From 4e1cc8a4862433db1e79969d983f5717d3fdce99 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 12 Sep 2017 00:53:58 +0300
Subject: [PATCH 001/106] - Added parameter for setting control pane height
 from settings-local.py - Refactored find_max_acts to work with settings file
 - Moved mean loading to separate function, to be used in find_max_acts -
 Added parameters caffe_net_raw_scale, caffe_net_image_dims, is_siamese

---
 caffevis/app.py               | 39 +++++++++++------------------------
 caffevis/caffe_proc_thread.py |  2 +-
 caffevis/caffevis_helper.py   | 28 ++++++++++++++++++++++++-
 find_maxes/find_max_acts.py   | 35 +++++++++++++++++++------------
 input_fetcher.py              |  6 +++---
 live_vis.py                   |  7 ++++---
 settings.py                   | 28 +++++++++++++++++++++----
 7 files changed, 93 insertions(+), 52 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 582da50f8..6a969d41f 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -11,13 +11,14 @@
 from misc import WithTimer
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
-from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio
+from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
+    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
 from caffevis_app_state import CaffeVisAppState
-from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, check_force_backward_true
-
+from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
+    check_force_backward_true, load_mean
 
 
 class CaffeVisApp(BaseApp):
@@ -36,7 +37,7 @@ def __init__(self, settings, key_bindings):
         else:
             self._net_channel_swap_inv = tuple([self._net_channel_swap.index(ii) for ii in range(len(self._net_channel_swap))])
 
-        self._range_scale = 1.0      # not needed; image already in [0,255]
+        self._range_scale = settings.caffe_net_raw_scale
 
         # Set the mode to CPU or GPU. Note: in the latest Caffe
         # versions, there is one Caffe object *per thread*, so the
@@ -56,32 +57,13 @@ def __init__(self, settings, key_bindings):
             mean = None,                                 # Set to None for now, assign later         # self._data_mean,
             channel_swap = self._net_channel_swap,
             raw_scale = self._range_scale,
+            image_dims=settings.caffe_net_image_dims,
         )
 
         if isinstance(settings.caffevis_data_mean, basestring):
             # If the mean is given as a filename, load the file
             try:
-
-                filename, file_extension = os.path.splitext(settings.caffevis_data_mean)
-                if file_extension == ".npy":
-                    # load mean from numpy array
-                    self._data_mean = np.load(settings.caffevis_data_mean)
-                    print "Loaded mean from numpy file, data_mean.shape: ", self._data_mean.shape
-
-                elif file_extension == ".binaryproto":
-
-                    # load mean from binary protobuf file
-                    blob = caffe.proto.caffe_pb2.BlobProto()
-                    data = open(settings.caffevis_data_mean, 'rb').read()
-                    blob.ParseFromString(data)
-                    self._data_mean = np.array(caffe.io.blobproto_to_array(blob))
-                    self._data_mean = np.squeeze(self._data_mean)
-                    print "Loaded mean from binaryproto file, data_mean.shape: ", self._data_mean.shape
-
-                else:
-                    # unknown file extension, trying to load as numpy array
-                    self._data_mean = np.load(settings.caffevis_data_mean)
-                    print "Loaded mean from numpy file, data_mean.shape: ", self._data_mean.shape
+                self._data_mean = load_mean(settings.caffevis_data_mean)
 
             except IOError:
                 print '\n\nCound not load mean file:', settings.caffevis_data_mean
@@ -529,7 +511,7 @@ def _draw_back_pane(self, pane):
             grad_blob = grad_blob[0]                    # bc01 -> c01
             grad_blob = grad_blob.transpose((1,2,0))    # c01 -> 01c
             if self._net_channel_swap_inv is None:
-                grad_img = grad_blob[:, :, :]  # e.g. BGR -> RGB
+                grad_img = grad_blob[:, :, :]  # do nothing
             else:
                 grad_img = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
                 
@@ -553,15 +535,18 @@ def _draw_back_pane(self, pane):
             if len(grad_img.shape) == 2:
                 grad_img = np.tile(grad_img[:,:,np.newaxis], 3)
 
-            if (self.settings.static_files_input_mode ==  "siamese_image_list") and (grad_img.shape[2] == 6):
+            if (self.settings.is_siamese) and (grad_img.shape[2] == 6):
 
+                # split gradient into two images, by channels
                 grad_img1 = grad_img[:, :, 0:3]
                 grad_img2 = grad_img[:, :, 3:6]
 
+                # resize each gradient image to half the pane size
                 half_pane_shape = (pane.data.shape[0] / 2, pane.data.shape[1])
                 grad_img_disp1 = cv2.resize(grad_img1[:], half_pane_shape)
                 grad_img_disp2 = cv2.resize(grad_img2[:], half_pane_shape)
 
+                # generate the pane image by concatenating both images
                 grad_img_disp = np.concatenate((grad_img_disp1, grad_img_disp2), axis=1)
 
             else:
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 82b4bb7dc..891799d07 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -85,7 +85,7 @@ def run(self):
                 #print 'TIMING:, processing frame'
                 self.frames_processed_fwd += 1
 
-                if self.settings.static_files_input_mode == "siamese_image_list":
+                if self.settings.is_siamese:
                     frame1 = frame[0]
                     frame2 = frame[1]
 
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index b2880f5bf..bdfe68eda 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,4 +1,6 @@
 import numpy as np
+import os
+import caffe
 
 from image_misc import get_tiles_height_width, caffe_load_image
 
@@ -6,7 +8,7 @@
 
 def net_preproc_forward(settings, net, img, data_hw):
 
-    if settings.static_files_input_mode == "siamese_image_list":
+    if settings.is_siamese:
         appropriate_shape = data_hw + (6,)
     else:
         appropriate_shape = data_hw + (3,)
@@ -121,3 +123,27 @@ def check_force_backward_true(prototxt_file):
         print 'to your prototxt file before continuing to force backprop to compute derivatives'
         print 'at the data layer as well.\n\n'
 
+
+def load_mean(data_mean_file):
+    filename, file_extension = os.path.splitext(data_mean_file)
+    if file_extension == ".npy":
+        # load mean from numpy array
+        data_mean = np.load(data_mean_file)
+        print "Loaded mean from numpy file, data_mean.shape: ", data_mean.shape
+
+    elif file_extension == ".binaryproto":
+
+        # load mean from binary protobuf file
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        data = open(data_mean_file, 'rb').read()
+        blob.ParseFromString(data)
+        data_mean = np.array(caffe.io.blobproto_to_array(blob))
+        data_mean = np.squeeze(data_mean)
+        print "Loaded mean from binaryproto file, data_mean.shape: ", data_mean.shape
+
+    else:
+        # unknown file extension, trying to load as numpy array
+        data_mean = np.load(data_mean_file)
+        print "Loaded mean from numpy file, data_mean.shape: ", data_mean.shape
+
+    return data_mean
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index c86eb2285..c0f48751c 100644
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -4,6 +4,9 @@
 import ipdb as pdb
 import cPickle as pickle
 
+import settings
+
+from caffevis.caffevis_helper import load_mean
 from loaders import load_imagenet_mean, load_labels, caffe
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes
@@ -13,21 +16,27 @@
 def main():
     parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
     parser.add_argument('--N', type = int, default = 9, help = 'note and save top N activations')
-    parser.add_argument('--gpu', action = 'store_true', help = 'use gpu')
-    parser.add_argument('net_prototxt', type = str, default = '', help = 'network prototxt to load')
-    parser.add_argument('net_weights', type = str, default = '', help = 'network weights to load')
-    parser.add_argument('datadir', type = str, default = '.', help = 'directory to look for files in')
-    parser.add_argument('filelist', type = str, help = 'list of image files to consider, one per line')
-    parser.add_argument('outfile', type = str, help = 'output filename for pkl')
-    #parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
+    parser.add_argument('--gpu', action = 'store_true', default=settings.caffevis_mode_gpu, help = 'use gpu')
+    parser.add_argument('--siamese', action = 'store_true', help = 'expected siamese network format')
+    parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
+    parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
+    parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
+    parser.add_argument('--filelist', type = str, default = settings.static_files_input_file, help = 'list of image files to consider, one per line')
+    parser.add_argument('--outfile', type=str, default = 'find_max_acts_output.pickled', help='output filename for pkl')
+    parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
-    imagenet_mean = load_imagenet_mean()
-    net = caffe.Classifier(args.net_prototxt, args.net_weights,
-                           mean=imagenet_mean,
-                           channel_swap=(2,1,0),
-                           raw_scale=255,
-                           image_dims=(256, 256))
+    if args.mean == "":
+        mean = load_imagenet_mean()
+    else:
+        mean = load_mean(args.mean)
+
+    net = caffe.Classifier(args.net_prototxt,
+                           args.net_weights,
+                           mean=mean,
+                           channel_swap=settings.caffe_net_channel_swap,
+                           raw_scale=settings.caffe_net_raw_scale,
+                           image_dims=settings.caffe_net_image_dims)
 
     if args.gpu:
         caffe.set_mode_gpu()
diff --git a/input_fetcher.py b/input_fetcher.py
index 95eb7a9fd..19967dfab 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -207,9 +207,9 @@ def check_increment_and_load_image(self):
             # available_files - local list of files
             if self.settings.static_files_input_mode == "directory":
                 available_files = self.get_files_from_directory()
-            elif self.settings.static_files_input_mode == "image_list":
+            elif (self.settings.static_files_input_mode == "image_list") and (not self.settings.is_siamese):
                 available_files = self.get_files_from_image_list()
-            elif self.settings.static_files_input_mode == "siamese_image_list":
+            elif (self.settings.static_files_input_mode == "image_list") and (self.settings.is_siamese):
                 available_files = self.get_files_from_siamese_image_list()
             else:
                 raise Exception(('Error: setting static_files_input_mode has invalid option (%s)' %
@@ -227,7 +227,7 @@ def check_increment_and_load_image(self):
             if self.latest_static_filename != available_files[self.static_file_idx] or self.latest_static_frame is None:
                 self.latest_static_filename = available_files[self.static_file_idx]
 
-                if self.settings.static_files_input_mode == "siamese_image_list":
+                if self.settings.is_siamese:
                     # loading two images for siamese network
                     im1 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]))
                     im2 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]))
diff --git a/live_vis.py b/live_vis.py
index 9dde3be99..0d4355ec9 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -316,17 +316,18 @@ def handle_key_post_apps(self, key):
                     key, hex(key), key_label, tag, tag)
 
     def display_frame(self, frame):
-        if self.settings.static_files_input_mode == "siamese_image_list":
+
+        full_pane_shape = self.panes['input'].data.shape[:2][::-1]
+        if self.settings.is_siamese:
             frame1 = frame[0]
             frame2 = frame[1]
-            full_pane_shape = self.panes['input'].data.shape[:2][::-1]
             half_pane_shape = (full_pane_shape[0] / 2, full_pane_shape[1])
             frame_disp1 = cv2.resize(frame1[:], half_pane_shape)
             frame_disp2 = cv2.resize(frame2[:], half_pane_shape)
             frame_disp = np.concatenate((frame_disp1, frame_disp2), axis=1)
 
         else:
-            frame_disp = cv2.resize(frame[:], self.panes['input'].data.shape[:2][::-1])
+            frame_disp = cv2.resize(frame[:], full_pane_shape)
 
         self.panes['input'].data[:] = frame_disp
 
diff --git a/settings.py b/settings.py
index 69c8a51bb..c1a5b0415 100644
--- a/settings.py
+++ b/settings.py
@@ -67,14 +67,17 @@
 # names), one can simply define control_pane_height. If more
 if 'default_window_panes' in locals():
     raise Exception('Override window panes in settings_local.py by defining window_panes, not default_window_panes')
+
+control_pane_height = locals().get('control_pane_height', 30)
+
 default_window_panes = (
     # (i, j, i_size, j_size)
     ('input',            (  0,    0,  300,   300)),    # This pane is required to show the input picture
     ('caffevis_aux',     (300,    0,  300,   300)),
     ('caffevis_back',    (600,    0,  300,   300)),
     ('caffevis_status',  (900,    0,   30,  1500)),
-    ('caffevis_control', (  0,  300,   30,   900)),
-    ('caffevis_layers',  ( 30,  300,  870,   900)),
+    ('caffevis_control', (  0,  300,   control_pane_height,   900)),
+    ('caffevis_layers',  ( control_pane_height,  300,  900-control_pane_height,   900)),
     ('caffevis_jpgvis',  (  0, 1200,  900,   300)),
 )
 window_panes = locals().get('window_panes', default_window_panes)
@@ -109,10 +112,13 @@
 # runtime via 'stretch_mode' key.)
 static_file_stretch_mode = locals().get('static_file_stretch_mode', False)
 
-# contains the input mode for reading static images, can be: 'directory', 'image_list', 'siamese_image_list'
+# is the network loaded a siamese network
+is_siamese = locals().get('is_siamese', False)
+
+# contains the input mode for reading static images, can be: 'directory', 'image_list'
 static_files_input_mode = locals().get('static_files_input_mode', 'directory')
 
-# contains the file name to read, relevant only when static_files_input_mode is 'image_list' or 'siamese_image_list'
+# contains the file name to read, relevant only when static_files_input_mode is 'image_list'
 static_files_input_file = locals().get('static_files_input_file', 'images_file_list.txt')
 
 # int, 0+. How many times to go through the main loop after a keypress
@@ -307,6 +313,15 @@
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 
+# caffe net parameter - raw scale
+caffe_net_raw_scale = locals().get('caffe_net_raw_scale', 1.0)
+
+# caffe net parameter - image dims
+caffe_net_image_dims = locals().get('caffe_net_image_dims', None)
+
+# # should we duplicate the mean file channels, this is used when we load a siamese network which has an input with 6
+# # channels (3 for each image)
+# duplicate_mean_channels = locals().get('duplicate_mean_channels', False)
 
 ####################################
 #
@@ -320,8 +335,13 @@ def assert_in_settings(setting_name):
     if not setting_name in bound_locals:
         raise Exception('The "%s" setting is required; be sure to define it in settings_local.py' % setting_name)
 
+# Set this to point to your compiled checkout of caffe
 assert_in_settings('caffevis_caffe_root')
+
+# Path to caffe deploy prototxt file. Minibatch size should be 1.
 assert_in_settings('caffevis_deploy_prototxt')
+
+# Path to network weights to load.
 assert_in_settings('caffevis_network_weights')
 assert_in_settings('caffevis_data_mean')
 

From 5d8fd0adc91302af935d0223f9e84d0d560964f4 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 12 Sep 2017 00:53:58 +0300
Subject: [PATCH 002/106] - Added parameter for setting control pane height
 from settings-local.py - Refactored find_max_acts to work with settings file
 - Moved mean loading to separate function, to be used in find_max_acts -
 Added parameters caffe_net_raw_scale, caffe_net_image_dims, is_siamese

---
 caffevis/app.py               | 39 +++++++++++------------------------
 caffevis/caffe_proc_thread.py |  2 +-
 caffevis/caffevis_helper.py   | 28 ++++++++++++++++++++++++-
 find_maxes/find_max_acts.py   | 35 +++++++++++++++++++------------
 input_fetcher.py              |  6 +++---
 live_vis.py                   |  7 ++++---
 settings.py                   | 28 +++++++++++++++++++++----
 7 files changed, 93 insertions(+), 52 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 582da50f8..6a969d41f 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -11,13 +11,14 @@
 from misc import WithTimer
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
-from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio
+from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
+    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
 from caffevis_app_state import CaffeVisAppState
-from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, check_force_backward_true
-
+from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
+    check_force_backward_true, load_mean
 
 
 class CaffeVisApp(BaseApp):
@@ -36,7 +37,7 @@ def __init__(self, settings, key_bindings):
         else:
             self._net_channel_swap_inv = tuple([self._net_channel_swap.index(ii) for ii in range(len(self._net_channel_swap))])
 
-        self._range_scale = 1.0      # not needed; image already in [0,255]
+        self._range_scale = settings.caffe_net_raw_scale
 
         # Set the mode to CPU or GPU. Note: in the latest Caffe
         # versions, there is one Caffe object *per thread*, so the
@@ -56,32 +57,13 @@ def __init__(self, settings, key_bindings):
             mean = None,                                 # Set to None for now, assign later         # self._data_mean,
             channel_swap = self._net_channel_swap,
             raw_scale = self._range_scale,
+            image_dims=settings.caffe_net_image_dims,
         )
 
         if isinstance(settings.caffevis_data_mean, basestring):
             # If the mean is given as a filename, load the file
             try:
-
-                filename, file_extension = os.path.splitext(settings.caffevis_data_mean)
-                if file_extension == ".npy":
-                    # load mean from numpy array
-                    self._data_mean = np.load(settings.caffevis_data_mean)
-                    print "Loaded mean from numpy file, data_mean.shape: ", self._data_mean.shape
-
-                elif file_extension == ".binaryproto":
-
-                    # load mean from binary protobuf file
-                    blob = caffe.proto.caffe_pb2.BlobProto()
-                    data = open(settings.caffevis_data_mean, 'rb').read()
-                    blob.ParseFromString(data)
-                    self._data_mean = np.array(caffe.io.blobproto_to_array(blob))
-                    self._data_mean = np.squeeze(self._data_mean)
-                    print "Loaded mean from binaryproto file, data_mean.shape: ", self._data_mean.shape
-
-                else:
-                    # unknown file extension, trying to load as numpy array
-                    self._data_mean = np.load(settings.caffevis_data_mean)
-                    print "Loaded mean from numpy file, data_mean.shape: ", self._data_mean.shape
+                self._data_mean = load_mean(settings.caffevis_data_mean)
 
             except IOError:
                 print '\n\nCound not load mean file:', settings.caffevis_data_mean
@@ -529,7 +511,7 @@ def _draw_back_pane(self, pane):
             grad_blob = grad_blob[0]                    # bc01 -> c01
             grad_blob = grad_blob.transpose((1,2,0))    # c01 -> 01c
             if self._net_channel_swap_inv is None:
-                grad_img = grad_blob[:, :, :]  # e.g. BGR -> RGB
+                grad_img = grad_blob[:, :, :]  # do nothing
             else:
                 grad_img = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
                 
@@ -553,15 +535,18 @@ def _draw_back_pane(self, pane):
             if len(grad_img.shape) == 2:
                 grad_img = np.tile(grad_img[:,:,np.newaxis], 3)
 
-            if (self.settings.static_files_input_mode ==  "siamese_image_list") and (grad_img.shape[2] == 6):
+            if (self.settings.is_siamese) and (grad_img.shape[2] == 6):
 
+                # split gradient into two images, by channels
                 grad_img1 = grad_img[:, :, 0:3]
                 grad_img2 = grad_img[:, :, 3:6]
 
+                # resize each gradient image to half the pane size
                 half_pane_shape = (pane.data.shape[0] / 2, pane.data.shape[1])
                 grad_img_disp1 = cv2.resize(grad_img1[:], half_pane_shape)
                 grad_img_disp2 = cv2.resize(grad_img2[:], half_pane_shape)
 
+                # generate the pane image by concatenating both images
                 grad_img_disp = np.concatenate((grad_img_disp1, grad_img_disp2), axis=1)
 
             else:
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 82b4bb7dc..891799d07 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -85,7 +85,7 @@ def run(self):
                 #print 'TIMING:, processing frame'
                 self.frames_processed_fwd += 1
 
-                if self.settings.static_files_input_mode == "siamese_image_list":
+                if self.settings.is_siamese:
                     frame1 = frame[0]
                     frame2 = frame[1]
 
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index b2880f5bf..bdfe68eda 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,4 +1,6 @@
 import numpy as np
+import os
+import caffe
 
 from image_misc import get_tiles_height_width, caffe_load_image
 
@@ -6,7 +8,7 @@
 
 def net_preproc_forward(settings, net, img, data_hw):
 
-    if settings.static_files_input_mode == "siamese_image_list":
+    if settings.is_siamese:
         appropriate_shape = data_hw + (6,)
     else:
         appropriate_shape = data_hw + (3,)
@@ -121,3 +123,27 @@ def check_force_backward_true(prototxt_file):
         print 'to your prototxt file before continuing to force backprop to compute derivatives'
         print 'at the data layer as well.\n\n'
 
+
+def load_mean(data_mean_file):
+    filename, file_extension = os.path.splitext(data_mean_file)
+    if file_extension == ".npy":
+        # load mean from numpy array
+        data_mean = np.load(data_mean_file)
+        print "Loaded mean from numpy file, data_mean.shape: ", data_mean.shape
+
+    elif file_extension == ".binaryproto":
+
+        # load mean from binary protobuf file
+        blob = caffe.proto.caffe_pb2.BlobProto()
+        data = open(data_mean_file, 'rb').read()
+        blob.ParseFromString(data)
+        data_mean = np.array(caffe.io.blobproto_to_array(blob))
+        data_mean = np.squeeze(data_mean)
+        print "Loaded mean from binaryproto file, data_mean.shape: ", data_mean.shape
+
+    else:
+        # unknown file extension, trying to load as numpy array
+        data_mean = np.load(data_mean_file)
+        print "Loaded mean from numpy file, data_mean.shape: ", data_mean.shape
+
+    return data_mean
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index c86eb2285..c0f48751c 100644
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -4,6 +4,9 @@
 import ipdb as pdb
 import cPickle as pickle
 
+import settings
+
+from caffevis.caffevis_helper import load_mean
 from loaders import load_imagenet_mean, load_labels, caffe
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes
@@ -13,21 +16,27 @@
 def main():
     parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
     parser.add_argument('--N', type = int, default = 9, help = 'note and save top N activations')
-    parser.add_argument('--gpu', action = 'store_true', help = 'use gpu')
-    parser.add_argument('net_prototxt', type = str, default = '', help = 'network prototxt to load')
-    parser.add_argument('net_weights', type = str, default = '', help = 'network weights to load')
-    parser.add_argument('datadir', type = str, default = '.', help = 'directory to look for files in')
-    parser.add_argument('filelist', type = str, help = 'list of image files to consider, one per line')
-    parser.add_argument('outfile', type = str, help = 'output filename for pkl')
-    #parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
+    parser.add_argument('--gpu', action = 'store_true', default=settings.caffevis_mode_gpu, help = 'use gpu')
+    parser.add_argument('--siamese', action = 'store_true', help = 'expected siamese network format')
+    parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
+    parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
+    parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
+    parser.add_argument('--filelist', type = str, default = settings.static_files_input_file, help = 'list of image files to consider, one per line')
+    parser.add_argument('--outfile', type=str, default = 'find_max_acts_output.pickled', help='output filename for pkl')
+    parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
-    imagenet_mean = load_imagenet_mean()
-    net = caffe.Classifier(args.net_prototxt, args.net_weights,
-                           mean=imagenet_mean,
-                           channel_swap=(2,1,0),
-                           raw_scale=255,
-                           image_dims=(256, 256))
+    if args.mean == "":
+        mean = load_imagenet_mean()
+    else:
+        mean = load_mean(args.mean)
+
+    net = caffe.Classifier(args.net_prototxt,
+                           args.net_weights,
+                           mean=mean,
+                           channel_swap=settings.caffe_net_channel_swap,
+                           raw_scale=settings.caffe_net_raw_scale,
+                           image_dims=settings.caffe_net_image_dims)
 
     if args.gpu:
         caffe.set_mode_gpu()
diff --git a/input_fetcher.py b/input_fetcher.py
index 95eb7a9fd..19967dfab 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -207,9 +207,9 @@ def check_increment_and_load_image(self):
             # available_files - local list of files
             if self.settings.static_files_input_mode == "directory":
                 available_files = self.get_files_from_directory()
-            elif self.settings.static_files_input_mode == "image_list":
+            elif (self.settings.static_files_input_mode == "image_list") and (not self.settings.is_siamese):
                 available_files = self.get_files_from_image_list()
-            elif self.settings.static_files_input_mode == "siamese_image_list":
+            elif (self.settings.static_files_input_mode == "image_list") and (self.settings.is_siamese):
                 available_files = self.get_files_from_siamese_image_list()
             else:
                 raise Exception(('Error: setting static_files_input_mode has invalid option (%s)' %
@@ -227,7 +227,7 @@ def check_increment_and_load_image(self):
             if self.latest_static_filename != available_files[self.static_file_idx] or self.latest_static_frame is None:
                 self.latest_static_filename = available_files[self.static_file_idx]
 
-                if self.settings.static_files_input_mode == "siamese_image_list":
+                if self.settings.is_siamese:
                     # loading two images for siamese network
                     im1 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]))
                     im2 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]))
diff --git a/live_vis.py b/live_vis.py
index 9dde3be99..0d4355ec9 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -316,17 +316,18 @@ def handle_key_post_apps(self, key):
                     key, hex(key), key_label, tag, tag)
 
     def display_frame(self, frame):
-        if self.settings.static_files_input_mode == "siamese_image_list":
+
+        full_pane_shape = self.panes['input'].data.shape[:2][::-1]
+        if self.settings.is_siamese:
             frame1 = frame[0]
             frame2 = frame[1]
-            full_pane_shape = self.panes['input'].data.shape[:2][::-1]
             half_pane_shape = (full_pane_shape[0] / 2, full_pane_shape[1])
             frame_disp1 = cv2.resize(frame1[:], half_pane_shape)
             frame_disp2 = cv2.resize(frame2[:], half_pane_shape)
             frame_disp = np.concatenate((frame_disp1, frame_disp2), axis=1)
 
         else:
-            frame_disp = cv2.resize(frame[:], self.panes['input'].data.shape[:2][::-1])
+            frame_disp = cv2.resize(frame[:], full_pane_shape)
 
         self.panes['input'].data[:] = frame_disp
 
diff --git a/settings.py b/settings.py
index 69c8a51bb..c1a5b0415 100644
--- a/settings.py
+++ b/settings.py
@@ -67,14 +67,17 @@
 # names), one can simply define control_pane_height. If more
 if 'default_window_panes' in locals():
     raise Exception('Override window panes in settings_local.py by defining window_panes, not default_window_panes')
+
+control_pane_height = locals().get('control_pane_height', 30)
+
 default_window_panes = (
     # (i, j, i_size, j_size)
     ('input',            (  0,    0,  300,   300)),    # This pane is required to show the input picture
     ('caffevis_aux',     (300,    0,  300,   300)),
     ('caffevis_back',    (600,    0,  300,   300)),
     ('caffevis_status',  (900,    0,   30,  1500)),
-    ('caffevis_control', (  0,  300,   30,   900)),
-    ('caffevis_layers',  ( 30,  300,  870,   900)),
+    ('caffevis_control', (  0,  300,   control_pane_height,   900)),
+    ('caffevis_layers',  ( control_pane_height,  300,  900-control_pane_height,   900)),
     ('caffevis_jpgvis',  (  0, 1200,  900,   300)),
 )
 window_panes = locals().get('window_panes', default_window_panes)
@@ -109,10 +112,13 @@
 # runtime via 'stretch_mode' key.)
 static_file_stretch_mode = locals().get('static_file_stretch_mode', False)
 
-# contains the input mode for reading static images, can be: 'directory', 'image_list', 'siamese_image_list'
+# is the network loaded a siamese network
+is_siamese = locals().get('is_siamese', False)
+
+# contains the input mode for reading static images, can be: 'directory', 'image_list'
 static_files_input_mode = locals().get('static_files_input_mode', 'directory')
 
-# contains the file name to read, relevant only when static_files_input_mode is 'image_list' or 'siamese_image_list'
+# contains the file name to read, relevant only when static_files_input_mode is 'image_list'
 static_files_input_file = locals().get('static_files_input_file', 'images_file_list.txt')
 
 # int, 0+. How many times to go through the main loop after a keypress
@@ -307,6 +313,15 @@
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 
+# caffe net parameter - raw scale
+caffe_net_raw_scale = locals().get('caffe_net_raw_scale', 1.0)
+
+# caffe net parameter - image dims
+caffe_net_image_dims = locals().get('caffe_net_image_dims', None)
+
+# # should we duplicate the mean file channels, this is used when we load a siamese network which has an input with 6
+# # channels (3 for each image)
+# duplicate_mean_channels = locals().get('duplicate_mean_channels', False)
 
 ####################################
 #
@@ -320,8 +335,13 @@ def assert_in_settings(setting_name):
     if not setting_name in bound_locals:
         raise Exception('The "%s" setting is required; be sure to define it in settings_local.py' % setting_name)
 
+# Set this to point to your compiled checkout of caffe
 assert_in_settings('caffevis_caffe_root')
+
+# Path to caffe deploy prototxt file. Minibatch size should be 1.
 assert_in_settings('caffevis_deploy_prototxt')
+
+# Path to network weights to load.
 assert_in_settings('caffevis_network_weights')
 assert_in_settings('caffevis_data_mean')
 

From 6bd2ab3b0e8093171e6fac1052954809a1f918de Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 21:53:12 +0300
Subject: [PATCH 003/106] - added new parameters in settings:   -
 convert_label_fb - function to apply on input label, e.g. convert probability
 into 0/1 categories   - layers_for_max_tracker - list of labels used to use
 in MaxTracker   - is_conv_fn - function which decides whether a layer is
 convolutional given its name   - normalize_layer_name_for_max_tracker_fn -
 function to normalize layer name used in NetMaxTracker   -
 siamese_layer_to_index_of_saved_image_fn - function which selects an image
 from a siamese input pair   - find_max_acts_output_file - location for max
 tracker output file

---
 settings.py | 21 ++++++++++++++++++---
 1 file changed, 18 insertions(+), 3 deletions(-)

diff --git a/settings.py b/settings.py
index c1a5b0415..a0e16db50 100644
--- a/settings.py
+++ b/settings.py
@@ -319,9 +319,24 @@
 # caffe net parameter - image dims
 caffe_net_image_dims = locals().get('caffe_net_image_dims', None)
 
-# # should we duplicate the mean file channels, this is used when we load a siamese network which has an input with 6
-# # channels (3 for each image)
-# duplicate_mean_channels = locals().get('duplicate_mean_channels', False)
+# function used to convert a string label into something else, can be an integer
+convert_label_fn = locals().get('convert_label_fn', lambda label: int(label.strip()))
+
+# layers list used in NetMaxTracker
+layers_for_max_tracker = locals().get('layers_for_max_tracker', ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob'])
+
+# function used to check if a layer is a convolutional
+is_conv_fn = locals().get('is_conv_fn', lambda layer_name: 'conv' in layer_name)
+
+# function to normalize layer name used in NetMaxTracker to combine activations of siamese layers
+# for example. this is used to make activations of layer "conv_1" and "conv_1_p" count the same
+normalize_layer_name_for_max_tracker_fn = locals().get('normalize_layer_name_for_max_tracker_fn', lambda layer_name: layer_name)
+
+# function which selects an image from a siamese input pair, given the layer name
+siamese_layer_to_index_of_saved_image_fn = locals().get('siamese_layer_to_index_of_saved_image_fn', lambda layer_name: 0)
+
+# location for max tracker output file
+find_max_acts_output_file = locals().get('find_max_acts_output_file', 'find_max_acts_output.pickled')
 
 ####################################
 #

From eff49e391050b163a4b49979c6af6027443121dd Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 21:54:43 +0300
Subject: [PATCH 004/106] - moved functions def get_files_from_directory,
 get_files_from_image_list and get_files_from_siamese_image_list into misc.py

---
 input_fetcher.py | 42 ++++--------------------------------------
 misc.py          | 46 +++++++++++++++++++++++++++++++++++++++++++++-
 2 files changed, 49 insertions(+), 39 deletions(-)

diff --git a/input_fetcher.py b/input_fetcher.py
index 19967dfab..276a177f7 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -7,7 +7,7 @@
 
 from codependent_thread import CodependentThread
 from image_misc import cv2_imshow_rgb, cv2_read_file_rgb, read_cam_frame, crop_to_square
-from misc import tsplit
+from misc import tsplit, get_files_from_directory, get_files_from_image_list, get_files_from_siamese_image_list
 
 
 class InputImageFetcher(CodependentThread):
@@ -159,41 +159,7 @@ def _increment_and_set_frame(self, frame, from_cam):
             self.latest_frame_data = frame
             self.latest_frame_is_from_cam = from_cam
 
-    def get_files_from_directory(self):
-        # returns list of files in requested directory
 
-        available_files = []
-        match_flags = re.IGNORECASE if self.settings.static_files_ignore_case else 0
-        for filename in os.listdir(self.settings.static_files_dir):
-            if re.match(self.settings.static_files_regexp, filename, match_flags):
-                available_files.append(filename)
-
-        return available_files
-
-    def get_files_from_image_list(self):
-        # returns list of files in requested image list file
-
-        available_files = []
-
-        with open(self.settings.static_files_input_file, 'r') as image_list_file:
-            lines = image_list_file.readlines()
-            # take first token from each line
-            available_files = [tsplit(line, True,' ',',','\t')[0] for line in lines if line.strip() != ""]
-
-        return available_files
-
-    def get_files_from_siamese_image_list(self):
-        # returns list of pair files in requested siamese image list file
-
-        available_files = []
-
-        with open(self.settings.static_files_input_file, 'r') as image_list_file:
-            lines = image_list_file.readlines()
-            # take first and second tokens from each line
-            available_files = [(tsplit(line, True, ' ', ',','\t')[0], tsplit(line, True, ' ', ',','\t')[1])
-                               for line in lines if line.strip() != ""]
-
-        return available_files
 
     def check_increment_and_load_image(self):
         with self.lock:
@@ -206,11 +172,11 @@ def check_increment_and_load_image(self):
 
             # available_files - local list of files
             if self.settings.static_files_input_mode == "directory":
-                available_files = self.get_files_from_directory()
+                available_files = get_files_from_directory(self.settings)
             elif (self.settings.static_files_input_mode == "image_list") and (not self.settings.is_siamese):
-                available_files = self.get_files_from_image_list()
+                available_files, labels = get_files_from_image_list(self.settings)
             elif (self.settings.static_files_input_mode == "image_list") and (self.settings.is_siamese):
-                available_files = self.get_files_from_siamese_image_list()
+                available_files, labels = get_files_from_siamese_image_list(self.settings)
             else:
                 raise Exception(('Error: setting static_files_input_mode has invalid option (%s)' %
                                 (self.settings.static_files_input_mode) ))
diff --git a/misc.py b/misc.py
index a9f43d5c6..523da97e0 100644
--- a/misc.py
+++ b/misc.py
@@ -59,4 +59,48 @@ def tsplit(string, no_empty_strings, *delimiters):
     if no_empty_strings:
         strings = filter(None, strings)
 
-    return strings
\ No newline at end of file
+    return strings
+
+
+def get_files_from_directory(settings):
+    # returns list of files in requested directory
+
+    available_files = []
+    match_flags = re.IGNORECASE if settings.static_files_ignore_case else 0
+    for filename in os.listdir(settings.static_files_dir):
+        if re.match(settings.static_files_regexp, filename, match_flags):
+            available_files.append(filename)
+
+    return available_files
+
+
+def get_files_from_image_list(settings):
+    # returns list of files in requested image list file
+
+    available_files = []
+    labels = []
+
+    with open(settings.static_files_input_file, 'r') as image_list_file:
+        lines = image_list_file.readlines()
+        # take first token from each line
+        available_files = [tsplit(line, True, ' ', ',', '\t')[0] for line in lines if line.strip() != ""]
+        labels = [tsplit(line, True, ' ', ',', '\t')[1] for line in lines if line.strip() != ""]
+
+    return available_files, labels
+
+
+def get_files_from_siamese_image_list(settings):
+    # returns list of pair files in requested siamese image list file
+
+    available_files = []
+    labels = []
+
+    with open(settings.static_files_input_file, 'r') as image_list_file:
+        lines = image_list_file.readlines()
+        # take first and second tokens from each line
+        available_files = [(tsplit(line, True, ' ', ',', '\t')[0], tsplit(line, True, ' ', ',', '\t')[1])
+                           for line in lines if line.strip() != ""]
+        labels = [tsplit(line, True, ' ', ',', '\t')[2] for line in lines if line.strip() != ""]
+
+    return available_files, labels
+

From 432ef1d901acd6faa5a2bd879c57b1861082e0f4 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 21:59:02 +0300
Subject: [PATCH 005/106] - revised find_max_acts.py and max_tracker.py, and
 subsequently crop_max_patches.py to work properly on siamese network   -
 handle siamese case where two layers generates activations for same neuron  
 - avoid input duplications while searching for max input images   - handle
 output generation for siamese network with duplicate layers

---
 find_maxes/crop_max_patches.py |  53 ++++++++----
 find_maxes/find_max_acts.py    |  29 ++++---
 find_maxes/max_tracker.py      | 154 ++++++++++++++++++++++++++-------
 3 files changed, 174 insertions(+), 62 deletions(-)
 mode change 100644 => 100755 find_maxes/crop_max_patches.py
 mode change 100644 => 100755 find_maxes/find_max_acts.py

diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
old mode 100644
new mode 100755
index 20161ace9..b33b346b8
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -4,6 +4,9 @@
 import ipdb as pdb
 import cPickle as pickle
 
+import settings
+from caffevis.caffevis_helper import load_mean
+
 from loaders import load_imagenet_mean, load_labels, caffe
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
@@ -13,7 +16,7 @@
 def main():
     parser = argparse.ArgumentParser(description='Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.')
     parser.add_argument('--N',           type = int, default = 9, help = 'Note and save top N activations.')
-    parser.add_argument('--gpu',         action = 'store_true', help = 'Use gpu.')
+    parser.add_argument('--gpu',         action = 'store_true', default=settings.caffevis_mode_gpu, help = 'Use gpu.')
     parser.add_argument('--do-maxes',    action = 'store_true', help = 'Output max patches.')
     parser.add_argument('--do-deconv',   action = 'store_true', help = 'Output deconv patches.')
     parser.add_argument('--do-deconv-norm', action = 'store_true', help = 'Output deconv-norm patches.')
@@ -23,14 +26,14 @@ def main():
     parser.add_argument('--idx-begin',   type = int, default = None, help = 'Start at this unit (default: all units).')
     parser.add_argument('--idx-end',     type = int, default = None, help = 'End at this unit (default: all units).')
     
-    parser.add_argument('nmt_pkl',       type = str, help = 'Which pickled NetMaxTracker to load.')
-    parser.add_argument('net_prototxt',  type = str, help = 'Network prototxt to load')
-    parser.add_argument('net_weights',   type = str, help = 'Network weights to load')
-    parser.add_argument('datadir',       type = str, help = 'Directory to look for files in')
-    parser.add_argument('filelist',      type = str, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
-    parser.add_argument('outdir',        type = str, help = r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
-    parser.add_argument('layer',         type = str, help = 'Which layer to output')
-    #parser.add_argument('--mean', type = str, default = '', help = 'data mean to load')
+    parser.add_argument('--nmt_pkl',       type = str, help = 'Which pickled NetMaxTracker to load.')
+    parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
+    parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
+    parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
+    parser.add_argument('--filelist',      type = str, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
+    parser.add_argument('--outdir',        type = str, help = r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
+    parser.add_argument('--layer',         type = str, help = 'Which layer to output')
+    parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
     if args.gpu:
@@ -38,26 +41,38 @@ def main():
     else:
         caffe.set_mode_cpu()
 
-    imagenet_mean = load_imagenet_mean()
-    net = caffe.Classifier(args.net_prototxt, args.net_weights,
-                           mean=imagenet_mean,
-                           channel_swap=(2,1,0),
-                           raw_scale=255,
-                           image_dims=(256, 256))
+    if args.mean == "":
+        data_mean = load_imagenet_mean()
+    else:
+        data_mean = load_mean(args.mean)
+
+    net = caffe.Classifier(args.net_prototxt,
+                           args.net_weights,
+                           mean=None,
+                           channel_swap=settings.caffe_net_channel_swap,
+                           raw_scale=settings.caffe_net_raw_scale,
+                           image_dims=settings.caffe_net_image_dims)
+
+    if data_mean is not None:
+        net.transformer.set_mean(net.inputs[0], data_mean)
 
     assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
 
     with open(args.nmt_pkl, 'rb') as ff:
         nmt = pickle.load(ff)
-    mt = nmt.max_trackers[args.layer]
+
+    normalized_layer_name = settings.normalize_layer_name_for_max_tracker_fn(args.layer)
+
+    # TODO: use settings.normalize_layer_name_for_max_tracker_fn to normalize layer name
+    mt = nmt.max_trackers[normalized_layer_name]
 
     if args.idx_begin is None:
         args.idx_begin = 0
     if args.idx_end is None:
         args.idx_end = mt.max_vals.shape[0]
-    
-    with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, args.layer, args.idx_begin, args.idx_end)):
-        output_max_patches(mt, net, args.layer, args.idx_begin, args.idx_end,
+
+    with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, args.idx_begin, args.idx_end)):
+        output_max_patches(mt, net, normalized_layer_name, args.idx_begin, args.idx_end,
                            args.N, args.datadir, args.filelist, args.outdir,
                            (args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
old mode 100644
new mode 100755
index c0f48751c..370baf33f
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -9,42 +9,45 @@
 from caffevis.caffevis_helper import load_mean
 from loaders import load_imagenet_mean, load_labels, caffe
 from jby_misc import WithTimer
-from max_tracker import scan_images_for_maxes
-
+from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
 
 
 def main():
     parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
     parser.add_argument('--N', type = int, default = 9, help = 'note and save top N activations')
     parser.add_argument('--gpu', action = 'store_true', default=settings.caffevis_mode_gpu, help = 'use gpu')
-    parser.add_argument('--siamese', action = 'store_true', help = 'expected siamese network format')
     parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
     parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
     parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
-    parser.add_argument('--filelist', type = str, default = settings.static_files_input_file, help = 'list of image files to consider, one per line')
-    parser.add_argument('--outfile', type=str, default = 'find_max_acts_output.pickled', help='output filename for pkl')
+    parser.add_argument('--outfile', type=str, default = settings.find_max_acts_output_file, help='output filename for pkl')
     parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
+    if args.gpu:
+        caffe.set_mode_gpu()
+    else:
+        caffe.set_mode_cpu()
+
     if args.mean == "":
-        mean = load_imagenet_mean()
+        data_mean = load_imagenet_mean()
     else:
-        mean = load_mean(args.mean)
+        data_mean = load_mean(args.mean)
 
     net = caffe.Classifier(args.net_prototxt,
                            args.net_weights,
-                           mean=mean,
+                           mean=None,
                            channel_swap=settings.caffe_net_channel_swap,
                            raw_scale=settings.caffe_net_raw_scale,
                            image_dims=settings.caffe_net_image_dims)
 
-    if args.gpu:
-        caffe.set_mode_gpu()
-    else:
-        caffe.set_mode_cpu()
+    if data_mean is not None:
+        net.transformer.set_mean(net.inputs[0], data_mean)
 
     with WithTimer('Scanning images'):
-        max_tracker = scan_images_for_maxes(net, args.datadir, args.filelist, args.N)
+        if settings.is_siamese:
+            max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N)
+        else: # normal operation
+            max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N)
     with WithTimer('Saving maxes'):
         with open(args.outfile, 'wb') as ff:
             pickle.dump(max_tracker, ff, -1)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 8bfd427de..e7df2079f 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -11,10 +11,8 @@
 from caffe_misc import shownet, RegionComputer, save_caffe_image
 import numpy as np
 
-
-
-default_layers  = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob']
-default_is_conv = [('conv' in ll) for ll in default_layers]
+from misc import get_files_from_image_list
+from misc import get_files_from_siamese_image_list
 
 def hardcoded_get():
     prototxt = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/deploy_1.prototxt'
@@ -54,17 +52,30 @@ def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype =
         self.max_vals = np.ones((n_channels, n_top), dtype = dtype) * initial_val
         self.n_top = n_top
         if is_conv:
-            self.max_locs = -np.ones((n_channels, n_top, 4), dtype = 'int')   # image_idx, image_class, i, j
+            self.max_locs = -np.ones((n_channels, n_top, 5), dtype = 'int')   # image_idx, image_class, selected_input_index, i, j
         else:
-            self.max_locs = -np.ones((n_channels, n_top, 2), dtype = 'int')      # image_idx, image_class
+            self.max_locs = -np.ones((n_channels, n_top, 3), dtype = 'int')      # image_idx, image_class, selected_input_index
+
+        # set of seen inputs, used to avoid updating on the same input twice
+        self.seen_inputs = set()
+
+    def update(self, blob, image_idx, image_class, selected_input_index, layer_unique_input_source):
+
+        if layer_unique_input_source in self.seen_inputs:
+            return
+
+        # add input identifier to seen inputs set
+        self.seen_inputs.add(layer_unique_input_source)
 
-    def update(self, blob, image_idx, image_class):
         data = blob[0]                                        # Note: makes a copy of blob, e.g. (96,55,55)
         n_channels = data.shape[0]
         data_unroll = data.reshape((n_channels, -1))          # Note: no copy eg (96,3025). Does nothing if not is_conv
 
         maxes = data_unroll.argmax(1)   # maxes for each channel, eg. (96,)
 
+        # if unique_input_source already exist, we can skip the update since we've already seen it
+
+
         #insertion_idx = zeros((n_channels,))
         #pdb.set_trace()
         for ii in xrange(n_channels):
@@ -80,59 +91,107 @@ def update(self, blob, image_idx, image_class):
             self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
             # store new location
             if self.is_conv:
-                self.max_locs[ii,idx-1] = (image_idx, image_class) + np.unravel_index(maxes[ii], data.shape[1:])
+                self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index) + np.unravel_index(maxes[ii], data.shape[1:])
             else:
-                self.max_locs[ii,idx-1] = (image_idx, image_class)
+                self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index)
 
 
 
 class NetMaxTracker(object):
-    def __init__(self, layers = default_layers, is_conv = default_is_conv, n_top = 10, initial_val = -1e99, dtype = 'float32'):
+    def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'float32'):
         self.layers = layers
-        self.is_conv = is_conv
         self.init_done = False
         self.n_top = n_top
         self.initial_val = initial_val
+        self.settings = settings
 
     def _init_with_net(self, net):
         self.max_trackers = {}
 
-        for layer,is_conv in zip(self.layers, self.is_conv):
+        for layer in self.layers:
             blob = net.blobs[layer].data
-            self.max_trackers[layer] = MaxTracker(is_conv, blob.shape[1], n_top = self.n_top,
-                                                  initial_val = self.initial_val,
-                                                  dtype = blob.dtype)
+
+            # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
+            # count as the same layer in terms of activations
+            normalized_layer_name = self.settings.normalize_layer_name_for_max_tracker_fn(layer)
+
+            is_conv = self.settings.is_conv_fn(layer)
+
+            # only add normalized layer once
+            if normalized_layer_name not in self.max_trackers:
+                self.max_trackers[normalized_layer_name] = MaxTracker(is_conv, blob.shape[1], n_top = self.n_top,
+                                                                      initial_val = self.initial_val,
+                                                                      dtype = blob.dtype)
+
         self.init_done = True
         
-    def update(self, net, image_idx, image_class):
+    def update(self, net, image_idx, image_class, net_unique_input_source):
         '''Updates the maxes found so far with the state of the given net. If a new max is found, it is stored together with the image_idx.'''
         if not self.init_done:
             self._init_with_net(net)
 
         for layer in self.layers:
             blob = net.blobs[layer].data
-            self.max_trackers[layer].update(blob, image_idx, image_class)
 
+            normalized_layer_name = self.settings.normalize_layer_name_for_max_tracker_fn(layer)
+
+            # in siamese network, we might need to select one of the images from the siamese pair
+            if self.settings.is_siamese:
+                selected_input_index = self.settings.siamese_layer_to_index_of_saved_image_fn(layer)
+
+                if selected_input_index == 0:
+                    # first image identifier is selected
+                    layer_unique_input_source = net_unique_input_source[0]
+                elif selected_input_index == 1:
+                    # second image identifier is selected
+                    layer_unique_input_source = net_unique_input_source[1]
+                elif selected_input_index == -1:
+                    # both images are selected
+                    layer_unique_input_source = net_unique_input_source
+            else:
+                layer_unique_input_source = net_unique_input_source
+                selected_input_index = -1
+
+            self.max_trackers[normalized_layer_name].update(blob, image_idx, image_class, selected_input_index, layer_unique_input_source)
 
-def load_file_list(filelist):
-    image_filenames = []
-    image_labels = []
-    with open(filelist, 'r') as ff:
-        for line in ff.readlines():
-            fields = line.strip().split()
-            image_filenames.append(fields[0])
-            image_labels.append(int(fields[1]))
-    return image_filenames, image_labels
+    def __getstate__(self):
+        # Copy the object's state from self.__dict__ which contains
+        # all our instance attributes. Always use the dict.copy()
+        # method to avoid modifying the original state.
+        state = self.__dict__.copy()
+        # Remove the unpicklable entries.
+        del state['settings']
+        return state
 
 
+    def __setstate__(self, state):
+        # Restore instance attributes (i.e., filename and lineno).
+        self.__dict__.update(state)
 
-def scan_images_for_maxes(net, datadir, filelist, n_top):
-    image_filenames, image_labels = load_file_list(filelist)
+        self.settings = None
+
+
+def load_file_list(settings):
+
+    if (settings.static_files_input_mode == "image_list") and (not settings.is_siamese):
+        available_files, labels = get_files_from_image_list(settings)
+
+    elif (settings.static_files_input_mode == "image_list") and (settings.is_siamese):
+        available_files, labels = get_files_from_siamese_image_list(settings)
+
+    converted_labels = [settings.convert_label_fn(label) for label in labels]
+
+    return available_files, converted_labels
+
+
+def scan_images_for_maxes(settings, net, datadir, n_top):
+    image_filenames, image_labels = load_file_list(settings)
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
 
-    tracker = NetMaxTracker(n_top = n_top)
+    tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_for_max_tracker)
     for image_idx in xrange(len(image_filenames)):
+
         filename = image_filenames[image_idx]
         image_class = image_labels[image_idx]
         #im = caffe.io.load_image('../../data/ilsvrc12/mini_ilsvrc_valid/sized/ILSVRC2012_val_00000610.JPEG')
@@ -144,12 +203,47 @@ def scan_images_for_maxes(net, datadir, filelist, n_top):
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Update    ', quiet = not do_print):
-            tracker.update(net, image_idx, image_class)
+            tracker.update(net, image_idx, image_class, net_unique_input_source=filename)
+
 
     print 'done!'
     return tracker
 
 
+def scan_pairs_for_maxes(settings, net, datadir, n_top):
+    image_filenames, image_labels = load_file_list(settings)
+    print 'Scanning %d pairs' % len(image_filenames)
+    print '  First pair', image_filenames[0]
+
+    tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_for_max_tracker)
+    for image_idx in xrange(len(image_filenames)):
+
+        images_pair = image_filenames[image_idx]
+        filename1 = images_pair[0]
+        filename2 = images_pair[1]
+        image_class = image_labels[image_idx]
+        # im = caffe.io.load_image('../../data/ilsvrc12/mini_ilsvrc_valid/sized/ILSVRC2012_val_00000610.JPEG')
+        do_print = (image_idx % 100 == 0)
+        if do_print:
+            print '%s   Pair %d/%d' % (datetime.now().ctime(), image_idx, len(image_filenames))
+        with WithTimer('Load image', quiet=not do_print):
+            im1 = caffe.io.load_image(os.path.join(datadir, filename1))
+            im2 = caffe.io.load_image(os.path.join(datadir, filename2))
+
+            net_input_dims = net.blobs['data'].data.shape[2:4]
+            im1 = caffe.io.resize_image(im1, net_input_dims)
+            im2 = caffe.io.resize_image(im2, net_input_dims)
+
+            im = np.concatenate((im1, im2), axis=2)
+
+        with WithTimer('Predict   ', quiet=not do_print):
+            net.predict([im], oversample=False)
+        with WithTimer('Update    ', quiet=not do_print):
+            tracker.update(net, image_idx, image_class, net_unique_input_source=images_pair)
+
+    print 'done!'
+    return tracker
+
 
 def save_representations(net, datadir, filelist, layer, first_N = None):
     image_filenames, image_labels = load_file_list(filelist)

From cde627f0f42712432243a8676700ba6c5b3ef1aa Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:03:11 +0300
Subject: [PATCH 006/106] - made max_tracker more generic - added support for
 siamese networks

---
 find_maxes/caffe_misc.py       |  26 +++---
 find_maxes/crop_max_patches.py |  59 ++++++------
 find_maxes/max_tracker.py      | 164 ++++++++++++++++++++++++++-------
 settings.py                    |  46 ++++++++-
 4 files changed, 219 insertions(+), 76 deletions(-)

diff --git a/find_maxes/caffe_misc.py b/find_maxes/caffe_misc.py
index d126650bb..1265e3fd7 100644
--- a/find_maxes/caffe_misc.py
+++ b/find_maxes/caffe_misc.py
@@ -81,21 +81,23 @@ def get_pool_converter(bot_size, top_size, filter_width = (1,1), stride = (1,1),
 
 
 class RegionComputer(object):
-    '''Computes regions of possible influcence from higher layers to lower layers.
+    '''Computes regions of possible influcence from higher layers to lower layers.'''
 
-    Woefully hardcoded'''
-
-    def __init__(self):
+    def __init__(self, layers_list):
         #self.net = net
+
         _tmp = []
-        _tmp.append(('data', None))
-        _tmp.append(('conv1', get_conv_converter((227,227), (55,55), (11,11), (4,4))))
-        _tmp.append(('pool1', get_pool_converter((55,55),   (27,27), (3,3),   (2,2))))
-        _tmp.append(('conv2', get_conv_converter((27,27),   (27,27), (5,5),   (1,1),  (2,2))))
-        _tmp.append(('pool2', get_pool_converter((27,27),   (13,13), (3,3),   (2,2))))
-        _tmp.append(('conv3', get_conv_converter((13,13),   (13,13), (3,3),   (1,1),  (1,1))))
-        _tmp.append(('conv4', get_conv_converter((13,13),   (13,13), (3,3),   (1,1),  (1,1))))
-        _tmp.append(('conv5', get_conv_converter((13,13),   (13,13), (3,3),   (1,1),  (1,1))))
+
+        for (name, type, input, output, filter, stride, pad) in layers_list:
+            if type == 'Input':
+                _tmp.append((name, None))
+            elif type == 'Convolution':
+                _tmp.append((name, get_conv_converter(input, output, filter, stride, pad)))
+            elif type == 'Pooling':
+                _tmp.append((name, get_pool_converter(input, output, filter, stride, pad)))
+            else:
+                continue # skip adding layer
+
         self.names = [tt[0] for tt in _tmp]
         self.converters = [tt[1] for tt in _tmp]
         
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index b33b346b8..16c667e07 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -15,25 +15,24 @@
 
 def main():
     parser = argparse.ArgumentParser(description='Loads a pickled NetMaxTracker and outputs one or more of {the patches of the image, a deconv patch, a backprop patch} associated with the maxes.')
-    parser.add_argument('--N',           type = int, default = 9, help = 'Note and save top N activations.')
-    parser.add_argument('--gpu',         action = 'store_true', default=settings.caffevis_mode_gpu, help = 'Use gpu.')
-    parser.add_argument('--do-maxes',    action = 'store_true', help = 'Output max patches.')
-    parser.add_argument('--do-deconv',   action = 'store_true', help = 'Output deconv patches.')
-    parser.add_argument('--do-deconv-norm', action = 'store_true', help = 'Output deconv-norm patches.')
-    parser.add_argument('--do-backprop', action = 'store_true', help = 'Output backprop patches.')
-    parser.add_argument('--do-backprop-norm', action = 'store_true', help = 'Output backprop-norm patches.')
-    parser.add_argument('--do-info',     action = 'store_true', help = 'Output info file containing max filenames and labels.')
-    parser.add_argument('--idx-begin',   type = int, default = None, help = 'Start at this unit (default: all units).')
-    parser.add_argument('--idx-end',     type = int, default = None, help = 'End at this unit (default: all units).')
+    parser.add_argument('--N',            type = int, default = 9, help = 'Note and save top N activations.')
+    parser.add_argument('--gpu',          action = 'store_true', default=settings.caffevis_mode_gpu, help = 'Use gpu.')
+    parser.add_argument('--do-maxes',     action = 'store_true', default=settings.max_tracker_do_maxes, help = 'Output max patches.')
+    parser.add_argument('--do-deconv',    action = 'store_true', default=settings.max_tracker_do_deconv, help = 'Output deconv patches.')
+    parser.add_argument('--do-deconv-norm', action = 'store_true', default=settings.max_tracker_do_deconv_norm, help = 'Output deconv-norm patches.')
+    parser.add_argument('--do-backprop',  action = 'store_true', default=settings.max_tracker_do_backprop, help = 'Output backprop patches.')
+    parser.add_argument('--do-backprop-norm', action = 'store_true', default=settings.max_tracker_do_backprop_norm, help = 'Output backprop-norm patches.')
+    parser.add_argument('--do-info',      action = 'store_true', default=settings.max_tracker_do_info, help = 'Output info file containing max filenames and labels.')
+    parser.add_argument('--idx-begin',    type = int, default = None, help = 'Start at this unit (default: all units).')
+    parser.add_argument('--idx-end',      type = int, default = None, help = 'End at this unit (default: all units).')
     
-    parser.add_argument('--nmt_pkl',       type = str, help = 'Which pickled NetMaxTracker to load.')
+    parser.add_argument('--nmt_pkl',      type = str, default = settings.find_max_acts_output_file, help = 'Which pickled NetMaxTracker to load.')
     parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
-    parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
-    parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
-    parser.add_argument('--filelist',      type = str, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
-    parser.add_argument('--outdir',        type = str, help = r'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
-    parser.add_argument('--layer',         type = str, help = 'Which layer to output')
-    parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
+    parser.add_argument('--net_weights',  type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
+    parser.add_argument('--datadir',      type = str, default = settings.static_files_dir, help = 'directory to look for files in')
+    parser.add_argument('--filelist',     type = str, default = settings.static_files_input_file, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
+    parser.add_argument('--outdir',       type = str, default = settings.max_tracker_output_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
+    parser.add_argument('--mean',         type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
     if args.gpu:
@@ -61,20 +60,24 @@ def main():
     with open(args.nmt_pkl, 'rb') as ff:
         nmt = pickle.load(ff)
 
-    normalized_layer_name = settings.normalize_layer_name_for_max_tracker_fn(args.layer)
+    for layer in settings.max_tracker_layers_to_output:
 
-    # TODO: use settings.normalize_layer_name_for_max_tracker_fn to normalize layer name
-    mt = nmt.max_trackers[normalized_layer_name]
+        print 'Started work on layer %s' % (layer)
 
-    if args.idx_begin is None:
-        args.idx_begin = 0
-    if args.idx_end is None:
-        args.idx_end = mt.max_vals.shape[0]
+        normalized_layer_name = settings.normalize_layer_name_for_max_tracker_fn(layer)
 
-    with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, args.idx_begin, args.idx_end)):
-        output_max_patches(mt, net, normalized_layer_name, args.idx_begin, args.idx_end,
-                           args.N, args.datadir, args.filelist, args.outdir,
-                           (args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
+        mt = nmt.max_trackers[normalized_layer_name]
+
+        if args.idx_begin is None:
+            args.idx_begin = 0
+        if args.idx_end is None:
+            args.idx_end = mt.max_vals.shape[0]
+
+        with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, args.idx_begin, args.idx_end)):
+
+            output_max_patches(settings, mt, net, normalized_layer_name, args.idx_begin, args.idx_end,
+                               args.N, args.datadir, args.filelist, args.outdir,
+                               (args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
 
 
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index e7df2079f..581d8d70b 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -54,7 +54,7 @@ def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype =
         if is_conv:
             self.max_locs = -np.ones((n_channels, n_top, 5), dtype = 'int')   # image_idx, image_class, selected_input_index, i, j
         else:
-            self.max_locs = -np.ones((n_channels, n_top, 3), dtype = 'int')      # image_idx, image_class, selected_input_index
+            self.max_locs = -np.ones((n_channels, n_top, 3), dtype = 'int')   # image_idx, image_class, selected_input_index
 
         # set of seen inputs, used to avoid updating on the same input twice
         self.seen_inputs = set()
@@ -124,7 +124,7 @@ def _init_with_net(self, net):
                                                                       dtype = blob.dtype)
 
         self.init_done = True
-        
+
     def update(self, net, image_idx, image_class, net_unique_input_source):
         '''Updates the maxes found so far with the state of the given net. If a new max is found, it is stored together with the image_idx.'''
         if not self.init_done:
@@ -293,16 +293,21 @@ def get_max_data_extent(net, layer, rc, is_conv):
 
 
 
-def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
+def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
     do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, do_info = do_which
     assert do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm or do_info, 'nothing to do'
 
     mt = max_tracker
-    rc = RegionComputer()
-    
-    image_filenames, image_labels = load_file_list(filelist)
-    print 'Loaded filenames and labels for %d files' % len(image_filenames)
-    print '  First file', os.path.join(datadir, image_filenames[0])
+    rc = RegionComputer(settings.max_tracker_layers_list)
+
+    image_filenames, image_labels = load_file_list(settings)
+
+    if settings.is_siamese:
+        print 'Loaded filenames and labels for %d pairs' % len(image_filenames)
+        print '  First pair', image_filenames[0]
+    else:
+        print 'Loaded filenames and labels for %d files' % len(image_filenames)
+        print '  First file', os.path.join(datadir, image_filenames[0])
 
     num_top_in_mt = mt.max_locs.shape[1]
     assert num_top <= num_top_in_mt, 'Requested %d top images but MaxTracker contains only %d' % (num_top, num_top_in_mt)
@@ -310,7 +315,7 @@ def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, dat
 
     size_ii, size_jj = get_max_data_extent(net, layer, rc, mt.is_conv)
     data_size_ii, data_size_jj = net.blobs['data'].data.shape[2:4]
-    
+
     n_total_images = (idx_end-idx_begin) * num_top
     for cc, channel_idx in enumerate(range(idx_begin, idx_end)):
         unit_dir = os.path.join(outdir, layer, 'unit_%04d' % channel_idx)
@@ -325,9 +330,9 @@ def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, dat
         for max_idx_0 in range(num_top):
             max_idx = num_top_in_mt - 1 - max_idx_0
             if mt.is_conv:
-                im_idx, im_class, ii, jj = mt.max_locs[channel_idx, max_idx]
+                im_idx, im_class, selected_input_index, ii, jj = mt.max_locs[channel_idx, max_idx]
             else:
-                im_idx, im_class = mt.max_locs[channel_idx, max_idx]
+                im_idx, im_class, selected_input_index = mt.max_locs[channel_idx, max_idx]
             recorded_val = mt.max_vals[channel_idx, max_idx]
             filename = image_filenames[im_idx]
             do_print = (max_idx_0 == 0)
@@ -340,12 +345,18 @@ def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, dat
                 data_indices = rc.convert_region(layer, 'data', layer_indices)
                 data_ii_start,data_ii_end,data_jj_start,data_jj_end = data_indices
 
+                # safe guard edges
+                data_ii_start = max(data_ii_start, 0)
+                data_jj_start = max(data_jj_start, 0)
+                data_ii_end = min(data_ii_end, data_size_ii)
+                data_jj_end = min(data_jj_end, data_size_jj)
+
                 touching_imin = (data_ii_start == 0)
                 touching_jmin = (data_jj_start == 0)
 
                 # Compute how much of the data slice falls outside the actual data [0,max] range
-                ii_outside = size_ii - (data_ii_end - data_ii_start)     # possibly 0
-                jj_outside = size_jj - (data_jj_end - data_jj_start)     # possibly 0
+                ii_outside = size_ii - (data_ii_end - data_ii_start)  # possibly 0
+                jj_outside = size_jj - (data_jj_end - data_jj_start)  # possibly 0
 
                 if touching_imin:
                     out_ii_start = ii_outside
@@ -368,9 +379,9 @@ def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, dat
             if do_info:
                 print >>info_file, 1 if mt.is_conv else 0, '%.6f' % mt.max_vals[channel_idx, max_idx],
                 if mt.is_conv:
-                    print >>info_file, '%d %d %d %d' % tuple(mt.max_locs[channel_idx, max_idx]),
+                    print >>info_file, '%d %d %d %d %d' % tuple(mt.max_locs[channel_idx, max_idx]),
                 else:
-                    print >>info_file, '%d %d' % tuple(mt.max_locs[channel_idx, max_idx]),
+                    print >>info_file, '%d %d %d' % tuple(mt.max_locs[channel_idx, max_idx]),
                 print >>info_file, filename
 
             if not (do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm):
@@ -378,55 +389,138 @@ def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, dat
 
 
             with WithTimer('Load image', quiet = not do_print):
-                im = caffe.io.load_image(os.path.join(datadir, filename))
+
+                if settings.is_siamese:
+                    # in siamese network, filename is a pair of image file names
+                    filename1 = filename[0]
+                    filename2 = filename[1]
+
+                    # load both images
+                    im1 = caffe.io.load_image(os.path.join(datadir, filename1))
+                    im2 = caffe.io.load_image(os.path.join(datadir, filename2))
+
+                    # resize images according to input dimension
+                    net_input_dims = net.blobs['data'].data.shape[2:4]
+                    im1 = caffe.io.resize_image(im1, net_input_dims)
+                    im2 = caffe.io.resize_image(im2, net_input_dims)
+
+                    # concatenate channelwise
+                    im = np.concatenate((im1, im2), axis=2)
+                else:
+                    im = caffe.io.load_image(os.path.join(datadir, filename))
+
             with WithTimer('Predict   ', quiet = not do_print):
-                net.predict([im], oversample = False)   # Just take center crop, same as in scan_images_for_maxes
+                net.predict([im], oversample = False)
+
 
-            if len(net.blobs[layer].data.shape) == 4:
-                reproduced_val = net.blobs[layer].data[0,channel_idx,ii,jj]
+            # in siamese network, we wish to return from the normalized layer name and selected input index to the denormalized layer name
+            # e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
+            denormalized_layer_name = settings.denormalize_layer_name_for_max_tracker_fn(layer, selected_input_index)
+
+            if len(net.blobs[denormalized_layer_name].data.shape) == 4:
+                reproduced_val = net.blobs[denormalized_layer_name].data[0,channel_idx,ii,jj]
             else:
-                reproduced_val = net.blobs[layer].data[0,channel_idx]
+                reproduced_val = net.blobs[denormalized_layer_name].data[0,channel_idx]
             if abs(reproduced_val - recorded_val) > .1:
                 print 'Warning: recorded value %s is suspiciously different from reproduced value %s. Is the filelist the same?' % (recorded_val, reproduced_val)
 
             if do_maxes:
                 #grab image from data layer, not from im (to ensure preprocessing / center crop details match between image and deconv/backprop)
-                out_arr = np.zeros((3,size_ii,size_jj), dtype='float32')
-                out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
+
+                if settings.is_siamese:
+
+                    # input is first image so select first 3 channels
+                    if selected_input_index == 0:
+                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                    # input is second image so select second 3 channels
+                    elif selected_input_index == 1:
+                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                    # input is both images so select concatenate data horizontally
+                    elif selected_input_index == -1:
+                        out_arr = np.zeros((3, size_ii, size_jj*2), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = net.blobs['data'].data[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                        out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = net.blobs['data'].data[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                    else:
+                        print "Error: invalid value for selected_input_index (", selected_input_index, ")"
+                else:
+                    out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
+
                 with WithTimer('Save img  ', quiet = not do_print):
                     save_caffe_image(out_arr, os.path.join(unit_dir, 'maxim_%03d.png' % max_idx_0),
                                      autoscale = False, autoscale_center = 0)
-                
+
             if do_deconv or do_deconv_norm:
-                diffs = net.blobs[layer].diff * 0
+                diffs = net.blobs[denormalized_layer_name].diff * 0
                 if len(diffs.shape) == 4:
                     diffs[0,channel_idx,ii,jj] = 1.0
                 else:
                     diffs[0,channel_idx] = 1.0
                 with WithTimer('Deconv    ', quiet = not do_print):
-                    net.deconv_from_layer(layer, diffs)
+                    net.deconv_from_layer(denormalized_layer_name, diffs)
+
+                if settings.is_siamese:
+
+                    # input is first image so select first 3 channels
+                    if selected_input_index == 0:
+                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                    # input is second image so select second 3 channels
+                    elif selected_input_index == 1:
+                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                    # input is both images so select concatenate data horizontally
+                    elif selected_input_index == -1:
+                        out_arr = np.zeros((3, size_ii, size_jj*2), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                        out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+
+                else: # normal operation
+                    out_arr = np.zeros((3,size_ii,size_jj), dtype='float32')
+                    out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
 
-                out_arr = np.zeros((3,size_ii,size_jj), dtype='float32')
-                out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
                 if out_arr.max() == 0:
                     print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
+
                 if do_deconv:
-                    with WithTimer('Save img  ', quiet = not do_print):
+                    with WithTimer('Save img  ', quiet=not do_print):
                         save_caffe_image(out_arr, os.path.join(unit_dir, 'deconv_%03d.png' % max_idx_0),
-                                         autoscale = False, autoscale_center = 0)
+                                         autoscale=False, autoscale_center=0)
                 if do_deconv_norm:
                     out_arr = np.linalg.norm(out_arr, axis=0)
-                    with WithTimer('Save img  ', quiet = not do_print):
+                    with WithTimer('Save img  ', quiet=not do_print):
                         save_caffe_image(out_arr, os.path.join(unit_dir, 'deconvnorm_%03d.png' % max_idx_0))
 
+
             if do_backprop or do_backprop_norm:
-                diffs = net.blobs[layer].diff * 0
+                diffs = net.blobs[denormalized_layer_name].diff * 0
                 diffs[0,channel_idx,ii,jj] = 1.0
                 with WithTimer('Backward  ', quiet = not do_print):
-                    net.backward_from_layer(layer, diffs)
+                    net.backward_from_layer(denormalized_layer_name, diffs)
+
+                if settings.is_siamese:
+
+                    # input is first image so select first 3 channels
+                    if selected_input_index == 0:
+                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+
+                    # input is second image so select second 3 channels
+                    elif selected_input_index == 1:
+                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+
+                    # input is both images so select concatenate data horizontally
+                    elif selected_input_index == -1:
+                        out_arr = np.zeros((3, size_ii, size_jj*2), dtype='float32')
+                        out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+                        out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
+
+                else: # normal operation
+                    out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                    out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
 
-                out_arr = np.zeros((3,size_ii,size_jj), dtype='float32')
-                out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
                 if out_arr.max() == 0:
                     print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
                 if do_backprop:
@@ -437,6 +531,6 @@ def output_max_patches(max_tracker, net, layer, idx_begin, idx_end, num_top, dat
                     out_arr = np.linalg.norm(out_arr, axis=0)
                     with WithTimer('Save img  ', quiet = not do_print):
                         save_caffe_image(out_arr, os.path.join(unit_dir, 'backpropnorm_%03d.png' % max_idx_0))
-                
+
         if do_info:
             info_file.close()
diff --git a/settings.py b/settings.py
index a0e16db50..3ff145ee8 100644
--- a/settings.py
+++ b/settings.py
@@ -8,6 +8,7 @@
 import sys
 
 # Import local / overridden settings. Turn off creation of settings_local.pyc to avoid stale settings if settings_local.py is removed.
+
 sys.dont_write_bytecode = True
 try:
     from settings_local import *
@@ -329,15 +330,58 @@
 is_conv_fn = locals().get('is_conv_fn', lambda layer_name: 'conv' in layer_name)
 
 # function to normalize layer name used in NetMaxTracker to combine activations of siamese layers
-# for example. this is used to make activations of layer "conv_1" and "conv_1_p" count the same
+# for example, this is used to make activations of layer "conv_1" and "conv_1_p" count the same
 normalize_layer_name_for_max_tracker_fn = locals().get('normalize_layer_name_for_max_tracker_fn', lambda layer_name: layer_name)
 
+# function to denormalize layer name used in NetMaxTracker to split combined activations of siamese layers
+# for example, given layer name "conv_1" and selected_input_index = 1, we might want to return "conv_1_p" to state that the twin layer should be used as source
+denormalize_layer_name_for_max_tracker_fn = locals().get('denormalize_layer_name_for_max_tracker_fn', lambda layer_name, selected_input_index: layer_name)
+
 # function which selects an image from a siamese input pair, given the layer name
 siamese_layer_to_index_of_saved_image_fn = locals().get('siamese_layer_to_index_of_saved_image_fn', lambda layer_name: 0)
 
 # location for max tracker output file
 find_max_acts_output_file = locals().get('find_max_acts_output_file', 'find_max_acts_output.pickled')
 
+# default value for do_maxes parameter in max_tracker
+max_tracker_do_maxes = locals().get('max_tracker_do_maxes', True)
+
+# default value for do_deconv parameter in max tracker
+max_tracker_do_deconv = locals().get('max_tracker_do_deconv', True)
+
+# default value for do_deconv_norm parameter in max tracker
+max_tracker_do_deconv_norm = locals().get('max_tracker_do_deconv_norm', True)
+
+# default value for do_backprop parameter in max tracker
+max_tracker_do_backprop = locals().get('max_tracker_do_backprop', True)
+
+# default value for do_backprop_norm parameter in max tracker
+max_tracker_do_backprop_norm = locals().get('max_tracker_do_backprop_norm', True)
+
+# default value for do_info parameter in max tracker
+max_tracker_do_info = locals().get('max_tracker_do_info', True)
+
+# default value for output_dir parameter in max tracker
+max_tracker_output_dir = locals().get('max_tracker_output_dir', True)
+
+# default value for layer parameter in max_tracker
+max_tracker_layers_to_output = locals().get('max_tracker_layers_to_output', [])
+
+# default value for list of layers used in max tracker
+# note the default is a hardcoded choice which was used in the original code,
+# for backward comparability we keep it as default, but you should change this parameter for each network
+default_max_tracker_layers_list = []
+# Description                           Name     Type           Input      Output   Filter   Stride  Pad
+default_max_tracker_layers_list.append(('data',  'Input',       None,      None,    None,    None,   None,))
+default_max_tracker_layers_list.append(('conv1', 'Convolution', (227,227), (55,55), (11,11), (4,4),  (0,0)))
+default_max_tracker_layers_list.append(('pool1', 'Pooling',     (55,55),   (27,27), (3,3),   (2,2),  (0,0)))
+default_max_tracker_layers_list.append(('conv2', 'Convolution', (27,27),   (27,27), (5,5),   (1,1),  (2,2)))
+default_max_tracker_layers_list.append(('pool2', 'Pooling',     (27,27),   (13,13), (3,3),   (2,2),  (0,0)))
+default_max_tracker_layers_list.append(('conv3', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
+default_max_tracker_layers_list.append(('conv4', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
+default_max_tracker_layers_list.append(('conv5', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
+max_tracker_layers_list = locals().get('max_tracker_layers_list', default_max_tracker_layers_list)
+
 ####################################
 #
 #  A few final sanity checks

From 99561607778e90d1ad3e1f341b7fa72a444baee8 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:03:53 +0300
Subject: [PATCH 007/106] fixed bug in backprop image generation

---
 find_maxes/max_tracker.py | 7 ++++++-
 1 file changed, 6 insertions(+), 1 deletion(-)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 581d8d70b..f8a693677 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -495,7 +495,12 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
             if do_backprop or do_backprop_norm:
                 diffs = net.blobs[denormalized_layer_name].diff * 0
-                diffs[0,channel_idx,ii,jj] = 1.0
+
+                if len(diffs.shape) == 4:
+                    diffs[0,channel_idx,ii,jj] = 1.0
+                else:
+                    diffs[0,channel_idx] = 1.0
+
                 with WithTimer('Backward  ', quiet = not do_print):
                     net.backward_from_layer(denormalized_layer_name, diffs)
 

From bc83b8c3d493ce10bb119dab16884e12b911a6aa Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:06:15 +0300
Subject: [PATCH 008/106] disabled generation of some of the outsputs by
 default

---
 find_maxes/crop_max_patches.py |  8 ++++----
 settings.py                    | 10 +++++-----
 2 files changed, 9 insertions(+), 9 deletions(-)

diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 16c667e07..2f020cd64 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -69,13 +69,13 @@ def main():
         mt = nmt.max_trackers[normalized_layer_name]
 
         if args.idx_begin is None:
-            args.idx_begin = 0
+            idx_begin = 0
         if args.idx_end is None:
-            args.idx_end = mt.max_vals.shape[0]
+            idx_end = mt.max_vals.shape[0]
 
-        with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, args.idx_begin, args.idx_end)):
+        with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, idx_begin, idx_end)):
 
-            output_max_patches(settings, mt, net, normalized_layer_name, args.idx_begin, args.idx_end,
+            output_max_patches(settings, mt, net, normalized_layer_name, idx_begin, idx_end,
                                args.N, args.datadir, args.filelist, args.outdir,
                                (args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
 
diff --git a/settings.py b/settings.py
index 3ff145ee8..2d03130c0 100644
--- a/settings.py
+++ b/settings.py
@@ -323,7 +323,7 @@
 # function used to convert a string label into something else, can be an integer
 convert_label_fn = locals().get('convert_label_fn', lambda label: int(label.strip()))
 
-# layers list used in NetMaxTracker
+# list of all the layer names which we want to track their maximal activations, usually convolutionals and fully-connecteds
 layers_for_max_tracker = locals().get('layers_for_max_tracker', ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob'])
 
 # function used to check if a layer is a convolutional
@@ -350,19 +350,19 @@
 max_tracker_do_deconv = locals().get('max_tracker_do_deconv', True)
 
 # default value for do_deconv_norm parameter in max tracker
-max_tracker_do_deconv_norm = locals().get('max_tracker_do_deconv_norm', True)
+max_tracker_do_deconv_norm = locals().get('max_tracker_do_deconv_norm', False)
 
 # default value for do_backprop parameter in max tracker
-max_tracker_do_backprop = locals().get('max_tracker_do_backprop', True)
+max_tracker_do_backprop = locals().get('max_tracker_do_backprop', False)
 
 # default value for do_backprop_norm parameter in max tracker
-max_tracker_do_backprop_norm = locals().get('max_tracker_do_backprop_norm', True)
+max_tracker_do_backprop_norm = locals().get('max_tracker_do_backprop_norm', False)
 
 # default value for do_info parameter in max tracker
 max_tracker_do_info = locals().get('max_tracker_do_info', True)
 
 # default value for output_dir parameter in max tracker
-max_tracker_output_dir = locals().get('max_tracker_output_dir', True)
+max_tracker_output_dir = locals().get('max_tracker_output_dir', './')
 
 # default value for layer parameter in max_tracker
 max_tracker_layers_to_output = locals().get('max_tracker_layers_to_output', [])

From a5b869db5100cc7de55d6d184489fd69af1b30d8 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:17:12 +0300
Subject: [PATCH 009/106] added conversion of layer name to top name in max
 search

---
 find_maxes/max_tracker.py | 27 +++++++++++++++++----------
 1 file changed, 17 insertions(+), 10 deletions(-)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index f8a693677..192dca4eb 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -109,7 +109,9 @@ def _init_with_net(self, net):
         self.max_trackers = {}
 
         for layer in self.layers:
-            blob = net.blobs[layer].data
+
+            top_name = net.top_names[layer][0]
+            blob = net.blobs[top_name].data
 
             # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
             # count as the same layer in terms of activations
@@ -131,7 +133,9 @@ def update(self, net, image_idx, image_class, net_unique_input_source):
             self._init_with_net(net)
 
         for layer in self.layers:
-            blob = net.blobs[layer].data
+
+            top_name = net.top_names[layer][0]
+            blob = net.blobs[top_name].data
 
             normalized_layer_name = self.settings.normalize_layer_name_for_max_tracker_fn(layer)
 
@@ -268,12 +272,13 @@ def save_representations(net, datadir, filelist, layer, first_N = None):
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Store     ', quiet = not do_print):
+            top_name = net.top_names[layer][0]
             if rep is None:
-                rep_shape = net.blobs[layer].data[0].shape   # e.g. (256,13,13)
+                rep_shape = net.blobs[top_name].data[0].shape   # e.g. (256,13,13)
                 rep = np.zeros((len(image_indices),) + rep_shape)   # e.g. (1000,256,13,13)
                 indices = [0] * len(image_indices)
             indices[ii] = image_idx
-            rep[ii] = net.blobs[layer].data[0]
+            rep[ii] = net.blobs[top_name].data[0]
 
     print 'done!'
     return indices,rep
@@ -283,7 +288,8 @@ def save_representations(net, datadir, filelist, layer, first_N = None):
 def get_max_data_extent(net, layer, rc, is_conv):
     '''Gets the maximum size of the data layer that can influence a unit on layer.'''
     if is_conv:
-        conv_size = net.blobs[layer].data.shape[2:4]        # e.g. (13,13) for conv5
+        top_name = net.top_names[layer][0]
+        conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
         layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
         data_slice = rc.convert_region(layer, 'data', layer_slice_middle)
         return data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
@@ -416,11 +422,12 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             # in siamese network, we wish to return from the normalized layer name and selected input index to the denormalized layer name
             # e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
             denormalized_layer_name = settings.denormalize_layer_name_for_max_tracker_fn(layer, selected_input_index)
+            denormalized_top_name = net.top_names[denormalized_layer_name][0]
 
-            if len(net.blobs[denormalized_layer_name].data.shape) == 4:
-                reproduced_val = net.blobs[denormalized_layer_name].data[0,channel_idx,ii,jj]
+            if len(net.blobs[denormalized_top_name].data.shape) == 4:
+                reproduced_val = net.blobs[denormalized_top_name].data[0,channel_idx,ii,jj]
             else:
-                reproduced_val = net.blobs[denormalized_layer_name].data[0,channel_idx]
+                reproduced_val = net.blobs[denormalized_top_name].data[0,channel_idx]
             if abs(reproduced_val - recorded_val) > .1:
                 print 'Warning: recorded value %s is suspiciously different from reproduced value %s. Is the filelist the same?' % (recorded_val, reproduced_val)
 
@@ -452,7 +459,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                                      autoscale = False, autoscale_center = 0)
 
             if do_deconv or do_deconv_norm:
-                diffs = net.blobs[denormalized_layer_name].diff * 0
+                diffs = net.blobs[denormalized_top_name].diff * 0
                 if len(diffs.shape) == 4:
                     diffs[0,channel_idx,ii,jj] = 1.0
                 else:
@@ -494,7 +501,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
 
             if do_backprop or do_backprop_norm:
-                diffs = net.blobs[denormalized_layer_name].diff * 0
+                diffs = net.blobs[denormalized_top_name].diff * 0
 
                 if len(diffs.shape) == 4:
                     diffs[0,channel_idx,ii,jj] = 1.0

From db6d833f3990311e05493fcb8a0372561ebdeb7b Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:36:52 +0300
Subject: [PATCH 010/106] - added support for loading offline visualization in
 the format which max tracker generates

---
 caffevis/jpg_vis_loading_thread.py | 153 ++++++++++++++++++++++-------
 image_misc.py                      |  48 +++++++++
 settings.py                        |   9 ++
 3 files changed, 173 insertions(+), 37 deletions(-)

diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 1bc69c52a..9c1a90e2a 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -1,9 +1,14 @@
 import os
 import time
+
+import cv2
 import numpy as np
+import glob
+import math
 
 from codependent_thread import CodependentThread
-from image_misc import caffe_load_image, ensure_uint255_and_resize_to_fit
+from image_misc import caffe_load_image, ensure_uint255_and_resize_to_fit, cv2_read_file_rgb, \
+    ensure_uint255_and_resize_without_fit
 from caffevis_helper import crop_to_corner
 
 
@@ -21,7 +26,71 @@ def __init__(self, settings, state, cache, loop_sleep, heartbeat_required):
         self.cache = cache
         self.loop_sleep = loop_sleep
         self.debug_level = 0
-        
+
+
+    def load_image_into_pane_original_format(self, state_layer, state_selected_unit, resize_shape, images, sub_folder,
+                                             file_pattern, image_index_to_set, should_crop_to_corner=False):
+
+        jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, sub_folder, state_layer,
+                                file_pattern % (state_layer, state_selected_unit))
+
+        try:
+            img = cv2_read_file_rgb(jpg_path)
+
+            if should_crop_to_corner:
+                img = crop_to_corner(img, 2)
+            images[image_index_to_set] = ensure_uint255_and_resize_without_fit(img, resize_shape)
+
+        except IOError:
+            print '\nAttempted to load file %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % (
+            jpg_path, state_layer)
+            pass
+
+    def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_unit, resize_shape, images,
+                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False):
+
+        unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer,
+                                        "unit_%04d" % (state_selected_unit),
+                                        file_search_pattern)
+
+        try:
+
+            # list unit images
+            unit_images_path = sorted(glob.glob(unit_folder_path))
+
+            # load all images
+            # unit_images = [caffe_load_image(unit_image_path, color=True) for unit_image_path in unit_images_path]
+            unit_images = [cv2_read_file_rgb(unit_image_path) for unit_image_path in unit_images_path]
+
+            if should_crop_to_corner:
+                unit_images = [crop_to_corner(img, 2) for img in unit_images]
+
+            # build mega image
+            (image_height, image_width, channels) = unit_images[0].shape
+            num_images = len(unit_images)
+            images_per_axis = int(math.ceil(math.sqrt(num_images)))
+            padding_pixel = 1
+            mega_image_height = images_per_axis * (image_height + 2*padding_pixel)
+            mega_image_width = images_per_axis * (image_width + 2*padding_pixel)
+            mega_image = np.zeros((mega_image_height,mega_image_width,channels))
+
+            for i in range(num_images):
+                cell_row = i % images_per_axis
+                cell_col = i / images_per_axis
+                mega_image_height_start = 1 + cell_row * (image_height + 2*padding_pixel)
+                mega_image_height_end = mega_image_height_start + image_height
+                mega_image_width_start = 1 + cell_col * (image_width + 2*padding_pixel)
+                mega_image_width_end = mega_image_width_start + image_width
+                mega_image[mega_image_height_start:mega_image_height_end,mega_image_width_start:mega_image_width_end,:] = unit_images[i]
+
+            images[image_index_to_set] = ensure_uint255_and_resize_without_fit(mega_image, resize_shape)
+
+        except:
+            print '\nAttempted to load files from %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % (
+                unit_folder_path, state_layer)
+            pass
+
+
     def run(self):
         print 'JPGVisLoadingThread.run called'
         
@@ -58,41 +127,51 @@ def run(self):
                 resize_shape = (data_shape[0]/3, data_shape[1])
             else:
                 resize_shape = (data_shape[0], data_shape[1]/3)
-            
-            # 0. e.g. regularized_opt/conv1/conv1_0037_montage.jpg
-            jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
-                                    'regularized_opt',
-                                    state_layer,
-                                    '%s_%04d_montage.jpg' % (state_layer, state_selected_unit))
-            try:
-                img = caffe_load_image(jpg_path, color = True)
-                img_corner = crop_to_corner(img, 2)
-                images[0] = ensure_uint255_and_resize_to_fit(img_corner, resize_shape)
-            except IOError:
-                print '\nAttempted to load file %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % (jpg_path, state_layer)
-                pass
-
-            # 1. e.g. max_im/conv1/conv1_0037.jpg
-            jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
-                                    'max_im',
-                                    state_layer,
-                                    '%s_%04d.jpg' % (state_layer, state_selected_unit))
-            try:
-                img = caffe_load_image(jpg_path, color = True)
-                images[1] = ensure_uint255_and_resize_to_fit(img, resize_shape)
-            except IOError:
-                pass                
-
-            # 2. e.g. max_deconv/conv1/conv1_0037.jpg
-            try:
-                jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
-                                        'max_deconv',
-                                        state_layer,
-                                        '%s_%04d.jpg' % (state_layer, state_selected_unit))
-                img = caffe_load_image(jpg_path, color = True)
-                images[2] = ensure_uint255_and_resize_to_fit(img, resize_shape)
-            except IOError:
-                pass
+
+
+            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+
+                # 0. e.g. regularized_opt/conv1/conv1_0037_montage.jpg
+                self.load_image_into_pane_original_format(state_layer, state_selected_unit, resize_shape, images,
+                                                          sub_folder='regularized_opt',
+                                                          file_pattern='%s_%04d_montage.jpg',
+                                                          image_index_to_set=0,
+                                                          should_crop_to_corner=True)
+
+            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                # self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                #                                              file_search_pattern='regularized_opt*.png',
+                #                                              image_index_to_set=0,
+                #                                              should_crop_to_corner=True)
+                # black image until we test it
+                images[0] = np.zeros((resize_shape[0],resize_shape[1],3))
+
+            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+
+                # 1. e.g. max_im/conv1/conv1_0037.jpg
+                self.load_image_into_pane_original_format(state_layer, state_selected_unit, resize_shape, images,
+                                                          sub_folder='max_im',
+                                                          file_pattern='%s_%04d.jpg',
+                                                          image_index_to_set=1)
+
+            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                                                             file_search_pattern='maxim*.png',
+                                                             image_index_to_set=1)
+
+
+            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+
+                # 2. e.g. max_deconv/conv1/conv1_0037.jpg
+                self.load_image_into_pane_original_format(state_layer, state_selected_unit, resize_shape, images,
+                                                          sub_folder='max_deconv',
+                                                          file_pattern='%s_%04d.jpg',
+                                                          image_index_to_set=2)
+
+            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                                                             file_search_pattern='deconv*.png',
+                                                             image_index_to_set=2)
 
             # Prune images that were not found:
             images = [im for im in images if im is not None]
diff --git a/image_misc.py b/image_misc.py
index 6c940d8cc..f16d0036d 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -277,6 +277,15 @@ def ensure_uint255_and_resize_to_fit(img, out_max_shape,
                          shrink_interpolation = shrink_interpolation,
                          grow_interpolation = grow_interpolation)
 
+def ensure_uint255_and_resize_without_fit(img, out_max_shape,
+                                     shrink_interpolation = cv2.INTER_LINEAR,
+                                     grow_interpolation = cv2.INTER_NEAREST):
+    as_uint255 = ensure_uint255(img)
+    return resize_without_fit(as_uint255, out_max_shape,
+                         dtype_out = 'uint8',
+                         shrink_interpolation = shrink_interpolation,
+                         grow_interpolation = grow_interpolation)
+
 
 def ensure_uint255(arr):
     '''If data is float, multiply by 255 and convert to uint8. Else leave as uint8.'''
@@ -362,6 +371,45 @@ def resize_to_fit(img, out_max_shape,
     return out
 
 
+def resize_without_fit(img, out_max_shape,
+                dtype_out = None,
+                shrink_interpolation = cv2.INTER_LINEAR,
+                grow_interpolation = cv2.INTER_NEAREST):
+    '''Resizes (without fit) to out_max_shape.
+
+    If one of the out_max_shape dimensions is None, then use only the other dimension to perform resizing.
+
+    '''
+
+    if dtype_out is not None and img.dtype != dtype_out:
+        dtype_in_size = img.dtype.itemsize
+        dtype_out_size = np.dtype(dtype_out).itemsize
+        convert_early = (dtype_out_size < dtype_in_size)
+        convert_late = not convert_early
+    else:
+        convert_early = False
+        convert_late = False
+    if out_max_shape[0] is None:
+        scale_0 = float(out_max_shape[1]) / img.shape[1]
+        scale_1 = float(out_max_shape[1]) / img.shape[1]
+    elif out_max_shape[1] is None:
+        scale_1 = float(out_max_shape[0]) / img.shape[0]
+        scale_0 = float(out_max_shape[0]) / img.shape[0]
+    else:
+
+        scale_0 = float(out_max_shape[0]) / img.shape[0]
+        scale_1 = float(out_max_shape[1]) / img.shape[1]
+
+    if convert_early:
+        img = np.array(img, dtype=dtype_out)
+    out = cv2.resize(img,
+            (int(img.shape[1] * scale_1), int(img.shape[0] * scale_0)),   # in (c,r) order
+                     interpolation = grow_interpolation if min(scale_0,scale_1) > 1 else shrink_interpolation)
+    if convert_late:
+        out = np.array(out, dtype=dtype_out)
+    return out
+
+
 class FormattedString(object):
     def __init__(self, string, defaults, face=None, fsize=None, clr=None, thick=None, align=None, width=None):
         self.string = string
diff --git a/settings.py b/settings.py
index 2d03130c0..30609cc32 100644
--- a/settings.py
+++ b/settings.py
@@ -193,6 +193,12 @@
 # visualizations from, if any. None to disable.
 caffevis_unit_jpg_dir = locals().get('caffevis_unit_jpg_dir', None)
 
+# what is the folder format for loading precomputed visualizations,
+# options are:
+#   "original_combined_single_image" - every unit has a single layer
+#   "max_tracker_output" - every unit has a list of images to be loaded
+caffevis_unit_jpg_dir_folder_format = locals().get('caffevis_unit_jpg_dir_folder_format', 'original_combined_single_image')
+
 # List. For which layers should jpgs be loaded for
 # visualization? If a layer name (full name, not prettified) is given
 # here, we will try to load jpgs to visualize each unit. This is used
@@ -382,6 +388,9 @@
 default_max_tracker_layers_list.append(('conv5', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
 max_tracker_layers_list = locals().get('max_tracker_layers_list', default_max_tracker_layers_list)
 
+
+
+
 ####################################
 #
 #  A few final sanity checks

From d3ca85267cf12942767a12ce15b88094fa3e409a Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:40:53 +0300
Subject: [PATCH 011/106] fixed crash when trying to deconv or backprop from
 invalid layers

---
 caffevis/caffe_proc_thread.py | 8 ++++++++
 1 file changed, 8 insertions(+)

diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 891799d07..4934da821 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -112,6 +112,10 @@ def run(self):
                         except AttributeError:
                             print 'ERROR: required bindings (backward_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
                             raise
+                        except ValueError:
+                            print "ERROR: probably impossible to backprop layer %s, ignoring to avoid crash" % (backprop_layer)
+                            with self.state.lock:
+                                self.state.back_enabled = False
                 else:
                     with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1):
                         #print '**** Doing deconv with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max())
@@ -120,6 +124,10 @@ def run(self):
                         except AttributeError:
                             print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
                             raise
+                        except ValueError:
+                            print "ERROR: probably impossible to deconv layer %s, ignoring to avoid crash" % (backprop_layer)
+                            with self.state.lock:
+                                self.state.back_enabled = False
 
                 with self.state.lock:
                     self.state.back_stale = False

From de2273f572968e0d5d145e8e02fc7155cf7d3fca Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:54:19 +0300
Subject: [PATCH 012/106] fixed so that current deconv shows only selected
 image deconvolution

---
 caffevis/app.py | 46 ++++++++++++++++++++++++++++++++++++++++------
 settings.py     |  4 +++-
 2 files changed, 43 insertions(+), 7 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 6a969d41f..c0ebce990 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -21,15 +21,37 @@
     check_force_backward_true, load_mean
 
 
+def process_settings(settings):
+
+    # convert layers_list_to_show to layer_to_selected_image, only relevant in siamese networks
+    if settings.is_siamese:
+        settings.siamese_layer_to_selected_image = dict()
+        for item in settings.siamese_layers_list:
+
+            # if simple layer name, selected both images
+            if type(item) is str:
+                settings.siamese_layer_to_selected_image[item] = -1
+
+            # if item is pair of layers
+            elif (type(item) is tuple) and (len(item) == 2):
+                settings.siamese_layer_to_selected_image[item[0]] = 0
+                settings.siamese_layer_to_selected_image[item[1]] = 1
+
+    return
+
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
 
     def __init__(self, settings, key_bindings):
         super(CaffeVisApp, self).__init__(settings, key_bindings)
+
+        process_settings(settings)
+
         print 'Got settings', settings
         self.settings = settings
         self.bindings = key_bindings
 
+
         self._net_channel_swap = settings.caffe_net_channel_swap
 
         if self._net_channel_swap is None:
@@ -537,17 +559,29 @@ def _draw_back_pane(self, pane):
 
             if (self.settings.is_siamese) and (grad_img.shape[2] == 6):
 
+                selected_image = -1
+                if state_layer in self.settings.siamese_layer_to_selected_image:
+                    selected_image = self.settings.siamese_layer_to_selected_image[state_layer]
+
                 # split gradient into two images, by channels
                 grad_img1 = grad_img[:, :, 0:3]
                 grad_img2 = grad_img[:, :, 3:6]
 
-                # resize each gradient image to half the pane size
-                half_pane_shape = (pane.data.shape[0] / 2, pane.data.shape[1])
-                grad_img_disp1 = cv2.resize(grad_img1[:], half_pane_shape)
-                grad_img_disp2 = cv2.resize(grad_img2[:], half_pane_shape)
+                # use both images
+                if selected_image == -1:
+                    # resize each gradient image to half the pane size
+                    half_pane_shape = (pane.data.shape[0] / 2, pane.data.shape[1])
+                    grad_img_disp1 = cv2.resize(grad_img1[:], half_pane_shape)
+                    grad_img_disp2 = cv2.resize(grad_img2[:], half_pane_shape)
+
+                    # generate the pane image by concatenating both images
+                    grad_img_disp = np.concatenate((grad_img_disp1, grad_img_disp2), axis=1)
+
+                elif selected_image == 0:
+                    grad_img_disp = grad_img1
 
-                # generate the pane image by concatenating both images
-                grad_img_disp = np.concatenate((grad_img_disp1, grad_img_disp2), axis=1)
+                elif selected_image == 1:
+                    grad_img_disp = grad_img2
 
             else:
                 grad_img_disp = grad_img
diff --git a/settings.py b/settings.py
index 30609cc32..5094e3750 100644
--- a/settings.py
+++ b/settings.py
@@ -388,7 +388,9 @@
 default_max_tracker_layers_list.append(('conv5', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
 max_tracker_layers_list = locals().get('max_tracker_layers_list', default_max_tracker_layers_list)
 
-
+# list of siamese layers/blobs to show
+# note: if an item in the list is a pair of layers, then it is a siamese layer
+siamese_layers_list = locals().get('siamese_layers_list', [])
 
 
 ####################################

From 5d374bf8d6802fe6028a3a6c2349d4f7bb151a05 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:57:18 +0300
Subject: [PATCH 013/106] - slight change in optimizer to be able to optimize
 over a Siamese network - added setting to set optimize_image output - added
 timestamps to log

---
 caffevis/app.py                |  1 -
 optimize/gradient_optimizer.py | 28 ++++++++--
 optimize_image.py              | 98 +++++++++++++++++++---------------
 settings.py                    | 33 ++++++++++++
 4 files changed, 110 insertions(+), 50 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index c0ebce990..87c76d646 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -51,7 +51,6 @@ def __init__(self, settings, key_bindings):
         self.settings = settings
         self.bindings = key_bindings
 
-
         self._net_channel_swap = settings.caffe_net_channel_swap
 
         if self._net_channel_swap is None:
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index e170a12cf..280139a85 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -3,6 +3,7 @@
 import os
 import errno
 import pickle
+import datetime
 import StringIO
 from pylab import *
 from scipy.ndimage.filters import gaussian_filter
@@ -154,7 +155,8 @@ def __str__(self):
 class GradientOptimizer(object):
     '''Finds images by gradient.'''
     
-    def __init__(self, net, data_mean, labels = None, label_layers = None, channel_swap_to_rgb = None):
+    def __init__(self, settings, net, data_mean, labels = None, label_layers = None, channel_swap_to_rgb = None):
+        self.settings = settings
         self.net = net
         self.data_mean = data_mean
         self.labels = labels if labels else ['labels not provided' for ii in range(1000)]
@@ -304,7 +306,7 @@ def _optimize(self, params, x0):
                 # constant fixed learning rate
                 lr = params.lr_params['lr']
             else:
-                raise Exception('Unimlemented lr_policy')
+                raise Exception('Unimplemented lr_policy')
 
             
             # 6. Apply gradient update and regularizations
@@ -345,6 +347,8 @@ def _optimize(self, params, x0):
                     smallaben3 = tile(smallaben[:,newaxis,:,:], (1,3,1,1))
                     xx = xx - xx*smallaben3
 
+            print '     timestamp:', datetime.datetime.now()
+
         if results.meta_result is None:
             if results.majority_obj is not None:
                 results.meta_result = 'Metaresult: majority success'
@@ -381,9 +385,23 @@ def save_results(self, params, results, prefix_template, brave = False, skipbig
                 saveimagesc('%smajority_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
 
         if results.best_xx is not None:
-            asimg = results.best_xx[self.channel_swap_to_rgb].transpose((1,2,0))
-            saveimagescc('%sbest_X.jpg' % prefix, asimg, 0)
-            saveimagesc('%sbest_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
+            # results.best_xx.shape is (6,224,224)
+
+            if (self.settings.is_siamese) and (results.best_xx.shape[0] == 6):
+                asimg1 = results.best_xx[self.channel_swap_to_rgb[[0,1,2]]].transpose((1, 2, 0))
+                asimg2 = results.best_xx[self.channel_swap_to_rgb[[3,4,5]]].transpose((1, 2, 0))
+
+                saveimagescc('%sbest_X_A.jpg' % prefix, asimg1, 0)
+                saveimagescc('%sbest_X_B.jpg' % prefix, asimg2, 0)
+
+                saveimagesc('%sbest_Xpm_A.jpg' % prefix, asimg1 + self._data_mean_rgb_img[:,:,[0,1,2]])  # PlusMean
+                saveimagesc('%sbest_Xpm_B.jpg' % prefix, asimg2 + self._data_mean_rgb_img[:,:,[3,4,5]])  # PlusMean
+
+
+            else:
+                asimg = results.best_xx[self.channel_swap_to_rgb].transpose((1,2,0))
+                saveimagescc('%sbest_X.jpg' % prefix, asimg, 0)
+                saveimagesc('%sbest_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
 
         with open('%sinfo.txt' % prefix, 'w') as ff:
             print >>ff, params
diff --git a/optimize_image.py b/optimize_image.py
index a555d9e02..ec825e225 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -7,7 +7,7 @@
 
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
-from caffevis.caffevis_helper import check_force_backward_true, read_label_file
+from caffevis.caffevis_helper import check_force_backward_true, read_label_file, load_mean
 
 LR_POLICY_CHOICES = ('constant', 'progress', 'progress01')
 
@@ -25,8 +25,6 @@ def get_parser():
                         help = 'Path to caffe network prototxt.')
     parser.add_argument('--net-weights', type = str, default = settings.caffevis_network_weights,
                         help = 'Path to caffe network weights.')
-    parser.add_argument('--mean', type = str, default = repr(settings.caffevis_data_mean),
-                        help = '''Mean. The mean may be None, a tuple of one mean value per channel, or a string specifying the path to a mean image to load. Because of the multiple datatypes supported, this argument must be specified as a string that evaluates to a valid Python object. For example: "None", "(10,20,30)", and "'mean.npy'" are all valid values. Note that to specify a string path to a mean file, it must be passed with quotes, which usually entails passing it with double quotes in the shell! Alternately, just provide the mean in settings_local.py.''')
     parser.add_argument('--channel-swap-to-rgb', type = str, default = '(2,1,0)',
                         help = 'Permutation to apply to channels to change to RGB space for plotting. Hint: (0,1,2) if your network is trained for RGB, (2,1,0) if it is trained for BGR.')
     parser.add_argument('--data-size', type = str, default = '(227,227)',
@@ -37,25 +35,25 @@ def get_parser():
     # Where to start
     parser.add_argument('--start-at', type = str, default = 'mean_plus_rand', choices = ('mean_plus_rand', 'randu', 'mean'),
                         help = 'How to generate x0, the initial point used in optimization.')
-    parser.add_argument('--rand-seed', type = int, default = 0,
+    parser.add_argument('--rand-seed', type = int, default = settings.optimize_image_rand_seed,
                         help = 'Random seed used for generating the start-at image (use different seeds to generate different images).')
 
     # What to optimize
-    parser.add_argument('--push-layer', type = str, default = 'fc8',
+    parser.add_argument('--push-layer', type = str, default = settings.optimize_image_push_layer,
                         help = 'Name of layer that contains the desired neuron whose value is optimized.')
-    parser.add_argument('--push-channel', type = int, default = '130',
+    parser.add_argument('--push-channel', type = int, default = settings.optimize_image_push_channel,
                         help = 'Channel number for desired neuron whose value is optimized (channel for conv, neuron index for FC).')
-    parser.add_argument('--push-spatial', type = str, default = 'None',
+    parser.add_argument('--push-spatial', type = str, default = settings.optimize_image_push_spatial,
                         help = 'Which spatial location to push for conv layers. For FC layers, set this to None. For conv layers, set it to a tuple, e.g. when using `--push-layer conv5` on AlexNet, --push-spatial (6,6) will maximize the center unit of the 13x13 spatial grid.')
     parser.add_argument('--push-dir', type = float, default = 1,
                         help = 'Which direction to push the activation of the selected neuron, that is, the value used to begin backprop. For example, use 1 to maximize the selected neuron activation and  -1 to minimize it.')
 
     # Use regularization?
-    parser.add_argument('--decay', type = float, default = 0,
+    parser.add_argument('--decay', type = float, default = settings.optimize_image_decay,
                         help = 'Amount of L2 decay to use.')
-    parser.add_argument('--blur-radius', type = float, default = 0,
+    parser.add_argument('--blur-radius', type = float, default = settings.optimize_image_blur_radius,
                         help = 'Radius in pixels of blur to apply after each BLUR_EVERY steps. If 0, perform no blurring. Blur sizes between 0 and 0.3 work poorly.')
-    parser.add_argument('--blur-every', type = int, default = 0,
+    parser.add_argument('--blur-every', type = int, default = settings.optimize_image_blue_every,
                         help = 'Blur every BLUR_EVERY steps. If 0, perform no blurring.')
     parser.add_argument('--small-val-percentile', type = float, default = 0,
                         help = 'Induce sparsity by setting pixels with absolute value under SMALL_VAL_PERCENTILE percentile to 0. Not discussed in paper. 0 to disable.')
@@ -67,21 +65,21 @@ def get_parser():
                         help = 'Induce sparsity by setting pixels with contribution under PX_BENEFIT_PERCENTILE percentile to 0. \\theta_{c_pct} from the paper. 0 to disable.')
 
     # How much to optimize
-    parser.add_argument('--lr-policy', type = str, default = 'constant', choices = LR_POLICY_CHOICES,
+    parser.add_argument('--lr-policy', type = str, default = settings.optimize_image_lr_policy, choices = LR_POLICY_CHOICES,
                         help = 'Learning rate policy. See description in lr-params.')
-    parser.add_argument('--lr-params', type = str, default = '{"lr": 1}',
+    parser.add_argument('--lr-params', type = str, default = settings.optimize_image_lr_params,
                         help = 'Learning rate params, specified as a string that evalutes to a Python dict. Params that must be provided dependon which lr-policy is selected. The "constant" policy requires the "lr" key and uses the constant given learning rate. The "progress" policy requires the "max_lr" and "desired_prog" keys and scales the learning rate such that the objective function will change by an amount equal to DESIRED_PROG under a linear objective assumption, except the LR is limited to MAX_LR. The "progress01" policy requires the "max_lr", "early_prog", and "late_prog_mult" keys and is tuned for optimizing neurons with outputs in the [0,1] range, e.g. neurons on a softmax layer. Under this policy optimization slows down as the output approaches 1 (see code for details).')
-    parser.add_argument('--max-iter', type = int, default = 500,
+    parser.add_argument('--max-iter', type = int, default = settings.optimize_image_max_iter,
                         help = 'Number of iterations of the optimization loop.')
 
     # Where to save results
-    parser.add_argument('--output-prefix', type = str, default = 'optimize_results/opt',
+    parser.add_argument('--output-prefix', type = str, default = settings.optimize_image_output_prefix,
                         help = 'Output path and filename prefix (default: optimize_results/opt)')
     parser.add_argument('--output-template', type = str, default = '%(p.push_layer)s_%(p.push_channel)04d_%(p.rand_seed)d',
                         help = 'Output filename template; see code for details (default: "%%(p.push_layer)s_%%(p.push_channel)04d_%%(p.rand_seed)d"). '
                         'The default output-prefix and output-template produce filenames like "optimize_results/opt_prob_0278_0_best_X.jpg"')
-    parser.add_argument('--brave', action = 'store_true', help = 'Allow overwriting existing results files. Default: off, i.e. cowardly refuse to overwrite existing files.')
-    parser.add_argument('--skipbig', action = 'store_true', help = 'Skip outputting large *info_big.pkl files (contains pickled version of x0, last x, best x, first x that attained max on the specified layer.')
+    parser.add_argument('--brave', action = 'store_true', default=True, help = 'Allow overwriting existing results files. Default: off, i.e. cowardly refuse to overwrite existing files.')
+    parser.add_argument('--skipbig', action = 'store_true', default=True, help = 'Skip outputting large *info_big.pkl files (contains pickled version of x0, last x, best x, first x that attained max on the specified layer.')
 
     return parser
 
@@ -135,22 +133,37 @@ def main():
     args = parser.parse_args()
     
     # Finish parsing args
-    channel_swap_to_rgb = eval(args.channel_swap_to_rgb)
-    assert isinstance(channel_swap_to_rgb, tuple) and len(channel_swap_to_rgb) > 0, 'channel_swap_to_rgb should be a tuple'
-    data_size = eval(args.data_size)
-    assert isinstance(data_size, tuple) and len(data_size) == 2, 'data_size should be a length 2 tuple'
-    #channel_swap_inv = tuple([net_channel_swap.index(ii) for ii in range(len(net_channel_swap))])
 
     lr_params = parse_and_validate_lr_params(parser, args.lr_policy, args.lr_params)
     push_spatial = parse_and_validate_push_spatial(parser, args.push_spatial)
-    
-    # Load mean
-    data_mean = eval(args.mean)
 
-    if isinstance(data_mean, basestring):
+    net_channel_swap = settings.caffe_net_channel_swap
+
+    range_scale = settings.caffe_net_raw_scale
+            
+    # Load network
+    sys.path.insert(0, os.path.join(args.caffe_root, 'python'))
+    import caffe
+    if settings.caffevis_mode_gpu:
+        caffe.set_mode_gpu()
+        print 'optimize_image mode (in main thread):     GPU'
+    else:
+        caffe.set_mode_cpu()
+        print 'optimize_image mode (in main thread):     CPU'
+
+    net = caffe.Classifier(
+        args.deploy_proto,
+        args.net_weights,
+        mean=None,  # Set to None for now, assign later         # self._data_mean,
+        channel_swap=net_channel_swap,
+        raw_scale=range_scale,
+        image_dims=settings.caffe_net_image_dims,
+    )
+
+    if isinstance(settings.caffevis_data_mean, basestring):
         # If the mean is given as a filename, load the file
         try:
-            data_mean = np.load(data_mean)
+            data_mean = load_mean(settings.caffevis_data_mean)
         except IOError:
             print '\n\nCound not load mean file:', data_mean
             print 'To fetch a default model and mean file, use:\n'
@@ -158,40 +171,37 @@ def main():
             print '  $ cp ./fetch.sh\n\n'
             print 'Or to use your own mean, change caffevis_data_mean in settings_local.py or override by running with `--mean MEAN_FILE` (see --help).\n'
             raise
+        input_shape = net.blobs[net.inputs[0]].data.shape[-2:]  # e.g. 227x227
+
         # Crop center region (e.g. 227x227) if mean is larger (e.g. 256x256)
-        excess_h = data_mean.shape[1] - data_size[0]
-        excess_w = data_mean.shape[2] - data_size[1]
-        assert excess_h >= 0 and excess_w >= 0, 'mean should be at least as large as %s' % repr(data_size)
-        data_mean = data_mean[:, (excess_h/2):(excess_h/2+data_size[0]), (excess_w/2):(excess_w/2+data_size[1])]
-    elif data_mean is None:
+        excess_h = data_mean.shape[1] - input_shape[0]
+        excess_w = data_mean.shape[2] - input_shape[1]
+        assert excess_h >= 0 and excess_w >= 0, 'mean should be at least as large as %s' % repr(input_shape)
+        data_mean = data_mean[:, (excess_h / 2):(excess_h / 2 + input_shape[0]),
+                    (excess_w / 2):(excess_w / 2 + input_shape[1])]
+    elif args.data_mean is None:
         pass
     else:
         # The mean has been given as a value or a tuple of values
-        data_mean = np.array(data_mean)
+        data_mean = np.array(settings.caffevis_data_mean)
         # Promote to shape C,1,1
         while len(data_mean.shape) < 3:
             data_mean = np.expand_dims(data_mean, -1)
 
     print 'Using mean:', repr(data_mean)
-            
-    # Load network
-    sys.path.insert(0, os.path.join(args.caffe_root, 'python'))
-    import caffe
-    net = caffe.Classifier(
-        args.deploy_proto,
-        args.net_weights,
-        mean = data_mean,
-        raw_scale = 1.0,
-    )
+
+    if data_mean is not None:
+        net.transformer.set_mean(net.inputs[0], data_mean)
+
     check_force_backward_true(settings.caffevis_deploy_prototxt)
 
     labels = None
     if settings.caffevis_labels:
         labels = read_label_file(settings.caffevis_labels)
 
-    optimizer = GradientOptimizer(net, data_mean, labels = labels,
+    optimizer = GradientOptimizer(settings, net, data_mean, labels = labels,
                                   label_layers = settings.caffevis_label_layers,
-                                  channel_swap_to_rgb = channel_swap_to_rgb)
+                                  channel_swap_to_rgb = net_channel_swap)
     
     params = FindParams(
         start_at = args.start_at,
diff --git a/settings.py b/settings.py
index 5094e3750..672c0b98c 100644
--- a/settings.py
+++ b/settings.py
@@ -392,6 +392,39 @@
 # note: if an item in the list is a pair of layers, then it is a siamese layer
 siamese_layers_list = locals().get('siamese_layers_list', [])
 
+# rand-seed parameter for optimize_image.py
+optimize_image_rand_seed = locals().get('optimize_image_rand_seed', 0)
+
+# push-layer parameter for optimize_image.py
+optimize_image_push_layer = locals().get('optimize_image_push_layer', 'fc8')
+
+# push-channel parameter for optimize_image.py
+optimize_image_push_channel = locals().get('optimize_image_push_channel', '130')
+
+# push-spatial parameter for optimize_image.py
+optimize_image_push_spatial = locals().get('optimize_image_push_spatial', 'None')
+
+# decay parameter for optimize_image.py
+optimize_image_decay = locals().get('optimize_image_decay', 0)
+
+# blur-radius parameter for optimize_image.py
+optimize_image_blur_radius = locals().get('optimize_image_blur_radius', 0)
+
+# blur-every parameter for optimize_image.py
+optimize_image_blue_every = locals().get('optimize_image_blue_every', 0)
+
+# lr-policy parameter for optimize_image.py
+optimize_image_lr_policy = locals().get('optimize_image_lr_policy', 'constant')
+
+# lr-params parameter for optimize_image.py
+optimize_image_lr_params = locals().get('optimize_image_lr_params', '{"lr": 1}')
+
+# max-iter parameter for optimize_image.py
+optimize_image_max_iter = locals().get('optimize_image_max_iter', 500)
+
+# output-prefix parameter for optimize_image.py
+optimize_image_output_prefix = locals().get('optimize_image_output_prefix', 'optimize_results/opt')
+
 
 ####################################
 #

From 6c96738f8da9280e74a3fa2f39b3a1baee708a5c Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:58:46 +0300
Subject: [PATCH 014/106] added support for camera mode in siamese network

---
 caffevis/caffe_proc_thread.py |  3 ++-
 caffevis/caffevis_helper.py   |  2 +-
 input_fetcher.py              | 15 +++++++++++++--
 live_vis.py                   |  2 +-
 4 files changed, 17 insertions(+), 5 deletions(-)

diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 4934da821..efbdb9cc4 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -85,7 +85,8 @@ def run(self):
                 #print 'TIMING:, processing frame'
                 self.frames_processed_fwd += 1
 
-                if self.settings.is_siamese:
+                if self.settings.is_siamese and ((type(frame), len(frame)) == (tuple, 2)):
+
                     frame1 = frame[0]
                     frame2 = frame[1]
 
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index bdfe68eda..cdfd0b50a 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -8,7 +8,7 @@
 
 def net_preproc_forward(settings, net, img, data_hw):
 
-    if settings.is_siamese:
+    if settings.is_siamese and img.shape[2] == 6:
         appropriate_shape = data_hw + (6,)
     else:
         appropriate_shape = data_hw + (3,)
diff --git a/input_fetcher.py b/input_fetcher.py
index 276a177f7..7b19ba4a0 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -125,14 +125,25 @@ def run(self):
                 if self.freeze_cam and self.latest_cam_frame is not None:
                     # If static file mode was switched to cam mode but cam is still frozen, we need to push the cam frame again
                     if not self.latest_frame_is_from_cam:
-                        self._increment_and_set_frame(self.latest_cam_frame, True)
+
+                        if self.settings.is_siamese:
+                            im = (self.latest_cam_frame, self.latest_cam_frame)
+                        else:
+                            im = self.latest_cam_frame
+
+                        self._increment_and_set_frame(im, True)
                 else:
                     frame_full = read_cam_frame(self.bound_cap_device)
                     #print '====> just read frame', frame_full.shape
                     frame = crop_to_square(frame_full)
                     with self.lock:
                         self.latest_cam_frame = frame
-                        self._increment_and_set_frame(self.latest_cam_frame, True)
+
+                        if self.settings.is_siamese:
+                            im = (self.latest_cam_frame, self.latest_cam_frame)
+                        else:
+                            im = self.latest_cam_frame
+                        self._increment_and_set_frame(im, True)
             
             time.sleep(self.sleep_after_read_frame)
             #print 'Reading one frame took', time.time() - start_time
diff --git a/live_vis.py b/live_vis.py
index 0d4355ec9..d5baf0035 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -318,7 +318,7 @@ def handle_key_post_apps(self, key):
     def display_frame(self, frame):
 
         full_pane_shape = self.panes['input'].data.shape[:2][::-1]
-        if self.settings.is_siamese:
+        if self.settings.is_siamese and ((type(frame),len(frame)) == (tuple,2)):
             frame1 = frame[0]
             frame2 = frame[1]
             half_pane_shape = (full_pane_shape[0] / 2, full_pane_shape[1])

From 813564ff4618791fa940fa209ac333fdc9d92c5e Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 15 Sep 2017 22:59:27 +0300
Subject: [PATCH 015/106] - added keyboard binding which will be used to change
 siamese input mode, currently does nothing

---
 bindings.py                    | 4 ++++
 caffevis/app.py                | 2 +-
 caffevis/caffevis_app_state.py | 4 ++++
 3 files changed, 9 insertions(+), 1 deletion(-)

diff --git a/bindings.py b/bindings.py
index 7de7f9dfd..bd429924a 100644
--- a/bindings.py
+++ b/bindings.py
@@ -124,4 +124,8 @@ def get_key_help(self, tag):
 _.add('toggle_unit_jpgs', '9',
        'Turn on or off display of loaded jpg visualization')
 
+_.add('siamese_input_mode', 'v',
+        'Cycle between siamese input modes {first image, second image, both images}')
+
+
 bindings = _
diff --git a/caffevis/app.py b/caffevis/app.py
index 87c76d646..795320d13 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -676,7 +676,7 @@ def draw_help(self, help_pane, locy):
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'pattern_mode',
                     'ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
-                    'boost_gamma', 'boost_individual', 'reset_state'):
+                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
             lines.append([FormattedString(label, defaults, width=120, align='right'),
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 904096d53..add5f8f18 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -51,6 +51,7 @@ def _reset_user_state(self):
         self.layers_show_back = False   # False: show forward activations. True: show backward diffs
         self.show_label_predictions = self.settings.caffevis_init_show_label_predictions
         self.show_unit_jpgs = self.settings.caffevis_init_show_unit_jpgs
+        self.siamese_input_mode = 2 # 0: first image, 1: second image, 2: both images
         self.drawing_stale = True
         kh,_ = self.bindings.get_key_help('help_mode')
         self.extra_msg = '%s for help' % kh[0]
@@ -173,6 +174,9 @@ def handle_key(self, key):
             elif tag == 'toggle_unit_jpgs':
                 self.show_unit_jpgs = not self.show_unit_jpgs
 
+            elif tag == 'siamese_input_mode':
+                self.siamese_input_mode = (self.siamese_input_mode + 1) % 3
+
             else:
                 key_handled = False
 

From c0d6041de605260ff1abd891f587067a2327a7e4 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 16 Sep 2017 23:50:02 +0300
Subject: [PATCH 016/106] - added support for SiameseInputMode.FIRST_IMAGE,
 SECOND_IMAGE, BOTH_IMAGES - renamed: layer_print_names to header_print_names,
 _layers to _headers

---
 caffevis/app.py                | 190 ++++++++++++++++++++-------------
 caffevis/caffe_proc_thread.py  |  37 +++++--
 caffevis/caffevis_app_state.py | 154 ++++++++++++++++++++++----
 live_vis.py                    |   1 -
 4 files changed, 280 insertions(+), 102 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 795320d13..0409cddcb 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -12,41 +12,21 @@
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
 from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
-    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio
+    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio, resize_without_fit
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
-from caffevis_app_state import CaffeVisAppState
+from caffevis_app_state import CaffeVisAppState, SiameseInputMode
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, load_mean
 
 
-def process_settings(settings):
-
-    # convert layers_list_to_show to layer_to_selected_image, only relevant in siamese networks
-    if settings.is_siamese:
-        settings.siamese_layer_to_selected_image = dict()
-        for item in settings.siamese_layers_list:
-
-            # if simple layer name, selected both images
-            if type(item) is str:
-                settings.siamese_layer_to_selected_image[item] = -1
-
-            # if item is pair of layers
-            elif (type(item) is tuple) and (len(item) == 2):
-                settings.siamese_layer_to_selected_image[item[0]] = 0
-                settings.siamese_layer_to_selected_image[item[1]] = 1
-
-    return
-
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
 
     def __init__(self, settings, key_bindings):
         super(CaffeVisApp, self).__init__(settings, key_bindings)
 
-        process_settings(settings)
-
         print 'Got settings', settings
         self.settings = settings
         self.bindings = key_bindings
@@ -151,7 +131,7 @@ def _populate_net_layer_info(self):
     def start(self):
         self.state = CaffeVisAppState(self.net, self.settings, self.bindings, self.net_layer_info)
         self.state.drawing_stale = True
-        self.layer_print_names = [get_pretty_layer_name(self.settings, nn) for nn in self.state._layers]
+        self.header_print_names = [get_pretty_layer_name(self.settings, nn) for nn in self.state._headers]
 
         if self.proc_thread is None or not self.proc_thread.is_alive():
             # Start thread if it's not already running
@@ -295,13 +275,13 @@ def _draw_control_pane(self, pane):
                     'clr':   to_255(self.settings.caffevis_control_clr),
                     'thick': self.settings.caffevis_control_thick}
 
-        for ii in range(len(self.layer_print_names)):
-            fs = FormattedString(self.layer_print_names[ii], defaults)
-            this_layer = self.state._layers[ii]
+        for ii in range(len(self.header_print_names)):
+            fs = FormattedString(self.header_print_names[ii], defaults)
+            this_layer = self.state._headers[ii]
             if self.state.backprop_selection_frozen and this_layer == self.state.backprop_layer:
                 fs.clr   = to_255(self.settings.caffevis_control_clr_bp)
                 fs.thick = self.settings.caffevis_control_thick_bp
-            if this_layer == self.state.layer:
+            if this_layer == self.state._headers[self.state.layer_idx]:
                 if self.state.cursor_area == 'top':
                     fs.clr = to_255(self.settings.caffevis_control_clr_cursor)
                     fs.thick = self.settings.caffevis_control_thick_cursor
@@ -328,7 +308,7 @@ def _draw_status_pane(self, pane):
         fps = self.proc_thread.approx_fps()
         with self.state.lock:
             print >>status, 'pattern' if self.state.pattern_mode else ('back' if self.state.layers_show_back else 'fwd'),
-            print >>status, '%s:%d |' % (self.state.layer, self.state.selected_unit),
+            print >>status, '%s:%d |' % (self.state.get_default_layer_name(), self.state.selected_unit),
             if not self.state.back_enabled:
                 print >>status, 'Back: off',
             else:
@@ -353,27 +333,56 @@ def _draw_status_pane(self, pane):
     
     def _draw_layer_pane(self, pane):
         '''Returns the data shown in highres format, b01c order.'''
-        
-        if self.state.layers_show_back:
-            layer_dat_3D = self.net.blobs[self.state.layer].diff[0]
+
+        default_layer_name = self.state.get_default_layer_name()
+
+        if self.state.siamese_input_mode_has_two_images(self.state.layer):
+
+            if self.state.layers_show_back:
+                layer_dat_3D_0 = self.net.blobs[self.state.layer[0]].diff[0]
+                layer_dat_3D_1 = self.net.blobs[self.state.layer[1]].diff[0]
+            else:
+                layer_dat_3D_0 = self.net.blobs[self.state.layer[0]].data[0]
+                layer_dat_3D_1 = self.net.blobs[self.state.layer[1]].data[0]
+
+            # Promote FC layers with shape (n) to have shape (n,1,1)
+            if len(layer_dat_3D_0.shape) == 1:
+                layer_dat_3D_0 = layer_dat_3D_0[:, np.newaxis, np.newaxis]
+                layer_dat_3D_1 = layer_dat_3D_1[:, np.newaxis, np.newaxis]
+
+                # we don't resize the images to half the size since there is no point in doing that in FC layers
+            else:
+                # resize images to half the size
+                half_pane_shape = (layer_dat_3D_0.shape[1], layer_dat_3D_0.shape[2] / 2)
+                layer_dat_3D_0 = resize_without_fit(layer_dat_3D_0.transpose((1, 2, 0)), half_pane_shape).transpose((2, 0, 1))
+                layer_dat_3D_1 = resize_without_fit(layer_dat_3D_1.transpose((1, 2, 0)), half_pane_shape).transpose((2, 0, 1))
+
+            # concatenate images side-by-side
+            layer_dat_3D = np.concatenate((layer_dat_3D_0, layer_dat_3D_1), axis=2)
+
         else:
-            layer_dat_3D = self.net.blobs[self.state.layer].data[0]
+            if self.state.layers_show_back:
+                layer_dat_3D = self.net.blobs[default_layer_name].diff[0]
+            else:
+                layer_dat_3D = self.net.blobs[default_layer_name].data[0]
+
         # Promote FC layers with shape (n) to have shape (n,1,1)
         if len(layer_dat_3D.shape) == 1:
-            layer_dat_3D = layer_dat_3D[:,np.newaxis,np.newaxis]
+            layer_dat_3D = layer_dat_3D[:, np.newaxis, np.newaxis]
 
         n_tiles = layer_dat_3D.shape[0]
-        tile_rows,tile_cols = self.net_layer_info[self.state.layer]['tiles_rc']
+
+        tile_rows, tile_cols = self.net_layer_info[default_layer_name]['tiles_rc']
 
         display_3D_highres = None
         if self.state.pattern_mode:
             # Show desired patterns loaded from disk
 
-            load_layer = self.state.layer
-            if self.settings.caffevis_jpgvis_remap and self.state.layer in self.settings.caffevis_jpgvis_remap:
-                load_layer = self.settings.caffevis_jpgvis_remap[self.state.layer]
+            load_layer = default_layer_name
+
+            if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
+                load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
 
-            
             if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers:
                 jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
                                         'regularized_opt', load_layer, 'whole_layer.jpg')
@@ -441,8 +450,12 @@ def _draw_layer_pane(self, pane):
             display_3D = np.tile(display_3D, (1, 1, 1, 3))
         # Upsample unit length tiles to give a more sane tile / highlight ratio
         #   e.g. (1000,1,1,3) -> (1000,3,3,3)
-        if display_3D.shape[1] == 1:
+        if (display_3D.shape[1] == 1) and (display_3D.shape[2] == 1):
             display_3D = np.tile(display_3D, (1, 3, 3, 1))
+        # Upsample pair of unit length tiles to give a more sane tile / highlight ratio (occurs on siamese FC layers)
+        #   e.g. (1000,1,2,3) -> (1000,2,2,3)
+        if (display_3D.shape[1] == 1) and (display_3D.shape[2] == 2):
+            display_3D = np.tile(display_3D, (1, 2, 1, 1))
         if self.state.layers_show_back and not self.state.pattern_mode:
             padval = self.settings.caffevis_layer_clr_back_background
         else:
@@ -452,7 +465,7 @@ def _draw_layer_pane(self, pane):
         with self.state.lock:
             if self.state.cursor_area == 'bottom':
                 highlights[self.state.selected_unit] = self.settings.caffevis_layer_clr_cursor  # in [0,1] range
-            if self.state.backprop_selection_frozen and self.state.layer == self.state.backprop_layer:
+            if self.state.backprop_selection_frozen and default_layer_name == self.state.backprop_layer:
                 highlights[self.state.backprop_unit] = self.settings.caffevis_layer_clr_back_sel  # in [0,1] range
 
         _, display_2D = tile_images_make_tiles(display_3D, hw = (tile_rows,tile_cols), padval = padval, highlights = highlights)
@@ -477,7 +490,7 @@ def _draw_layer_pane(self, pane):
         pane.data[:] = to_255(self.settings.window_background)
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
         
-        if self.settings.caffevis_label_layers and self.state.layer in self.settings.caffevis_label_layers and self.labels and self.state.cursor_area == 'bottom':
+        if self.settings.caffevis_label_layers and default_layer_name in self.settings.caffevis_label_layers and self.labels and self.state.cursor_area == 'bottom':
             # Display label annotation atop layers pane (e.g. for fc8/prob)
             defaults = {'face':  getattr(cv2, self.settings.caffevis_label_face),
                         'fsize': self.settings.caffevis_label_fsize,
@@ -539,51 +552,74 @@ def _draw_back_pane(self, pane):
             # Mode-specific processing
             assert back_mode in ('grad', 'deconv')
             assert back_filt_mode in ('raw', 'gray', 'norm', 'normblur')
-            if back_filt_mode == 'raw':
-                grad_img = norm01c(grad_img, 0)
-            elif back_filt_mode == 'gray':
-                grad_img = grad_img.mean(axis=2)
-                grad_img = norm01c(grad_img, 0)
-            elif back_filt_mode == 'norm':
-                grad_img = np.linalg.norm(grad_img, axis=2)
-                grad_img = norm01(grad_img)
-            else:
-                grad_img = np.linalg.norm(grad_img, axis=2)
-                cv2.GaussianBlur(grad_img, (0,0), self.settings.caffevis_grad_norm_blur_radius, grad_img)
-                grad_img = norm01(grad_img)
 
-            # If necessary, re-promote from grayscale to color
-            if len(grad_img.shape) == 2:
-                grad_img = np.tile(grad_img[:,:,np.newaxis], 3)
+            # define helper function ro run processing once or twice, in case of siamese network
+            def run_processing_once_or_twice(image, process_image_fn):
+
+                # if siamese network, run processing twice
+                if self.settings.is_siamese:
+
+                    # split image to image0 and image1
+                    image0 = image[:, :, 0:3]
+                    image1 = image[:, :, 3:6]
+
+                    # combine image0 and image1
+                    if self.state.siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                        # run processing on image0
+                        return process_image_fn(image0)
+
+                    elif self.state.siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                        # run processing on image1
+                        return process_image_fn(image1)
+
+                    elif self.state.siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
+
+                        # run processing on both image0 and image1
+                        image0 = process_image_fn(image0)
+                        image1 = process_image_fn(image1)
 
-            if (self.settings.is_siamese) and (grad_img.shape[2] == 6):
+                        # resize each gradient image to half the pane size
+                        half_pane_shape = (image.shape[1] / 2, image.shape[0])
+                        # half_pane_shape = (pane.data.shape[1] / 2, pane.data.shape[0])
 
-                selected_image = -1
-                if state_layer in self.settings.siamese_layer_to_selected_image:
-                    selected_image = self.settings.siamese_layer_to_selected_image[state_layer]
+                        image0 = cv2.resize(image0[:], half_pane_shape)
+                        image1 = cv2.resize(image1[:], half_pane_shape)
 
-                # split gradient into two images, by channels
-                grad_img1 = grad_img[:, :, 0:3]
-                grad_img2 = grad_img[:, :, 3:6]
+                        # generate the pane image by concatenating both images
+                        return np.concatenate((image0, image1), axis=1)
+
+                # else, normal network, run processing once
+                else:
+                    # run processing on image
+                    return process_image_fn(image)
+
+                raise Exception("flow should not arrive here")
 
-                # use both images
-                if selected_image == -1:
-                    # resize each gradient image to half the pane size
-                    half_pane_shape = (pane.data.shape[0] / 2, pane.data.shape[1])
-                    grad_img_disp1 = cv2.resize(grad_img1[:], half_pane_shape)
-                    grad_img_disp2 = cv2.resize(grad_img2[:], half_pane_shape)
 
-                    # generate the pane image by concatenating both images
-                    grad_img_disp = np.concatenate((grad_img_disp1, grad_img_disp2), axis=1)
+            if back_filt_mode == 'raw':
+                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0))
+
+            elif back_filt_mode == 'gray':
+                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img.mean(axis=2), 0))
 
-                elif selected_image == 0:
-                    grad_img_disp = grad_img1
+            elif back_filt_mode == 'norm':
+                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01(np.linalg.norm(grad_img, axis=2)))
 
-                elif selected_image == 1:
-                    grad_img_disp = grad_img2
+            elif back_filt_mode == 'normblur':
+                def do_norm_blur(grad_img):
+                    grad_img = np.linalg.norm(grad_img, axis=2)
+                    cv2.GaussianBlur(grad_img, (0, 0), self.settings.caffevis_grad_norm_blur_radius, grad_img)
+                    return norm01(grad_img)
+                grad_img = run_processing_once_or_twice(grad_img, do_norm_blur)
 
             else:
-                grad_img_disp = grad_img
+                raise Exception('Invalid option for back_filter_mode: %s' % (back_filt_mode))
+
+            # If necessary, re-promote from grayscale to color
+            if len(grad_img.shape) == 2:
+                grad_img = np.tile(grad_img[:,:,np.newaxis], 3)
+
+            grad_img_disp = grad_img
 
             grad_img_resize = ensure_uint255_and_resize_to_fit(grad_img_disp, pane.data.shape)
             pane.data[0:grad_img_resize.shape[0], 0:grad_img_resize.shape[1], :] = grad_img_resize
@@ -592,7 +628,7 @@ def _draw_jpgvis_pane(self, pane):
         pane.data[:] = to_255(self.settings.window_background)
 
         with self.state.lock:
-            state_layer, state_selected_unit, cursor_area, show_unit_jpgs = self.state.layer, self.state.selected_unit, self.state.cursor_area, self.state.show_unit_jpgs
+            state_layer, state_selected_unit, cursor_area, show_unit_jpgs = self.state.get_default_layer_name(), self.state.selected_unit, self.state.cursor_area, self.state.show_unit_jpgs
 
         try:
             # Some may be missing this setting
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index efbdb9cc4..8096f341f 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -5,7 +5,7 @@
 from codependent_thread import CodependentThread
 from misc import WithTimer
 from caffevis_helper import net_preproc_forward
-
+from caffevis_app_state import SiameseInputMode
 
 
 class CaffeProcThread(CodependentThread):
@@ -101,27 +101,52 @@ def run(self):
                     net_preproc_forward(self.settings, self.net, im_small, self.input_dims)
 
             if run_back:
-                diffs = self.net.blobs[backprop_layer].diff * 0
-                diffs[0][backprop_unit] = self.net.blobs[backprop_layer].data[0,backprop_unit]
+
+                default_backprop_layer = self.state.get_default_layer_name_for_layer(backprop_layer)
 
                 assert back_mode in ('grad', 'deconv')
                 if back_mode == 'grad':
                     with WithTimer('CaffeProcThread:backward', quiet = self.debug_level < 1):
                         #print '**** Doing backprop with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max())
                         try:
-                            self.net.backward_from_layer(backprop_layer, diffs, zero_higher = True)
+
+                            if self.state.siamese_input_mode_has_two_images(backprop_layer):
+                                diffs0 = self.net.blobs[backprop_layer[0]].diff * 0
+                                diffs0[0][backprop_unit] = self.net.blobs[backprop_layer[0]].data[0, backprop_unit]
+                                self.net.backward_from_layer(backprop_layer[0], diffs0, zero_higher = True)
+
+                                diffs1 = self.net.blobs[backprop_layer[1]].diff * 0
+                                diffs1[0][backprop_unit] = self.net.blobs[backprop_layer[1]].data[0, backprop_unit]
+                                self.net.backward_from_layer(backprop_layer[1], diffs1, zero_higher=True)
+
+                            else:
+                                diffs = self.net.blobs[default_backprop_layer].diff * 0
+                                diffs[0][backprop_unit] = self.net.blobs[default_backprop_layer].data[0, backprop_unit]
+                                self.net.backward_from_layer(default_backprop_layer, diffs, zero_higher = True)
                         except AttributeError:
                             print 'ERROR: required bindings (backward_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
                             raise
                         except ValueError:
-                            print "ERROR: probably impossible to backprop layer %s, ignoring to avoid crash" % (backprop_layer)
+                            print "ERROR: probably impossible to backprop layer %s, ignoring to avoid crash" % (str(backprop_layer))
                             with self.state.lock:
                                 self.state.back_enabled = False
                 else:
                     with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1):
                         #print '**** Doing deconv with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max())
                         try:
-                            self.net.deconv_from_layer(backprop_layer, diffs, zero_higher = True)
+                            if self.state.siamese_input_mode_has_two_images(backprop_layer):
+                                diffs0 = self.net.blobs[backprop_layer[0]].diff * 0
+                                diffs0[0][backprop_unit] = self.net.blobs[backprop_layer[0]].data[0, backprop_unit]
+                                self.net.deconv_from_layer(backprop_layer[0], diffs0, zero_higher=True)
+
+                                diffs1 = self.net.blobs[backprop_layer[1]].diff * 0
+                                diffs1[0][backprop_unit] = self.net.blobs[backprop_layer[1]].data[0, backprop_unit]
+                                self.net.deconv_from_layer(backprop_layer[1], diffs1, zero_higher=True)
+
+                            else:
+                                diffs = self.net.blobs[default_backprop_layer].diff * 0
+                                diffs[0][backprop_unit] = self.net.blobs[default_backprop_layer].data[0, backprop_unit]
+                                self.net.deconv_from_layer(default_backprop_layer, diffs, zero_higher = True)
                         except AttributeError:
                             print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
                             raise
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index add5f8f18..43cc89278 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -1,7 +1,13 @@
+import os
 import time
 from threading import Lock
 
 
+class SiameseInputMode:
+    FIRST_IMAGE = 0
+    SECOND_IMAGE = 1
+    BOTH_IMAGES = 2
+    NUMBER_OF_MODES = 3
 
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
@@ -10,13 +16,14 @@ def __init__(self, net, settings, bindings, net_layer_info):
         self.lock = Lock()  # State is accessed in multiple threads
         self.settings = settings
         self.bindings = bindings
-        self._layers = net.blobs.keys()
-        self._layers = self._layers[1:]  # chop off data layer
+
+        self._fill_headers_list(net, settings)
+
         if hasattr(self.settings, 'caffevis_filter_layers'):
-            for name in self._layers:
+            for name in self._headers:
                 if self.settings.caffevis_filter_layers(name):
                     print '  Layer filtered out by caffevis_filter_layers: %s' % name
-            self._layers = filter(lambda name: not self.settings.caffevis_filter_layers(name), self._layers)
+            self._headers = filter(lambda name: not self.settings.caffevis_filter_layers(name), self._headers)
         self.net_layer_info = net_layer_info
         self.layer_boost_indiv_choices = self.settings.caffevis_boost_indiv_choices   # 0-1, 0 is noop
         self.layer_boost_gamma_choices = self.settings.caffevis_boost_gamma_choices   # 0-inf, 1 is noop
@@ -30,9 +37,51 @@ def __init__(self, net, settings, bindings, net_layer_info):
 
         self._reset_user_state()
 
+    @staticmethod
+    def get_header_from_layer(layer):
+        '''
+        helper function which returns the header name, given a single layer, or layer pair
+        :param layer: can be either single layer string, or apir of layers
+        :return: header for layer
+        '''
+
+        # if we have only a single layer, the header is the layer name
+        if type(layer) is str:
+            return layer
+
+        # if we got a pair of layers
+        elif (type(layer) is tuple) and (len(layer) == 2):
+            # build header in format: common_prefix + first_postfix | second_postfix
+            prefix = os.path.commonprefix(layer)
+            prefix_len = len(prefix)
+            postfix0 = layer[0][prefix_len:]
+            postfix1 = layer[1][prefix_len:]
+            header_name = '%s%s|%s' % (prefix, postfix0, postfix1)
+            return header_name
+
+    def _fill_headers_list(self, net, settings):
+        '''
+        helper function which fills the headers list, using either the entire blob names, or the siamese layers definition
+        :param net: network to generate headers for
+        :param settings: settings holder
+        :return: N/A
+        '''
+
+        # if loading a siamese network, and layers list is not empty
+        if settings.is_siamese and settings.siamese_layers_list:
+
+            self._headers = list()
+            for item in settings.siamese_layers_list:
+                self._headers.append(self.get_header_from_layer(item))
+
+        else:
+            self._headers = net.blobs.keys()
+            self._headers = self._headers[1:]  # chop off data layer
+
     def _reset_user_state(self):
+        self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
         self.layer_idx = 0
-        self.layer = self._layers[0]
+        self._update_layer_name()
         self.layer_boost_indiv_idx = self.settings.caffevis_boost_indiv_default_idx
         self.layer_boost_indiv = self.layer_boost_indiv_choices[self.layer_boost_indiv_idx]
         self.layer_boost_gamma_idx = self.settings.caffevis_boost_gamma_default_idx
@@ -51,11 +100,31 @@ def _reset_user_state(self):
         self.layers_show_back = False   # False: show forward activations. True: show backward diffs
         self.show_label_predictions = self.settings.caffevis_init_show_label_predictions
         self.show_unit_jpgs = self.settings.caffevis_init_show_unit_jpgs
-        self.siamese_input_mode = 2 # 0: first image, 1: second image, 2: both images
         self.drawing_stale = True
         kh,_ = self.bindings.get_key_help('help_mode')
         self.extra_msg = '%s for help' % kh[0]
-        
+
+    def _update_layer_name(self):
+        '''
+        updates the selected layer, after layer_idx got updated
+        in Siamese networks, returns the relevant layer according to current input mode
+        on non-siamese networks, header name is the layer name
+        :return: N/A
+        '''
+
+        if self.settings.is_siamese and (self.settings.siamese_layers_list) and \
+                (type(self.settings.siamese_layers_list[self.layer_idx]) is tuple) and \
+                (len(self.settings.siamese_layers_list[self.layer_idx]) == 2):
+
+            if self.siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                self.layer = self.settings.siamese_layers_list[self.layer_idx][0]
+            elif self.siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                self.layer = self.settings.siamese_layers_list[self.layer_idx][1]
+            elif self.siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
+                self.layer = self.settings.siamese_layers_list[self.layer_idx]
+        else:
+            self.layer = self._headers[self.layer_idx]
+
     def handle_key(self, key):
         #print 'Ignoring key:', key
         if key == -1:
@@ -72,13 +141,14 @@ def handle_key(self, key):
                 #self.selected_unit = self.selected_unit % ww   # equivalent to scrolling all the way to the top row
                 #self.cursor_area = 'top' # Then to the control pane
                 self.layer_idx = max(0, self.layer_idx - 1)
-                self.layer = self._layers[self.layer_idx]
+                self._update_layer_name()
+
             elif tag == 'sel_layer_right':
                 #hh,ww = self.tiles_height_width
                 #self.selected_unit = self.selected_unit % ww   # equivalent to scrolling all the way to the top row
                 #self.cursor_area = 'top' # Then to the control pane
-                self.layer_idx = min(len(self._layers) - 1, self.layer_idx + 1)
-                self.layer = self._layers[self.layer_idx]
+                self.layer_idx = min(len(self._headers) - 1, self.layer_idx + 1)
+                self._update_layer_name()
 
             elif tag == 'sel_left':
                 self.move_selection('left')
@@ -175,7 +245,7 @@ def handle_key(self, key):
                 self.show_unit_jpgs = not self.show_unit_jpgs
 
             elif tag == 'siamese_input_mode':
-                self.siamese_input_mode = (self.siamese_input_mode + 1) % 3
+                self.siamese_input_mode = (self.siamese_input_mode + 1) % SiameseInputMode.NUMBER_OF_MODES
 
             else:
                 key_handled = False
@@ -190,35 +260,83 @@ def redraw_needed(self):
         with self.lock:
             return self.drawing_stale
 
+    def get_default_layer_name(self):
+        '''
+        helper function which returns the first layer in the layer tuple, or the only one
+        many parts of the code can take info from first layer, since it should be the same as second layer info
+        :return: layer name which can be used to get information which is not input specific
+        '''
+        return self.get_default_layer_name_for_layer(self.layer)
+
+        return default_layer_name
+
+
+    def get_default_layer_name_for_layer(self, layer):
+        '''
+        helper function which returns the first layer in the layer tuple, or the only one
+        many parts of the code can take info from first layer, since it should be the same as second layer info
+        :return: layer name which can be used to get information which is not input specific
+        '''
+
+        if self.siamese_input_mode_has_two_images(layer):
+            default_layer_name = layer[0]
+        elif self.siamese_input_mode == SiameseInputMode.FIRST_IMAGE and self.is_pair_of_layers(layer):
+            default_layer_name = layer[0]
+        elif self.siamese_input_mode == SiameseInputMode.SECOND_IMAGE and self.is_pair_of_layers(layer):
+            default_layer_name = layer[1]
+        else:
+            default_layer_name = layer
+
+        return default_layer_name
+
+    @staticmethod
+    def is_pair_of_layers(layer):
+        return (type(layer), len(layer)) == (tuple, 2)
+
+    def siamese_input_mode_has_two_images(self, layer):
+        '''
+        helper function which checks whether the input mode is two images, and the provided layer contains two layer names
+        :param layer: can be a single string layer name, or a pair of layer names
+        :return: True if both the input mode is BOTH_IMAGES and layer contains two layer names, False oherwise
+        '''
+
+        return self.siamese_input_mode == SiameseInputMode.BOTH_IMAGES and self.is_pair_of_layers(layer)
+
     def move_selection(self, direction, dist = 1):
+
+        default_layer_name = self.get_default_layer_name()
+
         if direction == 'left':
             if self.cursor_area == 'top':
                 self.layer_idx = max(0, self.layer_idx - dist)
-                self.layer = self._layers[self.layer_idx]
+                self._update_layer_name()
             else:
                 self.selected_unit -= dist
         elif direction == 'right':
             if self.cursor_area == 'top':
-                self.layer_idx = min(len(self._layers) - 1, self.layer_idx + dist)
-                self.layer = self._layers[self.layer_idx]
+                self.layer_idx = min(len(self._headers) - 1, self.layer_idx + dist)
+                self._update_layer_name()
             else:
                 self.selected_unit += dist
         elif direction == 'down':
             if self.cursor_area == 'top':
                 self.cursor_area = 'bottom'
             else:
-                self.selected_unit += self.net_layer_info[self.layer]['tile_cols'] * dist
+                self.selected_unit += self.net_layer_info[default_layer_name]['tile_cols'] * dist
         elif direction == 'up':
             if self.cursor_area == 'top':
                 pass
             else:
-                self.selected_unit -= self.net_layer_info[self.layer]['tile_cols'] * dist
+                self.selected_unit -= self.net_layer_info[default_layer_name]['tile_cols'] * dist
                 if self.selected_unit < 0:
-                    self.selected_unit += self.net_layer_info[self.layer]['tile_cols']
+                    self.selected_unit += self.net_layer_info[default_layer_name]['tile_cols']
                     self.cursor_area = 'top'
 
     def _ensure_valid_selected(self):
-        n_tiles = self.net_layer_info[self.layer]['n_tiles']
+
+        default_layer_name = self.get_default_layer_name()
+
+        n_tiles = self.net_layer_info[default_layer_name]['n_tiles']
 
         # Forward selection
         self.selected_unit = max(0, self.selected_unit)
diff --git a/live_vis.py b/live_vis.py
index d5baf0035..9543f8006 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -325,7 +325,6 @@ def display_frame(self, frame):
             frame_disp1 = cv2.resize(frame1[:], half_pane_shape)
             frame_disp2 = cv2.resize(frame2[:], half_pane_shape)
             frame_disp = np.concatenate((frame_disp1, frame_disp2), axis=1)
-
         else:
             frame_disp = cv2.resize(frame[:], full_pane_shape)
 

From 6aa65d914084cbaa1fa32b23a4f83cc597142126 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 16 Sep 2017 23:51:21 +0300
Subject: [PATCH 017/106] moved caffe dependency to local function in
 caffevis_helper.py

---
 caffevis/caffevis_helper.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index cdfd0b50a..0eb4b6a8c 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,6 +1,5 @@
 import numpy as np
 import os
-import caffe
 
 from image_misc import get_tiles_height_width, caffe_load_image
 
@@ -134,6 +133,7 @@ def load_mean(data_mean_file):
     elif file_extension == ".binaryproto":
 
         # load mean from binary protobuf file
+        import caffe
         blob = caffe.proto.caffe_pb2.BlobProto()
         data = open(data_mean_file, 'rb').read()
         blob.ParseFromString(data)

From 792e796f2429df575e8523f5dcf1fe176a38eec4 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:00:16 +0300
Subject: [PATCH 018/106] - added code to automatically go over all the
 channels of the given layer - removed push-channel and push-spatial from
 settings

---
 find_maxes/caffe_misc.py |  4 +--
 optimize_image.py        | 77 +++++++++++++++++++++++++++-------------
 settings.py              |  6 ----
 3 files changed, 55 insertions(+), 32 deletions(-)

diff --git a/find_maxes/caffe_misc.py b/find_maxes/caffe_misc.py
index 1265e3fd7..37832599a 100644
--- a/find_maxes/caffe_misc.py
+++ b/find_maxes/caffe_misc.py
@@ -92,9 +92,9 @@ def __init__(self, layers_list):
             if type == 'Input':
                 _tmp.append((name, None))
             elif type == 'Convolution':
-                _tmp.append((name, get_conv_converter(input, output, filter, stride, pad)))
+                _tmp.append((name, get_conv_converter(input[0:1], output[0:1], filter, stride, pad)))
             elif type == 'Pooling':
-                _tmp.append((name, get_pool_converter(input, output, filter, stride, pad)))
+                _tmp.append((name, get_pool_converter(input[0:1], output[0:1], filter, stride, pad)))
             else:
                 continue # skip adding layer
 
diff --git a/optimize_image.py b/optimize_image.py
index ec825e225..65d3e3d28 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -41,9 +41,9 @@ def get_parser():
     # What to optimize
     parser.add_argument('--push-layer', type = str, default = settings.optimize_image_push_layer,
                         help = 'Name of layer that contains the desired neuron whose value is optimized.')
-    parser.add_argument('--push-channel', type = int, default = settings.optimize_image_push_channel,
+    parser.add_argument('--push-channel', type = int, default = '130',
                         help = 'Channel number for desired neuron whose value is optimized (channel for conv, neuron index for FC).')
-    parser.add_argument('--push-spatial', type = str, default = settings.optimize_image_push_spatial,
+    parser.add_argument('--push-spatial', type = str, default = 'None',
                         help = 'Which spatial location to push for conv layers. For FC layers, set this to None. For conv layers, set it to a tuple, e.g. when using `--push-layer conv5` on AlexNet, --push-spatial (6,6) will maximize the center unit of the 13x13 spatial grid.')
     parser.add_argument('--push-dir', type = float, default = 1,
                         help = 'Which direction to push the activation of the selected neuron, that is, the value used to begin backprop. For example, use 1 to maximize the selected neuron activation and  -1 to minimize it.')
@@ -127,6 +127,20 @@ def parse_and_validate_push_spatial(parser, push_spatial):
     return push_spatial
 
 
+def get_layer_info(settings, layer_name):
+    '''
+        get layer info (name, type, input, output, filter, stride, pad) from settings
+
+    :param settings: contains script settings
+    :param layer_name: name of layer
+    :return: (name, type, input, output, filter, stride, pad)
+    '''
+
+    # go over layers
+    for (name, type, input, output, filter, stride, pad) in settings.max_tracker_layers_list:
+        if name == layer_name:
+            return (name, type, input, output, filter, stride, pad)
+
 
 def main():
     parser = get_parser()
@@ -202,29 +216,44 @@ def main():
     optimizer = GradientOptimizer(settings, net, data_mean, labels = labels,
                                   label_layers = settings.caffevis_label_layers,
                                   channel_swap_to_rgb = net_channel_swap)
-    
-    params = FindParams(
-        start_at = args.start_at,
-        rand_seed = args.rand_seed,
-        push_layer = args.push_layer,
-        push_channel = args.push_channel,
-        push_spatial = push_spatial,
-        push_dir = args.push_dir,
-        decay = args.decay,
-        blur_radius = args.blur_radius,
-        blur_every = args.blur_every,
-        small_val_percentile = args.small_val_percentile,
-        small_norm_percentile = args.small_norm_percentile,
-        px_benefit_percentile = args.px_benefit_percentile,
-        px_abs_benefit_percentile = args.px_abs_benefit_percentile,
-        lr_policy = args.lr_policy,
-        lr_params = lr_params,
-        max_iter = args.max_iter,
-    )
 
-    prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
-    im = optimizer.run_optimize(params, prefix_template = prefix_template,
-                                brave = args.brave, skipbig = args.skipbig)
+    # get layer type
+    (name, type, input, output, filter, stride, pad) = get_layer_info(settings, args.push_layer)
+
+    if type == 'FullyConnected':
+        # get number of units
+        channels = output
+        push_spatial = (0, 0)
+    elif type == 'Convolution':
+        # get number of channels
+        channels = output[2]
+        push_spatial = (filter[0]/2, filter[1]/2)
+
+    # go over channels
+    for current_channel in range(channels):
+
+        params = FindParams(
+            start_at = args.start_at,
+            rand_seed = args.rand_seed,
+            push_layer = args.push_layer,
+            push_channel = current_channel,
+            push_spatial = push_spatial,
+            push_dir = args.push_dir,
+            decay = args.decay,
+            blur_radius = args.blur_radius,
+            blur_every = args.blur_every,
+            small_val_percentile = args.small_val_percentile,
+            small_norm_percentile = args.small_norm_percentile,
+            px_benefit_percentile = args.px_benefit_percentile,
+            px_abs_benefit_percentile = args.px_abs_benefit_percentile,
+            lr_policy = args.lr_policy,
+            lr_params = lr_params,
+            max_iter = args.max_iter,
+        )
+
+        prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
+        im = optimizer.run_optimize(params, prefix_template = prefix_template,
+                                    brave = args.brave, skipbig = args.skipbig)
 
 
 
diff --git a/settings.py b/settings.py
index 672c0b98c..06fffb576 100644
--- a/settings.py
+++ b/settings.py
@@ -398,12 +398,6 @@
 # push-layer parameter for optimize_image.py
 optimize_image_push_layer = locals().get('optimize_image_push_layer', 'fc8')
 
-# push-channel parameter for optimize_image.py
-optimize_image_push_channel = locals().get('optimize_image_push_channel', '130')
-
-# push-spatial parameter for optimize_image.py
-optimize_image_push_spatial = locals().get('optimize_image_push_spatial', 'None')
-
 # decay parameter for optimize_image.py
 optimize_image_decay = locals().get('optimize_image_decay', 0)
 

From cee22214e2c6790f0b2303e10358a8e4b5611cc6 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:14:40 +0300
Subject: [PATCH 019/106] - fixed import modules issues in find_max and
 optimizer - only save the relevant part of the image, according to the
 receptive field - in siamese network, only save one relevant image - moved
 caffe_misc module to be able to use it from both max_tracker and optimizer

---
 find_maxes/caffe_misc.py => caffe_misc.py | 105 ++++++++++++-
 find_maxes/__init__.py                    |   0
 find_maxes/crop_max_patches.py            |  18 ++-
 find_maxes/find_max_acts.py               |  19 ++-
 find_maxes/loaders.py                     |  33 ++---
 find_maxes/max_tracker.py                 | 173 ++++++----------------
 optimize/gradient_optimizer.py            |  64 ++++++--
 optimize_image.py                         |   3 +-
 settings.py                               |   2 +
 9 files changed, 249 insertions(+), 168 deletions(-)
 rename find_maxes/caffe_misc.py => caffe_misc.py (50%)
 create mode 100644 find_maxes/__init__.py

diff --git a/find_maxes/caffe_misc.py b/caffe_misc.py
similarity index 50%
rename from find_maxes/caffe_misc.py
rename to caffe_misc.py
index 37832599a..715c85600 100644
--- a/find_maxes/caffe_misc.py
+++ b/caffe_misc.py
@@ -30,7 +30,7 @@ def shownet(net):
 def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
     '''
     Works for conv or pool
-    
+
 vector<int> ConvolutionLayer<Dtype>::JBY_region_of_influence(const vector<int>& slice) {
     +  CHECK_EQ(slice.size(), 4) << "slice must have length 4 (ii_start, ii_end, jj_start, jj_end)";
     +  // Crop region to output size
@@ -69,7 +69,7 @@ def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride
     bot_slice[1] = (top_slice[1]-1) * stride[0] + filter_width[0] - pad[0];
     bot_slice[2] = top_slice[2] * stride[1] - pad[1];
     bot_slice[3] = (top_slice[3]-1) * stride[1] + filter_width[1] - pad[1];
-    
+
     return bot_slice
 
 def get_conv_converter(bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
@@ -92,20 +92,20 @@ def __init__(self, layers_list):
             if type == 'Input':
                 _tmp.append((name, None))
             elif type == 'Convolution':
-                _tmp.append((name, get_conv_converter(input[0:1], output[0:1], filter, stride, pad)))
+                _tmp.append((name, get_conv_converter(input[0:2], output[0:2], filter, stride, pad)))
             elif type == 'Pooling':
-                _tmp.append((name, get_pool_converter(input[0:1], output[0:1], filter, stride, pad)))
+                _tmp.append((name, get_pool_converter(input[0:2], output[0:2], filter, stride, pad)))
             else:
                 continue # skip adding layer
 
         self.names = [tt[0] for tt in _tmp]
         self.converters = [tt[1] for tt in _tmp]
-        
+
     def convert_region(self, from_layer, to_layer, region, verbose = False):
         '''region is the slice of the from_layer in the following Python
             index format: (ii_start, ii_end, jj_start, jj_end)
         '''
-        
+
         from_idx = self.names.index(from_layer)
         to_idx = self.names.index(to_layer)
         assert from_idx >= to_idx, 'wrong order of from_layer and to_layer'
@@ -149,3 +149,96 @@ def save_caffe_image(img, filename, autoscale = True, autoscale_center = None):
         img -= img.min()
         img *= 1.0 / (img.max() + 1e-10)
     skimage.io.imsave(filename, img)
+
+
+def get_max_data_extent(net, layer, rc, is_conv):
+    '''Gets the maximum size of the data layer that can influence a unit on layer.'''
+    if is_conv:
+        top_name = net.top_names[layer][0]
+        conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
+        layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
+        data_slice = rc.convert_region(layer, 'data', layer_slice_middle)
+        return data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
+    else:
+        # Whole data region
+        return net.blobs['data'].data.shape[2:4]        # e.g. (227,227) for fc6,fc7,fc8,prop
+
+
+
+def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer, size_ii, size_jj, data_size_ii, data_size_jj):
+
+    if is_conv:
+
+        # Compute the focus area of the data layer
+        layer_indices = (ii, ii + 1, jj, jj + 1)
+        data_indices = region_computer.convert_region(layer, 'data', layer_indices)
+        data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
+
+        # safe guard edges
+        data_ii_start = max(data_ii_start, 0)
+        data_jj_start = max(data_jj_start, 0)
+        data_ii_end = min(data_ii_end, data_size_ii)
+        data_jj_end = min(data_jj_end, data_size_jj)
+
+        touching_imin = (data_ii_start == 0)
+        touching_jmin = (data_jj_start == 0)
+
+        # Compute how much of the data slice falls outside the actual data [0,max] range
+        ii_outside = size_ii - (data_ii_end - data_ii_start)  # possibly 0
+        jj_outside = size_jj - (data_jj_end - data_jj_start)  # possibly 0
+
+        if touching_imin:
+            out_ii_start = ii_outside
+            out_ii_end = size_ii
+        else:
+            out_ii_start = 0
+            out_ii_end = size_ii - ii_outside
+        if touching_jmin:
+            out_jj_start = jj_outside
+            out_jj_end = size_jj
+        else:
+            out_jj_start = 0
+            out_jj_end = size_jj - jj_outside
+
+    else:
+        ii, jj = 0, 0
+        data_ii_start, out_ii_start, data_jj_start, out_jj_start = 0, 0, 0, 0
+        data_ii_end, out_ii_end, data_jj_end, out_jj_end = size_ii, size_ii, size_jj, size_jj
+
+    return [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end]
+
+
+def extract_patch_from_image(data, net, selected_input_index, settings,
+                             data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                             out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj):
+    if settings.is_siamese:
+
+        # input is first image so select first 3 channels
+        if selected_input_index == 0:
+            out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+            out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = data[0:3,
+                                                                           data_ii_start:data_ii_end,
+                                                                           data_jj_start:data_jj_end]
+        # input is second image so select second 3 channels
+        elif selected_input_index == 1:
+            out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+            out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = data[3:6,
+                                                                           data_ii_start:data_ii_end,
+                                                                           data_jj_start:data_jj_end]
+        # input is both images so select concatenate data horizontally
+        elif selected_input_index == -1:
+            out_arr = np.zeros((3, size_ii, size_jj * 2), dtype='float32')
+            out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = data[0:3,
+                                                                                       data_ii_start:data_ii_end,
+                                                                                       data_jj_start:data_jj_end]
+            out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = data[3:6,
+                                                                                                   data_ii_start:data_ii_end,
+                                                                                                   data_jj_start:data_jj_end]
+        else:
+            print "Error: invalid value for selected_input_index (", selected_input_index, ")"
+    else:
+        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = data[:,
+                                                                       data_ii_start:data_ii_end,
+                                                                       data_jj_start:data_jj_end]
+    return out_arr
diff --git a/find_maxes/__init__.py b/find_maxes/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 2f020cd64..f03a80881 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -1,13 +1,19 @@
 #! /usr/bin/env python
 
+# add parent folder to search path, to enable import of core modules like settings
+import os,sys,inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.insert(0,parentdir)
+
 import argparse
-import ipdb as pdb
+#import ipdb as pdb
 import cPickle as pickle
 
 import settings
 from caffevis.caffevis_helper import load_mean
 
-from loaders import load_imagenet_mean, load_labels, caffe
+from loaders import load_imagenet_mean, load_labels
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
 
@@ -35,13 +41,19 @@ def main():
     parser.add_argument('--mean',         type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
     if args.gpu:
         caffe.set_mode_gpu()
+        print 'crop_max_patches mode (in main thread):     GPU'
+
     else:
         caffe.set_mode_cpu()
+        print 'crop_max_patches mode (in main thread):     CPU'
 
     if args.mean == "":
-        data_mean = load_imagenet_mean()
+        data_mean = load_imagenet_mean(settings)
     else:
         data_mean = load_mean(args.mean)
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 370baf33f..5455ab6fb 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -1,13 +1,19 @@
 #! /usr/bin/env python
 
+# add parent folder to search path, to enable import of core modules like settings
+import os,sys,inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.insert(0,parentdir)
+
 import argparse
-import ipdb as pdb
+#import ipdb as pdb
 import cPickle as pickle
 
 import settings
 
 from caffevis.caffevis_helper import load_mean
-from loaders import load_imagenet_mean, load_labels, caffe
+from loaders import load_imagenet_mean, load_labels
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
 
@@ -23,13 +29,20 @@ def main():
     parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
     if args.gpu:
         caffe.set_mode_gpu()
+        print 'find_max_acts mode (in main thread):     GPU'
+
     else:
         caffe.set_mode_cpu()
+        print 'find_max_acts mode (in main thread):     CPU'
+
 
     if args.mean == "":
-        data_mean = load_imagenet_mean()
+        data_mean = load_imagenet_mean(settings)
     else:
         data_mean = load_mean(args.mean)
 
diff --git a/find_maxes/loaders.py b/find_maxes/loaders.py
index a714da701..8d1869406 100644
--- a/find_maxes/loaders.py
+++ b/find_maxes/loaders.py
@@ -2,25 +2,13 @@
 
 from pylab import *
 
-# Make sure that caffe is on the python path:
-caffe_root = '../../'  # this file is expected to be in {caffe_root}/experiments/something
-import sys
-loadpath = caffe_root + 'python_cpu'
-print '= = = CAFFE LOADER: LOADING CPU VERSION from path: %s = = =' % loadpath
-sys.path.insert(0, loadpath)     # Use CPU compiled code for backprop vis/etc
-import caffe
-caffe.set_mode_cpu()
-
-
-
-def load_labels():
-    with open('%s/data/ilsvrc12/synset_words.txt' % caffe_root) as ff:
+def load_labels(settings):
+    with open('%s/data/ilsvrc12/synset_words.txt' % settings.caffevis_caffe_root) as ff:
         labels = [line.strip() for line in ff.readlines()]
     return labels
 
-
     
-def load_trained_net(model_prototxt = None, model_weights = None):
+def load_trained_net(settings, model_prototxt = None, model_weights = None):
     assert (model_prototxt is None) == (model_weights is None), 'Specify both model_prototxt and model_weights or neither'
     if model_prototxt is None:
         load_dir = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/'
@@ -30,14 +18,23 @@ def load_trained_net(model_prototxt = None, model_weights = None):
     print 'LOADER: loading net:'
     print '  ', model_prototxt
     print '  ', model_weights
+
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+    if settings.caffevis_mode_gpu:
+        caffe.set_mode_gpu()
+        print 'CaffeVisApp mode (in main thread):     GPU'
+    else:
+        caffe.set_mode_cpu()
+        print 'CaffeVisApp mode (in main thread):     CPU'
+
     net = caffe.Classifier(model_prototxt, model_weights)
     #net.set_phase_test()
 
     return net
 
-
     
-def load_imagenet_mean():
-    imagenet_mean = np.load(caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
+def load_imagenet_mean(settings):
+    imagenet_mean = np.load(settings.caffevis_caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
     imagenet_mean = imagenet_mean[:, 14:14+227, 14:14+227]    # (3,256,256) -> (3,227,227) Crop to center 227x227 section
     return imagenet_mean
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 192dca4eb..8994cd588 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -1,26 +1,37 @@
 #! /usr/bin/env python
 
-import os
-import ipdb as pdb
 import errno
+import os
+import sys
 from datetime import datetime
 
-#import caffe
-from loaders import load_imagenet_mean, load_labels, caffe
-from jby_misc import WithTimer
-from caffe_misc import shownet, RegionComputer, save_caffe_image
 import numpy as np
-
+from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
+    compute_data_layer_focus_area
 from misc import get_files_from_image_list
 from misc import get_files_from_siamese_image_list
 
-def hardcoded_get():
+from jby_misc import WithTimer
+from loaders import load_imagenet_mean, load_labels
+
+
+def hardcoded_get(settings):
     prototxt = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/deploy_1.prototxt'
     weights = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/caffe_imagenet_train_iter_450000'
     datadir = '/home/jyosinsk/imagenet2012/val'
     filelist = 'mini_valid.txt'
 
-    imagenet_mean = load_imagenet_mean()
+    imagenet_mean = load_imagenet_mean(settings)
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
+    if settings.caffevis_mode_gpu:
+        caffe.set_mode_gpu()
+        print 'CaffeVisApp mode (in main thread):     GPU'
+    else:
+        caffe.set_mode_cpu()
+        print 'CaffeVisApp mode (in main thread):     CPU'
+
     net = caffe.Classifier(prototxt, weights,
                            mean=imagenet_mean,
                            channel_swap=(2,1,0),
@@ -28,7 +39,7 @@ def hardcoded_get():
                            image_dims=(256, 256))
     net.set_phase_test()
     net.set_mode_cpu()
-    labels = load_labels()
+    labels = load_labels(settings)
 
     return net, imagenet_mean, labels, datadir, filelist
 
@@ -193,6 +204,9 @@ def scan_images_for_maxes(settings, net, datadir, n_top):
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
 
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
     tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_for_max_tracker)
     for image_idx in xrange(len(image_filenames)):
 
@@ -219,6 +233,9 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
     print 'Scanning %d pairs' % len(image_filenames)
     print '  First pair', image_filenames[0]
 
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
     tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_for_max_tracker)
     for image_idx in xrange(len(image_filenames)):
 
@@ -249,7 +266,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
     return tracker
 
 
-def save_representations(net, datadir, filelist, layer, first_N = None):
+def save_representations(settings, net, datadir, filelist, layer, first_N = None):
     image_filenames, image_labels = load_file_list(filelist)
     if first_N is None:
         first_N = len(image_filenames)
@@ -259,6 +276,9 @@ def save_representations(net, datadir, filelist, layer, first_N = None):
     assert len(image_indices) > 0
     print '  First file', os.path.join(datadir, image_filenames[image_indices[0]])
 
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
     indices = None
     rep = None
     for ii,image_idx in enumerate(image_indices):
@@ -284,25 +304,13 @@ def save_representations(net, datadir, filelist, layer, first_N = None):
     return indices,rep
 
 
-
-def get_max_data_extent(net, layer, rc, is_conv):
-    '''Gets the maximum size of the data layer that can influence a unit on layer.'''
-    if is_conv:
-        top_name = net.top_names[layer][0]
-        conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
-        layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
-        data_slice = rc.convert_region(layer, 'data', layer_slice_middle)
-        return data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
-    else:
-        # Whole data region
-        return net.blobs['data'].data.shape[2:4]        # e.g. (227,227) for fc6,fc7,fc8,prop
-
-
-
 def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
     do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, do_info = do_which
     assert do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm or do_info, 'nothing to do'
 
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+
     mt = max_tracker
     rc = RegionComputer(settings.max_tracker_layers_list)
 
@@ -345,42 +353,8 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             if do_print:
                 print '%s   Output file/image(s) %d/%d' % (datetime.now().ctime(), cc * num_top, n_total_images)
 
-            if mt.is_conv:
-                # Compute the focus area of the data layer
-                layer_indices = (ii,ii+1,jj,jj+1)
-                data_indices = rc.convert_region(layer, 'data', layer_indices)
-                data_ii_start,data_ii_end,data_jj_start,data_jj_end = data_indices
-
-                # safe guard edges
-                data_ii_start = max(data_ii_start, 0)
-                data_jj_start = max(data_jj_start, 0)
-                data_ii_end = min(data_ii_end, data_size_ii)
-                data_jj_end = min(data_jj_end, data_size_jj)
-
-                touching_imin = (data_ii_start == 0)
-                touching_jmin = (data_jj_start == 0)
-
-                # Compute how much of the data slice falls outside the actual data [0,max] range
-                ii_outside = size_ii - (data_ii_end - data_ii_start)  # possibly 0
-                jj_outside = size_jj - (data_jj_end - data_jj_start)  # possibly 0
-
-                if touching_imin:
-                    out_ii_start = ii_outside
-                    out_ii_end   = size_ii
-                else:
-                    out_ii_start = 0
-                    out_ii_end   = size_ii - ii_outside
-                if touching_jmin:
-                    out_jj_start = jj_outside
-                    out_jj_end   = size_jj
-                else:
-                    out_jj_start = 0
-                    out_jj_end   = size_jj - jj_outside
-            else:
-                ii,jj = 0,0
-                data_ii_start, out_ii_start, data_jj_start, out_jj_start = 0,0,0,0
-                data_ii_end, out_ii_end, data_jj_end, out_jj_end = size_ii, size_ii, size_jj, size_jj
-
+            [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
+                compute_data_layer_focus_area(mt.is_conv, ii, jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
 
             if do_info:
                 print >>info_file, 1 if mt.is_conv else 0, '%.6f' % mt.max_vals[channel_idx, max_idx],
@@ -393,7 +367,6 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             if not (do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm):
                 continue
 
-
             with WithTimer('Load image', quiet = not do_print):
 
                 if settings.is_siamese:
@@ -418,9 +391,8 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             with WithTimer('Predict   ', quiet = not do_print):
                 net.predict([im], oversample = False)
 
-
-            # in siamese network, we wish to return from the normalized layer name and selected input index to the denormalized layer name
-            # e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
+            # in siamese network, we wish to return from the normalized layer name and selected input index to the
+            # denormalized layer name, e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
             denormalized_layer_name = settings.denormalize_layer_name_for_max_tracker_fn(layer, selected_input_index)
             denormalized_top_name = net.top_names[denormalized_layer_name][0]
 
@@ -434,25 +406,9 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             if do_maxes:
                 #grab image from data layer, not from im (to ensure preprocessing / center crop details match between image and deconv/backprop)
 
-                if settings.is_siamese:
-
-                    # input is first image so select first 3 channels
-                    if selected_input_index == 0:
-                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                    # input is second image so select second 3 channels
-                    elif selected_input_index == 1:
-                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                    # input is both images so select concatenate data horizontally
-                    elif selected_input_index == -1:
-                        out_arr = np.zeros((3, size_ii, size_jj*2), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = net.blobs['data'].data[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                        out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = net.blobs['data'].data[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                    else:
-                        print "Error: invalid value for selected_input_index (", selected_input_index, ")"
-                else:
-                    out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].data[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
+                out_arr = extract_patch_from_image(net.blobs['data'].data[0], net, selected_input_index, settings,
+                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
 
                 with WithTimer('Save img  ', quiet = not do_print):
                     save_caffe_image(out_arr, os.path.join(unit_dir, 'maxim_%03d.png' % max_idx_0),
@@ -467,25 +423,9 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                 with WithTimer('Deconv    ', quiet = not do_print):
                     net.deconv_from_layer(denormalized_layer_name, diffs)
 
-                if settings.is_siamese:
-
-                    # input is first image so select first 3 channels
-                    if selected_input_index == 0:
-                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                    # input is second image so select second 3 channels
-                    elif selected_input_index == 1:
-                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                    # input is both images so select concatenate data horizontally
-                    elif selected_input_index == -1:
-                        out_arr = np.zeros((3, size_ii, size_jj*2), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                        out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-
-                else: # normal operation
-                    out_arr = np.zeros((3,size_ii,size_jj), dtype='float32')
-                    out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
+                out_arr = extract_patch_from_image(net.blobs['data'].diff[0], net, selected_input_index, settings,
+                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
 
                 if out_arr.max() == 0:
                     print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
@@ -511,27 +451,9 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                 with WithTimer('Backward  ', quiet = not do_print):
                     net.backward_from_layer(denormalized_layer_name, diffs)
 
-                if settings.is_siamese:
-
-                    # input is first image so select first 3 channels
-                    if selected_input_index == 0:
-                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-
-                    # input is second image so select second 3 channels
-                    elif selected_input_index == 1:
-                        out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-
-                    # input is both images so select concatenate data horizontally
-                    elif selected_input_index == -1:
-                        out_arr = np.zeros((3, size_ii, size_jj*2), dtype='float32')
-                        out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = net.blobs['data'].diff[0, 0:3, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-                        out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = net.blobs['data'].diff[0, 3:6, data_ii_start:data_ii_end, data_jj_start:data_jj_end]
-
-                else: # normal operation
-                    out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
-                    out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = net.blobs['data'].diff[0,:,data_ii_start:data_ii_end,data_jj_start:data_jj_end]
+                out_arr = extract_patch_from_image(net.blobs['data'].diff[0], net, selected_input_index, settings,
+                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
 
                 if out_arr.max() == 0:
                     print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
@@ -546,3 +468,4 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
         if do_info:
             info_file.close()
+
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 280139a85..8624cd234 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -14,6 +14,7 @@
 from misc import mkdir_p, combine_dicts
 from image_misc import saveimagesc, saveimagescc
 
+from caffe_misc import RegionComputer, get_max_data_extent, compute_data_layer_focus_area, extract_patch_from_image
 
 
 class FindParams(object):
@@ -35,6 +36,7 @@ def __init__(self, **kwargs):
             small_norm_percentile = None,
             px_benefit_percentile = None,
             px_abs_benefit_percentile = None,
+            layer_is_conv = False,
 
             lr_policy = 'constant',
             lr_params = {'lr': 10.0},
@@ -357,6 +359,25 @@ def _optimize(self, params, x0):
 
         return xx, results
 
+    def find_selected_input_index(self, layer):
+
+        for item in self.settings.siamese_layers_list:
+
+            # if we have only a single layer, the header is the layer name
+            if type(item) is str and item == layer:
+                return -1
+
+            # if we got a pair of layers
+            elif (type(item) is tuple) and (len(item) == 2):
+
+                if item[0] == layer:
+                    return 0
+
+                if item[1] == layer:
+                    return 1
+
+        return -1
+
     def save_results(self, params, results, prefix_template, brave = False, skipbig = False):
         if prefix_template is None:
             return
@@ -387,21 +408,42 @@ def save_results(self, params, results, prefix_template, brave = False, skipbig
         if results.best_xx is not None:
             # results.best_xx.shape is (6,224,224)
 
-            if (self.settings.is_siamese) and (results.best_xx.shape[0] == 6):
-                asimg1 = results.best_xx[self.channel_swap_to_rgb[[0,1,2]]].transpose((1, 2, 0))
-                asimg2 = results.best_xx[self.channel_swap_to_rgb[[3,4,5]]].transpose((1, 2, 0))
+            def save_output(data, channel_swap_to_rgb, best_X_image_name):
+                            # , best_Xpm_image_name, data_mean_rgb_img):
+                asimg = data[channel_swap_to_rgb].transpose((1, 2, 0))
+                saveimagescc(best_X_image_name, asimg, 0)
+                # saveimagesc(best_Xpm_image_name, asimg + data_mean_rgb_img)  # PlusMean
 
-                saveimagescc('%sbest_X_A.jpg' % prefix, asimg1, 0)
-                saveimagescc('%sbest_X_B.jpg' % prefix, asimg2, 0)
+            # get center position, relative to layer, of best maximum
+            [temp_ii, temp_jj] = results.idxmax[results.best_ii][1:3]
 
-                saveimagesc('%sbest_Xpm_A.jpg' % prefix, asimg1 + self._data_mean_rgb_img[:,:,[0,1,2]])  # PlusMean
-                saveimagesc('%sbest_Xpm_B.jpg' % prefix, asimg2 + self._data_mean_rgb_img[:,:,[3,4,5]])  # PlusMean
+            is_conv = params.layer_is_conv
+            rc = RegionComputer(self.settings.max_tracker_layers_list)
+            layer = params.push_layer
+            size_ii, size_jj = get_max_data_extent(self.net, layer, rc, is_conv)
+            data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
 
+            [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
+                compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
 
-            else:
-                asimg = results.best_xx[self.channel_swap_to_rgb].transpose((1,2,0))
-                saveimagescc('%sbest_X.jpg' % prefix, asimg, 0)
-                saveimagesc('%sbest_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
+            selected_input_index = self.find_selected_input_index(layer)
+
+            out_arr = extract_patch_from_image(results.best_xx, self.net, selected_input_index, self.settings,
+                                               data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                                               out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
+
+            save_output(out_arr,
+                        channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
+                        best_X_image_name='%sbest_X.jpg' % prefix)
+
+            if self.settings.optimize_image_generate_plus_mean:
+                out_arr_pm = extract_patch_from_image(results.best_xx + self.data_mean, self.net, selected_input_index, self.settings,
+                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
+
+                save_output(out_arr_pm,
+                            channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
+                            best_X_image_name='%sbest_Xpm.jpg' % prefix)
 
         with open('%sinfo.txt' % prefix, 'w') as ff:
             print >>ff, params
diff --git a/optimize_image.py b/optimize_image.py
index 65d3e3d28..756348c87 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -231,7 +231,6 @@ def main():
 
     # go over channels
     for current_channel in range(channels):
-
         params = FindParams(
             start_at = args.start_at,
             rand_seed = args.rand_seed,
@@ -249,6 +248,7 @@ def main():
             lr_policy = args.lr_policy,
             lr_params = lr_params,
             max_iter = args.max_iter,
+            layer_is_conv=(type == 'Convolution'),
         )
 
         prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
@@ -256,6 +256,5 @@ def main():
                                     brave = args.brave, skipbig = args.skipbig)
 
 
-
 if __name__ == '__main__':
     main()
diff --git a/settings.py b/settings.py
index 06fffb576..8aa2c997f 100644
--- a/settings.py
+++ b/settings.py
@@ -419,6 +419,8 @@
 # output-prefix parameter for optimize_image.py
 optimize_image_output_prefix = locals().get('optimize_image_output_prefix', 'optimize_results/opt')
 
+# parameter which marks whether we should generate also the plus mean image of the optmized image
+optimize_image_generate_plus_mean = locals().get('optimize_image_generate_plus_mean', False)
 
 ####################################
 #

From 57e9112d3f5ce2e161d60c5f9b734cf74c783f24 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:15:43 +0300
Subject: [PATCH 020/106] - fixed some layer/blob confusion in optimizer

---
 optimize/gradient_optimizer.py | 9 ++++++---
 1 file changed, 6 insertions(+), 3 deletions(-)

diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 8624cd234..8d53968d4 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -211,7 +211,8 @@ def _optimize(self, params, x0):
         is_labeled_unit = params.push_layer in self.label_layers
 
         # Sanity checks for conv vs FC layers
-        data_shape = self.net.blobs[params.push_layer].data.shape
+        top_name = self.net.top_names[params.push_layer][0]
+        data_shape = self.net.blobs[top_name].data.shape
         assert len(data_shape) in (2,4), 'Expected shape of length 2 (for FC) or 4 (for conv) layers but shape is %s' % repr(data_shape)
         is_conv = (len(data_shape) == 4)
 
@@ -240,7 +241,8 @@ def _optimize(self, params, x0):
             # 1. Push data through net
             out = self.net.forward_all(data = xx)
             #shownet(net)
-            acts = self.net.blobs[params.push_layer].data[0]    # chop off batch dimension
+            top_name = self.net.top_names[params.push_layer][0]
+            acts = self.net.blobs[top_name].data[0]    # chop off batch dimension
 
             if not is_conv:
                 # promote to 3D
@@ -274,7 +276,8 @@ def _optimize(self, params, x0):
 
 
             # 4. Do backward pass to get gradient
-            diffs = self.net.blobs[params.push_layer].diff * 0
+            top_name = self.net.top_names[params.push_layer][0]
+            diffs = self.net.blobs[top_name].diff * 0
             if not is_conv:
                 # Promote bc -> bc01
                 diffs = diffs[:,:,np.newaxis,np.newaxis]

From 6a901cddd53669b67a83084d29f0d81bf93ee6f9 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:17:27 +0300
Subject: [PATCH 021/106] added support for running on multiple layers in
 optimizer

---
 optimize_image.py | 81 ++++++++++++++++++++++++-----------------------
 settings.py       |  2 +-
 2 files changed, 43 insertions(+), 40 deletions(-)

diff --git a/optimize_image.py b/optimize_image.py
index 756348c87..bd1c732ec 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -39,8 +39,8 @@ def get_parser():
                         help = 'Random seed used for generating the start-at image (use different seeds to generate different images).')
 
     # What to optimize
-    parser.add_argument('--push-layer', type = str, default = settings.optimize_image_push_layer,
-                        help = 'Name of layer that contains the desired neuron whose value is optimized.')
+    parser.add_argument('--push-layers', type = str, default = settings.optimize_image_push_layers,
+                        help = 'Name of layers that contains the desired neuron whose value is optimized.')
     parser.add_argument('--push-channel', type = int, default = '130',
                         help = 'Channel number for desired neuron whose value is optimized (channel for conv, neuron index for FC).')
     parser.add_argument('--push-spatial', type = str, default = 'None',
@@ -217,43 +217,46 @@ def main():
                                   label_layers = settings.caffevis_label_layers,
                                   channel_swap_to_rgb = net_channel_swap)
 
-    # get layer type
-    (name, type, input, output, filter, stride, pad) = get_layer_info(settings, args.push_layer)
-
-    if type == 'FullyConnected':
-        # get number of units
-        channels = output
-        push_spatial = (0, 0)
-    elif type == 'Convolution':
-        # get number of channels
-        channels = output[2]
-        push_spatial = (filter[0]/2, filter[1]/2)
-
-    # go over channels
-    for current_channel in range(channels):
-        params = FindParams(
-            start_at = args.start_at,
-            rand_seed = args.rand_seed,
-            push_layer = args.push_layer,
-            push_channel = current_channel,
-            push_spatial = push_spatial,
-            push_dir = args.push_dir,
-            decay = args.decay,
-            blur_radius = args.blur_radius,
-            blur_every = args.blur_every,
-            small_val_percentile = args.small_val_percentile,
-            small_norm_percentile = args.small_norm_percentile,
-            px_benefit_percentile = args.px_benefit_percentile,
-            px_abs_benefit_percentile = args.px_abs_benefit_percentile,
-            lr_policy = args.lr_policy,
-            lr_params = lr_params,
-            max_iter = args.max_iter,
-            layer_is_conv=(type == 'Convolution'),
-        )
-
-        prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
-        im = optimizer.run_optimize(params, prefix_template = prefix_template,
-                                    brave = args.brave, skipbig = args.skipbig)
+    # go over push layers
+    for push_layer in args.push_layers:
+
+        # get layer type
+        (name, type, input, output, filter, stride, pad) = get_layer_info(settings, push_layer)
+
+        if type == 'FullyConnected':
+            # get number of units
+            channels = output
+            push_spatial = (0, 0)
+        elif type == 'Convolution':
+            # get number of channels
+            channels = output[2]
+            push_spatial = (filter[0]/2, filter[1]/2)
+
+        # go over channels
+        for current_channel in range(channels):
+            params = FindParams(
+                start_at = args.start_at,
+                rand_seed = args.rand_seed,
+                push_layer = push_layer,
+                push_channel = current_channel,
+                push_spatial = push_spatial,
+                push_dir = args.push_dir,
+                decay = args.decay,
+                blur_radius = args.blur_radius,
+                blur_every = args.blur_every,
+                small_val_percentile = args.small_val_percentile,
+                small_norm_percentile = args.small_norm_percentile,
+                px_benefit_percentile = args.px_benefit_percentile,
+                px_abs_benefit_percentile = args.px_abs_benefit_percentile,
+                lr_policy = args.lr_policy,
+                lr_params = lr_params,
+                max_iter = args.max_iter,
+                layer_is_conv=(type == 'Convolution'),
+            )
+
+            prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
+            im = optimizer.run_optimize(params, prefix_template = prefix_template,
+                                        brave = args.brave, skipbig = args.skipbig)
 
 
 if __name__ == '__main__':
diff --git a/settings.py b/settings.py
index 8aa2c997f..f4ded0286 100644
--- a/settings.py
+++ b/settings.py
@@ -396,7 +396,7 @@
 optimize_image_rand_seed = locals().get('optimize_image_rand_seed', 0)
 
 # push-layer parameter for optimize_image.py
-optimize_image_push_layer = locals().get('optimize_image_push_layer', 'fc8')
+optimize_image_push_layers = locals().get('optimize_image_push_layers', ['fc8'])
 
 # decay parameter for optimize_image.py
 optimize_image_decay = locals().get('optimize_image_decay', 0)

From 3f2a35d970034d5f345e33ad767dfac4ca0e8e41 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:20:00 +0300
Subject: [PATCH 022/106] added option to skip small files generation

---
 optimize/gradient_optimizer.py | 11 ++++++-----
 optimize_image.py              |  3 ++-
 2 files changed, 8 insertions(+), 6 deletions(-)

diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 8d53968d4..466dc4ba3 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -171,7 +171,7 @@ def __init__(self, settings, net, data_mean, labels = None, label_layers = None,
 
         self._data_mean_rgb_img = self.data_mean[self.channel_swap_to_rgb].transpose((1,2,0))  # Store as (227,227,3) in RGB order.
 
-    def run_optimize(self, params, prefix_template = None, brave = False, skipbig = False):
+    def run_optimize(self, params, prefix_template = None, brave = False, skipbig = False, skipsmall = False):
         '''All images are in Caffe format, e.g. shape (3, 227, 227) in BGR order.'''
 
         print '\n\nStarting optimization with the following parameters:'
@@ -179,7 +179,7 @@ def run_optimize(self, params, prefix_template = None, brave = False, skipbig =
         
         x0 = self._get_x0(params)
         xx, results = self._optimize(params, x0)
-        self.save_results(params, results, prefix_template, brave = brave, skipbig = skipbig)
+        self.save_results(params, results, prefix_template, brave = brave, skipbig = skipbig, skipsmall = skipsmall)
 
         print results.meta_result
         
@@ -381,7 +381,7 @@ def find_selected_input_index(self, layer):
 
         return -1
 
-    def save_results(self, params, results, prefix_template, brave = False, skipbig = False):
+    def save_results(self, params, results, prefix_template, brave = False, skipbig = False, skipsmall = False):
         if prefix_template is None:
             return
 
@@ -456,5 +456,6 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
             with open('%sinfo_big.pkl' % prefix, 'w') as ff:
                 pickle.dump((params, results), ff, protocol=-1)
         results.trim_arrays()
-        with open('%sinfo.pkl' % prefix, 'w') as ff:
-            pickle.dump((params, results), ff, protocol=-1)
+        if not skipsmall:
+            with open('%sinfo.pkl' % prefix, 'w') as ff:
+                pickle.dump((params, results), ff, protocol=-1)
diff --git a/optimize_image.py b/optimize_image.py
index bd1c732ec..e3999b145 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -80,6 +80,7 @@ def get_parser():
                         'The default output-prefix and output-template produce filenames like "optimize_results/opt_prob_0278_0_best_X.jpg"')
     parser.add_argument('--brave', action = 'store_true', default=True, help = 'Allow overwriting existing results files. Default: off, i.e. cowardly refuse to overwrite existing files.')
     parser.add_argument('--skipbig', action = 'store_true', default=True, help = 'Skip outputting large *info_big.pkl files (contains pickled version of x0, last x, best x, first x that attained max on the specified layer.')
+    parser.add_argument('--skipsmall', action = 'store_true', default=True, help = 'Skip outputting small *info.pkl files (contains pickled version of..')
 
     return parser
 
@@ -256,7 +257,7 @@ def main():
 
             prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
             im = optimizer.run_optimize(params, prefix_template = prefix_template,
-                                        brave = args.brave, skipbig = args.skipbig)
+                                        brave = args.brave, skipbig = args.skipbig, skipsmall = args.skipsmall)
 
 
 if __name__ == '__main__':

From f1abd5b0ab0a6f95b0c57925a5dcc30f4343949d Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:21:06 +0300
Subject: [PATCH 023/106] - added multiple max_iter per layer

---
 .idea/workspace.xml | 181 ++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 181 insertions(+)
 create mode 100644 .idea/workspace.xml

diff --git a/.idea/workspace.xml b/.idea/workspace.xml
new file mode 100644
index 000000000..ed33c7b58
--- /dev/null
+++ b/.idea/workspace.xml
@@ -0,0 +1,181 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ChangeListManager">
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From 95fde69223b004383d5b0c9e77b450bf4a0a39f2 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:22:32 +0300
Subject: [PATCH 024/106] - added batch support to optimize_image.py

---
 optimize/gradient_optimizer.py | 369 ++++++++++++++++++---------------
 optimize_image.py              |  25 ++-
 settings.py                    |   5 +-
 3 files changed, 220 insertions(+), 179 deletions(-)

diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 466dc4ba3..88d0ffcfe 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -23,6 +23,7 @@ def __init__(self, **kwargs):
             # Starting
             rand_seed = 0,
             start_at = 'mean_plus_rand',
+            batch_size = 9,
             
             # Optimization
             push_layer = 'prob',
@@ -81,7 +82,7 @@ def __str__(self):
 
 
 class FindResults(object):
-    def __init__(self):
+    def __init__(self,batch_index):
         self.ii = []
         self.obj = []
         self.idxmax = []
@@ -97,6 +98,7 @@ def __init__(self):
         self.last_obj = None
         self.last_xx = None
         self.meta_result = None
+        self.batch_index = batch_index
         
     def update(self, params, ii, acts, idxmax, xx, x0):
         assert params.push_dir > 0, 'push_dir < 0 not yet supported'
@@ -140,7 +142,7 @@ def trim_arrays(self):
 
     def __str__(self):
         ret = StringIO.StringIO()
-        print >>ret, 'FindResults:'
+        print >>ret, 'FindResults[%d]:' % self.batch_index
         for key in sorted(self.__dict__.keys()):
             val = self.__dict__[key]
             if isinstance(val, list) and len(val) > 4:
@@ -157,19 +159,20 @@ def __str__(self):
 class GradientOptimizer(object):
     '''Finds images by gradient.'''
     
-    def __init__(self, settings, net, data_mean, labels = None, label_layers = None, channel_swap_to_rgb = None):
+    def __init__(self, settings, net, batched_data_mean, labels = None, label_layers = None, channel_swap_to_rgb = None):
         self.settings = settings
         self.net = net
-        self.data_mean = data_mean
+        self.batched_data_mean = batched_data_mean
         self.labels = labels if labels else ['labels not provided' for ii in range(1000)]
         self.label_layers = label_layers if label_layers else tuple()
         if channel_swap_to_rgb:
             self.channel_swap_to_rgb = array(channel_swap_to_rgb)
         else:
-            data_n_channels = self.data_mean.shape[0]
+            data_n_channels = self.batched_data_mean.shape[0]
             self.channel_swap_to_rgb = arange(data_n_channels)   # Don't change order
 
-        self._data_mean_rgb_img = self.data_mean[self.channel_swap_to_rgb].transpose((1,2,0))  # Store as (227,227,3) in RGB order.
+        # since we have a batch of same data mean images, we can just take the first
+        self._data_mean_rgb_img = self.batched_data_mean[0, self.channel_swap_to_rgb].transpose((1,2,0))  # Store as (227,227,3) in RGB order.
 
     def run_optimize(self, params, prefix_template = None, brave = False, skipbig = False, skipsmall = False):
         '''All images are in Caffe format, e.g. shape (3, 227, 227) in BGR order.'''
@@ -181,21 +184,21 @@ def run_optimize(self, params, prefix_template = None, brave = False, skipbig =
         xx, results = self._optimize(params, x0)
         self.save_results(params, results, prefix_template, brave = brave, skipbig = skipbig, skipsmall = skipsmall)
 
-        print results.meta_result
+        print str([results[batch_index].meta_result for batch_index in range(params.batch_size)])
         
         return xx
 
     def _get_x0(self, params):
         '''Chooses a starting location'''
-        
+
         np.random.seed(params.rand_seed)
 
         if params.start_at == 'mean_plus_rand':
-            x0 = np.random.normal(0, 10, self.data_mean.shape)
+            x0 = np.random.normal(0, 10, self.batched_data_mean.shape)
         elif params.start_at == 'randu':
-            x0 = uniform(0, 255, self.data_mean.shape) - self.data_mean
+            x0 = uniform(0, 255, self.batched_data_mean.shape) - self.batched_data_mean
         elif params.start_at == 'mean':
-            x0 = zeros(self.data_mean.shape)
+            x0 = zeros(self.batched_data_mean.shape)
         else:
             raise Exception('Unknown start conditions: %s' % params.start_at)
 
@@ -203,9 +206,8 @@ def _get_x0(self, params):
         
     def _optimize(self, params, x0):
         xx = x0.copy()
-        xx = xx[newaxis,:]      # Promote 3D -> 4D
 
-        results = FindResults()
+        results = [FindResults(i) for i in range(params.batch_size)]
 
         # Whether or not the unit being optimized corresponds to a label (e.g. one of the 1000 imagenet classes)
         is_labeled_unit = params.push_layer in self.label_layers
@@ -232,47 +234,54 @@ def _optimize(self, params, x0):
             push_label = self.labels[params.push_unit[0]]
         else:
             push_label = None
-        
+
+        old_obj = np.zeros(params.batch_size)
         for ii in range(params.max_iter):
             # 0. Crop data
-            xx = minimum(255.0, maximum(0.0, xx + self.data_mean)) - self.data_mean     # Crop all values to [0,255]
+            xx = minimum(255.0, maximum(0.0, xx + self.batched_data_mean)) - self.batched_data_mean     # Crop all values to [0,255]
 
 
             # 1. Push data through net
             out = self.net.forward_all(data = xx)
             #shownet(net)
             top_name = self.net.top_names[params.push_layer][0]
-            acts = self.net.blobs[top_name].data[0]    # chop off batch dimension
+            acts = self.net.blobs[top_name].data
 
             if not is_conv:
-                # promote to 3D
-                acts = acts[:,np.newaxis,np.newaxis]
-            idxmax = unravel_index(acts.argmax(), acts.shape)
-            valmax = acts.max()
-            # idxmax for fc or prob layer will be like:  (278, 0, 0)
-            # idxmax for conv layer will be like:        (37, 4, 37)
-            obj = acts[params.push_unit]
+            #     promote to 3D
+                acts = np.reshape(acts, (params.batch_size, -1, 1, 1))
+            # else:
+            #     reshaped_acts = np.reshape(acts, (params.batch_size, -1))
 
-            
-            # 2. Update results
-            results.update(params, ii, acts, idxmax, xx[0], x0)
+            reshaped_acts = np.reshape(acts, (params.batch_size, -1))
 
-            
-            # 3. Print progress
-            if ii > 0:
-                if params.lr_policy == 'progress':
-                    print '%-4d  progress predicted: %g, actual: %g' % (ii, pred_prog, obj - old_obj)
+            idxmax = unravel_index(reshaped_acts.argmax(axis=1), acts.shape[1:])
+            valmax = reshaped_acts.max(axis=1)
+            # idxmax for fc or prob layer will be like:  (batch,278, 0, 0)
+            # idxmax for conv layer will be like:        (batch,37, 4, 37)
+            obj = acts[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]]
+
+            # 2. Update results
+            for batch_index in range(params.batch_size):
+                results[batch_index].update(params, ii, acts[batch_index], \
+                                            (idxmax[0][batch_index],idxmax[1][batch_index],idxmax[2][batch_index]), \
+                                            xx[batch_index], x0[batch_index])
+
+                # 3. Print progress
+                if ii > 0:
+                    if params.lr_policy == 'progress':
+                        print 'iter %-4d batch_index %d progress predicted: %g, actual: %g' % (ii, batch_index, pred_prog[batch_index], obj[batch_index] - old_obj[batch_index])
+                    else:
+                        print 'iter %-4d batch_index %d progress: %g' % (ii, batch_index, obj[batch_index] - old_obj[batch_index])
                 else:
-                    print '%-4d  progress: %g' % (ii, obj - old_obj)
-            else:
-                print '%d' % ii
-            old_obj = obj
+                    print 'iter %d batch_index %d' % (ii, batch_index)
+                old_obj[batch_index] = obj[batch_index]
 
-            push_label_str = ('(%s)' % push_label) if is_labeled_unit else ''
-            max_label_str  = ('(%s)' % self.labels[idxmax[0]]) if is_labeled_unit else ''
-            print '     push unit: %16s with value %g %s' % (params.push_unit, acts[params.push_unit], push_label_str)
-            print '       Max idx: %16s with value %g %s' % (idxmax, valmax, max_label_str)
-            print '             X:', xx.min(), xx.max(), norm(xx)
+                push_label_str = ('(%s)' % push_label) if is_labeled_unit else ''
+                max_label_str  = ('(%s)' % self.labels[idxmax[0][batch_index]]) if is_labeled_unit else ''
+                print '     push unit: %16s with value %g %s' % (params.push_unit, acts[batch_index][params.push_unit], push_label_str)
+                print '       Max idx: %16s with value %g %s' % ((idxmax[0][batch_index],idxmax[1][batch_index],idxmax[2][batch_index]), valmax[batch_index], max_label_str)
+                print '             X:', xx[batch_index].min(), xx[batch_index].max(), norm(xx[batch_index])
 
 
             # 4. Do backward pass to get gradient
@@ -281,84 +290,102 @@ def _optimize(self, params, x0):
             if not is_conv:
                 # Promote bc -> bc01
                 diffs = diffs[:,:,np.newaxis,np.newaxis]
-            diffs[0][params.push_unit] = params.push_dir
+
+            diffs[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
             backout = self.net.backward_from_layer(params.push_layer, diffs if is_conv else diffs[:,:,0,0])
 
             grad = backout['data'].copy()
-            print '          grad:', grad.min(), grad.max(), norm(grad)
-            if norm(grad) == 0:
-                print 'Grad exactly 0, failed'
-                results.meta_result = 'Metaresult: grad 0 failure'
-                break
-
+            reshaped_grad = np.reshape(grad, (params.batch_size, -1))
+            norm_grad = np.linalg.norm(reshaped_grad, axis=1)
+            min_grad = np.amin(reshaped_grad, axis=1)
+            max_grad = np.amax(reshaped_grad, axis=1)
+
+            for batch_index in range(params.batch_size):
+                print ' batch_index: %d         min grad: %f, max grad: %f, norm grad: %f' % (batch_index, min_grad[batch_index], max_grad[batch_index], norm_grad[batch_index])
+                if norm_grad[batch_index] == 0:
+                    print ' batch_index: %d, Grad exactly 0, failed' % batch_index
+                    results[batch_index].meta_result = 'Metaresult: grad 0 failure'
+                    break
 
             # 5. Pick gradient update per learning policy
             if params.lr_policy == 'progress01':
                 # Useful for softmax layer optimization, taper off near 1
                 late_prog = params.lr_params['late_prog_mult'] * (1-obj)
-                desired_prog = min(params.lr_params['early_prog'], late_prog)
-                prog_lr = desired_prog / norm(grad)**2
-                lr = min(params.lr_params['max_lr'], prog_lr)
-                print '    desired progress:', desired_prog, 'prog_lr:', prog_lr, 'lr:', lr
-                pred_prog = lr * dot(grad.flatten(), grad.flatten())
+                desired_prog = np.amin(np.stack((np.repeat(params.lr_params['early_prog'], params.batch_size), late_prog), axis=1), axis=1)
+                prog_lr = desired_prog / np.square(norm_grad)
+                lr = np.amin(np.stack((np.repeat(params.lr_params['max_lr'], params.batch_size), prog_lr), axis=1), axis=1)
+                print '    entire batch, desired progress:', desired_prog, 'prog_lr:', prog_lr, 'lr:', lr
+                pred_prog = lr * np.sum(np.abs(reshaped_grad) ** 2, axis=-1)
             elif params.lr_policy == 'progress':
                 # straight progress-based lr
-                prog_lr = params.lr_params['desired_prog'] / norm(grad)**2
-                lr = min(params.lr_params['max_lr'], prog_lr)
-                print '    desired progress:', params.lr_params['desired_prog'], 'prog_lr:', prog_lr, 'lr:', lr
-                pred_prog = lr * dot(grad.flatten(), grad.flatten())
+                prog_lr = params.lr_params['desired_prog'] / (norm_grad**2)
+                lr = np.amin(np.stack((np.repeat(params.lr_params['max_lr'], params.batch_size), prog_lr), axis=1), axis=1)
+                print '    entire batch, desired progress:', params.lr_params['desired_prog'], 'prog_lr:', prog_lr, 'lr:', lr
+                pred_prog = lr * np.sum(np.abs(reshaped_grad) ** 2, axis=-1)
             elif params.lr_policy == 'constant':
                 # constant fixed learning rate
-                lr = params.lr_params['lr']
+                lr = np.repeat(params.lr_params['lr'], params.batch_size)
             else:
                 raise Exception('Unimplemented lr_policy')
 
-            
-            # 6. Apply gradient update and regularizations
-            if ii < params.max_iter-1:
-                # Skip gradient and regularizations on the very last step (so the above printed info is valid for the last step)
-                xx += lr * grad
-                xx *= (1 - params.decay)
-
-                if params.blur_every is not 0 and params.blur_radius > 0:
-                    if params.blur_radius < .3:
-                        print 'Warning: blur-radius of .3 or less works very poorly'
-                        #raise Exception('blur-radius of .3 or less works very poorly')
-                    if ii % params.blur_every == 0:
-                        for channel in range(3):
-                            cimg = gaussian_filter(xx[0,channel], params.blur_radius)
-                            xx[0,channel] = cimg
-                if params.small_val_percentile > 0:
-                    small_entries = (abs(xx) < percentile(abs(xx), params.small_val_percentile))
-                    xx = xx - xx*small_entries   # e.g. set smallest 50% of xx to zero
-
-                if params.small_norm_percentile > 0:
-                    pxnorms = norm(xx, axis=1)
-                    smallpx = pxnorms < percentile(pxnorms, params.small_norm_percentile)
-                    smallpx3 = tile(smallpx[:,newaxis,:,:], (1,3,1,1))
-                    xx = xx - xx*smallpx3
-
-                if params.px_benefit_percentile > 0:
-                    pred_0_benefit = grad * -xx
-                    px_benefit = pred_0_benefit.sum(1)   # sum over color channels
-                    smallben = px_benefit < percentile(px_benefit, params.px_benefit_percentile)
-                    smallben3 = tile(smallben[:,newaxis,:,:], (1,3,1,1))
-                    xx = xx - xx*smallben3
-
-                if params.px_abs_benefit_percentile > 0:
-                    pred_0_benefit = grad * -xx
-                    px_benefit = pred_0_benefit.sum(1)   # sum over color channels
-                    smallaben = abs(px_benefit) < percentile(abs(px_benefit), params.px_abs_benefit_percentile)
-                    smallaben3 = tile(smallaben[:,newaxis,:,:], (1,3,1,1))
-                    xx = xx - xx*smallaben3
+            for batch_index in range(params.batch_size):
+
+                # 6. Apply gradient update and regularizations
+                if ii < params.max_iter-1:
+                    # Skip gradient and regularizations on the very last step (so the above printed info is valid for the last step)
+                    xx[batch_index] += lr[batch_index] * grad[batch_index]
+                    xx[batch_index] *= (1 - params.decay)
+
+                    if params.blur_every is not 0 and params.blur_radius > 0:
+                        if params.blur_radius < .3:
+                            print 'Warning: blur-radius of .3 or less works very poorly'
+                            #raise Exception('blur-radius of .3 or less works very poorly')
+                        if ii % params.blur_every == 0:
+                            channels = 6 if self.settings.is_siamese else 3
+                            for channel in range(channels):
+                                cimg = gaussian_filter(xx[batch_index,channel], params.blur_radius)
+                                xx[batch_index,channel] = cimg
+                    if params.small_val_percentile > 0:
+                        small_entries = (abs(xx[batch_index]) < percentile(abs(xx[batch_index]), params.small_val_percentile))
+                        xx[batch_index] = xx[batch_index] - xx[batch_index]*small_entries   # e.g. set smallest 50% of xx to zero
+
+                    if params.small_norm_percentile > 0:
+                        pxnorms = norm(xx[batch_index,np.newaxis,:,:,:], axis=1)
+                        smallpx = pxnorms < percentile(pxnorms, params.small_norm_percentile)
+                        if self.settings.is_siamese:
+                            smallpx3 = tile(smallpx[:,newaxis,:,:], (1,6,1,1))
+                        else:
+                            smallpx3 = tile(smallpx[:,newaxis,:,:], (1,3,1,1))
+                        xx[batch_index,:,:,:] = xx[batch_index,np.newaxis,:,:,:] - xx[batch_index,np.newaxis,:,:,:]*smallpx3
+
+                    if params.px_benefit_percentile > 0:
+                        pred_0_benefit = grad[batch_index,np.newaxis,:,:,:] * -xx[batch_index,np.newaxis,:,:,:]
+                        px_benefit = pred_0_benefit.sum(1)   # sum over color channels
+                        smallben = px_benefit < percentile(px_benefit, params.px_benefit_percentile)
+                        if self.settings.is_siamese:
+                            smallben3 = tile(smallben[:,newaxis,:,:], (1,6,1,1))
+                        else:
+                            smallben3 = tile(smallben[:,newaxis,:,:], (1,3,1,1))
+                        xx[batch_index,:,:,:] = xx[batch_index,np.newaxis,:,:,:] - xx[batch_index,np.newaxis,:,:,:]*smallben3
+
+                    if params.px_abs_benefit_percentile > 0:
+                        pred_0_benefit = grad[batch_index,np.newaxis,:,:,:] * -xx[batch_index,np.newaxis,:,:,:]
+                        px_benefit = pred_0_benefit.sum(1)   # sum over color channels
+                        smallaben = abs(px_benefit) < percentile(abs(px_benefit), params.px_abs_benefit_percentile)
+                        if self.settings.is_siamese:
+                            smallaben3 = tile(smallaben[:,newaxis,:,:], (1,6,1,1))
+                        else:
+                            smallaben3 = tile(smallaben[:,newaxis,:,:], (1,3,1,1))
+                        xx[batch_index,:,:,:] = xx[batch_index,np.newaxis,:,:,:] - xx[batch_index,np.newaxis,:,:,:]*smallaben3
 
             print '     timestamp:', datetime.datetime.now()
 
-        if results.meta_result is None:
-            if results.majority_obj is not None:
-                results.meta_result = 'Metaresult: majority success'
-            else:
-                results.meta_result = 'Metaresult: majority failure'
+        for batch_index in range(params.batch_size):
+            if results[batch_index].meta_result is None:
+                if results[batch_index].majority_obj is not None:
+                    results[batch_index].meta_result = 'batch_index: %d, Metaresult: majority success' % batch_index
+                else:
+                    results[batch_index].meta_result = 'batch_index: %d, Metaresult: majority failure' % batch_index
 
         return xx, results
 
@@ -385,77 +412,79 @@ def save_results(self, params, results, prefix_template, brave = False, skipbig
         if prefix_template is None:
             return
 
-        results_and_params = combine_dicts((('p.', params.__dict__),
-                                            ('r.', results.__dict__)))
-        prefix = prefix_template % results_and_params
-        
-        if os.path.isdir(prefix):
-            if prefix[-1] != '/':
-                prefix += '/'   # append slash for dir-only template
-        else:
-            dirname = os.path.dirname(prefix)
-            if dirname:
-                mkdir_p(dirname)
-
-        # Don't overwrite previous results
-        if os.path.exists('%sinfo.txt' % prefix) and not brave:
-            raise Exception('Cowardly refusing to overwrite ' + '%sinfo.txt' % prefix)
-
-        output_majority = False
-        if output_majority:
-            if results.majority_xx is not None:
-                asimg = results.majority_xx[self.channel_swap_to_rgb].transpose((1,2,0))
-                saveimagescc('%smajority_X.jpg' % prefix, asimg, 0)
-                saveimagesc('%smajority_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
-
-        if results.best_xx is not None:
-            # results.best_xx.shape is (6,224,224)
-
-            def save_output(data, channel_swap_to_rgb, best_X_image_name):
-                            # , best_Xpm_image_name, data_mean_rgb_img):
-                asimg = data[channel_swap_to_rgb].transpose((1, 2, 0))
-                saveimagescc(best_X_image_name, asimg, 0)
-                # saveimagesc(best_Xpm_image_name, asimg + data_mean_rgb_img)  # PlusMean
-
-            # get center position, relative to layer, of best maximum
-            [temp_ii, temp_jj] = results.idxmax[results.best_ii][1:3]
-
-            is_conv = params.layer_is_conv
-            rc = RegionComputer(self.settings.max_tracker_layers_list)
-            layer = params.push_layer
-            size_ii, size_jj = get_max_data_extent(self.net, layer, rc, is_conv)
-            data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
-
-            [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
-                compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
-
-            selected_input_index = self.find_selected_input_index(layer)
-
-            out_arr = extract_patch_from_image(results.best_xx, self.net, selected_input_index, self.settings,
-                                               data_ii_end, data_ii_start, data_jj_end, data_jj_start,
-                                               out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
-
-            save_output(out_arr,
-                        channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
-                        best_X_image_name='%sbest_X.jpg' % prefix)
-
-            if self.settings.optimize_image_generate_plus_mean:
-                out_arr_pm = extract_patch_from_image(results.best_xx + self.data_mean, self.net, selected_input_index, self.settings,
+        for batch_index in range(params.batch_size):
+
+            results_and_params = combine_dicts((('p.', params.__dict__),
+                                                ('r.', results[batch_index].__dict__)))
+            prefix = prefix_template % results_and_params
+
+            if os.path.isdir(prefix):
+                if prefix[-1] != '/':
+                    prefix += '/'   # append slash for dir-only template
+            else:
+                dirname = os.path.dirname(prefix)
+                if dirname:
+                    mkdir_p(dirname)
+
+            # Don't overwrite previous results
+            if os.path.exists('%sinfo.txt' % prefix) and not brave:
+                raise Exception('Cowardly refusing to overwrite ' + '%sinfo.txt' % prefix)
+
+            output_majority = False
+            if output_majority:
+                # NOTE: this section wasn't tested after changes to code, so some minor index tweaking are in order
+                if results[batch_index].majority_xx is not None:
+                    asimg = results[batch_index].majority_xx[self.channel_swap_to_rgb].transpose((1,2,0))
+                    saveimagescc('%smajority_X.jpg' % prefix, asimg, 0)
+                    saveimagesc('%smajority_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
+
+            if results[batch_index].best_xx is not None:
+                # results[batch_index].best_xx.shape is (6,224,224)
+
+                def save_output(data, channel_swap_to_rgb, best_X_image_name):
+                                # , best_Xpm_image_name, data_mean_rgb_img):
+                    asimg = data[channel_swap_to_rgb].transpose((1, 2, 0))
+                    saveimagescc(best_X_image_name, asimg, 0)
+
+                # get center position, relative to layer, of best maximum
+                [temp_ii, temp_jj] = results[batch_index].idxmax[results[batch_index].best_ii][1:3]
+
+                is_conv = params.layer_is_conv
+                rc = RegionComputer(self.settings.max_tracker_layers_list)
+                layer = params.push_layer
+                size_ii, size_jj = get_max_data_extent(self.net, layer, rc, is_conv)
+                data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
+
+                [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
+                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
+
+                selected_input_index = self.find_selected_input_index(layer)
+
+                out_arr = extract_patch_from_image(results[batch_index].best_xx, self.net, selected_input_index, self.settings,
                                                    data_ii_end, data_ii_start, data_jj_end, data_jj_start,
                                                    out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
 
-                save_output(out_arr_pm,
+                save_output(out_arr,
                             channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
-                            best_X_image_name='%sbest_Xpm.jpg' % prefix)
-
-        with open('%sinfo.txt' % prefix, 'w') as ff:
-            print >>ff, params
-            print >>ff
-            print >>ff, results
-        if not skipbig:
-            with open('%sinfo_big.pkl' % prefix, 'w') as ff:
-                pickle.dump((params, results), ff, protocol=-1)
-        results.trim_arrays()
-        if not skipsmall:
-            with open('%sinfo.pkl' % prefix, 'w') as ff:
-                pickle.dump((params, results), ff, protocol=-1)
+                            best_X_image_name='%sbest_X.jpg' % prefix)
+
+                if self.settings.optimize_image_generate_plus_mean:
+                    out_arr_pm = extract_patch_from_image(results[batch_index].best_xx + self.batched_data_mean, self.net, selected_input_index, self.settings,
+                                                       data_ii_end, data_ii_start, data_jj_end, data_jj_start,
+                                                       out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
+
+                    save_output(out_arr_pm,
+                                channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
+                                best_X_image_name='%sbest_Xpm.jpg' % prefix)
+
+            with open('%sinfo.txt' % prefix, 'w') as ff:
+                print >>ff, params
+                print >>ff
+                print >>ff, results[batch_index]
+            if not skipbig:
+                with open('%sinfo_big.pkl' % prefix, 'w') as ff:
+                    pickle.dump((params, results[batch_index]), ff, protocol=-1)
+            results[batch_index].trim_arrays()
+            if not skipsmall:
+                with open('%sinfo.pkl' % prefix, 'w') as ff:
+                    pickle.dump((params, results[batch_index]), ff, protocol=-1)
diff --git a/optimize_image.py b/optimize_image.py
index e3999b145..1c6cb9188 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -37,9 +37,11 @@ def get_parser():
                         help = 'How to generate x0, the initial point used in optimization.')
     parser.add_argument('--rand-seed', type = int, default = settings.optimize_image_rand_seed,
                         help = 'Random seed used for generating the start-at image (use different seeds to generate different images).')
+    parser.add_argument('--batch-size', type=int, default=settings.optimize_image_batch_size,
+                        help = 'Batch size used for generating several images, each index will be used as random seed')
 
     # What to optimize
-    parser.add_argument('--push-layers', type = str, default = settings.optimize_image_push_layers,
+    parser.add_argument('--push-layers', type = list, default = settings.optimize_image_push_layers,
                         help = 'Name of layers that contains the desired neuron whose value is optimized.')
     parser.add_argument('--push-channel', type = int, default = '130',
                         help = 'Channel number for desired neuron whose value is optimized (channel for conv, neuron index for FC).')
@@ -69,13 +71,13 @@ def get_parser():
                         help = 'Learning rate policy. See description in lr-params.')
     parser.add_argument('--lr-params', type = str, default = settings.optimize_image_lr_params,
                         help = 'Learning rate params, specified as a string that evalutes to a Python dict. Params that must be provided dependon which lr-policy is selected. The "constant" policy requires the "lr" key and uses the constant given learning rate. The "progress" policy requires the "max_lr" and "desired_prog" keys and scales the learning rate such that the objective function will change by an amount equal to DESIRED_PROG under a linear objective assumption, except the LR is limited to MAX_LR. The "progress01" policy requires the "max_lr", "early_prog", and "late_prog_mult" keys and is tuned for optimizing neurons with outputs in the [0,1] range, e.g. neurons on a softmax layer. Under this policy optimization slows down as the output approaches 1 (see code for details).')
-    parser.add_argument('--max-iter', type = int, default = settings.optimize_image_max_iter,
-                        help = 'Number of iterations of the optimization loop.')
+    parser.add_argument('--max-iters', type = list, default = settings.optimize_image_max_iters,
+                        help = 'List of number of iterations of the optimization loop.')
 
     # Where to save results
     parser.add_argument('--output-prefix', type = str, default = settings.optimize_image_output_prefix,
                         help = 'Output path and filename prefix (default: optimize_results/opt)')
-    parser.add_argument('--output-template', type = str, default = '%(p.push_layer)s_%(p.push_channel)04d_%(p.rand_seed)d',
+    parser.add_argument('--output-template', type = str, default = '%(p.push_layer)s_unit%(p.push_channel)04d_seed%(p.rand_seed)d_iter%(p.max_iter)04d_batch%(r.batch_index)d',
                         help = 'Output filename template; see code for details (default: "%%(p.push_layer)s_%%(p.push_channel)04d_%%(p.rand_seed)d"). '
                         'The default output-prefix and output-template produce filenames like "optimize_results/opt_prob_0278_0_best_X.jpg"')
     parser.add_argument('--brave', action = 'store_true', default=True, help = 'Allow overwriting existing results files. Default: off, i.e. cowardly refuse to overwrite existing files.')
@@ -203,23 +205,29 @@ def main():
         while len(data_mean.shape) < 3:
             data_mean = np.expand_dims(data_mean, -1)
 
-    print 'Using mean:', repr(data_mean)
+    print 'Using mean with shape:', data_mean.shape
 
     if data_mean is not None:
         net.transformer.set_mean(net.inputs[0], data_mean)
 
     check_force_backward_true(settings.caffevis_deploy_prototxt)
 
+    current_data_shape = net.blobs['data'].shape
+    net.blobs['data'].reshape(args.batch_size, current_data_shape[1], current_data_shape[2], current_data_shape[3])
+    net.reshape()
+
     labels = None
     if settings.caffevis_labels:
         labels = read_label_file(settings.caffevis_labels)
 
-    optimizer = GradientOptimizer(settings, net, data_mean, labels = labels,
+
+    batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
+    optimizer = GradientOptimizer(settings, net, batched_data_mean, labels = labels,
                                   label_layers = settings.caffevis_label_layers,
                                   channel_swap_to_rgb = net_channel_swap)
 
     # go over push layers
-    for push_layer in args.push_layers:
+    for count, push_layer in enumerate(args.push_layers):
 
         # get layer type
         (name, type, input, output, filter, stride, pad) = get_layer_info(settings, push_layer)
@@ -238,6 +246,7 @@ def main():
             params = FindParams(
                 start_at = args.start_at,
                 rand_seed = args.rand_seed,
+                batch_size = args.batch_size,
                 push_layer = push_layer,
                 push_channel = current_channel,
                 push_spatial = push_spatial,
@@ -251,7 +260,7 @@ def main():
                 px_abs_benefit_percentile = args.px_abs_benefit_percentile,
                 lr_policy = args.lr_policy,
                 lr_params = lr_params,
-                max_iter = args.max_iter,
+                max_iter = args.max_iters[count % len(args.max_iters)],
                 layer_is_conv=(type == 'Convolution'),
             )
 
diff --git a/settings.py b/settings.py
index f4ded0286..d65b2634f 100644
--- a/settings.py
+++ b/settings.py
@@ -414,7 +414,7 @@
 optimize_image_lr_params = locals().get('optimize_image_lr_params', '{"lr": 1}')
 
 # max-iter parameter for optimize_image.py
-optimize_image_max_iter = locals().get('optimize_image_max_iter', 500)
+optimize_image_max_iters = locals().get('optimize_image_max_iters', [500])
 
 # output-prefix parameter for optimize_image.py
 optimize_image_output_prefix = locals().get('optimize_image_output_prefix', 'optimize_results/opt')
@@ -422,6 +422,9 @@
 # parameter which marks whether we should generate also the plus mean image of the optmized image
 optimize_image_generate_plus_mean = locals().get('optimize_image_generate_plus_mean', False)
 
+# batch size used in optimize_image.py
+optimize_image_batch_size = locals().get('optimize_image_batch_size', 9)
+
 ####################################
 #
 #  A few final sanity checks

From f9a6d8736c7e21ac6f5ea7dcea97d2d1ddf41bd1 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:34:57 +0300
Subject: [PATCH 025/106] added support for skipping files for units which were
 already generated

---
 caffevis/app.py                    | 159 ++++++++++++++++++++++-------
 caffevis/jpg_vis_loading_thread.py |  30 +++---
 optimize/gradient_optimizer.py     |  83 +++++++++------
 optimize_image.py                  |  18 ++--
 settings.py                        |   5 +-
 5 files changed, 201 insertions(+), 94 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 0409cddcb..43f8654c9 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -7,12 +7,14 @@
 import numpy as np
 import time
 import StringIO
+import glob
 
 from misc import WithTimer
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
 from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
-    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio, resize_without_fit
+    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio, resize_without_fit, \
+    cv2_read_file_rgb, caffe_load_image
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
@@ -378,46 +380,17 @@ def _draw_layer_pane(self, pane):
         if self.state.pattern_mode:
             # Show desired patterns loaded from disk
 
-            load_layer = default_layer_name
+            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
-            if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
-                load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
+                display_3D, display_3D_highres = self.load_pattern_images_original_format(default_layer_name,
+                                                                                          layer_dat_3D, n_tiles, pane,
+                                                                                          tile_cols, tile_rows)
 
-            if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers:
-                jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
-                                        'regularized_opt', load_layer, 'whole_layer.jpg')
-
-                # Get highres version
-                #cache_before = str(self.img_cache)
-                display_3D_highres = self.img_cache.get((jpg_path, 'whole'), None)
-                #else:
-                #    display_3D_highres = None
-
-                if display_3D_highres is None:
-                    try:
-                        with WithTimer('CaffeVisApp:load_sprite_image', quiet = self.debug_level < 1):
-                            display_3D_highres = load_square_sprite_image(jpg_path, n_sprites = n_tiles)
-                    except IOError:
-                        # File does not exist, so just display disabled.
-                        pass
-                    else:
-                        self.img_cache.set((jpg_path, 'whole'), display_3D_highres)
-                #cache_after = str(self.img_cache)
-                #print 'Cache was / is:\n  %s\n  %s' % (cache_before, cache_after)
-
-            if display_3D_highres is not None:
-                # Get lowres version, maybe. Assume we want at least one pixel for selection border.
-                row_downsamp_factor = int(np.ceil(float(display_3D_highres.shape[1]) / (pane.data.shape[0] / tile_rows - 2)))
-                col_downsamp_factor = int(np.ceil(float(display_3D_highres.shape[2]) / (pane.data.shape[1] / tile_cols - 2)))
-                ds = max(row_downsamp_factor, col_downsamp_factor)
-                if ds > 1:
-                    #print 'Downsampling by', ds
-                    display_3D = display_3D_highres[:,::ds,::ds,:]
-                else:
-                    display_3D = display_3D_highres
-            else:
-                display_3D = layer_dat_3D * 0  # nothing to show
+            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
 
+                display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
+                                                                                          layer_dat_3D, n_tiles, pane,
+                                                                                          tile_cols, tile_rows)
         else:
 
             # Show data from network (activations or diffs)
@@ -502,6 +475,116 @@ def _draw_layer_pane(self, pane):
             
         return display_3D_highres
 
+    def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
+                                            tile_cols, tile_rows):
+        display_3D_highres = None
+        load_layer = default_layer_name
+        if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
+            load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
+        if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers:
+            jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
+                                    'regularized_opt', load_layer, 'whole_layer.jpg')
+
+            # Get highres version
+            # cache_before = str(self.img_cache)
+            display_3D_highres = self.img_cache.get((jpg_path, 'whole'), None)
+            # else:
+            #    display_3D_highres = None
+
+            if display_3D_highres is None:
+                try:
+                    with WithTimer('CaffeVisApp:load_sprite_image', quiet=self.debug_level < 1):
+                        display_3D_highres = load_square_sprite_image(jpg_path, n_sprites=n_tiles)
+                except IOError:
+                    # File does not exist, so just display disabled.
+                    pass
+                else:
+                    if display_3D_highres is not None:
+                        self.img_cache.set((jpg_path, 'whole'), display_3D_highres)
+                    # cache_after = str(self.img_cache)
+                    # print 'Cache was / is:\n  %s\n  %s' % (cache_before, cache_after)
+
+        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+        return display_3D, display_3D_highres
+
+    def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
+                                            tile_cols, tile_rows):
+        display_3D_highres = None
+        load_layer = default_layer_name
+        if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
+            load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
+        if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers:
+
+            # get number of units
+            units_num = layer_dat_3D.shape[0]
+
+            pattern_image_key = (self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d", units_num, 'opt*.jpg')
+
+            # Get highres version
+            display_3D_highres = self.img_cache.get(pattern_image_key, None)
+
+            if display_3D_highres is None:
+                try:
+                    with WithTimer('CaffeVisApp:load_first_image_per_unit', quiet=self.debug_level < 1):
+                        display_3D_highres = self.load_first_image_per_unit(display_3D_highres, load_layer, units_num)
+
+                except IOError:
+                    # File does not exist, so just display disabled.
+                    pass
+                else:
+                    if display_3D_highres is not None:
+                        self.img_cache.set(pattern_image_key, display_3D_highres)
+
+        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+        return display_3D, display_3D_highres
+
+    def load_first_image_per_unit(self, display_3D_highres, load_layer, units_num):
+        # for each neuron in layer
+        for unit_id in range(0, units_num):
+
+            # load first image for neuron
+            unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer,
+                                            "unit_%04d" % (unit_id), 'opt*.jpg')
+
+            try:
+
+                # load first image for unit
+                unit_first_image_path = sorted(glob.glob(unit_folder_path))[0]
+                unit_first_image = caffe_load_image(unit_first_image_path, color=True, as_uint=True)
+
+                # handle first generation of results container
+                if display_3D_highres is None:
+                    unit_first_image_shape = unit_first_image.shape
+                    display_3D_highres = np.zeros((units_num, unit_first_image_shape[0],
+                                                   unit_first_image_shape[1],
+                                                   unit_first_image_shape[2]), dtype=np.uint8)
+
+                # set in result
+                display_3D_highres[unit_id, :, ::] = unit_first_image
+
+            except:
+                print '\nAttempted to load file from %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % \
+                      (unit_folder_path, load_layer)
+                pass
+        return display_3D_highres
+
+    def downsample_display_3d(self, display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows):
+        if display_3D_highres is not None:
+            # Get lowres version, maybe. Assume we want at least one pixel for selection border.
+            row_downsamp_factor = int(
+                np.ceil(float(display_3D_highres.shape[1]) / (pane.data.shape[0] / tile_rows - 2)))
+            col_downsamp_factor = int(
+                np.ceil(float(display_3D_highres.shape[2]) / (pane.data.shape[1] / tile_cols - 2)))
+            ds = max(row_downsamp_factor, col_downsamp_factor)
+            if ds > 1:
+                # print 'Downsampling by', ds
+                display_3D = display_3D_highres[:, ::ds, ::ds, :]
+            else:
+                display_3D = display_3D_highres
+        else:
+            display_3D = layer_dat_3D * 0  # nothing to show
+        return display_3D
+
     def _draw_aux_pane(self, pane, layer_data_normalized):
         pane.data[:] = to_255(self.settings.window_background)
 
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 9c1a90e2a..3c2909d86 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -42,8 +42,10 @@ def load_image_into_pane_original_format(self, state_layer, state_selected_unit,
             images[image_index_to_set] = ensure_uint255_and_resize_without_fit(img, resize_shape)
 
         except IOError:
-            print '\nAttempted to load file %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % (
-            jpg_path, state_layer)
+            print '\nAttempted to load file %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % \
+                  (jpg_path, state_layer)
+            # set black image as place holder
+            images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
             pass
 
     def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_unit, resize_shape, images,
@@ -59,8 +61,7 @@ def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_un
             unit_images_path = sorted(glob.glob(unit_folder_path))
 
             # load all images
-            # unit_images = [caffe_load_image(unit_image_path, color=True) for unit_image_path in unit_images_path]
-            unit_images = [cv2_read_file_rgb(unit_image_path) for unit_image_path in unit_images_path]
+            unit_images = [caffe_load_image(unit_image_path, color=True, as_uint=True) for unit_image_path in unit_images_path]
 
             if should_crop_to_corner:
                 unit_images = [crop_to_corner(img, 2) for img in unit_images]
@@ -72,11 +73,11 @@ def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_un
             padding_pixel = 1
             mega_image_height = images_per_axis * (image_height + 2*padding_pixel)
             mega_image_width = images_per_axis * (image_width + 2*padding_pixel)
-            mega_image = np.zeros((mega_image_height,mega_image_width,channels))
+            mega_image = np.zeros((mega_image_height,mega_image_width,channels), dtype=np.uint8)
 
             for i in range(num_images):
-                cell_row = i % images_per_axis
-                cell_col = i / images_per_axis
+                cell_row = i / images_per_axis
+                cell_col = i % images_per_axis
                 mega_image_height_start = 1 + cell_row * (image_height + 2*padding_pixel)
                 mega_image_height_end = mega_image_height_start + image_height
                 mega_image_width_start = 1 + cell_col * (image_width + 2*padding_pixel)
@@ -86,8 +87,10 @@ def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_un
             images[image_index_to_set] = ensure_uint255_and_resize_without_fit(mega_image, resize_shape)
 
         except:
-            print '\nAttempted to load files from %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % (
-                unit_folder_path, state_layer)
+            print '\nAttempted to load files from %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % \
+                  (unit_folder_path, state_layer)
+            # set black image as place holder
+            images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
             pass
 
 
@@ -139,12 +142,9 @@ def run(self):
                                                           should_crop_to_corner=True)
 
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
-                # self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
-                #                                              file_search_pattern='regularized_opt*.png',
-                #                                              image_index_to_set=0,
-                #                                              should_crop_to_corner=True)
-                # black image until we test it
-                images[0] = np.zeros((resize_shape[0],resize_shape[1],3))
+                self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                                                             file_search_pattern='opt*.jpg',
+                                                             image_index_to_set=0)
 
             if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 88d0ffcfe..a6175dd7c 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -181,10 +181,10 @@ def run_optimize(self, params, prefix_template = None, brave = False, skipbig =
         print params
         
         x0 = self._get_x0(params)
-        xx, results = self._optimize(params, x0)
-        self.save_results(params, results, prefix_template, brave = brave, skipbig = skipbig, skipsmall = skipsmall)
-
-        print str([results[batch_index].meta_result for batch_index in range(params.batch_size)])
+        xx, results, results_generated = self._optimize(params, x0, prefix_template)
+        if results_generated:
+            self.save_results(params, results, prefix_template, brave = brave, skipbig = skipbig, skipsmall = skipsmall)
+            print str([results[batch_index].meta_result for batch_index in range(params.batch_size)])
         
         return xx
 
@@ -204,10 +204,17 @@ def _get_x0(self, params):
 
         return x0
         
-    def _optimize(self, params, x0):
+    def _optimize(self, params, x0, prefix_template):
         xx = x0.copy()
 
-        results = [FindResults(i) for i in range(params.batch_size)]
+        results = [FindResults(batch_index) for batch_index in range(params.batch_size)]
+
+        # check if all required outputs exist, in which case skip this optimization
+        all_outputs = [self.generate_output_names(batch_index, params, results, prefix_template) for batch_index in range(params.batch_size)]
+        relevant_outputs = [best_X_name for [best_X_name, best_Xpm_name, majority_X_name, majority_Xpm_name, info_name, info_pkl_name, info_big_pkl_name] in all_outputs]
+        relevant_outputs_exist = [os.path.exists(best_X_name) for best_X_name in relevant_outputs]
+        if all(relevant_outputs_exist):
+            return xx, results, False
 
         # Whether or not the unit being optimized corresponds to a label (e.g. one of the 1000 imagenet classes)
         is_labeled_unit = params.push_layer in self.label_layers
@@ -240,7 +247,6 @@ def _optimize(self, params, x0):
             # 0. Crop data
             xx = minimum(255.0, maximum(0.0, xx + self.batched_data_mean)) - self.batched_data_mean     # Crop all values to [0,255]
 
-
             # 1. Push data through net
             out = self.net.forward_all(data = xx)
             #shownet(net)
@@ -248,10 +254,8 @@ def _optimize(self, params, x0):
             acts = self.net.blobs[top_name].data
 
             if not is_conv:
-            #     promote to 3D
+                # promote to 4D
                 acts = np.reshape(acts, (params.batch_size, -1, 1, 1))
-            # else:
-            #     reshaped_acts = np.reshape(acts, (params.batch_size, -1))
 
             reshaped_acts = np.reshape(acts, (params.batch_size, -1))
 
@@ -387,7 +391,7 @@ def _optimize(self, params, x0):
                 else:
                     results[batch_index].meta_result = 'batch_index: %d, Metaresult: majority failure' % batch_index
 
-        return xx, results
+        return xx, results, True
 
     def find_selected_input_index(self, layer):
 
@@ -408,35 +412,49 @@ def find_selected_input_index(self, layer):
 
         return -1
 
+    def generate_output_names(self, batch_index, params, results, prefix_template):
+
+        results_and_params = combine_dicts((('p.', params.__dict__),
+                                            ('r.', results[batch_index].__dict__)))
+        prefix = prefix_template % results_and_params
+
+        if os.path.isdir(prefix):
+            if prefix[-1] != '/':
+                prefix += '/'  # append slash for dir-only template
+        else:
+            dirname = os.path.dirname(prefix)
+            if dirname:
+                mkdir_p(dirname)
+
+        best_X_name = '%s_best_X.jpg' % prefix
+        best_Xpm_name = '%s_best_Xpm.jpg' % prefix
+        majority_X_name = '%s_majority_X.jpg' % prefix
+        majority_Xpm_name = '%s_majority_Xpm.jpg' % prefix
+        info_name = '%s_info.txt' % prefix
+        info_pkl_name = '%s_info.pkl' % prefix
+        info_big_pkl_name = '%s_info_big.pkl' % prefix
+        return [best_X_name, best_Xpm_name, majority_X_name, majority_Xpm_name, info_name, info_pkl_name, info_big_pkl_name]
+
     def save_results(self, params, results, prefix_template, brave = False, skipbig = False, skipsmall = False):
         if prefix_template is None:
             return
 
         for batch_index in range(params.batch_size):
 
-            results_and_params = combine_dicts((('p.', params.__dict__),
-                                                ('r.', results[batch_index].__dict__)))
-            prefix = prefix_template % results_and_params
-
-            if os.path.isdir(prefix):
-                if prefix[-1] != '/':
-                    prefix += '/'   # append slash for dir-only template
-            else:
-                dirname = os.path.dirname(prefix)
-                if dirname:
-                    mkdir_p(dirname)
+            [best_X_name, best_Xpm_name, majority_X_name, majority_Xpm_name, info_name, info_pkl_name, info_big_pkl_name] = \
+                self.generate_output_names(batch_index, params, results, prefix_template)
 
             # Don't overwrite previous results
-            if os.path.exists('%sinfo.txt' % prefix) and not brave:
-                raise Exception('Cowardly refusing to overwrite ' + '%sinfo.txt' % prefix)
+            if os.path.exists(info_name) and not brave:
+                raise Exception('Cowardly refusing to overwrite ' + info_name)
 
             output_majority = False
             if output_majority:
                 # NOTE: this section wasn't tested after changes to code, so some minor index tweaking are in order
                 if results[batch_index].majority_xx is not None:
                     asimg = results[batch_index].majority_xx[self.channel_swap_to_rgb].transpose((1,2,0))
-                    saveimagescc('%smajority_X.jpg' % prefix, asimg, 0)
-                    saveimagesc('%smajority_Xpm.jpg' % prefix, asimg + self._data_mean_rgb_img)  # PlusMean
+                    saveimagescc(majority_X_name, asimg, 0)
+                    saveimagesc(majority_Xpm_name, asimg + self._data_mean_rgb_img)  # PlusMean
 
             if results[batch_index].best_xx is not None:
                 # results[batch_index].best_xx.shape is (6,224,224)
@@ -466,7 +484,7 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
 
                 save_output(out_arr,
                             channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
-                            best_X_image_name='%sbest_X.jpg' % prefix)
+                            best_X_image_name=best_X_name)
 
                 if self.settings.optimize_image_generate_plus_mean:
                     out_arr_pm = extract_patch_from_image(results[batch_index].best_xx + self.batched_data_mean, self.net, selected_input_index, self.settings,
@@ -475,16 +493,17 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
 
                     save_output(out_arr_pm,
                                 channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
-                                best_X_image_name='%sbest_Xpm.jpg' % prefix)
+                                best_X_image_name=best_Xpm_name)
 
-            with open('%sinfo.txt' % prefix, 'w') as ff:
+            with open(info_name, 'w') as ff:
                 print >>ff, params
                 print >>ff
                 print >>ff, results[batch_index]
             if not skipbig:
-                with open('%sinfo_big.pkl' % prefix, 'w') as ff:
+                with open(info_big_pkl_name, 'w') as ff:
                     pickle.dump((params, results[batch_index]), ff, protocol=-1)
-            results[batch_index].trim_arrays()
             if not skipsmall:
-                with open('%sinfo.pkl' % prefix, 'w') as ff:
+                results[batch_index].trim_arrays()
+                with open(info_pkl_name, 'w') as ff:
                     pickle.dump((params, results[batch_index]), ff, protocol=-1)
+
diff --git a/optimize_image.py b/optimize_image.py
index 1c6cb9188..4a0e112a1 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -76,10 +76,7 @@ def get_parser():
 
     # Where to save results
     parser.add_argument('--output-prefix', type = str, default = settings.optimize_image_output_prefix,
-                        help = 'Output path and filename prefix (default: optimize_results/opt)')
-    parser.add_argument('--output-template', type = str, default = '%(p.push_layer)s_unit%(p.push_channel)04d_seed%(p.rand_seed)d_iter%(p.max_iter)04d_batch%(r.batch_index)d',
-                        help = 'Output filename template; see code for details (default: "%%(p.push_layer)s_%%(p.push_channel)04d_%%(p.rand_seed)d"). '
-                        'The default output-prefix and output-template produce filenames like "optimize_results/opt_prob_0278_0_best_X.jpg"')
+                        help = 'Output path and filename prefix (default: outputs/%(p.push_layer)s/unit_%(p.push_channel)04d/opt_%(r.batch_index)03d)')
     parser.add_argument('--brave', action = 'store_true', default=True, help = 'Allow overwriting existing results files. Default: off, i.e. cowardly refuse to overwrite existing files.')
     parser.add_argument('--skipbig', action = 'store_true', default=True, help = 'Skip outputting large *info_big.pkl files (contains pickled version of x0, last x, best x, first x that attained max on the specified layer.')
     parser.add_argument('--skipsmall', action = 'store_true', default=True, help = 'Skip outputting small *info.pkl files (contains pickled version of..')
@@ -241,8 +238,14 @@ def main():
             channels = output[2]
             push_spatial = (filter[0]/2, filter[1]/2)
 
+        # if channels defined in settings file, use them
+        if settings.optimize_image_channels:
+            channels_list = settings.optimize_image_channels
+        else:
+            channels_list = range(channels)
+
         # go over channels
-        for current_channel in range(channels):
+        for current_channel in channels_list:
             params = FindParams(
                 start_at = args.start_at,
                 rand_seed = args.rand_seed,
@@ -264,9 +267,8 @@ def main():
                 layer_is_conv=(type == 'Convolution'),
             )
 
-            prefix_template = '%s_%s_' % (args.output_prefix, args.output_template)
-            im = optimizer.run_optimize(params, prefix_template = prefix_template,
-                                        brave = args.brave, skipbig = args.skipbig, skipsmall = args.skipsmall)
+            optimizer.run_optimize(params, prefix_template = args.output_prefix,
+                                   brave = args.brave, skipbig = args.skipbig, skipsmall = args.skipsmall)
 
 
 if __name__ == '__main__':
diff --git a/settings.py b/settings.py
index d65b2634f..9c0497875 100644
--- a/settings.py
+++ b/settings.py
@@ -417,7 +417,7 @@
 optimize_image_max_iters = locals().get('optimize_image_max_iters', [500])
 
 # output-prefix parameter for optimize_image.py
-optimize_image_output_prefix = locals().get('optimize_image_output_prefix', 'optimize_results/opt')
+optimize_image_output_prefix = locals().get('optimize_image_output_prefix', 'outputs/%(p.push_layer)s/unit_%(p.push_channel)04d/opt_%(r.batch_index)03d')
 
 # parameter which marks whether we should generate also the plus mean image of the optmized image
 optimize_image_generate_plus_mean = locals().get('optimize_image_generate_plus_mean', False)
@@ -425,6 +425,9 @@
 # batch size used in optimize_image.py
 optimize_image_batch_size = locals().get('optimize_image_batch_size', 9)
 
+# channels to generate in optimize_image.py, if list is empty we generate all the channels in the layer
+optimize_image_channels = locals().get('optimize_image_channels', [])
+
 ####################################
 #
 #  A few final sanity checks

From 925f9520e9bfd9784a49d3c1f28425c9bdfe40c7 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:37:05 +0300
Subject: [PATCH 026/106] - added option to show maximal input images as
 patterns - added option to show first or all images per neuron

---
 bindings.py                        |  8 +++-
 caffevis/app.py                    | 63 +++++++++++++++++++-----------
 caffevis/caffevis_app_state.py     | 28 +++++++++----
 caffevis/caffevis_helper.py        | 52 +++++++++++++++++++++++-
 caffevis/jpg_vis_loading_thread.py | 48 ++++-------------------
 5 files changed, 125 insertions(+), 74 deletions(-)

diff --git a/bindings.py b/bindings.py
index bd429924a..6fda47693 100644
--- a/bindings.py
+++ b/bindings.py
@@ -101,8 +101,12 @@ def get_key_help(self, tag):
 
 _.add('zoom_mode', 'z',
        'Cycle zooming through {currently selected unit, backprop results, none}')
-_.add('pattern_mode', 's',
-       'Toggle overlay of preferred input pattern (regularized optimized images)')
+_.add('next_pattern_mode', 's',
+       'Cycle overlay of preferred input pattern (opt image, max input, off)')
+_.add('prev_pattern_mode', 'S',
+       'Cycle overlay of preferred input pattern (opt image, max input, off)')
+_.add('pattern_first_only', '1',
+       'Toggle pattern loading first image only or loading all available images')
 
 _.add('ez_back_mode_loop', 'b',
        'Cycle through a few common backprop/deconv modes')
diff --git a/caffevis/app.py b/caffevis/app.py
index 43f8654c9..d52c079ce 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -18,9 +18,9 @@
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
-from caffevis_app_state import CaffeVisAppState, SiameseInputMode
+from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
-    check_force_backward_true, load_mean
+    check_force_backward_true, load_mean, get_image_from_files
 
 
 class CaffeVisApp(BaseApp):
@@ -309,7 +309,10 @@ def _draw_status_pane(self, pane):
         status = StringIO.StringIO()
         fps = self.proc_thread.approx_fps()
         with self.state.lock:
-            print >>status, 'pattern' if self.state.pattern_mode else ('back' if self.state.layers_show_back else 'fwd'),
+            pattern_first_mode = "first" if self.state.pattern_first_only else "all"
+            print >>status, 'pattern(' + pattern_first_mode + ' optimized max)' if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE else \
+                ('pattern(' + pattern_first_mode + ' input max)' if self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE else
+                ('back' if self.state.layers_show_back else 'fwd')),
             print >>status, '%s:%d |' % (self.state.get_default_layer_name(), self.state.selected_unit),
             if not self.state.back_enabled:
                 print >>status, 'Back: off',
@@ -377,20 +380,36 @@ def _draw_layer_pane(self, pane):
         tile_rows, tile_cols = self.net_layer_info[default_layer_name]['tiles_rc']
 
         display_3D_highres = None
-        if self.state.pattern_mode:
+        if self.state.pattern_mode != PatternMode.OFF:
             # Show desired patterns loaded from disk
 
-            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+            if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE:
 
-                display_3D, display_3D_highres = self.load_pattern_images_original_format(default_layer_name,
-                                                                                          layer_dat_3D, n_tiles, pane,
-                                                                                          tile_cols, tile_rows)
+                if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
-            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                    display_3D, display_3D_highres = self.load_pattern_images_original_format(default_layer_name,
+                                                                                              layer_dat_3D, n_tiles, pane,
+                                                                                              tile_cols, tile_rows)
 
-                display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
-                                                                                          layer_dat_3D, n_tiles, pane,
-                                                                                          tile_cols, tile_rows)
+                elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+
+                    display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
+                                                                                              layer_dat_3D, n_tiles, pane,
+                                                                                              tile_cols, tile_rows, self.state.pattern_first_only,
+                                                                                              file_search_pattern='opt*.jpg')
+            elif self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE:
+
+                # do something
+                if self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                    display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
+                                                                                               layer_dat_3D, n_tiles,
+                                                                                               pane,
+                                                                                               tile_cols, tile_rows, self.state.pattern_first_only,
+                                                                                               file_search_pattern='maxim*.png')
+
+
+            else:
+                raise Exception("Invalid value of pattern mode: %d" % self.state.pattern_mode)
         else:
 
             # Show data from network (activations or diffs)
@@ -429,7 +448,7 @@ def _draw_layer_pane(self, pane):
         #   e.g. (1000,1,2,3) -> (1000,2,2,3)
         if (display_3D.shape[1] == 1) and (display_3D.shape[2] == 2):
             display_3D = np.tile(display_3D, (1, 2, 1, 1))
-        if self.state.layers_show_back and not self.state.pattern_mode:
+        if self.state.layers_show_back and self.state.pattern_mode == PatternMode.OFF:
             padval = self.settings.caffevis_layer_clr_back_background
         else:
             padval = self.settings.window_background
@@ -508,7 +527,7 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
         return display_3D, display_3D_highres
 
     def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
-                                            tile_cols, tile_rows):
+                                            tile_cols, tile_rows, first_only, file_search_pattern):
         display_3D_highres = None
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
@@ -518,7 +537,7 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
             # get number of units
             units_num = layer_dat_3D.shape[0]
 
-            pattern_image_key = (self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d", units_num, 'opt*.jpg')
+            pattern_image_key = (self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d", units_num, file_search_pattern, first_only)
 
             # Get highres version
             display_3D_highres = self.img_cache.get(pattern_image_key, None)
@@ -526,7 +545,8 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
             if display_3D_highres is None:
                 try:
                     with WithTimer('CaffeVisApp:load_first_image_per_unit', quiet=self.debug_level < 1):
-                        display_3D_highres = self.load_first_image_per_unit(display_3D_highres, load_layer, units_num)
+                        resize_shape = pane.data.shape
+                        display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
 
                 except IOError:
                     # File does not exist, so just display disabled.
@@ -538,19 +558,16 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
         display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
         return display_3D, display_3D_highres
 
-    def load_first_image_per_unit(self, display_3D_highres, load_layer, units_num):
+    def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern):
         # for each neuron in layer
         for unit_id in range(0, units_num):
 
-            # load first image for neuron
             unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer,
-                                            "unit_%04d" % (unit_id), 'opt*.jpg')
+                                            "unit_%04d" % (unit_id), file_search_pattern)
 
             try:
 
-                # load first image for unit
-                unit_first_image_path = sorted(glob.glob(unit_folder_path))[0]
-                unit_first_image = caffe_load_image(unit_first_image_path, color=True, as_uint=True)
+                unit_first_image = get_image_from_files(unit_folder_path, False, resize_shape, first_only)
 
                 # handle first generation of results container
                 if display_3D_highres is None:
@@ -793,7 +810,7 @@ def draw_help(self, help_pane, locy):
         lines.append([FormattedString('', defaults, width=120, align='right'),
                       FormattedString(nav_string, defaults)])
             
-        for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'pattern_mode',
+        for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
                     'ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
                     'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode'):
             key_strings, help_string = self.bindings.get_key_help(tag)
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 43cc89278..833a157f1 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -9,6 +9,12 @@ class SiameseInputMode:
     BOTH_IMAGES = 2
     NUMBER_OF_MODES = 3
 
+class PatternMode:
+    OFF = 0
+    MAXIMAL_OPTIMIZED_IMAGE = 1
+    MAXIMAL_INPUT_IMAGE = 2
+    NUMBER_OF_MODES = 3
+
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
 
@@ -95,7 +101,8 @@ def _reset_user_state(self):
         self.back_enabled = False
         self.back_mode = 'grad'      # 'grad' or 'deconv'
         self.back_filt_mode = 'raw'  # 'raw', 'gray', 'norm', 'normblur'
-        self.pattern_mode = False    # Whether or not to show desired patterns instead of activations in layers pane
+        self.pattern_mode = PatternMode.OFF    # type of patterns to show instead of activations in layers pane: maximal optimized image, maximal input image, off
+        self.pattern_first_only = True         # should we load only the first pattern image for each neuron, or all the relevant images per neuron
         self.layers_pane_zoom_mode = 0       # 0: off, 1: zoom selected (and show pref in small pane), 2: zoom backprop
         self.layers_show_back = False   # False: show forward activations. True: show backward diffs
         self.show_label_predictions = self.settings.caffevis_init_show_label_predictions
@@ -174,15 +181,22 @@ def handle_key(self, key):
             elif tag == 'boost_gamma':
                 self.layer_boost_gamma_idx = (self.layer_boost_gamma_idx + 1) % len(self.layer_boost_gamma_choices)
                 self.layer_boost_gamma = self.layer_boost_gamma_choices[self.layer_boost_gamma_idx]
-            elif tag == 'pattern_mode':
-                self.pattern_mode = not self.pattern_mode
-                if self.pattern_mode and not hasattr(self.settings, 'caffevis_unit_jpg_dir'):
+            elif tag == 'next_pattern_mode':
+                self.pattern_mode = (self.pattern_mode + 1) % PatternMode.NUMBER_OF_MODES
+                if self.pattern_mode != PatternMode.OFF and not hasattr(self.settings, 'caffevis_unit_jpg_dir'):
+                    print 'Cannot switch to pattern mode; caffevis_unit_jpg_dir not defined in settings.py.'
+                    self.pattern_mode = PatternMode.OFF
+            elif tag == 'prev_pattern_mode':
+                self.pattern_mode = (self.pattern_mode - 1 + PatternMode.NUMBER_OF_MODES) % PatternMode.NUMBER_OF_MODES
+                if self.pattern_mode != PatternMode.OFF and not hasattr(self.settings, 'caffevis_unit_jpg_dir'):
                     print 'Cannot switch to pattern mode; caffevis_unit_jpg_dir not defined in settings.py.'
-                    self.pattern_mode = False
+                    self.pattern_mode = PatternMode.OFF
+            elif tag == 'pattern_first_only':
+                self.pattern_first_only = not self.pattern_first_only
             elif tag == 'show_back':
                 # If in pattern mode: switch to fwd/back. Else toggle fwd/back mode
-                if self.pattern_mode:
-                    self.pattern_mode = False
+                if self.pattern_mode != PatternMode.OFF:
+                    self.pattern_mode = PatternMode.OFF
                 else:
                     self.layers_show_back = not self.layers_show_back
                 if self.layers_show_back:
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index 0eb4b6a8c..cea81abdc 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,7 +1,8 @@
 import numpy as np
 import os
 
-from image_misc import get_tiles_height_width, caffe_load_image
+from image_misc import get_tiles_height_width, caffe_load_image, ensure_uint255_and_resize_without_fit
+import glob
 
 
 
@@ -147,3 +148,52 @@ def load_mean(data_mean_file):
         print "Loaded mean from numpy file, data_mean.shape: ", data_mean.shape
 
     return data_mean
+
+
+def get_image_from_files(unit_folder_path, should_crop_to_corner, resize_shape, first_only):
+    try:
+
+        # list unit images
+        unit_images_path = sorted(glob.glob(unit_folder_path))
+
+        # if no images
+        if not unit_images_path:
+            return np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
+
+        if first_only:
+            unit_images_path = [unit_images_path[0]]
+
+        # load all images
+        unit_images = [caffe_load_image(unit_image_path, color=True, as_uint=True) for unit_image_path in
+                       unit_images_path]
+
+        if should_crop_to_corner:
+            unit_images = [crop_to_corner(img, 2) for img in unit_images]
+
+        # build mega image
+        (image_height, image_width, channels) = unit_images[0].shape
+        num_images = len(unit_images)
+        images_per_axis = int(np.math.ceil(np.math.sqrt(num_images)))
+        padding_pixel = 1
+        mega_image_height = images_per_axis * (image_height + 2 * padding_pixel)
+        mega_image_width = images_per_axis * (image_width + 2 * padding_pixel)
+        mega_image = np.zeros((mega_image_height, mega_image_width, channels), dtype=np.uint8)
+
+        for i in range(num_images):
+            cell_row = i / images_per_axis
+            cell_col = i % images_per_axis
+            mega_image_height_start = 1 + cell_row * (image_height + 2 * padding_pixel)
+            mega_image_height_end = mega_image_height_start + image_height
+            mega_image_width_start = 1 + cell_col * (image_width + 2 * padding_pixel)
+            mega_image_width_end = mega_image_width_start + image_width
+            mega_image[mega_image_height_start:mega_image_height_end, mega_image_width_start:mega_image_width_end,:] = unit_images[i]
+
+        return ensure_uint255_and_resize_without_fit(mega_image, resize_shape)
+
+    except:
+        print '\nAttempted to load files from %s but failed. ' % unit_folder_path
+        # set black image as place holder
+        return np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
+        pass
+
+    return
\ No newline at end of file
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 3c2909d86..2554c0c95 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -9,8 +9,7 @@
 from codependent_thread import CodependentThread
 from image_misc import caffe_load_image, ensure_uint255_and_resize_to_fit, cv2_read_file_rgb, \
     ensure_uint255_and_resize_without_fit
-from caffevis_helper import crop_to_corner
-
+from caffevis_helper import crop_to_corner, get_image_from_files
 
 
 class JPGVisLoadingThread(CodependentThread):
@@ -48,51 +47,18 @@ def load_image_into_pane_original_format(self, state_layer, state_selected_unit,
             images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
             pass
 
+
+
+
     def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_unit, resize_shape, images,
-                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False):
+                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False):
 
         unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer,
                                         "unit_%04d" % (state_selected_unit),
                                         file_search_pattern)
 
-        try:
-
-            # list unit images
-            unit_images_path = sorted(glob.glob(unit_folder_path))
-
-            # load all images
-            unit_images = [caffe_load_image(unit_image_path, color=True, as_uint=True) for unit_image_path in unit_images_path]
-
-            if should_crop_to_corner:
-                unit_images = [crop_to_corner(img, 2) for img in unit_images]
-
-            # build mega image
-            (image_height, image_width, channels) = unit_images[0].shape
-            num_images = len(unit_images)
-            images_per_axis = int(math.ceil(math.sqrt(num_images)))
-            padding_pixel = 1
-            mega_image_height = images_per_axis * (image_height + 2*padding_pixel)
-            mega_image_width = images_per_axis * (image_width + 2*padding_pixel)
-            mega_image = np.zeros((mega_image_height,mega_image_width,channels), dtype=np.uint8)
-
-            for i in range(num_images):
-                cell_row = i / images_per_axis
-                cell_col = i % images_per_axis
-                mega_image_height_start = 1 + cell_row * (image_height + 2*padding_pixel)
-                mega_image_height_end = mega_image_height_start + image_height
-                mega_image_width_start = 1 + cell_col * (image_width + 2*padding_pixel)
-                mega_image_width_end = mega_image_width_start + image_width
-                mega_image[mega_image_height_start:mega_image_height_end,mega_image_width_start:mega_image_width_end,:] = unit_images[i]
-
-            images[image_index_to_set] = ensure_uint255_and_resize_without_fit(mega_image, resize_shape)
-
-        except:
-            print '\nAttempted to load files from %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % \
-                  (unit_folder_path, state_layer)
-            # set black image as place holder
-            images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
-            pass
-
+        images[image_index_to_set] = get_image_from_files(unit_folder_path, should_crop_to_corner, resize_shape, first_only)
+        return
 
     def run(self):
         print 'JPGVisLoadingThread.run called'

From 05bcbb044dbf4b7f72730be1d564c80a43de73fb Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:41:10 +0300
Subject: [PATCH 027/106] - added debug code to show calculation of network
 result - added option to show score of maximal images

---
 app_base.py                        |  2 +-
 bindings.py                        |  3 ++
 caffevis/app.py                    | 20 +++++++----
 caffevis/caffevis_app_state.py     |  5 +++
 caffevis/caffevis_helper.py        | 58 +++++++++++++++++++++++-------
 caffevis/jpg_vis_loading_thread.py | 34 +++++++++++++++---
 input_fetcher.py                   | 12 ++++++-
 live_vis.py                        |  5 +--
 settings.py                        |  7 ++++
 9 files changed, 119 insertions(+), 27 deletions(-)

diff --git a/app_base.py b/app_base.py
index 255855c22..0e0fd8ac6 100644
--- a/app_base.py
+++ b/app_base.py
@@ -6,7 +6,7 @@ class BaseApp(object):
     def __init__(self, settings, key_bindings):
         self.debug_level = 0
 
-    def handle_input(self, input_image, panes):
+    def handle_input(self, input_image, input_label, panes):
         pass
         
     def handle_key(self, key, panes):
diff --git a/bindings.py b/bindings.py
index 6fda47693..c268c560a 100644
--- a/bindings.py
+++ b/bindings.py
@@ -131,5 +131,8 @@ def get_key_help(self, tag):
 _.add('siamese_input_mode', 'v',
         'Cycle between siamese input modes {first image, second image, both images}')
 
+_.add('show_maximal_score', 'r',
+        'Toggle showing maximal score overlays {on, off}')
+
 
 bindings = _
diff --git a/caffevis/app.py b/caffevis/app.py
index d52c079ce..21342d9d8 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -175,7 +175,7 @@ def quit(self):
     def _can_skip_all(self, panes):
         return ('caffevis_layers' not in panes.keys())
         
-    def handle_input(self, input_image, panes):
+    def handle_input(self, input_image, input_label, panes):
         if self.debug_level > 1:
             print 'handle_input: frame number', self.handled_frames, 'is', 'None' if input_image is None else 'Available'
         self.handled_frames += 1
@@ -186,6 +186,7 @@ def handle_input(self, input_image, panes):
             if self.debug_level > 1:
                 print 'CaffeVisApp.handle_input: pushed frame'
             self.state.next_frame = input_image
+            self.state.next_label = input_label
             if self.debug_level > 1:
                 print 'CaffeVisApp.handle_input: caffe_net_state is:', self.state.caffe_net_state
     
@@ -327,6 +328,9 @@ def _draw_status_pane(self, pane):
             if fps > 0:
                 print >>status, '| FPS: %.01f' % fps
 
+            if self.state.next_label:
+                print >> status, '| GT Label: %s' % self.state.next_label
+
             if self.state.extra_msg:
                 print >>status, '|', self.state.extra_msg
                 self.state.extra_msg = ''
@@ -482,14 +486,18 @@ def _draw_layer_pane(self, pane):
         pane.data[:] = to_255(self.settings.window_background)
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
         
-        if self.settings.caffevis_label_layers and default_layer_name in self.settings.caffevis_label_layers and self.labels and self.state.cursor_area == 'bottom':
+        if self.settings.caffevis_label_layers and default_layer_name in self.settings.caffevis_label_layers and self.state.cursor_area == 'bottom':
             # Display label annotation atop layers pane (e.g. for fc8/prob)
             defaults = {'face':  getattr(cv2, self.settings.caffevis_label_face),
                         'fsize': self.settings.caffevis_label_fsize,
                         'clr':   to_255(self.settings.caffevis_label_clr),
                         'thick': self.settings.caffevis_label_thick}
             loc_base = self.settings.caffevis_label_loc[::-1]   # Reverse to OpenCV c,r order
-            lines = [FormattedString(self.labels[self.state.selected_unit], defaults)]
+
+            if self.labels:
+                lines = [FormattedString(self.labels[self.state.selected_unit], defaults)]
+            else:
+                lines = [FormattedString(str(self.net.blobs[default_layer_name].data[0,self.state.selected_unit]), defaults)]
             cv2_typeset_text(pane.data, lines, loc_base)
             
         return display_3D_highres
@@ -567,7 +575,7 @@ def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_o
 
             try:
 
-                unit_first_image = get_image_from_files(unit_folder_path, False, resize_shape, first_only)
+                unit_first_image = get_image_from_files(self.settings, unit_folder_path, False, resize_shape, first_only)
 
                 # handle first generation of results container
                 if display_3D_highres is None:
@@ -743,7 +751,7 @@ def _draw_jpgvis_pane(self, pane):
             img_key_layer = state_layer
 
         if self.settings.caffevis_jpgvis_layers and img_key_layer in self.settings.caffevis_jpgvis_layers and cursor_area == 'bottom' and show_unit_jpgs:
-            img_key = (img_key_layer, state_selected_unit, pane.data.shape)
+            img_key = (img_key_layer, state_selected_unit, pane.data.shape, self.state.show_maximal_score)
             img_resize = self.img_cache.get(img_key, None)
             if img_resize is None:
                 # If img_resize is None, loading has not yet been attempted, so show stale image and request load by JPGVisLoadingThread
@@ -812,7 +820,7 @@ def draw_help(self, help_pane, locy):
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
                     'ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
-                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode'):
+                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'show_maximal_score'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
             lines.append([FormattedString(label, defaults, width=120, align='right'),
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 833a157f1..84c4f8d46 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -37,6 +37,7 @@ def __init__(self, net, settings, bindings, net_layer_info):
         self.extra_msg = ''
         self.back_stale = True       # back becomes stale whenever the last back diffs were not computed using the current backprop unit and method (bprop or deconv)
         self.next_frame = None
+        self.next_label = None
         self.jpgvis_to_load_key = None
         self.last_key_at = 0
         self.quit = False
@@ -86,6 +87,7 @@ def _fill_headers_list(self, net, settings):
 
     def _reset_user_state(self):
         self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
+        self.show_maximal_score = False
         self.layer_idx = 0
         self._update_layer_name()
         self.layer_boost_indiv_idx = self.settings.caffevis_boost_indiv_default_idx
@@ -261,6 +263,9 @@ def handle_key(self, key):
             elif tag == 'siamese_input_mode':
                 self.siamese_input_mode = (self.siamese_input_mode + 1) % SiameseInputMode.NUMBER_OF_MODES
 
+            elif tag == 'show_maximal_score':
+                self.show_maximal_score = not self.show_maximal_score
+
             else:
                 key_handled = False
 
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index cea81abdc..9c06543bb 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,7 +1,10 @@
+#! /usr/bin/env python
+
 import numpy as np
 import os
 
-from image_misc import get_tiles_height_width, caffe_load_image, ensure_uint255_and_resize_without_fit
+from image_misc import get_tiles_height_width, caffe_load_image, ensure_uint255_and_resize_without_fit, FormattedString, \
+    cv2_typeset_text, to_255
 import glob
 
 
@@ -19,6 +22,17 @@ def net_preproc_forward(settings, net, img, data_hw):
     data_blob = net.transformer.preprocess('data', img)                # e.g. (3, 227, 227), mean subtracted and scaled to [0,255]
     data_blob = data_blob[np.newaxis,:,:,:]                   # e.g. (1, 3, 227, 227)
     output = net.forward(data=data_blob)
+
+    # DEBUG CODE
+    if False:
+        print "*** hidden1.weights.shape:", net.params['linear_logits'][0].data.shape
+        print "*** hidden1.weights.data:\n", net.params['linear_logits'][0].data
+        print "*** hidden1.bias.shape:", net.params['linear_logits'][1].data.shape
+        print "*** hidden1.bias.data:\n", net.params['linear_logits'][1].data
+        print "*** hidden1.shape:", net.blobs['hidden1'].data.shape
+        print "*** hidden1.data:\n", net.blobs['hidden1'].data
+        print "*** calculated prediction:", (np.dot(net.params['linear_logits'][0].data, net.blobs['hidden1'].data.transpose()) + net.params['linear_logits'][1].data)
+
     return output
 
 
@@ -150,15 +164,17 @@ def load_mean(data_mean_file):
     return data_mean
 
 
-def get_image_from_files(unit_folder_path, should_crop_to_corner, resize_shape, first_only):
+def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions = []):
     try:
 
         # list unit images
         unit_images_path = sorted(glob.glob(unit_folder_path))
 
+        mega_image = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
+
         # if no images
         if not unit_images_path:
-            return np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
+            return mega_image
 
         if first_only:
             unit_images_path = [unit_images_path[0]]
@@ -170,25 +186,41 @@ def get_image_from_files(unit_folder_path, should_crop_to_corner, resize_shape,
         if should_crop_to_corner:
             unit_images = [crop_to_corner(img, 2) for img in unit_images]
 
-        # build mega image
-        (image_height, image_width, channels) = unit_images[0].shape
         num_images = len(unit_images)
         images_per_axis = int(np.math.ceil(np.math.sqrt(num_images)))
         padding_pixel = 1
-        mega_image_height = images_per_axis * (image_height + 2 * padding_pixel)
-        mega_image_width = images_per_axis * (image_width + 2 * padding_pixel)
-        mega_image = np.zeros((mega_image_height, mega_image_width, channels), dtype=np.uint8)
+
+        if first_only:
+            single_resized_image_shape = (resize_shape[0] - 2*padding_pixel, resize_shape[1] - 2*padding_pixel)
+        else:
+            single_resized_image_shape = ((resize_shape[0] / images_per_axis) - 2*padding_pixel, (resize_shape[1] / images_per_axis) - 2*padding_pixel)
+        unit_images = [ensure_uint255_and_resize_without_fit(unit_image, single_resized_image_shape) for unit_image in unit_images]
+
+        # build mega image
+
+        should_add_caption = (len(captions) == num_images)
+        defaults = {'face': settings.caffevis_score_face,
+                    'fsize': settings.caffevis_score_fsize,
+                    'clr': to_255(settings.caffevis_score_clr),
+                    'thick': settings.caffevis_score_thick}
 
         for i in range(num_images):
+
+            # add caption if we have exactly one for each image
+            if should_add_caption:
+                loc = settings.caffevis_score_loc[::-1]   # Reverse to OpenCV c,r order
+                fs = FormattedString(captions[i], defaults)
+                cv2_typeset_text(unit_images[i], [[fs]], loc)
+
             cell_row = i / images_per_axis
             cell_col = i % images_per_axis
-            mega_image_height_start = 1 + cell_row * (image_height + 2 * padding_pixel)
-            mega_image_height_end = mega_image_height_start + image_height
-            mega_image_width_start = 1 + cell_col * (image_width + 2 * padding_pixel)
-            mega_image_width_end = mega_image_width_start + image_width
+            mega_image_height_start = 1 + cell_row * (single_resized_image_shape[0] + 2 * padding_pixel)
+            mega_image_height_end = mega_image_height_start + single_resized_image_shape[0]
+            mega_image_width_start = 1 + cell_col * (single_resized_image_shape[1] + 2 * padding_pixel)
+            mega_image_width_end = mega_image_width_start + single_resized_image_shape[1]
             mega_image[mega_image_height_start:mega_image_height_end, mega_image_width_start:mega_image_width_end,:] = unit_images[i]
 
-        return ensure_uint255_and_resize_without_fit(mega_image, resize_shape)
+        return mega_image
 
     except:
         print '\nAttempted to load files from %s but failed. ' % unit_folder_path
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 2554c0c95..8379fd9a0 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -12,6 +12,9 @@
 from caffevis_helper import crop_to_corner, get_image_from_files
 
 
+
+
+
 class JPGVisLoadingThread(CodependentThread):
     '''Loads JPGs necessary for caffevis_jpgvis pane in separate
     thread and inserts them into the cache.
@@ -47,17 +50,35 @@ def load_image_into_pane_original_format(self, state_layer, state_selected_unit,
             images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
             pass
 
+    def prepare_score_captions_for_max_input_images(self, state_layer, state_selected_unit):
+
+        info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer,
+                                        "unit_%04d" % (state_selected_unit),
+                                        "info.txt")
 
+        # open file
+        with open(info_file_path, 'r') as info_file:
+            lines = info_file.readlines()
 
+            # skip first line
+            lines = lines[1:]
+
+            # take second word from each line, and convert to float
+            second_column = [float(line.split(' ')[1]) for line in lines]
+
+            # convert to string with 2 decimal digits
+            captions = [('%.2f' % value) for value in second_column]
+
+        return captions
 
     def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_unit, resize_shape, images,
-                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False):
+                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False, captions = []):
 
         unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer,
                                         "unit_%04d" % (state_selected_unit),
                                         file_search_pattern)
 
-        images[image_index_to_set] = get_image_from_files(unit_folder_path, should_crop_to_corner, resize_shape, first_only)
+        images[image_index_to_set] = get_image_from_files(self.settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions)
         return
 
     def run(self):
@@ -80,7 +101,7 @@ def run(self):
                 time.sleep(self.loop_sleep)
                 continue
 
-            state_layer, state_selected_unit, data_shape = jpgvis_to_load_key
+            state_layer, state_selected_unit, data_shape, show_maximal_score = jpgvis_to_load_key
 
             # Load three images:
             images = [None] * 3
@@ -121,9 +142,14 @@ def run(self):
                                                           image_index_to_set=1)
 
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+
+                if self.state.show_maximal_score:
+                    captions = self.prepare_score_captions_for_max_input_images(state_layer, state_selected_unit)
+                else:
+                    captions = []
                 self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='maxim*.png',
-                                                             image_index_to_set=1)
+                                                             image_index_to_set=1, captions=captions)
 
 
             if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
diff --git a/input_fetcher.py b/input_fetcher.py
index 7b19ba4a0..fbaf3294f 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -45,6 +45,9 @@ def __init__(self, settings):
         # latest loaded image frame, holds the pixels and used to force reloading
         self.latest_static_frame = None
 
+        # latest label for loaded image
+        self.latest_label = None
+
         # keeps current index of loaded file, doesn't seem important
         self.static_file_idx = None
 
@@ -100,6 +103,7 @@ def set_mode_stretch_on(self):
             if not self.static_file_stretch_mode:
                 self.static_file_stretch_mode = True
                 self.latest_static_frame = None   # Force reload
+                self.latest_label = None
                 #self.latest_frame_is_from_cam = True  # Force reload
         
     def set_mode_stretch_off(self):
@@ -107,6 +111,7 @@ def set_mode_stretch_off(self):
             if self.static_file_stretch_mode:
                 self.static_file_stretch_mode = False
                 self.latest_static_frame = None   # Force reload
+                self.latest_label = None
                 #self.latest_frame_is_from_cam = True  # Force reload
         
     def toggle_stretch_mode(self):
@@ -157,7 +162,7 @@ def get_frame(self):
         is not valid.
         '''
         with self.lock:
-            return (self.latest_frame_idx, self.latest_frame_data)
+            return (self.latest_frame_idx, self.latest_frame_data, self.latest_label)
 
     def increment_static_file_idx(self, amount = 1):
         with self.lock:
@@ -184,6 +189,7 @@ def check_increment_and_load_image(self):
             # available_files - local list of files
             if self.settings.static_files_input_mode == "directory":
                 available_files = get_files_from_directory(self.settings)
+                labels = None
             elif (self.settings.static_files_input_mode == "image_list") and (not self.settings.is_siamese):
                 available_files, labels = get_files_from_image_list(self.settings)
             elif (self.settings.static_files_input_mode == "image_list") and (self.settings.is_siamese):
@@ -220,4 +226,8 @@ def check_increment_and_load_image(self):
                         im = crop_to_square(im)
 
                 self.latest_static_frame = im
+
+                # if we have labels, keep it
+                if labels:
+                    self.latest_label = labels[self.static_file_idx]
             self._increment_and_set_frame(self.latest_static_frame, False)
diff --git a/live_vis.py b/live_vis.py
index 9543f8006..205d72092 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -188,11 +188,12 @@ def run_loop(self):
                 redraw_needed |= app.redraw_needed()
 
             # Grab latest frame from input_updater thread
-            fr_idx,fr_data = self.input_updater.get_frame()
+            fr_idx,fr_data,fr_label = self.input_updater.get_frame()
             is_new_frame = (fr_idx != latest_frame_idx and fr_data is not None)
             if is_new_frame:
                 latest_frame_idx = fr_idx
                 latest_frame_data = fr_data
+                latest_label = fr_label
                 frame_for_apps = fr_data
 
             if is_new_frame:
@@ -206,7 +207,7 @@ def run_loop(self):
                 # Pass frame to apps for processing
                 for app_name, app in self.apps.iteritems():
                     with WithTimer('%s:handle_input' % app_name, quiet = self.debug_level < 1):
-                        app.handle_input(latest_frame_data, self.panes)
+                        app.handle_input(latest_frame_data, latest_label, self.panes)
                 frame_for_apps = None
 
             # Tell each app to draw
diff --git a/settings.py b/settings.py
index 9c0497875..83f0ea4ce 100644
--- a/settings.py
+++ b/settings.py
@@ -317,6 +317,13 @@
 caffevis_label_fsize = locals().get('caffevis_label_fsize', 1.0 * global_font_size)
 caffevis_label_thick = locals().get('caffevis_label_thick', 1)
 
+# Font settings for score overlay text (shown on maximal images on rightmost pane)
+caffevis_score_face = locals().get('caffevis_score_face', 5) # this is a hacky way to use FONT_HERSHEY_COMPLEX_SMALL
+caffevis_score_loc = locals().get('caffevis_score_loc', (20,10))   # r,c order
+caffevis_score_clr = locals().get('caffevis_score_clr', (.5,1,.5))
+caffevis_score_fsize = locals().get('caffevis_score_fsize', 1.0 * global_font_size)
+caffevis_score_thick = locals().get('caffevis_score_thick', 1)
+
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 

From 007b007cd15e2ba0b95672071046cfe63f03e626 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:42:39 +0300
Subject: [PATCH 028/106] fixed crash when max tracker working on fc

---
 caffe_misc.py             | 1 -
 find_maxes/max_tracker.py | 1 +
 2 files changed, 1 insertion(+), 1 deletion(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index 715c85600..d78ba4283 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -201,7 +201,6 @@ def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer, size_
             out_jj_end = size_jj - jj_outside
 
     else:
-        ii, jj = 0, 0
         data_ii_start, out_ii_start, data_jj_start, out_jj_start = 0, 0, 0, 0
         data_ii_end, out_ii_end, data_jj_end, out_jj_end = size_ii, size_ii, size_jj, size_jj
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 8994cd588..a40b06ad3 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -347,6 +347,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                 im_idx, im_class, selected_input_index, ii, jj = mt.max_locs[channel_idx, max_idx]
             else:
                 im_idx, im_class, selected_input_index = mt.max_locs[channel_idx, max_idx]
+                ii, jj = 0, 0
             recorded_val = mt.max_vals[channel_idx, max_idx]
             filename = image_filenames[im_idx]
             do_print = (max_idx_0 == 0)

From 0f1d809f8dd67483ea98ec2247e3df8afbc69eba Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 17 Sep 2017 00:46:50 +0300
Subject: [PATCH 029/106] - added more info to progress printouts - replaced
 caffe.io.load_image (which loads images in [0,1] range to cv2_read_file_rgb,
 which loads them in [0,255] range and is used in all other places in the code
 - replaced caffe.io.resize_image (which needs images in [0,1] range) with
 cv2.resize - moved load_imagenet_mean from loaders into caffevis_helper -
 clean up code around: range_scale, net_channel_swap - removed unused
 hardcoded functions: hardcoded_get, load_labels, load_trained_net - removed
 now empty file loaders.py

---
 caffevis/app.py                |  6 ++--
 caffevis/caffevis_helper.py    |  6 ++++
 find_maxes/crop_max_patches.py |  3 +-
 find_maxes/find_max_acts.py    |  3 +-
 find_maxes/max_tracker.py      | 57 +++++++++-------------------------
 optimize_image.py              | 12 +++----
 6 files changed, 28 insertions(+), 59 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 21342d9d8..a1003683d 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -40,8 +40,6 @@ def __init__(self, settings, key_bindings):
         else:
             self._net_channel_swap_inv = tuple([self._net_channel_swap.index(ii) for ii in range(len(self._net_channel_swap))])
 
-        self._range_scale = settings.caffe_net_raw_scale
-
         # Set the mode to CPU or GPU. Note: in the latest Caffe
         # versions, there is one Caffe object *per thread*, so the
         # mode must be set per thread! Here we set the mode for the
@@ -57,9 +55,9 @@ def __init__(self, settings, key_bindings):
         self.net = caffe.Classifier(
             settings.caffevis_deploy_prototxt,
             settings.caffevis_network_weights,
-            mean = None,                                 # Set to None for now, assign later         # self._data_mean,
+            mean = None, # Set to None for now, assign later self._data_mean
             channel_swap = self._net_channel_swap,
-            raw_scale = self._range_scale,
+            raw_scale = settings.caffe_net_raw_scale,
             image_dims=settings.caffe_net_image_dims,
         )
 
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index 9c06543bb..fa6c9862d 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -164,6 +164,12 @@ def load_mean(data_mean_file):
     return data_mean
 
 
+def load_imagenet_mean(settings):
+    imagenet_mean = np.load(settings.caffevis_caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
+    imagenet_mean = imagenet_mean[:, 14:14+227, 14:14+227]    # (3,256,256) -> (3,227,227) Crop to center 227x227 section
+    return imagenet_mean
+
+
 def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions = []):
     try:
 
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index f03a80881..c1ca83f1e 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -11,9 +11,8 @@
 import cPickle as pickle
 
 import settings
-from caffevis.caffevis_helper import load_mean
+from caffevis.caffevis_helper import load_mean, load_imagenet_mean
 
-from loaders import load_imagenet_mean, load_labels
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 5455ab6fb..64d371767 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -12,8 +12,7 @@
 
 import settings
 
-from caffevis.caffevis_helper import load_mean
-from loaders import load_imagenet_mean, load_labels
+from caffevis.caffevis_helper import load_mean, load_imagenet_mean
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index a40b06ad3..f3e1088b1 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -4,45 +4,16 @@
 import os
 import sys
 from datetime import datetime
+import cv2
 
 import numpy as np
+from image_misc import cv2_read_file_rgb
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
     compute_data_layer_focus_area
 from misc import get_files_from_image_list
 from misc import get_files_from_siamese_image_list
 
 from jby_misc import WithTimer
-from loaders import load_imagenet_mean, load_labels
-
-
-def hardcoded_get(settings):
-    prototxt = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/deploy_1.prototxt'
-    weights = '/home/jyosinsk/results/140311_234854_afadfd3_priv_netbase_upgraded/caffe_imagenet_train_iter_450000'
-    datadir = '/home/jyosinsk/imagenet2012/val'
-    filelist = 'mini_valid.txt'
-
-    imagenet_mean = load_imagenet_mean(settings)
-    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
-    import caffe
-
-    if settings.caffevis_mode_gpu:
-        caffe.set_mode_gpu()
-        print 'CaffeVisApp mode (in main thread):     GPU'
-    else:
-        caffe.set_mode_cpu()
-        print 'CaffeVisApp mode (in main thread):     CPU'
-
-    net = caffe.Classifier(prototxt, weights,
-                           mean=imagenet_mean,
-                           channel_swap=(2,1,0),
-                           raw_scale=255,
-                           image_dims=(256, 256))
-    net.set_phase_test()
-    net.set_mode_cpu()
-    labels = load_labels(settings)
-
-    return net, imagenet_mean, labels, datadir, filelist
-
 
 
 def mkdir_p(path):
@@ -217,7 +188,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top):
         if do_print:
             print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_filenames))
         with WithTimer('Load image', quiet = not do_print):
-            im = caffe.io.load_image(os.path.join(datadir, filename))
+            im = cv2_read_file_rgb(os.path.join(datadir, filename))
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Update    ', quiet = not do_print):
@@ -248,12 +219,12 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
         if do_print:
             print '%s   Pair %d/%d' % (datetime.now().ctime(), image_idx, len(image_filenames))
         with WithTimer('Load image', quiet=not do_print):
-            im1 = caffe.io.load_image(os.path.join(datadir, filename1))
-            im2 = caffe.io.load_image(os.path.join(datadir, filename2))
+            im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
+            im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
 
             net_input_dims = net.blobs['data'].data.shape[2:4]
-            im1 = caffe.io.resize_image(im1, net_input_dims)
-            im2 = caffe.io.resize_image(im2, net_input_dims)
+            im1 = cv2.resize(im1, net_input_dims)
+            im2 = cv2.resize(im2, net_input_dims)
 
             im = np.concatenate((im1, im2), axis=2)
 
@@ -288,7 +259,7 @@ def save_representations(settings, net, datadir, filelist, layer, first_N = None
         if do_print:
             print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_indices))
         with WithTimer('Load image', quiet = not do_print):
-            im = caffe.io.load_image(os.path.join(datadir, filename))
+            im = cv2_read_file_rgb(os.path.join(datadir, filename))
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Store     ', quiet = not do_print):
@@ -352,7 +323,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             filename = image_filenames[im_idx]
             do_print = (max_idx_0 == 0)
             if do_print:
-                print '%s   Output file/image(s) %d/%d' % (datetime.now().ctime(), cc * num_top, n_total_images)
+                print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), cc * num_top, n_total_images, layer,channel_idx)
 
             [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
                 compute_data_layer_focus_area(mt.is_conv, ii, jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
@@ -376,18 +347,18 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                     filename2 = filename[1]
 
                     # load both images
-                    im1 = caffe.io.load_image(os.path.join(datadir, filename1))
-                    im2 = caffe.io.load_image(os.path.join(datadir, filename2))
+                    im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
+                    im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
 
                     # resize images according to input dimension
                     net_input_dims = net.blobs['data'].data.shape[2:4]
-                    im1 = caffe.io.resize_image(im1, net_input_dims)
-                    im2 = caffe.io.resize_image(im2, net_input_dims)
+                    im1 = cv2.resize(im1, net_input_dims)
+                    im2 = cv2.resize(im2, net_input_dims)
 
                     # concatenate channelwise
                     im = np.concatenate((im1, im2), axis=2)
                 else:
-                    im = caffe.io.load_image(os.path.join(datadir, filename))
+                    im = cv2_read_file_rgb(os.path.join(datadir, filename))
 
             with WithTimer('Predict   ', quiet = not do_print):
                 net.predict([im], oversample = False)
diff --git a/optimize_image.py b/optimize_image.py
index 4a0e112a1..b382db579 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -151,10 +151,6 @@ def main():
     lr_params = parse_and_validate_lr_params(parser, args.lr_policy, args.lr_params)
     push_spatial = parse_and_validate_push_spatial(parser, args.push_spatial)
 
-    net_channel_swap = settings.caffe_net_channel_swap
-
-    range_scale = settings.caffe_net_raw_scale
-            
     # Load network
     sys.path.insert(0, os.path.join(args.caffe_root, 'python'))
     import caffe
@@ -168,9 +164,9 @@ def main():
     net = caffe.Classifier(
         args.deploy_proto,
         args.net_weights,
-        mean=None,  # Set to None for now, assign later         # self._data_mean,
-        channel_swap=net_channel_swap,
-        raw_scale=range_scale,
+        mean=None,  # Set to None for now, assign later self._data_mean
+        channel_swap=settings.caffe_net_channel_swap,
+        raw_scale=settings.caffe_net_raw_scale,
         image_dims=settings.caffe_net_image_dims,
     )
 
@@ -221,7 +217,7 @@ def main():
     batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
     optimizer = GradientOptimizer(settings, net, batched_data_mean, labels = labels,
                                   label_layers = settings.caffevis_label_layers,
-                                  channel_swap_to_rgb = net_channel_swap)
+                                  channel_swap_to_rgb = settings.caffe_net_channel_swap)
 
     # go over push layers
     for count, push_layer in enumerate(args.push_layers):

From dcd9e1a4d9deb5071cf9ea4a6bb7eea493581823 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:20:25 +0300
Subject: [PATCH 030/106] - added function get_top_to_layer_dict - fixed
 backprop/deconv for layers with top name different from layer name

---
 caffe_misc.py                 | 18 ++++++++++++++++++
 caffevis/caffe_proc_thread.py | 22 +++++++++++++++-------
 2 files changed, 33 insertions(+), 7 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index d78ba4283..5e08a673d 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -241,3 +241,21 @@ def extract_patch_from_image(data, net, selected_input_index, settings,
                                                                        data_ii_start:data_ii_end,
                                                                        data_jj_start:data_jj_end]
     return out_arr
+
+
+def get_top_to_layer_dict(net):
+
+    top_to_layer = dict()
+
+    # go over layers
+    for layer in list(net._layer_names):
+
+        if len(net.top_names[layer]) == 1 and len(net.bottom_names[layer]) == 1 and net.top_names[layer][0] == net.bottom_names[layer][0]:
+            # skip inplace layers
+            continue
+
+        # go over tops
+        for top_name in net.top_names[layer]:
+            top_to_layer[top_name] = layer
+
+    return top_to_layer
\ No newline at end of file
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 8096f341f..9e357299c 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -6,7 +6,7 @@
 from misc import WithTimer
 from caffevis_helper import net_preproc_forward
 from caffevis_app_state import SiameseInputMode
-
+from caffe_misc import get_top_to_layer_dict
 
 class CaffeProcThread(CodependentThread):
     '''Runs Caffe in separate thread.'''
@@ -28,6 +28,8 @@ def __init__(self, settings, net, state, loop_sleep, pause_after_keys, heartbeat
 
         self.settings = settings
 
+        self.top_to_layer_dict = get_top_to_layer_dict(self.net)
+
 
     def run(self):
         print 'CaffeProcThread.run called'
@@ -113,16 +115,19 @@ def run(self):
                             if self.state.siamese_input_mode_has_two_images(backprop_layer):
                                 diffs0 = self.net.blobs[backprop_layer[0]].diff * 0
                                 diffs0[0][backprop_unit] = self.net.blobs[backprop_layer[0]].data[0, backprop_unit]
-                                self.net.backward_from_layer(backprop_layer[0], diffs0, zero_higher = True)
+                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[0]]
+                                self.net.backward_from_layer(backprop_layer_name, diffs0, zero_higher = True)
 
                                 diffs1 = self.net.blobs[backprop_layer[1]].diff * 0
                                 diffs1[0][backprop_unit] = self.net.blobs[backprop_layer[1]].data[0, backprop_unit]
-                                self.net.backward_from_layer(backprop_layer[1], diffs1, zero_higher=True)
+                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[1]]
+                                self.net.backward_from_layer(backprop_layer_name, diffs1, zero_higher=True)
 
                             else:
                                 diffs = self.net.blobs[default_backprop_layer].diff * 0
                                 diffs[0][backprop_unit] = self.net.blobs[default_backprop_layer].data[0, backprop_unit]
-                                self.net.backward_from_layer(default_backprop_layer, diffs, zero_higher = True)
+                                backprop_layer_name = self.top_to_layer_dict[default_backprop_layer]
+                                self.net.backward_from_layer(backprop_layer_name, diffs, zero_higher = True)
                         except AttributeError:
                             print 'ERROR: required bindings (backward_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
                             raise
@@ -137,16 +142,19 @@ def run(self):
                             if self.state.siamese_input_mode_has_two_images(backprop_layer):
                                 diffs0 = self.net.blobs[backprop_layer[0]].diff * 0
                                 diffs0[0][backprop_unit] = self.net.blobs[backprop_layer[0]].data[0, backprop_unit]
-                                self.net.deconv_from_layer(backprop_layer[0], diffs0, zero_higher=True)
+                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[0]]
+                                self.net.deconv_from_layer(backprop_layer_name, diffs0, zero_higher=True)
 
                                 diffs1 = self.net.blobs[backprop_layer[1]].diff * 0
                                 diffs1[0][backprop_unit] = self.net.blobs[backprop_layer[1]].data[0, backprop_unit]
-                                self.net.deconv_from_layer(backprop_layer[1], diffs1, zero_higher=True)
+                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[1]]
+                                self.net.deconv_from_layer(backprop_layer_name, diffs1, zero_higher=True)
 
                             else:
                                 diffs = self.net.blobs[default_backprop_layer].diff * 0
                                 diffs[0][backprop_unit] = self.net.blobs[default_backprop_layer].data[0, backprop_unit]
-                                self.net.deconv_from_layer(default_backprop_layer, diffs, zero_higher = True)
+                                backprop_layer_name = self.top_to_layer_dict[default_backprop_layer]
+                                self.net.deconv_from_layer(backprop_layer_name, diffs, zero_higher = True)
                         except AttributeError:
                             print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
                             raise

From 58663830e8d076fac1122dd6d111d338b9874bac Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:21:08 +0300
Subject: [PATCH 031/106] added parameter channelwise_mean to transform a mean
 file into per channel mean

---
 find_maxes/find_max_acts.py | 5 +++++
 settings.py                 | 3 +++
 2 files changed, 8 insertions(+)

diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 64d371767..10419af81 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -26,6 +26,7 @@ def main():
     parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
     parser.add_argument('--outfile', type=str, default = settings.find_max_acts_output_file, help='output filename for pkl')
     parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
+    parser.add_argument('--channelwise_mean', action = 'store_true', default = settings.channelwise_mean, help = 'should we calculate the channelwise average of the input mean file')
     args = parser.parse_args()
 
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
@@ -45,6 +46,9 @@ def main():
     else:
         data_mean = load_mean(args.mean)
 
+    if args.channelwise_mean:
+        data_mean = data_mean.mean(1).mean(1)
+
     net = caffe.Classifier(args.net_prototxt,
                            args.net_weights,
                            mean=None,
@@ -52,6 +56,7 @@ def main():
                            raw_scale=settings.caffe_net_raw_scale,
                            image_dims=settings.caffe_net_image_dims)
 
+
     if data_mean is not None:
         net.transformer.set_mean(net.inputs[0], data_mean)
 
diff --git a/settings.py b/settings.py
index 83f0ea4ce..01524233e 100644
--- a/settings.py
+++ b/settings.py
@@ -176,6 +176,9 @@
 # channel or None.
 caffevis_data_mean = locals().get('caffevis_data_mean', None)
 
+# should we calculate the channelwise average of the input mean file
+channelwise_mean = locals().get('channelwise_mean', False)
+
 # Path to file listing labels in order, one per line, used for the
 # below two features. None to disable.
 caffevis_labels = locals().get('caffevis_labels', None)

From 7ae647a5c1e6b976e6cfbefe8551c77dc595dbda Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:22:03 +0300
Subject: [PATCH 032/106] refactor mean setting

---
 caffevis/app.py                | 42 ++++-----------------------------
 caffevis/caffevis_helper.py    | 43 ++++++++++++++++++++++++++++++----
 find_maxes/crop_max_patches.py | 11 ++-------
 find_maxes/find_max_acts.py    | 17 +++-----------
 optimize_image.py              | 37 +++--------------------------
 settings.py                    |  4 ++--
 6 files changed, 53 insertions(+), 101 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index a1003683d..d60678774 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -20,7 +20,7 @@
 from jpg_vis_loading_thread import JPGVisLoadingThread
 from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
-    check_force_backward_true, load_mean, get_image_from_files
+    check_force_backward_true, set_mean, get_image_from_files
 
 
 class CaffeVisApp(BaseApp):
@@ -55,46 +55,14 @@ def __init__(self, settings, key_bindings):
         self.net = caffe.Classifier(
             settings.caffevis_deploy_prototxt,
             settings.caffevis_network_weights,
-            mean = None, # Set to None for now, assign later self._data_mean
+            mean = None, # Set to None for now, assign later
             channel_swap = self._net_channel_swap,
             raw_scale = settings.caffe_net_raw_scale,
             image_dims=settings.caffe_net_image_dims,
         )
 
-        if isinstance(settings.caffevis_data_mean, basestring):
-            # If the mean is given as a filename, load the file
-            try:
-                self._data_mean = load_mean(settings.caffevis_data_mean)
-
-            except IOError:
-                print '\n\nCound not load mean file:', settings.caffevis_data_mean
-                print 'Ensure that the values in settings.py point to a valid model weights file, network'
-                print 'definition prototxt, and mean. To fetch a default model and mean file, use:\n'
-                print '$ cd models/caffenet-yos/'
-                print '$ ./fetch.sh\n\n'
-                raise
-            input_shape = self.net.blobs[self.net.inputs[0]].data.shape[-2:]   # e.g. 227x227
-            # Crop center region (e.g. 227x227) if mean is larger (e.g. 256x256)
-            excess_h = self._data_mean.shape[1] - input_shape[0]
-            excess_w = self._data_mean.shape[2] - input_shape[1]
-            assert excess_h >= 0 and excess_w >= 0, 'mean should be at least as large as %s' % repr(input_shape)
-            self._data_mean = self._data_mean[:, (excess_h/2):(excess_h/2+input_shape[0]),
-                                              (excess_w/2):(excess_w/2+input_shape[1])]
-        elif settings.caffevis_data_mean is None:
-            self._data_mean = None
-        else:
-            # The mean has been given as a value or a tuple of values
-            self._data_mean = np.array(settings.caffevis_data_mean)
-            # Promote to shape C,1,1
-            while len(self._data_mean.shape) < 1:
-                self._data_mean = np.expand_dims(self._data_mean, -1)
-            
-            #if not isinstance(self._data_mean, tuple):
-            #    # If given as int/float: promote to tuple
-            #    self._data_mean = tuple(self._data_mean)
-        if self._data_mean is not None:
-            self.net.transformer.set_mean(self.net.inputs[0], self._data_mean)
-        
+        self._data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, self.net)
+
         check_force_backward_true(settings.caffevis_deploy_prototxt)
 
         self.labels = None
@@ -654,7 +622,7 @@ def _draw_back_pane(self, pane):
                 grad_img = grad_blob[:, :, :]  # do nothing
             else:
                 grad_img = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
-                
+
             # Mode-specific processing
             assert back_mode in ('grad', 'deconv')
             assert back_filt_mode in ('raw', 'gray', 'norm', 'normblur')
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index fa6c9862d..0a826c550 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -138,7 +138,7 @@ def check_force_backward_true(prototxt_file):
         print 'at the data layer as well.\n\n'
 
 
-def load_mean(data_mean_file):
+def load_mean_file(data_mean_file):
     filename, file_extension = os.path.splitext(data_mean_file)
     if file_extension == ".npy":
         # load mean from numpy array
@@ -163,11 +163,44 @@ def load_mean(data_mean_file):
 
     return data_mean
 
+def set_mean(caffevis_data_mean, generate_channelwise_mean, net):
+
+    if isinstance(caffevis_data_mean, basestring):
+        # If the mean is given as a filename, load the file
+        try:
+            data_mean = load_mean_file(caffevis_data_mean)
+        except IOError:
+            print '\n\nCound not load mean file:', caffevis_data_mean
+            print 'Ensure that the values in settings.py point to a valid model weights file, network'
+            print 'definition prototxt, and mean. To fetch a default model and mean file, use:\n'
+            print '$ cd models/caffenet-yos/'
+            print '$ ./fetch.sh\n\n'
+            raise
+        input_shape = net.blobs[net.inputs[0]].data.shape[-2:]  # e.g. 227x227
+        # Crop center region (e.g. 227x227) if mean is larger (e.g. 256x256)
+        excess_h = data_mean.shape[1] - input_shape[0]
+        excess_w = data_mean.shape[2] - input_shape[1]
+        assert excess_h >= 0 and excess_w >= 0, 'mean should be at least as large as %s' % repr(input_shape)
+        data_mean = data_mean[:, (excess_h / 2):(excess_h / 2 + input_shape[0]),
+                          (excess_w / 2):(excess_w / 2 + input_shape[1])]
+    elif caffevis_data_mean is None:
+        data_mean = None
+    else:
+        # The mean has been given as a value or a tuple of values
+        data_mean = np.array(caffevis_data_mean)
+        # Promote to shape C,1,1
+        # while len(data_mean.shape) < 3:
+        #     data_mean = np.expand_dims(data_mean, -1)
+
+    if generate_channelwise_mean:
+        data_mean = data_mean.mean(1).mean(1)
+
+    print 'Using mean with shape:', data_mean.shape
 
-def load_imagenet_mean(settings):
-    imagenet_mean = np.load(settings.caffevis_caffe_root + 'python/caffe/imagenet/ilsvrc_2012_mean.npy')
-    imagenet_mean = imagenet_mean[:, 14:14+227, 14:14+227]    # (3,256,256) -> (3,227,227) Crop to center 227x227 section
-    return imagenet_mean
+    if data_mean is not None:
+        net.transformer.set_mean(net.inputs[0], data_mean)
+
+    return data_mean
 
 
 def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions = []):
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index c1ca83f1e..4d6e4b700 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -11,7 +11,7 @@
 import cPickle as pickle
 
 import settings
-from caffevis.caffevis_helper import load_mean, load_imagenet_mean
+from caffevis.caffevis_helper import set_mean
 
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
@@ -37,7 +37,6 @@ def main():
     parser.add_argument('--datadir',      type = str, default = settings.static_files_dir, help = 'directory to look for files in')
     parser.add_argument('--filelist',     type = str, default = settings.static_files_input_file, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
     parser.add_argument('--outdir',       type = str, default = settings.max_tracker_output_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
-    parser.add_argument('--mean',         type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
     args = parser.parse_args()
 
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
@@ -51,11 +50,6 @@ def main():
         caffe.set_mode_cpu()
         print 'crop_max_patches mode (in main thread):     CPU'
 
-    if args.mean == "":
-        data_mean = load_imagenet_mean(settings)
-    else:
-        data_mean = load_mean(args.mean)
-
     net = caffe.Classifier(args.net_prototxt,
                            args.net_weights,
                            mean=None,
@@ -63,8 +57,7 @@ def main():
                            raw_scale=settings.caffe_net_raw_scale,
                            image_dims=settings.caffe_net_image_dims)
 
-    if data_mean is not None:
-        net.transformer.set_mean(net.inputs[0], data_mean)
+    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
     assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 10419af81..441af2e31 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -9,10 +9,11 @@
 import argparse
 #import ipdb as pdb
 import cPickle as pickle
+import numpy as np
 
 import settings
 
-from caffevis.caffevis_helper import load_mean, load_imagenet_mean
+from caffevis.caffevis_helper import set_mean
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
 
@@ -25,8 +26,6 @@ def main():
     parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
     parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
     parser.add_argument('--outfile', type=str, default = settings.find_max_acts_output_file, help='output filename for pkl')
-    parser.add_argument('--mean', type = str, default = settings.caffevis_data_mean, help = 'data mean to load')
-    parser.add_argument('--channelwise_mean', action = 'store_true', default = settings.channelwise_mean, help = 'should we calculate the channelwise average of the input mean file')
     args = parser.parse_args()
 
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
@@ -41,14 +40,6 @@ def main():
         print 'find_max_acts mode (in main thread):     CPU'
 
 
-    if args.mean == "":
-        data_mean = load_imagenet_mean(settings)
-    else:
-        data_mean = load_mean(args.mean)
-
-    if args.channelwise_mean:
-        data_mean = data_mean.mean(1).mean(1)
-
     net = caffe.Classifier(args.net_prototxt,
                            args.net_weights,
                            mean=None,
@@ -56,9 +47,7 @@ def main():
                            raw_scale=settings.caffe_net_raw_scale,
                            image_dims=settings.caffe_net_image_dims)
 
-
-    if data_mean is not None:
-        net.transformer.set_mean(net.inputs[0], data_mean)
+    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
     with WithTimer('Scanning images'):
         if settings.is_siamese:
diff --git a/optimize_image.py b/optimize_image.py
index b382db579..a7932939e 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -7,7 +7,7 @@
 
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
-from caffevis.caffevis_helper import check_force_backward_true, read_label_file, load_mean
+from caffevis.caffevis_helper import check_force_backward_true, read_label_file, set_mean
 
 LR_POLICY_CHOICES = ('constant', 'progress', 'progress01')
 
@@ -164,44 +164,13 @@ def main():
     net = caffe.Classifier(
         args.deploy_proto,
         args.net_weights,
-        mean=None,  # Set to None for now, assign later self._data_mean
+        mean=None,  # Set to None for now, assign later
         channel_swap=settings.caffe_net_channel_swap,
         raw_scale=settings.caffe_net_raw_scale,
         image_dims=settings.caffe_net_image_dims,
     )
 
-    if isinstance(settings.caffevis_data_mean, basestring):
-        # If the mean is given as a filename, load the file
-        try:
-            data_mean = load_mean(settings.caffevis_data_mean)
-        except IOError:
-            print '\n\nCound not load mean file:', data_mean
-            print 'To fetch a default model and mean file, use:\n'
-            print '  $ cd models/caffenet-yos/'
-            print '  $ cp ./fetch.sh\n\n'
-            print 'Or to use your own mean, change caffevis_data_mean in settings_local.py or override by running with `--mean MEAN_FILE` (see --help).\n'
-            raise
-        input_shape = net.blobs[net.inputs[0]].data.shape[-2:]  # e.g. 227x227
-
-        # Crop center region (e.g. 227x227) if mean is larger (e.g. 256x256)
-        excess_h = data_mean.shape[1] - input_shape[0]
-        excess_w = data_mean.shape[2] - input_shape[1]
-        assert excess_h >= 0 and excess_w >= 0, 'mean should be at least as large as %s' % repr(input_shape)
-        data_mean = data_mean[:, (excess_h / 2):(excess_h / 2 + input_shape[0]),
-                    (excess_w / 2):(excess_w / 2 + input_shape[1])]
-    elif args.data_mean is None:
-        pass
-    else:
-        # The mean has been given as a value or a tuple of values
-        data_mean = np.array(settings.caffevis_data_mean)
-        # Promote to shape C,1,1
-        while len(data_mean.shape) < 3:
-            data_mean = np.expand_dims(data_mean, -1)
-
-    print 'Using mean with shape:', data_mean.shape
-
-    if data_mean is not None:
-        net.transformer.set_mean(net.inputs[0], data_mean)
+    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
     check_force_backward_true(settings.caffevis_deploy_prototxt)
 
diff --git a/settings.py b/settings.py
index 01524233e..5eb7f342f 100644
--- a/settings.py
+++ b/settings.py
@@ -176,8 +176,8 @@
 # channel or None.
 caffevis_data_mean = locals().get('caffevis_data_mean', None)
 
-# should we calculate the channelwise average of the input mean file
-channelwise_mean = locals().get('channelwise_mean', False)
+# should we generate the channelwise average of the input mean file
+generate_channelwise_mean = locals().get('generate_channelwise_mean', False)
 
 # Path to file listing labels in order, one per line, used for the
 # below two features. None to disable.

From 671feb77dfcfa8d985186440e092a666cda94153 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:22:35 +0300
Subject: [PATCH 033/106] fixed image loading in max tracker for a single
 network

---
 find_maxes/max_tracker.py | 11 +++++++++++
 1 file changed, 11 insertions(+)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index f3e1088b1..09ec0e4f1 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -189,6 +189,9 @@ def scan_images_for_maxes(settings, net, datadir, n_top):
             print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_filenames))
         with WithTimer('Load image', quiet = not do_print):
             im = cv2_read_file_rgb(os.path.join(datadir, filename))
+            net_input_dims = net.blobs['data'].data.shape[2:4]
+            im = cv2.resize(im, net_input_dims)
+            im = im.astype(np.float32)
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Update    ', quiet = not do_print):
@@ -358,8 +361,16 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                     # concatenate channelwise
                     im = np.concatenate((im1, im2), axis=2)
                 else:
+                    # load image
                     im = cv2_read_file_rgb(os.path.join(datadir, filename))
 
+                    # resize images according to input dimension
+                    net_input_dims = net.blobs['data'].data.shape[2:4]
+                    im = cv2.resize(im, net_input_dims)
+
+                    # convert to float to avoid caffe destroying the image in the scaling phase
+                    im = im.astype(np.float32)
+
             with WithTimer('Predict   ', quiet = not do_print):
                 net.predict([im], oversample = False)
 

From 04172a00485b2c7a39e56231bbade0012385f2ab Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:26:08 +0300
Subject: [PATCH 034/106] added batch support to find_max_acts

---
 .idea/modules.xml              |   8 +
 caffevis/caffe_proc_thread.py  |   6 +-
 find_maxes/crop_max_patches.py |   5 +
 find_maxes/find_max_acts.py    |   6 +-
 find_maxes/max_tracker.py      | 455 ++++++++++++++++++++++++---------
 image_misc.py                  |   4 +-
 optimize_image.py              |   6 +-
 settings.py                    |   5 +-
 8 files changed, 364 insertions(+), 131 deletions(-)
 create mode 100644 .idea/modules.xml

diff --git a/.idea/modules.xml b/.idea/modules.xml
new file mode 100644
index 000000000..66042cf49
--- /dev/null
+++ b/.idea/modules.xml
@@ -0,0 +1,8 @@
+<?xml version="1.0" encoding="UTF-8"?>
+<project version="4">
+  <component name="ProjectModuleManager">
+    <modules>
+      <module fileurl="file://$PROJECT_DIR$/.idea/deep-visualization-toolbox.iml" filepath="$PROJECT_DIR$/.idea/deep-visualization-toolbox.iml" />
+    </modules>
+  </component>
+</project>
\ No newline at end of file
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 9e357299c..ea27def14 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -92,12 +92,12 @@ def run(self):
                     frame1 = frame[0]
                     frame2 = frame[1]
 
-                    im_small1 = cv2.resize(frame1, self.input_dims)
-                    im_small2 = cv2.resize(frame2, self.input_dims)
+                    im_small1 = cv2.resize(frame1, self.input_dims[::-1])
+                    im_small2 = cv2.resize(frame2, self.input_dims[::-1])
                     im_small = np.concatenate( (im_small1, im_small2), axis=2)
 
                 else:
-                    im_small = cv2.resize(frame, self.input_dims)
+                    im_small = cv2.resize(frame, self.input_dims[::-1])
 
                 with WithTimer('CaffeProcThread:forward', quiet = self.debug_level < 1):
                     net_preproc_forward(self.settings, self.net, im_small, self.input_dims)
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 4d6e4b700..0ce620227 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -59,6 +59,11 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    # set network batch size
+    current_input_shape = net.blobs[net.inputs[0]].shape
+    current_input_shape[0] = settings.max_tracker_batch_size
+    net.blobs[net.inputs[0]].reshape(*current_input_shape)
+
     assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
 
     with open(args.nmt_pkl, 'rb') as ff:
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 441af2e31..e886aa511 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -39,7 +39,6 @@ def main():
         caffe.set_mode_cpu()
         print 'find_max_acts mode (in main thread):     CPU'
 
-
     net = caffe.Classifier(args.net_prototxt,
                            args.net_weights,
                            mean=None,
@@ -49,6 +48,11 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    # set network batch size
+    current_input_shape = net.blobs[net.inputs[0]].shape
+    current_input_shape[0] = settings.max_tracker_batch_size
+    net.blobs[net.inputs[0]].reshape(*current_input_shape)
+
     with WithTimer('Scanning images'):
         if settings.is_siamese:
             max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 09ec0e4f1..f78f5dce7 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -15,6 +15,60 @@
 
 from jby_misc import WithTimer
 
+# define records
+
+
+class MaxTrackerBatchRecord(object):
+
+    def __init__(self, image_idx = None, filename = None, image_class = None, im = None):
+        self.image_idx = image_idx
+        self.filename = filename
+        self.image_class = image_class
+        self.im = im
+
+
+class MaxTrackerCropBatchRecord(object):
+
+    def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_filenames = None,
+                 deconv_filenames = None, deconvnorm_filenames = None, backprop_filenames = None,
+                 backpropnorm_filenames = None, info_file = None, max_idx_0 = None, max_idx = None, im_idx = None,
+                 im_class = None, selected_input_index = None, ii = None, jj = None, recorded_val = None,
+                 out_ii_start = None, out_ii_end = None, out_jj_start = None, out_jj_end = None, data_ii_start = None,
+                 data_ii_end = None, data_jj_start = None, data_jj_end = None, im = None):
+        self.cc = cc
+        self.channel_idx = channel_idx
+        self.info_filename = info_filename
+        self.maxim_filenames = maxim_filenames
+        self.deconv_filenames = deconv_filenames
+        self.deconvnorm_filenames = deconvnorm_filenames
+        self.backprop_filenames = backprop_filenames
+        self.backpropnorm_filenames = backpropnorm_filenames
+        self.info_file = info_file
+        self.max_idx_0 = max_idx_0
+        self.max_idx = max_idx
+        self.im_idx = im_idx
+        self.im_class = im_class
+        self.selected_input_index = selected_input_index
+        self.ii = ii
+        self.jj = jj
+        self.recorded_val = recorded_val
+        self.out_ii_start = out_ii_start
+        self.out_ii_end = out_ii_end
+        self.out_jj_start = out_jj_start
+        self.out_jj_end = out_jj_end
+        self.data_ii_start = data_ii_start
+        self.data_ii_end = data_ii_end
+        self.data_jj_start = data_jj_start
+        self.data_jj_end = data_jj_end
+        self.im = im
+
+
+class InfoFileMetadata(object):
+
+    def __init__(self, info_file = None, ref_count = None):
+        self.info_file = info_file
+        self.ref_count = ref_count
+
 
 def mkdir_p(path):
     # From https://stackoverflow.com/questions/600268/mkdir-p-functionality-in-python
@@ -41,7 +95,7 @@ def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype =
         # set of seen inputs, used to avoid updating on the same input twice
         self.seen_inputs = set()
 
-    def update(self, blob, image_idx, image_class, selected_input_index, layer_unique_input_source):
+    def update(self, blob, image_idx, image_class, selected_input_index, layer_unique_input_source, batch_index):
 
         if layer_unique_input_source in self.seen_inputs:
             return
@@ -49,7 +103,7 @@ def update(self, blob, image_idx, image_class, selected_input_index, layer_uniqu
         # add input identifier to seen inputs set
         self.seen_inputs.add(layer_unique_input_source)
 
-        data = blob[0]                                        # Note: makes a copy of blob, e.g. (96,55,55)
+        data = blob[batch_index]                              # Note: makes a copy of blob, e.g. (96,55,55)
         n_channels = data.shape[0]
         data_unroll = data.reshape((n_channels, -1))          # Note: no copy eg (96,3025). Does nothing if not is_conv
 
@@ -109,7 +163,7 @@ def _init_with_net(self, net):
 
         self.init_done = True
 
-    def update(self, net, image_idx, image_class, net_unique_input_source):
+    def update(self, net, image_idx, image_class, net_unique_input_source, batch_index):
         '''Updates the maxes found so far with the state of the given net. If a new max is found, it is stored together with the image_idx.'''
         if not self.init_done:
             self._init_with_net(net)
@@ -138,7 +192,7 @@ def update(self, net, image_idx, image_class, net_unique_input_source):
                 layer_unique_input_source = net_unique_input_source
                 selected_input_index = -1
 
-            self.max_trackers[normalized_layer_name].update(blob, image_idx, image_class, selected_input_index, layer_unique_input_source)
+            self.max_trackers[normalized_layer_name].update(blob, image_idx, image_class, selected_input_index, layer_unique_input_source, batch_index)
 
     def __getstate__(self):
         # Copy the object's state from self.__dict__ which contains
@@ -179,24 +233,49 @@ def scan_images_for_maxes(settings, net, datadir, n_top):
     import caffe
 
     tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_for_max_tracker)
+
+    net_input_dims = net.blobs['data'].data.shape[2:4]
+
+    # prepare variables used for batches
+    batch = [None] * settings.max_tracker_batch_size
+    for i in range(0, settings.max_tracker_batch_size):
+        batch[i] = MaxTrackerBatchRecord()
+
+    batch_index = 0
+
     for image_idx in xrange(len(image_filenames)):
 
-        filename = image_filenames[image_idx]
-        image_class = image_labels[image_idx]
-        #im = caffe.io.load_image('../../data/ilsvrc12/mini_ilsvrc_valid/sized/ILSVRC2012_val_00000610.JPEG')
-        do_print = (image_idx % 100 == 0)
+        batch[batch_index].image_idx = image_idx
+        batch[batch_index].filename = image_filenames[image_idx]
+        batch[batch_index].image_class = image_labels[image_idx]
+
+        do_print = (batch[batch_index].image_idx % 100 == 0)
         if do_print:
-            print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_filenames))
+            print '%s   Image %d/%d' % (datetime.now().ctime(), batch[batch_index].image_idx, len(image_filenames))
+
         with WithTimer('Load image', quiet = not do_print):
-            im = cv2_read_file_rgb(os.path.join(datadir, filename))
-            net_input_dims = net.blobs['data'].data.shape[2:4]
-            im = cv2.resize(im, net_input_dims)
-            im = im.astype(np.float32)
-        with WithTimer('Predict   ', quiet = not do_print):
-            net.predict([im], oversample = False)   # Just take center crop
-        with WithTimer('Update    ', quiet = not do_print):
-            tracker.update(net, image_idx, image_class, net_unique_input_source=filename)
+            batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename))
+            batch[batch_index].im = cv2.resize(batch[batch_index].im, net_input_dims)
+            batch[batch_index].im = batch[batch_index].im.astype(np.float32)
+
+        batch_index += 1
+
+        # if current batch is full
+        if batch_index == settings.max_tracker_batch_size \
+            or image_idx == len(image_filenames) - 1:  # or last iteration
+
+            # batch predict
+            with WithTimer('Predict on batch  ', quiet = not do_print):
+                im_batch = [record.im for record in batch]
+                net.predict(im_batch, oversample = False)   # Just take center crop
 
+            # go over batch and update statistics
+            for i in range(0,batch_index):
+
+                with WithTimer('Update    ', quiet = not do_print):
+                    tracker.update(net, batch[i].image_idx, batch[i].image_class, net_unique_input_source=batch[i].filename, batch_index=i)
+
+            batch_index = 0
 
     print 'done!'
     return tracker
@@ -211,30 +290,50 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
     import caffe
 
     tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_for_max_tracker)
+
+    net_input_dims = net.blobs['data'].data.shape[2:4]
+
+    # prepare variables used for batches
+    batch = [None] * settings.max_tracker_batch_size
+    for i in range(0, settings.max_tracker_batch_size):
+        batch[i] = MaxTrackerBatchRecord()
+
+    batch_index = 0
+
     for image_idx in xrange(len(image_filenames)):
 
-        images_pair = image_filenames[image_idx]
-        filename1 = images_pair[0]
-        filename2 = images_pair[1]
-        image_class = image_labels[image_idx]
-        # im = caffe.io.load_image('../../data/ilsvrc12/mini_ilsvrc_valid/sized/ILSVRC2012_val_00000610.JPEG')
+        batch[batch_index].image_idx = image_idx
+        batch[batch_index].images_pair = image_filenames[image_idx]
+        filename1 = batch[batch_index].images_pair[0]
+        filename2 = batch[batch_index].images_pair[1]
+        batch[batch_index].image_class = image_labels[image_idx]
         do_print = (image_idx % 100 == 0)
         if do_print:
-            print '%s   Pair %d/%d' % (datetime.now().ctime(), image_idx, len(image_filenames))
+            print '%s   Pair %d/%d' % (datetime.now().ctime(), batch[batch_index].image_idx, len(image_filenames))
+
         with WithTimer('Load image', quiet=not do_print):
             im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
             im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
-
-            net_input_dims = net.blobs['data'].data.shape[2:4]
             im1 = cv2.resize(im1, net_input_dims)
             im2 = cv2.resize(im2, net_input_dims)
+            batch[batch_index].im = np.concatenate((im1, im2), axis=2)
+
+        batch_index += 1
+
+        # if current batch is full
+        if batch_index == settings.max_tracker_batch_size \
+            or image_idx == len(image_filenames) - 1:  # or last iteration
+
+            with WithTimer('Predict   ', quiet=not do_print):
+                im_batch = [record.im for record in batch]
+                net.predict(im_batch, oversample=False)
 
-            im = np.concatenate((im1, im2), axis=2)
+            # go over batch and update statistics
+            for i in range(0,batch_index):
+                with WithTimer('Update    ', quiet=not do_print):
+                    tracker.update(net, batch[i].image_idx, batch[i].image_class, net_unique_input_source=batch[i].images_pair, batch_index=i)
 
-        with WithTimer('Predict   ', quiet=not do_print):
-            net.predict([im], oversample=False)
-        with WithTimer('Update    ', quiet=not do_print):
-            tracker.update(net, image_idx, image_class, net_unique_input_source=images_pair)
+            batch_index = 0
 
     print 'done!'
     return tracker
@@ -278,6 +377,38 @@ def save_representations(settings, net, datadir, filelist, layer, first_N = None
     return indices,rep
 
 
+def generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm):
+
+    # init values
+    info_filename = []
+    maxim_filenames = []
+    deconv_filenames = []
+    deconvnorm_filenames = []
+    backprop_filenames = []
+    backpropnorm_filenames = []
+
+    if do_info:
+        info_filename = [os.path.join(unit_dir, 'info.txt')]
+
+    for max_idx_0 in range(num_top):
+        if do_maxes:
+            maxim_filenames.append(os.path.join(unit_dir, 'maxim_%03d.png' % max_idx_0))
+
+        if do_deconv:
+            deconv_filenames.append(os.path.join(unit_dir, 'deconv_%03d.png' % max_idx_0))
+
+        if  do_deconv_norm:
+            deconvnorm_filenames.append(os.path.join(unit_dir, 'deconvnorm_%03d.png' % max_idx_0))
+
+        if do_backprop:
+            backprop_filenames.append(os.path.join(unit_dir, 'backprop_%03d.png' % max_idx_0))
+
+        if do_backprop_norm:
+            backpropnorm_filenames.append(os.path.join(unit_dir, 'backpropnorm_%03d.png' % max_idx_0))
+
+    return (info_filename, maxim_filenames, deconv_filenames, deconvnorm_filenames, backprop_filenames, backpropnorm_filenames)
+
+
 def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
     do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, do_info = do_which
     assert do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm or do_info, 'nothing to do'
@@ -304,40 +435,96 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
     size_ii, size_jj = get_max_data_extent(net, layer, rc, mt.is_conv)
     data_size_ii, data_size_jj = net.blobs['data'].data.shape[2:4]
 
+    net_input_dims = net.blobs['data'].data.shape[2:4]
+
+    # prepare variables used for batches
+    batch = [None] * settings.max_tracker_batch_size
+    for i in range(0, settings.max_tracker_batch_size):
+        batch[i] = MaxTrackerCropBatchRecord()
+
+    batch_index = 0
+
+    channel_to_info_file = dict()
+
     n_total_images = (idx_end-idx_begin) * num_top
     for cc, channel_idx in enumerate(range(idx_begin, idx_end)):
+
         unit_dir = os.path.join(outdir, layer, 'unit_%04d' % channel_idx)
         mkdir_p(unit_dir)
 
+        # check if all required outputs exist, in which case skip this iteration
+        [info_filename,
+         maxim_filenames,
+         deconv_filenames,
+         deconvnorm_filenames,
+         backprop_filenames,
+         backpropnorm_filenames] = generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm)
+
+        relevant_outputs = info_filename + \
+                           maxim_filenames + \
+                           deconv_filenames + \
+                           deconvnorm_filenames + \
+                           backprop_filenames + \
+                           backpropnorm_filenames
+
+        # skip if all exist, unless we are in final iteration, in which case we redo it since we need to handle the partially filled batch
+        relevant_outputs_exist = [os.path.exists(file_name) for file_name in relevant_outputs]
+        if all(relevant_outputs_exist) and channel_idx != idx_end - 1:
+            print "skipped generation of channel %d in layer %s since files already exist" % (channel_idx, layer)
+            continue
+
         if do_info:
-            info_filename = os.path.join(unit_dir, 'info.txt')
-            info_file = open(info_filename, 'w')
-            print >>info_file, '# is_conv val image_idx image_class i(if is_conv) j(if is_conv) filename'
+            channel_to_info_file[channel_idx] = InfoFileMetadata()
+            channel_to_info_file[channel_idx].info_file = open(info_filename[0], 'w')
+            channel_to_info_file[channel_idx].ref_count = num_top
+
+            print >> channel_to_info_file[channel_idx].info_file, '# is_conv val image_idx image_class i(if is_conv) j(if is_conv) filename'
 
         # iterate through maxes from highest (at end) to lowest
         for max_idx_0 in range(num_top):
-            max_idx = num_top_in_mt - 1 - max_idx_0
+            batch[batch_index].cc = cc
+            batch[batch_index].channel_idx = channel_idx
+            batch[batch_index].info_filename = info_filename
+            batch[batch_index].maxim_filenames = maxim_filenames
+            batch[batch_index].deconv_filenames = deconv_filenames
+            batch[batch_index].deconvnorm_filenames = deconvnorm_filenames
+            batch[batch_index].backprop_filenames = backprop_filenames
+            batch[batch_index].backpropnorm_filenames = backpropnorm_filenames
+            batch[batch_index].info_file = channel_to_info_file[channel_idx].info_file
+
+            batch[batch_index].max_idx_0 = max_idx_0
+            batch[batch_index].max_idx = num_top_in_mt - 1 - batch[batch_index].max_idx_0
+
             if mt.is_conv:
-                im_idx, im_class, selected_input_index, ii, jj = mt.max_locs[channel_idx, max_idx]
+                batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index, batch[batch_index].ii, batch[batch_index].jj = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
             else:
-                im_idx, im_class, selected_input_index = mt.max_locs[channel_idx, max_idx]
-                ii, jj = 0, 0
-            recorded_val = mt.max_vals[channel_idx, max_idx]
-            filename = image_filenames[im_idx]
-            do_print = (max_idx_0 == 0)
-            if do_print:
-                print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), cc * num_top, n_total_images, layer,channel_idx)
+                batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+                batch[batch_index].ii, batch[batch_index].jj = 0, 0
 
-            [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
-                compute_data_layer_focus_area(mt.is_conv, ii, jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
+            batch[batch_index].recorded_val = mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+            batch[batch_index].filename = image_filenames[batch[batch_index].im_idx]
+            do_print = (batch[batch_index].max_idx_0 == 0)
+            if do_print:
+                print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), batch[batch_index].cc * num_top, n_total_images, layer, batch[batch_index].channel_idx)
+
+            [batch[batch_index].out_ii_start,
+             batch[batch_index].out_ii_end,
+             batch[batch_index].out_jj_start,
+             batch[batch_index].out_jj_end,
+             batch[batch_index].data_ii_start,
+             batch[batch_index].data_ii_end,
+             batch[batch_index].data_jj_start,
+             batch[batch_index].data_jj_end] = \
+                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer,
+                                              size_ii, size_jj, data_size_ii, data_size_jj)
 
             if do_info:
-                print >>info_file, 1 if mt.is_conv else 0, '%.6f' % mt.max_vals[channel_idx, max_idx],
+                print >> batch[batch_index].info_file, 1 if mt.is_conv else 0, '%.6f' % mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
                 if mt.is_conv:
-                    print >>info_file, '%d %d %d %d %d' % tuple(mt.max_locs[channel_idx, max_idx]),
+                    print >> batch[batch_index].info_file, '%d %d %d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
                 else:
-                    print >>info_file, '%d %d %d' % tuple(mt.max_locs[channel_idx, max_idx]),
-                print >>info_file, filename
+                    print >> batch[batch_index].info_file, '%d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
+                print >> batch[batch_index].info_file, batch[batch_index].filename
 
             if not (do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm):
                 continue
@@ -346,109 +533,131 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
                 if settings.is_siamese:
                     # in siamese network, filename is a pair of image file names
-                    filename1 = filename[0]
-                    filename2 = filename[1]
+                    filename1 = batch[batch_index].filename[0]
+                    filename2 = batch[batch_index].filename[1]
 
                     # load both images
                     im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
                     im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
 
                     # resize images according to input dimension
-                    net_input_dims = net.blobs['data'].data.shape[2:4]
                     im1 = cv2.resize(im1, net_input_dims)
                     im2 = cv2.resize(im2, net_input_dims)
 
                     # concatenate channelwise
-                    im = np.concatenate((im1, im2), axis=2)
+                    batch[batch_index].im = np.concatenate((im1, im2), axis=2)
                 else:
                     # load image
-                    im = cv2_read_file_rgb(os.path.join(datadir, filename))
+                    batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename))
 
                     # resize images according to input dimension
-                    net_input_dims = net.blobs['data'].data.shape[2:4]
-                    im = cv2.resize(im, net_input_dims)
+                    batch[batch_index].im = cv2.resize(batch[batch_index].im, net_input_dims)
 
                     # convert to float to avoid caffe destroying the image in the scaling phase
-                    im = im.astype(np.float32)
+                    batch[batch_index].im = batch[batch_index].im.astype(np.float32)
 
-            with WithTimer('Predict   ', quiet = not do_print):
-                net.predict([im], oversample = False)
+            batch_index += 1
+
+            # if current batch is full
+            if batch_index == settings.max_tracker_batch_size \
+                    or ((channel_idx == idx_end - 1) and (max_idx_0 == num_top - 1)):  # or last iteration
 
-            # in siamese network, we wish to return from the normalized layer name and selected input index to the
-            # denormalized layer name, e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
-            denormalized_layer_name = settings.denormalize_layer_name_for_max_tracker_fn(layer, selected_input_index)
-            denormalized_top_name = net.top_names[denormalized_layer_name][0]
+                with WithTimer('Predict on batch  ', quiet = not do_print):
+                    im_batch = [record.im for record in batch]
+                    net.predict(im_batch, oversample = False)
 
-            if len(net.blobs[denormalized_top_name].data.shape) == 4:
-                reproduced_val = net.blobs[denormalized_top_name].data[0,channel_idx,ii,jj]
-            else:
-                reproduced_val = net.blobs[denormalized_top_name].data[0,channel_idx]
-            if abs(reproduced_val - recorded_val) > .1:
-                print 'Warning: recorded value %s is suspiciously different from reproduced value %s. Is the filelist the same?' % (recorded_val, reproduced_val)
+                # go over batch and update statistics
+                for i in range(0, batch_index):
 
-            if do_maxes:
-                #grab image from data layer, not from im (to ensure preprocessing / center crop details match between image and deconv/backprop)
+                    # in siamese network, we wish to return from the normalized layer name and selected input index to the
+                    # denormalized layer name, e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
+                    denormalized_layer_name = settings.denormalize_layer_name_for_max_tracker_fn(layer, batch[i].selected_input_index)
+                    denormalized_top_name = net.top_names[denormalized_layer_name][0]
 
-                out_arr = extract_patch_from_image(net.blobs['data'].data[0], net, selected_input_index, settings,
-                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
-                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
+                    if len(net.blobs[denormalized_top_name].data.shape) == 4:
+                        reproduced_val = net.blobs[denormalized_top_name].data[i, batch[i].channel_idx, batch[i].ii, batch[i].jj]
+                    else:
+                        reproduced_val = net.blobs[denormalized_top_name].data[i, batch[i].channel_idx]
+                    if abs(reproduced_val - batch[i].recorded_val) > .1:
+                        print 'Warning: recorded value %s is suspiciously different from reproduced value %s. Is the filelist the same?' % (batch[i].recorded_val, reproduced_val)
 
-                with WithTimer('Save img  ', quiet = not do_print):
-                    save_caffe_image(out_arr, os.path.join(unit_dir, 'maxim_%03d.png' % max_idx_0),
-                                     autoscale = False, autoscale_center = 0)
+                    if do_maxes:
+                        #grab image from data layer, not from im (to ensure preprocessing / center crop details match between image and deconv/backprop)
 
-            if do_deconv or do_deconv_norm:
-                diffs = net.blobs[denormalized_top_name].diff * 0
-                if len(diffs.shape) == 4:
-                    diffs[0,channel_idx,ii,jj] = 1.0
-                else:
-                    diffs[0,channel_idx] = 1.0
-                with WithTimer('Deconv    ', quiet = not do_print):
-                    net.deconv_from_layer(denormalized_layer_name, diffs)
+                        out_arr = extract_patch_from_image(net.blobs['data'].data[i], net, batch[i].selected_input_index, settings,
+                                                           batch[i].data_ii_end, batch[i].data_ii_start, batch[i].data_jj_end, batch[i].data_jj_start,
+                                                           batch[i].out_ii_end, batch[i].out_ii_start, batch[i].out_jj_end, batch[i].out_jj_start, size_ii, size_jj)
 
-                out_arr = extract_patch_from_image(net.blobs['data'].diff[0], net, selected_input_index, settings,
-                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
-                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
+                        with WithTimer('Save img  ', quiet = not do_print):
+                            save_caffe_image(out_arr, batch[i].maxim_filenames[batch[i].max_idx_0],
+                                             autoscale = False, autoscale_center = 0)
 
-                if out_arr.max() == 0:
-                    print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
+                if do_deconv or do_deconv_norm:
 
-                if do_deconv:
-                    with WithTimer('Save img  ', quiet=not do_print):
-                        save_caffe_image(out_arr, os.path.join(unit_dir, 'deconv_%03d.png' % max_idx_0),
-                                         autoscale=False, autoscale_center=0)
-                if do_deconv_norm:
-                    out_arr = np.linalg.norm(out_arr, axis=0)
-                    with WithTimer('Save img  ', quiet=not do_print):
-                        save_caffe_image(out_arr, os.path.join(unit_dir, 'deconvnorm_%03d.png' % max_idx_0))
+                    diffs = net.blobs[denormalized_top_name].diff * 0
 
+                    for i in range(0, batch_index):
+                        if len(diffs.shape) == 4:
+                            diffs[i, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
+                        else:
+                            diffs[i, batch[i].channel_idx] = 1.0
 
-            if do_backprop or do_backprop_norm:
-                diffs = net.blobs[denormalized_top_name].diff * 0
+                    with WithTimer('Deconv    ', quiet = not do_print):
+                        net.deconv_from_layer(denormalized_layer_name, diffs)
 
-                if len(diffs.shape) == 4:
-                    diffs[0,channel_idx,ii,jj] = 1.0
-                else:
-                    diffs[0,channel_idx] = 1.0
-
-                with WithTimer('Backward  ', quiet = not do_print):
-                    net.backward_from_layer(denormalized_layer_name, diffs)
-
-                out_arr = extract_patch_from_image(net.blobs['data'].diff[0], net, selected_input_index, settings,
-                                                   data_ii_end, data_ii_start, data_jj_end, data_jj_start,
-                                                   out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
-
-                if out_arr.max() == 0:
-                    print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
-                if do_backprop:
-                    with WithTimer('Save img  ', quiet = not do_print):
-                        save_caffe_image(out_arr, os.path.join(unit_dir, 'backprop_%03d.png' % max_idx_0),
-                                         autoscale = False, autoscale_center = 0)
-                if do_backprop_norm:
-                    out_arr = np.linalg.norm(out_arr, axis=0)
-                    with WithTimer('Save img  ', quiet = not do_print):
-                        save_caffe_image(out_arr, os.path.join(unit_dir, 'backpropnorm_%03d.png' % max_idx_0))
+                    for i in range(0, batch_index):
+                        out_arr = extract_patch_from_image(net.blobs['data'].diff[i], net, batch[i].selected_input_index, settings,
+                                                           batch[i].data_ii_end, batch[i].data_ii_start, batch[i].data_jj_end, batch[i].data_jj_start,
+                                                           batch[i].out_ii_end, batch[i].out_ii_start, batch[i].out_jj_end, batch[i].out_jj_start, size_ii, size_jj)
 
-        if do_info:
-            info_file.close()
+                        if out_arr.max() == 0:
+                            print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
+
+                        if do_deconv:
+                            with WithTimer('Save img  ', quiet=not do_print):
+                                save_caffe_image(out_arr, batch[i].deconv_filenames[batch[i].max_idx_0],
+                                                 autoscale=False, autoscale_center=0)
+                        if do_deconv_norm:
+                            out_arr = np.linalg.norm(out_arr, axis=0)
+                            with WithTimer('Save img  ', quiet=not do_print):
+                                save_caffe_image(out_arr, batch[i].deconvnorm_filenames[batch[i].max_idx_0])
+
+                if do_backprop or do_backprop_norm:
+
+                    diffs = net.blobs[denormalized_top_name].diff * 0
+
+                    for i in range(0, batch_index):
+
+                        if len(diffs.shape) == 4:
+                            diffs[i, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
+                        else:
+                            diffs[i, batch[i].channel_idx] = 1.0
+
+                    with WithTimer('Backward batch  ', quiet = not do_print):
+                        net.backward_from_layer(denormalized_layer_name, diffs)
+
+                    for i in range(0, batch_index):
+
+                        out_arr = extract_patch_from_image(net.blobs['data'].diff[i], net, batch[i].selected_input_index, settings,
+                                                           batch[i].data_ii_end, batch[i].data_ii_start, batch[i].data_jj_end, batch[i].data_jj_start,
+                                                           batch[i].out_ii_end, batch[i].out_ii_start, batch[i].out_jj_end, batch[i].out_jj_start, size_ii, size_jj)
+
+                        if out_arr.max() == 0:
+                            print 'Warning: Deconv out_arr in range', out_arr.min(), 'to', out_arr.max(), 'ensure force_backward: true in prototxt'
+                        if do_backprop:
+                            with WithTimer('Save img  ', quiet = not do_print):
+                                save_caffe_image(out_arr, batch[i].backprop_filenames[batch[i].max_idx_0],
+                                                 autoscale = False, autoscale_center = 0)
+                        if do_backprop_norm:
+                            out_arr = np.linalg.norm(out_arr, axis=0)
+                            with WithTimer('Save img  ', quiet = not do_print):
+                                save_caffe_image(out_arr, batch[i].backpropnorm_filenames[batch[i].max_idx_0])
 
+                # close info files
+                for i in range(0, batch_index):
+                    channel_to_info_file[batch[i].channel_idx].ref_count -= 1
+                    if channel_to_info_file[batch[i].channel_idx].ref_count == 0:
+                        if do_info:
+                            channel_to_info_file[batch[i].channel_idx].info_file.close()
+
+                batch_index = 0
diff --git a/image_misc.py b/image_misc.py
index f16d0036d..2b72bbf4b 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -364,7 +364,7 @@ def resize_to_fit(img, out_max_shape,
     if convert_early:
         img = np.array(img, dtype=dtype_out)
     out = cv2.resize(img,
-            (int(img.shape[1] * scale), int(img.shape[0] * scale)),   # in (c,r) order
+            (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))),   # in (c,r) order
                      interpolation = grow_interpolation if scale > 1 else shrink_interpolation)
     if convert_late:
         out = np.array(out, dtype=dtype_out)
@@ -403,7 +403,7 @@ def resize_without_fit(img, out_max_shape,
     if convert_early:
         img = np.array(img, dtype=dtype_out)
     out = cv2.resize(img,
-            (int(img.shape[1] * scale_1), int(img.shape[0] * scale_0)),   # in (c,r) order
+            (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),   # in (c,r) order
                      interpolation = grow_interpolation if min(scale_0,scale_1) > 1 else shrink_interpolation)
     if convert_late:
         out = np.array(out, dtype=dtype_out)
diff --git a/optimize_image.py b/optimize_image.py
index a7932939e..2497b31ff 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -182,8 +182,12 @@ def main():
     if settings.caffevis_labels:
         labels = read_label_file(settings.caffevis_labels)
 
+    if len(data_mean.shape) == 3:
+        batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
+    elif len(data_mean.shape) == 1:
+        data_mean = data_mean[np.newaxis,:,np.newaxis,np.newaxis]
+        batched_data_mean = np.tile(data_mean, (args.batch_size,1,current_data_shape[2],current_data_shape[3]))
 
-    batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
     optimizer = GradientOptimizer(settings, net, batched_data_mean, labels = labels,
                                   label_layers = settings.caffevis_label_layers,
                                   channel_swap_to_rgb = settings.caffe_net_channel_swap)
diff --git a/settings.py b/settings.py
index 5eb7f342f..61847bfba 100644
--- a/settings.py
+++ b/settings.py
@@ -111,7 +111,7 @@
 static_files_ignore_case = locals().get('static_files_ignore_case', True)
 # True to stretch to square, False to crop to square. (Can change at
 # runtime via 'stretch_mode' key.)
-static_file_stretch_mode = locals().get('static_file_stretch_mode', False)
+static_file_stretch_mode = locals().get('static_file_stretch_mode', True)
 
 # is the network loaded a siamese network
 is_siamese = locals().get('is_siamese', False)
@@ -383,6 +383,9 @@
 # default value for layer parameter in max_tracker
 max_tracker_layers_to_output = locals().get('max_tracker_layers_to_output', [])
 
+# default batch size used in max_tracker
+max_tracker_batch_size = locals().get('max_tracker_batch_size', 1)
+
 # default value for list of layers used in max tracker
 # note the default is a hardcoded choice which was used in the original code,
 # for backward comparability we keep it as default, but you should change this parameter for each network

From 1b423cb6ae8b33927094ba06b55af7f43712b74a Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:26:27 +0300
Subject: [PATCH 035/106] added empty implementation to avoid crash when
 patterns view with maximal input images runs with
 settings.caffevis_unit_jpg_dir_folder_format ==
 'original_combined_single_image'"

---
 caffevis/app.py | 11 +++++++++--
 1 file changed, 9 insertions(+), 2 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index d60678774..689cd3176 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -369,8 +369,15 @@ def _draw_layer_pane(self, pane):
                                                                                               file_search_pattern='opt*.jpg')
             elif self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE:
 
-                # do something
-                if self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+                    # maximal input image patterns is not implemented in original format
+                    display_3D_highres = np.zeros((layer_dat_3D.shape[0], pane.data.shape[0],
+                                                   pane.data.shape[1],
+                                                   pane.data.shape[2]), dtype=np.uint8)
+                    display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+                    print "ERROR: patterns view with maximal input images is not implemented when settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image'"
+
+                elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
                     display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
                                                                                                layer_dat_3D, n_tiles,
                                                                                                pane,

From 7195e3563f5b9b90b13dbf2296db149c3aaea3f9 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:28:33 +0300
Subject: [PATCH 036/106] added 'B' key binding to back cycle backprop options
 in the UI

---
 bindings.py                    |  4 +++-
 caffevis/app.py                |  2 +-
 caffevis/caffevis_app_state.py | 18 +++++++++++++++++-
 3 files changed, 21 insertions(+), 3 deletions(-)

diff --git a/bindings.py b/bindings.py
index c268c560a..dedbe6b08 100644
--- a/bindings.py
+++ b/bindings.py
@@ -108,7 +108,9 @@ def get_key_help(self, tag):
 _.add('pattern_first_only', '1',
        'Toggle pattern loading first image only or loading all available images')
 
-_.add('ez_back_mode_loop', 'b',
+_.add('next_ez_back_mode_loop', 'b',
+       'Cycle through a few common backprop/deconv modes')
+_.add('prev_ez_back_mode_loop', 'B',
        'Cycle through a few common backprop/deconv modes')
 _.add('freeze_back_unit', 'd',
        'Freeze the bprop/deconv origin to be the currently selected unit')
diff --git a/caffevis/app.py b/caffevis/app.py
index 689cd3176..53afdefd4 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -792,7 +792,7 @@ def draw_help(self, help_pane, locy):
                       FormattedString(nav_string, defaults)])
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
-                    'ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
+                    'next_ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
                     'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'show_maximal_score'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 84c4f8d46..fd27cce19 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -225,7 +225,7 @@ def handle_key(self, key):
                         self.back_filt_mode = 'normblur'
                     else:
                         self.back_filt_mode = 'raw'
-            elif tag == 'ez_back_mode_loop':
+            elif tag == 'next_ez_back_mode_loop':
                 # Cycle:
                 # off -> grad (raw) -> grad(gray) -> grad(norm) -> grad(normblur) -> deconv
                 if not self.back_enabled:
@@ -241,6 +241,22 @@ def handle_key(self, key):
                     self.back_stale = True
                 else:
                     self.back_enabled = False
+            elif tag == 'prev_ez_back_mode_loop':
+                    # Cycle:
+                    # off -> grad (raw) -> grad(gray) -> grad(norm) -> grad(normblur) -> deconv
+                    if not self.back_enabled:
+                        self.back_enabled = True
+                        self.back_mode = 'deconv'
+                        self.back_filt_mode = 'raw'
+                        self.back_stale = True
+                    elif self.back_mode == 'deconv':
+                        self.back_mode = 'grad'
+                        self.back_filt_mode = 'norm'
+                        self.back_stale = True
+                    elif self.back_mode == 'grad' and self.back_filt_mode == 'norm':
+                        self.back_filt_mode = 'raw'
+                    else:
+                        self.back_enabled = False
             elif tag == 'freeze_back_unit':
                 # Freeze selected layer/unit as backprop unit
                 self.backprop_selection_frozen = not self.backprop_selection_frozen

From b7e28b6a77612e095354df9b7ba35432cce34b40 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:35:50 +0300
Subject: [PATCH 037/106] added support for loading Siamese networks when
 Siamese is implemented using the "batch trick"

---
 caffe_misc.py                                 |  87 ++--
 caffevis/app.py                               |  98 ++--
 caffevis/caffe_proc_thread.py                 |  64 +--
 caffevis/caffevis_app_state.py                | 258 ++++++-----
 caffevis/jpg_vis_loading_thread.py            |  32 +-
 deep-visualization-toolbox/siamese_helper.py  | 420 ++++++++++++++++++
 find_maxes/crop_max_patches.py                |  15 +-
 find_maxes/find_max_acts.py                   |  20 +
 find_maxes/max_tracker.py                     | 200 ++++++---
 input_fetcher.py                              |   1 +
 .../settings_local.template-bvlc-googlenet.py |   2 +-
 .../settings_local.template-squeezenet.py     |   2 +-
 optimize/gradient_optimizer.py                |  36 +-
 settings.py                                   |  25 +-
 14 files changed, 883 insertions(+), 377 deletions(-)
 create mode 100644 deep-visualization-toolbox/siamese_helper.py

diff --git a/caffe_misc.py b/caffe_misc.py
index 5e08a673d..4963e9745 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -27,7 +27,7 @@ def shownet(net):
 
 
 
-def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
+def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0), crop_to_boundary = True):
     '''
     Works for conv or pool
 
@@ -58,25 +58,29 @@ def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride
     # Crop top slice to allowable region
     top_slice = [ss for ss in top_slice]   # Copy list or array -> list
 
-    top_slice[0] = max(0, min(top_size[0], top_slice[0]))
-    top_slice[1] = max(0, min(top_size[0], top_slice[1]))
-    top_slice[2] = max(0, min(top_size[1], top_slice[2]))
-    top_slice[3] = max(0, min(top_size[1], top_slice[3]))
+    # should we crop to boundary
+    if crop_to_boundary:
+        top_slice[0] = max(0, min(top_size[0], top_slice[0]))
+        top_slice[1] = max(0, min(top_size[0], top_slice[1]))
+        top_slice[2] = max(0, min(top_size[1], top_slice[2]))
+        top_slice[3] = max(0, min(top_size[1], top_slice[3]))
 
     bot_slice = [-123] * 4
 
-    bot_slice[0] = top_slice[0] * stride[0] - pad[0];
-    bot_slice[1] = (top_slice[1]-1) * stride[0] + filter_width[0] - pad[0];
-    bot_slice[2] = top_slice[2] * stride[1] - pad[1];
-    bot_slice[3] = (top_slice[3]-1) * stride[1] + filter_width[1] - pad[1];
+    bot_slice[0] = top_slice[0] * stride[0] - pad[0]
+    bot_slice[1] = top_slice[1] * stride[0] - pad[0] + filter_width[0] - 1
+    bot_slice[2] = top_slice[2] * stride[1] - pad[1]
+    bot_slice[3] = top_slice[3] * stride[1] - pad[1] + filter_width[1] - 1
 
     return bot_slice
 
+
 def get_conv_converter(bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
-    return lambda top_slice : region_converter(top_slice, bot_size, top_size, filter_width, stride, pad)
+    return lambda top_slice, crop_to_boundary : region_converter(top_slice, bot_size, top_size, filter_width, stride, pad, crop_to_boundary)
+
 
 def get_pool_converter(bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
-    return lambda top_slice : region_converter(top_slice, bot_size, top_size, filter_width, stride, pad)
+    return lambda top_slice, crop_to_boundary : region_converter(top_slice, bot_size, top_size, filter_width, stride, pad, crop_to_boundary)
 
 
 
@@ -101,7 +105,7 @@ def __init__(self, layers_list):
         self.names = [tt[0] for tt in _tmp]
         self.converters = [tt[1] for tt in _tmp]
 
-    def convert_region(self, from_layer, to_layer, region, verbose = False):
+    def convert_region(self, from_layer, to_layer, region, verbose = False, crop_to_boundary = True):
         '''region is the slice of the from_layer in the following Python
             index format: (ii_start, ii_end, jj_start, jj_end)
         '''
@@ -115,7 +119,7 @@ def convert_region(self, from_layer, to_layer, region, verbose = False):
             converter = self.converters[ii]
             if verbose:
                 print 'pushing', self.names[ii], 'region', ret, 'through converter'
-            ret = converter(ret)
+            ret = converter(ret, crop_to_boundary)
         if verbose:
             print 'Final region at ', self.names[to_idx], 'is', ret
 
@@ -151,27 +155,36 @@ def save_caffe_image(img, filename, autoscale = True, autoscale_center = None):
     skimage.io.imsave(filename, img)
 
 
-def get_max_data_extent(net, layer, rc, is_conv):
+def layer_name_to_top_name(net, layer_name):
+    return net.top_names[layer_name][0]
+
+
+def get_max_data_extent(net, layer_name, rc, is_conv):
     '''Gets the maximum size of the data layer that can influence a unit on layer.'''
+
+    data_size = net.blobs['data'].data.shape[2:4]  # e.g. (227,227) for fc6,fc7,fc8,prop
+
     if is_conv:
-        top_name = net.top_names[layer][0]
+        top_name = layer_name_to_top_name(net, layer_name)
         conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
         layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
-        data_slice = rc.convert_region(layer, 'data', layer_slice_middle)
-        return data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
+        data_slice = rc.convert_region(layer_name, 'data', layer_slice_middle, crop_to_boundary = False)
+        data_slice_size = data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
+        # crop data slice size to data size
+        data_slice_size = min(data_slice_size[0], data_size[0]), min(data_slice_size[1], data_size[1])
+        return data_slice_size
     else:
         # Whole data region
-        return net.blobs['data'].data.shape[2:4]        # e.g. (227,227) for fc6,fc7,fc8,prop
+        return data_size
 
 
-
-def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer, size_ii, size_jj, data_size_ii, data_size_jj):
+def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
 
     if is_conv:
 
         # Compute the focus area of the data layer
         layer_indices = (ii, ii + 1, jj, jj + 1)
-        data_indices = region_computer.convert_region(layer, 'data', layer_indices)
+        data_indices = region_computer.convert_region(layer_name, 'data', layer_indices)
         data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
 
         # safe guard edges
@@ -243,19 +256,19 @@ def extract_patch_from_image(data, net, selected_input_index, settings,
     return out_arr
 
 
-def get_top_to_layer_dict(net):
-
-    top_to_layer = dict()
-
-    # go over layers
-    for layer in list(net._layer_names):
-
-        if len(net.top_names[layer]) == 1 and len(net.bottom_names[layer]) == 1 and net.top_names[layer][0] == net.bottom_names[layer][0]:
-            # skip inplace layers
-            continue
-
-        # go over tops
-        for top_name in net.top_names[layer]:
-            top_to_layer[top_name] = layer
-
-    return top_to_layer
\ No newline at end of file
+# def get_top_to_layer_dict(net):
+#
+#     top_to_layer = dict()
+#
+#     # go over layers
+#     for layer_name in list(net._layer_names):
+#
+#         if len(net.top_names[layer_name]) == 1 and len(net.bottom_names[layer_name]) == 1 and net.top_names[layer_name][0] == net.bottom_names[layer_name][0]:
+#             # skip inplace layers
+#             continue
+#
+#         # go over tops
+#         for top_name in net.top_names[layer_name]:
+#             top_to_layer[top_name] = layer_name
+#
+#     return top_to_layer
\ No newline at end of file
diff --git a/caffevis/app.py b/caffevis/app.py
index 53afdefd4..d920db63c 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -21,7 +21,7 @@
 from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
-
+from caffe_misc import layer_name_to_top_name
 
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
@@ -75,31 +75,10 @@ def __init__(self, settings, key_bindings):
             raise Exception('caffevis_jpg_cache_size must be at least 10MB for normal operation.')
         self.img_cache = FIFOLimitedArrayCache(settings.caffevis_jpg_cache_size)
 
-        self._populate_net_layer_info()
-
-    def _populate_net_layer_info(self):
-        '''For each layer, save the number of filters and precompute
-        tile arrangement (needed by CaffeVisAppState to handle
-        keyboard navigation).
-        '''
-        self.net_layer_info = {}
-        for key in self.net.blobs.keys():
-            self.net_layer_info[key] = {}
-            # Conv example: (1, 96, 55, 55)
-            # FC example: (1, 1000)
-            blob_shape = self.net.blobs[key].data.shape
-            assert len(blob_shape) in (2,4), 'Expected either 2 for FC or 4 for conv layer'
-            self.net_layer_info[key]['isconv'] = (len(blob_shape) == 4)
-            self.net_layer_info[key]['data_shape'] = blob_shape[1:]  # Chop off batch size
-            self.net_layer_info[key]['n_tiles'] = blob_shape[1]
-            self.net_layer_info[key]['tiles_rc'] = get_tiles_height_width_ratio(blob_shape[1], self.settings.caffevis_layers_aspect_ratio)
-            self.net_layer_info[key]['tile_rows'] = self.net_layer_info[key]['tiles_rc'][0]
-            self.net_layer_info[key]['tile_cols'] = self.net_layer_info[key]['tiles_rc'][1]
-
     def start(self):
-        self.state = CaffeVisAppState(self.net, self.settings, self.bindings, self.net_layer_info)
+        self.state = CaffeVisAppState(self.net, self.settings, self.bindings)
         self.state.drawing_stale = True
-        self.header_print_names = [get_pretty_layer_name(self.settings, nn) for nn in self.state._headers]
+        self.header_print_names = [get_pretty_layer_name(self.settings, nn) for nn in self.state.get_headers()]
 
         if self.proc_thread is None or not self.proc_thread.is_alive():
             # Start thread if it's not already running
@@ -246,16 +225,16 @@ def _draw_control_pane(self, pane):
 
         for ii in range(len(self.header_print_names)):
             fs = FormattedString(self.header_print_names[ii], defaults)
-            this_layer = self.state._headers[ii]
-            if self.state.backprop_selection_frozen and this_layer == self.state.backprop_layer:
-                fs.clr   = to_255(self.settings.caffevis_control_clr_bp)
+            this_layer_def = self.settings.layers_list[ii]
+            if self.state.backprop_selection_frozen and this_layer_def == self.state.get_current_backprop_layer_definition():
+                fs.clr = to_255(self.settings.caffevis_control_clr_bp)
                 fs.thick = self.settings.caffevis_control_thick_bp
-            if this_layer == self.state._headers[self.state.layer_idx]:
+            if this_layer_def == self.state.get_current_layer_definition():
                 if self.state.cursor_area == 'top':
                     fs.clr = to_255(self.settings.caffevis_control_clr_cursor)
                     fs.thick = self.settings.caffevis_control_thick_cursor
                 else:
-                    if not (self.state.backprop_selection_frozen and this_layer == self.state.backprop_layer):
+                    if not (self.state.backprop_selection_frozen and this_layer_def == self.state.get_current_backprop_layer_definition()):
                         fs.clr = to_255(self.settings.caffevis_control_clr_selected)
                         fs.thick = self.settings.caffevis_control_thick_selected
             strings.append(fs)
@@ -280,12 +259,13 @@ def _draw_status_pane(self, pane):
             print >>status, 'pattern(' + pattern_first_mode + ' optimized max)' if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE else \
                 ('pattern(' + pattern_first_mode + ' input max)' if self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE else
                 ('back' if self.state.layers_show_back else 'fwd')),
-            print >>status, '%s:%d |' % (self.state.get_default_layer_name(), self.state.selected_unit),
+            default_layer_name = self.state.get_default_layer_name()
+            print >>status, '%s:%d |' % (default_layer_name, self.state.selected_unit),
             if not self.state.back_enabled:
                 print >>status, 'Back: off',
             else:
                 print >>status, 'Back: %s' % ('deconv' if self.state.back_mode == 'deconv' else 'bprop'),
-                print >>status, '(from %s_%d, disp %s)' % (self.state.backprop_layer,
+                print >>status, '(from %s_%d, disp %s)' % (self.state.get_default_layer_name(self.state.get_current_backprop_layer_definition()) ,
                                                            self.state.backprop_unit,
                                                            self.state.back_filt_mode),
             print >>status, '|',
@@ -311,14 +291,13 @@ def _draw_layer_pane(self, pane):
 
         default_layer_name = self.state.get_default_layer_name()
 
-        if self.state.siamese_input_mode_has_two_images(self.state.layer):
+        if self.state.siamese_input_mode_has_two_images():
 
             if self.state.layers_show_back:
-                layer_dat_3D_0 = self.net.blobs[self.state.layer[0]].diff[0]
-                layer_dat_3D_1 = self.net.blobs[self.state.layer[1]].diff[0]
+
+                layer_dat_3D_0, layer_dat_3D_1 = self.state.get_siamese_selected_diff_blobs(self.net)
             else:
-                layer_dat_3D_0 = self.net.blobs[self.state.layer[0]].data[0]
-                layer_dat_3D_1 = self.net.blobs[self.state.layer[1]].data[0]
+                layer_dat_3D_0, layer_dat_3D_1 = self.state.get_siamese_selected_data_blobs(self.net)
 
             # Promote FC layers with shape (n) to have shape (n,1,1)
             if len(layer_dat_3D_0.shape) == 1:
@@ -337,9 +316,9 @@ def _draw_layer_pane(self, pane):
 
         else:
             if self.state.layers_show_back:
-                layer_dat_3D = self.net.blobs[default_layer_name].diff[0]
+                layer_dat_3D = self.state.get_single_selected_diff_blob(self.net)
             else:
-                layer_dat_3D = self.net.blobs[default_layer_name].data[0]
+                layer_dat_3D = self.state.get_single_selected_data_blob(self.net)
 
         # Promote FC layers with shape (n) to have shape (n,1,1)
         if len(layer_dat_3D.shape) == 1:
@@ -347,7 +326,8 @@ def _draw_layer_pane(self, pane):
 
         n_tiles = layer_dat_3D.shape[0]
 
-        tile_rows, tile_cols = self.net_layer_info[default_layer_name]['tiles_rc']
+        top_name = layer_name_to_top_name(self.net, default_layer_name)
+        tile_rows, tile_cols = self.state.net_blob_info[top_name]['tiles_rc']
 
         display_3D_highres = None
         if self.state.pattern_mode != PatternMode.OFF:
@@ -434,7 +414,7 @@ def _draw_layer_pane(self, pane):
         with self.state.lock:
             if self.state.cursor_area == 'bottom':
                 highlights[self.state.selected_unit] = self.settings.caffevis_layer_clr_cursor  # in [0,1] range
-            if self.state.backprop_selection_frozen and default_layer_name == self.state.backprop_layer:
+            if self.state.backprop_selection_frozen and self.state.get_current_layer_definition() == self.state.get_current_backprop_layer_definition():
                 highlights[self.state.backprop_unit] = self.settings.caffevis_layer_clr_back_sel  # in [0,1] range
 
         _, display_2D = tile_images_make_tiles(display_3D, hw = (tile_rows,tile_cols), padval = padval, highlights = highlights)
@@ -458,8 +438,11 @@ def _draw_layer_pane(self, pane):
 
         pane.data[:] = to_255(self.settings.window_background)
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
-        
-        if self.settings.caffevis_label_layers and default_layer_name in self.settings.caffevis_label_layers and self.state.cursor_area == 'bottom':
+
+        # check if label or score should be presented for the selected layer
+        if (default_layer_name in self.settings.caffevis_label_layers and self.state.cursor_area == 'bottom') or \
+           (default_layer_name in self.settings.caffevis_score_layers and self.state.cursor_area == 'bottom'):
+
             # Display label annotation atop layers pane (e.g. for fc8/prob)
             defaults = {'face':  getattr(cv2, self.settings.caffevis_label_face),
                         'fsize': self.settings.caffevis_label_fsize,
@@ -467,10 +450,23 @@ def _draw_layer_pane(self, pane):
                         'thick': self.settings.caffevis_label_thick}
             loc_base = self.settings.caffevis_label_loc[::-1]   # Reverse to OpenCV c,r order
 
-            if self.labels:
-                lines = [FormattedString(self.labels[self.state.selected_unit], defaults)]
-            else:
-                lines = [FormattedString(str(self.net.blobs[default_layer_name].data[0,self.state.selected_unit]), defaults)]
+            text_to_display = ""
+            if (self.labels) and (default_layer_name in self.settings.caffevis_label_layers):
+                text_to_display = self.labels[self.state.selected_unit] + " "
+
+            if (default_layer_name in self.settings.caffevis_score_layers):
+
+                if self.state.siamese_input_mode_has_two_images():
+                    blob1, blob2 = self.state.get_siamese_selected_data_blobs(self.net)
+                    value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
+                    text_to_display += str(value1) + " " +  str(value2)
+
+                else:
+                    blob = self.state.get_single_selected_data_blob(self.net)
+                    value = blob[self.state.selected_unit]
+                    text_to_display += str(value)
+
+            lines = [FormattedString(text_to_display, defaults)]
             cv2_typeset_text(pane.data, lines, loc_base)
             
         return display_3D_highres
@@ -606,8 +602,6 @@ def _draw_back_pane(self, pane):
             back_enabled = self.state.back_enabled
             back_mode = self.state.back_mode
             back_filt_mode = self.state.back_filt_mode
-            state_layer = self.state.layer
-            selected_unit = self.state.selected_unit
             back_what_to_disp = self.get_back_what_to_disp()
                 
         if back_what_to_disp == 'disabled':
@@ -709,7 +703,7 @@ def _draw_jpgvis_pane(self, pane):
         pane.data[:] = to_255(self.settings.window_background)
 
         with self.state.lock:
-            state_layer, state_selected_unit, cursor_area, show_unit_jpgs = self.state.get_default_layer_name(), self.state.selected_unit, self.state.cursor_area, self.state.show_unit_jpgs
+            state_layer_name, state_selected_unit, cursor_area, show_unit_jpgs = self.state.get_default_layer_name(), self.state.selected_unit, self.state.cursor_area, self.state.show_unit_jpgs
 
         try:
             # Some may be missing this setting
@@ -718,10 +712,10 @@ def _draw_jpgvis_pane(self, pane):
             print '\n\nNOTE: you need to upgrade your settings.py and settings_local.py files. See README.md.\n\n'
             raise
             
-        if self.settings.caffevis_jpgvis_remap and state_layer in self.settings.caffevis_jpgvis_remap:
-            img_key_layer = self.settings.caffevis_jpgvis_remap[state_layer]
+        if self.settings.caffevis_jpgvis_remap and state_layer_name in self.settings.caffevis_jpgvis_remap:
+            img_key_layer = self.settings.caffevis_jpgvis_remap[state_layer_name]
         else:
-            img_key_layer = state_layer
+            img_key_layer = state_layer_name
 
         if self.settings.caffevis_jpgvis_layers and img_key_layer in self.settings.caffevis_jpgvis_layers and cursor_area == 'bottom' and show_unit_jpgs:
             img_key = (img_key_layer, state_selected_unit, pane.data.shape, self.state.show_maximal_score)
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index ea27def14..7e75aec38 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -5,8 +5,6 @@
 from codependent_thread import CodependentThread
 from misc import WithTimer
 from caffevis_helper import net_preproc_forward
-from caffevis_app_state import SiameseInputMode
-from caffe_misc import get_top_to_layer_dict
 
 class CaffeProcThread(CodependentThread):
     '''Runs Caffe in separate thread.'''
@@ -25,11 +23,8 @@ def __init__(self, settings, net, state, loop_sleep, pause_after_keys, heartbeat
         self.pause_after_keys = pause_after_keys
         self.debug_level = 0
         self.mode_gpu = mode_gpu      # Needed so the mode can be set again in the spawned thread, because there is a separate Caffe object per thread.
-
         self.settings = settings
 
-        self.top_to_layer_dict = get_top_to_layer_dict(self.net)
-
 
     def run(self):
         print 'CaffeProcThread.run called'
@@ -70,9 +65,7 @@ def run(self):
                     back_enabled = self.state.back_enabled
                     back_mode = self.state.back_mode
                     back_stale = self.state.back_stale
-                    #state_layer = self.state.layer
-                    #selected_unit = self.state.selected_unit
-                    backprop_layer = self.state.backprop_layer
+                    backprop_layer_def = self.state.get_current_backprop_layer_definition()
                     backprop_unit = self.state.backprop_unit
 
                     # Forward should be run for every new frame
@@ -104,64 +97,13 @@ def run(self):
 
             if run_back:
 
-                default_backprop_layer = self.state.get_default_layer_name_for_layer(backprop_layer)
-
                 assert back_mode in ('grad', 'deconv')
                 if back_mode == 'grad':
                     with WithTimer('CaffeProcThread:backward', quiet = self.debug_level < 1):
-                        #print '**** Doing backprop with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max())
-                        try:
-
-                            if self.state.siamese_input_mode_has_two_images(backprop_layer):
-                                diffs0 = self.net.blobs[backprop_layer[0]].diff * 0
-                                diffs0[0][backprop_unit] = self.net.blobs[backprop_layer[0]].data[0, backprop_unit]
-                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[0]]
-                                self.net.backward_from_layer(backprop_layer_name, diffs0, zero_higher = True)
-
-                                diffs1 = self.net.blobs[backprop_layer[1]].diff * 0
-                                diffs1[0][backprop_unit] = self.net.blobs[backprop_layer[1]].data[0, backprop_unit]
-                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[1]]
-                                self.net.backward_from_layer(backprop_layer_name, diffs1, zero_higher=True)
-
-                            else:
-                                diffs = self.net.blobs[default_backprop_layer].diff * 0
-                                diffs[0][backprop_unit] = self.net.blobs[default_backprop_layer].data[0, backprop_unit]
-                                backprop_layer_name = self.top_to_layer_dict[default_backprop_layer]
-                                self.net.backward_from_layer(backprop_layer_name, diffs, zero_higher = True)
-                        except AttributeError:
-                            print 'ERROR: required bindings (backward_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
-                            raise
-                        except ValueError:
-                            print "ERROR: probably impossible to backprop layer %s, ignoring to avoid crash" % (str(backprop_layer))
-                            with self.state.lock:
-                                self.state.back_enabled = False
+                        self.state.backward_from_layer(self.net, backprop_layer_def, backprop_unit)
                 else:
                     with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1):
-                        #print '**** Doing deconv with %s diffs in [%s,%s]' % (backprop_layer, diffs.min(), diffs.max())
-                        try:
-                            if self.state.siamese_input_mode_has_two_images(backprop_layer):
-                                diffs0 = self.net.blobs[backprop_layer[0]].diff * 0
-                                diffs0[0][backprop_unit] = self.net.blobs[backprop_layer[0]].data[0, backprop_unit]
-                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[0]]
-                                self.net.deconv_from_layer(backprop_layer_name, diffs0, zero_higher=True)
-
-                                diffs1 = self.net.blobs[backprop_layer[1]].diff * 0
-                                diffs1[0][backprop_unit] = self.net.blobs[backprop_layer[1]].data[0, backprop_unit]
-                                backprop_layer_name = self.top_to_layer_dict[backprop_layer[1]]
-                                self.net.deconv_from_layer(backprop_layer_name, diffs1, zero_higher=True)
-
-                            else:
-                                diffs = self.net.blobs[default_backprop_layer].diff * 0
-                                diffs[0][backprop_unit] = self.net.blobs[default_backprop_layer].data[0, backprop_unit]
-                                backprop_layer_name = self.top_to_layer_dict[default_backprop_layer]
-                                self.net.deconv_from_layer(backprop_layer_name, diffs, zero_higher = True)
-                        except AttributeError:
-                            print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
-                            raise
-                        except ValueError:
-                            print "ERROR: probably impossible to deconv layer %s, ignoring to avoid crash" % (backprop_layer)
-                            with self.state.lock:
-                                self.state.back_enabled = False
+                        self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit)
 
                 with self.state.lock:
                     self.state.back_stale = False
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index fd27cce19..3a818ab6c 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -1,13 +1,9 @@
 import os
 import time
 from threading import Lock
-
-
-class SiameseInputMode:
-    FIRST_IMAGE = 0
-    SECOND_IMAGE = 1
-    BOTH_IMAGES = 2
-    NUMBER_OF_MODES = 3
+from siamese_helper import SiameseInputMode, SiameseHelper
+from caffe_misc import layer_name_to_top_name
+from image_misc import get_tiles_height_width_ratio
 
 class PatternMode:
     OFF = 0
@@ -18,19 +14,18 @@ class PatternMode:
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
 
-    def __init__(self, net, settings, bindings, net_layer_info):
+    def __init__(self, net, settings, bindings):
         self.lock = Lock()  # State is accessed in multiple threads
         self.settings = settings
         self.bindings = bindings
+        self.net = net
 
-        self._fill_headers_list(net, settings)
+        self.fill_layers_list(net)
+
+        self.siamese_helper = SiameseHelper(settings.layers_list)
+
+        self._populate_net_blob_info(net)
 
-        if hasattr(self.settings, 'caffevis_filter_layers'):
-            for name in self._headers:
-                if self.settings.caffevis_filter_layers(name):
-                    print '  Layer filtered out by caffevis_filter_layers: %s' % name
-            self._headers = filter(lambda name: not self.settings.caffevis_filter_layers(name), self._headers)
-        self.net_layer_info = net_layer_info
         self.layer_boost_indiv_choices = self.settings.caffevis_boost_indiv_choices   # 0-1, 0 is noop
         self.layer_boost_gamma_choices = self.settings.caffevis_boost_gamma_choices   # 0-inf, 1 is noop
         self.caffe_net_state = 'free'     # 'free', 'proc', or 'draw'
@@ -44,52 +39,36 @@ def __init__(self, net, settings, bindings, net_layer_info):
 
         self._reset_user_state()
 
-    @staticmethod
-    def get_header_from_layer(layer):
+    def _populate_net_blob_info(self, net):
+        '''For each blob, save the number of filters and precompute
+        tile arrangement (needed by CaffeVisAppState to handle keyboard navigation).
         '''
-        helper function which returns the header name, given a single layer, or layer pair
-        :param layer: can be either single layer string, or apir of layers
-        :return: header for layer
-        '''
-
-        # if we have only a single layer, the header is the layer name
-        if type(layer) is str:
-            return layer
-
-        # if we got a pair of layers
-        elif (type(layer) is tuple) and (len(layer) == 2):
-            # build header in format: common_prefix + first_postfix | second_postfix
-            prefix = os.path.commonprefix(layer)
-            prefix_len = len(prefix)
-            postfix0 = layer[0][prefix_len:]
-            postfix1 = layer[1][prefix_len:]
-            header_name = '%s%s|%s' % (prefix, postfix0, postfix1)
-            return header_name
-
-    def _fill_headers_list(self, net, settings):
-        '''
-        helper function which fills the headers list, using either the entire blob names, or the siamese layers definition
-        :param net: network to generate headers for
-        :param settings: settings holder
-        :return: N/A
-        '''
-
-        # if loading a siamese network, and layers list is not empty
-        if settings.is_siamese and settings.siamese_layers_list:
-
-            self._headers = list()
-            for item in settings.siamese_layers_list:
-                self._headers.append(self.get_header_from_layer(item))
-
-        else:
-            self._headers = net.blobs.keys()
-            self._headers = self._headers[1:]  # chop off data layer
+        self.net_blob_info = {}
+        for key in net.blobs.keys():
+            self.net_blob_info[key] = {}
+            # Conv example: (1, 96, 55, 55)
+            # FC example: (1, 1000)
+            blob_shape = net.blobs[key].data.shape
+            assert len(blob_shape) in (2,4), 'Expected either 2 for FC or 4 for conv layer'
+            self.net_blob_info[key]['isconv'] = (len(blob_shape) == 4)
+            self.net_blob_info[key]['data_shape'] = blob_shape[1:]  # Chop off batch size
+            self.net_blob_info[key]['n_tiles'] = blob_shape[1]
+            self.net_blob_info[key]['tiles_rc'] = get_tiles_height_width_ratio(blob_shape[1], self.settings.caffevis_layers_aspect_ratio)
+            self.net_blob_info[key]['tile_rows'] = self.net_blob_info[key]['tiles_rc'][0]
+            self.net_blob_info[key]['tile_cols'] = self.net_blob_info[key]['tiles_rc'][1]
+
+    def get_headers(self):
+
+        headers = list()
+        for layer_def in self.settings.layers_list:
+            headers.append(SiameseHelper.get_header_from_layer_def(layer_def))
+
+        return headers
 
     def _reset_user_state(self):
         self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
         self.show_maximal_score = False
         self.layer_idx = 0
-        self._update_layer_name()
         self.layer_boost_indiv_idx = self.settings.caffevis_boost_indiv_default_idx
         self.layer_boost_indiv = self.layer_boost_indiv_choices[self.layer_boost_indiv_idx]
         self.layer_boost_gamma_idx = self.settings.caffevis_boost_gamma_default_idx
@@ -97,7 +76,7 @@ def _reset_user_state(self):
         self.cursor_area = 'top'   # 'top' or 'bottom'
         self.selected_unit = 0
         # Which layer and unit (or channel) to use for backprop
-        self.backprop_layer = self.layer
+        self.backprop_layer_idx = self.layer_idx
         self.backprop_unit = self.selected_unit
         self.backprop_selection_frozen = False    # If false, backprop unit tracks selected unit
         self.back_enabled = False
@@ -113,27 +92,6 @@ def _reset_user_state(self):
         kh,_ = self.bindings.get_key_help('help_mode')
         self.extra_msg = '%s for help' % kh[0]
 
-    def _update_layer_name(self):
-        '''
-        updates the selected layer, after layer_idx got updated
-        in Siamese networks, returns the relevant layer according to current input mode
-        on non-siamese networks, header name is the layer name
-        :return: N/A
-        '''
-
-        if self.settings.is_siamese and (self.settings.siamese_layers_list) and \
-                (type(self.settings.siamese_layers_list[self.layer_idx]) is tuple) and \
-                (len(self.settings.siamese_layers_list[self.layer_idx]) == 2):
-
-            if self.siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
-                self.layer = self.settings.siamese_layers_list[self.layer_idx][0]
-            elif self.siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
-                self.layer = self.settings.siamese_layers_list[self.layer_idx][1]
-            elif self.siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
-                self.layer = self.settings.siamese_layers_list[self.layer_idx]
-        else:
-            self.layer = self._headers[self.layer_idx]
-
     def handle_key(self, key):
         #print 'Ignoring key:', key
         if key == -1:
@@ -150,14 +108,12 @@ def handle_key(self, key):
                 #self.selected_unit = self.selected_unit % ww   # equivalent to scrolling all the way to the top row
                 #self.cursor_area = 'top' # Then to the control pane
                 self.layer_idx = max(0, self.layer_idx - 1)
-                self._update_layer_name()
 
             elif tag == 'sel_layer_right':
                 #hh,ww = self.tiles_height_width
                 #self.selected_unit = self.selected_unit % ww   # equivalent to scrolling all the way to the top row
                 #self.cursor_area = 'top' # Then to the control pane
-                self.layer_idx = min(len(self._headers) - 1, self.layer_idx + 1)
-                self._update_layer_name()
+                self.layer_idx = min(len(self.settings.layers_list) - 1, self.layer_idx + 1)
 
             elif tag == 'sel_left':
                 self.move_selection('left')
@@ -262,7 +218,7 @@ def handle_key(self, key):
                 self.backprop_selection_frozen = not self.backprop_selection_frozen
                 if self.backprop_selection_frozen:
                     # Grap layer/selected_unit upon transition from non-frozen -> frozen
-                    self.backprop_layer = self.layer
+                    self.backprop_layer_idx = self.layer_idx
                     self.backprop_unit = self.selected_unit                    
             elif tag == 'zoom_mode':
                 self.layers_pane_zoom_mode = (self.layers_pane_zoom_mode + 1) % 3
@@ -295,83 +251,124 @@ def redraw_needed(self):
         with self.lock:
             return self.drawing_stale
 
-    def get_default_layer_name(self):
-        '''
-        helper function which returns the first layer in the layer tuple, or the only one
-        many parts of the code can take info from first layer, since it should be the same as second layer info
-        :return: layer name which can be used to get information which is not input specific
-        '''
-        return self.get_default_layer_name_for_layer(self.layer)
+    def get_current_layer_definition(self):
+        return self.settings.layers_list[self.layer_idx]
 
-        return default_layer_name
+    def get_current_backprop_layer_definition(self):
+        return self.settings.layers_list[self.backprop_layer_idx]
 
+    def get_single_selected_data_blob(self, net, layer_def = None):
 
-    def get_default_layer_name_for_layer(self, layer):
-        '''
-        helper function which returns the first layer in the layer tuple, or the only one
-        many parts of the code can take info from first layer, since it should be the same as second layer info
-        :return: layer name which can be used to get information which is not input specific
-        '''
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
+
+        return self.siamese_helper.get_single_selected_data_blob(net, layer_def, self.siamese_input_mode)
 
-        if self.siamese_input_mode_has_two_images(layer):
-            default_layer_name = layer[0]
-        elif self.siamese_input_mode == SiameseInputMode.FIRST_IMAGE and self.is_pair_of_layers(layer):
-            default_layer_name = layer[0]
-        elif self.siamese_input_mode == SiameseInputMode.SECOND_IMAGE and self.is_pair_of_layers(layer):
-            default_layer_name = layer[1]
-        else:
-            default_layer_name = layer
+    def get_single_selected_diff_blob(self, net, layer_def = None):
 
-        return default_layer_name
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
 
-    @staticmethod
-    def is_pair_of_layers(layer):
-        return (type(layer), len(layer)) == (tuple, 2)
+        return self.siamese_helper.get_single_selected_diff_blob(net, layer_def, self.siamese_input_mode)
 
-    def siamese_input_mode_has_two_images(self, layer):
+    def get_siamese_selected_data_blobs(self, net, layer_def = None):
+
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
+
+        return self.siamese_helper.get_siamese_selected_data_blobs(net, layer_def, self.siamese_input_mode)
+
+    def get_siamese_selected_diff_blobs(self, net, layer_def = None):
+
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
+
+        return self.siamese_helper.get_siamese_selected_diff_blobs(net, layer_def, self.siamese_input_mode)
+
+
+    def backward_from_layer(self, net, backprop_layer_def, backprop_unit):
+
+        try:
+            return SiameseHelper.backward_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_input_mode)
+        except AttributeError:
+            print 'ERROR: required bindings (backward_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
+            raise
+        except ValueError:
+            print "ERROR: probably impossible to backprop layer %s, ignoring to avoid crash" % (str(backprop_layer_def['name/s']))
+            with self.lock:
+                self.back_enabled = False
+
+    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit):
+
+        try:
+            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_input_mode)
+        except AttributeError:
+            print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
+            raise
+        except ValueError:
+            print "ERROR: probably impossible to deconv layer %s, ignoring to avoid crash" % (str(backprop_layer_def['name/s']))
+            with self.lock:
+                self.back_enabled = False
+
+    def get_default_layer_name(self, layer_def = None):
+
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
+
+        return self.siamese_helper.get_default_layer_name(layer_def)
+
+    def siamese_input_mode_has_two_images(self, layer_def = None):
         '''
         helper function which checks whether the input mode is two images, and the provided layer contains two layer names
         :param layer: can be a single string layer name, or a pair of layer names
         :return: True if both the input mode is BOTH_IMAGES and layer contains two layer names, False oherwise
         '''
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
 
-        return self.siamese_input_mode == SiameseInputMode.BOTH_IMAGES and self.is_pair_of_layers(layer)
+        return SiameseHelper.siamese_input_mode_has_two_images(layer_def, self.siamese_input_mode)
 
     def move_selection(self, direction, dist = 1):
 
         default_layer_name = self.get_default_layer_name()
+        default_top_name = layer_name_to_top_name(self.net, default_layer_name)
 
         if direction == 'left':
             if self.cursor_area == 'top':
                 self.layer_idx = max(0, self.layer_idx - dist)
-                self._update_layer_name()
             else:
                 self.selected_unit -= dist
         elif direction == 'right':
             if self.cursor_area == 'top':
-                self.layer_idx = min(len(self._headers) - 1, self.layer_idx + dist)
-                self._update_layer_name()
+                self.layer_idx = min(len(self.settings.layers_list) - 1, self.layer_idx + dist)
             else:
                 self.selected_unit += dist
         elif direction == 'down':
             if self.cursor_area == 'top':
                 self.cursor_area = 'bottom'
             else:
-                self.selected_unit += self.net_layer_info[default_layer_name]['tile_cols'] * dist
+                self.selected_unit += self.net_blob_info[default_top_name]['tile_cols'] * dist
         elif direction == 'up':
             if self.cursor_area == 'top':
                 pass
             else:
-                self.selected_unit -= self.net_layer_info[default_layer_name]['tile_cols'] * dist
+                self.selected_unit -= self.net_blob_info[default_top_name]['tile_cols'] * dist
                 if self.selected_unit < 0:
-                    self.selected_unit += self.net_layer_info[default_layer_name]['tile_cols']
+                    self.selected_unit += self.net_blob_info[default_top_name]['tile_cols']
                     self.cursor_area = 'top'
 
     def _ensure_valid_selected(self):
 
         default_layer_name = self.get_default_layer_name()
+        default_top_name = layer_name_to_top_name(self.net, default_layer_name)
 
-        n_tiles = self.net_layer_info[default_layer_name]['n_tiles']
+        n_tiles = self.net_blob_info[default_top_name]['n_tiles']
 
         # Forward selection
         self.selected_unit = max(0, self.selected_unit)
@@ -380,7 +377,32 @@ def _ensure_valid_selected(self):
         # Backward selection
         if not self.backprop_selection_frozen:
             # If backprop_selection is not frozen, backprop layer/unit follows selected unit
-            if not (self.backprop_layer == self.layer and self.backprop_unit == self.selected_unit):
-                self.backprop_layer = self.layer
+            if not (self.backprop_layer_idx == self.layer_idx and self.backprop_unit == self.selected_unit):
+                self.backprop_layer_idx = self.layer_idx
                 self.backprop_unit = self.selected_unit
                 self.back_stale = True    # If there is any change, back diffs are now stale
+
+    def fill_layers_list(self, net):
+
+        # if layers list is empty, fill it with layer names
+        if not self.settings.layers_list:
+
+            # go over layers
+            self.settings.layers_list = []
+            for layer_name in list(net._layer_names):
+
+                # skip inplace layers
+                if len(net.top_names[layer_name]) == 1 and len(net.bottom_names[layer_name]) == 1 and net.top_names[layer_name][0] == net.bottom_names[layer_name][0]:
+                    continue
+
+                self.settings.layers_list.append( {'format': 'normal', 'name/s': layer_name} )
+
+        # filter layers if needed
+        if hasattr(self.settings, 'caffevis_filter_layers'):
+            for layer_def in self.settings.layers_list:
+                if self.settings.caffevis_filter_layers(layer_def['name/s']):
+                    print '  Layer filtered out by caffevis_filter_layers: %s' % str(layer_def['name/s'])
+            self.settings.layers_list = filter(lambda layer_def: not self.settings.caffevis_filter_layers(layer_def['name/s']), self.settings.layers_list)
+
+
+        pass
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 8379fd9a0..737a9cfba 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -30,11 +30,11 @@ def __init__(self, settings, state, cache, loop_sleep, heartbeat_required):
         self.debug_level = 0
 
 
-    def load_image_into_pane_original_format(self, state_layer, state_selected_unit, resize_shape, images, sub_folder,
+    def load_image_into_pane_original_format(self, state_layer_name, state_selected_unit, resize_shape, images, sub_folder,
                                              file_pattern, image_index_to_set, should_crop_to_corner=False):
 
-        jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, sub_folder, state_layer,
-                                file_pattern % (state_layer, state_selected_unit))
+        jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, sub_folder, state_layer_name,
+                                file_pattern % (state_layer_name, state_selected_unit))
 
         try:
             img = cv2_read_file_rgb(jpg_path)
@@ -45,14 +45,14 @@ def load_image_into_pane_original_format(self, state_layer, state_selected_unit,
 
         except IOError:
             print '\nAttempted to load file %s but failed. To supress this warning, remove layer "%s" from settings.caffevis_jpgvis_layers' % \
-                  (jpg_path, state_layer)
+                  (jpg_path, state_layer_name)
             # set black image as place holder
             images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
             pass
 
-    def prepare_score_captions_for_max_input_images(self, state_layer, state_selected_unit):
+    def prepare_score_captions_for_max_input_images(self, state_layer_name, state_selected_unit):
 
-        info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer,
+        info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
                                         "unit_%04d" % (state_selected_unit),
                                         "info.txt")
 
@@ -71,10 +71,10 @@ def prepare_score_captions_for_max_input_images(self, state_layer, state_selecte
 
         return captions
 
-    def load_image_into_pane_max_tracker_format(self, state_layer, state_selected_unit, resize_shape, images,
+    def load_image_into_pane_max_tracker_format(self, state_layer_name, state_selected_unit, resize_shape, images,
                                                 file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False, captions = []):
 
-        unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer,
+        unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
                                         "unit_%04d" % (state_selected_unit),
                                         file_search_pattern)
 
@@ -101,7 +101,7 @@ def run(self):
                 time.sleep(self.loop_sleep)
                 continue
 
-            state_layer, state_selected_unit, data_shape, show_maximal_score = jpgvis_to_load_key
+            state_layer_name, state_selected_unit, data_shape, show_maximal_score = jpgvis_to_load_key
 
             # Load three images:
             images = [None] * 3
@@ -122,21 +122,21 @@ def run(self):
             if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
                 # 0. e.g. regularized_opt/conv1/conv1_0037_montage.jpg
-                self.load_image_into_pane_original_format(state_layer, state_selected_unit, resize_shape, images,
+                self.load_image_into_pane_original_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                           sub_folder='regularized_opt',
                                                           file_pattern='%s_%04d_montage.jpg',
                                                           image_index_to_set=0,
                                                           should_crop_to_corner=True)
 
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
-                self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                self.load_image_into_pane_max_tracker_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='opt*.jpg',
                                                              image_index_to_set=0)
 
             if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
                 # 1. e.g. max_im/conv1/conv1_0037.jpg
-                self.load_image_into_pane_original_format(state_layer, state_selected_unit, resize_shape, images,
+                self.load_image_into_pane_original_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                           sub_folder='max_im',
                                                           file_pattern='%s_%04d.jpg',
                                                           image_index_to_set=1)
@@ -144,10 +144,10 @@ def run(self):
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
 
                 if self.state.show_maximal_score:
-                    captions = self.prepare_score_captions_for_max_input_images(state_layer, state_selected_unit)
+                    captions = self.prepare_score_captions_for_max_input_images(state_layer_name, state_selected_unit)
                 else:
                     captions = []
-                self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                self.load_image_into_pane_max_tracker_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='maxim*.png',
                                                              image_index_to_set=1, captions=captions)
 
@@ -155,13 +155,13 @@ def run(self):
             if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
                 # 2. e.g. max_deconv/conv1/conv1_0037.jpg
-                self.load_image_into_pane_original_format(state_layer, state_selected_unit, resize_shape, images,
+                self.load_image_into_pane_original_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                           sub_folder='max_deconv',
                                                           file_pattern='%s_%04d.jpg',
                                                           image_index_to_set=2)
 
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
-                self.load_image_into_pane_max_tracker_format(state_layer, state_selected_unit, resize_shape, images,
+                self.load_image_into_pane_max_tracker_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='deconv*.png',
                                                              image_index_to_set=2)
 
diff --git a/deep-visualization-toolbox/siamese_helper.py b/deep-visualization-toolbox/siamese_helper.py
new file mode 100644
index 000000000..4a4e123af
--- /dev/null
+++ b/deep-visualization-toolbox/siamese_helper.py
@@ -0,0 +1,420 @@
+import os
+# import numpy as np
+from numpy import expand_dims, concatenate
+from caffe_misc import layer_name_to_top_name
+
+class SiameseInputMode:
+    FIRST_IMAGE = 0
+    SECOND_IMAGE = 1
+    BOTH_IMAGES = 2
+    NUMBER_OF_MODES = 3
+
+class SiameseHelper(object):
+    '''helper class for handling all sorts of operations related to siamese networks
+    this class should encapsulate the different types of siamese network implementation'''
+
+    def __init__(self, layers_list):
+
+        # define class members
+        self.layers_list = layers_list
+        self.layer_name_to_normalized_layer_name = dict()
+        self.normalized_layer_name_to_denormalized_layer_name = dict()
+        self.layer_name_to_index_of_saved_image = dict()
+
+        # init dictionaries
+        self._init_layer_name_to_normalized_layer_name()
+        self._init_normalized_layer_name_to_denormalized_layer_name()
+        self._init_layer_name_to_index_of_saved_image()
+
+        return
+
+    def _init_layer_name_to_normalized_layer_name(self):
+        '''
+        init layer_name_to_normalized_layer_name dictionary
+        :return: none
+        '''
+
+        for layer_def in self.layers_list:
+
+            layer_format = layer_def['format']
+            layer_names = layer_def['name/s']
+
+            if layer_format == 'normal':
+                self.layer_name_to_normalized_layer_name[layer_names] = layer_names
+
+            elif layer_format == 'siamese_layer_pair':
+                self.layer_name_to_normalized_layer_name[layer_names[0]] = layer_names[0]
+                self.layer_name_to_normalized_layer_name[layer_names[1]] = layer_names[0]
+
+            elif layer_format == 'siamese_batch_pair':
+                self.layer_name_to_normalized_layer_name[layer_names] = layer_names
+
+        return
+
+    def _init_normalized_layer_name_to_denormalized_layer_name(self):
+        '''
+        init normalized_layer_name_to_denormalized_layer_name dictionary
+        :return: none
+        '''
+
+        for layer_def in self.layers_list:
+
+            layer_format = layer_def['format']
+            layer_names = layer_def['name/s']
+
+            if layer_format == 'normal':
+                self.normalized_layer_name_to_denormalized_layer_name[layer_names] = layer_names
+
+            elif layer_format == 'siamese_layer_pair':
+                self.normalized_layer_name_to_denormalized_layer_name[layer_names[1]] = layer_names[1]
+                self.normalized_layer_name_to_denormalized_layer_name[layer_names[0]] = layer_names[1]
+
+            elif layer_format == 'siamese_batch_pair':
+                self.normalized_layer_name_to_denormalized_layer_name[layer_names] = layer_names
+
+        return
+
+    def _init_layer_name_to_index_of_saved_image(self):
+        '''
+        init layer_name_to_index_of_saved_image dictionary
+        :return: none
+        '''
+
+        for layer_def in self.layers_list:
+
+            layer_format = layer_def['format']
+            layer_names = layer_def['name/s']
+
+            if layer_format == 'normal':
+                self.layer_name_to_index_of_saved_image[layer_names] = -1
+
+            elif layer_format == 'siamese_layer_pair':
+                self.layer_name_to_index_of_saved_image[layer_names[0]] = 0
+                self.layer_name_to_index_of_saved_image[layer_names[1]] = 1
+
+            elif layer_format == 'siamese_batch_pair':
+                # raise NotImplementedError()
+                self.layer_name_to_index_of_saved_image[layer_names] = -1
+
+        return
+
+    def normalize_layer_name_for_max_tracker(self, layer_name):
+        '''
+        function used to normalize layer name, e.g. 'conv1' and 'conv1_p' will be normalized to 'conv1', given suitable
+        layers_list setting.
+        :param layer_name: layer name to normalize
+        :return: normalized layer name
+        '''
+
+        if self.layer_name_to_normalized_layer_name.has_key(layer_name):
+            return self.layer_name_to_normalized_layer_name[layer_name]
+
+        return layer_name
+
+    def denormalize_layer_name_for_max_tracker(self, normalized_layer_name, selected_input_index):
+        '''
+        function which returns the denormalized form of the layer name, given the normalized layer name and selected
+        input index which should be 0 or 1
+        e.g. denormalize_layer_name_for_max_tracker('conv1', 1) == 'conv1_p', given suitable layers_list setting
+        :param normalized_layer_name: normalized layer name
+        :param selected_input_index: selected input index, 0 or 1
+        :return: the denormalized layer name
+        '''
+
+        if selected_input_index == 0:
+            return normalized_layer_name
+
+        if self.normalized_layer_name_to_denormalized_layer_name.has_key(normalized_layer_name):
+            return self.normalized_layer_name_to_denormalized_layer_name[normalized_layer_name]
+
+        # this can happen for layer names which don't appear in the layers_list setting
+        return normalized_layer_name
+
+    def get_index_of_saved_image_by_layer_name(self, layer_name):
+        '''
+        function which returns the index of image to save (0 or 1) given the layer name
+        e.g. for conv1_p returns 1, for 'conv1' returns 0
+        the decision is done using the layers_list setting used in max tracker
+        :param layer_name: layer name
+        :return index of image in the pair, 0 or 1:
+        '''
+
+        if self.layer_name_to_index_of_saved_image.has_key(layer_name):
+            return self.layer_name_to_index_of_saved_image[layer_name]
+
+        # this can happen for layer names which don't appear in the layers_list setting
+        return -1
+
+    @staticmethod
+    def get_header_from_layer_def(layer_def):
+        '''
+        helper function which returns the header name, given a single layer, or layer pair
+        :param layer: can be either single layer string, or apir of layers
+        :return: header for layer
+        '''
+
+        # if we have only a single layer, the header is the layer name
+
+        if layer_def['format'] == 'normal':
+            return layer_def['name/s']
+
+        elif layer_def['format'] == 'siamese_layer_pair':
+            # build header in format: common_prefix + first_postfix | second_postfix
+            prefix = os.path.commonprefix(layer_def['name/s'])
+            prefix_len = len(prefix)
+            postfix0 = layer_def['name/s'][0][prefix_len:]
+            postfix1 = layer_def['name/s'][1][prefix_len:]
+            header_name = '%s%s|%s' % (prefix, postfix0, postfix1)
+            return header_name
+
+        elif layer_def['format'] == 'siamese_batch_pair':
+            return layer_def['name/s']
+
+    @staticmethod
+    def get_default_layer_name(layer_def):
+        '''
+        get layer name when the caller needs some 'default' choice
+        :param layer_def: layer definition object
+        :return: default_layer_name
+        '''
+
+        if layer_def['format'] == 'normal':
+            default_layer_name = layer_def['name/s']
+
+        elif layer_def['format'] == 'siamese_layer_pair':
+            default_layer_name = layer_def['name/s'][0]
+
+        elif layer_def['format'] == 'siamese_batch_pair':
+            default_layer_name = layer_def['name/s']
+
+        else:
+            raise Exception("get_default_layer_name() got invalid layer_def['format']=%s" % layer_def['format'])
+
+        return default_layer_name
+
+    @staticmethod
+    def _get_single_selected_layer_name(layer_def, siamese_input_mode):
+
+        if layer_def['format'] == 'normal':
+            return layer_def['name/s']
+
+        elif layer_def['format'] == 'siamese_layer_pair':
+            if siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                return layer_def['name/s'][0]
+            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                return layer_def['name/s'][1]
+            else:
+                raise Exception('in get_single_selected_blob() siamese_input_mode cant be BOTH')
+
+        elif layer_def['format'] == 'siamese_batch_pair':
+            return layer_def['name/s']
+
+        else:
+            raise Exception("get_single_selected_blob() got invalid layer_def['format']=%s" % layer_def['format'])
+
+    @staticmethod
+    def _get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector):
+        '''
+        function used to extract the single selected blob according to the specified layer and siamese input mode and
+        blob selector.
+        note that it is invalid to call this function when siamese input mode is BOTH
+        this is the main function which contains logic on the siamese network internal format structure
+        :param net: network containing the blob to extract
+        :param layer_def: layer requested
+        :param siamese_input_mode: siamese input mode
+        :param blob_selector: lambda function which lets us choose between data and diff blobs
+        :return: requested single blob
+        '''
+
+        if layer_def['format'] == 'normal':
+            return blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'])])[0]
+
+        elif layer_def['format'] == 'siamese_layer_pair':
+            if siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                selected_layer_name = layer_def['name/s'][0]
+            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                selected_layer_name = layer_def['name/s'][1]
+            else:
+                raise Exception('in get_single_selected_blob() siamese_input_mode cant be BOTH')
+            return blob_selector(net.blobs[layer_name_to_top_name(net, selected_layer_name)])[0]
+
+        elif layer_def['format'] == 'siamese_batch_pair':
+            if siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                selected_batch_index = 0
+            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                selected_batch_index = 1
+            else:
+                raise Exception('in get_single_selected_blob() siamese_input_mode cant be BOTH')
+            return blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'])])[selected_batch_index]
+
+        else:
+            raise Exception("get_single_selected_blob() got invalid layer_def['format']=%s" % layer_def['format'])
+
+
+    @staticmethod
+    def get_single_selected_data_blob(net, layer_def, siamese_input_mode):
+        '''
+        function used to extract the single selected DATA blob according to the specified layer and siamese input mode
+         note that it is invalid to call this function when siamese input mode is BOTH
+        :param net: network containing the blob to extract
+        :param layer_def: layer requested
+        :param siamese_input_mode: siamese input mode
+        :return: requested single data blob
+        '''
+
+        return SiameseHelper._get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.data)
+
+    @staticmethod
+    def get_single_selected_diff_blob(net, layer_def, siamese_input_mode):
+        '''
+        function used to extract the single selected DIFF blob according to the specified layer and siamese input mode
+         note that it is invalid to call this function when siamese input mode is BOTH
+        :param net: network containing the blob to extract
+        :param layer_def: layer requested
+        :param siamese_input_mode: siamese input mode
+        :return: requested single diff blob
+        '''
+
+        return SiameseHelper._get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.diff)
+
+    @staticmethod
+    def _get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selector):
+        '''
+        function used to extract both blobs according to the specified layer and siamese input mode and
+        blob selector.
+        note that it is invalid to call this function when siamese input mode is not BOTH
+        this is the main function which contains logic on the siamese network internal format structure
+        :param net: network containing the blob to extract
+        :param layer_def: layer requested
+        :param siamese_input_mode: siamese input mode
+        :param blob_selector:
+        :return: first_blob, second_blob
+        '''
+
+        assert siamese_input_mode == SiameseInputMode.BOTH_IMAGES
+
+        if layer_def['format'] == 'normal':
+            raise Exception('function get_siamese_blobs() should not be called when layer is in normal format')
+
+        elif layer_def['format'] == 'siamese_layer_pair':
+            return blob_selector(net.blobs[layer_def['name/s'][0]])[0], blob_selector(net.blobs[layer_def['name/s'][1]])[0]
+
+        elif layer_def['format'] == 'siamese_batch_pair':
+            return blob_selector(net.blobs[layer_def['name/s']])[0], blob_selector(net.blobs[layer_def['name/s']])[1]
+
+        else:
+            raise Exception("get_siamese_blobs() got invalid layer_def['format']=%s" % layer_def['format'])
+
+    @staticmethod
+    def get_siamese_selected_data_blobs(net, layer_def, siamese_input_mode):
+        '''
+        function used to extract both DATA blobs according to the specified layer and siamese input mode
+         note that it is invalid to call this function when siamese input mode is not BOTH
+        :param net: network containing the blob to extract
+        :param layer_def: layer requested
+        :param siamese_input_mode: siamese input mode
+        :return: first_blob, second_blob
+        '''
+
+        return SiameseHelper._get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.data)
+
+    @staticmethod
+    def get_siamese_selected_diff_blobs(net, layer_def, siamese_input_mode):
+        '''
+        function used to extract both DIFF blobs according to the specified layer and siamese input mode
+         note that it is invalid to call this function when siamese input mode is not BOTH
+        :param net: network containing the blob to extract
+        :param layer_def: layer requested
+        :param siamese_input_mode: siamese input mode
+        :return: first_blob, second_blob
+        '''
+
+        return SiameseHelper._get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.diff)
+
+    @staticmethod
+    def is_pair_of_layers(layer_def):
+
+        return layer_def['format'] in ['siamese_layer_pair', 'siamese_batch_pair']
+
+    @staticmethod
+    def siamese_input_mode_has_two_images(layer_def, siamese_input_mode):
+        '''
+        helper function which checks whether the input mode is two images, and the provided layer contains two layer names
+        :param layer: can be a single string layer name, or a pair of layer names
+        :return: True if both the input mode is BOTH_IMAGES and layer contains two layer names, False oherwise
+        '''
+
+        return siamese_input_mode == SiameseInputMode.BOTH_IMAGES and SiameseHelper.is_pair_of_layers(layer_def)
+
+    @staticmethod
+    def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode):
+
+        if SiameseHelper.siamese_input_mode_has_two_images(backprop_layer_def, siamese_input_mode):
+
+            diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_input_mode)
+            diffs0, diffs1 = diffs0 * 0, diffs1 * 0
+            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, backprop_layer_def, siamese_input_mode)
+            diffs0[backprop_unit], diffs1[backprop_unit] = data0[backprop_unit], data1[backprop_unit]
+
+            # add batch dimension
+            diffs0 = expand_dims(diffs0, 0)
+            diffs1 = expand_dims(diffs1, 0)
+
+            if backprop_layer_def['format'] == 'siamese_layer_pair':
+                net.backward_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True)
+                net.backward_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True)
+
+            elif backprop_layer_def['format'] == 'siamese_batch_pair':
+                # combine them to 2-batch and send once
+                diffs = concatenate((diffs0, diffs1), axis=0)
+                net.backward_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True)
+        else:
+
+            diffs = SiameseHelper.get_single_selected_diff_blob(net, backprop_layer_def, siamese_input_mode)
+            diffs = diffs * 0
+            data = SiameseHelper.get_single_selected_data_blob(net, backprop_layer_def, siamese_input_mode)
+            diffs[backprop_unit] = data[backprop_unit]
+
+            # add batch dimension
+            diffs = expand_dims(diffs, 0)
+
+            selected_backprop_layer_name = SiameseHelper._get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
+            net.backward_from_layer(selected_backprop_layer_name, diffs, zero_higher=True)
+
+        pass
+
+    @staticmethod
+    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode):
+
+        if SiameseHelper.siamese_input_mode_has_two_images(backprop_layer_def, siamese_input_mode):
+
+            diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_input_mode)
+            diffs0, diffs1 = diffs0 * 0, diffs1 * 0
+            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, backprop_layer_def, siamese_input_mode)
+            diffs0[backprop_unit], diffs1[backprop_unit] = data0[backprop_unit], data1[backprop_unit]
+
+            # add batch dimension
+            diffs0 = expand_dims(diffs0, 0)
+            diffs1 = expand_dims(diffs1, 0)
+
+            if backprop_layer_def['format'] == 'siamese_layer_pair':
+                net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True)
+                net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True)
+
+            elif backprop_layer_def['format'] == 'siamese_batch_pair':
+                # combine them to 2-batch and send once
+                diffs = concatenate((diffs0, diffs1), axis=0)
+                net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True)
+
+        else:
+
+            diffs = SiameseHelper.get_single_selected_diff_blob(net, backprop_layer_def, siamese_input_mode)
+            diffs = diffs * 0
+            data = SiameseHelper.get_single_selected_data_blob(net, backprop_layer_def, siamese_input_mode)
+            diffs[backprop_unit] = data[backprop_unit]
+
+            # add batch dimension
+            diffs = expand_dims(diffs, 0)
+
+            selected_backprop_layer_name = SiameseHelper._get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
+            net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True)
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 0ce620227..a6615df5f 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -12,6 +12,7 @@
 
 import settings
 from caffevis.caffevis_helper import set_mean
+from siamese_helper import SiameseHelper
 
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
@@ -59,6 +60,12 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    # validate batch size
+    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+        # currently, no batch support for siamese_batch_pair networks
+        # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
+        assert (settings.max_tracker_batch_size == 1)
+
     # set network batch size
     current_input_shape = net.blobs[net.inputs[0]].shape
     current_input_shape[0] = settings.max_tracker_batch_size
@@ -66,14 +73,16 @@ def main():
 
     assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
 
+    siamese_helper = SiameseHelper(settings.layers_list)
+
     with open(args.nmt_pkl, 'rb') as ff:
         nmt = pickle.load(ff)
 
-    for layer in settings.max_tracker_layers_to_output:
+    for layer_name in settings.max_tracker_layers_to_output:
 
-        print 'Started work on layer %s' % (layer)
+        print 'Started work on layer %s' % (layer_name)
 
-        normalized_layer_name = settings.normalize_layer_name_for_max_tracker_fn(layer)
+        normalized_layer_name = siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
         mt = nmt.max_trackers[normalized_layer_name]
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index e886aa511..a7e0d9a28 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -17,8 +17,20 @@
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
 
+def pickle_to_text(pickle_filename):
+
+    with open(pickle_filename, 'rb') as pickle_file:
+        data = pickle.load(pickle_file)
+
+    data_dict = data.__dict__.copy()
+
+    with open(pickle_filename + ".txt", 'wt') as text_file:
+        text_file.write(str(data_dict))
+
+    return
 
 def main():
+
     parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
     parser.add_argument('--N', type = int, default = 9, help = 'note and save top N activations')
     parser.add_argument('--gpu', action = 'store_true', default=settings.caffevis_mode_gpu, help = 'use gpu')
@@ -48,6 +60,12 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    # validate batch size
+    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+        # currently, no batch support for siamese_batch_pair networks
+        # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
+        assert (settings.max_tracker_batch_size == 1)
+
     # set network batch size
     current_input_shape = net.blobs[net.inputs[0]].shape
     current_input_shape[0] = settings.max_tracker_batch_size
@@ -61,6 +79,8 @@ def main():
     with WithTimer('Saving maxes'):
         with open(args.outfile, 'wb') as ff:
             pickle.dump(max_tracker, ff, -1)
+        # save text version of pickle file for easier debugging
+        pickle_to_text(args.outfile)
 
 
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index f78f5dce7..1246e9723 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -7,11 +7,12 @@
 import cv2
 
 import numpy as np
-from image_misc import cv2_read_file_rgb
+from image_misc import cv2_read_file_rgb, resize_without_fit
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
-    compute_data_layer_focus_area
+    compute_data_layer_focus_area, layer_name_to_top_name
 from misc import get_files_from_image_list
 from misc import get_files_from_siamese_image_list
+from siamese_helper import SiameseHelper
 
 from jby_misc import WithTimer
 
@@ -34,7 +35,8 @@ def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_fi
                  backpropnorm_filenames = None, info_file = None, max_idx_0 = None, max_idx = None, im_idx = None,
                  im_class = None, selected_input_index = None, ii = None, jj = None, recorded_val = None,
                  out_ii_start = None, out_ii_end = None, out_jj_start = None, out_jj_end = None, data_ii_start = None,
-                 data_ii_end = None, data_jj_start = None, data_jj_end = None, im = None):
+                 data_ii_end = None, data_jj_start = None, data_jj_end = None, im = None,
+                 denormalized_layer_name = None, denormalized_top_name = None):
         self.cc = cc
         self.channel_idx = channel_idx
         self.info_filename = info_filename
@@ -61,6 +63,9 @@ def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_fi
         self.data_jj_start = data_jj_start
         self.data_jj_end = data_jj_end
         self.im = im
+        self.denormalized_layer_name = denormalized_layer_name
+        self.denormalized_top_name = denormalized_top_name
+
 
 
 class InfoFileMetadata(object):
@@ -81,8 +86,8 @@ def mkdir_p(path):
             raise
 
 
-
 class MaxTracker(object):
+
     def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype = 'float32'):
         self.is_conv = is_conv
         self.max_vals = np.ones((n_channels, n_top), dtype = dtype) * initial_val
@@ -95,7 +100,25 @@ def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype =
         # set of seen inputs, used to avoid updating on the same input twice
         self.seen_inputs = set()
 
-    def update(self, blob, image_idx, image_class, selected_input_index, layer_unique_input_source, batch_index):
+    def __getstate__(self):
+        # Copy the object's state from self.__dict__ which contains
+        # all our instance attributes. Always use the dict.copy()
+        # method to avoid modifying the original state.
+        state = self.__dict__.copy()
+        # Remove the unpicklable entries.
+        del state['seen_inputs']
+        return state
+
+    def __setstate__(self, state):
+        # Restore instance attributes (i.e., filename and lineno).
+        self.__dict__.update(state)
+
+        self.seen_inputs = None
+
+    def __repr__(self):
+        return str(self.__dict__.copy())
+
+    def update(self, data, image_idx, image_class, selected_input_index, layer_unique_input_source, layer_name):
 
         if layer_unique_input_source in self.seen_inputs:
             return
@@ -103,7 +126,6 @@ def update(self, blob, image_idx, image_class, selected_input_index, layer_uniqu
         # add input identifier to seen inputs set
         self.seen_inputs.add(layer_unique_input_source)
 
-        data = blob[batch_index]                              # Note: makes a copy of blob, e.g. (96,55,55)
         n_channels = data.shape[0]
         data_unroll = data.reshape((n_channels, -1))          # Note: no copy eg (96,3025). Does nothing if not is_conv
 
@@ -115,6 +137,7 @@ def update(self, blob, image_idx, image_class, selected_input_index, layer_uniqu
         #insertion_idx = zeros((n_channels,))
         #pdb.set_trace()
         for ii in xrange(n_channels):
+
             idx = np.searchsorted(self.max_vals[ii], data_unroll[ii, maxes[ii]])
             if idx == 0:
                 # Smaller than all 10
@@ -132,7 +155,6 @@ def update(self, blob, image_idx, image_class, selected_input_index, layer_uniqu
                 self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index)
 
 
-
 class NetMaxTracker(object):
     def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'float32'):
         self.layers = layers
@@ -140,20 +162,21 @@ def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'f
         self.n_top = n_top
         self.initial_val = initial_val
         self.settings = settings
+        self.siamese_helper = SiameseHelper(self.settings.layers_list)
 
     def _init_with_net(self, net):
         self.max_trackers = {}
 
-        for layer in self.layers:
+        for layer_name in self.layers:
 
-            top_name = net.top_names[layer][0]
+            top_name = layer_name_to_top_name(net, layer_name)
             blob = net.blobs[top_name].data
 
             # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
             # count as the same layer in terms of activations
-            normalized_layer_name = self.settings.normalize_layer_name_for_max_tracker_fn(layer)
+            normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
-            is_conv = self.settings.is_conv_fn(layer)
+            is_conv = self.settings.is_conv_fn(layer_name)
 
             # only add normalized layer once
             if normalized_layer_name not in self.max_trackers:
@@ -165,34 +188,64 @@ def _init_with_net(self, net):
 
     def update(self, net, image_idx, image_class, net_unique_input_source, batch_index):
         '''Updates the maxes found so far with the state of the given net. If a new max is found, it is stored together with the image_idx.'''
+
         if not self.init_done:
             self._init_with_net(net)
 
-        for layer in self.layers:
+        for layer_name in self.layers:
 
-            top_name = net.top_names[layer][0]
+            print "processing layer %s" % layer_name
+
+            top_name = layer_name_to_top_name(net, layer_name)
             blob = net.blobs[top_name].data
 
-            normalized_layer_name = self.settings.normalize_layer_name_for_max_tracker_fn(layer)
+            normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
-            # in siamese network, we might need to select one of the images from the siamese pair
-            if self.settings.is_siamese:
-                selected_input_index = self.settings.siamese_layer_to_index_of_saved_image_fn(layer)
+            # in siamese network, implemented as pairs of layers, we might need to select one of the images from the siamese pair
+            if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_layer_pair':
+                selected_input_index = self.siamese_helper.get_index_of_saved_image_by_layer_name(layer_name)
 
                 if selected_input_index == 0:
                     # first image identifier is selected
-                    layer_unique_input_source = net_unique_input_source[0]
+                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, selected_input_index,
+                                                                    net_unique_input_source[0], layer_name)
+
                 elif selected_input_index == 1:
                     # second image identifier is selected
-                    layer_unique_input_source = net_unique_input_source[1]
+                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, selected_input_index,
+                                                                    net_unique_input_source[1], layer_name)
+
                 elif selected_input_index == -1:
                     # both images are selected
-                    layer_unique_input_source = net_unique_input_source
-            else:
-                layer_unique_input_source = net_unique_input_source
-                selected_input_index = -1
+                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, selected_input_index,
+                                                                    net_unique_input_source, layer_name)
+
+            # in siamese network, implemented as single layer with batch 2, we might need to select one of the images from the siamese pair
+            elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair':
+
+                assert (self.settings.max_tracker_batch_size == 1)
+
+                # if batch size is 2, then we have two outputs in this layer, so we update both of them
+                if blob.shape[0] == 2:
+
+                    # update first output
+                    self.max_trackers[normalized_layer_name].update(blob[0], image_idx, image_class, 0,
+                                                                    net_unique_input_source[0], layer_name)
+
+                    # update second output
+                    self.max_trackers[normalized_layer_name].update(blob[1], image_idx, image_class, 1,
+                                                                    net_unique_input_source[1], layer_name)
+
+                # we have single output
+                elif blob.shape[0] == 1:
+                    self.max_trackers[normalized_layer_name].update(blob[0], image_idx, image_class, -1,
+                                                                    net_unique_input_source, layer_name)
+
+            else:   # normal, non-siamese network
+                self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, -1, net_unique_input_source, layer_name)
+
+        pass
 
-            self.max_trackers[normalized_layer_name].update(blob, image_idx, image_class, selected_input_index, layer_unique_input_source, batch_index)
 
     def __getstate__(self):
         # Copy the object's state from self.__dict__ which contains
@@ -201,6 +254,7 @@ def __getstate__(self):
         state = self.__dict__.copy()
         # Remove the unpicklable entries.
         del state['settings']
+        del state['siamese_helper']
         return state
 
 
@@ -209,6 +263,7 @@ def __setstate__(self, state):
         self.__dict__.update(state)
 
         self.settings = None
+        self.siamese_helper = None
 
 
 def load_file_list(settings):
@@ -255,7 +310,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top):
 
         with WithTimer('Load image', quiet = not do_print):
             batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename))
-            batch[batch_index].im = cv2.resize(batch[batch_index].im, net_input_dims)
+            batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
             batch[batch_index].im = batch[batch_index].im.astype(np.float32)
 
         batch_index += 1
@@ -314,8 +369,8 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
         with WithTimer('Load image', quiet=not do_print):
             im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
             im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
-            im1 = cv2.resize(im1, net_input_dims)
-            im2 = cv2.resize(im2, net_input_dims)
+            im1 = resize_without_fit(im1, net_input_dims)
+            im2 = resize_without_fit(im2, net_input_dims)
             batch[batch_index].im = np.concatenate((im1, im2), axis=2)
 
         batch_index += 1
@@ -339,7 +394,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
     return tracker
 
 
-def save_representations(settings, net, datadir, filelist, layer, first_N = None):
+def save_representations(settings, net, datadir, filelist, layer_name, first_N = None):
     image_filenames, image_labels = load_file_list(filelist)
     if first_N is None:
         first_N = len(image_filenames)
@@ -365,7 +420,7 @@ def save_representations(settings, net, datadir, filelist, layer, first_N = None
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Store     ', quiet = not do_print):
-            top_name = net.top_names[layer][0]
+            top_name = layer_name_to_top_name(net, layer_name)
             if rep is None:
                 rep_shape = net.blobs[top_name].data[0].shape   # e.g. (256,13,13)
                 rep = np.zeros((len(image_indices),) + rep_shape)   # e.g. (1000,256,13,13)
@@ -409,7 +464,7 @@ def generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_de
     return (info_filename, maxim_filenames, deconv_filenames, deconvnorm_filenames, backprop_filenames, backpropnorm_filenames)
 
 
-def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
+def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
     do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, do_info = do_which
     assert do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm or do_info, 'nothing to do'
 
@@ -428,11 +483,13 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
         print 'Loaded filenames and labels for %d files' % len(image_filenames)
         print '  First file', os.path.join(datadir, image_filenames[0])
 
+    siamese_helper = SiameseHelper(settings.layers_list)
+
     num_top_in_mt = mt.max_locs.shape[1]
     assert num_top <= num_top_in_mt, 'Requested %d top images but MaxTracker contains only %d' % (num_top, num_top_in_mt)
     assert idx_end >= idx_begin, 'Range error'
 
-    size_ii, size_jj = get_max_data_extent(net, layer, rc, mt.is_conv)
+    size_ii, size_jj = get_max_data_extent(net, layer_name, rc, mt.is_conv)
     data_size_ii, data_size_jj = net.blobs['data'].data.shape[2:4]
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
@@ -449,7 +506,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
     n_total_images = (idx_end-idx_begin) * num_top
     for cc, channel_idx in enumerate(range(idx_begin, idx_end)):
 
-        unit_dir = os.path.join(outdir, layer, 'unit_%04d' % channel_idx)
+        unit_dir = os.path.join(outdir, layer_name, 'unit_%04d' % channel_idx)
         mkdir_p(unit_dir)
 
         # check if all required outputs exist, in which case skip this iteration
@@ -467,10 +524,14 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                            backprop_filenames + \
                            backpropnorm_filenames
 
-        # skip if all exist, unless we are in final iteration, in which case we redo it since we need to handle the partially filled batch
+        # we skip generation if:
+        # 1. all outputs exist, AND
+        # 2.1.   (not last iteration OR
+        # 2.2.    last iteration, but batch is empty)
         relevant_outputs_exist = [os.path.exists(file_name) for file_name in relevant_outputs]
-        if all(relevant_outputs_exist) and channel_idx != idx_end - 1:
-            print "skipped generation of channel %d in layer %s since files already exist" % (channel_idx, layer)
+        if all(relevant_outputs_exist) and \
+            ((channel_idx != idx_end - 1) or ((channel_idx == idx_end - 1) and (batch_index == 0))):
+            print "skipped generation of channel %d in layer %s since files already exist" % (channel_idx, layer_name)
             continue
 
         if do_info:
@@ -505,7 +566,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
             batch[batch_index].filename = image_filenames[batch[batch_index].im_idx]
             do_print = (batch[batch_index].max_idx_0 == 0)
             if do_print:
-                print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), batch[batch_index].cc * num_top, n_total_images, layer, batch[batch_index].channel_idx)
+                print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), batch[batch_index].cc * num_top, n_total_images, layer_name, batch[batch_index].channel_idx)
 
             [batch[batch_index].out_ii_start,
              batch[batch_index].out_ii_end,
@@ -515,7 +576,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
              batch[batch_index].data_ii_end,
              batch[batch_index].data_jj_start,
              batch[batch_index].data_jj_end] = \
-                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer,
+                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name,
                                               size_ii, size_jj, data_size_ii, data_size_jj)
 
             if do_info:
@@ -541,8 +602,8 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                     im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
 
                     # resize images according to input dimension
-                    im1 = cv2.resize(im1, net_input_dims)
-                    im2 = cv2.resize(im2, net_input_dims)
+                    im1 = resize_without_fit(im1, net_input_dims)
+                    im2 = resize_without_fit(im2, net_input_dims)
 
                     # concatenate channelwise
                     batch[batch_index].im = np.concatenate((im1, im2), axis=2)
@@ -551,7 +612,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                     batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename))
 
                     # resize images according to input dimension
-                    batch[batch_index].im = cv2.resize(batch[batch_index].im, net_input_dims)
+                    batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
 
                     # convert to float to avoid caffe destroying the image in the scaling phase
                     batch[batch_index].im = batch[batch_index].im.astype(np.float32)
@@ -571,13 +632,23 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
                     # in siamese network, we wish to return from the normalized layer name and selected input index to the
                     # denormalized layer name, e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
-                    denormalized_layer_name = settings.denormalize_layer_name_for_max_tracker_fn(layer, batch[i].selected_input_index)
-                    denormalized_top_name = net.top_names[denormalized_layer_name][0]
+                    batch[i].denormalized_layer_name = siamese_helper.denormalize_layer_name_for_max_tracker(layer_name, batch[i].selected_input_index)
+                    batch[i].denormalized_top_name = layer_name_to_top_name(net, batch[i].denormalized_layer_name)
+
+                    if len(net.blobs[batch[i].denormalized_top_name].data.shape) == 4:
+                        if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+                            reproduced_val = net.blobs[batch[i].denormalized_top_name].data[batch[i].selected_input_index, batch[i].channel_idx, batch[i].ii, batch[i].jj]
+
+                        else: # normal network, or siamese in siamese_layer_pair format
+                            reproduced_val = net.blobs[batch[i].denormalized_top_name].data[i, batch[i].channel_idx, batch[i].ii, batch[i].jj]
 
-                    if len(net.blobs[denormalized_top_name].data.shape) == 4:
-                        reproduced_val = net.blobs[denormalized_top_name].data[i, batch[i].channel_idx, batch[i].ii, batch[i].jj]
                     else:
-                        reproduced_val = net.blobs[denormalized_top_name].data[i, batch[i].channel_idx]
+                        if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+                            reproduced_val = net.blobs[batch[i].denormalized_top_name].data[batch[i].selected_input_index, batch[i].channel_idx]
+
+                        else:  # normal network, or siamese in siamese_layer_pair format
+                            reproduced_val = net.blobs[batch[i].denormalized_top_name].data[i, batch[i].channel_idx]
+
                     if abs(reproduced_val - batch[i].recorded_val) > .1:
                         print 'Warning: recorded value %s is suspiciously different from reproduced value %s. Is the filelist the same?' % (batch[i].recorded_val, reproduced_val)
 
@@ -594,18 +665,34 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
                 if do_deconv or do_deconv_norm:
 
-                    diffs = net.blobs[denormalized_top_name].diff * 0
+                    # TODO: we can improve performance by doing batch of deconv_from_layer, but only if we group
+                    # together instances which have the same selected_input_index, this can be done by holding to
+                    # separate batches
 
                     for i in range(0, batch_index):
-                        if len(diffs.shape) == 4:
-                            diffs[i, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
-                        else:
-                            diffs[i, batch[i].channel_idx] = 1.0
-
-                    with WithTimer('Deconv    ', quiet = not do_print):
-                        net.deconv_from_layer(denormalized_layer_name, diffs)
+                        diffs = net.blobs[batch[i].denormalized_top_name].diff * 0
+
+                        if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+                            if diffs.shape[0] == 2:
+                                if len(diffs.shape) == 4:
+                                    diffs[batch[i].selected_input_index, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
+                                else:
+                                    diffs[batch[i].selected_input_index, batch[i].channel_idx] = 1.0
+                            elif diffs.shape[0] == 1:
+                                if len(diffs.shape) == 4:
+                                    diffs[0, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
+                                else:
+                                    diffs[0, batch[i].channel_idx] = 1.0
+
+                        else: # normal network, or siamese in siamese_layer_pair format
+                            if len(diffs.shape) == 4:
+                                diffs[i, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
+                            else:
+                                diffs[i, batch[i].channel_idx] = 1.0
+
+                        with WithTimer('Deconv    ', quiet = not do_print):
+                            net.deconv_from_layer(batch[i].denormalized_layer_name, diffs)
 
-                    for i in range(0, batch_index):
                         out_arr = extract_patch_from_image(net.blobs['data'].diff[i], net, batch[i].selected_input_index, settings,
                                                            batch[i].data_ii_end, batch[i].data_ii_start, batch[i].data_jj_end, batch[i].data_jj_start,
                                                            batch[i].out_ii_end, batch[i].out_ii_start, batch[i].out_jj_end, batch[i].out_jj_start, size_ii, size_jj)
@@ -624,9 +711,8 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
 
                 if do_backprop or do_backprop_norm:
 
-                    diffs = net.blobs[denormalized_top_name].diff * 0
-
                     for i in range(0, batch_index):
+                        diffs = net.blobs[batch[i].denormalized_top_name].diff * 0
 
                         if len(diffs.shape) == 4:
                             diffs[i, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
@@ -634,7 +720,7 @@ def output_max_patches(settings, max_tracker, net, layer, idx_begin, idx_end, nu
                             diffs[i, batch[i].channel_idx] = 1.0
 
                     with WithTimer('Backward batch  ', quiet = not do_print):
-                        net.backward_from_layer(denormalized_layer_name, diffs)
+                        net.backward_from_layer(batch[i].denormalized_layer_name, diffs)
 
                     for i in range(0, batch_index):
 
diff --git a/input_fetcher.py b/input_fetcher.py
index fbaf3294f..7e1390d04 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -131,6 +131,7 @@ def run(self):
                     # If static file mode was switched to cam mode but cam is still frozen, we need to push the cam frame again
                     if not self.latest_frame_is_from_cam:
 
+                        # todo: implement more interesting combination of using a camera in sieamese mode
                         if self.settings.is_siamese:
                             im = (self.latest_cam_frame, self.latest_cam_frame)
                         else:
diff --git a/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py b/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
index 9df2a34f2..339a5738e 100644
--- a/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
+++ b/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
@@ -20,7 +20,7 @@
 caffevis_labels          = '%DVT_ROOT%/models/bvlc-googlenet/ilsvrc_2012_labels.txt'
 caffevis_jpgvis_layers   = []
 caffevis_prob_layer      = 'prob'
-caffevis_label_layers    = ('loss3/classifier', 'prob')
+caffevis_label_layers    = ['loss3/classifier', 'prob']
 def caffevis_layer_pretty_name_fn(name):
     # Shorten many layer names to fit in control pane (full layer name visible in status bar)
     name = name.replace('conv','c').replace('pool','p').replace('norm','n')
diff --git a/models/squeezenet/settings_local.template-squeezenet.py b/models/squeezenet/settings_local.template-squeezenet.py
index 1d320c15f..71b379ef1 100644
--- a/models/squeezenet/settings_local.template-squeezenet.py
+++ b/models/squeezenet/settings_local.template-squeezenet.py
@@ -22,7 +22,7 @@
 caffevis_labels          = '%DVT_ROOT%/models/squeezenet/ilsvrc_2012_labels.txt'
 caffevis_jpgvis_layers   = []
 caffevis_prob_layer      = 'prob'
-caffevis_label_layers    = ('conv10', 'relu_conv10', 'pool10', 'prob')
+caffevis_label_layers    = ['conv10', 'relu_conv10', 'pool10', 'prob']
 def caffevis_layer_pretty_name_fn(name):
     name = name.replace('fire','f').replace('relu_expand','re').replace('expand','e').replace('concat','c').replace('squeeze','s')
     name = name.replace('1x1_','').replace('1x1','')
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index a6175dd7c..fc0957b21 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -14,7 +14,8 @@
 from misc import mkdir_p, combine_dicts
 from image_misc import saveimagesc, saveimagescc
 
-from caffe_misc import RegionComputer, get_max_data_extent, compute_data_layer_focus_area, extract_patch_from_image
+from caffe_misc import RegionComputer, get_max_data_extent, compute_data_layer_focus_area, extract_patch_from_image, \
+    layer_name_to_top_name
 
 
 class FindParams(object):
@@ -159,12 +160,12 @@ def __str__(self):
 class GradientOptimizer(object):
     '''Finds images by gradient.'''
     
-    def __init__(self, settings, net, batched_data_mean, labels = None, label_layers = None, channel_swap_to_rgb = None):
+    def __init__(self, settings, net, batched_data_mean, labels = None, label_layers = [], channel_swap_to_rgb = None):
         self.settings = settings
         self.net = net
         self.batched_data_mean = batched_data_mean
         self.labels = labels if labels else ['labels not provided' for ii in range(1000)]
-        self.label_layers = label_layers if label_layers else tuple()
+        self.label_layers = label_layers if label_layers else list()
         if channel_swap_to_rgb:
             self.channel_swap_to_rgb = array(channel_swap_to_rgb)
         else:
@@ -220,7 +221,7 @@ def _optimize(self, params, x0, prefix_template):
         is_labeled_unit = params.push_layer in self.label_layers
 
         # Sanity checks for conv vs FC layers
-        top_name = self.net.top_names[params.push_layer][0]
+        top_name = layer_name_to_top_name(self.net, params.push_layer)
         data_shape = self.net.blobs[top_name].data.shape
         assert len(data_shape) in (2,4), 'Expected shape of length 2 (for FC) or 4 (for conv) layers but shape is %s' % repr(data_shape)
         is_conv = (len(data_shape) == 4)
@@ -250,7 +251,7 @@ def _optimize(self, params, x0, prefix_template):
             # 1. Push data through net
             out = self.net.forward_all(data = xx)
             #shownet(net)
-            top_name = self.net.top_names[params.push_layer][0]
+            top_name = layer_name_to_top_name(self.net, params.push_layer)
             acts = self.net.blobs[top_name].data
 
             if not is_conv:
@@ -289,7 +290,7 @@ def _optimize(self, params, x0, prefix_template):
 
 
             # 4. Do backward pass to get gradient
-            top_name = self.net.top_names[params.push_layer][0]
+            top_name = layer_name_to_top_name(self.net, params.push_layer)
             diffs = self.net.blobs[top_name].diff * 0
             if not is_conv:
                 # Promote bc -> bc01
@@ -393,23 +394,26 @@ def _optimize(self, params, x0, prefix_template):
 
         return xx, results, True
 
-    def find_selected_input_index(self, layer):
+    def find_selected_input_index(self, layer_name):
 
-        for item in self.settings.siamese_layers_list:
+        for item in self.settings.layers_list:
 
             # if we have only a single layer, the header is the layer name
-            if type(item) is str and item == layer:
+            if item['format'] == 'normal' and item['name/s'] == layer_name:
                 return -1
 
             # if we got a pair of layers
-            elif (type(item) is tuple) and (len(item) == 2):
+            elif item['format'] == 'siamese_layer_pair':
 
-                if item[0] == layer:
+                if item['name/s'][0] == layer_name:
                     return 0
 
-                if item[1] == layer:
+                if item['name/s'][1] == layer_name:
                     return 1
 
+            elif item['format'] == 'siamese_batch_pair':
+                raise NotImplementedError("in function find_selected_input_index()")
+
         return -1
 
     def generate_output_names(self, batch_index, params, results, prefix_template):
@@ -469,14 +473,14 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
 
                 is_conv = params.layer_is_conv
                 rc = RegionComputer(self.settings.max_tracker_layers_list)
-                layer = params.push_layer
-                size_ii, size_jj = get_max_data_extent(self.net, layer, rc, is_conv)
+                layer_name = params.push_layer
+                size_ii, size_jj = get_max_data_extent(self.net, layer_name, rc, is_conv)
                 data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
 
                 [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
-                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, layer, size_ii, size_jj, data_size_ii, data_size_jj)
+                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
 
-                selected_input_index = self.find_selected_input_index(layer)
+                selected_input_index = self.find_selected_input_index(layer_name)
 
                 out_arr = extract_patch_from_image(results[batch_index].best_xx, self.net, selected_input_index, self.settings,
                                                    data_ii_end, data_ii_start, data_jj_end, data_jj_start,
diff --git a/settings.py b/settings.py
index 61847bfba..cb6c3ee2d 100644
--- a/settings.py
+++ b/settings.py
@@ -38,7 +38,7 @@
 input_updater_sleep_after_read_frame = locals().get('input_updater_sleep_after_read_frame', 1.0/20)
 
 # Input updater thread die after this many seconds without a heartbeat. Useful during debugging to avoid other threads running after main thread has crashed.
-input_updater_heartbeat_required = locals().get('input_updater_heartbeat_required', 15.0)
+input_updater_heartbeat_required = locals().get('input_updater_heartbeat_required', 150.0 if __debug__ else 15.0)
 
 # How long to sleep while waiting for key presses and redraws. Recommendation: 1 (min: 1)
 main_loop_sleep_ms = locals().get('main_loop_sleep_ms', 1)
@@ -116,6 +116,9 @@
 # is the network loaded a siamese network
 is_siamese = locals().get('is_siamese', False)
 
+# siamese network format, can be either 'siamese_layer_pair' or 'siamese_batch_pair'
+siamese_network_format = locals().get('siamese_network_format', 'siamese_layer_pair')
+
 # contains the input mode for reading static images, can be: 'directory', 'image_list'
 static_files_input_mode = locals().get('static_files_input_mode', 'directory')
 
@@ -186,7 +189,10 @@
 # Which layers have channels/neurons corresponding to the order given
 # in the caffevis_labels file? Annotate these units with label text
 # (when those neurons are selected). None to disable.
-caffevis_label_layers = locals().get('caffevis_label_layers', None)
+caffevis_label_layers = locals().get('caffevis_label_layers', [])
+
+# Which layers should we print the channel score. makes sense for fully-connected layers. None to disable.
+caffevis_score_layers = locals().get('caffevis_score_layers', [])
 
 # Which layer to use for displaying class output numbers in left pane
 # (when no neurons are selected). None to disable.
@@ -245,7 +251,7 @@
 # CaffeProc thread dies after this many seconds without a
 # heartbeat. Useful during debugging to avoid other threads running
 # after main thread has crashed.
-caffevis_heartbeat_required = locals().get('caffevis_heartbeat_required', 15.0)
+caffevis_heartbeat_required = locals().get('caffevis_heartbeat_required', 150.0 if __debug__ else 15.0)
 
 # How far to move when using fast left/right/up/down keys
 caffevis_fast_move_dist = locals().get('caffevis_fast_move_dist', 3)
@@ -345,17 +351,6 @@
 # function used to check if a layer is a convolutional
 is_conv_fn = locals().get('is_conv_fn', lambda layer_name: 'conv' in layer_name)
 
-# function to normalize layer name used in NetMaxTracker to combine activations of siamese layers
-# for example, this is used to make activations of layer "conv_1" and "conv_1_p" count the same
-normalize_layer_name_for_max_tracker_fn = locals().get('normalize_layer_name_for_max_tracker_fn', lambda layer_name: layer_name)
-
-# function to denormalize layer name used in NetMaxTracker to split combined activations of siamese layers
-# for example, given layer name "conv_1" and selected_input_index = 1, we might want to return "conv_1_p" to state that the twin layer should be used as source
-denormalize_layer_name_for_max_tracker_fn = locals().get('denormalize_layer_name_for_max_tracker_fn', lambda layer_name, selected_input_index: layer_name)
-
-# function which selects an image from a siamese input pair, given the layer name
-siamese_layer_to_index_of_saved_image_fn = locals().get('siamese_layer_to_index_of_saved_image_fn', lambda layer_name: 0)
-
 # location for max tracker output file
 find_max_acts_output_file = locals().get('find_max_acts_output_file', 'find_max_acts_output.pickled')
 
@@ -403,7 +398,7 @@
 
 # list of siamese layers/blobs to show
 # note: if an item in the list is a pair of layers, then it is a siamese layer
-siamese_layers_list = locals().get('siamese_layers_list', [])
+layers_list = locals().get('layers_list', [])
 
 # rand-seed parameter for optimize_image.py
 optimize_image_rand_seed = locals().get('optimize_image_rand_seed', 0)

From 697f140b530ad3df64c910169e8777f0b05665b9 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:44:16 +0300
Subject: [PATCH 038/106] - added support for maximal input histograms for each
 neuron. the basic data is gathered during max tracker operation, but the
 actual histogram image generation can be done both in the offline script and
 during runtime

---
 bindings.py                    |   4 +-
 caffevis/app.py                | 166 ++++++++++++++++++++++++++++++---
 caffevis/caffevis_app_state.py |  11 +--
 find_maxes/crop_max_patches.py |   5 +-
 find_maxes/find_max_acts.py    |  27 +++++-
 find_maxes/max_tracker.py      | 151 ++++++++++++++++++++++++++++--
 input_fetcher.py               |   2 +-
 settings.py                    |  14 ++-
 8 files changed, 335 insertions(+), 45 deletions(-)

diff --git a/bindings.py b/bindings.py
index dedbe6b08..6847ac45e 100644
--- a/bindings.py
+++ b/bindings.py
@@ -102,9 +102,9 @@ def get_key_help(self, tag):
 _.add('zoom_mode', 'z',
        'Cycle zooming through {currently selected unit, backprop results, none}')
 _.add('next_pattern_mode', 's',
-       'Cycle overlay of preferred input pattern (opt image, max input, off)')
+       'Cycle channels overlay (max opt, max input, max hist, off)')
 _.add('prev_pattern_mode', 'S',
-       'Cycle overlay of preferred input pattern (opt image, max input, off)')
+       'Cycle channels overlay (max opt, max input, max hist, off)')
 _.add('pattern_first_only', '1',
        'Toggle pattern loading first image only or loading all available images')
 
diff --git a/caffevis/app.py b/caffevis/app.py
index d920db63c..bddd55b9f 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -1,20 +1,26 @@
 #! /usr/bin/env python
 # -*- coding: utf-8
 
-import sys
-import os
+# add parent folder to search path, to enable import of core modules like settings
+import os,sys,inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.insert(0,parentdir)
+
 import cv2
 import numpy as np
-import time
 import StringIO
-import glob
+
+from find_maxes.find_max_acts import load_max_tracker_from_file
+import find_maxes.max_tracker
+sys.modules['max_tracker'] = find_maxes.max_tracker
 
 from misc import WithTimer
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
 from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
     ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio, resize_without_fit, \
-    cv2_read_file_rgb, caffe_load_image
+    cv2_read_file_rgb, caffe_load_image, ensure_uint255_and_resize_without_fit
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
@@ -256,9 +262,17 @@ def _draw_status_pane(self, pane):
         fps = self.proc_thread.approx_fps()
         with self.state.lock:
             pattern_first_mode = "first" if self.state.pattern_first_only else "all"
-            print >>status, 'pattern(' + pattern_first_mode + ' optimized max)' if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE else \
-                ('pattern(' + pattern_first_mode + ' input max)' if self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE else
-                ('back' if self.state.layers_show_back else 'fwd')),
+            if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE:
+                print >> status, 'pattern(' + pattern_first_mode + ' optimized max)'
+            elif self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE:
+                print >> status, 'pattern(' + pattern_first_mode + ' input max)'
+            elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
+                print >> status, 'histogram(maximal activations)'
+            elif self.state.layers_show_back:
+                print >> status, 'back'
+            else:
+                print >> status, 'fwd'
+
             default_layer_name = self.state.get_default_layer_name()
             print >>status, '%s:%d |' % (default_layer_name, self.state.selected_unit),
             if not self.state.back_enabled:
@@ -330,6 +344,7 @@ def _draw_layer_pane(self, pane):
         tile_rows, tile_cols = self.state.net_blob_info[top_name]['tiles_rc']
 
         display_3D_highres = None
+        is_layer_summary_loaded = False
         if self.state.pattern_mode != PatternMode.OFF:
             # Show desired patterns loaded from disk
 
@@ -364,6 +379,24 @@ def _draw_layer_pane(self, pane):
                                                                                                tile_cols, tile_rows, self.state.pattern_first_only,
                                                                                                file_search_pattern='maxim*.png')
 
+            elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
+
+                    if self.settings.caffevis_histograms_format == 'load_from_file':
+                        display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
+                                                                                                  layer_dat_3D, n_tiles, pane,
+                                                                                                  tile_cols, tile_rows, True,
+                                                                                                  file_search_pattern='max_histogram.png',
+                                                                                                  show_layer_summary=self.state.cursor_area == 'top',
+                                                                                                  file_summary_pattern='layer_inactivity.png')
+
+                    elif self.settings.caffevis_histograms_format == 'calculate_in_realtime':
+                        display_3D, display_3D_highres = self.load_maximal_activations_histograms(default_layer_name,
+                                                                                                 layer_dat_3D, n_tiles, pane,
+                                                                                                 tile_cols, tile_rows,
+                                                                                                 show_layer_summary=self.state.cursor_area == 'top')
+
+                    is_layer_summary_loaded = display_3D_highres is not None and display_3D_highres.shape[0] == 1
+
 
             else:
                 raise Exception("Invalid value of pattern mode: %d" % self.state.pattern_mode)
@@ -417,7 +450,10 @@ def _draw_layer_pane(self, pane):
             if self.state.backprop_selection_frozen and self.state.get_current_layer_definition() == self.state.get_current_backprop_layer_definition():
                 highlights[self.state.backprop_unit] = self.settings.caffevis_layer_clr_back_sel  # in [0,1] range
 
-        _, display_2D = tile_images_make_tiles(display_3D, hw = (tile_rows,tile_cols), padval = padval, highlights = highlights)
+        if is_layer_summary_loaded:
+            display_2D = display_3D_highres[0]
+        else:
+            _, display_2D = tile_images_make_tiles(display_3D, hw = (tile_rows,tile_cols), padval = padval, highlights = highlights)
 
         if display_3D_highres is None:
             display_3D_highres = display_3D
@@ -504,7 +540,7 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
         return display_3D, display_3D_highres
 
     def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
-                                            tile_cols, tile_rows, first_only, file_search_pattern):
+                                            tile_cols, tile_rows, first_only, file_search_pattern, show_layer_summary = False, file_summary_pattern = ""):
         display_3D_highres = None
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
@@ -514,16 +550,28 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
             # get number of units
             units_num = layer_dat_3D.shape[0]
 
-            pattern_image_key = (self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d", units_num, file_search_pattern, first_only)
+            pattern_image_key = (self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d", units_num, file_search_pattern, first_only, show_layer_summary, file_summary_pattern)
 
             # Get highres version
             display_3D_highres = self.img_cache.get(pattern_image_key, None)
 
             if display_3D_highres is None:
                 try:
-                    with WithTimer('CaffeVisApp:load_first_image_per_unit', quiet=self.debug_level < 1):
-                        resize_shape = pane.data.shape
-                        display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
+
+                    resize_shape = pane.data.shape
+
+                    if show_layer_summary:
+                        # load layer summary image
+                        layer_summary_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, file_summary_pattern)
+                        layer_summary_image = caffe_load_image(layer_summary_image_path, color=True, as_uint=True)
+                        layer_summary_image = ensure_uint255_and_resize_without_fit(layer_summary_image, resize_shape)
+                        display_3D_highres = layer_summary_image
+                        display_3D_highres = np.expand_dims(display_3D_highres, 0)
+
+                    else:
+                        with WithTimer('CaffeVisApp:load_image_per_unit', quiet=self.debug_level < 1):
+                            # load all images
+                            display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
 
                 except IOError:
                     # File does not exist, so just display disabled.
@@ -579,6 +627,96 @@ def downsample_display_3d(self, display_3D_highres, layer_dat_3D, pane, tile_col
             display_3D = layer_dat_3D * 0  # nothing to show
         return display_3D
 
+    def fig2data(self, fig):
+        """
+        @brief Convert a Matplotlib figure to a 3D numpy array with RGB channels and return it
+        @param fig a matplotlib figure
+        @return a numpy 3D array of RGB values
+        """
+        # draw the renderer
+        fig.canvas.draw()
+
+        # Get the RGB buffer from the figure
+        w, h = fig.canvas.get_width_height()
+        buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
+        buf.shape = (w, h, 3)
+
+        return buf
+
+    def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
+
+        display_3D_highres = layer_dat_3D * 0
+        display_3D = layer_dat_3D * 0
+
+        if not self.settings.caffevis_maximum_activation_histogram_data_file:
+            return display_3D_highres, display_3D
+
+        pattern_image_key = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms", show_layer_summary)
+
+        # Get highres version
+        display_3D_highres = self.img_cache.get(pattern_image_key, None)
+
+        pane_shape = pane.data.shape
+
+        if display_3D_highres is None:
+            try:
+                # load pickle file
+                net_max_tracker = load_max_tracker_from_file(self.settings.caffevis_maximum_activation_histogram_data_file)
+
+                if not net_max_tracker.max_trackers.has_key(default_layer_name):
+                    return display_3D_highres, display_3D
+
+                channel_to_histogram = net_max_tracker.max_trackers[default_layer_name].channel_to_histogram
+
+                def channel_to_histogram_values(channel_idx):
+
+                    # get channel data
+                    hist, bin_edges = channel_to_histogram[channel_idx]
+
+                    return hist, bin_edges
+
+                display_3D_highres_list = [display_3D_highres, display_3D_highres]
+
+                def process_channel_figure(channel_idx, fig):
+                    figure_buffer = self.fig2data(fig)
+
+                    # handle first generation of results container
+                    if display_3D_highres_list[0] is None:
+                        first_shape = figure_buffer.shape
+                        display_3D_highres_list[0] = np.zeros((len(channel_to_histogram), first_shape[0],
+                                                       first_shape[1],
+                                                       first_shape[2]), dtype=np.uint8)
+
+                    display_3D_highres_list[0][channel_idx, :, ::] = figure_buffer
+                    pass
+
+                def process_layer_figure(fig):
+                    figure_buffer = self.fig2data(fig)
+                    display_3D_highres_list[1] = ensure_uint255_and_resize_without_fit(figure_buffer, pane_shape)
+                    display_3D_highres_list[1] = np.expand_dims(display_3D_highres_list[1], 0)
+                    pass
+
+                n_channels = len(channel_to_histogram)
+                find_maxes.max_tracker.prepare_histogram(default_layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
+
+                pattern_image_key_layer = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms",True)
+                pattern_image_key_details = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms",False)
+
+                self.img_cache.set(pattern_image_key_details, display_3D_highres_list[0])
+                self.img_cache.set(pattern_image_key_layer, display_3D_highres_list[1])
+
+                if show_layer_summary:
+                    display_3D_highres = display_3D_highres_list[1]
+                else:
+                    display_3D_highres = display_3D_highres_list[0]
+
+            except IOError:
+                # File does not exist, so just display disabled.
+                pass
+
+        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+        return display_3D, display_3D_highres
+
     def _draw_aux_pane(self, pane, layer_data_normalized):
         pane.data[:] = to_255(self.settings.window_background)
 
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 3a818ab6c..8fc27cedd 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -9,7 +9,8 @@ class PatternMode:
     OFF = 0
     MAXIMAL_OPTIMIZED_IMAGE = 1
     MAXIMAL_INPUT_IMAGE = 2
-    NUMBER_OF_MODES = 3
+    MAX_ACTIVATIONS_HISTOGRAM = 3
+    NUMBER_OF_MODES = 4
 
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
@@ -82,7 +83,7 @@ def _reset_user_state(self):
         self.back_enabled = False
         self.back_mode = 'grad'      # 'grad' or 'deconv'
         self.back_filt_mode = 'raw'  # 'raw', 'gray', 'norm', 'normblur'
-        self.pattern_mode = PatternMode.OFF    # type of patterns to show instead of activations in layers pane: maximal optimized image, maximal input image, off
+        self.pattern_mode = PatternMode.OFF    # type of patterns to show instead of activations in layers pane: maximal optimized image, maximal input image, maximal histogram, off
         self.pattern_first_only = True         # should we load only the first pattern image for each neuron, or all the relevant images per neuron
         self.layers_pane_zoom_mode = 0       # 0: off, 1: zoom selected (and show pref in small pane), 2: zoom backprop
         self.layers_show_back = False   # False: show forward activations. True: show backward diffs
@@ -141,14 +142,8 @@ def handle_key(self, key):
                 self.layer_boost_gamma = self.layer_boost_gamma_choices[self.layer_boost_gamma_idx]
             elif tag == 'next_pattern_mode':
                 self.pattern_mode = (self.pattern_mode + 1) % PatternMode.NUMBER_OF_MODES
-                if self.pattern_mode != PatternMode.OFF and not hasattr(self.settings, 'caffevis_unit_jpg_dir'):
-                    print 'Cannot switch to pattern mode; caffevis_unit_jpg_dir not defined in settings.py.'
-                    self.pattern_mode = PatternMode.OFF
             elif tag == 'prev_pattern_mode':
                 self.pattern_mode = (self.pattern_mode - 1 + PatternMode.NUMBER_OF_MODES) % PatternMode.NUMBER_OF_MODES
-                if self.pattern_mode != PatternMode.OFF and not hasattr(self.settings, 'caffevis_unit_jpg_dir'):
-                    print 'Cannot switch to pattern mode; caffevis_unit_jpg_dir not defined in settings.py.'
-                    self.pattern_mode = PatternMode.OFF
             elif tag == 'pattern_first_only':
                 self.pattern_first_only = not self.pattern_first_only
             elif tag == 'show_back':
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index a6615df5f..0af2476a5 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -16,7 +16,7 @@
 
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
-
+from find_max_acts import load_max_tracker_from_file
 
 
 def main():
@@ -75,8 +75,7 @@ def main():
 
     siamese_helper = SiameseHelper(settings.layers_list)
 
-    with open(args.nmt_pkl, 'rb') as ff:
-        nmt = pickle.load(ff)
+    nmt = load_max_tracker_from_file(args.nmt_pkl)
 
     for layer_name in settings.max_tracker_layers_to_output:
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index a7e0d9a28..4f4d56d3f 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -38,6 +38,9 @@ def main():
     parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
     parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
     parser.add_argument('--outfile', type=str, default = settings.find_max_acts_output_file, help='output filename for pkl')
+    parser.add_argument('--outdir', type = str, default = settings.max_tracker_output_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{max_histogram}.png')
+    parser.add_argument('--do-histograms', action = 'store_true', default=settings.max_tracker_do_histograms, help = 'Output histogram image file containing histogrma of max values per channel')
+
     args = parser.parse_args()
 
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
@@ -73,15 +76,29 @@ def main():
 
     with WithTimer('Scanning images'):
         if settings.is_siamese:
-            max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N)
+            net_max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N, args.outdir, args.do_histograms)
         else: # normal operation
-            max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N)
+            net_max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N, args.outdir, args.do_histograms)
+
+    save_max_tracker_to_file(args.outfile, net_max_tracker)
+
+
+def save_max_tracker_to_file(filename, net_max_tracker):
     with WithTimer('Saving maxes'):
-        with open(args.outfile, 'wb') as ff:
-            pickle.dump(max_tracker, ff, -1)
+        with open(filename, 'wb') as ff:
+            pickle.dump(net_max_tracker, ff, -1)
         # save text version of pickle file for easier debugging
-        pickle_to_text(args.outfile)
+        pickle_to_text(filename)
+
+
+def load_max_tracker_from_file(filename):
+
+    import max_tracker
+    # load pickle file
+    with open(filename, 'rb') as tracker_file:
+        net_max_tracker = pickle.load(tracker_file)
 
+    return net_max_tracker
 
 
 if __name__ == '__main__':
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 1246e9723..1d9477e93 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -86,6 +86,69 @@ def mkdir_p(path):
             raise
 
 
+def prepare_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure):
+
+    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+    from matplotlib.figure import Figure
+
+    fig = Figure(figsize=(10, 10))
+    canvas = FigureCanvas(fig)
+    ax = fig.add_subplot(111)
+
+    # for each channel
+    percent_dead = np.zeros((n_channels), dtype=np.float32)
+    for channel_idx in xrange(n_channels):
+
+        hist, bin_edges = channel_to_histogram_values(channel_idx)
+
+        # generate histogram image file
+        width = 0.7 * (bin_edges[1] - bin_edges[0])
+        center = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+        barlist = ax.bar(center, hist, align='center', width=width, color='g')
+
+        for i in range(len(hist)):
+            if 0 >= bin_edges[i] and 0 <= bin_edges[i+1]:
+                # mark dead bar in red
+                barlist[i].set_color('r')
+
+                # save percent dead
+                percent_dead[channel_idx] = 100.0 * hist[i] / sum(hist)
+
+                break
+
+        ax.xaxis.label.set_text('max activation value')
+        ax.yaxis.label.set_text('inputs count')
+
+        process_channel_figure(channel_idx, fig)
+
+        ax.cla()
+
+    # generate histogram for layer
+    num_bins = 20
+    hist, bin_edges = np.histogram(percent_dead, bins=num_bins, range=(0, 100))
+    width = 0.7 * (bin_edges[1] - bin_edges[0])
+    center = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+    bar_colors = [None] * num_bins
+    begin_color = np.array([0, 1.0, 0])
+    end_color = np.array([1.0, 0, 0])
+    color_step = (end_color - begin_color) / (num_bins - 1)
+    current_color = begin_color
+    for i in range(num_bins):
+        bar_colors[i] = tuple(current_color)
+        current_color += color_step
+
+    ax.bar(center, hist, align='center', width=width, color=bar_colors)
+
+    ax.xaxis.label.set_text('percent of inactivity')
+    ax.yaxis.label.set_text('channels count')
+
+    process_layer_figure(fig)
+
+    pass
+
+
 class MaxTracker(object):
 
     def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype = 'float32'):
@@ -100,6 +163,12 @@ def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype =
         # set of seen inputs, used to avoid updating on the same input twice
         self.seen_inputs = set()
 
+        # will hold a list of np array, each containing the max values of all the channels for one input
+        self.all_max_vals = list()
+
+        # keeps a map between channel index and histogram values
+        self.channel_to_histogram = [None] * n_channels
+
     def __getstate__(self):
         # Copy the object's state from self.__dict__ which contains
         # all our instance attributes. Always use the dict.copy()
@@ -107,6 +176,7 @@ def __getstate__(self):
         state = self.__dict__.copy()
         # Remove the unpicklable entries.
         del state['seen_inputs']
+        del state['all_max_vals']
         return state
 
     def __setstate__(self, state):
@@ -114,12 +184,14 @@ def __setstate__(self, state):
         self.__dict__.update(state)
 
         self.seen_inputs = None
+        self.all_max_vals = None
 
     def __repr__(self):
         return str(self.__dict__.copy())
 
     def update(self, data, image_idx, image_class, selected_input_index, layer_unique_input_source, layer_name):
 
+        # if unique_input_source already exist, we can skip the update since we've already seen it
         if layer_unique_input_source in self.seen_inputs:
             return
 
@@ -129,31 +201,75 @@ def update(self, data, image_idx, image_class, selected_input_index, layer_uniqu
         n_channels = data.shape[0]
         data_unroll = data.reshape((n_channels, -1))          # Note: no copy eg (96,3025). Does nothing if not is_conv
 
-        maxes = data_unroll.argmax(1)   # maxes for each channel, eg. (96,)
-
-        # if unique_input_source already exist, we can skip the update since we've already seen it
+        max_indexes = data_unroll.argmax(1)   # maxes for each channel, eg. (96,)
 
+        # add maxes for all channels to a list, bounded to avoid consuming too much memory
+        maxes = data_unroll[range(n_channels), max_indexes]
+        MAX_LIST_SIZE = 10000
+        if len(self.all_max_vals) < MAX_LIST_SIZE:
+            self.all_max_vals.append(maxes)
 
         #insertion_idx = zeros((n_channels,))
         #pdb.set_trace()
         for ii in xrange(n_channels):
 
-            idx = np.searchsorted(self.max_vals[ii], data_unroll[ii, maxes[ii]])
+            idx = np.searchsorted(self.max_vals[ii], data_unroll[ii, max_indexes[ii]])
             if idx == 0:
                 # Smaller than all 10
                 continue
             # Store new max in the proper order. Update both arrays:
             # self.max_vals:
             self.max_vals[ii,:idx-1] = self.max_vals[ii,1:idx]       # shift lower values
-            self.max_vals[ii,idx-1] = data_unroll[ii, maxes[ii]]     # store new max value
+            self.max_vals[ii,idx-1] = data_unroll[ii, max_indexes[ii]]     # store new max value
             # self.max_locs
             self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
             # store new location
             if self.is_conv:
-                self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index) + np.unravel_index(maxes[ii], data.shape[1:])
+                self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
             else:
                 self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index)
 
+    def run_post_processing(self, layer_name, outdir, do_histograms):
+
+        if do_histograms:
+            self.calculate_histogram(layer_name, outdir)
+
+        pass
+
+    def calculate_histogram(self, layer_name, outdir):
+
+        # convert list of arrays to numpy array
+        all_max_array = np.vstack(self.all_max_vals)
+
+        def channel_to_histogram_values(channel_idx):
+            # get values
+            max_for_single_channel = all_max_array[:, channel_idx]
+
+            # create histogram
+            hist, bin_edges = np.histogram(max_for_single_channel, bins=50)
+
+            # save histogram values
+            self.channel_to_histogram[channel_idx] = (hist, bin_edges)
+
+            return hist, bin_edges
+
+        def process_channel_figure(channel_idx, fig):
+            unit_dir = os.path.join(outdir, layer_name, 'unit_%04d' % channel_idx)
+            mkdir_p(unit_dir)
+            filename = os.path.join(unit_dir, 'max_histogram.png')
+            fig.savefig(filename)
+            pass
+
+        def process_layer_figure(fig):
+            filename = os.path.join(outdir, layer_name, 'layer_inactivity.png')
+            fig.savefig(filename)
+            pass
+
+        n_channels = all_max_array.shape[1]
+        prepare_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
+
+        pass
+
 
 class NetMaxTracker(object):
     def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'float32'):
@@ -194,7 +310,7 @@ def update(self, net, image_idx, image_class, net_unique_input_source, batch_ind
 
         for layer_name in self.layers:
 
-            print "processing layer %s" % layer_name
+            # print "processing layer %s" % layer_name
 
             top_name = layer_name_to_top_name(net, layer_name)
             blob = net.blobs[top_name].data
@@ -246,6 +362,17 @@ def update(self, net, image_idx, image_class, net_unique_input_source, batch_ind
 
         pass
 
+    def run_post_processing(self, outdir, do_histograms):
+
+        for layer_name in self.layers:
+
+            # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
+            # count as the same layer in terms of activations
+            normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
+
+            self.max_trackers[normalized_layer_name].run_post_processing(layer_name, outdir, do_histograms)
+
+        pass
 
     def __getstate__(self):
         # Copy the object's state from self.__dict__ which contains
@@ -279,7 +406,7 @@ def load_file_list(settings):
     return available_files, converted_labels
 
 
-def scan_images_for_maxes(settings, net, datadir, n_top):
+def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
     image_filenames, image_labels = load_file_list(settings)
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
@@ -332,11 +459,13 @@ def scan_images_for_maxes(settings, net, datadir, n_top):
 
             batch_index = 0
 
+    tracker.run_post_processing(outdir, do_histograms)
+
     print 'done!'
     return tracker
 
 
-def scan_pairs_for_maxes(settings, net, datadir, n_top):
+def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
     image_filenames, image_labels = load_file_list(settings)
     print 'Scanning %d pairs' % len(image_filenames)
     print '  First pair', image_filenames[0]
@@ -390,6 +519,8 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top):
 
             batch_index = 0
 
+    tracker.run_post_processing(outdir, do_histograms)
+
     print 'done!'
     return tracker
 
@@ -666,7 +797,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                 if do_deconv or do_deconv_norm:
 
                     # TODO: we can improve performance by doing batch of deconv_from_layer, but only if we group
-                    # together instances which have the same selected_input_index, this can be done by holding to
+                    # together instances which have the same selected_input_index, this can be done by holding two
                     # separate batches
 
                     for i in range(0, batch_index):
diff --git a/input_fetcher.py b/input_fetcher.py
index 7e1390d04..5df5cfac3 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -131,7 +131,7 @@ def run(self):
                     # If static file mode was switched to cam mode but cam is still frozen, we need to push the cam frame again
                     if not self.latest_frame_is_from_cam:
 
-                        # todo: implement more interesting combination of using a camera in sieamese mode
+                        # future feature: implement more interesting combination of using a camera in sieamese mode
                         if self.settings.is_siamese:
                             im = (self.latest_cam_frame, self.latest_cam_frame)
                         else:
diff --git a/settings.py b/settings.py
index cb6c3ee2d..e6ba7963e 100644
--- a/settings.py
+++ b/settings.py
@@ -208,6 +208,10 @@
 #   "max_tracker_output" - every unit has a list of images to be loaded
 caffevis_unit_jpg_dir_folder_format = locals().get('caffevis_unit_jpg_dir_folder_format', 'original_combined_single_image')
 
+# the location of the pickle data file which contains the information about the maximum activation histograms
+# usually named 'find_max_acts_output.pickle'
+caffevis_maximum_activation_histogram_data_file = locals().get('caffevis_maximum_activation_histogram_data_file', None)
+
 # List. For which layers should jpgs be loaded for
 # visualization? If a layer name (full name, not prettified) is given
 # here, we will try to load jpgs to visualize each unit. This is used
@@ -251,14 +255,14 @@
 # CaffeProc thread dies after this many seconds without a
 # heartbeat. Useful during debugging to avoid other threads running
 # after main thread has crashed.
-caffevis_heartbeat_required = locals().get('caffevis_heartbeat_required', 150.0 if __debug__ else 15.0)
+caffevis_heartbeat_required = locals().get('caffevis_heartbeat_required', 150.0 if __debug__ else 30.0)
 
 # How far to move when using fast left/right/up/down keys
 caffevis_fast_move_dist = locals().get('caffevis_fast_move_dist', 3)
 # Size of jpg reading cache in bytes (default: 2GB)
 # Note: largest fc6/fc7 images are ~600MB. Cache smaller than this will be painfully slow when using patterns_mode for fc6 and fc7.
 # Cache use when all layers have been loaded is ~1.6GB
-caffevis_jpg_cache_size  = locals().get('caffevis_jpg_cache_size', 2000*1024**2)
+caffevis_jpg_cache_size  = locals().get('caffevis_jpg_cache_size', 4000*1024**2)
 
 caffevis_grad_norm_blur_radius = locals().get('caffevis_grad_norm_blur_radius', 4.0)
 
@@ -333,6 +337,9 @@
 caffevis_score_fsize = locals().get('caffevis_score_fsize', 1.0 * global_font_size)
 caffevis_score_thick = locals().get('caffevis_score_thick', 1)
 
+# how should histograms be loaded: 'calculate_in_realtime' or 'load_from_file'
+caffevis_histograms_format = locals().get('caffevis_histograms_format','calculate_in_realtime')
+
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 
@@ -372,6 +379,9 @@
 # default value for do_info parameter in max tracker
 max_tracker_do_info = locals().get('max_tracker_do_info', True)
 
+# default value for do_histograms parameter in max tracker
+max_tracker_do_histograms = locals().get('max_tracker_do_histograms', True)
+
 # default value for output_dir parameter in max tracker
 max_tracker_output_dir = locals().get('max_tracker_output_dir', './')
 

From 3077e3079c42683a3926b1e246d56835f928078d Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:45:43 +0300
Subject: [PATCH 039/106] fixed crush due to old file format

---
 caffevis/app.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/caffevis/app.py b/caffevis/app.py
index bddd55b9f..5c689db16 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -666,6 +666,11 @@ def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D,
                 if not net_max_tracker.max_trackers.has_key(default_layer_name):
                     return display_3D_highres, display_3D
 
+                # check if
+                if not hasattr(net_max_tracker.max_trackers[default_layer_name], 'channel_to_histogram'):
+                    print "ERROR: file %s is missing the field channel_to_histogram, try rerun find_max_acts to generate it" % (self.settings.caffevis_maximum_activation_histogram_data_file)
+                    return display_3D_highres, display_3D
+
                 channel_to_histogram = net_max_tracker.max_trackers[default_layer_name].channel_to_histogram
 
                 def channel_to_histogram_values(channel_idx):

From cc2219385dbe94abfee5c0597888119eb45ff9e8 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:46:10 +0300
Subject: [PATCH 040/106] fix some other crash in case input file is invalid

---
 caffevis/app.py | 10 +++++-----
 1 file changed, 5 insertions(+), 5 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 5c689db16..cb64fe25c 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -645,11 +645,10 @@ def fig2data(self, fig):
 
     def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
 
-        display_3D_highres = layer_dat_3D * 0
-        display_3D = layer_dat_3D * 0
+        empty_display_3D = layer_dat_3D * 0
 
         if not self.settings.caffevis_maximum_activation_histogram_data_file:
-            return display_3D_highres, display_3D
+            return empty_display_3D, empty_display_3D
 
         pattern_image_key = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms", show_layer_summary)
 
@@ -664,12 +663,12 @@ def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D,
                 net_max_tracker = load_max_tracker_from_file(self.settings.caffevis_maximum_activation_histogram_data_file)
 
                 if not net_max_tracker.max_trackers.has_key(default_layer_name):
-                    return display_3D_highres, display_3D
+                    return empty_display_3D, empty_display_3D
 
                 # check if
                 if not hasattr(net_max_tracker.max_trackers[default_layer_name], 'channel_to_histogram'):
                     print "ERROR: file %s is missing the field channel_to_histogram, try rerun find_max_acts to generate it" % (self.settings.caffevis_maximum_activation_histogram_data_file)
-                    return display_3D_highres, display_3D
+                    return empty_display_3D, empty_display_3D
 
                 channel_to_histogram = net_max_tracker.max_trackers[default_layer_name].channel_to_histogram
 
@@ -716,6 +715,7 @@ def process_layer_figure(fig):
                     display_3D_highres = display_3D_highres_list[0]
 
             except IOError:
+                return empty_display_3D, empty_display_3D
                 # File does not exist, so just display disabled.
                 pass
 

From bab2f1cd8b06ed701f1b7b6649df1262d4a6960b Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:46:25 +0300
Subject: [PATCH 041/106] added progress report to histogram calculation

---
 find_maxes/max_tracker.py | 5 +++++
 1 file changed, 5 insertions(+)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 1d9477e93..7d6db48d2 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -99,6 +99,9 @@ def prepare_histogram(layer_name, n_channels, channel_to_histogram_values, proce
     percent_dead = np.zeros((n_channels), dtype=np.float32)
     for channel_idx in xrange(n_channels):
 
+        if channel_idx % 100 == 0:
+            print "calculating histogram for channel %d our of %d" % (channel_idx, n_channels)
+
         hist, bin_edges = channel_to_histogram_values(channel_idx)
 
         # generate histogram image file
@@ -364,7 +367,9 @@ def update(self, net, image_idx, image_class, net_unique_input_source, batch_ind
 
     def run_post_processing(self, outdir, do_histograms):
 
+        print "run_post_processing on network"
         for layer_name in self.layers:
+            print "run_post_processing on layer %s" % layer_name
 
             # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
             # count as the same layer in terms of activations

From f1fe6d3ec0416e3d8fe24036dbcc2604f15e5fc6 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:47:18 +0300
Subject: [PATCH 042/106] - fixed size of selected activation - fixed size of
 actual activations

---
 caffevis/app.py           | 25 ++++++++++++++++++++-----
 find_maxes/max_tracker.py |  2 +-
 settings.py               |  2 ++
 3 files changed, 23 insertions(+), 6 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index cb64fe25c..dfc5048a4 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -463,14 +463,23 @@ def _draw_layer_pane(self, pane):
         assert state_layers_pane_zoom_mode in (0,1,2)
         if state_layers_pane_zoom_mode == 0:
             # Mode 0: normal display (activations or patterns)
-            display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape)
+            if self.settings.caffevis_keep_aspect_ratio:
+                display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape)
+            else:
+                display_2D_resize = ensure_uint255_and_resize_without_fit(display_2D, pane.data.shape)
         elif state_layers_pane_zoom_mode == 1:
             # Mode 1: zoomed selection
             unit_data = display_3D_highres[self.state.selected_unit]
-            display_2D_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
+            if self.settings.caffevis_keep_aspect_ratio:
+                display_2D_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
+            else:
+                display_2D_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
         else:
             # Mode 2: zoomed backprop pane
-            display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape) * 0
+            if self.settings.caffevis_keep_aspect_ratio:
+                display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape) * 0
+            else:
+                display_2D_resize = ensure_uint255_and_resize_without_fit(display_2D, pane.data.shape) * 0
 
         pane.data[:] = to_255(self.settings.window_background)
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
@@ -734,7 +743,10 @@ def _draw_aux_pane(self, pane, layer_data_normalized):
                 
         if mode == 'selected':
             unit_data = layer_data_normalized[self.state.selected_unit]
-            unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
+            if self.settings.caffevis_keep_aspect_ratio:
+                unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
+            else:
+                unit_data_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
             pane.data[0:unit_data_resize.shape[0], 0:unit_data_resize.shape[1], :] = unit_data_resize
         elif mode == 'prob_labels':
             self._draw_prob_labels_pane(pane)
@@ -839,7 +851,10 @@ def do_norm_blur(grad_img):
 
             grad_img_disp = grad_img
 
-            grad_img_resize = ensure_uint255_and_resize_to_fit(grad_img_disp, pane.data.shape)
+            if self.settings.caffevis_keep_aspect_ratio:
+                grad_img_resize = ensure_uint255_and_resize_to_fit(grad_img_disp, pane.data.shape)
+            else:
+                grad_img_resize = ensure_uint255_and_resize_without_fit(grad_img_disp, pane.data.shape)
             pane.data[0:grad_img_resize.shape[0], 0:grad_img_resize.shape[1], :] = grad_img_resize
 
     def _draw_jpgvis_pane(self, pane):
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 7d6db48d2..13d694756 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -100,7 +100,7 @@ def prepare_histogram(layer_name, n_channels, channel_to_histogram_values, proce
     for channel_idx in xrange(n_channels):
 
         if channel_idx % 100 == 0:
-            print "calculating histogram for channel %d our of %d" % (channel_idx, n_channels)
+            print "calculating histogram for channel %d out of %d" % (channel_idx, n_channels)
 
         hist, bin_edges = channel_to_histogram_values(channel_idx)
 
diff --git a/settings.py b/settings.py
index e6ba7963e..86d96ea4f 100644
--- a/settings.py
+++ b/settings.py
@@ -285,6 +285,8 @@
 # Initially show jpg vis or not (toggle with default key '9')
 caffevis_init_show_unit_jpgs = locals().get('caffevis_init_show_unit_jpgs', True)
 
+caffevis_keep_aspect_ratio = locals().get('caffevis_keep_aspect_ratio', False)
+
 # extra pixel spacing between lines. Default: 4 = not much space / tight layout
 caffevis_control_line_spacing = locals().get('caffevis_control_line_spacing', 4)
 # Font settings for control pane (list of layers)

From 912e52acd3148358b156db779522e15104456462 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:47:42 +0300
Subject: [PATCH 043/106] - cleared images with 0 max value

---
 caffevis/caffevis_helper.py        |  9 ++++++++-
 caffevis/jpg_vis_loading_thread.py | 25 ++++++++++++++-----------
 2 files changed, 22 insertions(+), 12 deletions(-)

diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index 0a826c550..f15b2f340 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,4 +1,5 @@
 #! /usr/bin/env python
+from sys import float_info
 
 import numpy as np
 import os
@@ -203,7 +204,7 @@ def set_mean(caffevis_data_mean, generate_channelwise_mean, net):
     return data_mean
 
 
-def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions = []):
+def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions = [], values = []):
     try:
 
         # list unit images
@@ -222,6 +223,12 @@ def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resi
         unit_images = [caffe_load_image(unit_image_path, color=True, as_uint=True) for unit_image_path in
                        unit_images_path]
 
+        # clear images with 0 value
+        if values:
+            for i in range(len(values)):
+                if values[i] < float_info.epsilon:
+                    unit_images[i] *= 0
+
         if should_crop_to_corner:
             unit_images = [crop_to_corner(img, 2) for img in unit_images]
 
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 737a9cfba..59f9edc50 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -50,7 +50,7 @@ def load_image_into_pane_original_format(self, state_layer_name, state_selected_
             images[image_index_to_set] = np.zeros((resize_shape[0], resize_shape[1], 3), dtype=np.uint8)
             pass
 
-    def prepare_score_captions_for_max_input_images(self, state_layer_name, state_selected_unit):
+    def get_score_values_for_max_input_images(self, state_layer_name, state_selected_unit):
 
         info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
                                         "unit_%04d" % (state_selected_unit),
@@ -64,21 +64,18 @@ def prepare_score_captions_for_max_input_images(self, state_layer_name, state_se
             lines = lines[1:]
 
             # take second word from each line, and convert to float
-            second_column = [float(line.split(' ')[1]) for line in lines]
+            values = [float(line.split(' ')[1]) for line in lines]
 
-            # convert to string with 2 decimal digits
-            captions = [('%.2f' % value) for value in second_column]
-
-        return captions
+        return values
 
     def load_image_into_pane_max_tracker_format(self, state_layer_name, state_selected_unit, resize_shape, images,
-                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False, captions = []):
+                                                file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False, captions = [], values = []):
 
         unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
                                         "unit_%04d" % (state_selected_unit),
                                         file_search_pattern)
 
-        images[image_index_to_set] = get_image_from_files(self.settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions)
+        images[image_index_to_set] = get_image_from_files(self.settings, unit_folder_path, should_crop_to_corner, resize_shape, first_only, captions, values)
         return
 
     def run(self):
@@ -143,13 +140,16 @@ def run(self):
 
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
 
+                # convert to string with 2 decimal digits
+                values = self.get_score_values_for_max_input_images(state_layer_name, state_selected_unit)
+
                 if self.state.show_maximal_score:
-                    captions = self.prepare_score_captions_for_max_input_images(state_layer_name, state_selected_unit)
+                    captions = [('%.2f' % value) for value in values]
                 else:
                     captions = []
                 self.load_image_into_pane_max_tracker_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='maxim*.png',
-                                                             image_index_to_set=1, captions=captions)
+                                                             image_index_to_set=1, captions=captions, values=values)
 
 
             if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
@@ -161,9 +161,12 @@ def run(self):
                                                           image_index_to_set=2)
 
             elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                # convert to string with 2 decimal digits
+                values = self.get_score_values_for_max_input_images(state_layer_name, state_selected_unit)
+
                 self.load_image_into_pane_max_tracker_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='deconv*.png',
-                                                             image_index_to_set=2)
+                                                             image_index_to_set=2, values=values)
 
             # Prune images that were not found:
             images = [im for im in images if im is not None]

From 2b63b16f92f8a7a54da54479cdcbc5f95e507112 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:48:53 +0300
Subject: [PATCH 044/106] - added weights histogram view

---
 caffevis/app.py                | 132 ++++++++++++++++++++++++++++++++-
 caffevis/caffevis_app_state.py |   5 +-
 find_maxes/max_tracker.py      |   4 +-
 3 files changed, 136 insertions(+), 5 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index dfc5048a4..c2502e52a 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -266,6 +266,8 @@ def _draw_status_pane(self, pane):
                 print >> status, 'pattern(' + pattern_first_mode + ' optimized max)'
             elif self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE:
                 print >> status, 'pattern(' + pattern_first_mode + ' input max)'
+            elif self.state.pattern_mode == PatternMode.WEIGHTS_HISTOGRAM:
+                print >> status, 'histogram(weights)'
             elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
                 print >> status, 'histogram(maximal activations)'
             elif self.state.layers_show_back:
@@ -379,6 +381,16 @@ def _draw_layer_pane(self, pane):
                                                                                                tile_cols, tile_rows, self.state.pattern_first_only,
                                                                                                file_search_pattern='maxim*.png')
 
+            elif self.state.pattern_mode == PatternMode.WEIGHTS_HISTOGRAM:
+
+                    display_3D, display_3D_highres = self.load_weights_histograms(self.net,
+                                                                                  default_layer_name,
+                                                                                  layer_dat_3D, n_tiles, pane,
+                                                                                  tile_cols, tile_rows,
+                                                                                  show_layer_summary=self.state.cursor_area == 'top')
+
+                    is_layer_summary_loaded = display_3D_highres is not None and display_3D_highres.shape[0] == 1
+
             elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
 
                     if self.settings.caffevis_histograms_format == 'load_from_file':
@@ -652,6 +664,124 @@ def fig2data(self, fig):
 
         return buf
 
+
+    def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
+
+        empty_display_3D = layer_dat_3D * 0
+        # empty_display_3D = np.expand_dims(empty_display_3D, 0)
+
+        pattern_image_key = (layer_name, "weights_histogram", show_layer_summary)
+
+        # Get highres version
+        display_3D_highres = self.img_cache.get(pattern_image_key, None)
+
+        pane_shape = pane.data.shape
+
+        if display_3D_highres is None:
+            try:
+
+                n_channels = n_tiles
+
+                # check if layer has weights at all
+                if not net.params.has_key(layer_name):
+                    return empty_display_3D, empty_display_3D
+
+                from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+                from matplotlib.figure import Figure
+
+                fig = Figure(figsize=(10, 10))
+                canvas = FigureCanvas(fig)
+                ax = fig.add_subplot(111)
+
+                # for each channel
+                for channel_idx in xrange(n_channels):
+
+                    if channel_idx % 100 == 0:
+                        print "calculating weights histogram for channel %d out of %d" % (channel_idx, n_channels)
+
+                    # get vector of weights
+                    weights = net.params[layer_name][0].data[channel_idx].flatten()
+                    bias = net.params[layer_name][1].data[channel_idx]
+
+                    # create histogram
+                    hist, bin_edges = np.histogram(weights, bins=50)
+
+                    # generate histogram image file
+                    width = 0.7 * (bin_edges[1] - bin_edges[0])
+                    center = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+                    ax.bar(center, hist, align='center', width=width, color='g')
+
+                    fig.suptitle('weights for unit %d, bias is %f' % (channel_idx, bias))
+                    ax.xaxis.label.set_text('weight value')
+                    ax.yaxis.label.set_text('count')
+
+                    figure_buffer = self.fig2data(fig)
+
+                    # handle first generation of results container
+                    if display_3D_highres is None:
+                        first_shape = figure_buffer.shape
+                        display_3D_highres = np.zeros((n_channels, first_shape[0], first_shape[1], first_shape[2]), dtype=np.uint8)
+
+                    display_3D_highres[channel_idx, :, ::] = figure_buffer
+
+                    ax.cla()
+
+                half_pane_shape = (pane_shape[0], pane_shape[1]/2)
+
+                # generate weights histogram for layer
+                weights = net.params[layer_name][0].data.flatten()
+                hist, bin_edges = np.histogram(weights, bins=50)
+
+                width = 0.7 * (bin_edges[1] - bin_edges[0])
+                center = (bin_edges[:-1] + bin_edges[1:]) / 2
+                ax.bar(center, hist, align='center', width=width, color='g')
+
+                fig.suptitle('weights for layer %s' % layer_name)
+                ax.xaxis.label.set_text('weight value')
+                ax.yaxis.label.set_text('count')
+
+                figure_buffer = self.fig2data(fig)
+                display_3D_highres_summary_weights = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
+
+                ax.cla()
+
+                # generate bias histogram for layer
+                bias = net.params[layer_name][1].data.flatten()
+                hist, bin_edges = np.histogram(bias, bins=50)
+
+                width = 0.7 * (bin_edges[1] - bin_edges[0])
+                center = (bin_edges[:-1] + bin_edges[1:]) / 2
+                ax.bar(center, hist, align='center', width=width, color='g')
+
+                fig.suptitle('bias for layer %s' % layer_name)
+                ax.xaxis.label.set_text('bias value')
+                ax.yaxis.label.set_text('count')
+
+                figure_buffer = self.fig2data(fig)
+                display_3D_highres_summary_bias = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
+
+                display_3D_highres_summary = np.concatenate((display_3D_highres_summary_weights, display_3D_highres_summary_bias), axis=1)
+                display_3D_highres_summary = np.expand_dims(display_3D_highres_summary, 0)
+
+                pattern_image_key_layer = (layer_name, "weights_histogram", True)
+                pattern_image_key_details = (layer_name, "weights_histogram", False)
+
+                self.img_cache.set(pattern_image_key_details, display_3D_highres)
+                self.img_cache.set(pattern_image_key_layer, display_3D_highres_summary)
+
+                if show_layer_summary:
+                    display_3D_highres = display_3D_highres_summary
+
+            except IOError:
+                return empty_display_3D, empty_display_3D
+                # File does not exist, so just display disabled.
+                pass
+
+        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+        return display_3D, display_3D_highres
+
+
     def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
 
         empty_display_3D = layer_dat_3D * 0
@@ -710,7 +840,7 @@ def process_layer_figure(fig):
                     pass
 
                 n_channels = len(channel_to_histogram)
-                find_maxes.max_tracker.prepare_histogram(default_layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
+                find_maxes.max_tracker.prepare_max_histogram(default_layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
 
                 pattern_image_key_layer = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms",True)
                 pattern_image_key_details = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms",False)
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 8fc27cedd..ead9da5fe 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -9,8 +9,9 @@ class PatternMode:
     OFF = 0
     MAXIMAL_OPTIMIZED_IMAGE = 1
     MAXIMAL_INPUT_IMAGE = 2
-    MAX_ACTIVATIONS_HISTOGRAM = 3
-    NUMBER_OF_MODES = 4
+    WEIGHTS_HISTOGRAM = 3
+    MAX_ACTIVATIONS_HISTOGRAM = 4
+    NUMBER_OF_MODES = 5
 
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 13d694756..888310630 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -86,7 +86,7 @@ def mkdir_p(path):
             raise
 
 
-def prepare_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure):
+def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure):
 
     from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
     from matplotlib.figure import Figure
@@ -269,7 +269,7 @@ def process_layer_figure(fig):
             pass
 
         n_channels = all_max_array.shape[1]
-        prepare_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
+        prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
 
         pass
 

From 4408a57b47eb1c50eca6c9a56a480254012fb513 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:49:25 +0300
Subject: [PATCH 045/106] - added proof of concept for web ui

---
 deep-visualization-toolbox/run_webui.py       | 72 +++++++++++++++++++
 .../templates/index.html                      |  9 +++
 2 files changed, 81 insertions(+)
 create mode 100644 deep-visualization-toolbox/run_webui.py
 create mode 100644 deep-visualization-toolbox/templates/index.html

diff --git a/deep-visualization-toolbox/run_webui.py b/deep-visualization-toolbox/run_webui.py
new file mode 100644
index 000000000..47ad22908
--- /dev/null
+++ b/deep-visualization-toolbox/run_webui.py
@@ -0,0 +1,72 @@
+#! /usr/bin/env python
+
+import os
+import thread
+from live_vis import LiveVis
+from bindings import bindings
+try:
+    import settings
+except:
+    print '\nError importing settings.py. Check the error message below for more information.'
+    print "If you haven't already, you'll want to copy one of the settings_local.template-*.py files"
+    print 'to settings_local.py and edit it to point to your caffe checkout. E.g. via:'
+    print
+    print '  $ cp models/caffenet_yos/settings_local_template_caffenet_yos.py settings_local.py'
+    print '  $ < edit settings_local.py >\n'
+    raise
+
+if not os.path.exists(settings.caffevis_caffe_root):
+    raise Exception('ERROR: Set caffevis_caffe_root in settings.py first.')
+
+import cv2
+from flask import Flask, render_template, Response
+
+app = Flask(__name__)
+
+@app.route('/')
+def index():
+    return render_template('index.html')
+
+
+def gen():
+    while True:
+        frame = get_frame()
+        yield (b'--frame\r\n'
+               b'Content-Type: image/jpeg\r\n\r\n' + frame + b'\r\n\r\n')
+
+
+def get_frame():
+    # We are using Motion JPEG, but OpenCV defaults to capture raw images,
+    # so we must encode it into JPEG in order to correctly display the
+    # video stream.
+
+    global lv
+
+    ret, jpeg = cv2.imencode('.jpg', lv.window_buffer[:,:,::-1])
+    return jpeg.tobytes()
+
+
+@app.route('/video_feed')
+def video_feed():
+    return Response(gen(), mimetype='multipart/x-mixed-replace; boundary=frame')
+
+if __name__ == '__main__':
+
+    global lv
+
+    def someFunc():
+        print "someFunc was called"
+        lv.run_loop()
+
+
+    if os.environ.get("WERKZEUG_RUN_MAIN") == "true":
+        # The reloader has already run - do what you want to do here
+
+        lv = LiveVis(settings)
+        help_keys, _ = bindings.get_key_help('help_mode')
+        quit_keys, _ = bindings.get_key_help('quit')
+        print '\n\nRunning toolbox. Push %s for help or %s to quit.\n\n' % (help_keys[0], quit_keys[0])
+
+        thread.start_new_thread(someFunc, ())
+
+    app.run(host='127.0.0.1', debug=True)
diff --git a/deep-visualization-toolbox/templates/index.html b/deep-visualization-toolbox/templates/index.html
new file mode 100644
index 000000000..b4dc90eff
--- /dev/null
+++ b/deep-visualization-toolbox/templates/index.html
@@ -0,0 +1,9 @@
+<html>
+  <head>
+    <title>Video Streaming Demonstration</title>
+  </head>
+  <body>
+    <h1>Video Streaming Demonstration</h1>
+    <img id="bg" src="{{ url_for('video_feed') }}">
+  </body>
+</html>
\ No newline at end of file

From cc7ccc475d84875747e3641b815e229112d7fb7d Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:57:49 +0300
Subject: [PATCH 046/106] - extracted new method: _add_label_or_score_overlay
 to simplified code structure - fixed crash when showing zoom mode while layer
 summary is presented

---
 caffevis/app.py | 28 +++++++++++++++++++---------
 1 file changed, 19 insertions(+), 9 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index c2502e52a..8638bddc5 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -479,33 +479,45 @@ def _draw_layer_pane(self, pane):
                 display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape)
             else:
                 display_2D_resize = ensure_uint255_and_resize_without_fit(display_2D, pane.data.shape)
-        elif state_layers_pane_zoom_mode == 1:
+
+        elif state_layers_pane_zoom_mode == 1 and not is_layer_summary_loaded:
             # Mode 1: zoomed selection
             unit_data = display_3D_highres[self.state.selected_unit]
             if self.settings.caffevis_keep_aspect_ratio:
                 display_2D_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
             else:
                 display_2D_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
-        else:
+
+        elif state_layers_pane_zoom_mode == 2 and not is_layer_summary_loaded:
             # Mode 2: zoomed backprop pane
             if self.settings.caffevis_keep_aspect_ratio:
                 display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape) * 0
             else:
                 display_2D_resize = ensure_uint255_and_resize_without_fit(display_2D, pane.data.shape) * 0
 
+        else:   # any other case = zoom_mode + is_layer_summary_loaded
+            if self.settings.caffevis_keep_aspect_ratio:
+                display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape)
+            else:
+                display_2D_resize = ensure_uint255_and_resize_without_fit(display_2D, pane.data.shape)
         pane.data[:] = to_255(self.settings.window_background)
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
 
+        self._add_label_or_score_overlay(default_layer_name, pane)
+            
+        return display_3D_highres
+
+    def _add_label_or_score_overlay(self, default_layer_name, pane):
         # check if label or score should be presented for the selected layer
         if (default_layer_name in self.settings.caffevis_label_layers and self.state.cursor_area == 'bottom') or \
-           (default_layer_name in self.settings.caffevis_score_layers and self.state.cursor_area == 'bottom'):
+                (default_layer_name in self.settings.caffevis_score_layers and self.state.cursor_area == 'bottom'):
 
             # Display label annotation atop layers pane (e.g. for fc8/prob)
-            defaults = {'face':  getattr(cv2, self.settings.caffevis_label_face),
+            defaults = {'face': getattr(cv2, self.settings.caffevis_label_face),
                         'fsize': self.settings.caffevis_label_fsize,
-                        'clr':   to_255(self.settings.caffevis_label_clr),
+                        'clr': to_255(self.settings.caffevis_label_clr),
                         'thick': self.settings.caffevis_label_thick}
-            loc_base = self.settings.caffevis_label_loc[::-1]   # Reverse to OpenCV c,r order
+            loc_base = self.settings.caffevis_label_loc[::-1]  # Reverse to OpenCV c,r order
 
             text_to_display = ""
             if (self.labels) and (default_layer_name in self.settings.caffevis_label_layers):
@@ -516,7 +528,7 @@ def _draw_layer_pane(self, pane):
                 if self.state.siamese_input_mode_has_two_images():
                     blob1, blob2 = self.state.get_siamese_selected_data_blobs(self.net)
                     value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
-                    text_to_display += str(value1) + " " +  str(value2)
+                    text_to_display += str(value1) + " " + str(value2)
 
                 else:
                     blob = self.state.get_single_selected_data_blob(self.net)
@@ -525,8 +537,6 @@ def _draw_layer_pane(self, pane):
 
             lines = [FormattedString(text_to_display, defaults)]
             cv2_typeset_text(pane.data, lines, loc_base)
-            
-        return display_3D_highres
 
     def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
                                             tile_cols, tile_rows):

From 563bced785579dd8f91fb6eb695eca93dd625053 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Tue, 19 Sep 2017 23:58:39 +0300
Subject: [PATCH 047/106] - extracted new function
 _display_pane_based_on_zoom_mode to organize the code

---
 caffevis/app.py | 17 ++++++++++-------
 1 file changed, 10 insertions(+), 7 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 8638bddc5..82549f1c1 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -469,10 +469,17 @@ def _draw_layer_pane(self, pane):
 
         if display_3D_highres is None:
             display_3D_highres = display_3D
-        
+
+        self._display_pane_based_on_zoom_mode(display_2D, display_3D_highres, is_layer_summary_loaded, pane)
+
+        self._add_label_or_score_overlay(default_layer_name, pane)
+            
+        return display_3D_highres
+
+    def _display_pane_based_on_zoom_mode(self, display_2D, display_3D_highres, is_layer_summary_loaded, pane):
         # Display pane based on layers_pane_zoom_mode
         state_layers_pane_zoom_mode = self.state.layers_pane_zoom_mode
-        assert state_layers_pane_zoom_mode in (0,1,2)
+        assert state_layers_pane_zoom_mode in (0, 1, 2)
         if state_layers_pane_zoom_mode == 0:
             # Mode 0: normal display (activations or patterns)
             if self.settings.caffevis_keep_aspect_ratio:
@@ -495,7 +502,7 @@ def _draw_layer_pane(self, pane):
             else:
                 display_2D_resize = ensure_uint255_and_resize_without_fit(display_2D, pane.data.shape) * 0
 
-        else:   # any other case = zoom_mode + is_layer_summary_loaded
+        else:  # any other case = zoom_mode + is_layer_summary_loaded
             if self.settings.caffevis_keep_aspect_ratio:
                 display_2D_resize = ensure_uint255_and_resize_to_fit(display_2D, pane.data.shape)
             else:
@@ -503,10 +510,6 @@ def _draw_layer_pane(self, pane):
         pane.data[:] = to_255(self.settings.window_background)
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
 
-        self._add_label_or_score_overlay(default_layer_name, pane)
-            
-        return display_3D_highres
-
     def _add_label_or_score_overlay(self, default_layer_name, pane):
         # check if label or score should be presented for the selected layer
         if (default_layer_name in self.settings.caffevis_label_layers and self.state.cursor_area == 'bottom') or \

From f091e94f51fe454a708b413a923262d064533aa3 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 00:00:22 +0300
Subject: [PATCH 048/106] - improved weights histogram loading time by caching
 the 2d results into image files and load them on the fly - refactored
 function _draw_layer_pane

---
 caffevis/app.py | 373 ++++++++++++++++++++++++++++++------------------
 1 file changed, 234 insertions(+), 139 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 82549f1c1..a843b659f 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -27,7 +27,7 @@
 from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
-from caffe_misc import layer_name_to_top_name
+from caffe_misc import layer_name_to_top_name, save_caffe_image
 
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
@@ -153,7 +153,7 @@ def draw(self, panes):
         with self.state.lock:
             # Hold lock throughout drawing
             do_draw = self.state.drawing_stale and self.state.caffe_net_state == 'free'
-            #print 'CaffeProcThread.draw: caffe_net_state is:', self.state.caffe_net_state
+            # print 'CaffeProcThread.draw: caffe_net_state is:', self.state.caffe_net_state
             if do_draw:
                 self.state.caffe_net_state = 'draw'
 
@@ -301,7 +301,26 @@ def _draw_status_pane(self, pane):
 
         cv2_typeset_text(pane.data, strings, loc,
                          line_spacing = self.settings.caffevis_status_line_spacing)
-    
+
+    def prepare_tile_image(self, display_3D, highlight_selected, n_tiles, tile_rows, tile_cols):
+
+        if self.state.layers_show_back and self.state.pattern_mode == PatternMode.OFF:
+            padval = self.settings.caffevis_layer_clr_back_background
+        else:
+            padval = self.settings.window_background
+
+        highlights = [None] * n_tiles
+        if highlight_selected:
+            with self.state.lock:
+                if self.state.cursor_area == 'bottom':
+                    highlights[self.state.selected_unit] = self.settings.caffevis_layer_clr_cursor  # in [0,1] range
+                if self.state.backprop_selection_frozen and self.state.get_current_layer_definition() == self.state.get_current_backprop_layer_definition():
+                    highlights[self.state.backprop_unit] = self.settings.caffevis_layer_clr_back_sel  # in [0,1] range
+
+        _, display_2D = tile_images_make_tiles(display_3D, hw=(tile_rows, tile_cols), padval=padval, highlights=highlights)
+
+        return display_2D
+
     def _draw_layer_pane(self, pane):
         '''Returns the data shown in highres format, b01c order.'''
 
@@ -345,6 +364,7 @@ def _draw_layer_pane(self, pane):
         top_name = layer_name_to_top_name(self.net, default_layer_name)
         tile_rows, tile_cols = self.state.net_blob_info[top_name]['tiles_rc']
 
+        display_2D = None
         display_3D_highres = None
         is_layer_summary_loaded = False
         if self.state.pattern_mode != PatternMode.OFF:
@@ -354,16 +374,15 @@ def _draw_layer_pane(self, pane):
 
                 if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
 
-                    display_3D, display_3D_highres = self.load_pattern_images_original_format(default_layer_name,
-                                                                                              layer_dat_3D, n_tiles, pane,
-                                                                                              tile_cols, tile_rows)
+                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_original_format(
+                        default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows)
 
                 elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
 
-                    display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
-                                                                                              layer_dat_3D, n_tiles, pane,
-                                                                                              tile_cols, tile_rows, self.state.pattern_first_only,
-                                                                                              file_search_pattern='opt*.jpg')
+                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
+                        default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                        self.state.pattern_first_only, file_search_pattern='opt*.jpg')
+
             elif self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE:
 
                 if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
@@ -375,40 +394,29 @@ def _draw_layer_pane(self, pane):
                     print "ERROR: patterns view with maximal input images is not implemented when settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image'"
 
                 elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
-                    display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
-                                                                                               layer_dat_3D, n_tiles,
-                                                                                               pane,
-                                                                                               tile_cols, tile_rows, self.state.pattern_first_only,
-                                                                                               file_search_pattern='maxim*.png')
+                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
+                        default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                        self.state.pattern_first_only, file_search_pattern='maxim*.png')
 
             elif self.state.pattern_mode == PatternMode.WEIGHTS_HISTOGRAM:
 
-                    display_3D, display_3D_highres = self.load_weights_histograms(self.net,
-                                                                                  default_layer_name,
-                                                                                  layer_dat_3D, n_tiles, pane,
-                                                                                  tile_cols, tile_rows,
-                                                                                  show_layer_summary=self.state.cursor_area == 'top')
-
-                    is_layer_summary_loaded = display_3D_highres is not None and display_3D_highres.shape[0] == 1
+                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_weights_histograms(
+                        self.net, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                        show_layer_summary=self.state.cursor_area == 'top')
 
             elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
 
                     if self.settings.caffevis_histograms_format == 'load_from_file':
-                        display_3D, display_3D_highres = self.load_pattern_images_optimizer_format(default_layer_name,
-                                                                                                  layer_dat_3D, n_tiles, pane,
-                                                                                                  tile_cols, tile_rows, True,
-                                                                                                  file_search_pattern='max_histogram.png',
-                                                                                                  show_layer_summary=self.state.cursor_area == 'top',
-                                                                                                  file_summary_pattern='layer_inactivity.png')
+                        display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
+                            default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, True,
+                            file_search_pattern='max_histogram.png',
+                            show_layer_summary=self.state.cursor_area == 'top',
+                            file_summary_pattern='layer_inactivity.png')
 
                     elif self.settings.caffevis_histograms_format == 'calculate_in_realtime':
-                        display_3D, display_3D_highres = self.load_maximal_activations_histograms(default_layer_name,
-                                                                                                 layer_dat_3D, n_tiles, pane,
-                                                                                                 tile_cols, tile_rows,
-                                                                                                 show_layer_summary=self.state.cursor_area == 'top')
-
-                    is_layer_summary_loaded = display_3D_highres is not None and display_3D_highres.shape[0] == 1
-
+                        display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_maximal_activations_histograms(
+                            default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                            show_layer_summary=self.state.cursor_area == 'top')
 
             else:
                 raise Exception("Invalid value of pattern mode: %d" % self.state.pattern_mode)
@@ -430,7 +438,6 @@ def _draw_layer_pane(self, pane):
                 layer_dat_3D_normalized = tile_images_normalize(layer_dat_3D,
                                                                 boost_indiv = self.state.layer_boost_indiv,
                                                                 boost_gamma = self.state.layer_boost_gamma)
-            #print ' ===layer_dat_3D_normalized.shape', layer_dat_3D_normalized.shape, 'layer_dat_3D_normalized dtype', layer_dat_3D_normalized.dtype, 'range', layer_dat_3D_normalized.min(), layer_dat_3D_normalized.max()
 
             display_3D         = layer_dat_3D_normalized
 
@@ -450,26 +457,15 @@ def _draw_layer_pane(self, pane):
         #   e.g. (1000,1,2,3) -> (1000,2,2,3)
         if (display_3D.shape[1] == 1) and (display_3D.shape[2] == 2):
             display_3D = np.tile(display_3D, (1, 2, 1, 1))
-        if self.state.layers_show_back and self.state.pattern_mode == PatternMode.OFF:
-            padval = self.settings.caffevis_layer_clr_back_background
-        else:
-            padval = self.settings.window_background
-
-        highlights = [None] * n_tiles
-        with self.state.lock:
-            if self.state.cursor_area == 'bottom':
-                highlights[self.state.selected_unit] = self.settings.caffevis_layer_clr_cursor  # in [0,1] range
-            if self.state.backprop_selection_frozen and self.state.get_current_layer_definition() == self.state.get_current_backprop_layer_definition():
-                highlights[self.state.backprop_unit] = self.settings.caffevis_layer_clr_back_sel  # in [0,1] range
-
-        if is_layer_summary_loaded:
-            display_2D = display_3D_highres[0]
-        else:
-            _, display_2D = tile_images_make_tiles(display_3D, hw = (tile_rows,tile_cols), padval = padval, highlights = highlights)
 
         if display_3D_highres is None:
             display_3D_highres = display_3D
 
+
+        # generate 2D display by tiling the 3D images and add highlights, unless already generated
+        if display_2D is None:
+            display_2D = self.prepare_tile_image(display_3D, True, n_tiles, tile_rows, tile_cols)
+
         self._display_pane_based_on_zoom_mode(display_2D, display_3D_highres, is_layer_summary_loaded, pane)
 
         self._add_label_or_score_overlay(default_layer_name, pane)
@@ -543,7 +539,9 @@ def _add_label_or_score_overlay(self, default_layer_name, pane):
 
     def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
                                             tile_cols, tile_rows):
+        display_2D = None
         display_3D_highres = None
+        is_layer_summary_loaded = False
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
             load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
@@ -552,10 +550,7 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
                                     'regularized_opt', load_layer, 'whole_layer.jpg')
 
             # Get highres version
-            # cache_before = str(self.img_cache)
             display_3D_highres = self.img_cache.get((jpg_path, 'whole'), None)
-            # else:
-            #    display_3D_highres = None
 
             if display_3D_highres is None:
                 try:
@@ -567,14 +562,14 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
                 else:
                     if display_3D_highres is not None:
                         self.img_cache.set((jpg_path, 'whole'), display_3D_highres)
-                    # cache_after = str(self.img_cache)
-                    # print 'Cache was / is:\n  %s\n  %s' % (cache_before, cache_after)
 
         display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
-        return display_3D, display_3D_highres
+        return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
     def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D, n_tiles, pane,
                                             tile_cols, tile_rows, first_only, file_search_pattern, show_layer_summary = False, file_summary_pattern = ""):
+        is_layer_summary_loaded = False
+        display_2D = None
         display_3D_highres = None
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
@@ -601,6 +596,8 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
                         layer_summary_image = ensure_uint255_and_resize_without_fit(layer_summary_image, resize_shape)
                         display_3D_highres = layer_summary_image
                         display_3D_highres = np.expand_dims(display_3D_highres, 0)
+                        display_2D = display_3D_highres[0]
+                        is_layer_summary_loaded = True
 
                     else:
                         with WithTimer('CaffeVisApp:load_image_per_unit', quiet=self.debug_level < 1):
@@ -613,9 +610,15 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
                 else:
                     if display_3D_highres is not None:
                         self.img_cache.set(pattern_image_key, display_3D_highres)
+            else:
+
+                # if layer found in cache, mark it as loaded
+                if show_layer_summary:
+                    display_2D = display_3D_highres[0]
+                    is_layer_summary_loaded = True
 
         display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
-        return display_3D, display_3D_highres
+        return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
     def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern):
         # for each neuron in layer
@@ -678,129 +681,213 @@ def fig2data(self, fig):
         return buf
 
 
-    def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
+    def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pane, tile_cols, tile_rows, show_layer_summary):
 
-        empty_display_3D = layer_dat_3D * 0
-        # empty_display_3D = np.expand_dims(empty_display_3D, 0)
+        is_layer_summary_loaded = False
+        display_2D = None
+        display_3D = None
+        empty_display_3D = np.zeros(layer_dat_3D.shape + (3,))
 
-        pattern_image_key = (layer_name, "weights_histogram", show_layer_summary)
+        pattern_image_key_3d = (layer_name, "weights_histogram", show_layer_summary, self.state.selected_unit, "3D")
+        pattern_image_key_2d = (layer_name, "weights_histogram", show_layer_summary, self.state.selected_unit, "2D")
 
         # Get highres version
-        display_3D_highres = self.img_cache.get(pattern_image_key, None)
+        display_3D_highres = self.img_cache.get(pattern_image_key_3d, None)
+        display_2D = self.img_cache.get(pattern_image_key_2d, None)
+
+        if display_3D_highres is None or display_2D is None:
+            pane_shape = pane.data.shape
+
+            cache_layer_weights_histogram_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name, 'layer_weights_histogram.png')
+            cache_details_weights_histogram_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name, 'details_weights_histogram.png')
+
+            # plotting objects needed for
+            # 1. calculating size of results array
+            # 2. generating weights histogram for selected unit
+            # 3. generating weights histograms for all units
+            from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+            from matplotlib.figure import Figure
+            fig = Figure(figsize=(10, 10))
+            canvas = FigureCanvas(fig)
+            ax = fig.add_subplot(111)
+
+            def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print):
+
+                if do_print and channel_idx % 100 == 0:
+                    print "calculating weights histogram for channel %d out of %d" % (channel_idx, n_channels)
+
+                # get vector of weights
+                weights = net.params[layer_name][0].data[channel_idx].flatten()
+                bias = net.params[layer_name][1].data[channel_idx]
+
+                # create histogram
+                hist, bin_edges = np.histogram(weights, bins=50)
+
+                # generate histogram image file
+                width = 0.7 * (bin_edges[1] - bin_edges[0])
+                center = (bin_edges[:-1] + bin_edges[1:]) / 2
+
+                ax.bar(center, hist, align='center', width=width, color='g')
+
+                fig.suptitle('weights for unit %d, bias is %f' % (channel_idx, bias))
+                ax.xaxis.label.set_text('weight value')
+                ax.yaxis.label.set_text('count')
+
+                figure_buffer = self.fig2data(fig)
+
+                display_3D_highres[channel_idx, :, ::] = figure_buffer
+
+                ax.cla()
 
-        pane_shape = pane.data.shape
 
-        if display_3D_highres is None:
             try:
 
-                n_channels = n_tiles
+                # handle generation of results container
+                figure_buffer = self.fig2data(fig)
+                first_shape = figure_buffer.shape
+                display_3D_highres = np.zeros((n_channels, first_shape[0], first_shape[1], first_shape[2]), dtype=np.uint8)
 
-                # check if layer has weights at all
-                if not net.params.has_key(layer_name):
-                    return empty_display_3D, empty_display_3D
+                # try load from cache
+                if show_layer_summary:
 
-                from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-                from matplotlib.figure import Figure
+                    # try load cache file for layer weight histogram
+                    if os.path.exists(cache_layer_weights_histogram_image_path):
 
-                fig = Figure(figsize=(10, 10))
-                canvas = FigureCanvas(fig)
-                ax = fig.add_subplot(111)
+                        # load 2d image from cache file
+                        display_2D = caffe_load_image(cache_layer_weights_histogram_image_path, color=True, as_uint=False)
 
-                # for each channel
-                for channel_idx in xrange(n_channels):
+                        is_layer_summary_loaded = True
 
-                    if channel_idx % 100 == 0:
-                        print "calculating weights histogram for channel %d out of %d" % (channel_idx, n_channels)
+                else:
 
-                    # get vector of weights
-                    weights = net.params[layer_name][0].data[channel_idx].flatten()
-                    bias = net.params[layer_name][1].data[channel_idx]
+                    # try load cache file for details weights histogram
+                    if os.path.exists(cache_details_weights_histogram_image_path):
 
-                    # create histogram
-                    hist, bin_edges = np.histogram(weights, bins=50)
+                        # load 2d image from cache file
+                        display_2D = caffe_load_image(cache_details_weights_histogram_image_path, color=True, as_uint=False)
 
-                    # generate histogram image file
-                    width = 0.7 * (bin_edges[1] - bin_edges[0])
-                    center = (bin_edges[:-1] + bin_edges[1:]) / 2
+                        # calculate weights histogram for selected unit
+                        calculate_weights_histogram_for_specific_unit(self.state.selected_unit, fig, ax, do_print=False)
 
-                    ax.bar(center, hist, align='center', width=width, color='g')
+                        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
 
-                    fig.suptitle('weights for unit %d, bias is %f' % (channel_idx, bias))
-                    ax.xaxis.label.set_text('weight value')
-                    ax.yaxis.label.set_text('count')
+                        # generate empty highlights
+                        display_2D_highlights_only = self.prepare_tile_image(display_3D * 0, True, n_channels, tile_rows, tile_cols)
 
-                    figure_buffer = self.fig2data(fig)
+                        # mix highlights with cached image
+                        display_2D = (display_2D_highlights_only != 0) * display_2D_highlights_only + (display_2D_highlights_only == 0) * display_2D
 
-                    # handle first generation of results container
-                    if display_3D_highres is None:
-                        first_shape = figure_buffer.shape
-                        display_3D_highres = np.zeros((n_channels, first_shape[0], first_shape[1], first_shape[2]), dtype=np.uint8)
+                # if not loaded from cache, generate the data
+                if display_2D is None:
 
-                    display_3D_highres[channel_idx, :, ::] = figure_buffer
+                    # calculate weights histogram image
 
-                    ax.cla()
+                    # check if layer has weights at all
+                    if not net.params.has_key(layer_name):
+                        return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
 
-                half_pane_shape = (pane_shape[0], pane_shape[1]/2)
+                    # pattern_image_key_layer = (layer_name, "weights_histogram", True)
+                    # pattern_image_key_details = (layer_name, "weights_histogram", False)
 
-                # generate weights histogram for layer
-                weights = net.params[layer_name][0].data.flatten()
-                hist, bin_edges = np.histogram(weights, bins=50)
+                    # self.img_cache.set(pattern_image_key_details, display_3D_highres)
+                    # self.img_cache.set(pattern_image_key_layer, display_3D_highres_summary)
 
-                width = 0.7 * (bin_edges[1] - bin_edges[0])
-                center = (bin_edges[:-1] + bin_edges[1:]) / 2
-                ax.bar(center, hist, align='center', width=width, color='g')
+                    if show_layer_summary:
 
-                fig.suptitle('weights for layer %s' % layer_name)
-                ax.xaxis.label.set_text('weight value')
-                ax.yaxis.label.set_text('count')
+                        half_pane_shape = (pane_shape[0], pane_shape[1] / 2)
 
-                figure_buffer = self.fig2data(fig)
-                display_3D_highres_summary_weights = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
+                        # generate weights histogram for layer
+                        weights = net.params[layer_name][0].data.flatten()
+                        hist, bin_edges = np.histogram(weights, bins=50)
 
-                ax.cla()
+                        width = 0.7 * (bin_edges[1] - bin_edges[0])
+                        center = (bin_edges[:-1] + bin_edges[1:]) / 2
+                        ax.bar(center, hist, align='center', width=width, color='g')
 
-                # generate bias histogram for layer
-                bias = net.params[layer_name][1].data.flatten()
-                hist, bin_edges = np.histogram(bias, bins=50)
+                        fig.suptitle('weights for layer %s' % layer_name)
+                        ax.xaxis.label.set_text('weight value')
+                        ax.yaxis.label.set_text('count')
 
-                width = 0.7 * (bin_edges[1] - bin_edges[0])
-                center = (bin_edges[:-1] + bin_edges[1:]) / 2
-                ax.bar(center, hist, align='center', width=width, color='g')
+                        figure_buffer = self.fig2data(fig)
+                        display_3D_highres_summary_weights = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
 
-                fig.suptitle('bias for layer %s' % layer_name)
-                ax.xaxis.label.set_text('bias value')
-                ax.yaxis.label.set_text('count')
+                        ax.cla()
 
-                figure_buffer = self.fig2data(fig)
-                display_3D_highres_summary_bias = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
+                        # generate bias histogram for layer
+                        bias = net.params[layer_name][1].data.flatten()
+                        hist, bin_edges = np.histogram(bias, bins=50)
 
-                display_3D_highres_summary = np.concatenate((display_3D_highres_summary_weights, display_3D_highres_summary_bias), axis=1)
-                display_3D_highres_summary = np.expand_dims(display_3D_highres_summary, 0)
+                        width = 0.7 * (bin_edges[1] - bin_edges[0])
+                        center = (bin_edges[:-1] + bin_edges[1:]) / 2
+                        ax.bar(center, hist, align='center', width=width, color='g')
 
-                pattern_image_key_layer = (layer_name, "weights_histogram", True)
-                pattern_image_key_details = (layer_name, "weights_histogram", False)
+                        fig.suptitle('bias for layer %s' % layer_name)
+                        ax.xaxis.label.set_text('bias value')
+                        ax.yaxis.label.set_text('count')
 
-                self.img_cache.set(pattern_image_key_details, display_3D_highres)
-                self.img_cache.set(pattern_image_key_layer, display_3D_highres_summary)
+                        figure_buffer = self.fig2data(fig)
+                        display_3D_highres_summary_bias = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
 
-                if show_layer_summary:
-                    display_3D_highres = display_3D_highres_summary
+                        display_3D_highres_summary = np.concatenate((display_3D_highres_summary_weights, display_3D_highres_summary_bias), axis=1)
+                        display_3D_highres_summary = np.expand_dims(display_3D_highres_summary, 0)
+
+                        display_3D_highres = display_3D_highres_summary
+                        display_2D = display_3D_highres[0]
+                        is_layer_summary_loaded = True
+
+                        save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
+
+                    else:
+
+                        # for each channel
+                        for channel_idx in xrange(n_channels):
+                            calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print=True)
+
+                        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+
+                        # generate display of details weights histogram image
+                        display_2D = self.prepare_tile_image(display_3D, False, n_channels, tile_rows, tile_cols)
+
+                        # save histogram image to cache
+                        save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
+
+                        # generate empty highlights
+                        display_2D_highlights_only = self.prepare_tile_image(display_3D * 0, True, n_channels, tile_rows, tile_cols)
+
+                        # mix highlights with cached image
+                        display_2D = (display_2D_highlights_only != 0) * display_2D_highlights_only + (display_2D_highlights_only == 0) * display_2D
 
             except IOError:
-                return empty_display_3D, empty_display_3D
+                return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
                 # File does not exist, so just display disabled.
                 pass
 
-        display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
-        return display_3D, display_3D_highres
+            else:
+                self.img_cache.set(pattern_image_key_3d, display_3D_highres)
+                self.img_cache.set(pattern_image_key_2d, display_2D)
+
+        else:
+            # here we can safely assume that display_2D is not None, so we only need to check if show_layer_summary was requested
+            if show_layer_summary:
+                is_layer_summary_loaded = True
+
+            pass
+
+        if display_3D is None:
+            display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+
+        return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
 
     def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
 
-        empty_display_3D = layer_dat_3D * 0
+        display_2D = None
+        empty_display_3D = np.zeros(layer_dat_3D.shape + (3,))
+
+        is_layer_summary_loaded = False
 
         if not self.settings.caffevis_maximum_activation_histogram_data_file:
-            return empty_display_3D, empty_display_3D
+            return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
 
         pattern_image_key = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms", show_layer_summary)
 
@@ -815,12 +902,12 @@ def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D,
                 net_max_tracker = load_max_tracker_from_file(self.settings.caffevis_maximum_activation_histogram_data_file)
 
                 if not net_max_tracker.max_trackers.has_key(default_layer_name):
-                    return empty_display_3D, empty_display_3D
+                    return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
 
                 # check if
                 if not hasattr(net_max_tracker.max_trackers[default_layer_name], 'channel_to_histogram'):
                     print "ERROR: file %s is missing the field channel_to_histogram, try rerun find_max_acts to generate it" % (self.settings.caffevis_maximum_activation_histogram_data_file)
-                    return empty_display_3D, empty_display_3D
+                    return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
 
                 channel_to_histogram = net_max_tracker.max_trackers[default_layer_name].channel_to_histogram
 
@@ -863,16 +950,24 @@ def process_layer_figure(fig):
 
                 if show_layer_summary:
                     display_3D_highres = display_3D_highres_list[1]
+                    display_2D = display_3D_highres[0]
+                    is_layer_summary_loaded = True
                 else:
                     display_3D_highres = display_3D_highres_list[0]
 
             except IOError:
-                return empty_display_3D, empty_display_3D
+                return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
                 # File does not exist, so just display disabled.
                 pass
+        else:
+
+            # if layer found in cache, mark it as loaded
+            if show_layer_summary:
+                display_2D = display_3D_highres[0]
+                is_layer_summary_loaded = True
 
         display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
-        return display_3D, display_3D_highres
+        return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
     def _draw_aux_pane(self, pane, layer_data_normalized):
         pane.data[:] = to_255(self.settings.window_background)

From 67e16167bec6d9551df5380e33f778936a249cbc Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 00:00:58 +0300
Subject: [PATCH 049/106] fixed crash when info.txt is missing

---
 caffevis/jpg_vis_loading_thread.py | 28 +++++++++++++++++-----------
 1 file changed, 17 insertions(+), 11 deletions(-)

diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 59f9edc50..5461d8567 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -52,21 +52,27 @@ def load_image_into_pane_original_format(self, state_layer_name, state_selected_
 
     def get_score_values_for_max_input_images(self, state_layer_name, state_selected_unit):
 
-        info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
-                                        "unit_%04d" % (state_selected_unit),
-                                        "info.txt")
+        try:
+
+            info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
+                                            "unit_%04d" % (state_selected_unit),
+                                            "info.txt")
 
-        # open file
-        with open(info_file_path, 'r') as info_file:
-            lines = info_file.readlines()
+            # open file
+            with open(info_file_path, 'r') as info_file:
+                lines = info_file.readlines()
 
-            # skip first line
-            lines = lines[1:]
+                # skip first line
+                lines = lines[1:]
 
-            # take second word from each line, and convert to float
-            values = [float(line.split(' ')[1]) for line in lines]
+                # take second word from each line, and convert to float
+                values = [float(line.split(' ')[1]) for line in lines]
 
-        return values
+            return values
+
+        except IOError:
+            return []
+            pass
 
     def load_image_into_pane_max_tracker_format(self, state_layer_name, state_selected_unit, resize_shape, images,
                                                 file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False, captions = [], values = []):

From 93e6c5c688f8b68ce788211156236004168cf1f3 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 00:01:41 +0300
Subject: [PATCH 050/106] - fixed bug when cache folder doesn't exist -
 organized used of mkdir_p

---
 caffevis/app.py           |  9 ++++++---
 find_maxes/max_tracker.py | 12 +-----------
 2 files changed, 7 insertions(+), 14 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index a843b659f..4a9993a75 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -15,7 +15,7 @@
 import find_maxes.max_tracker
 sys.modules['max_tracker'] = find_maxes.max_tracker
 
-from misc import WithTimer
+from misc import WithTimer, mkdir_p
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
 from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
@@ -698,8 +698,9 @@ def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pan
         if display_3D_highres is None or display_2D is None:
             pane_shape = pane.data.shape
 
-            cache_layer_weights_histogram_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name, 'layer_weights_histogram.png')
-            cache_details_weights_histogram_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name, 'details_weights_histogram.png')
+            folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name)
+            cache_layer_weights_histogram_image_path = os.path.join(folder_path, 'layer_weights_histogram.png')
+            cache_details_weights_histogram_image_path = os.path.join(folder_path, 'details_weights_histogram.png')
 
             # plotting objects needed for
             # 1. calculating size of results array
@@ -835,6 +836,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         display_2D = display_3D_highres[0]
                         is_layer_summary_loaded = True
 
+                        mkdir_p(folder_path)
                         save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
 
                     else:
@@ -849,6 +851,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         display_2D = self.prepare_tile_image(display_3D, False, n_channels, tile_rows, tile_cols)
 
                         # save histogram image to cache
+                        mkdir_p(folder_path)
                         save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
 
                         # generate empty highlights
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 888310630..102d08e30 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -7,6 +7,7 @@
 import cv2
 
 import numpy as np
+from misc import mkdir_p
 from image_misc import cv2_read_file_rgb, resize_without_fit
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
     compute_data_layer_focus_area, layer_name_to_top_name
@@ -75,17 +76,6 @@ def __init__(self, info_file = None, ref_count = None):
         self.ref_count = ref_count
 
 
-def mkdir_p(path):
-    # From https://stackoverflow.com/questions/600268/mkdir-p-functionality-in-python
-    try:
-        os.makedirs(path)
-    except OSError as exc: # Python >2.5
-        if exc.errno == errno.EEXIST and os.path.isdir(path):
-            pass
-        else:
-            raise
-
-
 def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure):
 
     from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas

From 008f40fe237a3eb4aa6b625709680f5a4c2fad31 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:20:01 +0300
Subject: [PATCH 051/106] added assertion

---
 find_maxes/max_tracker.py | 12 ++++++++++++
 1 file changed, 12 insertions(+)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 102d08e30..9b332e19f 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -694,6 +694,9 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             if do_print:
                 print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), batch[batch_index].cc * num_top, n_total_images, layer_name, batch[batch_index].channel_idx)
 
+
+            # print "DEBUG: (mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)", str((mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj))
+
             [batch[batch_index].out_ii_start,
              batch[batch_index].out_ii_end,
              batch[batch_index].out_jj_start,
@@ -705,6 +708,13 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                 compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name,
                                               size_ii, size_jj, data_size_ii, data_size_jj)
 
+            # print "DEBUG: channel:%d out_ii_start:%d out_ii_end:%d out_jj_start:%d out_jj_end:%d data_ii_start:%d data_ii_end:%d data_jj_start:%d data_jj_end:%d" % \
+            #       (channel_idx,
+            #        batch[batch_index].out_ii_start, batch[batch_index].out_ii_end,
+            #        batch[batch_index].out_jj_start, batch[batch_index].out_jj_end,
+            #        batch[batch_index].data_ii_start, batch[batch_index].data_ii_end,
+            #        batch[batch_index].data_jj_start, batch[batch_index].data_jj_end)
+
             if do_info:
                 print >> batch[batch_index].info_file, 1 if mt.is_conv else 0, '%.6f' % mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
                 if mt.is_conv:
@@ -803,6 +813,8 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                                 if len(diffs.shape) == 4:
                                     diffs[batch[i].selected_input_index, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
                                 else:
+                                    # note: the following will not crash, since we already checked we have 2 outputs, so selected_input_index is either 0 or 1
+                                    assert batch[i].selected_input_index != -1
                                     diffs[batch[i].selected_input_index, batch[i].channel_idx] = 1.0
                             elif diffs.shape[0] == 1:
                                 if len(diffs.shape) == 4:

From fa95dd9f00bd0b5b1374f1961ef735c691acb553 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:22:22 +0300
Subject: [PATCH 052/106] changed default batch size for optimize_image to 1

---
 settings.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/settings.py b/settings.py
index 86d96ea4f..59aa97204 100644
--- a/settings.py
+++ b/settings.py
@@ -443,7 +443,7 @@
 optimize_image_generate_plus_mean = locals().get('optimize_image_generate_plus_mean', False)
 
 # batch size used in optimize_image.py
-optimize_image_batch_size = locals().get('optimize_image_batch_size', 9)
+optimize_image_batch_size = locals().get('optimize_image_batch_size', 1)
 
 # channels to generate in optimize_image.py, if list is empty we generate all the channels in the layer
 optimize_image_channels = locals().get('optimize_image_channels', [])

From 4e4abc0e85fe6dce95c46183c96edb04344239b3 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:23:53 +0300
Subject: [PATCH 053/106] - replaced use of cv2.resize with
 image_misc.resize_without_fit which improve visual results

---
 caffevis/app.py               |  6 +++---
 caffevis/caffe_proc_thread.py |  7 ++++---
 live_vis.py                   | 11 ++++++-----
 3 files changed, 13 insertions(+), 11 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 4a9993a75..3d2556805 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -1050,11 +1050,11 @@ def run_processing_once_or_twice(image, process_image_fn):
                         image1 = process_image_fn(image1)
 
                         # resize each gradient image to half the pane size
-                        half_pane_shape = (image.shape[1] / 2, image.shape[0])
+                        half_pane_shape = (image.shape[0], image.shape[1] / 2)
                         # half_pane_shape = (pane.data.shape[1] / 2, pane.data.shape[0])
 
-                        image0 = cv2.resize(image0[:], half_pane_shape)
-                        image1 = cv2.resize(image1[:], half_pane_shape)
+                        image0 = resize_without_fit(image0[:], half_pane_shape)
+                        image1 = resize_without_fit(image1[:], half_pane_shape)
 
                         # generate the pane image by concatenating both images
                         return np.concatenate((image0, image1), axis=1)
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 7e75aec38..56ce30dec 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -5,6 +5,7 @@
 from codependent_thread import CodependentThread
 from misc import WithTimer
 from caffevis_helper import net_preproc_forward
+from image_misc import resize_without_fit
 
 class CaffeProcThread(CodependentThread):
     '''Runs Caffe in separate thread.'''
@@ -85,12 +86,12 @@ def run(self):
                     frame1 = frame[0]
                     frame2 = frame[1]
 
-                    im_small1 = cv2.resize(frame1, self.input_dims[::-1])
-                    im_small2 = cv2.resize(frame2, self.input_dims[::-1])
+                    im_small1 = resize_without_fit(frame1, self.input_dims)
+                    im_small2 = resize_without_fit(frame2, self.input_dims)
                     im_small = np.concatenate( (im_small1, im_small2), axis=2)
 
                 else:
-                    im_small = cv2.resize(frame, self.input_dims[::-1])
+                    im_small = resize_without_fit(frame, self.input_dims)
 
                 with WithTimer('CaffeProcThread:forward', quiet = self.debug_level < 1):
                     net_preproc_forward(self.settings, self.net, im_small, self.input_dims)
diff --git a/live_vis.py b/live_vis.py
index 205d72092..dd5f6fef6 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -15,7 +15,7 @@
     raise
 
 from misc import WithTimer
-from image_misc import cv2_imshow_rgb, FormattedString, cv2_typeset_text, to_255
+from image_misc import cv2_imshow_rgb, FormattedString, cv2_typeset_text, to_255, resize_without_fit
 from bindings import bindings
 from input_fetcher import InputImageFetcher
 
@@ -322,12 +322,13 @@ def display_frame(self, frame):
         if self.settings.is_siamese and ((type(frame),len(frame)) == (tuple,2)):
             frame1 = frame[0]
             frame2 = frame[1]
-            half_pane_shape = (full_pane_shape[0] / 2, full_pane_shape[1])
-            frame_disp1 = cv2.resize(frame1[:], half_pane_shape)
-            frame_disp2 = cv2.resize(frame2[:], half_pane_shape)
+            # half_pane_shape = (full_pane_shape[0] / 2, full_pane_shape[1])
+            half_pane_shape = (full_pane_shape[0], full_pane_shape[1]/2)
+            frame_disp1 = resize_without_fit(frame1[:], half_pane_shape)
+            frame_disp2 = resize_without_fit(frame2[:], half_pane_shape)
             frame_disp = np.concatenate((frame_disp1, frame_disp2), axis=1)
         else:
-            frame_disp = cv2.resize(frame[:], full_pane_shape)
+            frame_disp = resize_without_fit(frame[:], full_pane_shape)
 
         self.panes['input'].data[:] = frame_disp
 

From 86659f179ac639d958f69960e6726ca110947240 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:24:48 +0300
Subject: [PATCH 054/106] - added option to overlay activation/gradient over
 original image

---
 bindings.py                                  |  4 +++-
 caffevis/app.py                              | 16 +++++++++++++-
 caffevis/caffevis_app_state.py               |  7 +++++-
 deep-visualization-toolbox/siamese_helper.py | 23 ++++++++++++++++++++
 live_vis.py                                  |  1 -
 5 files changed, 47 insertions(+), 4 deletions(-)

diff --git a/bindings.py b/bindings.py
index 6847ac45e..137b37048 100644
--- a/bindings.py
+++ b/bindings.py
@@ -133,8 +133,10 @@ def get_key_help(self, tag):
 _.add('siamese_input_mode', 'v',
         'Cycle between siamese input modes {first image, second image, both images}')
 
-_.add('show_maximal_score', 'r',
+_.add('toggle_maximal_score', 'r',
         'Toggle showing maximal score overlays {on, off}')
+_.add('toggle_input_overlay_in_aux_pane', 'y',
+        'Toggle showing input overlay in aux pane {on, off}')
 
 
 bindings = _
diff --git a/caffevis/app.py b/caffevis/app.py
index 3d2556805..abc67dcf3 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -28,6 +28,7 @@
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
 from caffe_misc import layer_name_to_top_name, save_caffe_image
+from siamese_helper import SiameseHelper
 
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
@@ -140,6 +141,8 @@ def handle_input(self, input_image, input_label, panes):
             self.state.next_label = input_label
             if self.debug_level > 1:
                 print 'CaffeVisApp.handle_input: caffe_net_state is:', self.state.caffe_net_state
+
+            self.state.last_frame = input_image
     
     def redraw_needed(self):
         return self.state.redraw_needed()
@@ -491,6 +494,11 @@ def _display_pane_based_on_zoom_mode(self, display_2D, display_3D_highres, is_la
             else:
                 display_2D_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
 
+            if self.state.show_input_overlay_in_aux_pane and self.state.pattern_mode == PatternMode.OFF:
+                input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese, pane.data.shape, self.state.siamese_input_mode)
+                normalized_mask = display_2D_resize / 255.0
+                display_2D_resize = normalized_mask * input_image + (1 - normalized_mask) * display_2D_resize
+
         elif state_layers_pane_zoom_mode == 2 and not is_layer_summary_loaded:
             # Mode 2: zoomed backprop pane
             if self.settings.caffevis_keep_aspect_ratio:
@@ -988,6 +996,12 @@ def _draw_aux_pane(self, pane, layer_data_normalized):
                 unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
             else:
                 unit_data_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
+
+            if self.state.show_input_overlay_in_aux_pane and self.state.pattern_mode == PatternMode.OFF:
+                input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese, pane.data.shape, self.state.siamese_input_mode)
+                normalized_mask = unit_data_resize / 255.0
+                unit_data_resize = normalized_mask * input_image + (1-normalized_mask) * unit_data_resize
+
             pane.data[0:unit_data_resize.shape[0], 0:unit_data_resize.shape[1], :] = unit_data_resize
         elif mode == 'prob_labels':
             self._draw_prob_labels_pane(pane)
@@ -1186,7 +1200,7 @@ def draw_help(self, help_pane, locy):
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
                     'next_ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
-                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'show_maximal_score'):
+                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'toggle_maximal_score', 'toggle_input_overlay_in_aux_pane'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
             lines.append([FormattedString(label, defaults, width=120, align='right'),
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index ead9da5fe..370d7aad2 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -35,6 +35,7 @@ def __init__(self, net, settings, bindings):
         self.back_stale = True       # back becomes stale whenever the last back diffs were not computed using the current backprop unit and method (bprop or deconv)
         self.next_frame = None
         self.next_label = None
+        self.last_frame = None
         self.jpgvis_to_load_key = None
         self.last_key_at = 0
         self.quit = False
@@ -70,6 +71,7 @@ def get_headers(self):
     def _reset_user_state(self):
         self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
         self.show_maximal_score = False
+        self.show_input_overlay_in_aux_pane = False
         self.layer_idx = 0
         self.layer_boost_indiv_idx = self.settings.caffevis_boost_indiv_default_idx
         self.layer_boost_indiv = self.layer_boost_indiv_choices[self.layer_boost_indiv_idx]
@@ -231,9 +233,12 @@ def handle_key(self, key):
             elif tag == 'siamese_input_mode':
                 self.siamese_input_mode = (self.siamese_input_mode + 1) % SiameseInputMode.NUMBER_OF_MODES
 
-            elif tag == 'show_maximal_score':
+            elif tag == 'toggle_maximal_score':
                 self.show_maximal_score = not self.show_maximal_score
 
+            elif tag == 'toggle_input_overlay_in_aux_pane':
+                self.show_input_overlay_in_aux_pane = not self.show_input_overlay_in_aux_pane
+
             else:
                 key_handled = False
 
diff --git a/deep-visualization-toolbox/siamese_helper.py b/deep-visualization-toolbox/siamese_helper.py
index 4a4e123af..0748d9b05 100644
--- a/deep-visualization-toolbox/siamese_helper.py
+++ b/deep-visualization-toolbox/siamese_helper.py
@@ -2,6 +2,7 @@
 # import numpy as np
 from numpy import expand_dims, concatenate
 from caffe_misc import layer_name_to_top_name
+from image_misc import resize_without_fit
 
 class SiameseInputMode:
     FIRST_IMAGE = 0
@@ -418,3 +419,25 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode
 
             selected_backprop_layer_name = SiameseHelper._get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
             net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True)
+
+    @staticmethod
+    def get_image_from_frame(frame, is_siamese, image_shape, siamese_input_mode):
+
+        if is_siamese and ((type(frame),len(frame)) == (tuple,2)):
+
+            if siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
+                frame1 = frame[0]
+                frame2 = frame[1]
+                half_pane_shape = (image_shape[0], image_shape[1]/2)
+                frame_disp1 = resize_without_fit(frame1[:], half_pane_shape)
+                frame_disp2 = resize_without_fit(frame2[:], half_pane_shape)
+                frame_disp = concatenate((frame_disp1, frame_disp2), axis=1)
+            elif siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                frame_disp = resize_without_fit(frame[0], image_shape)
+            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                frame_disp = resize_without_fit(frame[1], image_shape)
+
+        else:
+            frame_disp = resize_without_fit(frame[:], image_shape)
+
+        return frame_disp
\ No newline at end of file
diff --git a/live_vis.py b/live_vis.py
index dd5f6fef6..ba36fb617 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -322,7 +322,6 @@ def display_frame(self, frame):
         if self.settings.is_siamese and ((type(frame),len(frame)) == (tuple,2)):
             frame1 = frame[0]
             frame2 = frame[1]
-            # half_pane_shape = (full_pane_shape[0] / 2, full_pane_shape[1])
             half_pane_shape = (full_pane_shape[0], full_pane_shape[1]/2)
             frame_disp1 = resize_without_fit(frame1[:], half_pane_shape)
             frame_disp2 = resize_without_fit(frame2[:], half_pane_shape)

From fdb4cc576c0ce5366341564f84cfe8d9bd29d733 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:43:13 +0300
Subject: [PATCH 055/106] added user settings file which contains model_to_load
 and gpu settings

---
 deep-visualization-toolbox/.gitignore              |  1 +
 .../settings_user_template.py                      | 14 ++++++++++++++
 settings.py                                        |  7 ++++---
 3 files changed, 19 insertions(+), 3 deletions(-)
 create mode 100644 deep-visualization-toolbox/.gitignore
 create mode 100644 deep-visualization-toolbox/settings_user_template.py

diff --git a/deep-visualization-toolbox/.gitignore b/deep-visualization-toolbox/.gitignore
new file mode 100644
index 000000000..5aca41de4
--- /dev/null
+++ b/deep-visualization-toolbox/.gitignore
@@ -0,0 +1 @@
+/settings_user.py
diff --git a/deep-visualization-toolbox/settings_user_template.py b/deep-visualization-toolbox/settings_user_template.py
new file mode 100644
index 000000000..24665fd93
--- /dev/null
+++ b/deep-visualization-toolbox/settings_user_template.py
@@ -0,0 +1,14 @@
+
+##################################### user related settings #####################################
+
+import os
+
+# base folder for all relative paths
+base_folder = os.path.expanduser('~')
+
+# Use GPU? Default is True.
+# caffevis_mode_gpu = False
+
+# network selection, for network settings see file settings_local.py
+model_to_load = 'caffenet_yos'
+
diff --git a/settings.py b/settings.py
index 59aa97204..759e3cc82 100644
--- a/settings.py
+++ b/settings.py
@@ -4,11 +4,9 @@
 # settings, define them in settings_local.py rather than changing them
 # here.
 
+# Import network settings. Turn off creation of X.pyc to avoid stale settings if X.py is removed.
 import os
 import sys
-
-# Import local / overridden settings. Turn off creation of settings_local.pyc to avoid stale settings if settings_local.py is removed.
-
 sys.dont_write_bytecode = True
 try:
     from settings_local import *
@@ -28,6 +26,9 @@
 #
 ####################################
 
+# base folder for paths defined in the settings
+base_folder = locals().get('base_folder', '')
+
 # Which device to use for webcam input. On Mac the default device, 0,
 # works for builtin camera or external USB webcam, if plugged in. If
 # you have multiple cameras, you might need to update this value. To

From 2952df5f776ec5321f13a31d62052589ad39299e Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:46:49 +0300
Subject: [PATCH 056/106] - fixed bug: when moving between layers and siamese
 mode is first or second, we can't move into the other modes (nothing happens)

---
 caffevis/caffevis_app_state.py               | 11 ++++++++---
 deep-visualization-toolbox/siamese_helper.py |  8 ++++----
 2 files changed, 12 insertions(+), 7 deletions(-)

diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 370d7aad2..914c4493e 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -69,7 +69,6 @@ def get_headers(self):
         return headers
 
     def _reset_user_state(self):
-        self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
         self.show_maximal_score = False
         self.show_input_overlay_in_aux_pane = False
         self.layer_idx = 0
@@ -79,10 +78,13 @@ def _reset_user_state(self):
         self.layer_boost_gamma = self.layer_boost_gamma_choices[self.layer_boost_gamma_idx]
         self.cursor_area = 'top'   # 'top' or 'bottom'
         self.selected_unit = 0
+        self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
+
         # Which layer and unit (or channel) to use for backprop
         self.backprop_layer_idx = self.layer_idx
         self.backprop_unit = self.selected_unit
         self.backprop_selection_frozen = False    # If false, backprop unit tracks selected unit
+        self.backprop_siamese_input_mode = SiameseInputMode.BOTH_IMAGES
         self.back_enabled = False
         self.back_mode = 'grad'      # 'grad' or 'deconv'
         self.back_filt_mode = 'raw'  # 'raw', 'gray', 'norm', 'normblur'
@@ -217,7 +219,8 @@ def handle_key(self, key):
                 if self.backprop_selection_frozen:
                     # Grap layer/selected_unit upon transition from non-frozen -> frozen
                     self.backprop_layer_idx = self.layer_idx
-                    self.backprop_unit = self.selected_unit                    
+                    self.backprop_unit = self.selected_unit
+                    self.backprop_siamese_input_mode = self.siamese_input_mode
             elif tag == 'zoom_mode':
                 self.layers_pane_zoom_mode = (self.layers_pane_zoom_mode + 1) % 3
                 if self.layers_pane_zoom_mode == 2 and not self.back_enabled:
@@ -233,6 +236,7 @@ def handle_key(self, key):
             elif tag == 'siamese_input_mode':
                 self.siamese_input_mode = (self.siamese_input_mode + 1) % SiameseInputMode.NUMBER_OF_MODES
 
+
             elif tag == 'toggle_maximal_score':
                 self.show_maximal_score = not self.show_maximal_score
 
@@ -378,9 +382,10 @@ def _ensure_valid_selected(self):
         # Backward selection
         if not self.backprop_selection_frozen:
             # If backprop_selection is not frozen, backprop layer/unit follows selected unit
-            if not (self.backprop_layer_idx == self.layer_idx and self.backprop_unit == self.selected_unit):
+            if not (self.backprop_layer_idx == self.layer_idx and self.backprop_unit == self.selected_unit and self.backprop_siamese_input_mode == self.siamese_input_mode):
                 self.backprop_layer_idx = self.layer_idx
                 self.backprop_unit = self.selected_unit
+                self.backprop_siamese_input_mode = self.siamese_input_mode
                 self.back_stale = True    # If there is any change, back diffs are now stale
 
     def fill_layers_list(self, net):
diff --git a/deep-visualization-toolbox/siamese_helper.py b/deep-visualization-toolbox/siamese_helper.py
index 0748d9b05..5847db7ca 100644
--- a/deep-visualization-toolbox/siamese_helper.py
+++ b/deep-visualization-toolbox/siamese_helper.py
@@ -283,7 +283,6 @@ def _get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selecto
         '''
         function used to extract both blobs according to the specified layer and siamese input mode and
         blob selector.
-        note that it is invalid to call this function when siamese input mode is not BOTH
         this is the main function which contains logic on the siamese network internal format structure
         :param net: network containing the blob to extract
         :param layer_def: layer requested
@@ -292,8 +291,6 @@ def _get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selecto
         :return: first_blob, second_blob
         '''
 
-        assert siamese_input_mode == SiameseInputMode.BOTH_IMAGES
-
         if layer_def['format'] == 'normal':
             raise Exception('function get_siamese_blobs() should not be called when layer is in normal format')
 
@@ -387,7 +384,10 @@ def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mo
     @staticmethod
     def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode):
 
-        if SiameseHelper.siamese_input_mode_has_two_images(backprop_layer_def, siamese_input_mode):
+        # if we are in siamese_batch_pair, we don't care of siamese_input_mode since we must do deconv on the 2-batch
+        # otherwise, if we are in siamese_layer_pair, we do it on both layers only if both deconv are requested
+        if (backprop_layer_def['format'] == 'siamese_batch_pair') or \
+            (backprop_layer_def['format'] == 'siamese_layer_pair' and siamese_input_mode == SiameseInputMode.BOTH_IMAGES):
 
             diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_input_mode)
             diffs0, diffs1 = diffs0 * 0, diffs1 * 0

From 34bb15fb8d4b87521f7507131c6b92d1b971a3e5 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:50:08 +0300
Subject: [PATCH 057/106] adjusted optimize_image script for siamese networks
 in "siamese_batch_format"

---
 optimize/gradient_optimizer.py | 57 +++++++++++++++++++++++++++-------
 optimize_image.py              |  6 ++++
 2 files changed, 51 insertions(+), 12 deletions(-)

diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index fc0957b21..c1f63eb03 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -244,6 +244,7 @@ def _optimize(self, params, x0, prefix_template):
             push_label = None
 
         old_obj = np.zeros(params.batch_size)
+        obj = np.zeros(params.batch_size)
         for ii in range(params.max_iter):
             # 0. Crop data
             xx = minimum(255.0, maximum(0.0, xx + self.batched_data_mean)) - self.batched_data_mean     # Crop all values to [0,255]
@@ -254,17 +255,44 @@ def _optimize(self, params, x0, prefix_template):
             top_name = layer_name_to_top_name(self.net, params.push_layer)
             acts = self.net.blobs[top_name].data
 
-            if not is_conv:
-                # promote to 4D
-                acts = np.reshape(acts, (params.batch_size, -1, 1, 1))
+            # note: no batch support in 'siamese_batch_pair'
+            if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 2:
+
+                if not is_conv:
+                    # promote to 4D
+                    acts = np.reshape(acts, (2, -1, 1, 1))
+                reshaped_acts = np.reshape(acts, (2, -1))
+                idxmax = unravel_index(reshaped_acts.argmax(axis=1), acts.shape[1:])
+                valmax = reshaped_acts.max(axis=1)
+
+                # idxmax for fc or prob layer will be like:  (batch,278, 0, 0)
+                # idxmax for conv layer will be like:        (batch,37, 4, 37)
+                obj[0] = acts[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]]
+
+            elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 1:
 
-            reshaped_acts = np.reshape(acts, (params.batch_size, -1))
+                if not is_conv:
+                    # promote to 4D
+                    acts = np.reshape(acts, (1, -1, 1, 1))
+                reshaped_acts = np.reshape(acts, (1, -1))
+                idxmax = unravel_index(reshaped_acts.argmax(axis=1), acts.shape[1:])
+                valmax = reshaped_acts.max(axis=1)
 
-            idxmax = unravel_index(reshaped_acts.argmax(axis=1), acts.shape[1:])
-            valmax = reshaped_acts.max(axis=1)
-            # idxmax for fc or prob layer will be like:  (batch,278, 0, 0)
-            # idxmax for conv layer will be like:        (batch,37, 4, 37)
-            obj = acts[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]]
+                # idxmax for fc or prob layer will be like:  (batch,278, 0, 0)
+                # idxmax for conv layer will be like:        (batch,37, 4, 37)
+                obj[0] = acts[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]]
+
+            else:
+                if not is_conv:
+                    # promote to 4D
+                    acts = np.reshape(acts, (params.batch_size, -1, 1, 1))
+                reshaped_acts = np.reshape(acts, (params.batch_size, -1))
+                idxmax = unravel_index(reshaped_acts.argmax(axis=1), acts.shape[1:])
+                valmax = reshaped_acts.max(axis=1)
+
+                # idxmax for fc or prob layer will be like:  (batch,278, 0, 0)
+                # idxmax for conv layer will be like:        (batch,37, 4, 37)
+                obj = acts[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]]
 
             # 2. Update results
             for batch_index in range(params.batch_size):
@@ -296,7 +324,12 @@ def _optimize(self, params, x0, prefix_template):
                 # Promote bc -> bc01
                 diffs = diffs[:,:,np.newaxis,np.newaxis]
 
-            diffs[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
+            if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 2:
+                diffs[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
+            elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 1:
+                diffs[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
+            else:
+                diffs[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
             backout = self.net.backward_from_layer(params.push_layer, diffs if is_conv else diffs[:,:,0,0])
 
             grad = backout['data'].copy()
@@ -411,8 +444,8 @@ def find_selected_input_index(self, layer_name):
                 if item['name/s'][1] == layer_name:
                     return 1
 
-            elif item['format'] == 'siamese_batch_pair':
-                raise NotImplementedError("in function find_selected_input_index()")
+            elif item['format'] == 'siamese_batch_pair' and item['name/s'] == layer_name:
+                return 0
 
         return -1
 
diff --git a/optimize_image.py b/optimize_image.py
index 2497b31ff..9977e154e 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -172,6 +172,12 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    # validate batch size
+    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+        # currently, no batch support for siamese_batch_pair networks
+        # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
+        assert (settings.max_tracker_batch_size == 1)
+
     check_force_backward_true(settings.caffevis_deploy_prototxt)
 
     current_data_shape = net.blobs['data'].shape

From e6a987df43057133dc88ef4b9e851165b8526e11 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:53:07 +0300
Subject: [PATCH 058/106] - added parameter to deconv to select between running
 "Zeiler & Fergus" deconv, and "guided backprop" deconv

---
 bindings.py                                  |  12 +-
 caffevis/app.py                              |  36 +++---
 caffevis/caffe_proc_thread.py                |  13 +-
 caffevis/caffevis_app_state.py               | 121 ++++++++++---------
 deep-visualization-toolbox/siamese_helper.py |  10 +-
 find_maxes/max_tracker.py                    |   2 +-
 6 files changed, 106 insertions(+), 88 deletions(-)

diff --git a/bindings.py b/bindings.py
index 137b37048..e870c3879 100644
--- a/bindings.py
+++ b/bindings.py
@@ -109,17 +109,17 @@ def get_key_help(self, tag):
        'Toggle pattern loading first image only or loading all available images')
 
 _.add('next_ez_back_mode_loop', 'b',
-       'Cycle through a few common backprop/deconv modes')
+       'Cycle through backprop modes (backprop, deconv zf, deconv gb, off)')
 _.add('prev_ez_back_mode_loop', 'B',
-       'Cycle through a few common backprop/deconv modes')
+       'Cycle through backprop modes (backprop, deconv zf, deconv gb, off)')
 _.add('freeze_back_unit', 'd',
        'Freeze the bprop/deconv origin to be the currently selected unit')
 _.add('show_back', 'a',
        'Toggle between showing forward activations and back/deconv diffs')
-_.add('back_mode', 'n',
-       '(expert) Change back mode directly.')
-_.add('back_filt_mode', 'm',
-       '(expert) Change back output filter directly.')
+_.add('next_back_view_option', 'n',
+       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur)')
+_.add('prev_back_view_option', 'N',
+       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur)')
 
 _.add('boost_gamma', 't',
        'Boost contrast using gamma correction')
diff --git a/caffevis/app.py b/caffevis/app.py
index abc67dcf3..884aa165e 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -24,7 +24,7 @@
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
-from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode
+from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode, BackpropMode, BackpropViewOption
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
 from caffe_misc import layer_name_to_top_name, save_caffe_image
@@ -283,10 +283,8 @@ def _draw_status_pane(self, pane):
             if not self.state.back_enabled:
                 print >>status, 'Back: off',
             else:
-                print >>status, 'Back: %s' % ('deconv' if self.state.back_mode == 'deconv' else 'bprop'),
-                print >>status, '(from %s_%d, disp %s)' % (self.state.get_default_layer_name(self.state.get_current_backprop_layer_definition()) ,
-                                                           self.state.backprop_unit,
-                                                           self.state.back_filt_mode),
+                print >>status, 'Back: %s (%s)' % (BackpropMode.to_string(self.state.back_mode), BackpropViewOption.to_string(self.state.back_view_option)),
+                print >>status, '(from %s_%d)' % (self.state.get_default_layer_name(self.state.get_current_backprop_layer_definition()), self.state.backprop_unit),
             print >>status, '|',
             print >>status, 'Boost: %g/%g' % (self.state.layer_boost_indiv, self.state.layer_boost_gamma)
 
@@ -1011,7 +1009,7 @@ def _draw_back_pane(self, pane):
         with self.state.lock:
             back_enabled = self.state.back_enabled
             back_mode = self.state.back_mode
-            back_filt_mode = self.state.back_filt_mode
+            back_view_option = self.state.back_view_option
             back_what_to_disp = self.get_back_what_to_disp()
                 
         if back_what_to_disp == 'disabled':
@@ -1034,10 +1032,6 @@ def _draw_back_pane(self, pane):
             else:
                 grad_img = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
 
-            # Mode-specific processing
-            assert back_mode in ('grad', 'deconv')
-            assert back_filt_mode in ('raw', 'gray', 'norm', 'normblur')
-
             # define helper function ro run processing once or twice, in case of siamese network
             def run_processing_once_or_twice(image, process_image_fn):
 
@@ -1080,17 +1074,22 @@ def run_processing_once_or_twice(image, process_image_fn):
 
                 raise Exception("flow should not arrive here")
 
-
-            if back_filt_mode == 'raw':
+            if back_view_option == BackpropViewOption.RAW:
                 grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0))
 
-            elif back_filt_mode == 'gray':
+            elif back_view_option == BackpropViewOption.RAW_POS:
+                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0) * (grad_img > 0) + (0.5) * (grad_img <= 0))
+
+            elif back_view_option == BackpropViewOption.RAW_NEG:
+                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0) * (grad_img < 0) + (0.5) * (grad_img >= 0))
+
+            elif back_view_option == BackpropViewOption.GRAY:
                 grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img.mean(axis=2), 0))
 
-            elif back_filt_mode == 'norm':
+            elif back_view_option == BackpropViewOption.NORM:
                 grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01(np.linalg.norm(grad_img, axis=2)))
 
-            elif back_filt_mode == 'normblur':
+            elif back_view_option == BackpropViewOption.NORM_BLUR:
                 def do_norm_blur(grad_img):
                     grad_img = np.linalg.norm(grad_img, axis=2)
                     cv2.GaussianBlur(grad_img, (0, 0), self.settings.caffevis_grad_norm_blur_radius, grad_img)
@@ -1098,7 +1097,7 @@ def do_norm_blur(grad_img):
                 grad_img = run_processing_once_or_twice(grad_img, do_norm_blur)
 
             else:
-                raise Exception('Invalid option for back_filter_mode: %s' % (back_filt_mode))
+                raise Exception('Invalid option for back_view_option: %s' % (back_view_option))
 
             # If necessary, re-promote from grayscale to color
             if len(grad_img.shape) == 2:
@@ -1199,8 +1198,9 @@ def draw_help(self, help_pane, locy):
                       FormattedString(nav_string, defaults)])
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
-                    'next_ez_back_mode_loop', 'freeze_back_unit', 'show_back', 'back_mode', 'back_filt_mode',
-                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'toggle_maximal_score', 'toggle_input_overlay_in_aux_pane'):
+                    'next_ez_back_mode_loop', 'next_back_view_option', 'freeze_back_unit', 'show_back',
+                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'toggle_maximal_score',
+                    'toggle_input_overlay_in_aux_pane'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
             lines.append([FormattedString(label, defaults, width=120, align='right'),
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 56ce30dec..62e8efa7f 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -6,6 +6,7 @@
 from misc import WithTimer
 from caffevis_helper import net_preproc_forward
 from image_misc import resize_without_fit
+from caffevis_app_state import BackpropMode
 
 class CaffeProcThread(CodependentThread):
     '''Runs Caffe in separate thread.'''
@@ -98,13 +99,17 @@ def run(self):
 
             if run_back:
 
-                assert back_mode in ('grad', 'deconv')
-                if back_mode == 'grad':
+                if back_mode == BackpropMode.GRAD:
                     with WithTimer('CaffeProcThread:backward', quiet = self.debug_level < 1):
                         self.state.backward_from_layer(self.net, backprop_layer_def, backprop_unit)
-                else:
+
+                elif back_mode == BackpropMode.DECONV_ZF:
                     with WithTimer('CaffeProcThread:deconv', quiet = self.debug_level < 1):
-                        self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit)
+                        self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit, 'Zeiler & Fergus')
+
+                elif back_mode == BackpropMode.DECONV_GB:
+                    with WithTimer('CaffeProcThread:deconv', quiet=self.debug_level < 1):
+                        self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit, 'Guided Backprop')
 
                 with self.state.lock:
                     self.state.back_stale = False
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 914c4493e..1287d755e 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -13,6 +13,52 @@ class PatternMode:
     MAX_ACTIVATIONS_HISTOGRAM = 4
     NUMBER_OF_MODES = 5
 
+class BackpropMode:
+    OFF = 0
+    GRAD = 1
+    DECONV_ZF = 2
+    DECONV_GB = 3
+    NUMBER_OF_MODES = 4
+
+    @staticmethod
+    def to_string(back_mode):
+        if back_mode == BackpropMode.OFF:
+            return 'off'
+        elif back_mode == BackpropMode.GRAD:
+            return 'grad'
+        elif back_mode == BackpropMode.DECONV_ZF:
+            return 'deconv zf'
+        elif back_mode == BackpropMode.DECONV_GB:
+            return 'deconv gb'
+
+        return 'n/a'
+
+class BackpropViewOption:
+    RAW = 0
+    RAW_POS = 1
+    RAW_NEG = 2
+    GRAY = 3
+    NORM = 4
+    NORM_BLUR = 5
+    NUMBER_OF_OPTIONS = 6
+
+    @staticmethod
+    def to_string(back_view_option):
+        if back_view_option == BackpropViewOption.RAW:
+            return 'raw'
+        elif back_view_option == BackpropViewOption.RAW_POS:
+            return 'raw>0'
+        elif back_view_option == BackpropViewOption.RAW_NEG:
+            return 'raw<0'
+        elif back_view_option == BackpropViewOption.GRAY:
+            return 'gray'
+        elif back_view_option == BackpropViewOption.NORM:
+            return 'norm'
+        elif back_view_option == BackpropViewOption.NORM_BLUR:
+            return 'normblur'
+
+        return 'n/a'
+
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
 
@@ -86,8 +132,8 @@ def _reset_user_state(self):
         self.backprop_selection_frozen = False    # If false, backprop unit tracks selected unit
         self.backprop_siamese_input_mode = SiameseInputMode.BOTH_IMAGES
         self.back_enabled = False
-        self.back_mode = 'grad'      # 'grad' or 'deconv'
-        self.back_filt_mode = 'raw'  # 'raw', 'gray', 'norm', 'normblur'
+        self.back_mode = BackpropMode.OFF
+        self.back_view_option = BackpropViewOption.RAW
         self.pattern_mode = PatternMode.OFF    # type of patterns to show instead of activations in layers pane: maximal optimized image, maximal input image, maximal histogram, off
         self.pattern_first_only = True         # should we load only the first pattern image for each neuron, or all the relevant images per neuron
         self.layers_pane_zoom_mode = 0       # 0: off, 1: zoom selected (and show pref in small pane), 2: zoom backprop
@@ -159,60 +205,27 @@ def handle_key(self, key):
                     self.layers_show_back = not self.layers_show_back
                 if self.layers_show_back:
                     if not self.back_enabled:
+                        if self.back_mode == BackpropMode.OFF:
+                            self.back_mode = BackpropMode.GRAD
                         self.back_enabled = True
                         self.back_stale = True
-            elif tag == 'back_mode':
-                if not self.back_enabled:
-                    self.back_enabled = True
-                    self.back_mode = 'grad'
-                    self.back_stale = True
-                else:
-                    if self.back_mode == 'grad':
-                        self.back_mode = 'deconv'
-                        self.back_stale = True
-                    else:
-                        self.back_enabled = False
-            elif tag == 'back_filt_mode':
-                    if self.back_filt_mode == 'raw':
-                        self.back_filt_mode = 'gray'
-                    elif self.back_filt_mode == 'gray':
-                        self.back_filt_mode = 'norm'
-                    elif self.back_filt_mode == 'norm':
-                        self.back_filt_mode = 'normblur'
-                    else:
-                        self.back_filt_mode = 'raw'
+
             elif tag == 'next_ez_back_mode_loop':
-                # Cycle:
-                # off -> grad (raw) -> grad(gray) -> grad(norm) -> grad(normblur) -> deconv
-                if not self.back_enabled:
-                    self.back_enabled = True
-                    self.back_mode = 'grad'
-                    self.back_filt_mode = 'raw'
-                    self.back_stale = True
-                elif self.back_mode == 'grad' and self.back_filt_mode == 'raw':
-                    self.back_filt_mode = 'norm'
-                elif self.back_mode == 'grad' and self.back_filt_mode == 'norm':
-                    self.back_mode = 'deconv'
-                    self.back_filt_mode = 'raw'
-                    self.back_stale = True
-                else:
-                    self.back_enabled = False
+                self.back_mode = (self.back_mode + 1) % BackpropMode.NUMBER_OF_MODES
+                self.back_enabled = (self.back_mode != BackpropMode.OFF)
+                self.back_stale = True
+
             elif tag == 'prev_ez_back_mode_loop':
-                    # Cycle:
-                    # off -> grad (raw) -> grad(gray) -> grad(norm) -> grad(normblur) -> deconv
-                    if not self.back_enabled:
-                        self.back_enabled = True
-                        self.back_mode = 'deconv'
-                        self.back_filt_mode = 'raw'
-                        self.back_stale = True
-                    elif self.back_mode == 'deconv':
-                        self.back_mode = 'grad'
-                        self.back_filt_mode = 'norm'
-                        self.back_stale = True
-                    elif self.back_mode == 'grad' and self.back_filt_mode == 'norm':
-                        self.back_filt_mode = 'raw'
-                    else:
-                        self.back_enabled = False
+                self.back_mode = (self.back_mode - 1 + BackpropMode.NUMBER_OF_MODES) % BackpropMode.NUMBER_OF_MODES
+                self.back_enabled = (self.back_mode != BackpropMode.OFF)
+                self.back_stale = True
+
+            elif tag == 'next_back_view_option':
+                self.back_view_option = (self.back_view_option + 1) % BackpropViewOption.NUMBER_OF_OPTIONS
+
+            elif tag == 'prev_back_view_option':
+                self.back_view_option = (self.back_view_option - 1 + BackpropViewOption.NUMBER_OF_OPTIONS) % BackpropViewOption.NUMBER_OF_OPTIONS
+
             elif tag == 'freeze_back_unit':
                 # Freeze selected layer/unit as backprop unit
                 self.backprop_selection_frozen = not self.backprop_selection_frozen
@@ -307,10 +320,10 @@ def backward_from_layer(self, net, backprop_layer_def, backprop_unit):
             with self.lock:
                 self.back_enabled = False
 
-    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit):
+    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit, deconv_type):
 
         try:
-            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_input_mode)
+            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_input_mode, deconv_type)
         except AttributeError:
             print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
             raise
diff --git a/deep-visualization-toolbox/siamese_helper.py b/deep-visualization-toolbox/siamese_helper.py
index 5847db7ca..5f2891635 100644
--- a/deep-visualization-toolbox/siamese_helper.py
+++ b/deep-visualization-toolbox/siamese_helper.py
@@ -382,7 +382,7 @@ def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mo
         pass
 
     @staticmethod
-    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode):
+    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode, deconv_type):
 
         # if we are in siamese_batch_pair, we don't care of siamese_input_mode since we must do deconv on the 2-batch
         # otherwise, if we are in siamese_layer_pair, we do it on both layers only if both deconv are requested
@@ -399,13 +399,13 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode
             diffs1 = expand_dims(diffs1, 0)
 
             if backprop_layer_def['format'] == 'siamese_layer_pair':
-                net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True)
-                net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True)
+                net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True, deconv_type=deconv_type)
+                net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True, deconv_type=deconv_type)
 
             elif backprop_layer_def['format'] == 'siamese_batch_pair':
                 # combine them to 2-batch and send once
                 diffs = concatenate((diffs0, diffs1), axis=0)
-                net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True)
+                net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True, deconv_type=deconv_type)
 
         else:
 
@@ -418,7 +418,7 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode
             diffs = expand_dims(diffs, 0)
 
             selected_backprop_layer_name = SiameseHelper._get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
-            net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True)
+            net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
 
     @staticmethod
     def get_image_from_frame(frame, is_siamese, image_shape, siamese_input_mode):
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 9b332e19f..113e6cb63 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -829,7 +829,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                                 diffs[i, batch[i].channel_idx] = 1.0
 
                         with WithTimer('Deconv    ', quiet = not do_print):
-                            net.deconv_from_layer(batch[i].denormalized_layer_name, diffs)
+                            net.deconv_from_layer(batch[i].denormalized_layer_name, diffs, 'Guided Backprop')
 
                         out_arr = extract_patch_from_image(net.blobs['data'].diff[i], net, batch[i].selected_input_index, settings,
                                                            batch[i].data_ii_end, batch[i].data_ii_start, batch[i].data_jj_end, batch[i].data_jj_start,

From 7aff13950245d5f28d51b47613f02a36a2b902a8 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 07:57:13 +0300
Subject: [PATCH 059/106] added softmax raw view option, and histogram view
 option

---
 caffe_misc.py                  | 33 +----------------
 caffevis/app.py                | 60 +++++++++++++------------------
 caffevis/caffevis_app_state.py | 22 +++++++-----
 image_misc.py                  | 65 ++++++++++++++++++++++++++++++++++
 4 files changed, 105 insertions(+), 75 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index 4963e9745..a015ecffb 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -2,7 +2,7 @@
 
 import skimage.io
 import numpy as np
-
+from image_misc import norm01c
 
 
 def shownet(net):
@@ -126,19 +126,6 @@ def convert_region(self, from_layer, to_layer, region, verbose = False, crop_to_
         return ret
 
 
-
-def norm01c(arr, center):
-    '''Maps the input range to [0,1] such that the center value maps to .5'''
-    arr = arr.copy()
-    arr -= center
-    arr /= max(2 * arr.max(), -2 * arr.min()) + 1e-10
-    arr += .5
-    assert arr.min() >= 0
-    assert arr.max() <= 1
-    return arr
-
-
-
 def save_caffe_image(img, filename, autoscale = True, autoscale_center = None):
     '''Takes an image in caffe format (01) or (c01, BGR) and saves it to a file'''
     if len(img.shape) == 2:
@@ -254,21 +241,3 @@ def extract_patch_from_image(data, net, selected_input_index, settings,
                                                                        data_ii_start:data_ii_end,
                                                                        data_jj_start:data_jj_end]
     return out_arr
-
-
-# def get_top_to_layer_dict(net):
-#
-#     top_to_layer = dict()
-#
-#     # go over layers
-#     for layer_name in list(net._layer_names):
-#
-#         if len(net.top_names[layer_name]) == 1 and len(net.bottom_names[layer_name]) == 1 and net.top_names[layer_name][0] == net.bottom_names[layer_name][0]:
-#             # skip inplace layers
-#             continue
-#
-#         # go over tops
-#         for top_name in net.top_names[layer_name]:
-#             top_to_layer[top_name] = layer_name
-#
-#     return top_to_layer
\ No newline at end of file
diff --git a/caffevis/app.py b/caffevis/app.py
index 884aa165e..7ad0fac80 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -18,9 +18,9 @@
 from misc import WithTimer, mkdir_p
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
-from image_misc import norm01, norm01c, norm0255, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
-    ensure_uint255_and_resize_to_fit, get_tiles_height_width, get_tiles_height_width_ratio, resize_without_fit, \
-    cv2_read_file_rgb, caffe_load_image, ensure_uint255_and_resize_without_fit
+from image_misc import norm01, norm01c, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
+    ensure_uint255_and_resize_to_fit, resize_without_fit, \
+    caffe_load_image, ensure_uint255_and_resize_without_fit, softmax_image, array_histogram, fig2data
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
@@ -670,22 +670,6 @@ def downsample_display_3d(self, display_3D_highres, layer_dat_3D, pane, tile_col
             display_3D = layer_dat_3D * 0  # nothing to show
         return display_3D
 
-    def fig2data(self, fig):
-        """
-        @brief Convert a Matplotlib figure to a 3D numpy array with RGB channels and return it
-        @param fig a matplotlib figure
-        @return a numpy 3D array of RGB values
-        """
-        # draw the renderer
-        fig.canvas.draw()
-
-        # Get the RGB buffer from the figure
-        w, h = fig.canvas.get_width_height()
-        buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
-        buf.shape = (w, h, 3)
-
-        return buf
-
 
     def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pane, tile_cols, tile_rows, show_layer_summary):
 
@@ -740,7 +724,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                 ax.xaxis.label.set_text('weight value')
                 ax.yaxis.label.set_text('count')
 
-                figure_buffer = self.fig2data(fig)
+                figure_buffer = fig2data(fig)
 
                 display_3D_highres[channel_idx, :, ::] = figure_buffer
 
@@ -750,7 +734,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
             try:
 
                 # handle generation of results container
-                figure_buffer = self.fig2data(fig)
+                figure_buffer = fig2data(fig)
                 first_shape = figure_buffer.shape
                 display_3D_highres = np.zeros((n_channels, first_shape[0], first_shape[1], first_shape[2]), dtype=np.uint8)
 
@@ -815,7 +799,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         ax.xaxis.label.set_text('weight value')
                         ax.yaxis.label.set_text('count')
 
-                        figure_buffer = self.fig2data(fig)
+                        figure_buffer = fig2data(fig)
                         display_3D_highres_summary_weights = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
 
                         ax.cla()
@@ -832,7 +816,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         ax.xaxis.label.set_text('bias value')
                         ax.yaxis.label.set_text('count')
 
-                        figure_buffer = self.fig2data(fig)
+                        figure_buffer = fig2data(fig)
                         display_3D_highres_summary_bias = ensure_uint255_and_resize_without_fit(figure_buffer, half_pane_shape)
 
                         display_3D_highres_summary = np.concatenate((display_3D_highres_summary_weights, display_3D_highres_summary_bias), axis=1)
@@ -930,7 +914,7 @@ def channel_to_histogram_values(channel_idx):
                 display_3D_highres_list = [display_3D_highres, display_3D_highres]
 
                 def process_channel_figure(channel_idx, fig):
-                    figure_buffer = self.fig2data(fig)
+                    figure_buffer = fig2data(fig)
 
                     # handle first generation of results container
                     if display_3D_highres_list[0] is None:
@@ -943,7 +927,7 @@ def process_channel_figure(channel_idx, fig):
                     pass
 
                 def process_layer_figure(fig):
-                    figure_buffer = self.fig2data(fig)
+                    figure_buffer = fig2data(fig)
                     display_3D_highres_list[1] = ensure_uint255_and_resize_without_fit(figure_buffer, pane_shape)
                     display_3D_highres_list[1] = np.expand_dims(display_3D_highres_list[1], 0)
                     pass
@@ -1033,7 +1017,7 @@ def _draw_back_pane(self, pane):
                 grad_img = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
 
             # define helper function ro run processing once or twice, in case of siamese network
-            def run_processing_once_or_twice(image, process_image_fn):
+            def run_processing_once_or_twice(image, resize_shape, process_image_fn):
 
                 # if siamese network, run processing twice
                 if self.settings.is_siamese:
@@ -1058,8 +1042,7 @@ def run_processing_once_or_twice(image, process_image_fn):
                         image1 = process_image_fn(image1)
 
                         # resize each gradient image to half the pane size
-                        half_pane_shape = (image.shape[0], image.shape[1] / 2)
-                        # half_pane_shape = (pane.data.shape[1] / 2, pane.data.shape[0])
+                        half_pane_shape = (resize_shape[0], resize_shape[1] / 2)
 
                         image0 = resize_without_fit(image0[:], half_pane_shape)
                         image1 = resize_without_fit(image1[:], half_pane_shape)
@@ -1074,27 +1057,34 @@ def run_processing_once_or_twice(image, process_image_fn):
 
                 raise Exception("flow should not arrive here")
 
-            if back_view_option == BackpropViewOption.RAW:
-                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0))
+            if back_view_option == BackpropViewOption.SOFTMAX_RAW:
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: softmax_image(grad_img))
+
+            elif back_view_option == BackpropViewOption.RAW:
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0))
 
             elif back_view_option == BackpropViewOption.RAW_POS:
-                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0) * (grad_img > 0) + (0.5) * (grad_img <= 0))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0) * (grad_img > 0) + (0.5) * (grad_img <= 0))
 
             elif back_view_option == BackpropViewOption.RAW_NEG:
-                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img, 0) * (grad_img < 0) + (0.5) * (grad_img >= 0))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0) * (grad_img < 0) + (0.5) * (grad_img >= 0))
 
             elif back_view_option == BackpropViewOption.GRAY:
-                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01c(grad_img.mean(axis=2), 0))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img.mean(axis=2), 0))
 
             elif back_view_option == BackpropViewOption.NORM:
-                grad_img = run_processing_once_or_twice(grad_img, lambda grad_img: norm01(np.linalg.norm(grad_img, axis=2)))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01(np.linalg.norm(grad_img, axis=2)))
 
             elif back_view_option == BackpropViewOption.NORM_BLUR:
                 def do_norm_blur(grad_img):
                     grad_img = np.linalg.norm(grad_img, axis=2)
                     cv2.GaussianBlur(grad_img, (0, 0), self.settings.caffevis_grad_norm_blur_radius, grad_img)
                     return norm01(grad_img)
-                grad_img = run_processing_once_or_twice(grad_img, do_norm_blur)
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_norm_blur)
+
+            elif back_view_option == BackpropViewOption.HISTOGRAM:
+                half_pane_shape = (pane.data.shape[0],pane.data.shape[1]/2,3)
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: array_histogram(grad_img, half_pane_shape, BackpropMode.to_string(back_mode)+' histogram', 'values', 'count'))
 
             else:
                 raise Exception('Invalid option for back_view_option: %s' % (back_view_option))
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 1287d755e..4b8b7933d 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -34,17 +34,21 @@ def to_string(back_mode):
         return 'n/a'
 
 class BackpropViewOption:
-    RAW = 0
-    RAW_POS = 1
-    RAW_NEG = 2
-    GRAY = 3
-    NORM = 4
-    NORM_BLUR = 5
-    NUMBER_OF_OPTIONS = 6
+    SOFTMAX_RAW = 0
+    RAW = 1
+    RAW_POS = 2
+    RAW_NEG = 3
+    GRAY = 4
+    NORM = 5
+    NORM_BLUR = 6
+    HISTOGRAM = 7
+    NUMBER_OF_OPTIONS = 8
 
     @staticmethod
     def to_string(back_view_option):
-        if back_view_option == BackpropViewOption.RAW:
+        if back_view_option == BackpropViewOption.SOFTMAX_RAW:
+            return 'softmax raw'
+        elif back_view_option == BackpropViewOption.RAW:
             return 'raw'
         elif back_view_option == BackpropViewOption.RAW_POS:
             return 'raw>0'
@@ -56,6 +60,8 @@ def to_string(back_view_option):
             return 'norm'
         elif back_view_option == BackpropViewOption.NORM_BLUR:
             return 'normblur'
+        elif back_view_option == BackpropViewOption.HISTOGRAM:
+            return 'histogram'
 
         return 'n/a'
 
diff --git a/image_misc.py b/image_misc.py
index 2b72bbf4b..2f5fa2e9c 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -8,6 +8,71 @@
 
 from misc import WithTimer
 
+def softmax_image(arr):
+    '''Maps the input range to [0,1] using softmax'''
+
+    arr = arr.copy()
+    exp_arr = np.exp(arr)
+    sum_exp_arr = np.sum(exp_arr, axis=(0,1))
+    normalized_exp_arr = exp_arr / sum_exp_arr
+
+    print "arr", arr
+    print "exp_arr", exp_arr
+    print "sum_exp_arr", sum_exp_arr
+    print "normalized_exp_arr", normalized_exp_arr
+
+
+    print "arr.shape", arr.shape, arr.max(), arr.min()
+    print "exp_arr.shape", exp_arr.shape, exp_arr.max(), exp_arr.min()
+    print "sum_exp_arr.shape", sum_exp_arr.shape, sum_exp_arr.max(), sum_exp_arr.min()
+    print "normalized_exp_arr.shape", normalized_exp_arr.shape, normalized_exp_arr.max(), normalized_exp_arr.min()
+
+    assert normalized_exp_arr.min() >= 0
+    assert normalized_exp_arr.max() <= 1
+    return normalized_exp_arr
+
+
+def fig2data(fig):
+    """
+    @brief Convert a Matplotlib figure to a 3D numpy array with RGB channels and return it
+    @param fig a matplotlib figure
+    @return a numpy 3D array of RGB values
+    """
+    # draw the renderer
+    fig.canvas.draw()
+
+    # Get the RGB buffer from the figure
+    w, h = fig.canvas.get_width_height()
+    buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
+    buf.shape = (w, h, 3)
+
+    return buf
+
+
+def array_histogram(arr, histogram_pane_shape, title, xlabel, ylabel):
+
+    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
+    from matplotlib.figure import Figure
+    fig = Figure(figsize=(10, 10))
+    canvas = FigureCanvas(fig)
+    ax = fig.add_subplot(111)
+
+    # generate histogram
+    values = arr.flatten()
+    hist, bin_edges = np.histogram(values, bins=50)
+
+    width = 0.7 * (bin_edges[1] - bin_edges[0])
+    center = (bin_edges[:-1] + bin_edges[1:]) / 2
+    ax.bar(center, hist, align='center', width=width, color='g')
+
+    fig.suptitle(title)
+    ax.xaxis.label.set_text(xlabel)
+    ax.yaxis.label.set_text(ylabel)
+
+    figure_buffer = fig2data(fig)
+    # result = ensure_uint255_and_resize_without_fit(figure_buffer, histogram_pane_shape)
+    return figure_buffer
+
 
 def norm01(arr):
     arr = arr.copy()

From 751298fec18898a4d03ef2a21696eff4a2464495 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:00:49 +0300
Subject: [PATCH 060/106] organized a little the help screen

---
 bindings.py     | 4 ++--
 caffevis/app.py | 4 ++--
 2 files changed, 4 insertions(+), 4 deletions(-)

diff --git a/bindings.py b/bindings.py
index e870c3879..e0ee978f5 100644
--- a/bindings.py
+++ b/bindings.py
@@ -102,9 +102,9 @@ def get_key_help(self, tag):
 _.add('zoom_mode', 'z',
        'Cycle zooming through {currently selected unit, backprop results, none}')
 _.add('next_pattern_mode', 's',
-       'Cycle channels overlay (max opt, max input, max hist, off)')
+       'Cycle channels overlay (max opt, max input, weights hist, max hist, off)')
 _.add('prev_pattern_mode', 'S',
-       'Cycle channels overlay (max opt, max input, max hist, off)')
+       'Cycle patterns overlay (max opt, max input, weights hist, max hist, off)')
 _.add('pattern_first_only', '1',
        'Toggle pattern loading first image only or loading all available images')
 
diff --git a/caffevis/app.py b/caffevis/app.py
index 7ad0fac80..1ed55177c 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -1189,8 +1189,8 @@ def draw_help(self, help_pane, locy):
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
                     'next_ez_back_mode_loop', 'next_back_view_option', 'freeze_back_unit', 'show_back',
-                    'boost_gamma', 'boost_individual', 'reset_state', 'siamese_input_mode', 'toggle_maximal_score',
-                    'toggle_input_overlay_in_aux_pane'):
+                    'boost_gamma', 'boost_individual', 'siamese_input_mode', 'toggle_maximal_score',
+                    'toggle_input_overlay_in_aux_pane', 'reset_state'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
             lines.append([FormattedString(label, defaults, width=120, align='right'),

From 44943e0d6dc23c29fc0f82697df72bfefec7c456 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:09:35 +0300
Subject: [PATCH 061/106] added settings_model_selector.py renamed file
 settings_user_template.py to settings_user.py.example

---
 caffevis/app.py                               | 26 +++++++++++++------
 caffevis/caffevis_helper.py                   |  4 +--
 deep-visualization-toolbox/run_webui.py       |  7 ++---
 .../settings_model_selector.py                | 23 ++++++++++++++++
 ...r_template.py => settings_user.py.example} |  0
 .../settings_local.template-bvlc-googlenet.py |  6 +----
 .../settings_local.template-squeezenet.py     |  6 +----
 optimize_image.py                             |  2 +-
 run_toolbox.py                                |  7 ++---
 settings.py                                   | 16 ++++++------
 10 files changed, 58 insertions(+), 39 deletions(-)
 create mode 100644 deep-visualization-toolbox/settings_model_selector.py
 rename deep-visualization-toolbox/{settings_user_template.py => settings_user.py.example} (100%)

diff --git a/caffevis/app.py b/caffevis/app.py
index 1ed55177c..035f5994c 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -531,14 +531,24 @@ def _add_label_or_score_overlay(self, default_layer_name, pane):
             if (default_layer_name in self.settings.caffevis_score_layers):
 
                 if self.state.siamese_input_mode_has_two_images():
-                    blob1, blob2 = self.state.get_siamese_selected_data_blobs(self.net)
-                    value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
-                    text_to_display += str(value1) + " " + str(value2)
+                    if self.state.layers_show_back:
+                        blob1, blob2 = self.state.get_siamese_selected_diff_blobs(self.net)
+                        value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
+                        text_to_display += 'grad: ' + str(value1) + " " + str(value2)
+                    else:
+                        blob1, blob2 = self.state.get_siamese_selected_data_blobs(self.net)
+                        value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
+                        text_to_display += 'act: ' + str(value1) + " " + str(value2)
 
                 else:
-                    blob = self.state.get_single_selected_data_blob(self.net)
-                    value = blob[self.state.selected_unit]
-                    text_to_display += str(value)
+                    if self.state.layers_show_back:
+                        blob = self.state.get_single_selected_diff_blob(self.net)
+                        value = blob[self.state.selected_unit]
+                        text_to_display += 'grad: ' + str(value)
+                    else:
+                        blob = self.state.get_single_selected_data_blob(self.net)
+                        value = blob[self.state.selected_unit]
+                        text_to_display += 'act: ' + str(value)
 
             lines = [FormattedString(text_to_display, defaults)]
             cv2_typeset_text(pane.data, lines, loc_base)
@@ -1058,7 +1068,7 @@ def run_processing_once_or_twice(image, resize_shape, process_image_fn):
                 raise Exception("flow should not arrive here")
 
             if back_view_option == BackpropViewOption.SOFTMAX_RAW:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: softmax_image(grad_img))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: softmax_image(norm01c(grad_img, 0)))
 
             elif back_view_option == BackpropViewOption.RAW:
                 grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0))
@@ -1111,7 +1121,7 @@ def _draw_jpgvis_pane(self, pane):
             # Some may be missing this setting
             self.settings.caffevis_jpgvis_layers
         except:
-            print '\n\nNOTE: you need to upgrade your settings.py and settings_local.py files. See README.md.\n\n'
+            print '\n\nNOTE: you need to upgrade your settings.py and settings_model_selector.py files. See README.md.\n\n'
             raise
             
         if self.settings.caffevis_jpgvis_remap and state_layer_name in self.settings.caffevis_jpgvis_remap:
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index f15b2f340..5469638ad 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -41,9 +41,9 @@ def get_pretty_layer_name(settings, layer_name):
     has_old_settings = hasattr(settings, 'caffevis_layer_pretty_names')
     has_new_settings = hasattr(settings, 'caffevis_layer_pretty_name_fn')
     if has_old_settings and not has_new_settings:
-        print ('WARNING: Your settings.py and/or settings_local.py are out of date.'
+        print ('WARNING: Your settings.py and/or settings_model_selector.py are out of date.'
                'caffevis_layer_pretty_names has been replaced with caffevis_layer_pretty_name_fn.'
-               'Update your settings.py and/or settings_local.py (see documentation in'
+               'Update your settings.py and/or settings_model_selector.py (see documentation in'
                'setttings.py) to remove this warning.')
         return settings.caffevis_layer_pretty_names.get(layer_name, layer_name)
 
diff --git a/deep-visualization-toolbox/run_webui.py b/deep-visualization-toolbox/run_webui.py
index 47ad22908..8e41ba7aa 100644
--- a/deep-visualization-toolbox/run_webui.py
+++ b/deep-visualization-toolbox/run_webui.py
@@ -8,11 +8,8 @@
     import settings
 except:
     print '\nError importing settings.py. Check the error message below for more information.'
-    print "If you haven't already, you'll want to copy one of the settings_local.template-*.py files"
-    print 'to settings_local.py and edit it to point to your caffe checkout. E.g. via:'
-    print
-    print '  $ cp models/caffenet_yos/settings_local_template_caffenet_yos.py settings_local.py'
-    print '  $ < edit settings_local.py >\n'
+    print "If you haven't already, you'll want to open the settings_model_selector.py file"
+    print 'and edit it to point to your caffe checkout.\n'
     raise
 
 if not os.path.exists(settings.caffevis_caffe_root):
diff --git a/deep-visualization-toolbox/settings_model_selector.py b/deep-visualization-toolbox/settings_model_selector.py
new file mode 100644
index 000000000..933c1b9f1
--- /dev/null
+++ b/deep-visualization-toolbox/settings_model_selector.py
@@ -0,0 +1,23 @@
+
+# Import user settings. Turn off creation of X.pyc to avoid stale settings if X.py is removed.
+import os
+import sys
+sys.dont_write_bytecode = True
+try:
+    from settings_user import *
+except ImportError:
+    if not os.path.exists('settings_user.py'):
+        raise Exception('Could not import settings_user. Did you create it from the template? Start with:\n\n  $ cp settings_user_template.py settings_user.py')
+    else:
+        raise
+# Resume usual pyc creation
+sys.dont_write_bytecode = False
+
+caffevis_caffe_root = base_folder + '/caffe'
+
+if model_to_load == 'caffenet_yos':
+    from models.caffenet_yos.settings_caffenet_yos import *
+
+elif model_to_load == 'other_model':
+    from models.other_model.settings_other_model import *
+
diff --git a/deep-visualization-toolbox/settings_user_template.py b/deep-visualization-toolbox/settings_user.py.example
similarity index 100%
rename from deep-visualization-toolbox/settings_user_template.py
rename to deep-visualization-toolbox/settings_user.py.example
diff --git a/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py b/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
index 339a5738e..b81eaf949 100644
--- a/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
+++ b/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
@@ -1,9 +1,5 @@
 # Define critical settings and/or override defaults specified in
-# settings.py. Copy this file to settings_local.py in the same
-# directory as settings.py and edit. Any settings defined here
-# will override those defined in settings.py
-
-
+# settings.py. Any settings defined here will override those defined in settings.py
 
 # Set this to point to your compiled checkout of caffe
 caffevis_caffe_root      = '/path/to/caffe'
diff --git a/models/squeezenet/settings_local.template-squeezenet.py b/models/squeezenet/settings_local.template-squeezenet.py
index 71b379ef1..90398d2f0 100644
--- a/models/squeezenet/settings_local.template-squeezenet.py
+++ b/models/squeezenet/settings_local.template-squeezenet.py
@@ -1,9 +1,5 @@
 # Define critical settings and/or override defaults specified in
-# settings.py. Copy this file to settings_local.py in the same
-# directory as settings.py and edit. Any settings defined here
-# will override those defined in settings.py
-
-
+# settings.py. Any settings defined here will override those defined in settings.py
 
 # Set this to point to your compiled checkout of caffe
 caffevis_caffe_root      = '/path/to/caffe'
diff --git a/optimize_image.py b/optimize_image.py
index 9977e154e..89ac91d89 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -14,7 +14,7 @@
 
 
 def get_parser():
-    parser = argparse.ArgumentParser(description='Script to find, with or without regularization, images that cause high or low activations of specific neurons in a network via numerical optimization. Settings are read from settings.py, overridden in settings_local.py, and may be further overridden on the command line.',
+    parser = argparse.ArgumentParser(description='Script to find, with or without regularization, images that cause high or low activations of specific neurons in a network via numerical optimization. Settings are read from settings.py, overridden in settings_MODEL.py and settings_user.py, and may be further overridden on the command line.',
                                      formatter_class=lambda prog: argparse.ArgumentDefaultsHelpFormatter(prog, width=100)
     )
 
diff --git a/run_toolbox.py b/run_toolbox.py
index b342fd4ae..97ba4f2e8 100755
--- a/run_toolbox.py
+++ b/run_toolbox.py
@@ -7,11 +7,8 @@
     import settings
 except:
     print '\nError importing settings.py. Check the error message below for more information.'
-    print "If you haven't already, you'll want to copy one of the settings_local.template-*.py files"
-    print 'to settings_local.py and edit it to point to your caffe checkout. E.g. via:'
-    print
-    print '  $ cp models/caffenet-yos/settings_local.template-caffenet-yos.py settings_local.py'
-    print '  $ < edit settings_local.py >\n'
+    print "If you haven't already, you'll want to open the settings_model_selector.py file"
+    print 'and edit it to point to your caffe checkout.\n'
     raise
 
 if not os.path.exists(settings.caffevis_caffe_root):
diff --git a/settings.py b/settings.py
index 759e3cc82..989bfe24c 100644
--- a/settings.py
+++ b/settings.py
@@ -1,18 +1,17 @@
 # Settings for Deep Visualization Toolbox
 #
 # Note: Probably don't change anything in this file. To override
-# settings, define them in settings_local.py rather than changing them
-# here.
+# settings, define them in your network specific settings file or settings_user.py rather than changing them here.
 
 # Import network settings. Turn off creation of X.pyc to avoid stale settings if X.py is removed.
 import os
 import sys
 sys.dont_write_bytecode = True
 try:
-    from settings_local import *
+    from settings_model_selector import *
 except ImportError:
-    if not os.path.exists('settings_local.py'):
-        raise Exception('Could not import settings_local. Did you create it from the template? See README and start with:\n\n  $ cp models/caffenet-yos/settings_local.template-caffenet-yos.py settings_local.py')
+    if not os.path.exists('settings_model_selector.py'):
+        raise Exception('Could not import settings_model_selector.py')
     else:
         raise
 # Resume usual pyc creation
@@ -68,7 +67,7 @@
 # height of the control panel (to accomodate varying length of layer
 # names), one can simply define control_pane_height. If more
 if 'default_window_panes' in locals():
-    raise Exception('Override window panes in settings_local.py by defining window_panes, not default_window_panes')
+    raise Exception('Override window panes in settings_MODEL.py by defining window_panes, not default_window_panes')
 
 control_pane_height = locals().get('control_pane_height', 30)
 
@@ -459,7 +458,7 @@
 bound_locals = locals()
 def assert_in_settings(setting_name):
     if not setting_name in bound_locals:
-        raise Exception('The "%s" setting is required; be sure to define it in settings_local.py' % setting_name)
+        raise Exception('The "%s" setting is required; be sure to define it in settings_MODEL.py' % setting_name)
 
 # Set this to point to your compiled checkout of caffe
 assert_in_settings('caffevis_caffe_root')
@@ -473,4 +472,5 @@ def assert_in_settings(setting_name):
 
 # Check that caffe directory actually exists
 if not os.path.exists(caffevis_caffe_root):
-    raise Exception('The Caffe directory specified in settings_local.py, %s, does not exist. Set the caffevis_caffe_root variable in your settings_local.py to the path of your compiled Caffe checkout.' % caffevis_caffe_root)
+    raise Exception('The Caffe directory specified in settings_model_selector.py, %s, does not exist. Set the caffevis_caffe_root variable in your settings_model_selector.py to the path of your compiled Caffe checkout.' % caffevis_caffe_root)
+

From b25cc29cb6637e939de64e7367386ba229a980b7 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:11:37 +0300
Subject: [PATCH 062/106] fixed bug when data_mean is empty

---
 caffevis/app.py             | 6 +++---
 caffevis/caffevis_helper.py | 3 +--
 2 files changed, 4 insertions(+), 5 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 035f5994c..c72e1280e 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -62,9 +62,9 @@ def __init__(self, settings, key_bindings):
         self.net = caffe.Classifier(
             settings.caffevis_deploy_prototxt,
             settings.caffevis_network_weights,
-            mean = None, # Set to None for now, assign later
-            channel_swap = self._net_channel_swap,
-            raw_scale = settings.caffe_net_raw_scale,
+            mean=None, # Set to None for now, assign later
+            channel_swap=self._net_channel_swap,
+            raw_scale=settings.caffe_net_raw_scale,
             image_dims=settings.caffe_net_image_dims,
         )
 
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index 5469638ad..eae8d97c1 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -196,9 +196,8 @@ def set_mean(caffevis_data_mean, generate_channelwise_mean, net):
     if generate_channelwise_mean:
         data_mean = data_mean.mean(1).mean(1)
 
-    print 'Using mean with shape:', data_mean.shape
-
     if data_mean is not None:
+        print 'Using mean with shape:', data_mean.shape
         net.transformer.set_mean(net.inputs[0], data_mean)
 
     return data_mean

From 497349e9464ecb34623cfc20ec9798e4ba5bb843 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:25:32 +0300
Subject: [PATCH 063/106] added support for grayscale models

---
 caffe_misc.py                               | 21 +++---
 caffevis/app.py                             | 72 ++++++++++++---------
 caffevis/caffevis_helper.py                 |  4 +-
 deep-visualization-toolbox/settings_misc.py | 30 +++++++++
 find_maxes/crop_max_patches.py              |  3 +
 find_maxes/find_max_acts.py                 |  8 +++
 find_maxes/max_tracker.py                   | 58 ++++++++---------
 image_misc.py                               | 48 +++++++++++---
 input_fetcher.py                            | 23 ++-----
 live_vis.py                                 |  5 +-
 misc.py                                     | 19 ++++++
 optimize/gradient_optimizer.py              | 69 ++++++++++++--------
 optimize_image.py                           | 60 +++++++++++++----
 settings.py                                 |  8 ++-
 14 files changed, 285 insertions(+), 143 deletions(-)
 create mode 100644 deep-visualization-toolbox/settings_misc.py

diff --git a/caffe_misc.py b/caffe_misc.py
index a015ecffb..1e57974ac 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -92,15 +92,18 @@ def __init__(self, layers_list):
 
         _tmp = []
 
-        for (name, type, input, output, filter, stride, pad) in layers_list:
-            if type == 'Input':
-                _tmp.append((name, None))
-            elif type == 'Convolution':
-                _tmp.append((name, get_conv_converter(input[0:2], output[0:2], filter, stride, pad)))
-            elif type == 'Pooling':
-                _tmp.append((name, get_pool_converter(input[0:2], output[0:2], filter, stride, pad)))
-            else:
-                continue # skip adding layer
+        (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = (None, None, None, None, None, None, None)
+        for (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad) in layers_list:
+            if current_type == 'Input':
+                _tmp.append((current_name, None))
+            elif current_type == 'Convolution':
+                _tmp.append((current_name, get_conv_converter(current_input[0:2], current_output[0:2], current_filter, current_stride, current_pad)))
+            elif current_type == 'Pooling':
+                _tmp.append((current_name, get_pool_converter(current_input[0:2], current_output[0:2], current_filter, current_stride, current_pad)))
+            elif current_type == 'Eltwise' and previous_type == 'Convolution':
+                _tmp.append((current_name, get_conv_converter(previous_input[0:2], previous_output[0:2], previous_filter, previous_stride, previous_pad)))
+
+            (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad)
 
         self.names = [tt[0] for tt in _tmp]
         self.converters = [tt[1] for tt in _tmp]
diff --git a/caffevis/app.py b/caffevis/app.py
index c72e1280e..1f715b460 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -29,6 +29,7 @@
     check_force_backward_true, set_mean, get_image_from_files
 from caffe_misc import layer_name_to_top_name, save_caffe_image
 from siamese_helper import SiameseHelper
+from settings_misc import deduce_calculated_settings
 
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
@@ -68,6 +69,8 @@ def __init__(self, settings, key_bindings):
             image_dims=settings.caffe_net_image_dims,
         )
 
+        deduce_calculated_settings(settings, self.net)
+
         self._data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, self.net)
 
         check_force_backward_true(settings.caffevis_deploy_prototxt)
@@ -561,9 +564,8 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
             load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
-        if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers:
-            jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir,
-                                    'regularized_opt', load_layer, 'whole_layer.jpg')
+        if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers and self.settings.caffevis_unit_jpg_dir:
+            jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, 'regularized_opt', load_layer, 'whole_layer.jpg')
 
             # Get highres version
             display_3D_highres = self.img_cache.get((jpg_path, 'whole'), None)
@@ -603,22 +605,23 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
             if display_3D_highres is None:
                 try:
 
-                    resize_shape = pane.data.shape
+                    if self.settings.caffevis_unit_jpg_dir:
+                        resize_shape = pane.data.shape
 
-                    if show_layer_summary:
-                        # load layer summary image
-                        layer_summary_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, file_summary_pattern)
-                        layer_summary_image = caffe_load_image(layer_summary_image_path, color=True, as_uint=True)
-                        layer_summary_image = ensure_uint255_and_resize_without_fit(layer_summary_image, resize_shape)
-                        display_3D_highres = layer_summary_image
-                        display_3D_highres = np.expand_dims(display_3D_highres, 0)
-                        display_2D = display_3D_highres[0]
-                        is_layer_summary_loaded = True
+                        if show_layer_summary:
+                            # load layer summary image
+                            layer_summary_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, file_summary_pattern)
+                            layer_summary_image = caffe_load_image(layer_summary_image_path, color=True, as_uint=True)
+                            layer_summary_image = ensure_uint255_and_resize_without_fit(layer_summary_image, resize_shape)
+                            display_3D_highres = layer_summary_image
+                            display_3D_highres = np.expand_dims(display_3D_highres, 0)
+                            display_2D = display_3D_highres[0]
+                            is_layer_summary_loaded = True
 
-                    else:
-                        with WithTimer('CaffeVisApp:load_image_per_unit', quiet=self.debug_level < 1):
-                            # load all images
-                            display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
+                        else:
+                            with WithTimer('CaffeVisApp:load_image_per_unit', quiet=self.debug_level < 1):
+                                # load all images
+                                display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
 
                 except IOError:
                     # File does not exist, so just display disabled.
@@ -640,8 +643,7 @@ def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_o
         # for each neuron in layer
         for unit_id in range(0, units_num):
 
-            unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer,
-                                            "unit_%04d" % (unit_id), file_search_pattern)
+            unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d" % (unit_id), file_search_pattern)
 
             try:
 
@@ -696,11 +698,17 @@ def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pan
         display_2D = self.img_cache.get(pattern_image_key_2d, None)
 
         if display_3D_highres is None or display_2D is None:
+
             pane_shape = pane.data.shape
 
-            folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name)
-            cache_layer_weights_histogram_image_path = os.path.join(folder_path, 'layer_weights_histogram.png')
-            cache_details_weights_histogram_image_path = os.path.join(folder_path, 'details_weights_histogram.png')
+            if not self.settings.caffevis_unit_jpg_dir:
+                folder_path = None
+                cache_layer_weights_histogram_image_path = None
+                cache_details_weights_histogram_image_path = None
+            else:
+                folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name)
+                cache_layer_weights_histogram_image_path = os.path.join(folder_path, 'layer_weights_histogram.png')
+                cache_details_weights_histogram_image_path = os.path.join(folder_path, 'details_weights_histogram.png')
 
             # plotting objects needed for
             # 1. calculating size of results array
@@ -752,7 +760,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                 if show_layer_summary:
 
                     # try load cache file for layer weight histogram
-                    if os.path.exists(cache_layer_weights_histogram_image_path):
+                    if cache_layer_weights_histogram_image_path and os.path.exists(cache_layer_weights_histogram_image_path):
 
                         # load 2d image from cache file
                         display_2D = caffe_load_image(cache_layer_weights_histogram_image_path, color=True, as_uint=False)
@@ -762,7 +770,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                 else:
 
                     # try load cache file for details weights histogram
-                    if os.path.exists(cache_details_weights_histogram_image_path):
+                    if cache_details_weights_histogram_image_path and os.path.exists(cache_details_weights_histogram_image_path):
 
                         # load 2d image from cache file
                         display_2D = caffe_load_image(cache_details_weights_histogram_image_path, color=True, as_uint=False)
@@ -836,8 +844,11 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         display_2D = display_3D_highres[0]
                         is_layer_summary_loaded = True
 
-                        mkdir_p(folder_path)
-                        save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
+                        if folder_path:
+                            mkdir_p(folder_path)
+                            save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
+                        else:
+                            print "WARNING: unable to save weight histogram to cache since caffevis_unit_jpg_dir is not set"
 
                     else:
 
@@ -850,9 +861,12 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         # generate display of details weights histogram image
                         display_2D = self.prepare_tile_image(display_3D, False, n_channels, tile_rows, tile_cols)
 
-                        # save histogram image to cache
-                        mkdir_p(folder_path)
-                        save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
+                        if folder_path:
+                            # save histogram image to cache
+                            mkdir_p(folder_path)
+                            save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
+                        else:
+                            print "WARNING: unable to save weight histogram to cache since caffevis_unit_jpg_dir is not set"
 
                         # generate empty highlights
                         display_2D_highlights_only = self.prepare_tile_image(display_3D * 0, True, n_channels, tile_rows, tile_cols)
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index eae8d97c1..0113829c9 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -14,7 +14,9 @@ def net_preproc_forward(settings, net, img, data_hw):
 
     if settings.is_siamese and img.shape[2] == 6:
         appropriate_shape = data_hw + (6,)
-    else:
+    elif settings._calculated_is_gray_model:
+        appropriate_shape = data_hw + (1,)
+    else: # default is color
         appropriate_shape = data_hw + (3,)
 
     assert img.shape == appropriate_shape, 'img is wrong size (got %s but expected %s)' % (img.shape, appropriate_shape)
diff --git a/deep-visualization-toolbox/settings_misc.py b/deep-visualization-toolbox/settings_misc.py
new file mode 100644
index 000000000..3bc5c1bba
--- /dev/null
+++ b/deep-visualization-toolbox/settings_misc.py
@@ -0,0 +1,30 @@
+
+def deduce_calculated_settings(settings, net):
+    set_calculated_is_gray_model(settings, net)
+    set_calculated_image_dims(settings, net)
+
+
+def set_calculated_is_gray_model(settings, net):
+    if settings.is_gray_model is not None:
+        settings._calculated_is_gray_model = settings.is_gray_model
+    else:
+        input_shape = net.blobs[net.inputs[0]].data.shape
+        channels = input_shape[1]
+        if channels == 1:
+            settings._calculated_is_gray_model = True
+        elif channels == 2 and setings.is_siamese:
+            settings._calculated_is_gray_model = True
+        elif channels == 3:
+            settings._calculated_is_gray_model = False
+        elif channels == 6 and settings.is_siamese:
+            settings._calculated_is_gray_model = False
+        else:
+            settings._calculated_is_gray_model = None
+
+
+def set_calculated_image_dims(settings, net):
+    if settings.caffe_net_image_dims is not None:
+        settings._calculated_image_dims = settings.caffe_net_image_dims
+    else:
+        input_shape = net.blobs[net.inputs[0]].data.shape
+        settings._calculated_image_dims = input_shape[2:4]
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 0af2476a5..848df2b64 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -17,6 +17,7 @@
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
 from find_max_acts import load_max_tracker_from_file
+from settings_misc import deduce_calculated_settings
 
 
 def main():
@@ -60,6 +61,8 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    deduce_calculated_settings(settings, net)
+
     # validate batch size
     if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 4f4d56d3f..f231ffc31 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -16,6 +16,8 @@
 from caffevis.caffevis_helper import set_mean
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
+from settings_misc import deduce_calculated_settings
+from misc import mkdir_p
 
 def pickle_to_text(pickle_filename):
 
@@ -63,6 +65,8 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    deduce_calculated_settings(settings, net)
+
     # validate batch size
     if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
@@ -84,6 +88,10 @@ def main():
 
 
 def save_max_tracker_to_file(filename, net_max_tracker):
+
+    dir_name = os.path.dirname(filename)
+    mkdir_p(dir_name)
+
     with WithTimer('Saving maxes'):
         with open(filename, 'wb') as ff:
             pickle.dump(net_max_tracker, ff, -1)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 113e6cb63..b1c8ed234 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -7,12 +7,10 @@
 import cv2
 
 import numpy as np
-from misc import mkdir_p
+from misc import mkdir_p, get_files_list
 from image_misc import cv2_read_file_rgb, resize_without_fit
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
     compute_data_layer_focus_area, layer_name_to_top_name
-from misc import get_files_from_image_list
-from misc import get_files_from_siamese_image_list
 from siamese_helper import SiameseHelper
 
 from jby_misc import WithTimer
@@ -387,22 +385,8 @@ def __setstate__(self, state):
         self.settings = None
         self.siamese_helper = None
 
-
-def load_file_list(settings):
-
-    if (settings.static_files_input_mode == "image_list") and (not settings.is_siamese):
-        available_files, labels = get_files_from_image_list(settings)
-
-    elif (settings.static_files_input_mode == "image_list") and (settings.is_siamese):
-        available_files, labels = get_files_from_siamese_image_list(settings)
-
-    converted_labels = [settings.convert_label_fn(label) for label in labels]
-
-    return available_files, converted_labels
-
-
 def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
-    image_filenames, image_labels = load_file_list(settings)
+    image_filenames, image_labels = get_files_list(settings, True)
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
 
@@ -424,14 +408,17 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
         batch[batch_index].image_idx = image_idx
         batch[batch_index].filename = image_filenames[image_idx]
-        batch[batch_index].image_class = image_labels[image_idx]
+        if image_labels:
+            batch[batch_index].image_class = image_labels[image_idx]
+        else: # no labels so just put a default value
+            batch[batch_index].image_class = 0
 
         do_print = (batch[batch_index].image_idx % 100 == 0)
         if do_print:
             print '%s   Image %d/%d' % (datetime.now().ctime(), batch[batch_index].image_idx, len(image_filenames))
 
         with WithTimer('Load image', quiet = not do_print):
-            batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename))
+            batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
             batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
             batch[batch_index].im = batch[batch_index].im.astype(np.float32)
 
@@ -461,7 +448,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
 
 def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
-    image_filenames, image_labels = load_file_list(settings)
+    image_filenames, image_labels = get_files_list(settings, True)
     print 'Scanning %d pairs' % len(image_filenames)
     print '  First pair', image_filenames[0]
 
@@ -485,14 +472,18 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
         batch[batch_index].images_pair = image_filenames[image_idx]
         filename1 = batch[batch_index].images_pair[0]
         filename2 = batch[batch_index].images_pair[1]
-        batch[batch_index].image_class = image_labels[image_idx]
+        if image_labels:
+            batch[batch_index].image_class = image_labels[image_idx]
+        else:  # no labels so just put a default value
+            batch[batch_index].image_class = 0
+
         do_print = (image_idx % 100 == 0)
         if do_print:
             print '%s   Pair %d/%d' % (datetime.now().ctime(), batch[batch_index].image_idx, len(image_filenames))
 
         with WithTimer('Load image', quiet=not do_print):
-            im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
-            im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
+            im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
+            im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
             im1 = resize_without_fit(im1, net_input_dims)
             im2 = resize_without_fit(im2, net_input_dims)
             batch[batch_index].im = np.concatenate((im1, im2), axis=2)
@@ -521,7 +512,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
 
 def save_representations(settings, net, datadir, filelist, layer_name, first_N = None):
-    image_filenames, image_labels = load_file_list(filelist)
+    image_filenames, image_labels = get_files_list(filelist)
     if first_N is None:
         first_N = len(image_filenames)
     assert first_N <= len(image_filenames)
@@ -537,12 +528,15 @@ def save_representations(settings, net, datadir, filelist, layer_name, first_N =
     rep = None
     for ii,image_idx in enumerate(image_indices):
         filename = image_filenames[image_idx]
-        image_class = image_labels[image_idx]
+        if image_labels:
+            image_class = image_labels[image_idx]
+        else:
+            image_class = 0
         do_print = (image_idx % 10 == 0)
         if do_print:
             print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_indices))
         with WithTimer('Load image', quiet = not do_print):
-            im = cv2_read_file_rgb(os.path.join(datadir, filename))
+            im = cv2_read_file_rgb(os.path.join(datadir, filename), as_grayscale=settings._calculated_is_gray_model)
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Store     ', quiet = not do_print):
@@ -600,7 +594,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
     mt = max_tracker
     rc = RegionComputer(settings.max_tracker_layers_list)
 
-    image_filenames, image_labels = load_file_list(settings)
+    image_filenames, image_labels = get_files_list(settings)
 
     if settings.is_siamese:
         print 'Loaded filenames and labels for %d pairs' % len(image_filenames)
@@ -734,8 +728,8 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                     filename2 = batch[batch_index].filename[1]
 
                     # load both images
-                    im1 = cv2_read_file_rgb(os.path.join(datadir, filename1))
-                    im2 = cv2_read_file_rgb(os.path.join(datadir, filename2))
+                    im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
+                    im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
 
                     # resize images according to input dimension
                     im1 = resize_without_fit(im1, net_input_dims)
@@ -745,7 +739,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                     batch[batch_index].im = np.concatenate((im1, im2), axis=2)
                 else:
                     # load image
-                    batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename))
+                    batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
 
                     # resize images according to input dimension
                     batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
@@ -829,7 +823,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                                 diffs[i, batch[i].channel_idx] = 1.0
 
                         with WithTimer('Deconv    ', quiet = not do_print):
-                            net.deconv_from_layer(batch[i].denormalized_layer_name, diffs, 'Guided Backprop')
+                            net.deconv_from_layer(batch[i].denormalized_layer_name, diffs, zero_higher=True, deconv_type='Guided Backprop')
 
                         out_arr = extract_patch_from_image(net.blobs['data'].diff[i], net, batch[i].selected_input_index, settings,
                                                            batch[i].data_ii_end, batch[i].data_ii_start, batch[i].data_jj_end, batch[i].data_jj_start,
diff --git a/image_misc.py b/image_misc.py
index 2f5fa2e9c..415a2c54f 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -116,19 +116,35 @@ def cv2_read_cap_rgb(cap, saveto = None):
     frame = frame[:,:,::-1]   # Convert native OpenCV BGR -> RGB
     return frame
 
-    
-def cv2_read_file_rgb(filename):
-    '''Reads an image from file. Always returns (x,y,3)'''
-    im = cv2.imread(filename)
+
+def gray_to_color(im):
+
     if len(im.shape) == 2:
         # Upconvert single channel grayscale to color
-        im = im[:,:,np.newaxis]
+        im = im[:, :, np.newaxis]
     if im.shape[2] == 1:
-        im = np.tile(im, (1,1,3))
-    if im.shape[2] > 3:
-        # Chop off transparency
-        im = im[:,:,:3]
-    im = im[:,:,::-1]   # Convert native OpenCV BGR -> RGB
+        im = np.tile(im, (1, 1, 3))
+
+    return im
+
+
+def cv2_read_file_rgb(filename, as_grayscale = False):
+    '''Reads an image from file. Returns (x,y,3) or (x,y,1) depending on as_grayscale parameter'''
+
+    if as_grayscale:
+        im = cv2.imread(filename, cv2.CV_LOAD_IMAGE_GRAYSCALE)
+        if len(im.shape) == 2:
+            # Upconvert single channel grayscale to color
+            im = im[:, :, np.newaxis]
+
+    else:
+        im = cv2.imread(filename)
+        im = gray_to_color(im)
+        if im.shape[2] > 3:
+            # Chop off transparency
+            im = im[:,:,:3]
+        im = im[:,:,::-1]   # Convert native OpenCV BGR -> RGB
+
     return im
 
     
@@ -431,8 +447,14 @@ def resize_to_fit(img, out_max_shape,
     out = cv2.resize(img,
             (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))),   # in (c,r) order
                      interpolation = grow_interpolation if scale > 1 else shrink_interpolation)
+
     if convert_late:
         out = np.array(out, dtype=dtype_out)
+
+    # fix resize of grayscale images
+    if len(img.shape) == 3 and img.shape[2] == 1 and len(out.shape) == 2:
+        out = out[:,:,np.newaxis]
+
     return out
 
 
@@ -470,8 +492,14 @@ def resize_without_fit(img, out_max_shape,
     out = cv2.resize(img,
             (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),   # in (c,r) order
                      interpolation = grow_interpolation if min(scale_0,scale_1) > 1 else shrink_interpolation)
+
     if convert_late:
         out = np.array(out, dtype=dtype_out)
+
+    # fix resize of grayscale images
+    if len(img.shape) == 3 and img.shape[2] == 1 and len(out.shape) == 2:
+        out = out[:, :, np.newaxis]
+
     return out
 
 
diff --git a/input_fetcher.py b/input_fetcher.py
index 5df5cfac3..818b357ec 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -7,7 +7,7 @@
 
 from codependent_thread import CodependentThread
 from image_misc import cv2_imshow_rgb, cv2_read_file_rgb, read_cam_frame, crop_to_square
-from misc import tsplit, get_files_from_directory, get_files_from_image_list, get_files_from_siamese_image_list
+from misc import tsplit, get_files_list
 
 
 class InputImageFetcher(CodependentThread):
@@ -187,21 +187,8 @@ def check_increment_and_load_image(self):
                 # Skip if a static frame is already loaded and there is no increment
                 return
 
-            # available_files - local list of files
-            if self.settings.static_files_input_mode == "directory":
-                available_files = get_files_from_directory(self.settings)
-                labels = None
-            elif (self.settings.static_files_input_mode == "image_list") and (not self.settings.is_siamese):
-                available_files, labels = get_files_from_image_list(self.settings)
-            elif (self.settings.static_files_input_mode == "image_list") and (self.settings.is_siamese):
-                available_files, labels = get_files_from_siamese_image_list(self.settings)
-            else:
-                raise Exception(('Error: setting static_files_input_mode has invalid option (%s)' %
-                                (self.settings.static_files_input_mode) ))
+            available_files, labels = get_files_list(self.settings)
 
-            #print 'Found files:'
-            #for filename in available_files:
-            #    print '   %s' % filename
             assert len(available_files) != 0, ('Error: No files found in %s matching %s (current working directory is %s)' %
                                                (self.settings.static_files_dir, self.settings.static_files_regexp, os.getcwd()))
             if self.static_file_idx is None:
@@ -213,8 +200,8 @@ def check_increment_and_load_image(self):
 
                 if self.settings.is_siamese:
                     # loading two images for siamese network
-                    im1 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]))
-                    im2 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]))
+                    im1 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]), as_grayscale=self.settings._calculated_is_gray_model)
+                    im2 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]), as_grayscale=self.settings._calculated_is_gray_model)
                     if not self.static_file_stretch_mode:
                         im1 = crop_to_square(im1)
                         im2 = crop_to_square(im2)
@@ -222,7 +209,7 @@ def check_increment_and_load_image(self):
                     im = (im1,im2)
 
                 else:
-                    im = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename))
+                    im = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename), as_grayscale=self.settings._calculated_is_gray_model)
                     if not self.static_file_stretch_mode:
                         im = crop_to_square(im)
 
diff --git a/live_vis.py b/live_vis.py
index ba36fb617..eee5f9673 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -15,7 +15,7 @@
     raise
 
 from misc import WithTimer
-from image_misc import cv2_imshow_rgb, FormattedString, cv2_typeset_text, to_255, resize_without_fit
+from image_misc import cv2_imshow_rgb, FormattedString, cv2_typeset_text, to_255, resize_without_fit, gray_to_color
 from bindings import bindings
 from input_fetcher import InputImageFetcher
 
@@ -329,6 +329,9 @@ def display_frame(self, frame):
         else:
             frame_disp = resize_without_fit(frame[:], full_pane_shape)
 
+        if self.settings._calculated_is_gray_model:
+            frame_disp = gray_to_color(frame_disp)
+
         self.panes['input'].data[:] = frame_disp
 
     def draw_help(self):
diff --git a/misc.py b/misc.py
index 523da97e0..2526c60f9 100644
--- a/misc.py
+++ b/misc.py
@@ -104,3 +104,22 @@ def get_files_from_siamese_image_list(settings):
 
     return available_files, labels
 
+
+def get_files_list(settings, should_convert_labels = False):
+
+    # available_files - local list of files
+    if settings.static_files_input_mode == "directory":
+        available_files = get_files_from_directory(settings)
+        labels = None
+    elif (settings.static_files_input_mode == "image_list") and (not settings.is_siamese):
+        available_files, labels = get_files_from_image_list(settings)
+    elif (settings.static_files_input_mode == "image_list") and (settings.is_siamese):
+        available_files, labels = get_files_from_siamese_image_list(settings)
+    else:
+        raise Exception(('Error: setting static_files_input_mode has invalid option (%s)' %
+                         (settings.static_files_input_mode)))
+
+    if should_convert_labels and labels:
+        labels = [settings.convert_label_fn(label) for label in labels]
+
+    return available_files, labels
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index c1f63eb03..2de01b2d1 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -169,11 +169,17 @@ def __init__(self, settings, net, batched_data_mean, labels = None, label_layers
         if channel_swap_to_rgb:
             self.channel_swap_to_rgb = array(channel_swap_to_rgb)
         else:
-            data_n_channels = self.batched_data_mean.shape[0]
-            self.channel_swap_to_rgb = arange(data_n_channels)   # Don't change order
+
+            if settings._calculated_is_gray_model:
+                self.channel_swap_to_rgb = arange(1)
+            else:
+                self.channel_swap_to_rgb = arange(3)  # Don't change order
 
         # since we have a batch of same data mean images, we can just take the first
-        self._data_mean_rgb_img = self.batched_data_mean[0, self.channel_swap_to_rgb].transpose((1,2,0))  # Store as (227,227,3) in RGB order.
+        if batched_data_mean:
+            self._data_mean_rgb_img = self.batched_data_mean[0, self.channel_swap_to_rgb].transpose((1,2,0))  # Store as (227,227,3) in RGB order.
+        else:
+            self._data_mean_rgb_img = None
 
     def run_optimize(self, params, prefix_template = None, brave = False, skipbig = False, skipsmall = False):
         '''All images are in Caffe format, e.g. shape (3, 227, 227) in BGR order.'''
@@ -194,12 +200,17 @@ def _get_x0(self, params):
 
         np.random.seed(params.rand_seed)
 
+        input_shape = self.net.blobs['data'].data.shape
+
         if params.start_at == 'mean_plus_rand':
-            x0 = np.random.normal(0, 10, self.batched_data_mean.shape)
+            x0 = np.random.normal(0, 10, input_shape)
         elif params.start_at == 'randu':
-            x0 = uniform(0, 255, self.batched_data_mean.shape) - self.batched_data_mean
+            if self.batched_data_mean:
+                x0 = uniform(0, 255, input_shape) - self.batched_data_mean
+            else:
+                x0 = uniform(0, 255, input_shape)
         elif params.start_at == 'mean':
-            x0 = zeros(self.batched_data_mean.shape)
+            x0 = zeros(input_shape)
         else:
             raise Exception('Unknown start conditions: %s' % params.start_at)
 
@@ -247,8 +258,10 @@ def _optimize(self, params, x0, prefix_template):
         obj = np.zeros(params.batch_size)
         for ii in range(params.max_iter):
             # 0. Crop data
-            xx = minimum(255.0, maximum(0.0, xx + self.batched_data_mean)) - self.batched_data_mean     # Crop all values to [0,255]
-
+            if self.batched_data_mean:
+                xx = minimum(255.0, maximum(0.0, xx + self.batched_data_mean)) - self.batched_data_mean     # Crop all values to [0,255]
+            else:
+                xx = minimum(255.0, maximum(0.0, xx)) # Crop all values to [0,255]
             # 1. Push data through net
             out = self.net.forward_all(data = xx)
             #shownet(net)
@@ -374,12 +387,13 @@ def _optimize(self, params, x0, prefix_template):
                     xx[batch_index] += lr[batch_index] * grad[batch_index]
                     xx[batch_index] *= (1 - params.decay)
 
+                    channels = xx.shape[1]
+
                     if params.blur_every is not 0 and params.blur_radius > 0:
                         if params.blur_radius < .3:
                             print 'Warning: blur-radius of .3 or less works very poorly'
                             #raise Exception('blur-radius of .3 or less works very poorly')
                         if ii % params.blur_every == 0:
-                            channels = 6 if self.settings.is_siamese else 3
                             for channel in range(channels):
                                 cimg = gaussian_filter(xx[batch_index,channel], params.blur_radius)
                                 xx[batch_index,channel] = cimg
@@ -390,30 +404,21 @@ def _optimize(self, params, x0, prefix_template):
                     if params.small_norm_percentile > 0:
                         pxnorms = norm(xx[batch_index,np.newaxis,:,:,:], axis=1)
                         smallpx = pxnorms < percentile(pxnorms, params.small_norm_percentile)
-                        if self.settings.is_siamese:
-                            smallpx3 = tile(smallpx[:,newaxis,:,:], (1,6,1,1))
-                        else:
-                            smallpx3 = tile(smallpx[:,newaxis,:,:], (1,3,1,1))
+                        smallpx3 = tile(smallpx[:,newaxis,:,:], (1,channels,1,1))
                         xx[batch_index,:,:,:] = xx[batch_index,np.newaxis,:,:,:] - xx[batch_index,np.newaxis,:,:,:]*smallpx3
 
                     if params.px_benefit_percentile > 0:
                         pred_0_benefit = grad[batch_index,np.newaxis,:,:,:] * -xx[batch_index,np.newaxis,:,:,:]
                         px_benefit = pred_0_benefit.sum(1)   # sum over color channels
                         smallben = px_benefit < percentile(px_benefit, params.px_benefit_percentile)
-                        if self.settings.is_siamese:
-                            smallben3 = tile(smallben[:,newaxis,:,:], (1,6,1,1))
-                        else:
-                            smallben3 = tile(smallben[:,newaxis,:,:], (1,3,1,1))
+                        smallben3 = tile(smallben[:,newaxis,:,:], (1,channels,1,1))
                         xx[batch_index,:,:,:] = xx[batch_index,np.newaxis,:,:,:] - xx[batch_index,np.newaxis,:,:,:]*smallben3
 
                     if params.px_abs_benefit_percentile > 0:
                         pred_0_benefit = grad[batch_index,np.newaxis,:,:,:] * -xx[batch_index,np.newaxis,:,:,:]
                         px_benefit = pred_0_benefit.sum(1)   # sum over color channels
                         smallaben = abs(px_benefit) < percentile(abs(px_benefit), params.px_abs_benefit_percentile)
-                        if self.settings.is_siamese:
-                            smallaben3 = tile(smallaben[:,newaxis,:,:], (1,6,1,1))
-                        else:
-                            smallaben3 = tile(smallaben[:,newaxis,:,:], (1,3,1,1))
+                        smallaben3 = tile(smallaben[:,newaxis,:,:], (1,channels,1,1))
                         xx[batch_index,:,:,:] = xx[batch_index,np.newaxis,:,:,:] - xx[batch_index,np.newaxis,:,:,:]*smallaben3
 
             print '     timestamp:', datetime.datetime.now()
@@ -519,18 +524,28 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
                                                    data_ii_end, data_ii_start, data_jj_end, data_jj_start,
                                                    out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
 
-                save_output(out_arr,
-                            channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
-                            best_X_image_name=best_X_name)
+                if self.settings.is_siamese:
+                    save_output(out_arr,
+                                channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
+                                best_X_image_name=best_X_name)
+                else:
+                    save_output(out_arr,
+                                channel_swap_to_rgb=self.channel_swap_to_rgb,
+                                best_X_image_name=best_X_name)
 
                 if self.settings.optimize_image_generate_plus_mean:
                     out_arr_pm = extract_patch_from_image(results[batch_index].best_xx + self.batched_data_mean, self.net, selected_input_index, self.settings,
                                                        data_ii_end, data_ii_start, data_jj_end, data_jj_start,
                                                        out_ii_end, out_ii_start, out_jj_end, out_jj_start, size_ii, size_jj)
 
-                    save_output(out_arr_pm,
-                                channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
-                                best_X_image_name=best_Xpm_name)
+                    if self.settings.is_siamese:
+                        save_output(out_arr_pm,
+                                    channel_swap_to_rgb=self.channel_swap_to_rgb[[0, 1, 2]],
+                                    best_X_image_name=best_Xpm_name)
+                    else:
+                        save_output(out_arr_pm,
+                                    channel_swap_to_rgb=self.channel_swap_to_rgb,
+                                    best_X_image_name=best_Xpm_name)
 
             with open(info_name, 'w') as ff:
                 print >>ff, params
diff --git a/optimize_image.py b/optimize_image.py
index 89ac91d89..bebdf5502 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -8,6 +8,7 @@
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
 from caffevis.caffevis_helper import check_force_backward_true, read_label_file, set_mean
+from settings_misc import deduce_calculated_settings
 
 LR_POLICY_CHOICES = ('constant', 'progress', 'progress01')
 
@@ -133,13 +134,19 @@ def get_layer_info(settings, layer_name):
 
     :param settings: contains script settings
     :param layer_name: name of layer
-    :return: (name, type, input, output, filter, stride, pad)
+    :return: current_layer and previous_layer tuples of (name, type, input, output, filter, stride, pad)
     '''
 
     # go over layers
+
+    previous_layer = None
     for (name, type, input, output, filter, stride, pad) in settings.max_tracker_layers_list:
         if name == layer_name:
-            return (name, type, input, output, filter, stride, pad)
+            current_layer = (name, type, input, output, filter, stride, pad)
+            return current_layer, previous_layer
+
+        # update previous layer
+        previous_layer = (name, type, input, output, filter, stride, pad)
 
 
 def main():
@@ -172,6 +179,8 @@ def main():
 
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
+    deduce_calculated_settings(settings, net)
+
     # validate batch size
     if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
@@ -188,30 +197,53 @@ def main():
     if settings.caffevis_labels:
         labels = read_label_file(settings.caffevis_labels)
 
-    if len(data_mean.shape) == 3:
-        batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
-    elif len(data_mean.shape) == 1:
-        data_mean = data_mean[np.newaxis,:,np.newaxis,np.newaxis]
-        batched_data_mean = np.tile(data_mean, (args.batch_size,1,current_data_shape[2],current_data_shape[3]))
+    if data_mean:
+        if len(data_mean.shape) == 3:
+            batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
+        elif len(data_mean.shape) == 1:
+            data_mean = data_mean[np.newaxis,:,np.newaxis,np.newaxis]
+            batched_data_mean = np.tile(data_mean, (args.batch_size,1,current_data_shape[2],current_data_shape[3]))
+    else:
+        batched_data_mean = data_mean
 
     optimizer = GradientOptimizer(settings, net, batched_data_mean, labels = labels,
                                   label_layers = settings.caffevis_label_layers,
                                   channel_swap_to_rgb = settings.caffe_net_channel_swap)
 
+    if not args.push_layers:
+        print "ERROR: No layers to work on, please set optimize_image_push_layers to list of layers"
+        return
+
     # go over push layers
     for count, push_layer in enumerate(args.push_layers):
 
         # get layer type
-        (name, type, input, output, filter, stride, pad) = get_layer_info(settings, push_layer)
+        current_layer, previous_layer = get_layer_info(settings, push_layer)
+        (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad) = current_layer
+        (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = previous_layer
 
-        if type == 'FullyConnected':
+        if current_type == 'FullyConnected':
             # get number of units
-            channels = output
+            channels = current_output
             push_spatial = (0, 0)
-        elif type == 'Convolution':
+            layer_is_conv = False
+
+        elif current_type == 'Convolution':
+            # get number of channels
+            channels = current_output[2]
+            push_spatial = (current_filter[0]/2, current_filter[1]/2)
+            layer_is_conv = True
+
+        elif current_type == 'Eltwise' and previous_type == 'FullyConnected':
+            channels = current_output
+            push_spatial = (0, 0)
+            layer_is_conv = False
+
+        elif current_type == 'Eltwise' and previous_type == 'Convolution':
             # get number of channels
-            channels = output[2]
-            push_spatial = (filter[0]/2, filter[1]/2)
+            channels = current_output[2]
+            push_spatial = (previous_filter[0]/2, previous_filter[1]/2)
+            layer_is_conv = True
 
         # if channels defined in settings file, use them
         if settings.optimize_image_channels:
@@ -239,7 +271,7 @@ def main():
                 lr_policy = args.lr_policy,
                 lr_params = lr_params,
                 max_iter = args.max_iters[count % len(args.max_iters)],
-                layer_is_conv=(type == 'Convolution'),
+                layer_is_conv=layer_is_conv,
             )
 
             optimizer.run_optimize(params, prefix_template = args.output_prefix,
diff --git a/settings.py b/settings.py
index 989bfe24c..f5ee1277e 100644
--- a/settings.py
+++ b/settings.py
@@ -69,7 +69,7 @@
 if 'default_window_panes' in locals():
     raise Exception('Override window panes in settings_MODEL.py by defining window_panes, not default_window_panes')
 
-control_pane_height = locals().get('control_pane_height', 30)
+control_pane_height = locals().get('control_pane_height', 4*20)
 
 default_window_panes = (
     # (i, j, i_size, j_size)
@@ -125,6 +125,10 @@
 # contains the file name to read, relevant only when static_files_input_mode is 'image_list'
 static_files_input_file = locals().get('static_files_input_file', 'images_file_list.txt')
 
+# set to True if the model expects grayscale inputs, False otherwise.
+# If value is None we set this parameter according to the network structure
+is_gray_model = locals().get('is_gray_model', None)
+
 # int, 0+. How many times to go through the main loop after a keypress
 # before resuming handling frames (0 to handle every frame as it
 # arrives). Setting this to a value > 0 can enable more responsive
@@ -416,7 +420,7 @@
 optimize_image_rand_seed = locals().get('optimize_image_rand_seed', 0)
 
 # push-layer parameter for optimize_image.py
-optimize_image_push_layers = locals().get('optimize_image_push_layers', ['fc8'])
+optimize_image_push_layers = locals().get('optimize_image_push_layers', [])
 
 # decay parameter for optimize_image.py
 optimize_image_decay = locals().get('optimize_image_decay', 0)

From 2a835465b8fad267ff421c3f42f868deea4dbc9c Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:26:31 +0300
Subject: [PATCH 064/106] - fixed bug when input images list is too short for
 max tracker - fixed bug related to use of maxout

---
 deep-visualization-toolbox/settings_misc.py | 21 +++++++++++++++++++
 find_maxes/max_tracker.py                   | 12 ++++++++++-
 optimize_image.py                           | 23 +--------------------
 3 files changed, 33 insertions(+), 23 deletions(-)

diff --git a/deep-visualization-toolbox/settings_misc.py b/deep-visualization-toolbox/settings_misc.py
index 3bc5c1bba..f08567d5d 100644
--- a/deep-visualization-toolbox/settings_misc.py
+++ b/deep-visualization-toolbox/settings_misc.py
@@ -28,3 +28,24 @@ def set_calculated_image_dims(settings, net):
     else:
         input_shape = net.blobs[net.inputs[0]].data.shape
         settings._calculated_image_dims = input_shape[2:4]
+
+
+def get_layer_info(settings, layer_name):
+    '''
+        get layer info (name, type, input, output, filter, stride, pad) from settings
+
+    :param settings: contains script settings
+    :param layer_name: name of layer
+    :return: current_layer and previous_layer tuples of (name, type, input, output, filter, stride, pad)
+    '''
+
+    # go over layers
+
+    previous_layer = None
+    for (name, type, input, output, filter, stride, pad) in settings.max_tracker_layers_list:
+        if name == layer_name:
+            current_layer = (name, type, input, output, filter, stride, pad)
+            return current_layer, previous_layer
+
+        # update previous layer
+        previous_layer = (name, type, input, output, filter, stride, pad)
\ No newline at end of file
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index b1c8ed234..30b32fb3c 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -12,6 +12,7 @@
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
     compute_data_layer_focus_area, layer_name_to_top_name
 from siamese_helper import SiameseHelper
+from settings_misc import get_layer_info
 
 from jby_misc import WithTimer
 
@@ -283,7 +284,12 @@ def _init_with_net(self, net):
             # count as the same layer in terms of activations
             normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
-            is_conv = self.settings.is_conv_fn(layer_name)
+            current_layer, previous_layer = get_layer_info(self.settings, normalized_layer_name)
+            (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad) = current_layer
+            (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = previous_layer
+
+            is_conv = (current_type == 'Convolution') or (previous_type == 'Convolution' and current_type == 'Eltwise')
+            # is_conv = self.settings.is_conv_fn(layer_name)
 
             # only add normalized layer once
             if normalized_layer_name not in self.max_trackers:
@@ -682,6 +688,10 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                 batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
                 batch[batch_index].ii, batch[batch_index].jj = 0, 0
 
+            # if ii and jj are invalid then there is no data for this "top" image, so we can skip it
+            if (batch[batch_index].ii, batch[batch_index].jj) == (-1,-1):
+                continue
+
             batch[batch_index].recorded_val = mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx]
             batch[batch_index].filename = image_filenames[batch[batch_index].im_idx]
             do_print = (batch[batch_index].max_idx_0 == 0)
diff --git a/optimize_image.py b/optimize_image.py
index bebdf5502..e844d7328 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -8,7 +8,7 @@
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
 from caffevis.caffevis_helper import check_force_backward_true, read_label_file, set_mean
-from settings_misc import deduce_calculated_settings
+from settings_misc import deduce_calculated_settings, get_layer_info
 
 LR_POLICY_CHOICES = ('constant', 'progress', 'progress01')
 
@@ -128,27 +128,6 @@ def parse_and_validate_push_spatial(parser, push_spatial):
     return push_spatial
 
 
-def get_layer_info(settings, layer_name):
-    '''
-        get layer info (name, type, input, output, filter, stride, pad) from settings
-
-    :param settings: contains script settings
-    :param layer_name: name of layer
-    :return: current_layer and previous_layer tuples of (name, type, input, output, filter, stride, pad)
-    '''
-
-    # go over layers
-
-    previous_layer = None
-    for (name, type, input, output, filter, stride, pad) in settings.max_tracker_layers_list:
-        if name == layer_name:
-            current_layer = (name, type, input, output, filter, stride, pad)
-            return current_layer, previous_layer
-
-        # update previous layer
-        previous_layer = (name, type, input, output, filter, stride, pad)
-
-
 def main():
     parser = get_parser()
     args = parser.parse_args()

From cd35999a66c12ff9d749eec7ccaed7512e7761d4 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:28:23 +0300
Subject: [PATCH 065/106] added caffevis_clear_negative_activations which is
 False by default to allow viewing max input images with negative activations

---
 caffevis/caffevis_helper.py | 11 ++++++-----
 settings.py                 |  3 +++
 2 files changed, 9 insertions(+), 5 deletions(-)

diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index 0113829c9..b81d7b653 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -224,11 +224,12 @@ def get_image_from_files(settings, unit_folder_path, should_crop_to_corner, resi
         unit_images = [caffe_load_image(unit_image_path, color=True, as_uint=True) for unit_image_path in
                        unit_images_path]
 
-        # clear images with 0 value
-        if values:
-            for i in range(len(values)):
-                if values[i] < float_info.epsilon:
-                    unit_images[i] *= 0
+        if settings.caffevis_clear_negative_activations:
+            # clear images with 0 value
+            if values:
+                for i in range(len(values)):
+                    if values[i] < float_info.epsilon:
+                        unit_images[i] *= 0
 
         if should_crop_to_corner:
             unit_images = [crop_to_corner(img, 2) for img in unit_images]
diff --git a/settings.py b/settings.py
index f5ee1277e..d7a35b9d5 100644
--- a/settings.py
+++ b/settings.py
@@ -346,6 +346,9 @@
 # how should histograms be loaded: 'calculate_in_realtime' or 'load_from_file'
 caffevis_histograms_format = locals().get('caffevis_histograms_format','calculate_in_realtime')
 
+# should we black maximal input images with zero or negative activation score
+caffevis_clear_negative_activations = locals().get('caffevis_clear_negative_activations', False)
+
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 

From fc78c7397b51b15bcaef58b799cc9be13cdf263f Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:29:50 +0300
Subject: [PATCH 066/106] - fixed bug in backprop when using siamese view,
 first image only

---
 deep-visualization-toolbox/siamese_helper.py | 5 ++++-
 1 file changed, 4 insertions(+), 1 deletion(-)

diff --git a/deep-visualization-toolbox/siamese_helper.py b/deep-visualization-toolbox/siamese_helper.py
index 5f2891635..3aa22d3eb 100644
--- a/deep-visualization-toolbox/siamese_helper.py
+++ b/deep-visualization-toolbox/siamese_helper.py
@@ -347,7 +347,10 @@ def siamese_input_mode_has_two_images(layer_def, siamese_input_mode):
     @staticmethod
     def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode):
 
-        if SiameseHelper.siamese_input_mode_has_two_images(backprop_layer_def, siamese_input_mode):
+        # if we are in siamese_batch_pair, we don't care of siamese_input_mode since we must do deconv on the 2-batch
+        # otherwise, if we are in siamese_layer_pair, we do it on both layers only if backprop deconv are requested
+        if (backprop_layer_def['format'] == 'siamese_batch_pair') or \
+            (backprop_layer_def['format'] == 'siamese_layer_pair' and siamese_input_mode == SiameseInputMode.BOTH_IMAGES):
 
             diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_input_mode)
             diffs0, diffs1 = diffs0 * 0, diffs1 * 0

From 7514d3fe310addde6cf2dee3dfe3e0e549af9bf3 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:30:11 +0300
Subject: [PATCH 067/106] fixed missing field in info file

---
 find_maxes/max_tracker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 30b32fb3c..4080ff833 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -665,7 +665,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             channel_to_info_file[channel_idx].info_file = open(info_filename[0], 'w')
             channel_to_info_file[channel_idx].ref_count = num_top
 
-            print >> channel_to_info_file[channel_idx].info_file, '# is_conv val image_idx image_class i(if is_conv) j(if is_conv) filename'
+            print >> channel_to_info_file[channel_idx].info_file, '# is_conv val image_idx image_class selected_input_index i(if is_conv) j(if is_conv) filename'
 
         # iterate through maxes from highest (at end) to lowest
         for max_idx_0 in range(num_top):

From 234c35cd08a5207db4ce43840ebec46aaa0cef31 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 20 Sep 2017 08:50:35 +0300
Subject: [PATCH 068/106] added color maps options

---
 bindings.py                    |  18 ++--
 caffevis/app.py                | 162 ++++++++++++++++++++++-----------
 caffevis/caffevis_app_state.py |  86 ++++++++++++++---
 image_misc.py                  |  12 ++-
 4 files changed, 204 insertions(+), 74 deletions(-)

diff --git a/bindings.py b/bindings.py
index e0ee978f5..acd7430ea 100644
--- a/bindings.py
+++ b/bindings.py
@@ -109,17 +109,21 @@ def get_key_help(self, tag):
        'Toggle pattern loading first image only or loading all available images')
 
 _.add('next_ez_back_mode_loop', 'b',
-       'Cycle through backprop modes (backprop, deconv zf, deconv gb, off)')
+       'Cycle through backprop modes (grad, deconv zf, deconv gb, off)')
 _.add('prev_ez_back_mode_loop', 'B',
-       'Cycle through backprop modes (backprop, deconv zf, deconv gb, off)')
+       'Cycle through backprop modes (grad, deconv zf, deconv gb, off)')
 _.add('freeze_back_unit', 'd',
        'Freeze the bprop/deconv origin to be the currently selected unit')
 _.add('show_back', 'a',
        'Toggle between showing forward activations and back/deconv diffs')
 _.add('next_back_view_option', 'n',
-       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur)')
+       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, max abs, histogram)')
 _.add('prev_back_view_option', 'N',
-       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur)')
+       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, max abs, histogram)')
+_.add('next_color_map', 'm',
+       'Cycle through colormaps options (grayscale, jet, plasma)')
+_.add('prev_color_map', 'M',
+       'Cycle through colormaps options (grayscale, jet, plasma)')
 
 _.add('boost_gamma', 't',
        'Boost contrast using gamma correction')
@@ -135,8 +139,10 @@ def get_key_help(self, tag):
 
 _.add('toggle_maximal_score', 'r',
         'Toggle showing maximal score overlays {on, off}')
-_.add('toggle_input_overlay_in_aux_pane', 'y',
-        'Toggle showing input overlay in aux pane {on, off}')
+_.add('next_input_overlay', 'y',
+      'Cycle input overlay {off, over active, over inactive}')
+_.add('prev_input_overlay', 'Y',
+      'Cycle input overlay {off, over active, over inactive}')
 
 
 bindings = _
diff --git a/caffevis/app.py b/caffevis/app.py
index 1f715b460..3a5b62d32 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -19,18 +19,20 @@
 from numpy_cache import FIFOLimitedArrayCache
 from app_base import BaseApp
 from image_misc import norm01, norm01c, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
-    ensure_uint255_and_resize_to_fit, resize_without_fit, \
+    ensure_uint255_and_resize_to_fit, resize_without_fit, ensure_uint255, \
     caffe_load_image, ensure_uint255_and_resize_without_fit, softmax_image, array_histogram, fig2data
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from jpg_vis_loading_thread import JPGVisLoadingThread
-from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode, BackpropMode, BackpropViewOption
+from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode, BackpropMode, BackpropViewOption, \
+    ColorMapOption, InputOverlayOption
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
 from caffe_misc import layer_name_to_top_name, save_caffe_image
 from siamese_helper import SiameseHelper
 from settings_misc import deduce_calculated_settings
 
+
 class CaffeVisApp(BaseApp):
     '''App to visualize using caffe.'''
 
@@ -489,16 +491,7 @@ def _display_pane_based_on_zoom_mode(self, display_2D, display_3D_highres, is_la
 
         elif state_layers_pane_zoom_mode == 1 and not is_layer_summary_loaded:
             # Mode 1: zoomed selection
-            unit_data = display_3D_highres[self.state.selected_unit]
-            if self.settings.caffevis_keep_aspect_ratio:
-                display_2D_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
-            else:
-                display_2D_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
-
-            if self.state.show_input_overlay_in_aux_pane and self.state.pattern_mode == PatternMode.OFF:
-                input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese, pane.data.shape, self.state.siamese_input_mode)
-                normalized_mask = display_2D_resize / 255.0
-                display_2D_resize = normalized_mask * input_image + (1 - normalized_mask) * display_2D_resize
+            display_2D_resize = self.get_processed_selected_unit(display_3D_highres, pane.data.shape, use_colored_data=False)
 
         elif state_layers_pane_zoom_mode == 2 and not is_layer_summary_loaded:
             # Mode 2: zoomed backprop pane
@@ -986,6 +979,67 @@ def process_layer_figure(fig):
         display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
         return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
+    def get_processed_selected_unit(self, all_units, resize_shape, use_colored_data = False):
+
+        unit_data = all_units[self.state.selected_unit]
+        if self.settings.caffevis_keep_aspect_ratio:
+            unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, resize_shape)
+        else:
+            unit_data_resize = ensure_uint255_and_resize_without_fit(unit_data, resize_shape)
+
+        if self.state.pattern_mode == PatternMode.OFF:
+            input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese,
+                                                             resize_shape, self.state.siamese_input_mode)
+            normalized_mask = unit_data_resize / 255.0
+
+            if use_colored_data:
+                unit_data_resize = self.state.gray_to_colormap(unit_data_resize)
+                normalized_mask = np.tile(normalized_mask[:, :, np.newaxis], 3)
+
+            if self.state.input_overlay_option == InputOverlayOption.OFF:
+                pass
+
+            elif self.state.input_overlay_option == InputOverlayOption.OVER_ACTIVE:
+
+                unit_data_resize = normalized_mask * input_image + (1 - normalized_mask) * unit_data_resize
+
+            elif self.state.input_overlay_option == InputOverlayOption.OVER_INACTIVE:
+                unit_data_resize = (normalized_mask < 0.1) * input_image + (normalized_mask >= 0.1) * unit_data_resize
+                pass
+
+        return unit_data_resize
+
+
+    def _mix_input_overlay_with_colormap(self, unit_data, resize_shape, input_image):
+
+        if self.settings.caffevis_keep_aspect_ratio:
+            unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, resize_shape)
+            input_image_resize = ensure_uint255_and_resize_to_fit(input_image, resize_shape)
+        else:
+            unit_data_resize = ensure_uint255_and_resize_without_fit(unit_data, resize_shape)
+            input_image_resize = ensure_uint255_and_resize_without_fit(input_image, resize_shape)
+
+        normalized_mask = unit_data_resize / 255.0
+        normalized_mask = np.tile(normalized_mask[:, :, np.newaxis], 3)
+
+        colored_unit_data_resize = self.state.gray_to_colormap(unit_data_resize)
+        colored_unit_data_resize = ensure_uint255(colored_unit_data_resize)
+        if len(colored_unit_data_resize.shape) == 2:
+            colored_unit_data_resize = np.tile(colored_unit_data_resize[:, :, np.newaxis], 3)
+
+        if self.state.input_overlay_option == InputOverlayOption.OFF:
+            pass
+
+        elif self.state.input_overlay_option == InputOverlayOption.OVER_ACTIVE:
+            colored_unit_data_resize = np.array(normalized_mask * input_image_resize + (1 - normalized_mask) * colored_unit_data_resize, dtype = 'uint8')
+
+        elif self.state.input_overlay_option == InputOverlayOption.OVER_INACTIVE:
+            MAGIC_THRESHOLD_NUMBER = 0.3
+            colored_unit_data_resize = (normalized_mask < MAGIC_THRESHOLD_NUMBER) * input_image_resize + (normalized_mask >= MAGIC_THRESHOLD_NUMBER) * colored_unit_data_resize
+            pass
+
+        return colored_unit_data_resize
+
     def _draw_aux_pane(self, pane, layer_data_normalized):
         pane.data[:] = to_255(self.settings.window_background)
 
@@ -997,18 +1051,9 @@ def _draw_aux_pane(self, pane, layer_data_normalized):
                 mode = 'prob_labels'
                 
         if mode == 'selected':
-            unit_data = layer_data_normalized[self.state.selected_unit]
-            if self.settings.caffevis_keep_aspect_ratio:
-                unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, pane.data.shape)
-            else:
-                unit_data_resize = ensure_uint255_and_resize_without_fit(unit_data, pane.data.shape)
-
-            if self.state.show_input_overlay_in_aux_pane and self.state.pattern_mode == PatternMode.OFF:
-                input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese, pane.data.shape, self.state.siamese_input_mode)
-                normalized_mask = unit_data_resize / 255.0
-                unit_data_resize = normalized_mask * input_image + (1-normalized_mask) * unit_data_resize
-
+            unit_data_resize = self.get_processed_selected_unit(layer_data_normalized, pane.data.shape, use_colored_data=False)
             pane.data[0:unit_data_resize.shape[0], 0:unit_data_resize.shape[1], :] = unit_data_resize
+
         elif mode == 'prob_labels':
             self._draw_prob_labels_pane(pane)
 
@@ -1026,9 +1071,8 @@ def _draw_back_pane(self, pane):
         elif back_what_to_disp == 'stale':
             pane.data[:] = to_255(self.settings.stale_background)
 
-        else:
-            # One of the backprop modes is enabled and the back computation (gradient or deconv) is up to date
-            
+        else: # One of the backprop modes is enabled and the back computation (gradient or deconv) is up to date
+
             grad_blob = self.net.blobs['data'].diff
 
             # Manually deprocess (skip mean subtraction and rescaling)
@@ -1053,21 +1097,21 @@ def run_processing_once_or_twice(image, resize_shape, process_image_fn):
                     # combine image0 and image1
                     if self.state.siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
                         # run processing on image0
-                        return process_image_fn(image0)
+                        return process_image_fn(image0, resize_shape, self.state.last_frame[0])
 
                     elif self.state.siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
                         # run processing on image1
-                        return process_image_fn(image1)
+                        return process_image_fn(image1, resize_shape, self.state.last_frame[1])
 
                     elif self.state.siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
 
-                        # run processing on both image0 and image1
-                        image0 = process_image_fn(image0)
-                        image1 = process_image_fn(image1)
-
                         # resize each gradient image to half the pane size
                         half_pane_shape = (resize_shape[0], resize_shape[1] / 2)
 
+                        # run processing on both image0 and image1
+                        image0 = process_image_fn(image0, half_pane_shape, self.state.last_frame[0])
+                        image1 = process_image_fn(image1, half_pane_shape, self.state.last_frame[1])
+
                         image0 = resize_without_fit(image0[:], half_pane_shape)
                         image1 = resize_without_fit(image1[:], half_pane_shape)
 
@@ -1077,38 +1121,55 @@ def run_processing_once_or_twice(image, resize_shape, process_image_fn):
                 # else, normal network, run processing once
                 else:
                     # run processing on image
-                    return process_image_fn(image)
+                    return process_image_fn(image, resize_shape, self.state.last_frame)
 
                 raise Exception("flow should not arrive here")
 
-            if back_view_option == BackpropViewOption.SOFTMAX_RAW:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: softmax_image(norm01c(grad_img, 0)))
+            # if back_view_option == BackpropViewOption.SOFTMAX_RAW:
+            #     grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: softmax_image(norm01c(grad_img, 0)))
 
-            elif back_view_option == BackpropViewOption.RAW:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0))
+            if back_view_option == BackpropViewOption.RAW:
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img, 0))
 
             elif back_view_option == BackpropViewOption.RAW_POS:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0) * (grad_img > 0) + (0.5) * (grad_img <= 0))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img, 0) * (grad_img > 0) + (0.5) * (grad_img <= 0))
 
             elif back_view_option == BackpropViewOption.RAW_NEG:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img, 0) * (grad_img < 0) + (0.5) * (grad_img >= 0))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img, 0) * (grad_img < 0) + (0.5) * (grad_img >= 0))
 
             elif back_view_option == BackpropViewOption.GRAY:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01c(grad_img.mean(axis=2), 0))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img.mean(axis=2), 0))
 
             elif back_view_option == BackpropViewOption.NORM:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: norm01(np.linalg.norm(grad_img, axis=2)))
+                def do_norm(grad_img, resize_shape, input_image):
+                    norm_grad_img = norm01(np.linalg.norm(grad_img, axis=2))
+                    # return self.state.gray_to_colormap(norm_grad_img)
+                    return self._mix_input_overlay_with_colormap(norm_grad_img, resize_shape, input_image)
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_norm)
 
             elif back_view_option == BackpropViewOption.NORM_BLUR:
-                def do_norm_blur(grad_img):
+                def do_norm_blur(grad_img, resize_shape, input_image):
                     grad_img = np.linalg.norm(grad_img, axis=2)
                     cv2.GaussianBlur(grad_img, (0, 0), self.settings.caffevis_grad_norm_blur_radius, grad_img)
-                    return norm01(grad_img)
+                    norm_grad_img = norm01(grad_img)
+                    # return self.state.gray_to_colormap(norm_grad_img)
+                    return self._mix_input_overlay_with_colormap(norm_grad_img, resize_shape, input_image)
                 grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_norm_blur)
 
+            elif back_view_option == BackpropViewOption.MAX_ABS:
+                def do_max_abs(grad_img, resize_shape, input_image):
+                    sigma = 0.02 * max(grad_img.shape[0:2])
+                    blur_grad_img = cv2.GaussianBlur(grad_img, (0, 0), sigma)
+                    abs_grad_img = np.abs(blur_grad_img)
+                    max_abs_grad_img = np.max(abs_grad_img, axis=2)
+                    normalized_max_abs_grad_img = norm01(max_abs_grad_img)
+                    # return self.state.gray_to_colormap(normalized_max_abs_grad_img)
+                    return self._mix_input_overlay_with_colormap(normalized_max_abs_grad_img, resize_shape, input_image)
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_max_abs)
+
             elif back_view_option == BackpropViewOption.HISTOGRAM:
                 half_pane_shape = (pane.data.shape[0],pane.data.shape[1]/2,3)
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img: array_histogram(grad_img, half_pane_shape, BackpropMode.to_string(back_mode)+' histogram', 'values', 'count'))
+                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: array_histogram(grad_img, half_pane_shape, BackpropMode.to_string(back_mode)+' histogram', 'values', 'count'))
 
             else:
                 raise Exception('Invalid option for back_view_option: %s' % (back_view_option))
@@ -1117,12 +1178,11 @@ def do_norm_blur(grad_img):
             if len(grad_img.shape) == 2:
                 grad_img = np.tile(grad_img[:,:,np.newaxis], 3)
 
-            grad_img_disp = grad_img
-
             if self.settings.caffevis_keep_aspect_ratio:
-                grad_img_resize = ensure_uint255_and_resize_to_fit(grad_img_disp, pane.data.shape)
+                grad_img_resize = ensure_uint255_and_resize_to_fit(grad_img, pane.data.shape)
             else:
-                grad_img_resize = ensure_uint255_and_resize_without_fit(grad_img_disp, pane.data.shape)
+                grad_img_resize = ensure_uint255_and_resize_without_fit(grad_img, pane.data.shape)
+
             pane.data[0:grad_img_resize.shape[0], 0:grad_img_resize.shape[1], :] = grad_img_resize
 
     def _draw_jpgvis_pane(self, pane):
@@ -1212,9 +1272,9 @@ def draw_help(self, help_pane, locy):
                       FormattedString(nav_string, defaults)])
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
-                    'next_ez_back_mode_loop', 'next_back_view_option', 'freeze_back_unit', 'show_back',
-                    'boost_gamma', 'boost_individual', 'siamese_input_mode', 'toggle_maximal_score',
-                    'toggle_input_overlay_in_aux_pane', 'reset_state'):
+                    'next_input_overlay', 'next_ez_back_mode_loop', 'next_back_view_option', 'next_color_map',
+                    'freeze_back_unit', 'show_back', 'boost_gamma', 'boost_individual', 'siamese_input_mode',
+                    'toggle_maximal_score', 'reset_state'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
             lines.append([FormattedString(label, defaults, width=120, align='right'),
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 4b8b7933d..6301903bd 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -3,7 +3,7 @@
 from threading import Lock
 from siamese_helper import SiameseInputMode, SiameseHelper
 from caffe_misc import layer_name_to_top_name
-from image_misc import get_tiles_height_width_ratio
+from image_misc import get_tiles_height_width_ratio, gray_to_colormap
 
 class PatternMode:
     OFF = 0
@@ -34,21 +34,22 @@ def to_string(back_mode):
         return 'n/a'
 
 class BackpropViewOption:
-    SOFTMAX_RAW = 0
-    RAW = 1
-    RAW_POS = 2
-    RAW_NEG = 3
-    GRAY = 4
-    NORM = 5
-    NORM_BLUR = 6
+    # SOFTMAX_RAW = 0
+    RAW = 0
+    RAW_POS = 1
+    RAW_NEG = 2
+    GRAY = 3
+    NORM = 4
+    NORM_BLUR = 5
+    MAX_ABS = 6
     HISTOGRAM = 7
     NUMBER_OF_OPTIONS = 8
 
     @staticmethod
     def to_string(back_view_option):
-        if back_view_option == BackpropViewOption.SOFTMAX_RAW:
-            return 'softmax raw'
-        elif back_view_option == BackpropViewOption.RAW:
+        # if back_view_option == BackpropViewOption.SOFTMAX_RAW:
+        #     return 'softmax raw'
+        if back_view_option == BackpropViewOption.RAW:
             return 'raw'
         elif back_view_option == BackpropViewOption.RAW_POS:
             return 'raw>0'
@@ -62,6 +63,42 @@ def to_string(back_view_option):
             return 'normblur'
         elif back_view_option == BackpropViewOption.HISTOGRAM:
             return 'histogram'
+        elif back_view_option == BackpropViewOption.MAX_ABS:
+            return 'max abs'
+
+        return 'n/a'
+
+class ColorMapOption:
+    GRAY = 0
+    JET = 1
+    PLASMA = 2
+    NUMBER_OF_OPTIONS = 3
+
+    @staticmethod
+    def to_string(color_map_option):
+        if color_map_option == ColorMapOption.GRAY:
+            return 'gray'
+        elif color_map_option == ColorMapOption.JET:
+            return 'jet'
+        elif color_map_option == ColorMapOption.PLASMA:
+            return 'plasma'
+
+        return 'n/a'
+
+class InputOverlayOption:
+    OFF = 0
+    OVER_ACTIVE = 1
+    OVER_INACTIVE = 2
+    NUMBER_OF_OPTIONS = 3
+
+    @staticmethod
+    def to_string(input_overlay_option):
+        if input_overlay_option == InputOverlayOption.OFF:
+            return 'off'
+        elif input_overlay_option == InputOverlayOption.OVER_ACTIVE:
+            return 'over active'
+        elif input_overlay_option == InputOverlayOption.OVER_INACTIVE:
+            return 'over inactive'
 
         return 'n/a'
 
@@ -122,7 +159,7 @@ def get_headers(self):
 
     def _reset_user_state(self):
         self.show_maximal_score = False
-        self.show_input_overlay_in_aux_pane = False
+        self.input_overlay_option = InputOverlayOption.OFF
         self.layer_idx = 0
         self.layer_boost_indiv_idx = self.settings.caffevis_boost_indiv_default_idx
         self.layer_boost_indiv = self.layer_boost_indiv_choices[self.layer_boost_indiv_idx]
@@ -140,6 +177,7 @@ def _reset_user_state(self):
         self.back_enabled = False
         self.back_mode = BackpropMode.OFF
         self.back_view_option = BackpropViewOption.RAW
+        self.color_map_option = ColorMapOption.GRAY
         self.pattern_mode = PatternMode.OFF    # type of patterns to show instead of activations in layers pane: maximal optimized image, maximal input image, maximal histogram, off
         self.pattern_first_only = True         # should we load only the first pattern image for each neuron, or all the relevant images per neuron
         self.layers_pane_zoom_mode = 0       # 0: off, 1: zoom selected (and show pref in small pane), 2: zoom backprop
@@ -232,6 +270,12 @@ def handle_key(self, key):
             elif tag == 'prev_back_view_option':
                 self.back_view_option = (self.back_view_option - 1 + BackpropViewOption.NUMBER_OF_OPTIONS) % BackpropViewOption.NUMBER_OF_OPTIONS
 
+            elif tag == 'next_color_map':
+                self.color_map_option = (self.color_map_option  + 1) % ColorMapOption.NUMBER_OF_OPTIONS
+
+            elif tag == 'prev_color_map':
+                self.color_map_option  = (self.color_map_option  - 1 + ColorMapOption.NUMBER_OF_OPTIONS) % ColorMapOption.NUMBER_OF_OPTIONS
+
             elif tag == 'freeze_back_unit':
                 # Freeze selected layer/unit as backprop unit
                 self.backprop_selection_frozen = not self.backprop_selection_frozen
@@ -259,8 +303,11 @@ def handle_key(self, key):
             elif tag == 'toggle_maximal_score':
                 self.show_maximal_score = not self.show_maximal_score
 
-            elif tag == 'toggle_input_overlay_in_aux_pane':
-                self.show_input_overlay_in_aux_pane = not self.show_input_overlay_in_aux_pane
+            elif tag == 'next_input_overlay':
+                self.input_overlay_option = (self.input_overlay_option + 1) % InputOverlayOption.NUMBER_OF_OPTIONS
+
+            elif tag == 'prev_input_overlay':
+                self.input_overlay_option  = (self.input_overlay_option  - 1 + InputOverlayOption.NUMBER_OF_OPTIONS) % InputOverlayOption.NUMBER_OF_OPTIONS
 
             else:
                 key_handled = False
@@ -431,3 +478,14 @@ def fill_layers_list(self, net):
 
 
         pass
+
+    def gray_to_colormap(self, gray_image):
+
+        if self.color_map_option == ColorMapOption.GRAY:
+            return gray_image
+
+        elif self.color_map_option == ColorMapOption.JET:
+            return gray_to_colormap('jet', gray_image)
+
+        elif self.color_map_option == ColorMapOption.PLASMA:
+            return gray_to_colormap('plasma', gray_image)
diff --git a/image_misc.py b/image_misc.py
index 415a2c54f..c56de1dbf 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -5,6 +5,7 @@
 import skimage
 import skimage.io
 from copy import deepcopy
+import matplotlib.pyplot as plt
 
 from misc import WithTimer
 
@@ -594,7 +595,6 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
     return locy
 
 
-
 def saveimage(filename, im):
     '''Saves an image with pixel values in [0,1]'''
     #matplotlib.image.imsave(filename, im)
@@ -605,11 +605,17 @@ def saveimage(filename, im):
         cv2.imwrite(filename, 255*im)
 
 
-
 def saveimagesc(filename, im):
     saveimage(filename, norm01(im))
 
 
-
 def saveimagescc(filename, im, center):
     saveimage(filename, norm01c(im, center))
+
+
+def gray_to_colormap(map_name, gray_image):
+
+    cmap = plt.get_cmap(map_name)
+    rgba_image = cmap(gray_image)
+    rgb_image = np.delete(rgba_image, 3, 2)
+    return rgb_image

From e7bb7938caab2a7e72a46e23f170670188c6b17a Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 23 Sep 2017 23:39:15 +0300
Subject: [PATCH 069/106] added option to select deconv target layer

---
 .idea/modules.xml                            |   8 -
 .idea/workspace.xml                          | 181 -------------------
 caffevis/app.py                              |  13 +-
 deep-visualization-toolbox/siamese_helper.py |   6 +-
 4 files changed, 15 insertions(+), 193 deletions(-)
 delete mode 100644 .idea/modules.xml
 delete mode 100644 .idea/workspace.xml

diff --git a/.idea/modules.xml b/.idea/modules.xml
deleted file mode 100644
index 66042cf49..000000000
--- a/.idea/modules.xml
+++ /dev/null
@@ -1,8 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ProjectModuleManager">
-    <modules>
-      <module fileurl="file://$PROJECT_DIR$/.idea/deep-visualization-toolbox.iml" filepath="$PROJECT_DIR$/.idea/deep-visualization-toolbox.iml" />
-    </modules>
-  </component>
-</project>
\ No newline at end of file
diff --git a/.idea/workspace.xml b/.idea/workspace.xml
deleted file mode 100644
index ed33c7b58..000000000
--- a/.idea/workspace.xml
+++ /dev/null
@@ -1,181 +0,0 @@
-<?xml version="1.0" encoding="UTF-8"?>
-<project version="4">
-  <component name="ChangeListManager">
-    <list default="true" id="acddccbe-2b82-4fca-b4ea-7a946b6fd129" name="Default" comment="" />
-    <option name="EXCLUDED_CONVERTED_TO_IGNORED" value="true" />
-    <option name="TRACKING_ENABLED" value="true" />
-    <option name="SHOW_DIALOG" value="false" />
-    <option name="HIGHLIGHT_CONFLICTS" value="true" />
-    <option name="HIGHLIGHT_NON_ACTIVE_CHANGELIST" value="false" />
-    <option name="LAST_RESOLUTION" value="IGNORE" />
-  </component>
-  <component name="FileEditorManager">
-    <leaf>
-      <file leaf-file-name="crop_max_patches.py" pinned="false" current-in-tab="false">
-        <entry file="file://$PROJECT_DIR$/find_maxes/crop_max_patches.py">
-          <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="436">
-              <caret line="66" column="8" lean-forward="false" selection-start-line="66" selection-start-column="8" selection-end-line="66" selection-end-column="8" />
-              <folding />
-            </state>
-          </provider>
-        </entry>
-      </file>
-      <file leaf-file-name="settings.py" pinned="false" current-in-tab="true">
-        <entry file="file://$PROJECT_DIR$/settings.py">
-          <provider selected="true" editor-type-id="text-editor">
-            <state relative-caret-position="271">
-              <caret line="70" column="0" lean-forward="false" selection-start-line="70" selection-start-column="0" selection-end-line="70" selection-end-column="0" />
-              <folding />
-            </state>
-          </provider>
-        </entry>
-      </file>
-    </leaf>
-  </component>
-  <component name="FindInProjectRecents">
-    <findStrings>
-      <find>todo</find>
-      <find>TODO</find>
-      <find>normalized_layer_name</find>
-      <find>control</find>
-    </findStrings>
-  </component>
-  <component name="Git.Settings">
-    <option name="RECENT_GIT_ROOT_PATH" value="$PROJECT_DIR$" />
-  </component>
-  <component name="IdeDocumentHistory">
-    <option name="CHANGED_PATHS">
-      <list>
-        <option value="$PROJECT_DIR$/settings.py" />
-      </list>
-    </option>
-  </component>
-  <component name="JsBuildToolGruntFileManager" detection-done="true" sorting="DEFINITION_ORDER" />
-  <component name="JsBuildToolPackageJson" detection-done="true" sorting="DEFINITION_ORDER" />
-  <component name="JsGulpfileManager">
-    <detection-done>true</detection-done>
-    <sorting>DEFINITION_ORDER</sorting>
-  </component>
-  <component name="ProjectFrameBounds" extendedState="6">
-    <option name="x" value="74" />
-    <option name="y" value="24" />
-    <option name="width" value="1855" />
-    <option name="height" value="1176" />
-  </component>
-  <component name="ProjectLevelVcsManager">
-    <ConfirmationsSetting value="2" id="Add" />
-  </component>
-  <component name="ProjectView">
-    <navigator currentView="ProjectPane" proportions="" version="1">
-      <flattenPackages />
-      <showMembers />
-      <showModules />
-      <showLibraryContents />
-      <hideEmptyPackages />
-      <abbreviatePackageNames />
-      <autoscrollToSource />
-      <autoscrollFromSource />
-      <sortByType />
-      <manualOrder />
-      <foldersAlwaysOnTop value="true" />
-    </navigator>
-    <panes>
-      <pane id="Scope" />
-      <pane id="Scratches" />
-      <pane id="ProjectPane">
-        <subPane>
-          <expand>
-            <path>
-              <item name="deep-visualization-toolbox" type="b2602c69:ProjectViewProjectNode" />
-              <item name="deep-visualization-toolbox" type="462c0819:PsiDirectoryNode" />
-            </path>
-          </expand>
-          <select />
-        </subPane>
-      </pane>
-    </panes>
-  </component>
-  <component name="PropertiesComponent">
-    <property name="WebServerToolWindowFactoryState" value="false" />
-    <property name="last_opened_file_path" value="$PROJECT_DIR$" />
-  </component>
-  <component name="RunDashboard">
-    <option name="ruleStates">
-      <list>
-        <RuleState>
-          <option name="name" value="ConfigurationTypeDashboardGroupingRule" />
-        </RuleState>
-        <RuleState>
-          <option name="name" value="StatusDashboardGroupingRule" />
-        </RuleState>
-      </list>
-    </option>
-  </component>
-  <component name="ShelveChangesManager" show_recycled="false">
-    <option name="remove_strategy" value="false" />
-  </component>
-  <component name="TaskManager">
-    <task active="true" id="Default" summary="Default task">
-      <changelist id="acddccbe-2b82-4fca-b4ea-7a946b6fd129" name="Default" comment="" />
-      <created>1505539193562</created>
-      <option name="number" value="Default" />
-      <option name="presentableId" value="Default" />
-      <updated>1505539193562</updated>
-    </task>
-    <servers />
-  </component>
-  <component name="ToolWindowManager">
-    <frame x="65" y="-4" width="1855" height="1084" extended-state="6" />
-    <editor active="true" />
-    <layout>
-      <window_info id="TODO" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="6" side_tool="false" content_ui="tabs" />
-      <window_info id="Event Log" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="true" content_ui="tabs" />
-      <window_info id="Version Control" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Python Console" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Run" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="2" side_tool="false" content_ui="tabs" />
-      <window_info id="Terminal" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Project" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="true" show_stripe_button="true" weight="0.2498618" sideWeight="0.5" order="0" side_tool="false" content_ui="combo" />
-      <window_info id="Database" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Find" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
-      <window_info id="Structure" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
-      <window_info id="Favorites" active="false" anchor="left" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="true" content_ui="tabs" />
-      <window_info id="Debug" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="3" side_tool="false" content_ui="tabs" />
-      <window_info id="Data View" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="-1" side_tool="false" content_ui="tabs" />
-      <window_info id="Cvs" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="4" side_tool="false" content_ui="tabs" />
-      <window_info id="Hierarchy" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="2" side_tool="false" content_ui="combo" />
-      <window_info id="Message" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.33" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
-      <window_info id="Commander" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="0" side_tool="false" content_ui="tabs" />
-      <window_info id="Inspection" active="false" anchor="bottom" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.4" sideWeight="0.5" order="5" side_tool="false" content_ui="tabs" />
-      <window_info id="Ant Build" active="false" anchor="right" auto_hide="false" internal_type="DOCKED" type="DOCKED" visible="false" show_stripe_button="true" weight="0.25" sideWeight="0.5" order="1" side_tool="false" content_ui="tabs" />
-    </layout>
-  </component>
-  <component name="TypeScriptGeneratedFilesManager">
-    <option name="version" value="1" />
-  </component>
-  <component name="VcsContentAnnotationSettings">
-    <option name="myLimit" value="2678400000" />
-  </component>
-  <component name="XDebuggerManager">
-    <breakpoint-manager />
-    <watches-manager />
-  </component>
-  <component name="editorHistoryManager">
-    <entry file="file://$PROJECT_DIR$/find_maxes/crop_max_patches.py">
-      <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="436">
-          <caret line="66" column="8" lean-forward="false" selection-start-line="66" selection-start-column="8" selection-end-line="66" selection-end-column="8" />
-          <folding />
-        </state>
-      </provider>
-    </entry>
-    <entry file="file://$PROJECT_DIR$/settings.py">
-      <provider selected="true" editor-type-id="text-editor">
-        <state relative-caret-position="271">
-          <caret line="70" column="0" lean-forward="false" selection-start-line="70" selection-start-column="0" selection-end-line="70" selection-end-column="0" />
-          <folding />
-        </state>
-      </provider>
-    </entry>
-  </component>
-</project>
\ No newline at end of file
diff --git a/caffevis/app.py b/caffevis/app.py
index 3a5b62d32..5f4fa7cd0 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -1073,7 +1073,18 @@ def _draw_back_pane(self, pane):
 
         else: # One of the backprop modes is enabled and the back computation (gradient or deconv) is up to date
 
-            grad_blob = self.net.blobs['data'].diff
+            # if selection is not frozen we use the input layer as target for visualization
+            if not self.state.backprop_selection_frozen:
+                grad_blob = self.net.blobs['data'].diff
+            else: # otherwise, we use the currently selected layer as target for visualization
+                selected_layer_def = self.state.get_current_layer_definition()
+                selected_layer_name = SiameseHelper.get_single_selected_layer_name(selected_layer_def, self.state.siamese_input_mode)
+
+                if self.net.blobs.has_key(selected_layer_name) and len(self.net.blobs[selected_layer_name].diff.shape) == 4:
+                    grad_blob = self.net.blobs[selected_layer_name].diff
+                else:
+                    # fallback to input layer
+                    grad_blob = self.net.blobs['data'].diff
 
             # Manually deprocess (skip mean subtraction and rescaling)
             #grad_img = self.net.deprocess('data', diff_blob)
diff --git a/deep-visualization-toolbox/siamese_helper.py b/deep-visualization-toolbox/siamese_helper.py
index 3aa22d3eb..8f62e9aa7 100644
--- a/deep-visualization-toolbox/siamese_helper.py
+++ b/deep-visualization-toolbox/siamese_helper.py
@@ -194,7 +194,7 @@ def get_default_layer_name(layer_def):
         return default_layer_name
 
     @staticmethod
-    def _get_single_selected_layer_name(layer_def, siamese_input_mode):
+    def get_single_selected_layer_name(layer_def, siamese_input_mode):
 
         if layer_def['format'] == 'normal':
             return layer_def['name/s']
@@ -379,7 +379,7 @@ def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mo
             # add batch dimension
             diffs = expand_dims(diffs, 0)
 
-            selected_backprop_layer_name = SiameseHelper._get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
+            selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
             net.backward_from_layer(selected_backprop_layer_name, diffs, zero_higher=True)
 
         pass
@@ -420,7 +420,7 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode
             # add batch dimension
             diffs = expand_dims(diffs, 0)
 
-            selected_backprop_layer_name = SiameseHelper._get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
+            selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
             net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
 
     @staticmethod

From 4a939164ba1bcd82c53e190482bd35f237345312 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 23 Sep 2017 23:52:03 +0300
Subject: [PATCH 070/106] moved some code

---
 .gitignore                                         |  6 ++----
 caffevis/caffevis_app_state.py                     |  6 +++++-
 deep-visualization-toolbox/.gitignore              |  1 -
 find_maxes/max_tracker.py                          | 14 +++++++++-----
 optimize/gradient_optimizer.py                     |  8 ++++----
 optimize_image.py                                  |  2 +-
 .../run_webui.py => run_webui.py                   |  0
 .../settings_misc.py => settings_misc.py           |  7 +++++--
 ...model_selector.py => settings_model_selector.py |  1 -
 ...ngs_user.py.example => settings_user.py.example |  6 +++---
 .../siamese_helper.py => siamese_helper.py         |  0
 .../templates => templates}/index.html             |  0
 12 files changed, 29 insertions(+), 22 deletions(-)
 delete mode 100644 deep-visualization-toolbox/.gitignore
 rename deep-visualization-toolbox/run_webui.py => run_webui.py (100%)
 rename deep-visualization-toolbox/settings_misc.py => settings_misc.py (91%)
 rename deep-visualization-toolbox/settings_model_selector.py => settings_model_selector.py (99%)
 rename deep-visualization-toolbox/settings_user.py.example => settings_user.py.example (77%)
 rename deep-visualization-toolbox/siamese_helper.py => siamese_helper.py (100%)
 rename {deep-visualization-toolbox/templates => templates}/index.html (100%)

diff --git a/.gitignore b/.gitignore
index aaa8b111e..b438bdc1b 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,4 +1,2 @@
-settings_local.py
-settings.py.templatec
-*.pyc
-*.DS_Store
+settings_user.py
+
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 6301903bd..57a4ae28b 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -141,7 +141,11 @@ def _populate_net_blob_info(self, net):
             # Conv example: (1, 96, 55, 55)
             # FC example: (1, 1000)
             blob_shape = net.blobs[key].data.shape
-            assert len(blob_shape) in (2,4), 'Expected either 2 for FC or 4 for conv layer'
+
+            # handle case when output is a single number per image in the batch
+            if (len(blob_shape) == 1):
+                blob_shape = (blob_shape[0], 1)
+
             self.net_blob_info[key]['isconv'] = (len(blob_shape) == 4)
             self.net_blob_info[key]['data_shape'] = blob_shape[1:]  # Chop off batch size
             self.net_blob_info[key]['n_tiles'] = blob_shape[1]
diff --git a/deep-visualization-toolbox/.gitignore b/deep-visualization-toolbox/.gitignore
deleted file mode 100644
index 5aca41de4..000000000
--- a/deep-visualization-toolbox/.gitignore
+++ /dev/null
@@ -1 +0,0 @@
-/settings_user.py
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 4080ff833..0c9404e2d 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -277,6 +277,7 @@ def _init_with_net(self, net):
 
         for layer_name in self.layers:
 
+            print 'init layer: ', layer_name
             top_name = layer_name_to_top_name(net, layer_name)
             blob = net.blobs[top_name].data
 
@@ -488,11 +489,14 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
             print '%s   Pair %d/%d' % (datetime.now().ctime(), batch[batch_index].image_idx, len(image_filenames))
 
         with WithTimer('Load image', quiet=not do_print):
-            im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
-            im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
-            im1 = resize_without_fit(im1, net_input_dims)
-            im2 = resize_without_fit(im2, net_input_dims)
-            batch[batch_index].im = np.concatenate((im1, im2), axis=2)
+            try:
+                im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
+                im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
+                im1 = resize_without_fit(im1, net_input_dims)
+                im2 = resize_without_fit(im2, net_input_dims)
+                batch[batch_index].im = np.concatenate((im1, im2), axis=2)
+            except:
+                continue
 
         batch_index += 1
 
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 2de01b2d1..c56acf062 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -176,7 +176,7 @@ def __init__(self, settings, net, batched_data_mean, labels = None, label_layers
                 self.channel_swap_to_rgb = arange(3)  # Don't change order
 
         # since we have a batch of same data mean images, we can just take the first
-        if batched_data_mean:
+        if batched_data_mean is not None:
             self._data_mean_rgb_img = self.batched_data_mean[0, self.channel_swap_to_rgb].transpose((1,2,0))  # Store as (227,227,3) in RGB order.
         else:
             self._data_mean_rgb_img = None
@@ -205,7 +205,7 @@ def _get_x0(self, params):
         if params.start_at == 'mean_plus_rand':
             x0 = np.random.normal(0, 10, input_shape)
         elif params.start_at == 'randu':
-            if self.batched_data_mean:
+            if self.batched_data_mean is not None:
                 x0 = uniform(0, 255, input_shape) - self.batched_data_mean
             else:
                 x0 = uniform(0, 255, input_shape)
@@ -258,7 +258,7 @@ def _optimize(self, params, x0, prefix_template):
         obj = np.zeros(params.batch_size)
         for ii in range(params.max_iter):
             # 0. Crop data
-            if self.batched_data_mean:
+            if self.batched_data_mean is not None:
                 xx = minimum(255.0, maximum(0.0, xx + self.batched_data_mean)) - self.batched_data_mean     # Crop all values to [0,255]
             else:
                 xx = minimum(255.0, maximum(0.0, xx)) # Crop all values to [0,255]
@@ -352,7 +352,7 @@ def _optimize(self, params, x0, prefix_template):
             max_grad = np.amax(reshaped_grad, axis=1)
 
             for batch_index in range(params.batch_size):
-                print ' batch_index: %d         min grad: %f, max grad: %f, norm grad: %f' % (batch_index, min_grad[batch_index], max_grad[batch_index], norm_grad[batch_index])
+                print ' layer: %s, channel: %d, batch_index: %d    min grad: %f, max grad: %f, norm grad: %f' % (params.push_layer, params.push_unit[0], batch_index, min_grad[batch_index], max_grad[batch_index], norm_grad[batch_index])
                 if norm_grad[batch_index] == 0:
                     print ' batch_index: %d, Grad exactly 0, failed' % batch_index
                     results[batch_index].meta_result = 'Metaresult: grad 0 failure'
diff --git a/optimize_image.py b/optimize_image.py
index e844d7328..f36f59c6a 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -176,7 +176,7 @@ def main():
     if settings.caffevis_labels:
         labels = read_label_file(settings.caffevis_labels)
 
-    if data_mean:
+    if data_mean is not None:
         if len(data_mean.shape) == 3:
             batched_data_mean = np.repeat(data_mean[np.newaxis, :, :, :], args.batch_size, axis=0)
         elif len(data_mean.shape) == 1:
diff --git a/deep-visualization-toolbox/run_webui.py b/run_webui.py
similarity index 100%
rename from deep-visualization-toolbox/run_webui.py
rename to run_webui.py
diff --git a/deep-visualization-toolbox/settings_misc.py b/settings_misc.py
similarity index 91%
rename from deep-visualization-toolbox/settings_misc.py
rename to settings_misc.py
index f08567d5d..1e7868ec1 100644
--- a/deep-visualization-toolbox/settings_misc.py
+++ b/settings_misc.py
@@ -41,11 +41,14 @@ def get_layer_info(settings, layer_name):
 
     # go over layers
 
-    previous_layer = None
+    previous_layer = (None, None, None, None, None, None, None)
+    current_layer = (None, None, None, None, None, None, None)
     for (name, type, input, output, filter, stride, pad) in settings.max_tracker_layers_list:
         if name == layer_name:
             current_layer = (name, type, input, output, filter, stride, pad)
             return current_layer, previous_layer
 
         # update previous layer
-        previous_layer = (name, type, input, output, filter, stride, pad)
\ No newline at end of file
+        previous_layer = (name, type, input, output, filter, stride, pad)
+
+    return current_layer, previous_layer
\ No newline at end of file
diff --git a/deep-visualization-toolbox/settings_model_selector.py b/settings_model_selector.py
similarity index 99%
rename from deep-visualization-toolbox/settings_model_selector.py
rename to settings_model_selector.py
index 933c1b9f1..b8ff35089 100644
--- a/deep-visualization-toolbox/settings_model_selector.py
+++ b/settings_model_selector.py
@@ -20,4 +20,3 @@
 
 elif model_to_load == 'other_model':
     from models.other_model.settings_other_model import *
-
diff --git a/deep-visualization-toolbox/settings_user.py.example b/settings_user.py.example
similarity index 77%
rename from deep-visualization-toolbox/settings_user.py.example
rename to settings_user.py.example
index 24665fd93..29c2ee3e0 100644
--- a/deep-visualization-toolbox/settings_user.py.example
+++ b/settings_user.py.example
@@ -7,8 +7,8 @@ import os
 base_folder = os.path.expanduser('~')
 
 # Use GPU? Default is True.
-# caffevis_mode_gpu = False
+#caffevis_mode_gpu = False
 
 # network selection, for network settings see file settings_local.py
-model_to_load = 'caffenet_yos'
-
+model_to_load = 'model'
+#model_to_load = 'caffenet_yos'
diff --git a/deep-visualization-toolbox/siamese_helper.py b/siamese_helper.py
similarity index 100%
rename from deep-visualization-toolbox/siamese_helper.py
rename to siamese_helper.py
diff --git a/deep-visualization-toolbox/templates/index.html b/templates/index.html
similarity index 100%
rename from deep-visualization-toolbox/templates/index.html
rename to templates/index.html

From 1ab937bddcc019c7eed32003fc9d05092dda0254 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 23 Sep 2017 23:54:40 +0300
Subject: [PATCH 071/106] handle missing inputs in max tracker

---
 find_maxes/max_tracker.py | 13 ++++++++++---
 1 file changed, 10 insertions(+), 3 deletions(-)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 0c9404e2d..4e663d429 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -425,9 +425,14 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
             print '%s   Image %d/%d' % (datetime.now().ctime(), batch[batch_index].image_idx, len(image_filenames))
 
         with WithTimer('Load image', quiet = not do_print):
-            batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
-            batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
-            batch[batch_index].im = batch[batch_index].im.astype(np.float32)
+            try:
+                batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
+                batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
+                batch[batch_index].im = batch[batch_index].im.astype(np.float32)
+            except:
+                # skip bad/missing inputs
+                print "WARNING: skipping bad/missing input:", batch[batch_index].filename
+                continue
 
         batch_index += 1
 
@@ -496,6 +501,8 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
                 im2 = resize_without_fit(im2, net_input_dims)
                 batch[batch_index].im = np.concatenate((im1, im2), axis=2)
             except:
+                # skip bad/missing inputs
+                print "WARNING: skipping bad/missing inputs:", filename1, filename2
                 continue
 
         batch_index += 1

From d87afa1bf03f7386923da2d3a2a14f68d2f0cb01 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 23 Sep 2017 23:56:29 +0300
Subject: [PATCH 072/106] fixed bug related to missing layer name to top name
 conversion

---
 caffevis/app.py   | 8 ++++----
 siamese_helper.py | 4 ++--
 2 files changed, 6 insertions(+), 6 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 5f4fa7cd0..d4c9ca15a 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -207,7 +207,7 @@ def _draw_prob_labels_pane(self, pane):
         clr_0 = to_255(self.settings.caffevis_class_clr_0)
         clr_1 = to_255(self.settings.caffevis_class_clr_1)
 
-        probs_flat = self.net.blobs[self.settings.caffevis_prob_layer].data.flatten()
+        probs_flat = self.net.blobs[layer_name_to_top_name(self.net, self.settings.caffevis_prob_layer)].data.flatten()
         top_5 = probs_flat.argsort()[-1:-6:-1]
 
         strings = []
@@ -1079,9 +1079,9 @@ def _draw_back_pane(self, pane):
             else: # otherwise, we use the currently selected layer as target for visualization
                 selected_layer_def = self.state.get_current_layer_definition()
                 selected_layer_name = SiameseHelper.get_single_selected_layer_name(selected_layer_def, self.state.siamese_input_mode)
-
-                if self.net.blobs.has_key(selected_layer_name) and len(self.net.blobs[selected_layer_name].diff.shape) == 4:
-                    grad_blob = self.net.blobs[selected_layer_name].diff
+                selected_top_name = layer_name_to_top_name(self.net, selected_layer_name)
+                if self.net.blobs.has_key(selected_top_name) and len(self.net.blobs[selected_top_name].diff.shape) == 4:
+                    grad_blob = self.net.blobs[selected_top_name].diff
                 else:
                     # fallback to input layer
                     grad_blob = self.net.blobs['data'].diff
diff --git a/siamese_helper.py b/siamese_helper.py
index 8f62e9aa7..e543e1f75 100644
--- a/siamese_helper.py
+++ b/siamese_helper.py
@@ -295,10 +295,10 @@ def _get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selecto
             raise Exception('function get_siamese_blobs() should not be called when layer is in normal format')
 
         elif layer_def['format'] == 'siamese_layer_pair':
-            return blob_selector(net.blobs[layer_def['name/s'][0]])[0], blob_selector(net.blobs[layer_def['name/s'][1]])[0]
+            return blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'][0])])[0], blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'][1])])[0]
 
         elif layer_def['format'] == 'siamese_batch_pair':
-            return blob_selector(net.blobs[layer_def['name/s']])[0], blob_selector(net.blobs[layer_def['name/s']])[1]
+            return blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'])])[0], blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'])])[1]
 
         else:
             raise Exception("get_siamese_blobs() got invalid layer_def['format']=%s" % layer_def['format'])

From e5149ca0b3d43fd6ad4bb12a03b31a9ea25b1367 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 24 Sep 2017 00:00:51 +0300
Subject: [PATCH 073/106] added support for siamese_input_mode to be
 'concat_along_width', existing was only 'concat_channelwise'

---
 caffe_misc.py                 | 23 ++++++++++++++------
 caffevis/app.py               | 10 +++++++--
 caffevis/caffe_proc_thread.py | 12 ++++++++---
 caffevis/caffevis_helper.py   | 10 ---------
 find_maxes/max_tracker.py     | 40 ++++++++++++++++++++++++++++-------
 settings.py                   |  3 +++
 6 files changed, 68 insertions(+), 30 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index 1e57974ac..c7f6f568d 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -217,6 +217,7 @@ def extract_patch_from_image(data, net, selected_input_index, settings,
 
         # input is first image so select first 3 channels
         if selected_input_index == 0:
+
             out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
             out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = data[0:3,
                                                                            data_ii_start:data_ii_end,
@@ -229,13 +230,21 @@ def extract_patch_from_image(data, net, selected_input_index, settings,
                                                                            data_jj_start:data_jj_end]
         # input is both images so select concatenate data horizontally
         elif selected_input_index == -1:
-            out_arr = np.zeros((3, size_ii, size_jj * 2), dtype='float32')
-            out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = data[0:3,
-                                                                                       data_ii_start:data_ii_end,
-                                                                                       data_jj_start:data_jj_end]
-            out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = data[3:6,
-                                                                                                   data_ii_start:data_ii_end,
-                                                                                                   data_jj_start:data_jj_end]
+
+            if settings.siamese_input_mode == 'concat_channelwise':
+                out_arr = np.zeros((3, size_ii, size_jj * 2), dtype='float32')
+                out_arr[:, out_ii_start:out_ii_end, (0 + out_jj_start):(0 + out_jj_end)] = data[0:3,
+                                                                                           data_ii_start:data_ii_end,
+                                                                                           data_jj_start:data_jj_end]
+                out_arr[:, out_ii_start:out_ii_end, (size_jj + out_jj_start):(size_jj + out_jj_end)] = data[3:6,
+                                                                                                       data_ii_start:data_ii_end,
+                                                                                                       data_jj_start:data_jj_end]
+            elif settings.siamese_input_mode == 'concat_along_width':
+                out_arr = np.zeros((3, size_ii, size_jj), dtype='float32')
+                out_arr[:, out_ii_start:out_ii_end, out_jj_start:out_jj_end] = data[:,
+                                                                               data_ii_start:data_ii_end,
+                                                                               data_jj_start:data_jj_end]
+
         else:
             print "Error: invalid value for selected_input_index (", selected_input_index, ")"
     else:
diff --git a/caffevis/app.py b/caffevis/app.py
index d4c9ca15a..dedfdfeea 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -1102,8 +1102,14 @@ def run_processing_once_or_twice(image, resize_shape, process_image_fn):
                 if self.settings.is_siamese:
 
                     # split image to image0 and image1
-                    image0 = image[:, :, 0:3]
-                    image1 = image[:, :, 3:6]
+                    if self.settings.siamese_input_mode == 'concat_channelwise':
+                        image0 = image[:, :, 0:3]
+                        image1 = image[:, :, 3:6]
+
+                    elif self.settings.siamese_input_mode == 'concat_along_width':
+                        half_width = image.shape[1] / 2
+                        image0 = image[:, :half_width, :]
+                        image1 = image[:, half_width:, :]
 
                     # combine image0 and image1
                     if self.state.siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index 62e8efa7f..a13554acd 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -87,9 +87,15 @@ def run(self):
                     frame1 = frame[0]
                     frame2 = frame[1]
 
-                    im_small1 = resize_without_fit(frame1, self.input_dims)
-                    im_small2 = resize_without_fit(frame2, self.input_dims)
-                    im_small = np.concatenate( (im_small1, im_small2), axis=2)
+                    if self.settings.siamese_input_mode == 'concat_channelwise':
+                        im_small1 = resize_without_fit(frame1, self.input_dims)
+                        im_small2 = resize_without_fit(frame2, self.input_dims)
+                        im_small = np.concatenate( (im_small1, im_small2), axis=2)
+                    elif self.settings.siamese_input_mode == 'concat_along_width':
+                        half_input_dims = (self.input_dims[0],self.input_dims[1]/2)
+                        im_small1 = resize_without_fit(frame1, half_input_dims)
+                        im_small2 = resize_without_fit(frame2, half_input_dims)
+                        im_small = np.concatenate( (im_small1, im_small2), axis=1)
 
                 else:
                     im_small = resize_without_fit(frame, self.input_dims)
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index b81d7b653..b514c541e 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -26,16 +26,6 @@ def net_preproc_forward(settings, net, img, data_hw):
     data_blob = data_blob[np.newaxis,:,:,:]                   # e.g. (1, 3, 227, 227)
     output = net.forward(data=data_blob)
 
-    # DEBUG CODE
-    if False:
-        print "*** hidden1.weights.shape:", net.params['linear_logits'][0].data.shape
-        print "*** hidden1.weights.data:\n", net.params['linear_logits'][0].data
-        print "*** hidden1.bias.shape:", net.params['linear_logits'][1].data.shape
-        print "*** hidden1.bias.data:\n", net.params['linear_logits'][1].data
-        print "*** hidden1.shape:", net.blobs['hidden1'].data.shape
-        print "*** hidden1.data:\n", net.blobs['hidden1'].data
-        print "*** calculated prediction:", (np.dot(net.params['linear_logits'][0].data, net.blobs['hidden1'].data.transpose()) + net.params['linear_logits'][1].data)
-
     return output
 
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 4e663d429..af4b8734e 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -497,9 +497,18 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
             try:
                 im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
                 im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
-                im1 = resize_without_fit(im1, net_input_dims)
-                im2 = resize_without_fit(im2, net_input_dims)
-                batch[batch_index].im = np.concatenate((im1, im2), axis=2)
+
+                if settings.siamese_input_mode == 'concat_channelwise':
+                    im1 = resize_without_fit(im1, net_input_dims)
+                    im2 = resize_without_fit(im2, net_input_dims)
+                    batch[batch_index].im = np.concatenate((im1, im2), axis=2)
+
+                elif settings.siamese_input_mode == 'concat_along_width':
+                    half_input_dims = (net_input_dims[0], net_input_dims[1] / 2)
+                    im1 = resize_without_fit(im1, half_input_dims)
+                    im2 = resize_without_fit(im2, half_input_dims)
+                    batch[batch_index].im = np.concatenate((im1, im2), axis=1)
+
             except:
                 # skip bad/missing inputs
                 print "WARNING: skipping bad/missing inputs:", filename1, filename2
@@ -752,12 +761,27 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                     im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
                     im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
 
-                    # resize images according to input dimension
-                    im1 = resize_without_fit(im1, net_input_dims)
-                    im2 = resize_without_fit(im2, net_input_dims)
+                    if settings.siamese_input_mode == 'concat_channelwise':
+
+                        # resize images according to input dimension
+                        im1 = resize_without_fit(im1, net_input_dims)
+                        im2 = resize_without_fit(im2, net_input_dims)
+
+                        # concatenate channelwise
+                        batch[batch_index].im = np.concatenate((im1, im2), axis=2)
+
+                        # convert to float to avoid caffe destroying the image in the scaling phase
+                        batch[batch_index].im = batch[batch_index].im.astype(np.float32)
+
+                    elif settings.siamese_input_mode == 'concat_along_width':
+                        half_input_dims = (net_input_dims[0], net_input_dims[1] / 2)
+                        im1 = resize_without_fit(im1, half_input_dims)
+                        im2 = resize_without_fit(im2, half_input_dims)
+                        batch[batch_index].im = np.concatenate((im1, im2), axis=1)
+
+                        # convert to float to avoid caffe destroying the image in the scaling phase
+                        batch[batch_index].im = batch[batch_index].im.astype(np.float32)
 
-                    # concatenate channelwise
-                    batch[batch_index].im = np.concatenate((im1, im2), axis=2)
                 else:
                     # load image
                     batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
diff --git a/settings.py b/settings.py
index d7a35b9d5..77366b69a 100644
--- a/settings.py
+++ b/settings.py
@@ -119,6 +119,9 @@
 # siamese network format, can be either 'siamese_layer_pair' or 'siamese_batch_pair'
 siamese_network_format = locals().get('siamese_network_format', 'siamese_layer_pair')
 
+# siamese input mode, can be either 'concat_channelwise' or 'concat_along_width'
+siamese_input_mode = locals().get('siamese_input_mode', 'concat_channelwise')
+
 # contains the input mode for reading static images, can be: 'directory', 'image_list'
 static_files_input_mode = locals().get('static_files_input_mode', 'directory')
 

From 84975caba12e29995b0a31fe9b39f397c49536d5 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 00:55:35 +0300
Subject: [PATCH 074/106] - implemented unique deconv - renamed enum
 SiameseInputMode to SiameseViewMode - moved network loading into
 settings_misc::load_network()

---
 bindings.py                        |   8 +-
 caffevis/app.py                    | 258 +++++++++++++++++------------
 caffevis/caffe_proc_thread.py      |  18 +-
 caffevis/caffevis_app_state.py     |  49 +++---
 caffevis/jpg_vis_loading_thread.py |   8 +-
 find_maxes/crop_max_patches.py     |  25 +--
 find_maxes/find_max_acts.py        |  34 ++--
 find_maxes/max_tracker.py          |  43 ++---
 input_fetcher.py                   |   9 +-
 live_vis.py                        |  12 +-
 optimize_image.py                  |  27 +--
 settings.py                        |  11 +-
 settings_misc.py                   |  41 ++++-
 siamese_helper.py                  | 218 ++++++++++++++++++------
 14 files changed, 452 insertions(+), 309 deletions(-)

diff --git a/bindings.py b/bindings.py
index acd7430ea..3442ef4b1 100644
--- a/bindings.py
+++ b/bindings.py
@@ -117,9 +117,9 @@ def get_key_help(self, tag):
 _.add('show_back', 'a',
        'Toggle between showing forward activations and back/deconv diffs')
 _.add('next_back_view_option', 'n',
-       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, max abs, histogram)')
+       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, sum>0, histogram)')
 _.add('prev_back_view_option', 'N',
-       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, max abs, histogram)')
+       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, sum>0, histogram)')
 _.add('next_color_map', 'm',
        'Cycle through colormaps options (grayscale, jet, plasma)')
 _.add('prev_color_map', 'M',
@@ -134,8 +134,8 @@ def get_key_help(self, tag):
 _.add('toggle_unit_jpgs', '9',
        'Turn on or off display of loaded jpg visualization')
 
-_.add('siamese_input_mode', 'v',
-        'Cycle between siamese input modes {first image, second image, both images}')
+_.add('siamese_view_mode', 'v',
+        'Cycle between siamese view modes {first image, second image, both images}')
 
 _.add('toggle_maximal_score', 'r',
         'Toggle showing maximal score overlays {on, off}')
diff --git a/caffevis/app.py b/caffevis/app.py
index dedfdfeea..fc24049f9 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -23,14 +23,13 @@
     caffe_load_image, ensure_uint255_and_resize_without_fit, softmax_image, array_histogram, fig2data
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
-from jpg_vis_loading_thread import JPGVisLoadingThread
-from caffevis_app_state import CaffeVisAppState, SiameseInputMode, PatternMode, BackpropMode, BackpropViewOption, \
+from caffevis_app_state import CaffeVisAppState, SiameseViewMode, PatternMode, BackpropMode, BackpropViewOption, \
     ColorMapOption, InputOverlayOption
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
 from caffe_misc import layer_name_to_top_name, save_caffe_image
 from siamese_helper import SiameseHelper
-from settings_misc import deduce_calculated_settings
+from settings_misc import load_network
 
 
 class CaffeVisApp(BaseApp):
@@ -50,30 +49,7 @@ def __init__(self, settings, key_bindings):
         else:
             self._net_channel_swap_inv = tuple([self._net_channel_swap.index(ii) for ii in range(len(self._net_channel_swap))])
 
-        # Set the mode to CPU or GPU. Note: in the latest Caffe
-        # versions, there is one Caffe object *per thread*, so the
-        # mode must be set per thread! Here we set the mode for the
-        # main thread; it is also separately set in CaffeProcThread.
-        sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
-        import caffe
-        if settings.caffevis_mode_gpu:
-            caffe.set_mode_gpu()
-            print 'CaffeVisApp mode (in main thread):     GPU'
-        else:
-            caffe.set_mode_cpu()
-            print 'CaffeVisApp mode (in main thread):     CPU'
-        self.net = caffe.Classifier(
-            settings.caffevis_deploy_prototxt,
-            settings.caffevis_network_weights,
-            mean=None, # Set to None for now, assign later
-            channel_swap=self._net_channel_swap,
-            raw_scale=settings.caffe_net_raw_scale,
-            image_dims=settings.caffe_net_image_dims,
-        )
-
-        deduce_calculated_settings(settings, self.net)
-
-        self._data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, self.net)
+        self.net, self._data_mean = load_network(settings)
 
         check_force_backward_true(settings.caffevis_deploy_prototxt)
 
@@ -88,6 +64,8 @@ def __init__(self, settings, key_bindings):
         self.img_cache = FIFOLimitedArrayCache(settings.caffevis_jpg_cache_size)
 
     def start(self):
+        from jpg_vis_loading_thread import JPGVisLoadingThread
+
         self.state = CaffeVisAppState(self.net, self.settings, self.bindings)
         self.state.drawing_stale = True
         self.header_print_names = [get_pretty_layer_name(self.settings, nn) for nn in self.state.get_headers()]
@@ -332,7 +310,7 @@ def _draw_layer_pane(self, pane):
 
         default_layer_name = self.state.get_default_layer_name()
 
-        if self.state.siamese_input_mode_has_two_images():
+        if self.state.siamese_view_mode_has_two_images():
 
             if self.state.layers_show_back:
 
@@ -526,7 +504,7 @@ def _add_label_or_score_overlay(self, default_layer_name, pane):
 
             if (default_layer_name in self.settings.caffevis_score_layers):
 
-                if self.state.siamese_input_mode_has_two_images():
+                if self.state.siamese_view_mode_has_two_images():
                     if self.state.layers_show_back:
                         blob1, blob2 = self.state.get_siamese_selected_diff_blobs(self.net)
                         value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
@@ -983,14 +961,14 @@ def get_processed_selected_unit(self, all_units, resize_shape, use_colored_data
 
         unit_data = all_units[self.state.selected_unit]
         if self.settings.caffevis_keep_aspect_ratio:
-            unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, resize_shape)
+            unit_data_resize = resize_to_fit(unit_data, resize_shape)
         else:
-            unit_data_resize = ensure_uint255_and_resize_without_fit(unit_data, resize_shape)
+            unit_data_resize = resize_without_fit(unit_data, resize_shape)
 
         if self.state.pattern_mode == PatternMode.OFF:
             input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese,
-                                                             resize_shape, self.state.siamese_input_mode)
-            normalized_mask = unit_data_resize / 255.0
+                                                             resize_shape, self.state.siamese_view_mode)
+            normalized_mask = unit_data_resize
 
             if use_colored_data:
                 unit_data_resize = self.state.gray_to_colormap(unit_data_resize)
@@ -1007,10 +985,11 @@ def get_processed_selected_unit(self, all_units, resize_shape, use_colored_data
                 unit_data_resize = (normalized_mask < 0.1) * input_image + (normalized_mask >= 0.1) * unit_data_resize
                 pass
 
+        unit_data_resize = ensure_uint255(unit_data_resize)
         return unit_data_resize
 
 
-    def _mix_input_overlay_with_colormap(self, unit_data, resize_shape, input_image):
+    def _mix_input_overlay_with_colormap_old(self, unit_data, resize_shape, input_image):
 
         if self.settings.caffevis_keep_aspect_ratio:
             unit_data_resize = ensure_uint255_and_resize_to_fit(unit_data, resize_shape)
@@ -1040,6 +1019,43 @@ def _mix_input_overlay_with_colormap(self, unit_data, resize_shape, input_image)
 
         return colored_unit_data_resize
 
+    def _mix_input_overlay_with_colormap(self, unit_data, resize_shape, input_image):
+
+        # resize
+        if self.settings.caffevis_keep_aspect_ratio:
+            input_image_resize = resize_to_fit(input_image, resize_shape)
+            unit_data_resize = resize_to_fit(unit_data, resize_shape)
+        else:
+            input_image_resize = resize_without_fit(input_image, resize_shape)
+            unit_data_resize = resize_without_fit(unit_data, resize_shape)
+
+        sigma = 0.02 * max(unit_data_resize.shape[0:2])
+        blur_unit_data_resize = cv2.GaussianBlur(unit_data_resize, (0, 0), sigma)
+        normalized_blur_unit_data_resize = norm01(blur_unit_data_resize)
+
+        colored_normalized_blur_unit_data_resize = self.state.gray_to_colormap(normalized_blur_unit_data_resize)
+        if len(colored_normalized_blur_unit_data_resize.shape) == 2:
+            colored_normalized_blur_unit_data_resize = np.tile(colored_normalized_blur_unit_data_resize[:, :, np.newaxis], 3)
+
+        if self.state.input_overlay_option == InputOverlayOption.OFF:
+            attMap = colored_normalized_blur_unit_data_resize
+            pass
+
+        elif self.state.input_overlay_option == InputOverlayOption.OVER_ACTIVE:
+            MAGIC_NUMBER = 0.8
+            boost_normalized_blur_unit_data_resize = normalized_blur_unit_data_resize ** MAGIC_NUMBER
+            boost_normalized_blur_unit_data_resize = boost_normalized_blur_unit_data_resize.reshape(boost_normalized_blur_unit_data_resize.shape + (1,))
+            attMap = (boost_normalized_blur_unit_data_resize) * input_image_resize + (1 - boost_normalized_blur_unit_data_resize) * colored_normalized_blur_unit_data_resize
+
+        elif self.state.input_overlay_option == InputOverlayOption.OVER_INACTIVE:
+            MAGIC_NUMBER = 0.8
+            boost_normalized_blur_unit_data_resize = normalized_blur_unit_data_resize ** MAGIC_NUMBER
+            boost_normalized_blur_unit_data_resize = boost_normalized_blur_unit_data_resize.reshape(boost_normalized_blur_unit_data_resize.shape + (1,))
+            attMap = (1 - boost_normalized_blur_unit_data_resize) * input_image_resize + (boost_normalized_blur_unit_data_resize) * colored_normalized_blur_unit_data_resize
+
+        return attMap
+
+
     def _draw_aux_pane(self, pane, layer_data_normalized):
         pane.data[:] = to_255(self.settings.window_background)
 
@@ -1073,120 +1089,152 @@ def _draw_back_pane(self, pane):
 
         else: # One of the backprop modes is enabled and the back computation (gradient or deconv) is up to date
 
-            # if selection is not frozen we use the input layer as target for visualization
-            if not self.state.backprop_selection_frozen:
-                grad_blob = self.net.blobs['data'].diff
-            else: # otherwise, we use the currently selected layer as target for visualization
-                selected_layer_def = self.state.get_current_layer_definition()
-                selected_layer_name = SiameseHelper.get_single_selected_layer_name(selected_layer_def, self.state.siamese_input_mode)
-                selected_top_name = layer_name_to_top_name(self.net, selected_layer_name)
-                if self.net.blobs.has_key(selected_top_name) and len(self.net.blobs[selected_top_name].diff.shape) == 4:
-                    grad_blob = self.net.blobs[selected_top_name].diff
-                else:
-                    # fallback to input layer
-                    grad_blob = self.net.blobs['data'].diff
+            # define helper function to run processing once or twice, in case of siamese network
+            def run_processing_once_or_twice(resize_shape, process_image_fn):
 
-            # Manually deprocess (skip mean subtraction and rescaling)
-            #grad_img = self.net.deprocess('data', diff_blob)
-            grad_blob = grad_blob[0]                    # bc01 -> c01
-            grad_blob = grad_blob.transpose((1,2,0))    # c01 -> 01c
-            if self._net_channel_swap_inv is None:
-                grad_img = grad_blob[:, :, :]  # do nothing
-            else:
-                grad_img = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
+                has_pair_inputs = False
+                no_spatial_info = False;
 
-            # define helper function ro run processing once or twice, in case of siamese network
-            def run_processing_once_or_twice(image, resize_shape, process_image_fn):
+                # if selection is frozen we use the currently selected layer as target for visualization
+                if self.state.backprop_selection_frozen:
+                    if self.state.siamese_view_mode_has_two_images():
+                        grad_blob1, grad_blob2 = self.state.get_siamese_selected_diff_blobs(self.net)
 
-                # if siamese network, run processing twice
-                if self.settings.is_siamese:
+                        if len(grad_blob1.shape) == 1:
+                            no_spatial_info = True
+
+                        if len(grad_blob1.shape) == 3:
+                            grad_blob1 = grad_blob1.transpose((1, 2, 0))  # c01 -> 01c
+                            grad_blob2 = grad_blob2.transpose((1, 2, 0))  # c01 -> 01c
+
+                        has_pair_inputs = True
+
+                    else:
+                        grad_blob = self.state.get_single_selected_diff_blob(self.net)
+                        if len(grad_blob.shape) == 1:
+                            no_spatial_info = True
+                        if len(grad_blob.shape) == 3:
+                            grad_blob = grad_blob.transpose((1, 2, 0))  # c01 -> 01c
+
+                # if selection is not frozen we use the input layer as target for visualization
+                if (not self.state.backprop_selection_frozen) or no_spatial_info:
+                    grad_blob = self.net.blobs['data'].diff
+
+                    grad_blob = grad_blob[0]  # bc01 -> c01
+                    grad_blob = grad_blob.transpose((1, 2, 0))  # c01 -> 01c
+
+                    if self._net_channel_swap_inv:
+                        grad_blob = grad_blob[:, :, self._net_channel_swap_inv]  # e.g. BGR -> RGB
 
                     # split image to image0 and image1
-                    if self.settings.siamese_input_mode == 'concat_channelwise':
-                        image0 = image[:, :, 0:3]
-                        image1 = image[:, :, 3:6]
+                    if self.settings.is_siamese:
+                        # split image to image0 and image1
+                        if self.settings.siamese_input_mode == 'concat_channelwise':
+                            [grad_blob1, grad_blob2] = np.split(grad_blob, 2, axis=2)
+
+                        elif self.settings.siamese_input_mode == 'concat_along_width':
+                            half_width = grad_blob.shape[1] / 2
+                            grad_blob1 = grad_blob[:, :half_width, :]
+                            grad_blob2 = grad_blob[:, half_width:, :]
+
+                        has_pair_inputs = True
 
-                    elif self.settings.siamese_input_mode == 'concat_along_width':
-                        half_width = image.shape[1] / 2
-                        image0 = image[:, :half_width, :]
-                        image1 = image[:, half_width:, :]
+                # if siamese network, run processing twice
+                if self.settings.is_siamese:
 
                     # combine image0 and image1
-                    if self.state.siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+                    if self.state.siamese_view_mode == SiameseViewMode.FIRST_IMAGE:
                         # run processing on image0
-                        return process_image_fn(image0, resize_shape, self.state.last_frame[0])
+                        return process_image_fn(grad_blob, resize_shape, self.state.last_frame[0])
 
-                    elif self.state.siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+                    elif self.state.siamese_view_mode == SiameseViewMode.SECOND_IMAGE:
                         # run processing on image1
-                        return process_image_fn(image1, resize_shape, self.state.last_frame[1])
+                        return process_image_fn(grad_blob, resize_shape, self.state.last_frame[1])
 
-                    elif self.state.siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
+                    elif self.state.siamese_view_mode == SiameseViewMode.BOTH_IMAGES and has_pair_inputs:
 
                         # resize each gradient image to half the pane size
                         half_pane_shape = (resize_shape[0], resize_shape[1] / 2)
 
                         # run processing on both image0 and image1
-                        image0 = process_image_fn(image0, half_pane_shape, self.state.last_frame[0])
-                        image1 = process_image_fn(image1, half_pane_shape, self.state.last_frame[1])
+                        image1 = process_image_fn(grad_blob1, half_pane_shape, self.state.last_frame[0])
+                        image2 = process_image_fn(grad_blob2, half_pane_shape, self.state.last_frame[1])
 
-                        image0 = resize_without_fit(image0[:], half_pane_shape)
                         image1 = resize_without_fit(image1[:], half_pane_shape)
+                        image2 = resize_without_fit(image2[:], half_pane_shape)
 
                         # generate the pane image by concatenating both images
-                        return np.concatenate((image0, image1), axis=1)
+                        return np.concatenate((image1, image2), axis=1)
+                    elif self.state.siamese_view_mode == SiameseViewMode.BOTH_IMAGES and not has_pair_inputs:
+                        processed_input = self.state.convert_image_pair_to_network_input_format(self.settings, self.state.last_frame, resize_shape)
+                        return process_image_fn(grad_blob, resize_shape, processed_input)
+
+                    else:
+                        return process_image_fn(grad_blob, resize_shape, self.state.last_frame)
 
                 # else, normal network, run processing once
                 else:
                     # run processing on image
-                    return process_image_fn(image, resize_shape, self.state.last_frame)
+                    return process_image_fn(grad_blob, resize_shape, self.state.last_frame)
 
                 raise Exception("flow should not arrive here")
 
             # if back_view_option == BackpropViewOption.SOFTMAX_RAW:
-            #     grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: softmax_image(norm01c(grad_img, 0)))
+            #     grad_img = run_processing_once_or_twice(pane.data.shape, lambda grad_img, resize_shape, input_image: softmax_image(norm01c(grad_img, 0)))
 
             if back_view_option == BackpropViewOption.RAW:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img, 0))
+                def do_raw(grad_blob, resize_shape, input_image):
+                    if len(grad_blob.shape) == 3 and grad_blob.shape[2] != 3:
+                        return np.zeros(resize_shape)
+                    return norm01c(grad_blob, 0)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_raw)
 
             elif back_view_option == BackpropViewOption.RAW_POS:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img, 0) * (grad_img > 0) + (0.5) * (grad_img <= 0))
+                def do_raw_pos(grad_blob, resize_shape, input_image):
+                    if len(grad_blob.shape) == 3 and grad_blob.shape[2] != 3:
+                        return np.zeros(resize_shape)
+                    return norm01c(grad_blob, 0) * (grad_blob > 0) + (0.5) * (grad_blob <= 0)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_raw_pos)
 
             elif back_view_option == BackpropViewOption.RAW_NEG:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img, 0) * (grad_img < 0) + (0.5) * (grad_img >= 0))
+                def do_raw_neg(grad_blob, resize_shape, input_image):
+                    if len(grad_blob.shape) == 3 and grad_blob.shape[2] != 3:
+                        return np.zeros(resize_shape)
+                    return norm01c(grad_blob, 0) * (grad_blob < 0) + (0.5) * (grad_blob >= 0)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_raw_neg)
 
             elif back_view_option == BackpropViewOption.GRAY:
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: norm01c(grad_img.mean(axis=2), 0))
+                def do_gray(grad_blob, resize_shape, input_image):
+                    return norm01c(grad_blob.mean(axis=2), 0)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_gray)
 
             elif back_view_option == BackpropViewOption.NORM:
-                def do_norm(grad_img, resize_shape, input_image):
-                    norm_grad_img = norm01(np.linalg.norm(grad_img, axis=2))
-                    # return self.state.gray_to_colormap(norm_grad_img)
-                    return self._mix_input_overlay_with_colormap(norm_grad_img, resize_shape, input_image)
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_norm)
+                def do_norm(grad_blob, resize_shape, input_image):
+                    norm_grad_blob = norm01(np.linalg.norm(grad_blob, axis=2))
+                    return self._mix_input_overlay_with_colormap(norm_grad_blob, resize_shape, input_image)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_norm)
 
             elif back_view_option == BackpropViewOption.NORM_BLUR:
-                def do_norm_blur(grad_img, resize_shape, input_image):
-                    grad_img = np.linalg.norm(grad_img, axis=2)
-                    cv2.GaussianBlur(grad_img, (0, 0), self.settings.caffevis_grad_norm_blur_radius, grad_img)
-                    norm_grad_img = norm01(grad_img)
-                    # return self.state.gray_to_colormap(norm_grad_img)
-                    return self._mix_input_overlay_with_colormap(norm_grad_img, resize_shape, input_image)
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_norm_blur)
-
-            elif back_view_option == BackpropViewOption.MAX_ABS:
-                def do_max_abs(grad_img, resize_shape, input_image):
-                    sigma = 0.02 * max(grad_img.shape[0:2])
-                    blur_grad_img = cv2.GaussianBlur(grad_img, (0, 0), sigma)
-                    abs_grad_img = np.abs(blur_grad_img)
-                    max_abs_grad_img = np.max(abs_grad_img, axis=2)
-                    normalized_max_abs_grad_img = norm01(max_abs_grad_img)
-                    # return self.state.gray_to_colormap(normalized_max_abs_grad_img)
-                    return self._mix_input_overlay_with_colormap(normalized_max_abs_grad_img, resize_shape, input_image)
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, do_max_abs)
+                def do_norm_blur(grad_blob, resize_shape, input_image):
+                    grad_blob = np.linalg.norm(grad_blob, axis=2)
+                    cv2.GaussianBlur(grad_blob, (0, 0), self.settings.caffevis_grad_norm_blur_radius, grad_blob)
+                    norm_grad_blob = norm01(grad_blob)
+                    return self._mix_input_overlay_with_colormap(norm_grad_blob, resize_shape, input_image)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_norm_blur)
+
+            elif back_view_option == BackpropViewOption.POS_SUM:
+                def do_pos_sum(grad_blob, resize_shape, input_image):
+                    grad_blob = np.maximum(grad_blob.sum(-1), 0)
+                    norm_grad_blob = norm01(grad_blob)
+                    return self._mix_input_overlay_with_colormap(norm_grad_blob, resize_shape, input_image)
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_pos_sum)
 
             elif back_view_option == BackpropViewOption.HISTOGRAM:
+                def do_histogram(grad_blob, resize_shape, input_image):
+                    return array_histogram(grad_blob, half_pane_shape, BackpropMode.to_string(back_mode)+' histogram', 'values', 'count')
+
                 half_pane_shape = (pane.data.shape[0],pane.data.shape[1]/2,3)
-                grad_img = run_processing_once_or_twice(grad_img, pane.data.shape, lambda grad_img, resize_shape, input_image: array_histogram(grad_img, half_pane_shape, BackpropMode.to_string(back_mode)+' histogram', 'values', 'count'))
+                grad_img = run_processing_once_or_twice(pane.data.shape, do_histogram)
 
             else:
                 raise Exception('Invalid option for back_view_option: %s' % (back_view_option))
@@ -1290,7 +1338,7 @@ def draw_help(self, help_pane, locy):
             
         for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
                     'next_input_overlay', 'next_ez_back_mode_loop', 'next_back_view_option', 'next_color_map',
-                    'freeze_back_unit', 'show_back', 'boost_gamma', 'boost_individual', 'siamese_input_mode',
+                    'freeze_back_unit', 'show_back', 'boost_gamma', 'boost_individual', 'siamese_view_mode',
                     'toggle_maximal_score', 'reset_state'):
             key_strings, help_string = self.bindings.get_key_help(tag)
             label = '%10s:' % (','.join(key_strings))
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index a13554acd..f5808d9dc 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -83,19 +83,7 @@ def run(self):
                 self.frames_processed_fwd += 1
 
                 if self.settings.is_siamese and ((type(frame), len(frame)) == (tuple, 2)):
-
-                    frame1 = frame[0]
-                    frame2 = frame[1]
-
-                    if self.settings.siamese_input_mode == 'concat_channelwise':
-                        im_small1 = resize_without_fit(frame1, self.input_dims)
-                        im_small2 = resize_without_fit(frame2, self.input_dims)
-                        im_small = np.concatenate( (im_small1, im_small2), axis=2)
-                    elif self.settings.siamese_input_mode == 'concat_along_width':
-                        half_input_dims = (self.input_dims[0],self.input_dims[1]/2)
-                        im_small1 = resize_without_fit(frame1, half_input_dims)
-                        im_small2 = resize_without_fit(frame2, half_input_dims)
-                        im_small = np.concatenate( (im_small1, im_small2), axis=1)
+                    im_small = self.state.convert_image_pair_to_network_input_format(self.settings, frame, self.input_dims)
 
                 else:
                     im_small = resize_without_fit(frame, self.input_dims)
@@ -117,6 +105,10 @@ def run(self):
                     with WithTimer('CaffeProcThread:deconv', quiet=self.debug_level < 1):
                         self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit, 'Guided Backprop')
 
+                elif back_mode == BackpropMode.DECONV_UGB:
+                    with WithTimer('CaffeProcThread:deconv', quiet=self.debug_level < 1):
+                        self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit, 'Guided Backprop', unique=True)
+
                 with self.state.lock:
                     self.state.back_stale = False
 
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 57a4ae28b..2ed1ed348 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -1,7 +1,7 @@
 import os
 import time
 from threading import Lock
-from siamese_helper import SiameseInputMode, SiameseHelper
+from siamese_helper import SiameseViewMode, SiameseHelper
 from caffe_misc import layer_name_to_top_name
 from image_misc import get_tiles_height_width_ratio, gray_to_colormap
 
@@ -18,7 +18,8 @@ class BackpropMode:
     GRAD = 1
     DECONV_ZF = 2
     DECONV_GB = 3
-    NUMBER_OF_MODES = 4
+    DECONV_UGB = 4
+    NUMBER_OF_MODES = 5
 
     @staticmethod
     def to_string(back_mode):
@@ -30,6 +31,8 @@ def to_string(back_mode):
             return 'deconv zf'
         elif back_mode == BackpropMode.DECONV_GB:
             return 'deconv gb'
+        elif back_mode == BackpropMode.DECONV_UGB:
+            return 'deconv ugb'
 
         return 'n/a'
 
@@ -41,7 +44,7 @@ class BackpropViewOption:
     GRAY = 3
     NORM = 4
     NORM_BLUR = 5
-    MAX_ABS = 6
+    POS_SUM = 6
     HISTOGRAM = 7
     NUMBER_OF_OPTIONS = 8
 
@@ -63,8 +66,8 @@ def to_string(back_view_option):
             return 'normblur'
         elif back_view_option == BackpropViewOption.HISTOGRAM:
             return 'histogram'
-        elif back_view_option == BackpropViewOption.MAX_ABS:
-            return 'max abs'
+        elif back_view_option == BackpropViewOption.POS_SUM:
+            return 'sum>0'
 
         return 'n/a'
 
@@ -171,13 +174,13 @@ def _reset_user_state(self):
         self.layer_boost_gamma = self.layer_boost_gamma_choices[self.layer_boost_gamma_idx]
         self.cursor_area = 'top'   # 'top' or 'bottom'
         self.selected_unit = 0
-        self.siamese_input_mode = SiameseInputMode.BOTH_IMAGES
+        self.siamese_view_mode = SiameseViewMode.BOTH_IMAGES
 
         # Which layer and unit (or channel) to use for backprop
         self.backprop_layer_idx = self.layer_idx
         self.backprop_unit = self.selected_unit
         self.backprop_selection_frozen = False    # If false, backprop unit tracks selected unit
-        self.backprop_siamese_input_mode = SiameseInputMode.BOTH_IMAGES
+        self.backprop_siamese_view_mode = SiameseViewMode.BOTH_IMAGES
         self.back_enabled = False
         self.back_mode = BackpropMode.OFF
         self.back_view_option = BackpropViewOption.RAW
@@ -287,7 +290,7 @@ def handle_key(self, key):
                     # Grap layer/selected_unit upon transition from non-frozen -> frozen
                     self.backprop_layer_idx = self.layer_idx
                     self.backprop_unit = self.selected_unit
-                    self.backprop_siamese_input_mode = self.siamese_input_mode
+                    self.backprop_siamese_view_mode = self.siamese_view_mode
             elif tag == 'zoom_mode':
                 self.layers_pane_zoom_mode = (self.layers_pane_zoom_mode + 1) % 3
                 if self.layers_pane_zoom_mode == 2 and not self.back_enabled:
@@ -300,9 +303,8 @@ def handle_key(self, key):
             elif tag == 'toggle_unit_jpgs':
                 self.show_unit_jpgs = not self.show_unit_jpgs
 
-            elif tag == 'siamese_input_mode':
-                self.siamese_input_mode = (self.siamese_input_mode + 1) % SiameseInputMode.NUMBER_OF_MODES
-
+            elif tag == 'siamese_view_mode':
+                self.siamese_view_mode = (self.siamese_view_mode + 1) % SiameseViewMode.NUMBER_OF_MODES
 
             elif tag == 'toggle_maximal_score':
                 self.show_maximal_score = not self.show_maximal_score
@@ -338,7 +340,7 @@ def get_single_selected_data_blob(self, net, layer_def = None):
         if layer_def is None:
             layer_def = self.get_current_layer_definition()
 
-        return self.siamese_helper.get_single_selected_data_blob(net, layer_def, self.siamese_input_mode)
+        return self.siamese_helper.get_single_selected_data_blob(net, layer_def, self.siamese_view_mode)
 
     def get_single_selected_diff_blob(self, net, layer_def = None):
 
@@ -346,7 +348,7 @@ def get_single_selected_diff_blob(self, net, layer_def = None):
         if layer_def is None:
             layer_def = self.get_current_layer_definition()
 
-        return self.siamese_helper.get_single_selected_diff_blob(net, layer_def, self.siamese_input_mode)
+        return self.siamese_helper.get_single_selected_diff_blob(net, layer_def, self.siamese_view_mode)
 
     def get_siamese_selected_data_blobs(self, net, layer_def = None):
 
@@ -354,7 +356,7 @@ def get_siamese_selected_data_blobs(self, net, layer_def = None):
         if layer_def is None:
             layer_def = self.get_current_layer_definition()
 
-        return self.siamese_helper.get_siamese_selected_data_blobs(net, layer_def, self.siamese_input_mode)
+        return self.siamese_helper.get_siamese_selected_data_blobs(net, layer_def, self.siamese_view_mode)
 
     def get_siamese_selected_diff_blobs(self, net, layer_def = None):
 
@@ -362,13 +364,13 @@ def get_siamese_selected_diff_blobs(self, net, layer_def = None):
         if layer_def is None:
             layer_def = self.get_current_layer_definition()
 
-        return self.siamese_helper.get_siamese_selected_diff_blobs(net, layer_def, self.siamese_input_mode)
+        return self.siamese_helper.get_siamese_selected_diff_blobs(net, layer_def, self.siamese_view_mode)
 
 
     def backward_from_layer(self, net, backprop_layer_def, backprop_unit):
 
         try:
-            return SiameseHelper.backward_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_input_mode)
+            return SiameseHelper.backward_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_view_mode)
         except AttributeError:
             print 'ERROR: required bindings (backward_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
             raise
@@ -377,10 +379,10 @@ def backward_from_layer(self, net, backprop_layer_def, backprop_unit):
             with self.lock:
                 self.back_enabled = False
 
-    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit, deconv_type):
+    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit, deconv_type, unique=False):
 
         try:
-            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_input_mode, deconv_type)
+            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_view_mode, deconv_type, unique)
         except AttributeError:
             print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
             raise
@@ -397,7 +399,7 @@ def get_default_layer_name(self, layer_def = None):
 
         return self.siamese_helper.get_default_layer_name(layer_def)
 
-    def siamese_input_mode_has_two_images(self, layer_def = None):
+    def siamese_view_mode_has_two_images(self, layer_def = None):
         '''
         helper function which checks whether the input mode is two images, and the provided layer contains two layer names
         :param layer: can be a single string layer name, or a pair of layer names
@@ -407,7 +409,7 @@ def siamese_input_mode_has_two_images(self, layer_def = None):
         if layer_def is None:
             layer_def = self.get_current_layer_definition()
 
-        return SiameseHelper.siamese_input_mode_has_two_images(layer_def, self.siamese_input_mode)
+        return SiameseHelper.siamese_view_mode_has_two_images(layer_def, self.siamese_view_mode)
 
     def move_selection(self, direction, dist = 1):
 
@@ -452,10 +454,10 @@ def _ensure_valid_selected(self):
         # Backward selection
         if not self.backprop_selection_frozen:
             # If backprop_selection is not frozen, backprop layer/unit follows selected unit
-            if not (self.backprop_layer_idx == self.layer_idx and self.backprop_unit == self.selected_unit and self.backprop_siamese_input_mode == self.siamese_input_mode):
+            if not (self.backprop_layer_idx == self.layer_idx and self.backprop_unit == self.selected_unit and self.backprop_siamese_view_mode == self.siamese_view_mode):
                 self.backprop_layer_idx = self.layer_idx
                 self.backprop_unit = self.selected_unit
-                self.backprop_siamese_input_mode = self.siamese_input_mode
+                self.backprop_siamese_view_mode = self.siamese_view_mode
                 self.back_stale = True    # If there is any change, back diffs are now stale
 
     def fill_layers_list(self, net):
@@ -493,3 +495,6 @@ def gray_to_colormap(self, gray_image):
 
         elif self.color_map_option == ColorMapOption.PLASMA:
             return gray_to_colormap('plasma', gray_image)
+
+    def convert_image_pair_to_network_input_format(self, settings, frame_pair, resize_shape):
+        return SiameseHelper.convert_image_pair_to_network_input_format(frame_pair, resize_shape, settings.siamese_input_mode)
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index 5461d8567..fdbce01fa 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -7,13 +7,11 @@
 import math
 
 from codependent_thread import CodependentThread
-from image_misc import caffe_load_image, ensure_uint255_and_resize_to_fit, cv2_read_file_rgb, \
+from image_misc import caffe_load_image, ensure_uint255_and_resize_to_fit, \
     ensure_uint255_and_resize_without_fit
 from caffevis_helper import crop_to_corner, get_image_from_files
 
-
-
-
+import caffe
 
 class JPGVisLoadingThread(CodependentThread):
     '''Loads JPGs necessary for caffevis_jpgvis pane in separate
@@ -37,7 +35,7 @@ def load_image_into_pane_original_format(self, state_layer_name, state_selected_
                                 file_pattern % (state_layer_name, state_selected_unit))
 
         try:
-            img = cv2_read_file_rgb(jpg_path)
+            img = caffe.io.load_image(jpg_path)
 
             if should_crop_to_corner:
                 img = crop_to_corner(img, 2)
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 848df2b64..6be67e792 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -17,7 +17,7 @@
 from jby_misc import WithTimer
 from max_tracker import output_max_patches
 from find_max_acts import load_max_tracker_from_file
-from settings_misc import deduce_calculated_settings
+from settings_misc import load_network
 
 
 def main():
@@ -41,27 +41,10 @@ def main():
     parser.add_argument('--outdir',       type = str, default = settings.max_tracker_output_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
     args = parser.parse_args()
 
-    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
-    import caffe
+    settings.caffevis_deploy_prototxt = args.net_prototxt
+    settings.caffevis_network_weights = args.net_weights
 
-    if args.gpu:
-        caffe.set_mode_gpu()
-        print 'crop_max_patches mode (in main thread):     GPU'
-
-    else:
-        caffe.set_mode_cpu()
-        print 'crop_max_patches mode (in main thread):     CPU'
-
-    net = caffe.Classifier(args.net_prototxt,
-                           args.net_weights,
-                           mean=None,
-                           channel_swap=settings.caffe_net_channel_swap,
-                           raw_scale=settings.caffe_net_raw_scale,
-                           image_dims=settings.caffe_net_image_dims)
-
-    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
-
-    deduce_calculated_settings(settings, net)
+    net, data_mean = load_network(settings)
 
     # validate batch size
     if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index f231ffc31..207bac59d 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -16,7 +16,8 @@
 from caffevis.caffevis_helper import set_mean
 from jby_misc import WithTimer
 from max_tracker import scan_images_for_maxes, scan_pairs_for_maxes
-from settings_misc import deduce_calculated_settings
+from settings_misc import load_network
+
 from misc import mkdir_p
 
 def pickle_to_text(pickle_filename):
@@ -45,27 +46,10 @@ def main():
 
     args = parser.parse_args()
 
-    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
-    import caffe
-
-    if args.gpu:
-        caffe.set_mode_gpu()
-        print 'find_max_acts mode (in main thread):     GPU'
-
-    else:
-        caffe.set_mode_cpu()
-        print 'find_max_acts mode (in main thread):     CPU'
-
-    net = caffe.Classifier(args.net_prototxt,
-                           args.net_weights,
-                           mean=None,
-                           channel_swap=settings.caffe_net_channel_swap,
-                           raw_scale=settings.caffe_net_raw_scale,
-                           image_dims=settings.caffe_net_image_dims)
+    settings.caffevis_deploy_prototxt = args.net_prototxt
+    settings.caffevis_network_weights = args.net_weights
 
-    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
-
-    deduce_calculated_settings(settings, net)
+    net, data_mean = load_network(settings)
 
     # validate batch size
     if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
@@ -80,12 +64,16 @@ def main():
 
     with WithTimer('Scanning images'):
         if settings.is_siamese:
-            net_max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N, args.outdir, args.do_histograms)
+            net_max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N, args.outdir)
         else: # normal operation
-            net_max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N, args.outdir, args.do_histograms)
+            net_max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N, args.outdir)
 
     save_max_tracker_to_file(args.outfile, net_max_tracker)
 
+    if args.do_histograms:
+        net_max_tracker.calculate_histograms(args.outdir)
+
+
 
 def save_max_tracker_to_file(filename, net_max_tracker):
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index af4b8734e..3eaf50c8a 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -8,7 +8,7 @@
 
 import numpy as np
 from misc import mkdir_p, get_files_list
-from image_misc import cv2_read_file_rgb, resize_without_fit
+from image_misc import resize_without_fit
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
     compute_data_layer_focus_area, layer_name_to_top_name
 from siamese_helper import SiameseHelper
@@ -221,13 +221,6 @@ def update(self, data, image_idx, image_class, selected_input_index, layer_uniqu
             else:
                 self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index)
 
-    def run_post_processing(self, layer_name, outdir, do_histograms):
-
-        if do_histograms:
-            self.calculate_histogram(layer_name, outdir)
-
-        pass
-
     def calculate_histogram(self, layer_name, outdir):
 
         # convert list of arrays to numpy array
@@ -360,17 +353,17 @@ def update(self, net, image_idx, image_class, net_unique_input_source, batch_ind
 
         pass
 
-    def run_post_processing(self, outdir, do_histograms):
+    def calculate_histograms(self, outdir):
 
-        print "run_post_processing on network"
+        print "calculate_histograms on network"
         for layer_name in self.layers:
-            print "run_post_processing on layer %s" % layer_name
+            print "calculate_histogram on layer %s" % layer_name
 
             # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
             # count as the same layer in terms of activations
             normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
-            self.max_trackers[normalized_layer_name].run_post_processing(layer_name, outdir, do_histograms)
+            self.max_trackers[normalized_layer_name].calculate_histogram(layer_name, outdir)
 
         pass
 
@@ -392,7 +385,7 @@ def __setstate__(self, state):
         self.settings = None
         self.siamese_helper = None
 
-def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
+def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
     image_filenames, image_labels = get_files_list(settings, True)
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
@@ -426,7 +419,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
         with WithTimer('Load image', quiet = not do_print):
             try:
-                batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
+                batch[batch_index].im = caffe.io.load_image(os.path.join(datadir, batch[batch_index].filename), color=not settings._calculated_is_gray_model)
                 batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
                 batch[batch_index].im = batch[batch_index].im.astype(np.float32)
             except:
@@ -453,13 +446,11 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
             batch_index = 0
 
-    tracker.run_post_processing(outdir, do_histograms)
-
     print 'done!'
     return tracker
 
 
-def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
+def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
     image_filenames, image_labels = get_files_list(settings, True)
     print 'Scanning %d pairs' % len(image_filenames)
     print '  First pair', image_filenames[0]
@@ -495,9 +486,9 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
         with WithTimer('Load image', quiet=not do_print):
             try:
-                im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
-                im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
-
+                im1 = caffe.io.load_image(os.path.join(datadir, filename1), color=not settings._calculated_is_gray_model)
+                im2 = caffe.io.load_image(os.path.join(datadir, filename2), color=not settings._calculated_is_gray_model)
+                
                 if settings.siamese_input_mode == 'concat_channelwise':
                     im1 = resize_without_fit(im1, net_input_dims)
                     im2 = resize_without_fit(im2, net_input_dims)
@@ -508,7 +499,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
                     im1 = resize_without_fit(im1, half_input_dims)
                     im2 = resize_without_fit(im2, half_input_dims)
                     batch[batch_index].im = np.concatenate((im1, im2), axis=1)
-
+                    
             except:
                 # skip bad/missing inputs
                 print "WARNING: skipping bad/missing inputs:", filename1, filename2
@@ -531,8 +522,6 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, do_histograms):
 
             batch_index = 0
 
-    tracker.run_post_processing(outdir, do_histograms)
-
     print 'done!'
     return tracker
 
@@ -562,7 +551,7 @@ def save_representations(settings, net, datadir, filelist, layer_name, first_N =
         if do_print:
             print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_indices))
         with WithTimer('Load image', quiet = not do_print):
-            im = cv2_read_file_rgb(os.path.join(datadir, filename), as_grayscale=settings._calculated_is_gray_model)
+            im = caffe.io.load_image(os.path.join(datadir, filename), color=not settings._calculated_is_gray_model)
         with WithTimer('Predict   ', quiet = not do_print):
             net.predict([im], oversample = False)   # Just take center crop
         with WithTimer('Store     ', quiet = not do_print):
@@ -758,8 +747,8 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                     filename2 = batch[batch_index].filename[1]
 
                     # load both images
-                    im1 = cv2_read_file_rgb(os.path.join(datadir, filename1), as_grayscale=settings._calculated_is_gray_model)
-                    im2 = cv2_read_file_rgb(os.path.join(datadir, filename2), as_grayscale=settings._calculated_is_gray_model)
+                    im1 = caffe.io.load_image(os.path.join(datadir, filename1), color=not settings._calculated_is_gray_model)
+                    im2 = caffe.io.load_image(os.path.join(datadir, filename2), color=not settings._calculated_is_gray_model)
 
                     if settings.siamese_input_mode == 'concat_channelwise':
 
@@ -784,7 +773,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
 
                 else:
                     # load image
-                    batch[batch_index].im = cv2_read_file_rgb(os.path.join(datadir, batch[batch_index].filename), as_grayscale=settings._calculated_is_gray_model)
+                    batch[batch_index].im = caffe.io.load_image(os.path.join(datadir, batch[batch_index].filename), color=not settings._calculated_is_gray_model)
 
                     # resize images according to input dimension
                     batch[batch_index].im = resize_without_fit(batch[batch_index].im, net_input_dims)
diff --git a/input_fetcher.py b/input_fetcher.py
index 818b357ec..412946afc 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -6,9 +6,10 @@
 import numpy as np
 
 from codependent_thread import CodependentThread
-from image_misc import cv2_imshow_rgb, cv2_read_file_rgb, read_cam_frame, crop_to_square
+from image_misc import cv2_imshow_rgb, read_cam_frame, crop_to_square
 from misc import tsplit, get_files_list
 
+import caffe
 
 class InputImageFetcher(CodependentThread):
     '''Fetches images from a webcam or loads from a directory.'''
@@ -200,8 +201,8 @@ def check_increment_and_load_image(self):
 
                 if self.settings.is_siamese:
                     # loading two images for siamese network
-                    im1 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]), as_grayscale=self.settings._calculated_is_gray_model)
-                    im2 = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]), as_grayscale=self.settings._calculated_is_gray_model)
+                    im1 = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]), color=not self.settings._calculated_is_gray_model)
+                    im2 = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]), color=not self.settings._calculated_is_gray_model)
                     if not self.static_file_stretch_mode:
                         im1 = crop_to_square(im1)
                         im2 = crop_to_square(im2)
@@ -209,7 +210,7 @@ def check_increment_and_load_image(self):
                     im = (im1,im2)
 
                 else:
-                    im = cv2_read_file_rgb(os.path.join(self.settings.static_files_dir, self.latest_static_filename), as_grayscale=self.settings._calculated_is_gray_model)
+                    im = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename), color=not self.settings._calculated_is_gray_model)
                     if not self.static_file_stretch_mode:
                         im = crop_to_square(im)
 
diff --git a/live_vis.py b/live_vis.py
index eee5f9673..24b2b9233 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -15,9 +15,8 @@
     raise
 
 from misc import WithTimer
-from image_misc import cv2_imshow_rgb, FormattedString, cv2_typeset_text, to_255, resize_without_fit, gray_to_color
+from image_misc import cv2_imshow_rgb, FormattedString, cv2_typeset_text, to_255, gray_to_color, ensure_uint255_and_resize_without_fit
 from bindings import bindings
-from input_fetcher import InputImageFetcher
 
 pane_debug_clr = (255, 64, 64)
 
@@ -120,6 +119,8 @@ def run_loop(self):
         # Setup
         self.init_window()
         #cap = cv2.VideoCapture(self.settings.capture_device)
+        from input_fetcher import InputImageFetcher
+
         self.input_updater = InputImageFetcher(self.settings)
         self.input_updater.bind_camera()
         self.input_updater.start()
@@ -322,12 +323,13 @@ def display_frame(self, frame):
         if self.settings.is_siamese and ((type(frame),len(frame)) == (tuple,2)):
             frame1 = frame[0]
             frame2 = frame[1]
+
             half_pane_shape = (full_pane_shape[0], full_pane_shape[1]/2)
-            frame_disp1 = resize_without_fit(frame1[:], half_pane_shape)
-            frame_disp2 = resize_without_fit(frame2[:], half_pane_shape)
+            frame_disp1 = ensure_uint255_and_resize_without_fit(frame1[:], half_pane_shape)
+            frame_disp2 = ensure_uint255_and_resize_without_fit(frame2[:], half_pane_shape)
             frame_disp = np.concatenate((frame_disp1, frame_disp2), axis=1)
         else:
-            frame_disp = resize_without_fit(frame[:], full_pane_shape)
+            frame_disp = ensure_uint255_and_resize_without_fit(frame[:], full_pane_shape)
 
         if self.settings._calculated_is_gray_model:
             frame_disp = gray_to_color(frame_disp)
diff --git a/optimize_image.py b/optimize_image.py
index f36f59c6a..2d4bacee0 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -8,7 +8,8 @@
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
 from caffevis.caffevis_helper import check_force_backward_true, read_label_file, set_mean
-from settings_misc import deduce_calculated_settings, get_layer_info
+from settings_misc import load_network, get_layer_info
+
 
 LR_POLICY_CHOICES = ('constant', 'progress', 'progress01')
 
@@ -137,28 +138,10 @@ def main():
     lr_params = parse_and_validate_lr_params(parser, args.lr_policy, args.lr_params)
     push_spatial = parse_and_validate_push_spatial(parser, args.push_spatial)
 
-    # Load network
-    sys.path.insert(0, os.path.join(args.caffe_root, 'python'))
-    import caffe
-    if settings.caffevis_mode_gpu:
-        caffe.set_mode_gpu()
-        print 'optimize_image mode (in main thread):     GPU'
-    else:
-        caffe.set_mode_cpu()
-        print 'optimize_image mode (in main thread):     CPU'
-
-    net = caffe.Classifier(
-        args.deploy_proto,
-        args.net_weights,
-        mean=None,  # Set to None for now, assign later
-        channel_swap=settings.caffe_net_channel_swap,
-        raw_scale=settings.caffe_net_raw_scale,
-        image_dims=settings.caffe_net_image_dims,
-    )
-
-    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
+    settings.caffevis_deploy_prototxt = args.deploy_proto
+    settings.caffevis_network_weights = args.net_weights
 
-    deduce_calculated_settings(settings, net)
+    net, data_mean = load_network(settings)
 
     # validate batch size
     if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
diff --git a/settings.py b/settings.py
index 77366b69a..0c00a0aec 100644
--- a/settings.py
+++ b/settings.py
@@ -355,8 +355,15 @@
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 
-# caffe net parameter - raw scale
-caffe_net_raw_scale = locals().get('caffe_net_raw_scale', 1.0)
+# caffe net parameter - transpose, used to convert HxWxK to KxHxW, when None uses caffe default which is (2,0,1)
+# this parameter should rarely change
+caffe_net_transpose = locals().get('caffe_net_transpose', None)
+
+# caffe net parameter - raw scale, multiplies input BEFORE mean subtraction
+caffe_net_raw_scale = locals().get('caffe_net_raw_scale', None)
+
+# caffe net parameter - input scale, multiplies input AFTER mean subtraction
+caffe_net_input_scale = locals().get('caffe_net_input_scale', None)
 
 # caffe net parameter - image dims
 caffe_net_image_dims = locals().get('caffe_net_image_dims', None)
diff --git a/settings_misc.py b/settings_misc.py
index 1e7868ec1..35595ed10 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -1,3 +1,9 @@
+import os,sys,inspect
+currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
+parentdir = os.path.dirname(currentdir)
+sys.path.insert(0,parentdir)
+
+from caffevis.caffevis_helper import set_mean
 
 def deduce_calculated_settings(settings, net):
     set_calculated_is_gray_model(settings, net)
@@ -51,4 +57,37 @@ def get_layer_info(settings, layer_name):
         # update previous layer
         previous_layer = (name, type, input, output, filter, stride, pad)
 
-    return current_layer, previous_layer
\ No newline at end of file
+    return current_layer, previous_layer
+
+def load_network(settings):
+
+    # Set the mode to CPU or GPU. Note: in the latest Caffe
+    # versions, there is one Caffe object *per thread*, so the
+    # mode must be set per thread! Here we set the mode for the
+    # main thread; it is also separately set in CaffeProcThread.
+    sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
+    import caffe
+    if settings.caffevis_mode_gpu:
+        caffe.set_mode_gpu()
+        print 'CaffeVisApp mode (in main thread):     GPU'
+    else:
+        caffe.set_mode_cpu()
+        print 'CaffeVisApp mode (in main thread):     CPU'
+
+    net = caffe.Classifier(
+        settings.caffevis_deploy_prototxt,
+        settings.caffevis_network_weights,
+        image_dims=settings.caffe_net_image_dims,
+        mean=None,  # Set to None for now, assign later
+        input_scale=settings.caffe_net_input_scale,
+        raw_scale=settings.caffe_net_raw_scale,
+        channel_swap=settings.caffe_net_channel_swap)
+
+    deduce_calculated_settings(settings, net)
+
+    if settings.caffe_net_transpose:
+        net.transformer.set_transpose(net.inputs[0], settings.caffe_net_transpose)
+
+    data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
+
+    return net, data_mean
diff --git a/siamese_helper.py b/siamese_helper.py
index e543e1f75..d57dc600c 100644
--- a/siamese_helper.py
+++ b/siamese_helper.py
@@ -1,10 +1,10 @@
 import os
-# import numpy as np
+import numpy as np
 from numpy import expand_dims, concatenate
 from caffe_misc import layer_name_to_top_name
 from image_misc import resize_without_fit
 
-class SiameseInputMode:
+class SiameseViewMode:
     FIRST_IMAGE = 0
     SECOND_IMAGE = 1
     BOTH_IMAGES = 2
@@ -194,18 +194,18 @@ def get_default_layer_name(layer_def):
         return default_layer_name
 
     @staticmethod
-    def get_single_selected_layer_name(layer_def, siamese_input_mode):
+    def get_single_selected_layer_name(layer_def, siamese_view_mode):
 
         if layer_def['format'] == 'normal':
             return layer_def['name/s']
 
         elif layer_def['format'] == 'siamese_layer_pair':
-            if siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+            if siamese_view_mode == SiameseViewMode.FIRST_IMAGE:
                 return layer_def['name/s'][0]
-            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+            elif siamese_view_mode == SiameseViewMode.SECOND_IMAGE:
                 return layer_def['name/s'][1]
             else:
-                raise Exception('in get_single_selected_blob() siamese_input_mode cant be BOTH')
+                raise Exception('in get_single_selected_blob() siamese_view_mode cant be BOTH')
 
         elif layer_def['format'] == 'siamese_batch_pair':
             return layer_def['name/s']
@@ -214,7 +214,7 @@ def get_single_selected_layer_name(layer_def, siamese_input_mode):
             raise Exception("get_single_selected_blob() got invalid layer_def['format']=%s" % layer_def['format'])
 
     @staticmethod
-    def _get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector):
+    def _get_single_selected_blob(net, layer_def, siamese_view_mode, blob_selector):
         '''
         function used to extract the single selected blob according to the specified layer and siamese input mode and
         blob selector.
@@ -222,7 +222,7 @@ def _get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector)
         this is the main function which contains logic on the siamese network internal format structure
         :param net: network containing the blob to extract
         :param layer_def: layer requested
-        :param siamese_input_mode: siamese input mode
+        :param siamese_view_mode: siamese view mode
         :param blob_selector: lambda function which lets us choose between data and diff blobs
         :return: requested single blob
         '''
@@ -231,21 +231,21 @@ def _get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector)
             return blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'])])[0]
 
         elif layer_def['format'] == 'siamese_layer_pair':
-            if siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+            if siamese_view_mode == SiameseViewMode.FIRST_IMAGE:
                 selected_layer_name = layer_def['name/s'][0]
-            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+            elif siamese_view_mode == SiameseViewMode.SECOND_IMAGE:
                 selected_layer_name = layer_def['name/s'][1]
             else:
-                raise Exception('in get_single_selected_blob() siamese_input_mode cant be BOTH')
+                raise Exception('in get_single_selected_blob() siamese_view_mode cant be BOTH')
             return blob_selector(net.blobs[layer_name_to_top_name(net, selected_layer_name)])[0]
 
         elif layer_def['format'] == 'siamese_batch_pair':
-            if siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+            if siamese_view_mode == SiameseViewMode.FIRST_IMAGE:
                 selected_batch_index = 0
-            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+            elif siamese_view_mode == SiameseViewMode.SECOND_IMAGE:
                 selected_batch_index = 1
             else:
-                raise Exception('in get_single_selected_blob() siamese_input_mode cant be BOTH')
+                raise Exception('in get_single_selected_blob() siamese_view_mode cant be BOTH')
             return blob_selector(net.blobs[layer_name_to_top_name(net, layer_def['name/s'])])[selected_batch_index]
 
         else:
@@ -253,40 +253,40 @@ def _get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector)
 
 
     @staticmethod
-    def get_single_selected_data_blob(net, layer_def, siamese_input_mode):
+    def get_single_selected_data_blob(net, layer_def, siamese_view_mode):
         '''
         function used to extract the single selected DATA blob according to the specified layer and siamese input mode
          note that it is invalid to call this function when siamese input mode is BOTH
         :param net: network containing the blob to extract
         :param layer_def: layer requested
-        :param siamese_input_mode: siamese input mode
+        :param siamese_view_mode: siamese view mode
         :return: requested single data blob
         '''
 
-        return SiameseHelper._get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.data)
+        return SiameseHelper._get_single_selected_blob(net, layer_def, siamese_view_mode, blob_selector=lambda layer_object: layer_object.data)
 
     @staticmethod
-    def get_single_selected_diff_blob(net, layer_def, siamese_input_mode):
+    def get_single_selected_diff_blob(net, layer_def, siamese_view_mode):
         '''
         function used to extract the single selected DIFF blob according to the specified layer and siamese input mode
          note that it is invalid to call this function when siamese input mode is BOTH
         :param net: network containing the blob to extract
         :param layer_def: layer requested
-        :param siamese_input_mode: siamese input mode
+        :param siamese_view_mode: siamese view mode
         :return: requested single diff blob
         '''
 
-        return SiameseHelper._get_single_selected_blob(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.diff)
+        return SiameseHelper._get_single_selected_blob(net, layer_def, siamese_view_mode, blob_selector=lambda layer_object: layer_object.diff)
 
     @staticmethod
-    def _get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selector):
+    def _get_siamese_selected_blobs(net, layer_def, siamese_view_mode, blob_selector):
         '''
         function used to extract both blobs according to the specified layer and siamese input mode and
         blob selector.
         this is the main function which contains logic on the siamese network internal format structure
         :param net: network containing the blob to extract
         :param layer_def: layer requested
-        :param siamese_input_mode: siamese input mode
+        :param siamese_view_mode: siamese view mode
         :param blob_selector:
         :return: first_blob, second_blob
         '''
@@ -304,30 +304,30 @@ def _get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selecto
             raise Exception("get_siamese_blobs() got invalid layer_def['format']=%s" % layer_def['format'])
 
     @staticmethod
-    def get_siamese_selected_data_blobs(net, layer_def, siamese_input_mode):
+    def get_siamese_selected_data_blobs(net, layer_def, siamese_view_mode):
         '''
         function used to extract both DATA blobs according to the specified layer and siamese input mode
          note that it is invalid to call this function when siamese input mode is not BOTH
         :param net: network containing the blob to extract
         :param layer_def: layer requested
-        :param siamese_input_mode: siamese input mode
+        :param siamese_view_mode: siamese view mode
         :return: first_blob, second_blob
         '''
 
-        return SiameseHelper._get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.data)
+        return SiameseHelper._get_siamese_selected_blobs(net, layer_def, siamese_view_mode, blob_selector=lambda layer_object: layer_object.data)
 
     @staticmethod
-    def get_siamese_selected_diff_blobs(net, layer_def, siamese_input_mode):
+    def get_siamese_selected_diff_blobs(net, layer_def, siamese_view_mode):
         '''
         function used to extract both DIFF blobs according to the specified layer and siamese input mode
          note that it is invalid to call this function when siamese input mode is not BOTH
         :param net: network containing the blob to extract
         :param layer_def: layer requested
-        :param siamese_input_mode: siamese input mode
+        :param siamese_view_mode: siamese view mode
         :return: first_blob, second_blob
         '''
 
-        return SiameseHelper._get_siamese_selected_blobs(net, layer_def, siamese_input_mode, blob_selector=lambda layer_object: layer_object.diff)
+        return SiameseHelper._get_siamese_selected_blobs(net, layer_def, siamese_view_mode, blob_selector=lambda layer_object: layer_object.diff)
 
     @staticmethod
     def is_pair_of_layers(layer_def):
@@ -335,26 +335,26 @@ def is_pair_of_layers(layer_def):
         return layer_def['format'] in ['siamese_layer_pair', 'siamese_batch_pair']
 
     @staticmethod
-    def siamese_input_mode_has_two_images(layer_def, siamese_input_mode):
+    def siamese_view_mode_has_two_images(layer_def, siamese_view_mode):
         '''
         helper function which checks whether the input mode is two images, and the provided layer contains two layer names
         :param layer: can be a single string layer name, or a pair of layer names
         :return: True if both the input mode is BOTH_IMAGES and layer contains two layer names, False oherwise
         '''
 
-        return siamese_input_mode == SiameseInputMode.BOTH_IMAGES and SiameseHelper.is_pair_of_layers(layer_def)
+        return siamese_view_mode == SiameseViewMode.BOTH_IMAGES and SiameseHelper.is_pair_of_layers(layer_def)
 
     @staticmethod
-    def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode):
+    def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mode):
 
-        # if we are in siamese_batch_pair, we don't care of siamese_input_mode since we must do deconv on the 2-batch
+        # if we are in siamese_batch_pair, we don't care of siamese_view_mode since we must do deconv on the 2-batch
         # otherwise, if we are in siamese_layer_pair, we do it on both layers only if backprop deconv are requested
         if (backprop_layer_def['format'] == 'siamese_batch_pair') or \
-            (backprop_layer_def['format'] == 'siamese_layer_pair' and siamese_input_mode == SiameseInputMode.BOTH_IMAGES):
+            (backprop_layer_def['format'] == 'siamese_layer_pair' and siamese_view_mode == SiameseViewMode.BOTH_IMAGES):
 
-            diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_input_mode)
+            diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_view_mode)
             diffs0, diffs1 = diffs0 * 0, diffs1 * 0
-            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, backprop_layer_def, siamese_input_mode)
+            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, backprop_layer_def, siamese_view_mode)
             diffs0[backprop_unit], diffs1[backprop_unit] = data0[backprop_unit], data1[backprop_unit]
 
             # add batch dimension
@@ -371,30 +371,30 @@ def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mo
                 net.backward_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True)
         else:
 
-            diffs = SiameseHelper.get_single_selected_diff_blob(net, backprop_layer_def, siamese_input_mode)
+            diffs = SiameseHelper.get_single_selected_diff_blob(net, backprop_layer_def, siamese_view_mode)
             diffs = diffs * 0
-            data = SiameseHelper.get_single_selected_data_blob(net, backprop_layer_def, siamese_input_mode)
+            data = SiameseHelper.get_single_selected_data_blob(net, backprop_layer_def, siamese_view_mode)
             diffs[backprop_unit] = data[backprop_unit]
 
             # add batch dimension
             diffs = expand_dims(diffs, 0)
 
-            selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
+            selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_view_mode)
             net.backward_from_layer(selected_backprop_layer_name, diffs, zero_higher=True)
 
         pass
 
     @staticmethod
-    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode, deconv_type):
+    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mode, deconv_type, unique=False):
 
-        # if we are in siamese_batch_pair, we don't care of siamese_input_mode since we must do deconv on the 2-batch
+        # if we are in siamese_batch_pair, we don't care of siamese_view_mode since we must do deconv on the 2-batch
         # otherwise, if we are in siamese_layer_pair, we do it on both layers only if both deconv are requested
         if (backprop_layer_def['format'] == 'siamese_batch_pair') or \
-            (backprop_layer_def['format'] == 'siamese_layer_pair' and siamese_input_mode == SiameseInputMode.BOTH_IMAGES):
+            (backprop_layer_def['format'] == 'siamese_layer_pair' and siamese_view_mode == SiameseViewMode.BOTH_IMAGES):
 
-            diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_input_mode)
+            diffs0, diffs1 = SiameseHelper.get_siamese_selected_diff_blobs(net, backprop_layer_def, siamese_view_mode)
             diffs0, diffs1 = diffs0 * 0, diffs1 * 0
-            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, backprop_layer_def, siamese_input_mode)
+            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, backprop_layer_def, siamese_view_mode)
             diffs0[backprop_unit], diffs1[backprop_unit] = data0[backprop_unit], data1[backprop_unit]
 
             # add batch dimension
@@ -402,45 +402,153 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_input_mode
             diffs1 = expand_dims(diffs1, 0)
 
             if backprop_layer_def['format'] == 'siamese_layer_pair':
-                net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True, deconv_type=deconv_type)
-                net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True, deconv_type=deconv_type)
+                if not unique:
+                    net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True, deconv_type=deconv_type)
+                    net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True, deconv_type=deconv_type)
+                else:
+                    SiameseHelper.siamese_unique_deconv_from_layer(net, backprop_layer_def['name/s'][0], backprop_layer_def['name/s'][1], diffs0, diffs1, deconv_type)
 
             elif backprop_layer_def['format'] == 'siamese_batch_pair':
                 # combine them to 2-batch and send once
                 diffs = concatenate((diffs0, diffs1), axis=0)
-                net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True, deconv_type=deconv_type)
 
-        else:
+                if not unique:
+                    net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True, deconv_type=deconv_type)
+
+                else:  # unique deconv
+                    SiameseHelper.unique_deconv_from_layer(net, backprop_layer_def['name/s'], diffs, deconv_type)
 
-            diffs = SiameseHelper.get_single_selected_diff_blob(net, backprop_layer_def, siamese_input_mode)
+        else: # normal layer, or siamese layer but siamese input mode is 'first' or 'second'
+
+            diffs = SiameseHelper.get_single_selected_diff_blob(net, backprop_layer_def, siamese_view_mode)
             diffs = diffs * 0
-            data = SiameseHelper.get_single_selected_data_blob(net, backprop_layer_def, siamese_input_mode)
+            data = SiameseHelper.get_single_selected_data_blob(net, backprop_layer_def, siamese_view_mode)
             diffs[backprop_unit] = data[backprop_unit]
 
             # add batch dimension
             diffs = expand_dims(diffs, 0)
 
-            selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_input_mode)
+            selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_view_mode)
+
+            if not unique:
+                net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
+
+            else: # unique deconv
+                SiameseHelper.unique_deconv_from_layer(net, selected_backprop_layer_name, diffs, deconv_type)
+
+    @staticmethod
+    def unique_deconv_from_layer(net, selected_backprop_layer_name, diffs, deconv_type):
+        try:
+            # invert weights of selected layer
+            net.params[selected_backprop_layer_name][0].data[...] *= -1
+            # run deconv using invereted weights on last layer
+            net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
+            # save gradients of first selected unit
+            first_dict = dict()
+            for key in net.blobs.keys():
+                # L1 normalize first signal
+                first = net.blobs[key].diff.copy()
+                sum_first = np.sum(np.abs(first))
+                first /= (sum_first + np.finfo(np.float32).eps)
+
+                # keep first signal
+                first_dict[key] = first
+
+            # invert weights back
+            net.params[selected_backprop_layer_name][0].data[...] *= -1
+            # run deconv using normal weights on last layer
             net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
+            for key in net.blobs.keys():
+                # L1 normalize current signal
+                current = net.blobs[key].diff.copy()
+                sum_current = np.sum(np.abs(current))
+                current /= (sum_current + np.finfo(np.float32).eps)
+
+                # get normalized first signal
+                first = first_dict[key]
+
+                net.blobs[key].diff[...] = current - first
+        except Exception as err:
+            print "ERROR while running unique_deconv_from_layer(), error:", str(err)
+            for key in net.blobs.keys():
+                net.blobs[key].diff[...] = 0
+
+    @staticmethod
+    def siamese_unique_deconv_from_layer(net, backprop_layer_name1, backprop_layer_name2, diffs1, diffs2, deconv_type):
+        try:
+            # invert weights of selected layer
+            # note: we only need to inverse the weights of the first layer, since they are shared
+            net.params[backprop_layer_name1][0].data[...] *= -1
+            # run deconv using invereted weights on last layer
+            net.deconv_from_layer(backprop_layer_name1, diffs1, zero_higher=True, deconv_type=deconv_type)
+            net.deconv_from_layer(backprop_layer_name2, diffs2, zero_higher=True, deconv_type=deconv_type)
+            # save gradients of first selected unit
+            first_dict = dict()
+            for key in net.blobs.keys():
+                # L1 normalize first signal
+                first = net.blobs[key].diff.copy()
+                sum_first = np.sum(np.abs(first))
+                first /= (sum_first + np.finfo(np.float32).eps)
+
+                # keep first signal
+                first_dict[key] = first
+
+            # invert weights back
+            net.params[backprop_layer_name1][0].data[...] *= -1
+            # run deconv using normal weights on last layer
+            net.deconv_from_layer(backprop_layer_name1, diffs1, zero_higher=True, deconv_type=deconv_type)
+            net.deconv_from_layer(backprop_layer_name2, diffs2, zero_higher=True, deconv_type=deconv_type)
+            for key in net.blobs.keys():
+                # L1 normalize current signal
+                current = net.blobs[key].diff.copy()
+                sum_current = np.sum(np.abs(current))
+                current /= (sum_current + np.finfo(np.float32).eps)
+
+                # get normalized first signal
+                first = first_dict[key]
+
+                net.blobs[key].diff[...] = current - first
+
+        except Exception as err:
+            print "ERROR while running siamese_unique_deconv_from_layer(), error:", str(err)
+            for key in net.blobs.keys():
+                net.blobs[key].diff[...] = 0
 
     @staticmethod
-    def get_image_from_frame(frame, is_siamese, image_shape, siamese_input_mode):
+    def get_image_from_frame(frame, is_siamese, image_shape, siamese_view_mode):
 
         if is_siamese and ((type(frame),len(frame)) == (tuple,2)):
 
-            if siamese_input_mode == SiameseInputMode.BOTH_IMAGES:
+            if siamese_view_mode == SiameseViewMode.BOTH_IMAGES:
                 frame1 = frame[0]
                 frame2 = frame[1]
                 half_pane_shape = (image_shape[0], image_shape[1]/2)
                 frame_disp1 = resize_without_fit(frame1[:], half_pane_shape)
                 frame_disp2 = resize_without_fit(frame2[:], half_pane_shape)
                 frame_disp = concatenate((frame_disp1, frame_disp2), axis=1)
-            elif siamese_input_mode == SiameseInputMode.FIRST_IMAGE:
+
+            elif siamese_view_mode == SiameseViewMode.FIRST_IMAGE:
                 frame_disp = resize_without_fit(frame[0], image_shape)
-            elif siamese_input_mode == SiameseInputMode.SECOND_IMAGE:
+
+            elif siamese_view_mode == SiameseViewMode.SECOND_IMAGE:
                 frame_disp = resize_without_fit(frame[1], image_shape)
 
         else:
             frame_disp = resize_without_fit(frame[:], image_shape)
 
-        return frame_disp
\ No newline at end of file
+        return frame_disp
+
+    @staticmethod
+    def convert_image_pair_to_network_input_format(frame_pair, resize_shape, siamese_input_mode):
+
+        if siamese_input_mode == 'concat_channelwise':
+            im_small1 = resize_without_fit(frame_pair[0], resize_shape)
+            im_small2 = resize_without_fit(frame_pair[1], resize_shape)
+            im_small = np.concatenate((im_small1, im_small2), axis=2)
+        elif siamese_input_mode == 'concat_along_width':
+            half_input_dims = (resize_shape[0], resize_shape[1] / 2)
+            im_small1 = resize_without_fit(frame_pair[0], half_input_dims)
+            im_small2 = resize_without_fit(frame_pair[1], half_input_dims)
+            im_small = np.concatenate((im_small1, im_small2), axis=1)
+
+        return im_small

From 1b27aefa968176f023b87e0901e616f48d560b85 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 01:05:20 +0300
Subject: [PATCH 075/106] - make camera feature work again - add camera support
 in grayscale model

---
 image_misc.py    | 10 ++++++++--
 input_fetcher.py |  2 +-
 2 files changed, 9 insertions(+), 3 deletions(-)

diff --git a/image_misc.py b/image_misc.py
index c56de1dbf..7e10fc7d0 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -149,12 +149,18 @@ def cv2_read_file_rgb(filename, as_grayscale = False):
     return im
 
     
-def read_cam_frame(cap, saveto = None):
+def read_cam_frame(cap, saveto = None, color = True):
     #frame = np.array(cv2_read_cap_rgb(cap, saveto = saveto), dtype='float32')
     frame = cv2_read_cap_rgb(cap, saveto = saveto)
+    if not color:
+        frame = cv2.cvtColor(frame, cv2.COLOR_RGB2GRAY)
+        frame = frame[:, :, np.newaxis]
+
     frame = frame[:,::-1,:]  # flip L-R for display
     frame -= frame.min()
-    frame = frame * (255.0 / (frame.max() + 1e-6))
+    frame = frame * (1.0 / (frame.max() + 1e-6))
+
+
     return frame
 
 
diff --git a/input_fetcher.py b/input_fetcher.py
index 412946afc..3ba18309b 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -140,7 +140,7 @@ def run(self):
 
                         self._increment_and_set_frame(im, True)
                 else:
-                    frame_full = read_cam_frame(self.bound_cap_device)
+                    frame_full = read_cam_frame(self.bound_cap_device, color=not self.settings._calculated_is_gray_model)
                     #print '====> just read frame', frame_full.shape
                     frame = crop_to_square(frame_full)
                     with self.lock:

From f6fdb509ef29f867be9c9cd37e75d0f787ae8fd3 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 10:59:48 +0300
Subject: [PATCH 076/106] removed softmax raw view option leftovers from the
 code

---
 caffevis/app.py                |  5 +----
 caffevis/caffevis_app_state.py |  7 ++-----
 image_misc.py                  | 23 -----------------------
 3 files changed, 3 insertions(+), 32 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index fc24049f9..24e9d07ac 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -20,7 +20,7 @@
 from app_base import BaseApp
 from image_misc import norm01, norm01c, tile_images_normalize, ensure_float01, tile_images_make_tiles, \
     ensure_uint255_and_resize_to_fit, resize_without_fit, ensure_uint255, \
-    caffe_load_image, ensure_uint255_and_resize_without_fit, softmax_image, array_histogram, fig2data
+    caffe_load_image, ensure_uint255_and_resize_without_fit, array_histogram, fig2data
 from image_misc import FormattedString, cv2_typeset_text, to_255
 from caffe_proc_thread import CaffeProcThread
 from caffevis_app_state import CaffeVisAppState, SiameseViewMode, PatternMode, BackpropMode, BackpropViewOption, \
@@ -1179,9 +1179,6 @@ def run_processing_once_or_twice(resize_shape, process_image_fn):
 
                 raise Exception("flow should not arrive here")
 
-            # if back_view_option == BackpropViewOption.SOFTMAX_RAW:
-            #     grad_img = run_processing_once_or_twice(pane.data.shape, lambda grad_img, resize_shape, input_image: softmax_image(norm01c(grad_img, 0)))
-
             if back_view_option == BackpropViewOption.RAW:
                 def do_raw(grad_blob, resize_shape, input_image):
                     if len(grad_blob.shape) == 3 and grad_blob.shape[2] != 3:
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 2ed1ed348..6d17e28b6 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -37,7 +37,6 @@ def to_string(back_mode):
         return 'n/a'
 
 class BackpropViewOption:
-    # SOFTMAX_RAW = 0
     RAW = 0
     RAW_POS = 1
     RAW_NEG = 2
@@ -50,8 +49,6 @@ class BackpropViewOption:
 
     @staticmethod
     def to_string(back_view_option):
-        # if back_view_option == BackpropViewOption.SOFTMAX_RAW:
-        #     return 'softmax raw'
         if back_view_option == BackpropViewOption.RAW:
             return 'raw'
         elif back_view_option == BackpropViewOption.RAW_POS:
@@ -64,10 +61,10 @@ def to_string(back_view_option):
             return 'norm'
         elif back_view_option == BackpropViewOption.NORM_BLUR:
             return 'normblur'
-        elif back_view_option == BackpropViewOption.HISTOGRAM:
-            return 'histogram'
         elif back_view_option == BackpropViewOption.POS_SUM:
             return 'sum>0'
+        elif back_view_option == BackpropViewOption.HISTOGRAM:
+            return 'histogram'
 
         return 'n/a'
 
diff --git a/image_misc.py b/image_misc.py
index 7e10fc7d0..16be9b3d9 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -9,29 +9,6 @@
 
 from misc import WithTimer
 
-def softmax_image(arr):
-    '''Maps the input range to [0,1] using softmax'''
-
-    arr = arr.copy()
-    exp_arr = np.exp(arr)
-    sum_exp_arr = np.sum(exp_arr, axis=(0,1))
-    normalized_exp_arr = exp_arr / sum_exp_arr
-
-    print "arr", arr
-    print "exp_arr", exp_arr
-    print "sum_exp_arr", sum_exp_arr
-    print "normalized_exp_arr", normalized_exp_arr
-
-
-    print "arr.shape", arr.shape, arr.max(), arr.min()
-    print "exp_arr.shape", exp_arr.shape, exp_arr.max(), exp_arr.min()
-    print "sum_exp_arr.shape", sum_exp_arr.shape, sum_exp_arr.max(), sum_exp_arr.min()
-    print "normalized_exp_arr.shape", normalized_exp_arr.shape, normalized_exp_arr.max(), normalized_exp_arr.min()
-
-    assert normalized_exp_arr.min() >= 0
-    assert normalized_exp_arr.max() <= 1
-    return normalized_exp_arr
-
 
 def fig2data(fig):
     """

From 7a3f17ce301fdef51b477ca2512542dbe192a554 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 11:22:51 +0300
Subject: [PATCH 077/106] refactor loading method of network settings file

---
 settings_model_selector.py | 9 ++++-----
 1 file changed, 4 insertions(+), 5 deletions(-)

diff --git a/settings_model_selector.py b/settings_model_selector.py
index b8ff35089..d9e34e39e 100644
--- a/settings_model_selector.py
+++ b/settings_model_selector.py
@@ -15,8 +15,7 @@
 
 caffevis_caffe_root = base_folder + '/caffe'
 
-if model_to_load == 'caffenet_yos':
-    from models.caffenet_yos.settings_caffenet_yos import *
-
-elif model_to_load == 'other_model':
-    from models.other_model.settings_other_model import *
+# the following code runs dynamically the import command:
+# from models.YOUR_MODEL.settings_YOUR_MODEL import *
+import_code = 'from models.' + model_to_load + '.settings_' + model_to_load + ' import *'
+exec (import_code, globals())

From d4985d35e1de16386e042df9324c0c63164945cb Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 11:25:17 +0300
Subject: [PATCH 078/106] added code to support replacing of
 max_tracker_layers_list with automatic data from network definition

---
 caffe_misc.py                  | 200 ++++++++++++++++++++++++++++++++-
 find_maxes/max_tracker.py      |   2 +-
 optimize/gradient_optimizer.py |   2 +-
 settings_misc.py               |  76 +++++++++++++
 4 files changed, 275 insertions(+), 5 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index c7f6f568d..674e427f0 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -49,8 +49,6 @@ def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride
     +}
     '''
     assert len(top_slice) == 4
-    assert len(bot_size) == 2
-    assert len(top_size) == 2
     assert len(filter_width) == 2
     assert len(stride) == 2
     assert len(pad) == 2
@@ -60,6 +58,9 @@ def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride
 
     # should we crop to boundary
     if crop_to_boundary:
+        assert len(bot_size) == 2
+        assert len(top_size) == 2
+
         top_slice[0] = max(0, min(top_size[0], top_slice[0]))
         top_slice[1] = max(0, min(top_size[0], top_slice[1]))
         top_slice[2] = max(0, min(top_size[1], top_slice[2]))
@@ -129,6 +130,173 @@ def convert_region(self, from_layer, to_layer, region, verbose = False, crop_to_
         return ret
 
 
+    @staticmethod
+    def merge_regions(region1, region2):
+
+        region1_x_start, region1_x_end, region1_y_start, region1_y_end = region1
+        region2_x_start, region2_x_end, region2_y_start, region2_y_end = region2
+
+        merged_x_start = min(region1_x_start, region2_x_start)
+        merged_x_end = max(region1_x_end, region2_x_end)
+        merged_y_start = min(region1_y_start, region2_y_start)
+        merged_y_end = max(region1_y_end, region2_y_end)
+
+        merged_region = (merged_x_start, merged_x_end, merged_y_start, merged_y_end)
+
+        return merged_region
+
+
+    @staticmethod
+    def convert_region_dag_v1(settings, from_layer, to_layer, region, verbose = False, crop_to_boundary = True):
+
+        total_region = None
+
+        # handle leaf
+        if from_layer == to_layer:
+            total_region = region
+
+        else:
+            # get all the layers which have their bottom blob set to one of the from_layer top blobs
+            tops = settings._layer_to_tops[to_layer]
+
+            for top in tops:
+
+                # get next layers, excluding inplace layers
+                next_layers = list(set(settings._bottom_to_layers[top]) - set(settings._inplace_layers))
+
+                for next_layer in next_layers:
+
+                    # calculate convert_region_dag on each one
+                    current_region = RegionComputer.convert_region_dag(settings, from_layer, next_layer, region, verbose, crop_to_boundary)
+
+                    # aggregate results
+                    if total_region is None:
+                        total_region = current_region
+                    else:
+                        total_region = RegionComputer.merge_regions(total_region, current_region)
+
+        if to_layer not in settings._layer_name_to_def:
+            # fallback to doing nothing
+            return total_region
+
+        # do final step by layer type
+        layer_def = settings._layer_name_to_def[to_layer]
+
+        if layer_def.type == 'Convolution':
+            filter = list(layer_def.convolution_param.kernel_size)
+            if len(filter) == 1:
+                filter *= 2
+            pad = list(layer_def.convolution_param.pad)
+            if len(pad) == 0:
+                pad = [0, 0]
+            elif len(pad) == 1:
+                pad *= 2
+            stride = list(layer_def.convolution_param.stride)
+            if len(stride) == 0:
+                stride = [1, 1]
+            elif len(stride) == 1:
+                stride *= 2
+            return region_converter(total_region, None, None, filter, stride, pad, crop_to_boundary=False)
+
+        elif layer_def.type == 'Pooling':
+            filter = [layer_def.pooling_param.kernel_size]
+            if len(filter) == 1:
+                filter *= 2
+            pad = [layer_def.pooling_param.pad]
+            if len(pad) == 0:
+                pad = [0, 0]
+            elif len(pad) == 1:
+                pad *= 2
+            stride = [layer_def.pooling_param.stride]
+            if len(stride) == 0:
+                stride = [1, 1]
+            elif len(stride) == 1:
+                stride *= 2
+            return region_converter(total_region, None, None, filter, stride, pad, crop_to_boundary=False)
+
+        else:
+            # fallback to doing nothing
+            return total_region
+
+        return total_region
+
+
+    @staticmethod
+    def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, crop_to_boundary=True):
+
+        step_region = None
+
+        # do single step to convert according to from_layer
+        if from_layer not in settings._layer_name_to_def:
+            # fallback to doing nothing
+            step_region = region
+        else:
+
+            layer_def = settings._layer_name_to_def[from_layer]
+
+            if layer_def.type == 'Convolution':
+                filter = list(layer_def.convolution_param.kernel_size)
+                if len(filter) == 1:
+                    filter *= 2
+                pad = list(layer_def.convolution_param.pad)
+                if len(pad) == 0:
+                    pad = [0, 0]
+                elif len(pad) == 1:
+                    pad *= 2
+                stride = list(layer_def.convolution_param.stride)
+                if len(stride) == 0:
+                    stride = [1, 1]
+                elif len(stride) == 1:
+                    stride *= 2
+                step_region = region_converter(region, None, None, filter, stride, pad, crop_to_boundary=False)
+
+            elif layer_def.type == 'Pooling':
+                filter = [layer_def.pooling_param.kernel_size]
+                if len(filter) == 1:
+                    filter *= 2
+                pad = [layer_def.pooling_param.pad]
+                if len(pad) == 0:
+                    pad = [0, 0]
+                elif len(pad) == 1:
+                    pad *= 2
+                stride = [layer_def.pooling_param.stride]
+                if len(stride) == 0:
+                    stride = [1, 1]
+                elif len(stride) == 1:
+                    stride *= 2
+                step_region = region_converter(region, None, None, filter, stride, pad, crop_to_boundary=False)
+
+            else:
+                # fallback to doing nothing
+                step_region = region
+
+        if from_layer == to_layer:
+            return step_region
+
+        # handle the rest
+        total_region = None
+
+        bottoms = settings._layer_to_bottoms[from_layer]
+
+        for bottom in bottoms:
+
+            # get prev layers, excluding inplace layers
+            prev_layers = list(set(settings._top_to_layers[bottom]) - set(settings._inplace_layers))
+
+            for prev_layer in prev_layers:
+
+                # calculate convert_region_dag on each one
+                current_region = RegionComputer.convert_region_dag(settings, prev_layer, to_layer, step_region, verbose, crop_to_boundary)
+
+                # aggregate results
+                if total_region is None:
+                    total_region = current_region
+                else:
+                    total_region = RegionComputer.merge_regions(total_region, current_region)
+
+        return total_region
+
+
 def save_caffe_image(img, filename, autoscale = True, autoscale_center = None):
     '''Takes an image in caffe format (01) or (c01, BGR) and saves it to a file'''
     if len(img.shape) == 2:
@@ -168,7 +336,7 @@ def get_max_data_extent(net, layer_name, rc, is_conv):
         return data_size
 
 
-def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
+def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
 
     if is_conv:
 
@@ -176,6 +344,11 @@ def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer_name,
         layer_indices = (ii, ii + 1, jj, jj + 1)
         data_indices = region_computer.convert_region(layer_name, 'data', layer_indices)
         data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
+        data_indices2 = region_computer.convert_region(layer_name, 'data', layer_indices, crop_to_boundary=False)
+        data_ii_start2, data_ii_end2, data_jj_start2, data_jj_end2 = data_indices2
+
+        data_indices3 = RegionComputer.convert_region_dag(settings, layer_name, 'data', layer_indices, crop_to_boundary=False)
+        data_ii_start3, data_ii_end3, data_jj_start3, data_jj_end3 = data_indices3
 
         # safe guard edges
         data_ii_start = max(data_ii_start, 0)
@@ -183,6 +356,27 @@ def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, layer_name,
         data_ii_end = min(data_ii_end, data_size_ii)
         data_jj_end = min(data_jj_end, data_size_jj)
 
+        data_ii_start2 = max(data_ii_start2, 0)
+        data_jj_start2 = max(data_jj_start2, 0)
+        data_ii_end2 = min(data_ii_end2, data_size_ii)
+        data_jj_end2 = min(data_jj_end2, data_size_jj)
+
+        data_ii_start3 = max(data_ii_start3, 0)
+        data_jj_start3 = max(data_jj_start3, 0)
+        data_ii_end3 = min(data_ii_end3, data_size_ii)
+        data_jj_end3 = min(data_jj_end3, data_size_jj)
+
+        assert(data_ii_start2 == data_ii_start)
+        assert(data_jj_start2 == data_jj_start)
+        assert(data_ii_end2 == data_ii_end)
+        assert(data_jj_end2 == data_jj_end)
+
+        assert(data_ii_start3 == data_ii_start)
+        assert(data_jj_start3 == data_jj_start)
+        assert(data_ii_end3 == data_ii_end)
+        assert(data_jj_end3 == data_jj_end)
+
+
         touching_imin = (data_ii_start == 0)
         touching_jmin = (data_jj_start == 0)
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 3eaf50c8a..b1a783623 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -718,7 +718,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
              batch[batch_index].data_ii_end,
              batch[batch_index].data_jj_start,
              batch[batch_index].data_jj_end] = \
-                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name,
+                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, settings, layer_name,
                                               size_ii, size_jj, data_size_ii, data_size_jj)
 
             # print "DEBUG: channel:%d out_ii_start:%d out_ii_end:%d out_jj_start:%d out_jj_end:%d data_ii_start:%d data_ii_end:%d data_jj_start:%d data_jj_end:%d" % \
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index c56acf062..f987725a1 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -516,7 +516,7 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
                 data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
 
                 [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
-                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
+                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, self.settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
 
                 selected_input_index = self.find_selected_input_index(layer_name)
 
diff --git a/settings_misc.py b/settings_misc.py
index 35595ed10..85ad4fb15 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -5,9 +5,11 @@
 
 from caffevis.caffevis_helper import set_mean
 
+
 def deduce_calculated_settings(settings, net):
     set_calculated_is_gray_model(settings, net)
     set_calculated_image_dims(settings, net)
+    read_network_dag(settings)
 
 
 def set_calculated_is_gray_model(settings, net):
@@ -59,6 +61,7 @@ def get_layer_info(settings, layer_name):
 
     return current_layer, previous_layer
 
+
 def load_network(settings):
 
     # Set the mode to CPU or GPU. Note: in the latest Caffe
@@ -91,3 +94,76 @@ def load_network(settings):
     data_mean = set_mean(settings.caffevis_data_mean, settings.generate_channelwise_mean, net)
 
     return net, data_mean
+
+
+def read_network_dag(settings):
+    from caffe.proto import caffe_pb2
+    from google.protobuf import text_format
+
+    # load prototxt file
+    network_def = caffe_pb2.NetParameter()
+    f = open(settings.caffevis_deploy_prototxt, 'r')
+    text_format.Merge(str(f.read()), network_def)
+
+    # map blob name to list of layers having this blob name as input
+    bottom_to_layers = dict()
+    for layer in network_def.layer:
+        # no specific phase, or TEST phase is specifically asked for
+        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
+            for bottom in layer.bottom:
+                if bottom not in bottom_to_layers:
+                    bottom_to_layers[bottom] = list()
+                bottom_to_layers[bottom].append(layer.name)
+
+    top_to_layers = dict()
+    for layer in network_def.layer:
+        # no specific phase, or TEST phase is specifically asked for
+        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
+            for top in layer.top:
+                if top not in top_to_layers:
+                    top_to_layers[top] = list()
+                top_to_layers[top].append(layer.name)
+    if 'data' not in top_to_layers:
+        top_to_layers['data'] = ['data']
+
+    layer_to_tops = dict()
+    for layer in network_def.layer:
+        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
+            for top in layer.top:
+                if layer.name not in layer_to_tops:
+                    layer_to_tops[layer.name] = list()
+                layer_to_tops[layer.name].append(top)
+    if 'data' not in layer_to_tops:
+        layer_to_tops['data'] = ['data']
+
+    layer_to_bottoms = dict()
+    for layer in network_def.layer:
+        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
+            for bottom in layer.bottom:
+                if layer.name not in layer_to_bottoms:
+                    layer_to_bottoms[layer.name] = list()
+                layer_to_bottoms[layer.name].append(bottom)
+    # if 'data' not in layer_to_bottoms:
+    #     layer_to_bottoms['data'] = ['data']
+
+    inplace_layers = list()
+    for layer in network_def.layer:
+        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
+            if len(layer.top) == 1 and len(layer.bottom) == 1 and layer.top[0] == layer.bottom[0]:
+                inplace_layers.append(layer.name)
+
+    layer_name_to_def = dict()
+    for layer in network_def.layer:
+        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
+            layer_name_to_def[layer.name] = layer
+
+    # keep helper variables in settings
+    settings._network_def = network_def
+    settings._bottom_to_layers = bottom_to_layers
+    settings._top_to_layers = top_to_layers
+    settings._layer_to_tops = layer_to_tops
+    settings._layer_to_bottoms = layer_to_bottoms
+    settings._inplace_layers = inplace_layers
+    settings._layer_name_to_def = layer_name_to_def
+
+    return

From 220c0d6d43400a90262fc108208f391f5567b408 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 11:29:58 +0300
Subject: [PATCH 079/106] - use RegionComputer.convert_region_dag in
 get_max_data_extent() and compute_data_layer_focus_area()

---
 caffe_misc.py                  | 243 ++++++---------------------------
 find_maxes/max_tracker.py      |   7 +-
 optimize/gradient_optimizer.py |   5 +-
 3 files changed, 46 insertions(+), 209 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index 674e427f0..558102285 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -26,108 +26,47 @@ def shownet(net):
         print '(%g, %g)' % (v.diff.min(), v.diff.max())
 
 
-
-def region_converter(top_slice, bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0), crop_to_boundary = True):
-    '''
-    Works for conv or pool
-
-vector<int> ConvolutionLayer<Dtype>::JBY_region_of_influence(const vector<int>& slice) {
-    +  CHECK_EQ(slice.size(), 4) << "slice must have length 4 (ii_start, ii_end, jj_start, jj_end)";
-    +  // Crop region to output size
-    +  vector<int> sl = vector<int>(slice);
-    +  sl[0] = max(0, min(height_out_, slice[0]));
-    +  sl[1] = max(0, min(height_out_, slice[1]));
-    +  sl[2] = max(0, min(width_out_, slice[2]));
-    +  sl[3] = max(0, min(width_out_, slice[3]));
-    +  vector<int> roi;
-    +  roi.resize(4);
-    +  roi[0] = sl[0] * stride_h_ - pad_h_;
-    +  roi[1] = (sl[1]-1) * stride_h_ + kernel_h_ - pad_h_;
-    +  roi[2] = sl[2] * stride_w_ - pad_w_;
-    +  roi[3] = (sl[3]-1) * stride_w_ + kernel_w_ - pad_w_;
-    +  return roi;
-    +}
-    '''
-    assert len(top_slice) == 4
-    assert len(filter_width) == 2
-    assert len(stride) == 2
-    assert len(pad) == 2
-
-    # Crop top slice to allowable region
-    top_slice = [ss for ss in top_slice]   # Copy list or array -> list
-
-    # should we crop to boundary
-    if crop_to_boundary:
-        assert len(bot_size) == 2
-        assert len(top_size) == 2
-
-        top_slice[0] = max(0, min(top_size[0], top_slice[0]))
-        top_slice[1] = max(0, min(top_size[0], top_slice[1]))
-        top_slice[2] = max(0, min(top_size[1], top_slice[2]))
-        top_slice[3] = max(0, min(top_size[1], top_slice[3]))
-
-    bot_slice = [-123] * 4
-
-    bot_slice[0] = top_slice[0] * stride[0] - pad[0]
-    bot_slice[1] = top_slice[1] * stride[0] - pad[0] + filter_width[0] - 1
-    bot_slice[2] = top_slice[2] * stride[1] - pad[1]
-    bot_slice[3] = top_slice[3] * stride[1] - pad[1] + filter_width[1] - 1
-
-    return bot_slice
-
-
-def get_conv_converter(bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
-    return lambda top_slice, crop_to_boundary : region_converter(top_slice, bot_size, top_size, filter_width, stride, pad, crop_to_boundary)
-
-
-def get_pool_converter(bot_size, top_size, filter_width = (1,1), stride = (1,1), pad = (0,0)):
-    return lambda top_slice, crop_to_boundary : region_converter(top_slice, bot_size, top_size, filter_width, stride, pad, crop_to_boundary)
-
-
-
 class RegionComputer(object):
     '''Computes regions of possible influcence from higher layers to lower layers.'''
 
-    def __init__(self, layers_list):
-        #self.net = net
-
-        _tmp = []
-
-        (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = (None, None, None, None, None, None, None)
-        for (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad) in layers_list:
-            if current_type == 'Input':
-                _tmp.append((current_name, None))
-            elif current_type == 'Convolution':
-                _tmp.append((current_name, get_conv_converter(current_input[0:2], current_output[0:2], current_filter, current_stride, current_pad)))
-            elif current_type == 'Pooling':
-                _tmp.append((current_name, get_pool_converter(current_input[0:2], current_output[0:2], current_filter, current_stride, current_pad)))
-            elif current_type == 'Eltwise' and previous_type == 'Convolution':
-                _tmp.append((current_name, get_conv_converter(previous_input[0:2], previous_output[0:2], previous_filter, previous_stride, previous_pad)))
-
-            (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad)
-
-        self.names = [tt[0] for tt in _tmp]
-        self.converters = [tt[1] for tt in _tmp]
-
-    def convert_region(self, from_layer, to_layer, region, verbose = False, crop_to_boundary = True):
-        '''region is the slice of the from_layer in the following Python
-            index format: (ii_start, ii_end, jj_start, jj_end)
+    @staticmethod
+    def region_converter(top_slice, filter_width=(1, 1), stride=(1, 1), pad=(0, 0)):
+        '''
+        Works for conv or pool
+
+        vector<int> ConvolutionLayer<Dtype>::JBY_region_of_influence(const vector<int>& slice) {
+        +  CHECK_EQ(slice.size(), 4) << "slice must have length 4 (ii_start, ii_end, jj_start, jj_end)";
+        +  // Crop region to output size
+        +  vector<int> sl = vector<int>(slice);
+        +  sl[0] = max(0, min(height_out_, slice[0]));
+        +  sl[1] = max(0, min(height_out_, slice[1]));
+        +  sl[2] = max(0, min(width_out_, slice[2]));
+        +  sl[3] = max(0, min(width_out_, slice[3]));
+        +  vector<int> roi;
+        +  roi.resize(4);
+        +  roi[0] = sl[0] * stride_h_ - pad_h_;
+        +  roi[1] = (sl[1]-1) * stride_h_ + kernel_h_ - pad_h_;
+        +  roi[2] = sl[2] * stride_w_ - pad_w_;
+        +  roi[3] = (sl[3]-1) * stride_w_ + kernel_w_ - pad_w_;
+        +  return roi;
+        +}
         '''
+        assert len(top_slice) == 4
+        assert len(filter_width) == 2
+        assert len(stride) == 2
+        assert len(pad) == 2
+
+        # Crop top slice to allowable region
+        top_slice = [ss for ss in top_slice]  # Copy list or array -> list
 
-        from_idx = self.names.index(from_layer)
-        to_idx = self.names.index(to_layer)
-        assert from_idx >= to_idx, 'wrong order of from_layer and to_layer'
+        bot_slice = [-123] * 4
 
-        ret = region
-        for ii in range(from_idx, to_idx, -1):
-            converter = self.converters[ii]
-            if verbose:
-                print 'pushing', self.names[ii], 'region', ret, 'through converter'
-            ret = converter(ret, crop_to_boundary)
-        if verbose:
-            print 'Final region at ', self.names[to_idx], 'is', ret
+        bot_slice[0] = top_slice[0] * stride[0] - pad[0]
+        bot_slice[1] = top_slice[1] * stride[0] - pad[0] + filter_width[0] - 1
+        bot_slice[2] = top_slice[2] * stride[1] - pad[1]
+        bot_slice[3] = top_slice[3] * stride[1] - pad[1] + filter_width[1] - 1
 
-        return ret
+        return bot_slice
 
 
     @staticmethod
@@ -146,81 +85,6 @@ def merge_regions(region1, region2):
         return merged_region
 
 
-    @staticmethod
-    def convert_region_dag_v1(settings, from_layer, to_layer, region, verbose = False, crop_to_boundary = True):
-
-        total_region = None
-
-        # handle leaf
-        if from_layer == to_layer:
-            total_region = region
-
-        else:
-            # get all the layers which have their bottom blob set to one of the from_layer top blobs
-            tops = settings._layer_to_tops[to_layer]
-
-            for top in tops:
-
-                # get next layers, excluding inplace layers
-                next_layers = list(set(settings._bottom_to_layers[top]) - set(settings._inplace_layers))
-
-                for next_layer in next_layers:
-
-                    # calculate convert_region_dag on each one
-                    current_region = RegionComputer.convert_region_dag(settings, from_layer, next_layer, region, verbose, crop_to_boundary)
-
-                    # aggregate results
-                    if total_region is None:
-                        total_region = current_region
-                    else:
-                        total_region = RegionComputer.merge_regions(total_region, current_region)
-
-        if to_layer not in settings._layer_name_to_def:
-            # fallback to doing nothing
-            return total_region
-
-        # do final step by layer type
-        layer_def = settings._layer_name_to_def[to_layer]
-
-        if layer_def.type == 'Convolution':
-            filter = list(layer_def.convolution_param.kernel_size)
-            if len(filter) == 1:
-                filter *= 2
-            pad = list(layer_def.convolution_param.pad)
-            if len(pad) == 0:
-                pad = [0, 0]
-            elif len(pad) == 1:
-                pad *= 2
-            stride = list(layer_def.convolution_param.stride)
-            if len(stride) == 0:
-                stride = [1, 1]
-            elif len(stride) == 1:
-                stride *= 2
-            return region_converter(total_region, None, None, filter, stride, pad, crop_to_boundary=False)
-
-        elif layer_def.type == 'Pooling':
-            filter = [layer_def.pooling_param.kernel_size]
-            if len(filter) == 1:
-                filter *= 2
-            pad = [layer_def.pooling_param.pad]
-            if len(pad) == 0:
-                pad = [0, 0]
-            elif len(pad) == 1:
-                pad *= 2
-            stride = [layer_def.pooling_param.stride]
-            if len(stride) == 0:
-                stride = [1, 1]
-            elif len(stride) == 1:
-                stride *= 2
-            return region_converter(total_region, None, None, filter, stride, pad, crop_to_boundary=False)
-
-        else:
-            # fallback to doing nothing
-            return total_region
-
-        return total_region
-
-
     @staticmethod
     def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, crop_to_boundary=True):
 
@@ -248,7 +112,7 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
                     stride = [1, 1]
                 elif len(stride) == 1:
                     stride *= 2
-                step_region = region_converter(region, None, None, filter, stride, pad, crop_to_boundary=False)
+                step_region = RegionComputer.region_converter(region, filter, stride, pad)
 
             elif layer_def.type == 'Pooling':
                 filter = [layer_def.pooling_param.kernel_size]
@@ -264,7 +128,7 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
                     stride = [1, 1]
                 elif len(stride) == 1:
                     stride *= 2
-                step_region = region_converter(region, None, None, filter, stride, pad, crop_to_boundary=False)
+                step_region = RegionComputer.region_converter(region, filter, stride, pad)
 
             else:
                 # fallback to doing nothing
@@ -317,7 +181,7 @@ def layer_name_to_top_name(net, layer_name):
     return net.top_names[layer_name][0]
 
 
-def get_max_data_extent(net, layer_name, rc, is_conv):
+def get_max_data_extent(net, settings, layer_name, is_conv):
     '''Gets the maximum size of the data layer that can influence a unit on layer.'''
 
     data_size = net.blobs['data'].data.shape[2:4]  # e.g. (227,227) for fc6,fc7,fc8,prop
@@ -326,7 +190,7 @@ def get_max_data_extent(net, layer_name, rc, is_conv):
         top_name = layer_name_to_top_name(net, layer_name)
         conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
         layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
-        data_slice = rc.convert_region(layer_name, 'data', layer_slice_middle, crop_to_boundary = False)
+        data_slice = RegionComputer.convert_region_dag(settings, layer_name, 'data', layer_slice_middle, crop_to_boundary=False)
         data_slice_size = data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
         # crop data slice size to data size
         data_slice_size = min(data_slice_size[0], data_size[0]), min(data_slice_size[1], data_size[1])
@@ -336,19 +200,15 @@ def get_max_data_extent(net, layer_name, rc, is_conv):
         return data_size
 
 
-def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
+def compute_data_layer_focus_area(is_conv, ii, jj, settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
 
     if is_conv:
 
         # Compute the focus area of the data layer
         layer_indices = (ii, ii + 1, jj, jj + 1)
-        data_indices = region_computer.convert_region(layer_name, 'data', layer_indices)
-        data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
-        data_indices2 = region_computer.convert_region(layer_name, 'data', layer_indices, crop_to_boundary=False)
-        data_ii_start2, data_ii_end2, data_jj_start2, data_jj_end2 = data_indices2
 
-        data_indices3 = RegionComputer.convert_region_dag(settings, layer_name, 'data', layer_indices, crop_to_boundary=False)
-        data_ii_start3, data_ii_end3, data_jj_start3, data_jj_end3 = data_indices3
+        data_indices = RegionComputer.convert_region_dag(settings, layer_name, 'data', layer_indices, crop_to_boundary=False)
+        data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
 
         # safe guard edges
         data_ii_start = max(data_ii_start, 0)
@@ -356,27 +216,6 @@ def compute_data_layer_focus_area(is_conv, ii, jj, region_computer, settings, la
         data_ii_end = min(data_ii_end, data_size_ii)
         data_jj_end = min(data_jj_end, data_size_jj)
 
-        data_ii_start2 = max(data_ii_start2, 0)
-        data_jj_start2 = max(data_jj_start2, 0)
-        data_ii_end2 = min(data_ii_end2, data_size_ii)
-        data_jj_end2 = min(data_jj_end2, data_size_jj)
-
-        data_ii_start3 = max(data_ii_start3, 0)
-        data_jj_start3 = max(data_jj_start3, 0)
-        data_ii_end3 = min(data_ii_end3, data_size_ii)
-        data_jj_end3 = min(data_jj_end3, data_size_jj)
-
-        assert(data_ii_start2 == data_ii_start)
-        assert(data_jj_start2 == data_jj_start)
-        assert(data_ii_end2 == data_ii_end)
-        assert(data_jj_end2 == data_jj_end)
-
-        assert(data_ii_start3 == data_ii_start)
-        assert(data_jj_start3 == data_jj_start)
-        assert(data_ii_end3 == data_ii_end)
-        assert(data_jj_end3 == data_jj_end)
-
-
         touching_imin = (data_ii_start == 0)
         touching_jmin = (data_jj_start == 0)
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index b1a783623..f09d9098a 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -607,7 +607,6 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
     import caffe
 
     mt = max_tracker
-    rc = RegionComputer(settings.max_tracker_layers_list)
 
     image_filenames, image_labels = get_files_list(settings)
 
@@ -624,7 +623,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
     assert num_top <= num_top_in_mt, 'Requested %d top images but MaxTracker contains only %d' % (num_top, num_top_in_mt)
     assert idx_end >= idx_begin, 'Range error'
 
-    size_ii, size_jj = get_max_data_extent(net, layer_name, rc, mt.is_conv)
+    size_ii, size_jj = get_max_data_extent(net, settings, layer_name, mt.is_conv)
     data_size_ii, data_size_jj = net.blobs['data'].data.shape[2:4]
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
@@ -708,7 +707,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                 print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), batch[batch_index].cc * num_top, n_total_images, layer_name, batch[batch_index].channel_idx)
 
 
-            # print "DEBUG: (mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)", str((mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj))
+            # print "DEBUG: (mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)", str((mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj))
 
             [batch[batch_index].out_ii_start,
              batch[batch_index].out_ii_end,
@@ -718,7 +717,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
              batch[batch_index].data_ii_end,
              batch[batch_index].data_jj_start,
              batch[batch_index].data_jj_end] = \
-                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, settings, layer_name,
+                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, settings, layer_name,
                                               size_ii, size_jj, data_size_ii, data_size_jj)
 
             # print "DEBUG: channel:%d out_ii_start:%d out_ii_end:%d out_jj_start:%d out_jj_end:%d data_ii_start:%d data_ii_end:%d data_jj_start:%d data_jj_end:%d" % \
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index f987725a1..b0cb9feb9 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -510,13 +510,12 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
                 [temp_ii, temp_jj] = results[batch_index].idxmax[results[batch_index].best_ii][1:3]
 
                 is_conv = params.layer_is_conv
-                rc = RegionComputer(self.settings.max_tracker_layers_list)
                 layer_name = params.push_layer
-                size_ii, size_jj = get_max_data_extent(self.net, layer_name, rc, is_conv)
+                size_ii, size_jj = get_max_data_extent(self.net, self.settings, layer_name, is_conv)
                 data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
 
                 [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
-                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, rc, self.settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
+                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, self.settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
 
                 selected_input_index = self.find_selected_input_index(layer_name)
 

From 8d5e99571c030e66e41440bc373f9bf0d9185376 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 11:31:14 +0300
Subject: [PATCH 080/106] added progress reports when loading images and
 calculating histograms

---
 caffevis/app.py | 6 ++++--
 1 file changed, 4 insertions(+), 2 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 24e9d07ac..df84d3a09 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -617,6 +617,8 @@ def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_o
             unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d" % (unit_id), file_search_pattern)
 
             try:
+                if unit_id % 10 == 0:
+                    print "loading %s images for layer %s channel %d out of %d" % (file_search_pattern, load_layer, unit_id, units_num)
 
                 unit_first_image = get_image_from_files(self.settings, unit_folder_path, False, resize_shape, first_only)
 
@@ -693,8 +695,8 @@ def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pan
 
             def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print):
 
-                if do_print and channel_idx % 100 == 0:
-                    print "calculating weights histogram for channel %d out of %d" % (channel_idx, n_channels)
+                if do_print and channel_idx % 10 == 0:
+                    print "calculating weights histogram for layer %s channel %d out of %d" % (layer_name, channel_idx, n_channels)
 
                 # get vector of weights
                 weights = net.params[layer_name][0].data[channel_idx].flatten()

From 0abe730337c4db863642b327c77d2367d811098b Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 21:35:41 +0300
Subject: [PATCH 081/106] - removed get_layer_info function - added parent
 children link in layer_def - identify maxout structures - added
 layer_name_to_def in settings_misc - removed layer_to_tops, layer_to_bottoms
 - refactored convert_region_dag - renamed is_conv with is_spatial

---
 caffe_misc.py                  |  50 +++---------
 find_maxes/max_tracker.py      |  34 ++++----
 optimize/gradient_optimizer.py |  22 +++---
 optimize_image.py              |  38 +++------
 settings_misc.py               | 140 +++++++++++++++++++++------------
 5 files changed, 137 insertions(+), 147 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index 558102285..8eb6bc8ae 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -90,45 +90,17 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
 
         step_region = None
 
+        layer_def = settings._layer_name_to_def[from_layer] if from_layer in settings._layer_name_to_def else None
+
         # do single step to convert according to from_layer
-        if from_layer not in settings._layer_name_to_def:
+        if not layer_def:
             # fallback to doing nothing
             step_region = region
+
         else:
 
-            layer_def = settings._layer_name_to_def[from_layer]
-
-            if layer_def.type == 'Convolution':
-                filter = list(layer_def.convolution_param.kernel_size)
-                if len(filter) == 1:
-                    filter *= 2
-                pad = list(layer_def.convolution_param.pad)
-                if len(pad) == 0:
-                    pad = [0, 0]
-                elif len(pad) == 1:
-                    pad *= 2
-                stride = list(layer_def.convolution_param.stride)
-                if len(stride) == 0:
-                    stride = [1, 1]
-                elif len(stride) == 1:
-                    stride *= 2
-                step_region = RegionComputer.region_converter(region, filter, stride, pad)
-
-            elif layer_def.type == 'Pooling':
-                filter = [layer_def.pooling_param.kernel_size]
-                if len(filter) == 1:
-                    filter *= 2
-                pad = [layer_def.pooling_param.pad]
-                if len(pad) == 0:
-                    pad = [0, 0]
-                elif len(pad) == 1:
-                    pad *= 2
-                stride = [layer_def.pooling_param.stride]
-                if len(stride) == 0:
-                    stride = [1, 1]
-                elif len(stride) == 1:
-                    stride *= 2
-                step_region = RegionComputer.region_converter(region, filter, stride, pad)
+            if layer_def.type in ['Convolution', 'Pooling']:
+                step_region = RegionComputer.region_converter(region, layer_def.filter, layer_def.stride, layer_def.pad)
 
             else:
                 # fallback to doing nothing
@@ -140,7 +112,7 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
         # handle the rest
         total_region = None
 
-        bottoms = settings._layer_to_bottoms[from_layer]
+        bottoms = layer_def.bottoms if layer_def else []
 
         for bottom in bottoms:
 
@@ -181,12 +153,12 @@ def layer_name_to_top_name(net, layer_name):
     return net.top_names[layer_name][0]
 
 
-def get_max_data_extent(net, settings, layer_name, is_conv):
+def get_max_data_extent(net, settings, layer_name, is_spatial):
     '''Gets the maximum size of the data layer that can influence a unit on layer.'''
 
     data_size = net.blobs['data'].data.shape[2:4]  # e.g. (227,227) for fc6,fc7,fc8,prop
 
-    if is_conv:
+    if is_spatial:
         top_name = layer_name_to_top_name(net, layer_name)
         conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
         layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
@@ -200,9 +172,9 @@ def get_max_data_extent(net, settings, layer_name, is_conv):
         return data_size
 
 
-def compute_data_layer_focus_area(is_conv, ii, jj, settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
+def compute_data_layer_focus_area(is_spatial, ii, jj, settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj):
 
-    if is_conv:
+    if is_spatial:
 
         # Compute the focus area of the data layer
         layer_indices = (ii, ii + 1, jj, jj + 1)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index f09d9098a..2d35bd73a 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -12,7 +12,6 @@
 from caffe_misc import RegionComputer, save_caffe_image, get_max_data_extent, extract_patch_from_image, \
     compute_data_layer_focus_area, layer_name_to_top_name
 from siamese_helper import SiameseHelper
-from settings_misc import get_layer_info
 
 from jby_misc import WithTimer
 
@@ -143,11 +142,11 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
 
 class MaxTracker(object):
 
-    def __init__(self, is_conv, n_channels, n_top = 10, initial_val = -1e99, dtype = 'float32'):
-        self.is_conv = is_conv
+    def __init__(self, is_spatial, n_channels, n_top = 10, initial_val = -1e99, dtype = 'float32'):
+        self.is_spatial = is_spatial
         self.max_vals = np.ones((n_channels, n_top), dtype = dtype) * initial_val
         self.n_top = n_top
-        if is_conv:
+        if is_spatial:
             self.max_locs = -np.ones((n_channels, n_top, 5), dtype = 'int')   # image_idx, image_class, selected_input_index, i, j
         else:
             self.max_locs = -np.ones((n_channels, n_top, 3), dtype = 'int')   # image_idx, image_class, selected_input_index
@@ -191,7 +190,7 @@ def update(self, data, image_idx, image_class, selected_input_index, layer_uniqu
         self.seen_inputs.add(layer_unique_input_source)
 
         n_channels = data.shape[0]
-        data_unroll = data.reshape((n_channels, -1))          # Note: no copy eg (96,3025). Does nothing if not is_conv
+        data_unroll = data.reshape((n_channels, -1))          # Note: no copy eg (96,3025). Does nothing if not is_spatial
 
         max_indexes = data_unroll.argmax(1)   # maxes for each channel, eg. (96,)
 
@@ -216,7 +215,7 @@ def update(self, data, image_idx, image_class, selected_input_index, layer_uniqu
             # self.max_locs
             self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
             # store new location
-            if self.is_conv:
+            if self.is_spatial:
                 self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
             else:
                 self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index)
@@ -278,16 +277,11 @@ def _init_with_net(self, net):
             # count as the same layer in terms of activations
             normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
-            current_layer, previous_layer = get_layer_info(self.settings, normalized_layer_name)
-            (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad) = current_layer
-            (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = previous_layer
-
-            is_conv = (current_type == 'Convolution') or (previous_type == 'Convolution' and current_type == 'Eltwise')
-            # is_conv = self.settings.is_conv_fn(layer_name)
+            is_spatial = (len(blob.shape) == 4)
 
             # only add normalized layer once
             if normalized_layer_name not in self.max_trackers:
-                self.max_trackers[normalized_layer_name] = MaxTracker(is_conv, blob.shape[1], n_top = self.n_top,
+                self.max_trackers[normalized_layer_name] = MaxTracker(is_spatial, blob.shape[1], n_top = self.n_top,
                                                                       initial_val = self.initial_val,
                                                                       dtype = blob.dtype)
 
@@ -623,7 +617,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
     assert num_top <= num_top_in_mt, 'Requested %d top images but MaxTracker contains only %d' % (num_top, num_top_in_mt)
     assert idx_end >= idx_begin, 'Range error'
 
-    size_ii, size_jj = get_max_data_extent(net, settings, layer_name, mt.is_conv)
+    size_ii, size_jj = get_max_data_extent(net, settings, layer_name, mt.is_spatial)
     data_size_ii, data_size_jj = net.blobs['data'].data.shape[2:4]
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
@@ -673,7 +667,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             channel_to_info_file[channel_idx].info_file = open(info_filename[0], 'w')
             channel_to_info_file[channel_idx].ref_count = num_top
 
-            print >> channel_to_info_file[channel_idx].info_file, '# is_conv val image_idx image_class selected_input_index i(if is_conv) j(if is_conv) filename'
+            print >> channel_to_info_file[channel_idx].info_file, '# is_spatial val image_idx image_class selected_input_index i(if is_spatial) j(if is_spatial) filename'
 
         # iterate through maxes from highest (at end) to lowest
         for max_idx_0 in range(num_top):
@@ -690,7 +684,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             batch[batch_index].max_idx_0 = max_idx_0
             batch[batch_index].max_idx = num_top_in_mt - 1 - batch[batch_index].max_idx_0
 
-            if mt.is_conv:
+            if mt.is_spatial:
                 batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index, batch[batch_index].ii, batch[batch_index].jj = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
             else:
                 batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
@@ -707,7 +701,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                 print '%s   Output file/image(s) %d/%d   layer %s channel %d' % (datetime.now().ctime(), batch[batch_index].cc * num_top, n_total_images, layer_name, batch[batch_index].channel_idx)
 
 
-            # print "DEBUG: (mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)", str((mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj))
+            # print "DEBUG: (mt.is_spatial, batch[batch_index].ii, batch[batch_index].jj, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)", str((mt.is_spatial, batch[batch_index].ii, batch[batch_index].jj, rc, layer_name, size_ii, size_jj, data_size_ii, data_size_jj))
 
             [batch[batch_index].out_ii_start,
              batch[batch_index].out_ii_end,
@@ -717,7 +711,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
              batch[batch_index].data_ii_end,
              batch[batch_index].data_jj_start,
              batch[batch_index].data_jj_end] = \
-                compute_data_layer_focus_area(mt.is_conv, batch[batch_index].ii, batch[batch_index].jj, settings, layer_name,
+                compute_data_layer_focus_area(mt.is_spatial, batch[batch_index].ii, batch[batch_index].jj, settings, layer_name,
                                               size_ii, size_jj, data_size_ii, data_size_jj)
 
             # print "DEBUG: channel:%d out_ii_start:%d out_ii_end:%d out_jj_start:%d out_jj_end:%d data_ii_start:%d data_ii_end:%d data_jj_start:%d data_jj_end:%d" % \
@@ -728,8 +722,8 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             #        batch[batch_index].data_jj_start, batch[batch_index].data_jj_end)
 
             if do_info:
-                print >> batch[batch_index].info_file, 1 if mt.is_conv else 0, '%.6f' % mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
-                if mt.is_conv:
+                print >> batch[batch_index].info_file, 1 if mt.is_spatial else 0, '%.6f' % mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
+                if mt.is_spatial:
                     print >> batch[batch_index].info_file, '%d %d %d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
                 else:
                     print >> batch[batch_index].info_file, '%d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index b0cb9feb9..8ccb6f29c 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -38,7 +38,7 @@ def __init__(self, **kwargs):
             small_norm_percentile = None,
             px_benefit_percentile = None,
             px_abs_benefit_percentile = None,
-            layer_is_conv = False,
+            is_spatial = False,
 
             lr_policy = 'constant',
             lr_params = {'lr': 10.0},
@@ -235,9 +235,9 @@ def _optimize(self, params, x0, prefix_template):
         top_name = layer_name_to_top_name(self.net, params.push_layer)
         data_shape = self.net.blobs[top_name].data.shape
         assert len(data_shape) in (2,4), 'Expected shape of length 2 (for FC) or 4 (for conv) layers but shape is %s' % repr(data_shape)
-        is_conv = (len(data_shape) == 4)
+        is_spatial = (len(data_shape) == 4)
 
-        if is_conv:
+        if is_spatial:
             if params.push_spatial == (0,0):
                 recommended_spatial = (data_shape[2]/2, data_shape[3]/2)
                 print ('WARNING: A unit on a conv layer (%s) is being optimized, but push_spatial\n'
@@ -271,7 +271,7 @@ def _optimize(self, params, x0, prefix_template):
             # note: no batch support in 'siamese_batch_pair'
             if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 2:
 
-                if not is_conv:
+                if not is_spatial:
                     # promote to 4D
                     acts = np.reshape(acts, (2, -1, 1, 1))
                 reshaped_acts = np.reshape(acts, (2, -1))
@@ -284,7 +284,7 @@ def _optimize(self, params, x0, prefix_template):
 
             elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 1:
 
-                if not is_conv:
+                if not is_spatial:
                     # promote to 4D
                     acts = np.reshape(acts, (1, -1, 1, 1))
                 reshaped_acts = np.reshape(acts, (1, -1))
@@ -296,7 +296,7 @@ def _optimize(self, params, x0, prefix_template):
                 obj[0] = acts[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]]
 
             else:
-                if not is_conv:
+                if not is_spatial:
                     # promote to 4D
                     acts = np.reshape(acts, (params.batch_size, -1, 1, 1))
                 reshaped_acts = np.reshape(acts, (params.batch_size, -1))
@@ -333,7 +333,7 @@ def _optimize(self, params, x0, prefix_template):
             # 4. Do backward pass to get gradient
             top_name = layer_name_to_top_name(self.net, params.push_layer)
             diffs = self.net.blobs[top_name].diff * 0
-            if not is_conv:
+            if not is_spatial:
                 # Promote bc -> bc01
                 diffs = diffs[:,:,np.newaxis,np.newaxis]
 
@@ -343,7 +343,7 @@ def _optimize(self, params, x0, prefix_template):
                 diffs[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
             else:
                 diffs[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
-            backout = self.net.backward_from_layer(params.push_layer, diffs if is_conv else diffs[:,:,0,0])
+            backout = self.net.backward_from_layer(params.push_layer, diffs if is_spatial else diffs[:,:,0,0])
 
             grad = backout['data'].copy()
             reshaped_grad = np.reshape(grad, (params.batch_size, -1))
@@ -509,13 +509,13 @@ def save_output(data, channel_swap_to_rgb, best_X_image_name):
                 # get center position, relative to layer, of best maximum
                 [temp_ii, temp_jj] = results[batch_index].idxmax[results[batch_index].best_ii][1:3]
 
-                is_conv = params.layer_is_conv
+                is_spatial = params.is_spatial
                 layer_name = params.push_layer
-                size_ii, size_jj = get_max_data_extent(self.net, self.settings, layer_name, is_conv)
+                size_ii, size_jj = get_max_data_extent(self.net, self.settings, layer_name, is_spatial)
                 data_size_ii, data_size_jj = self.net.blobs['data'].data.shape[2:4]
 
                 [out_ii_start, out_ii_end, out_jj_start, out_jj_end, data_ii_start, data_ii_end, data_jj_start, data_jj_end] = \
-                    compute_data_layer_focus_area(is_conv, temp_ii, temp_jj, self.settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
+                    compute_data_layer_focus_area(is_spatial, temp_ii, temp_jj, self.settings, layer_name, size_ii, size_jj, data_size_ii, data_size_jj)
 
                 selected_input_index = self.find_selected_input_index(layer_name)
 
diff --git a/optimize_image.py b/optimize_image.py
index 2d4bacee0..f0cd38e56 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -8,7 +8,8 @@
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
 from caffevis.caffevis_helper import check_force_backward_true, read_label_file, set_mean
-from settings_misc import load_network, get_layer_info
+from settings_misc import load_network
+from caffe_misc import layer_name_to_top_name
 
 
 LR_POLICY_CHOICES = ('constant', 'progress', 'progress01')
@@ -179,33 +180,18 @@ def main():
     # go over push layers
     for count, push_layer in enumerate(args.push_layers):
 
-        # get layer type
-        current_layer, previous_layer = get_layer_info(settings, push_layer)
-        (current_name, current_type, current_input, current_output, current_filter, current_stride, current_pad) = current_layer
-        (previous_name, previous_type, previous_input, previous_output, previous_filter, previous_stride, previous_pad) = previous_layer
+        top_name = layer_name_to_top_name(net, push_layer)
+        blob = net.blobs[top_name].data
+        is_spatial = (len(blob.shape) == 4)
+        channels = blob.shape[1]
 
-        if current_type == 'FullyConnected':
-            # get number of units
-            channels = current_output
-            push_spatial = (0, 0)
-            layer_is_conv = False
-
-        elif current_type == 'Convolution':
-            # get number of channels
-            channels = current_output[2]
-            push_spatial = (current_filter[0]/2, current_filter[1]/2)
-            layer_is_conv = True
+        # get layer definition
+        layer_def = settings._layer_name_to_def[push_layer]
 
-        elif current_type == 'Eltwise' and previous_type == 'FullyConnected':
-            channels = current_output
+        if is_spatial:
+            push_spatial = (layer_def.filter[0] / 2, layer_def.filter[1] / 2)
+        else:
             push_spatial = (0, 0)
-            layer_is_conv = False
-
-        elif current_type == 'Eltwise' and previous_type == 'Convolution':
-            # get number of channels
-            channels = current_output[2]
-            push_spatial = (previous_filter[0]/2, previous_filter[1]/2)
-            layer_is_conv = True
 
         # if channels defined in settings file, use them
         if settings.optimize_image_channels:
@@ -233,7 +219,7 @@ def main():
                 lr_policy = args.lr_policy,
                 lr_params = lr_params,
                 max_iter = args.max_iters[count % len(args.max_iters)],
-                layer_is_conv=layer_is_conv,
+                is_spatial = is_spatial,
             )
 
             optimizer.run_optimize(params, prefix_template = args.output_prefix,
diff --git a/settings_misc.py b/settings_misc.py
index 85ad4fb15..7802df69f 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -38,30 +38,6 @@ def set_calculated_image_dims(settings, net):
         settings._calculated_image_dims = input_shape[2:4]
 
 
-def get_layer_info(settings, layer_name):
-    '''
-        get layer info (name, type, input, output, filter, stride, pad) from settings
-
-    :param settings: contains script settings
-    :param layer_name: name of layer
-    :return: current_layer and previous_layer tuples of (name, type, input, output, filter, stride, pad)
-    '''
-
-    # go over layers
-
-    previous_layer = (None, None, None, None, None, None, None)
-    current_layer = (None, None, None, None, None, None, None)
-    for (name, type, input, output, filter, stride, pad) in settings.max_tracker_layers_list:
-        if name == layer_name:
-            current_layer = (name, type, input, output, filter, stride, pad)
-            return current_layer, previous_layer
-
-        # update previous layer
-        previous_layer = (name, type, input, output, filter, stride, pad)
-
-    return current_layer, previous_layer
-
-
 def load_network(settings):
 
     # Set the mode to CPU or GPU. Note: in the latest Caffe
@@ -96,6 +72,64 @@ def load_network(settings):
     return net, data_mean
 
 
+class LayerRecord:
+
+    def __init__(self, layer_def):
+
+        self.layer_def = layer_def
+        self.type = layer_def.type
+
+        # keep filter, stride and pad
+        if layer_def.type == 'Convolution':
+            self.filter = list(layer_def.convolution_param.kernel_size)
+            if len(self.filter) == 1:
+                self.filter *= 2
+            self.pad = list(layer_def.convolution_param.pad)
+            if len(self.pad) == 0:
+                self.pad = [0, 0]
+            elif len(self.pad) == 1:
+                self.pad *= 2
+            self.stride = list(layer_def.convolution_param.stride)
+            if len(self.stride) == 0:
+                self.stride = [1, 1]
+            elif len(self.stride) == 1:
+                self.stride *= 2
+
+        elif layer_def.type == 'Pooling':
+            self.filter = [layer_def.pooling_param.kernel_size]
+            if len(self.filter) == 1:
+                self.filter *= 2
+            self.pad = [layer_def.pooling_param.pad]
+            if len(self.pad) == 0:
+                self.pad = [0, 0]
+            elif len(self.pad) == 1:
+                self.pad *= 2
+            self.stride = [layer_def.pooling_param.stride]
+            if len(self.stride) == 0:
+                self.stride = [1, 1]
+            elif len(self.stride) == 1:
+                self.stride *= 2
+
+        else:
+            self.filter = [0, 0]
+            self.pad = [0, 0]
+            self.stride = [1, 1]
+
+        # keep tops
+        self.tops = list(layer_def.top)
+
+        # keep bottoms
+        self.bottoms = list(layer_def.bottom)
+
+        # list of parent layers
+        self.parents = []
+
+        # list of child layers
+        self.children = []
+
+    pass
+
+
 def read_network_dag(settings):
     from caffe.proto import caffe_pb2
     from google.protobuf import text_format
@@ -105,6 +139,12 @@ def read_network_dag(settings):
     f = open(settings.caffevis_deploy_prototxt, 'r')
     text_format.Merge(str(f.read()), network_def)
 
+    # map layer name to layer record
+    layer_name_to_def = dict()
+    for layer_def in network_def.layer:
+        if (len(layer_def.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer_def.include]):
+            layer_name_to_def[layer_def.name] = LayerRecord(layer_def)
+
     # map blob name to list of layers having this blob name as input
     bottom_to_layers = dict()
     for layer in network_def.layer:
@@ -126,25 +166,29 @@ def read_network_dag(settings):
     if 'data' not in top_to_layers:
         top_to_layers['data'] = ['data']
 
-    layer_to_tops = dict()
-    for layer in network_def.layer:
-        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
-            for top in layer.top:
-                if layer.name not in layer_to_tops:
-                    layer_to_tops[layer.name] = list()
-                layer_to_tops[layer.name].append(top)
-    if 'data' not in layer_to_tops:
-        layer_to_tops['data'] = ['data']
-
-    layer_to_bottoms = dict()
-    for layer in network_def.layer:
-        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
-            for bottom in layer.bottom:
-                if layer.name not in layer_to_bottoms:
-                    layer_to_bottoms[layer.name] = list()
-                layer_to_bottoms[layer.name].append(bottom)
-    # if 'data' not in layer_to_bottoms:
-    #     layer_to_bottoms['data'] = ['data']
+    # find parents and children of all layers
+    for child_layer_name in layer_name_to_def.keys():
+        child_layer_def = layer_name_to_def[child_layer_name]
+        for bottom in child_layer_def.bottoms:
+            for parent_layer_name in top_to_layers[bottom]:
+                if parent_layer_name in layer_name_to_def:
+                    parent_layer_def = layer_name_to_def[parent_layer_name]
+                    if parent_layer_def not in child_layer_def.parents:
+                        child_layer_def.parents.append(parent_layer_def)
+                    if child_layer_def not in parent_layer_def.children:
+                        parent_layer_def.children.append(child_layer_def)
+
+    # update filter, strid, pad for maxout "structures"
+    for layer_name in layer_name_to_def.keys():
+        layer_def = layer_name_to_def[layer_name]
+        if layer_def.type == 'Eltwise' and \
+           len(layer_def.parents) == 1 and \
+           layer_def.parents[0].type == 'Slice' and \
+           len(layer_def.parents[0].parents) == 1 and \
+           layer_def.parents[0].parents[0].type in ['Convolution', 'InnerProduct']:
+            layer_def.filter = layer_def.parents[0].parents[0].filter
+            layer_def.stride = layer_def.parents[0].parents[0].stride
+            layer_def.pad = layer_def.parents[0].parents[0].pad
 
     inplace_layers = list()
     for layer in network_def.layer:
@@ -152,18 +196,12 @@ def read_network_dag(settings):
             if len(layer.top) == 1 and len(layer.bottom) == 1 and layer.top[0] == layer.bottom[0]:
                 inplace_layers.append(layer.name)
 
-    layer_name_to_def = dict()
-    for layer in network_def.layer:
-        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
-            layer_name_to_def[layer.name] = layer
-
     # keep helper variables in settings
     settings._network_def = network_def
+    settings._layer_name_to_def = layer_name_to_def
+
     settings._bottom_to_layers = bottom_to_layers
     settings._top_to_layers = top_to_layers
-    settings._layer_to_tops = layer_to_tops
-    settings._layer_to_bottoms = layer_to_bottoms
     settings._inplace_layers = inplace_layers
-    settings._layer_name_to_def = layer_name_to_def
 
     return

From 4433092a6956edd3e359a649f8313e1088cf4bbe Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 23:44:12 +0300
Subject: [PATCH 082/106] - added function to handle missing force_backwards
 and to upgrade network definition to latest

---
 settings_misc.py | 36 +++++++++++++++++++++++++++++++++---
 1 file changed, 33 insertions(+), 3 deletions(-)

diff --git a/settings_misc.py b/settings_misc.py
index 7802df69f..4bca180de 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -38,6 +38,34 @@ def set_calculated_image_dims(settings, net):
         settings._calculated_image_dims = input_shape[2:4]
 
 
+def process_network_proto(settings):
+
+    settings._processed_deploy_prototxt = settings.caffevis_deploy_prototxt + ".processed_by_deepvis"
+
+    # check if force_backwards is missing
+    found_force_backwards = False
+    with open(settings.caffevis_deploy_prototxt, 'r') as proto_file:
+        for line in proto_file:
+            fields = line.strip().split()
+            if len(fields) == 2 and fields[0] == 'force_backward:' and fields[1] == 'true':
+                found_force_backwards = True
+                break
+
+    # write file, adding force_backward if needed
+    with open(settings.caffevis_deploy_prototxt, 'r') as proto_file:
+        with open(settings._processed_deploy_prototxt, 'w') as new_proto_file:
+            if not found_force_backwards:
+                new_proto_file.write('force_backward: true')
+            for line in proto_file:
+                new_proto_file.write(line)
+
+    # run upgrade tool on new file name (same output file)
+    upgrade_tool_command_line = settings.caffevis_caffe_root + '/build/tools/upgrade_net_proto_text.bin ' + settings._processed_deploy_prototxt + ' ' + settings._processed_deploy_prototxt
+    os.system(upgrade_tool_command_line)
+
+    return
+
+
 def load_network(settings):
 
     # Set the mode to CPU or GPU. Note: in the latest Caffe
@@ -53,8 +81,10 @@ def load_network(settings):
         caffe.set_mode_cpu()
         print 'CaffeVisApp mode (in main thread):     CPU'
 
+    process_network_proto(settings)
+
     net = caffe.Classifier(
-        settings.caffevis_deploy_prototxt,
+        settings._processed_deploy_prototxt,
         settings.caffevis_network_weights,
         image_dims=settings.caffe_net_image_dims,
         mean=None,  # Set to None for now, assign later
@@ -136,8 +166,8 @@ def read_network_dag(settings):
 
     # load prototxt file
     network_def = caffe_pb2.NetParameter()
-    f = open(settings.caffevis_deploy_prototxt, 'r')
-    text_format.Merge(str(f.read()), network_def)
+    with open(settings._processed_deploy_prototxt, 'r') as proto_file:
+        text_format.Merge(str(proto_file.read()), network_def)
 
     # map layer name to layer record
     layer_name_to_def = dict()

From f0c742a3c0b6deccd6c639965ac556173029cf29 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 23:49:19 +0300
Subject: [PATCH 083/106] - when caffevis_jpgvis_layers is None (which is now
 the default) we try to load offline images - added print in get_files_list -
 fixed bug in writing force_backward - removed inplace_layers - refactored
 more

---
 caffe_misc.py                                 | 23 +++++-----
 caffevis/app.py                               | 14 ++----
 find_maxes/max_tracker.py                     |  4 ++
 misc.py                                       |  4 ++
 .../settings_local.template-squeezenet.py     |  1 -
 optimize_image.py                             |  2 +-
 settings.py                                   |  4 +-
 settings_misc.py                              | 43 +++++--------------
 8 files changed, 38 insertions(+), 57 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index 8eb6bc8ae..dcb01177e 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -90,7 +90,7 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
 
         step_region = None
 
-        layer_def = settings._layer_name_to_def[from_layer] if from_layer in settings._layer_name_to_def else None
+        layer_def = settings._layer_name_to_record[from_layer] if from_layer in settings._layer_name_to_record else None
 
         # do single step to convert according to from_layer
         if not layer_def:
@@ -112,17 +112,15 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
         # handle the rest
         total_region = None
 
-        bottoms = layer_def.bottoms if layer_def else []
+        if layer_def is not None:
+            for parent_layer in layer_def.parents:
 
-        for bottom in bottoms:
-
-            # get prev layers, excluding inplace layers
-            prev_layers = list(set(settings._top_to_layers[bottom]) - set(settings._inplace_layers))
-
-            for prev_layer in prev_layers:
+                # skip inplace layers
+                if len(parent_layer.tops) == 1 and len(parent_layer.bottoms) == 1 and parent_layer.tops[0] == parent_layer.bottoms[0]:
+                    continue
 
                 # calculate convert_region_dag on each one
-                current_region = RegionComputer.convert_region_dag(settings, prev_layer, to_layer, step_region, verbose, crop_to_boundary)
+                current_region = RegionComputer.convert_region_dag(settings, parent_layer.name, to_layer, step_region, verbose, crop_to_boundary)
 
                 # aggregate results
                 if total_region is None:
@@ -130,6 +128,9 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
                 else:
                     total_region = RegionComputer.merge_regions(total_region, current_region)
 
+        if total_region is None:
+            return step_region
+
         return total_region
 
 
@@ -162,7 +163,7 @@ def get_max_data_extent(net, settings, layer_name, is_spatial):
         top_name = layer_name_to_top_name(net, layer_name)
         conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
         layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
-        data_slice = RegionComputer.convert_region_dag(settings, layer_name, 'data', layer_slice_middle, crop_to_boundary=False)
+        data_slice = RegionComputer.convert_region_dag(settings, layer_name, 'input', layer_slice_middle, crop_to_boundary=False)
         data_slice_size = data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
         # crop data slice size to data size
         data_slice_size = min(data_slice_size[0], data_size[0]), min(data_slice_size[1], data_size[1])
@@ -179,7 +180,7 @@ def compute_data_layer_focus_area(is_spatial, ii, jj, settings, layer_name, size
         # Compute the focus area of the data layer
         layer_indices = (ii, ii + 1, jj, jj + 1)
 
-        data_indices = RegionComputer.convert_region_dag(settings, layer_name, 'data', layer_indices, crop_to_boundary=False)
+        data_indices = RegionComputer.convert_region_dag(settings, layer_name, 'input', layer_indices, crop_to_boundary=False)
         data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
 
         # safe guard edges
diff --git a/caffevis/app.py b/caffevis/app.py
index df84d3a09..d71aa001d 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -535,7 +535,7 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
             load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
-        if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers and self.settings.caffevis_unit_jpg_dir:
+        if ((self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers) or (self.settings.caffevis_jpgvis_layers is None)) and self.settings.caffevis_unit_jpg_dir:
             jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, 'regularized_opt', load_layer, 'whole_layer.jpg')
 
             # Get highres version
@@ -563,7 +563,7 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
             load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
-        if self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers:
+        if (self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers) or (self.settings.caffevis_jpgvis_layers is None):
 
             # get number of units
             units_num = layer_dat_3D.shape[0]
@@ -1255,19 +1255,13 @@ def _draw_jpgvis_pane(self, pane):
         with self.state.lock:
             state_layer_name, state_selected_unit, cursor_area, show_unit_jpgs = self.state.get_default_layer_name(), self.state.selected_unit, self.state.cursor_area, self.state.show_unit_jpgs
 
-        try:
-            # Some may be missing this setting
-            self.settings.caffevis_jpgvis_layers
-        except:
-            print '\n\nNOTE: you need to upgrade your settings.py and settings_model_selector.py files. See README.md.\n\n'
-            raise
-            
         if self.settings.caffevis_jpgvis_remap and state_layer_name in self.settings.caffevis_jpgvis_remap:
             img_key_layer = self.settings.caffevis_jpgvis_remap[state_layer_name]
         else:
             img_key_layer = state_layer_name
 
-        if self.settings.caffevis_jpgvis_layers and img_key_layer in self.settings.caffevis_jpgvis_layers and cursor_area == 'bottom' and show_unit_jpgs:
+        if ((self.settings.caffevis_jpgvis_layers and img_key_layer in self.settings.caffevis_jpgvis_layers) or (self.settings.caffevis_jpgvis_layers is None)) and \
+                        cursor_area == 'bottom' and show_unit_jpgs:
             img_key = (img_key_layer, state_selected_unit, pane.data.shape, self.state.show_maximal_score)
             img_resize = self.img_cache.get(img_key, None)
             if img_resize is None:
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 2d35bd73a..868c01261 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -617,6 +617,10 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
     assert num_top <= num_top_in_mt, 'Requested %d top images but MaxTracker contains only %d' % (num_top, num_top_in_mt)
     assert idx_end >= idx_begin, 'Range error'
 
+    # minor fix for backwards compatability
+    if hasattr(mt, 'is_conv'):
+        mt.is_spatial = mt.is_conv
+
     size_ii, size_jj = get_max_data_extent(net, settings, layer_name, mt.is_spatial)
     data_size_ii, data_size_jj = net.blobs['data'].data.shape[2:4]
 
diff --git a/misc.py b/misc.py
index 2526c60f9..2a3916ba0 100644
--- a/misc.py
+++ b/misc.py
@@ -107,6 +107,8 @@ def get_files_from_siamese_image_list(settings):
 
 def get_files_list(settings, should_convert_labels = False):
 
+    print 'Getting image list...'
+
     # available_files - local list of files
     if settings.static_files_input_mode == "directory":
         available_files = get_files_from_directory(settings)
@@ -122,4 +124,6 @@ def get_files_list(settings, should_convert_labels = False):
     if should_convert_labels and labels:
         labels = [settings.convert_label_fn(label) for label in labels]
 
+    print 'Getting image list... Done.'
+
     return available_files, labels
diff --git a/models/squeezenet/settings_local.template-squeezenet.py b/models/squeezenet/settings_local.template-squeezenet.py
index 90398d2f0..00fa05f71 100644
--- a/models/squeezenet/settings_local.template-squeezenet.py
+++ b/models/squeezenet/settings_local.template-squeezenet.py
@@ -16,7 +16,6 @@
 # Other optional settings; see complete documentation for each in settings.py.
 caffevis_data_mean       = (104, 117, 123)   # per-channel mean
 caffevis_labels          = '%DVT_ROOT%/models/squeezenet/ilsvrc_2012_labels.txt'
-caffevis_jpgvis_layers   = []
 caffevis_prob_layer      = 'prob'
 caffevis_label_layers    = ['conv10', 'relu_conv10', 'pool10', 'prob']
 def caffevis_layer_pretty_name_fn(name):
diff --git a/optimize_image.py b/optimize_image.py
index f0cd38e56..15d3bb103 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -186,7 +186,7 @@ def main():
         channels = blob.shape[1]
 
         # get layer definition
-        layer_def = settings._layer_name_to_def[push_layer]
+        layer_def = settings._layer_name_to_record[push_layer]
 
         if is_spatial:
             push_spatial = (layer_def.filter[0] / 2, layer_def.filter[1] / 2)
diff --git a/settings.py b/settings.py
index 0c00a0aec..7c38ea081 100644
--- a/settings.py
+++ b/settings.py
@@ -223,8 +223,8 @@
 # visualization? If a layer name (full name, not prettified) is given
 # here, we will try to load jpgs to visualize each unit. This is used
 # for pattern mode ('s' key by default) and for the right
-# caffevis_jpgvis pane ('9' key by default). Empty list to disable.
-caffevis_jpgvis_layers = locals().get('caffevis_jpgvis_layers', [])
+# caffevis_jpgvis pane ('9' key by default). None disables filtering (thus taking all), empty list to tries nothing
+caffevis_jpgvis_layers = locals().get('caffevis_jpgvis_layers', None)
 
 # Dict specifying string:string mapping. Steal pattern mode and right
 # jpgvis pane visualizations for certain layers (e.g. pool1) from
diff --git a/settings_misc.py b/settings_misc.py
index 4bca180de..ddd1007ca 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -55,7 +55,7 @@ def process_network_proto(settings):
     with open(settings.caffevis_deploy_prototxt, 'r') as proto_file:
         with open(settings._processed_deploy_prototxt, 'w') as new_proto_file:
             if not found_force_backwards:
-                new_proto_file.write('force_backward: true')
+                new_proto_file.write('force_backward: true\n')
             for line in proto_file:
                 new_proto_file.write(line)
 
@@ -107,6 +107,7 @@ class LayerRecord:
     def __init__(self, layer_def):
 
         self.layer_def = layer_def
+        self.name = layer_def.name
         self.type = layer_def.type
 
         # keep filter, stride and pad
@@ -170,20 +171,10 @@ def read_network_dag(settings):
         text_format.Merge(str(proto_file.read()), network_def)
 
     # map layer name to layer record
-    layer_name_to_def = dict()
+    layer_name_to_record = dict()
     for layer_def in network_def.layer:
         if (len(layer_def.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer_def.include]):
-            layer_name_to_def[layer_def.name] = LayerRecord(layer_def)
-
-    # map blob name to list of layers having this blob name as input
-    bottom_to_layers = dict()
-    for layer in network_def.layer:
-        # no specific phase, or TEST phase is specifically asked for
-        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
-            for bottom in layer.bottom:
-                if bottom not in bottom_to_layers:
-                    bottom_to_layers[bottom] = list()
-                bottom_to_layers[bottom].append(layer.name)
+            layer_name_to_record[layer_def.name] = LayerRecord(layer_def)
 
     top_to_layers = dict()
     for layer in network_def.layer:
@@ -193,24 +184,22 @@ def read_network_dag(settings):
                 if top not in top_to_layers:
                     top_to_layers[top] = list()
                 top_to_layers[top].append(layer.name)
-    if 'data' not in top_to_layers:
-        top_to_layers['data'] = ['data']
 
     # find parents and children of all layers
-    for child_layer_name in layer_name_to_def.keys():
-        child_layer_def = layer_name_to_def[child_layer_name]
+    for child_layer_name in layer_name_to_record.keys():
+        child_layer_def = layer_name_to_record[child_layer_name]
         for bottom in child_layer_def.bottoms:
             for parent_layer_name in top_to_layers[bottom]:
-                if parent_layer_name in layer_name_to_def:
-                    parent_layer_def = layer_name_to_def[parent_layer_name]
+                if parent_layer_name in layer_name_to_record:
+                    parent_layer_def = layer_name_to_record[parent_layer_name]
                     if parent_layer_def not in child_layer_def.parents:
                         child_layer_def.parents.append(parent_layer_def)
                     if child_layer_def not in parent_layer_def.children:
                         parent_layer_def.children.append(child_layer_def)
 
     # update filter, strid, pad for maxout "structures"
-    for layer_name in layer_name_to_def.keys():
-        layer_def = layer_name_to_def[layer_name]
+    for layer_name in layer_name_to_record.keys():
+        layer_def = layer_name_to_record[layer_name]
         if layer_def.type == 'Eltwise' and \
            len(layer_def.parents) == 1 and \
            layer_def.parents[0].type == 'Slice' and \
@@ -220,18 +209,8 @@ def read_network_dag(settings):
             layer_def.stride = layer_def.parents[0].parents[0].stride
             layer_def.pad = layer_def.parents[0].parents[0].pad
 
-    inplace_layers = list()
-    for layer in network_def.layer:
-        if (len(layer.include) == 0) or (caffe_pb2.TEST in [item.phase for item in layer.include]):
-            if len(layer.top) == 1 and len(layer.bottom) == 1 and layer.top[0] == layer.bottom[0]:
-                inplace_layers.append(layer.name)
-
     # keep helper variables in settings
     settings._network_def = network_def
-    settings._layer_name_to_def = layer_name_to_def
-
-    settings._bottom_to_layers = bottom_to_layers
-    settings._top_to_layers = top_to_layers
-    settings._inplace_layers = inplace_layers
+    settings._layer_name_to_record = layer_name_to_record
 
     return

From 93b55067ee4099e27cd6a4d8b79cc1e4ae53aaa5 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Wed, 27 Sep 2017 23:50:39 +0300
Subject: [PATCH 084/106] - replaced max_tracker_layers_to_output,
 layers_for_max_tracker and optimize_image_push_layers with
 max_tracker_layers_to_output

---
 find_maxes/crop_max_patches.py |  2 +-
 find_maxes/max_tracker.py      |  4 ++--
 optimize_image.py              |  4 ++--
 settings.py                    | 12 +++---------
 4 files changed, 8 insertions(+), 14 deletions(-)

diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 6be67e792..ae02c3b9a 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -63,7 +63,7 @@ def main():
 
     nmt = load_max_tracker_from_file(args.nmt_pkl)
 
-    for layer_name in settings.max_tracker_layers_to_output:
+    for layer_name in settings.layers_to_output_in_offline_scripts:
 
         print 'Started work on layer %s' % (layer_name)
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 868c01261..7cfefec8e 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -387,7 +387,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
     import caffe
 
-    tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_for_max_tracker)
+    tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_to_output_in_offline_scripts)
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
 
@@ -452,7 +452,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
     import caffe
 
-    tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_for_max_tracker)
+    tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_to_output_in_offline_scripts)
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
 
diff --git a/optimize_image.py b/optimize_image.py
index 15d3bb103..be7c5c8c6 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -44,7 +44,7 @@ def get_parser():
                         help = 'Batch size used for generating several images, each index will be used as random seed')
 
     # What to optimize
-    parser.add_argument('--push-layers', type = list, default = settings.optimize_image_push_layers,
+    parser.add_argument('--push-layers', type = list, default = settings.layers_to_output_in_offline_scripts,
                         help = 'Name of layers that contains the desired neuron whose value is optimized.')
     parser.add_argument('--push-channel', type = int, default = '130',
                         help = 'Channel number for desired neuron whose value is optimized (channel for conv, neuron index for FC).')
@@ -174,7 +174,7 @@ def main():
                                   channel_swap_to_rgb = settings.caffe_net_channel_swap)
 
     if not args.push_layers:
-        print "ERROR: No layers to work on, please set optimize_image_push_layers to list of layers"
+        print "ERROR: No layers to work on, please set layers_to_output_in_offline_scripts to list of layers"
         return
 
     # go over push layers
diff --git a/settings.py b/settings.py
index 7c38ea081..7d806fedf 100644
--- a/settings.py
+++ b/settings.py
@@ -371,9 +371,6 @@
 # function used to convert a string label into something else, can be an integer
 convert_label_fn = locals().get('convert_label_fn', lambda label: int(label.strip()))
 
-# list of all the layer names which we want to track their maximal activations, usually convolutionals and fully-connecteds
-layers_for_max_tracker = locals().get('layers_for_max_tracker', ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob'])
-
 # function used to check if a layer is a convolutional
 is_conv_fn = locals().get('is_conv_fn', lambda layer_name: 'conv' in layer_name)
 
@@ -404,12 +401,12 @@
 # default value for output_dir parameter in max tracker
 max_tracker_output_dir = locals().get('max_tracker_output_dir', './')
 
-# default value for layer parameter in max_tracker
-max_tracker_layers_to_output = locals().get('max_tracker_layers_to_output', [])
-
 # default batch size used in max_tracker
 max_tracker_batch_size = locals().get('max_tracker_batch_size', 1)
 
+# list of layers to output when using offlien scripts
+layers_to_output_in_offline_scripts = locals().get('layers_to_output_in_offline_scripts', [])
+
 # default value for list of layers used in max tracker
 # note the default is a hardcoded choice which was used in the original code,
 # for backward comparability we keep it as default, but you should change this parameter for each network
@@ -432,9 +429,6 @@
 # rand-seed parameter for optimize_image.py
 optimize_image_rand_seed = locals().get('optimize_image_rand_seed', 0)
 
-# push-layer parameter for optimize_image.py
-optimize_image_push_layers = locals().get('optimize_image_push_layers', [])
-
 # decay parameter for optimize_image.py
 optimize_image_decay = locals().get('optimize_image_decay', 0)
 

From e0951a338e5642ad2bcc969757b90fb4aa2d630a Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Thu, 28 Sep 2017 10:15:43 +0300
Subject: [PATCH 085/106] - removed
 caffevis_maximum_activation_histogram_data_file - removed
 optimize_image_output_prefix - replaced max_tracker_output_dir with
 caffevis_outputs_dir - removed find_max_acts_output_file setting, file name
 is deduced automatically - renamed caffevis_unit_jpg_dir to
 caffevis_outputs_dir - added backward compatability hack after removing image
 class - removed convert_label_fn and image class in info.txt - removed
 max_tracker_layers_list leftovers from model settings files - changed
 caffevis_unit_jpg_dir_folder_format == 'max_tracker_output' to be the default
 - removed caffevis_histograms_format from model settings - removed settings
 which are the default from model files

---
 caffevis/app.py                          | 44 +++++++-------
 caffevis/jpg_vis_loading_thread.py       | 18 +++---
 doc/computing_per_unit_visualizations.md |  2 +-
 find_maxes/crop_max_patches.py           |  4 +-
 find_maxes/find_max_acts.py              |  8 +--
 find_maxes/max_tracker.py                | 73 ++++++++++++------------
 misc.py                                  |  5 +-
 optimize/gradient_optimizer.py           |  8 ++-
 settings.py                              | 55 +++++-------------
 9 files changed, 91 insertions(+), 126 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index d71aa001d..671811e8d 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -356,12 +356,12 @@ def _draw_layer_pane(self, pane):
 
             if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE:
 
-                if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+                if self.settings.caffevis_outputs_dir_folder_format == 'original_combined_single_image':
 
                     display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_original_format(
                         default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows)
 
-                elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                elif self.settings.caffevis_outputs_dir_folder_format == 'max_tracker_output':
 
                     display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
                         default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
@@ -369,15 +369,15 @@ def _draw_layer_pane(self, pane):
 
             elif self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE:
 
-                if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+                if self.settings.caffevis_outputs_dir_folder_format == 'original_combined_single_image':
                     # maximal input image patterns is not implemented in original format
                     display_3D_highres = np.zeros((layer_dat_3D.shape[0], pane.data.shape[0],
                                                    pane.data.shape[1],
                                                    pane.data.shape[2]), dtype=np.uint8)
                     display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
-                    print "ERROR: patterns view with maximal input images is not implemented when settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image'"
+                    print "ERROR: patterns view with maximal input images is not implemented when settings.caffevis_outputs_dir_folder_format == 'original_combined_single_image'"
 
-                elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+                elif self.settings.caffevis_outputs_dir_folder_format == 'max_tracker_output':
                     display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
                         default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
                         self.state.pattern_first_only, file_search_pattern='maxim*.png')
@@ -535,8 +535,8 @@ def load_pattern_images_original_format(self, default_layer_name, layer_dat_3D,
         load_layer = default_layer_name
         if self.settings.caffevis_jpgvis_remap and load_layer in self.settings.caffevis_jpgvis_remap:
             load_layer = self.settings.caffevis_jpgvis_remap[load_layer]
-        if ((self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers) or (self.settings.caffevis_jpgvis_layers is None)) and self.settings.caffevis_unit_jpg_dir:
-            jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, 'regularized_opt', load_layer, 'whole_layer.jpg')
+        if ((self.settings.caffevis_jpgvis_layers and load_layer in self.settings.caffevis_jpgvis_layers) or (self.settings.caffevis_jpgvis_layers is None)) and self.settings.caffevis_outputs_dir:
+            jpg_path = os.path.join(self.settings.caffevis_outputs_dir, 'regularized_opt', load_layer, 'whole_layer.jpg')
 
             # Get highres version
             display_3D_highres = self.img_cache.get((jpg_path, 'whole'), None)
@@ -568,7 +568,7 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
             # get number of units
             units_num = layer_dat_3D.shape[0]
 
-            pattern_image_key = (self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d", units_num, file_search_pattern, first_only, show_layer_summary, file_summary_pattern)
+            pattern_image_key = (self.settings.caffevis_outputs_dir, load_layer, "unit_%04d", units_num, file_search_pattern, first_only, show_layer_summary, file_summary_pattern)
 
             # Get highres version
             display_3D_highres = self.img_cache.get(pattern_image_key, None)
@@ -576,12 +576,12 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
             if display_3D_highres is None:
                 try:
 
-                    if self.settings.caffevis_unit_jpg_dir:
+                    if self.settings.caffevis_outputs_dir:
                         resize_shape = pane.data.shape
 
                         if show_layer_summary:
                             # load layer summary image
-                            layer_summary_image_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, file_summary_pattern)
+                            layer_summary_image_path = os.path.join(self.settings.caffevis_outputs_dir, load_layer, file_summary_pattern)
                             layer_summary_image = caffe_load_image(layer_summary_image_path, color=True, as_uint=True)
                             layer_summary_image = ensure_uint255_and_resize_without_fit(layer_summary_image, resize_shape)
                             display_3D_highres = layer_summary_image
@@ -614,7 +614,7 @@ def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_o
         # for each neuron in layer
         for unit_id in range(0, units_num):
 
-            unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, load_layer, "unit_%04d" % (unit_id), file_search_pattern)
+            unit_folder_path = os.path.join(self.settings.caffevis_outputs_dir, load_layer, "unit_%04d" % (unit_id), file_search_pattern)
 
             try:
                 if unit_id % 10 == 0:
@@ -674,12 +674,12 @@ def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pan
 
             pane_shape = pane.data.shape
 
-            if not self.settings.caffevis_unit_jpg_dir:
+            if not self.settings.caffevis_outputs_dir:
                 folder_path = None
                 cache_layer_weights_histogram_image_path = None
                 cache_details_weights_histogram_image_path = None
             else:
-                folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, layer_name)
+                folder_path = os.path.join(self.settings.caffevis_outputs_dir, layer_name)
                 cache_layer_weights_histogram_image_path = os.path.join(folder_path, 'layer_weights_histogram.png')
                 cache_details_weights_histogram_image_path = os.path.join(folder_path, 'details_weights_histogram.png')
 
@@ -821,7 +821,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                             mkdir_p(folder_path)
                             save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
                         else:
-                            print "WARNING: unable to save weight histogram to cache since caffevis_unit_jpg_dir is not set"
+                            print "WARNING: unable to save weight histogram to cache since caffevis_outputs_dir is not set"
 
                     else:
 
@@ -839,7 +839,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                             mkdir_p(folder_path)
                             save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
                         else:
-                            print "WARNING: unable to save weight histogram to cache since caffevis_unit_jpg_dir is not set"
+                            print "WARNING: unable to save weight histogram to cache since caffevis_outputs_dir is not set"
 
                         # generate empty highlights
                         display_2D_highlights_only = self.prepare_tile_image(display_3D * 0, True, n_channels, tile_rows, tile_cols)
@@ -876,10 +876,8 @@ def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D,
 
         is_layer_summary_loaded = False
 
-        if not self.settings.caffevis_maximum_activation_histogram_data_file:
-            return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
-
-        pattern_image_key = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms", show_layer_summary)
+        maximum_activation_histogram_data_file = os.path.join(settings.caffevis_outputs_dir, 'find_max_acts_output.pickled')
+        pattern_image_key = (maximum_activation_histogram_data_file, default_layer_name, "max histograms", show_layer_summary)
 
         # Get highres version
         display_3D_highres = self.img_cache.get(pattern_image_key, None)
@@ -889,14 +887,14 @@ def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D,
         if display_3D_highres is None:
             try:
                 # load pickle file
-                net_max_tracker = load_max_tracker_from_file(self.settings.caffevis_maximum_activation_histogram_data_file)
+                net_max_tracker = load_max_tracker_from_file(maximum_activation_histogram_data_file)
 
                 if not net_max_tracker.max_trackers.has_key(default_layer_name):
                     return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
 
                 # check if
                 if not hasattr(net_max_tracker.max_trackers[default_layer_name], 'channel_to_histogram'):
-                    print "ERROR: file %s is missing the field channel_to_histogram, try rerun find_max_acts to generate it" % (self.settings.caffevis_maximum_activation_histogram_data_file)
+                    print "ERROR: file %s is missing the field channel_to_histogram, try rerun find_max_acts to generate it" % (maximum_activation_histogram_data_file)
                     return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
 
                 channel_to_histogram = net_max_tracker.max_trackers[default_layer_name].channel_to_histogram
@@ -932,8 +930,8 @@ def process_layer_figure(fig):
                 n_channels = len(channel_to_histogram)
                 find_maxes.max_tracker.prepare_max_histogram(default_layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure)
 
-                pattern_image_key_layer = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms",True)
-                pattern_image_key_details = (self.settings.caffevis_maximum_activation_histogram_data_file, default_layer_name, "max histograms",False)
+                pattern_image_key_layer = (maximum_activation_histogram_data_file, default_layer_name, "max histograms",True)
+                pattern_image_key_details = (maximum_activation_histogram_data_file, default_layer_name, "max histograms",False)
 
                 self.img_cache.set(pattern_image_key_details, display_3D_highres_list[0])
                 self.img_cache.set(pattern_image_key_layer, display_3D_highres_list[1])
diff --git a/caffevis/jpg_vis_loading_thread.py b/caffevis/jpg_vis_loading_thread.py
index fdbce01fa..c204114c2 100644
--- a/caffevis/jpg_vis_loading_thread.py
+++ b/caffevis/jpg_vis_loading_thread.py
@@ -31,7 +31,7 @@ def __init__(self, settings, state, cache, loop_sleep, heartbeat_required):
     def load_image_into_pane_original_format(self, state_layer_name, state_selected_unit, resize_shape, images, sub_folder,
                                              file_pattern, image_index_to_set, should_crop_to_corner=False):
 
-        jpg_path = os.path.join(self.settings.caffevis_unit_jpg_dir, sub_folder, state_layer_name,
+        jpg_path = os.path.join(self.settings.caffevis_outputs_dir, sub_folder, state_layer_name,
                                 file_pattern % (state_layer_name, state_selected_unit))
 
         try:
@@ -52,7 +52,7 @@ def get_score_values_for_max_input_images(self, state_layer_name, state_selected
 
         try:
 
-            info_file_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
+            info_file_path = os.path.join(self.settings.caffevis_outputs_dir, state_layer_name,
                                             "unit_%04d" % (state_selected_unit),
                                             "info.txt")
 
@@ -75,7 +75,7 @@ def get_score_values_for_max_input_images(self, state_layer_name, state_selected
     def load_image_into_pane_max_tracker_format(self, state_layer_name, state_selected_unit, resize_shape, images,
                                                 file_search_pattern, image_index_to_set, should_crop_to_corner=False, first_only = False, captions = [], values = []):
 
-        unit_folder_path = os.path.join(self.settings.caffevis_unit_jpg_dir, state_layer_name,
+        unit_folder_path = os.path.join(self.settings.caffevis_outputs_dir, state_layer_name,
                                         "unit_%04d" % (state_selected_unit),
                                         file_search_pattern)
 
@@ -120,7 +120,7 @@ def run(self):
                 resize_shape = (data_shape[0], data_shape[1]/3)
 
 
-            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+            if self.settings.caffevis_outputs_dir_folder_format == 'original_combined_single_image':
 
                 # 0. e.g. regularized_opt/conv1/conv1_0037_montage.jpg
                 self.load_image_into_pane_original_format(state_layer_name, state_selected_unit, resize_shape, images,
@@ -129,12 +129,12 @@ def run(self):
                                                           image_index_to_set=0,
                                                           should_crop_to_corner=True)
 
-            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+            elif self.settings.caffevis_outputs_dir_folder_format == 'max_tracker_output':
                 self.load_image_into_pane_max_tracker_format(state_layer_name, state_selected_unit, resize_shape, images,
                                                              file_search_pattern='opt*.jpg',
                                                              image_index_to_set=0)
 
-            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+            if self.settings.caffevis_outputs_dir_folder_format == 'original_combined_single_image':
 
                 # 1. e.g. max_im/conv1/conv1_0037.jpg
                 self.load_image_into_pane_original_format(state_layer_name, state_selected_unit, resize_shape, images,
@@ -142,7 +142,7 @@ def run(self):
                                                           file_pattern='%s_%04d.jpg',
                                                           image_index_to_set=1)
 
-            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+            elif self.settings.caffevis_outputs_dir_folder_format == 'max_tracker_output':
 
                 # convert to string with 2 decimal digits
                 values = self.get_score_values_for_max_input_images(state_layer_name, state_selected_unit)
@@ -156,7 +156,7 @@ def run(self):
                                                              image_index_to_set=1, captions=captions, values=values)
 
 
-            if self.settings.caffevis_unit_jpg_dir_folder_format == 'original_combined_single_image':
+            if self.settings.caffevis_outputs_dir_folder_format == 'original_combined_single_image':
 
                 # 2. e.g. max_deconv/conv1/conv1_0037.jpg
                 self.load_image_into_pane_original_format(state_layer_name, state_selected_unit, resize_shape, images,
@@ -164,7 +164,7 @@ def run(self):
                                                           file_pattern='%s_%04d.jpg',
                                                           image_index_to_set=2)
 
-            elif self.settings.caffevis_unit_jpg_dir_folder_format == 'max_tracker_output':
+            elif self.settings.caffevis_outputs_dir_folder_format == 'max_tracker_output':
                 # convert to string with 2 decimal digits
                 values = self.get_score_values_for_max_input_images(state_layer_name, state_selected_unit)
 
diff --git a/doc/computing_per_unit_visualizations.md b/doc/computing_per_unit_visualizations.md
index e1bca78e2..84e61fea9 100644
--- a/doc/computing_per_unit_visualizations.md
+++ b/doc/computing_per_unit_visualizations.md
@@ -8,4 +8,4 @@ But the per-unit visualizations can be computed for any network:
 
 * To find images (for FC layers) or crops (for conv layers) from a set of images (e.g. the ImageNet training or validation set) that cause highest activation, use the [find_max_acts.py](/find_maxes/find_max_acts.py) script to go through the set of images and note the top K images/crops and then [crop_max_patches.py](/find_maxes/crop_max_patches.py) to use the noted max images / max locations to output the crops and/or deconv of the crops.
 
-Results of both of the above steps will be saved as per-unit jpg image files, which can be loaded by the toolbox when browsing units. To do so, just point the `caffevis_unit_jpg_dir` setting to the directory containing the per-unit images.
+Results of both of the above steps will be saved as per-unit jpg image files, which can be loaded by the toolbox when browsing units. To do so, just point the `caffevis_outputs_dir` setting to the directory containing the per-unit images.
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index ae02c3b9a..4ba120c2a 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -33,12 +33,12 @@ def main():
     parser.add_argument('--idx-begin',    type = int, default = None, help = 'Start at this unit (default: all units).')
     parser.add_argument('--idx-end',      type = int, default = None, help = 'End at this unit (default: all units).')
     
-    parser.add_argument('--nmt_pkl',      type = str, default = settings.find_max_acts_output_file, help = 'Which pickled NetMaxTracker to load.')
+    parser.add_argument('--nmt_pkl',      type = str, default = os.path.join(settings.caffevis_outputs_dir, 'find_max_acts_output.pickled'), help = 'Which pickled NetMaxTracker to load.')
     parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
     parser.add_argument('--net_weights',  type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
     parser.add_argument('--datadir',      type = str, default = settings.static_files_dir, help = 'directory to look for files in')
     parser.add_argument('--filelist',     type = str, default = settings.static_files_input_file, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
-    parser.add_argument('--outdir',       type = str, default = settings.max_tracker_output_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
+    parser.add_argument('--outdir',       type = str, default = settings.caffevis_outputs_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
     args = parser.parse_args()
 
     settings.caffevis_deploy_prototxt = args.net_prototxt
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 207bac59d..aa5527b23 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -36,13 +36,13 @@ def main():
 
     parser = argparse.ArgumentParser(description='Finds images in a training set that cause max activation for a network; saves results in a pickled NetMaxTracker.')
     parser.add_argument('--N', type = int, default = 9, help = 'note and save top N activations')
-    parser.add_argument('--gpu', action = 'store_true', default=settings.caffevis_mode_gpu, help = 'use gpu')
+    parser.add_argument('--gpu', action = 'store_true', default = settings.caffevis_mode_gpu, help = 'use gpu')
     parser.add_argument('--net_prototxt', type = str, default = settings.caffevis_deploy_prototxt, help = 'network prototxt to load')
     parser.add_argument('--net_weights', type = str, default = settings.caffevis_network_weights, help = 'network weights to load')
     parser.add_argument('--datadir', type = str, default = settings.static_files_dir, help = 'directory to look for files in')
-    parser.add_argument('--outfile', type=str, default = settings.find_max_acts_output_file, help='output filename for pkl')
-    parser.add_argument('--outdir', type = str, default = settings.max_tracker_output_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{max_histogram}.png')
-    parser.add_argument('--do-histograms', action = 'store_true', default=settings.max_tracker_do_histograms, help = 'Output histogram image file containing histogrma of max values per channel')
+    parser.add_argument('--outfile', type=str, default = os.path.join(settings.caffevis_outputs_dir, 'find_max_acts_output.pickled'), help='output filename for pkl')
+    parser.add_argument('--outdir', type = str, default = settings.caffevis_outputs_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{max_histogram}.png')
+    parser.add_argument('--do-histograms', action = 'store_true', default = settings.max_tracker_do_histograms, help = 'Output histogram image file containing histogrma of max values per channel')
 
     args = parser.parse_args()
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 7cfefec8e..111ab7c87 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -20,10 +20,9 @@
 
 class MaxTrackerBatchRecord(object):
 
-    def __init__(self, image_idx = None, filename = None, image_class = None, im = None):
+    def __init__(self, image_idx = None, filename = None, im = None):
         self.image_idx = image_idx
         self.filename = filename
-        self.image_class = image_class
         self.im = im
 
 
@@ -32,7 +31,7 @@ class MaxTrackerCropBatchRecord(object):
     def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_filenames = None,
                  deconv_filenames = None, deconvnorm_filenames = None, backprop_filenames = None,
                  backpropnorm_filenames = None, info_file = None, max_idx_0 = None, max_idx = None, im_idx = None,
-                 im_class = None, selected_input_index = None, ii = None, jj = None, recorded_val = None,
+                 selected_input_index = None, ii = None, jj = None, recorded_val = None,
                  out_ii_start = None, out_ii_end = None, out_jj_start = None, out_jj_end = None, data_ii_start = None,
                  data_ii_end = None, data_jj_start = None, data_jj_end = None, im = None,
                  denormalized_layer_name = None, denormalized_top_name = None):
@@ -48,7 +47,6 @@ def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_fi
         self.max_idx_0 = max_idx_0
         self.max_idx = max_idx
         self.im_idx = im_idx
-        self.im_class = im_class
         self.selected_input_index = selected_input_index
         self.ii = ii
         self.jj = jj
@@ -147,9 +145,9 @@ def __init__(self, is_spatial, n_channels, n_top = 10, initial_val = -1e99, dtyp
         self.max_vals = np.ones((n_channels, n_top), dtype = dtype) * initial_val
         self.n_top = n_top
         if is_spatial:
-            self.max_locs = -np.ones((n_channels, n_top, 5), dtype = 'int')   # image_idx, image_class, selected_input_index, i, j
+            self.max_locs = -np.ones((n_channels, n_top, 4), dtype = 'int')   # image_idx, selected_input_index, i, j
         else:
-            self.max_locs = -np.ones((n_channels, n_top, 3), dtype = 'int')   # image_idx, image_class, selected_input_index
+            self.max_locs = -np.ones((n_channels, n_top, 2), dtype = 'int')   # image_idx, selected_input_index
 
         # set of seen inputs, used to avoid updating on the same input twice
         self.seen_inputs = set()
@@ -180,7 +178,7 @@ def __setstate__(self, state):
     def __repr__(self):
         return str(self.__dict__.copy())
 
-    def update(self, data, image_idx, image_class, selected_input_index, layer_unique_input_source, layer_name):
+    def update(self, data, image_idx, selected_input_index, layer_unique_input_source, layer_name):
 
         # if unique_input_source already exist, we can skip the update since we've already seen it
         if layer_unique_input_source in self.seen_inputs:
@@ -216,9 +214,9 @@ def update(self, data, image_idx, image_class, selected_input_index, layer_uniqu
             self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
             # store new location
             if self.is_spatial:
-                self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
+                self.max_locs[ii,idx-1] = (image_idx, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
             else:
-                self.max_locs[ii,idx-1] = (image_idx, image_class, selected_input_index)
+                self.max_locs[ii,idx-1] = (image_idx, selected_input_index)
 
     def calculate_histogram(self, layer_name, outdir):
 
@@ -287,7 +285,7 @@ def _init_with_net(self, net):
 
         self.init_done = True
 
-    def update(self, net, image_idx, image_class, net_unique_input_source, batch_index):
+    def update(self, net, image_idx, net_unique_input_source, batch_index):
         '''Updates the maxes found so far with the state of the given net. If a new max is found, it is stored together with the image_idx.'''
 
         if not self.init_done:
@@ -308,17 +306,17 @@ def update(self, net, image_idx, image_class, net_unique_input_source, batch_ind
 
                 if selected_input_index == 0:
                     # first image identifier is selected
-                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, selected_input_index,
+                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, selected_input_index,
                                                                     net_unique_input_source[0], layer_name)
 
                 elif selected_input_index == 1:
                     # second image identifier is selected
-                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, selected_input_index,
+                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, selected_input_index,
                                                                     net_unique_input_source[1], layer_name)
 
                 elif selected_input_index == -1:
                     # both images are selected
-                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, selected_input_index,
+                    self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, selected_input_index,
                                                                     net_unique_input_source, layer_name)
 
             # in siamese network, implemented as single layer with batch 2, we might need to select one of the images from the siamese pair
@@ -330,20 +328,20 @@ def update(self, net, image_idx, image_class, net_unique_input_source, batch_ind
                 if blob.shape[0] == 2:
 
                     # update first output
-                    self.max_trackers[normalized_layer_name].update(blob[0], image_idx, image_class, 0,
+                    self.max_trackers[normalized_layer_name].update(blob[0], image_idx, 0,
                                                                     net_unique_input_source[0], layer_name)
 
                     # update second output
-                    self.max_trackers[normalized_layer_name].update(blob[1], image_idx, image_class, 1,
+                    self.max_trackers[normalized_layer_name].update(blob[1], image_idx, 1,
                                                                     net_unique_input_source[1], layer_name)
 
                 # we have single output
                 elif blob.shape[0] == 1:
-                    self.max_trackers[normalized_layer_name].update(blob[0], image_idx, image_class, -1,
+                    self.max_trackers[normalized_layer_name].update(blob[0], image_idx, -1,
                                                                     net_unique_input_source, layer_name)
 
             else:   # normal, non-siamese network
-                self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, image_class, -1, net_unique_input_source, layer_name)
+                self.max_trackers[normalized_layer_name].update(blob[batch_index], image_idx, -1, net_unique_input_source, layer_name)
 
         pass
 
@@ -380,7 +378,7 @@ def __setstate__(self, state):
         self.siamese_helper = None
 
 def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
-    image_filenames, image_labels = get_files_list(settings, True)
+    image_filenames, image_labels = get_files_list(settings)
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
 
@@ -402,10 +400,6 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
 
         batch[batch_index].image_idx = image_idx
         batch[batch_index].filename = image_filenames[image_idx]
-        if image_labels:
-            batch[batch_index].image_class = image_labels[image_idx]
-        else: # no labels so just put a default value
-            batch[batch_index].image_class = 0
 
         do_print = (batch[batch_index].image_idx % 100 == 0)
         if do_print:
@@ -436,7 +430,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
             for i in range(0,batch_index):
 
                 with WithTimer('Update    ', quiet = not do_print):
-                    tracker.update(net, batch[i].image_idx, batch[i].image_class, net_unique_input_source=batch[i].filename, batch_index=i)
+                    tracker.update(net, batch[i].image_idx, net_unique_input_source=batch[i].filename, batch_index=i)
 
             batch_index = 0
 
@@ -445,7 +439,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
 
 
 def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
-    image_filenames, image_labels = get_files_list(settings, True)
+    image_filenames, image_labels = get_files_list(settings)
     print 'Scanning %d pairs' % len(image_filenames)
     print '  First pair', image_filenames[0]
 
@@ -469,10 +463,6 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
         batch[batch_index].images_pair = image_filenames[image_idx]
         filename1 = batch[batch_index].images_pair[0]
         filename2 = batch[batch_index].images_pair[1]
-        if image_labels:
-            batch[batch_index].image_class = image_labels[image_idx]
-        else:  # no labels so just put a default value
-            batch[batch_index].image_class = 0
 
         do_print = (image_idx % 100 == 0)
         if do_print:
@@ -512,7 +502,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
             # go over batch and update statistics
             for i in range(0,batch_index):
                 with WithTimer('Update    ', quiet=not do_print):
-                    tracker.update(net, batch[i].image_idx, batch[i].image_class, net_unique_input_source=batch[i].images_pair, batch_index=i)
+                    tracker.update(net, batch[i].image_idx, net_unique_input_source=batch[i].images_pair, batch_index=i)
 
             batch_index = 0
 
@@ -537,10 +527,6 @@ def save_representations(settings, net, datadir, filelist, layer_name, first_N =
     rep = None
     for ii,image_idx in enumerate(image_indices):
         filename = image_filenames[image_idx]
-        if image_labels:
-            image_class = image_labels[image_idx]
-        else:
-            image_class = 0
         do_print = (image_idx % 10 == 0)
         if do_print:
             print '%s   Image %d/%d' % (datetime.now().ctime(), image_idx, len(image_indices))
@@ -671,7 +657,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             channel_to_info_file[channel_idx].info_file = open(info_filename[0], 'w')
             channel_to_info_file[channel_idx].ref_count = num_top
 
-            print >> channel_to_info_file[channel_idx].info_file, '# is_spatial val image_idx image_class selected_input_index i(if is_spatial) j(if is_spatial) filename'
+            print >> channel_to_info_file[channel_idx].info_file, '# is_spatial val image_idx selected_input_index i(if is_spatial) j(if is_spatial) filename'
 
         # iterate through maxes from highest (at end) to lowest
         for max_idx_0 in range(num_top):
@@ -689,9 +675,20 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             batch[batch_index].max_idx = num_top_in_mt - 1 - batch[batch_index].max_idx_0
 
             if mt.is_spatial:
-                batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index, batch[batch_index].ii, batch[batch_index].jj = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+
+                # fix for backward compatability
+                if mt.max_locs.shape[2] == 5:
+                    # remove second column
+                    mt.max_locs = np.delete(mt.max_locs, 1, 2)
+
+                batch[batch_index].im_idx, batch[batch_index].selected_input_index, batch[batch_index].ii, batch[batch_index].jj = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
             else:
-                batch[batch_index].im_idx, batch[batch_index].im_class, batch[batch_index].selected_input_index = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+                # fix for backward compatability
+                if mt.max_locs.shape[2] == 3:
+                    # remove second column
+                    mt.max_locs = np.delete(mt.max_locs, 1, 2)
+
+                batch[batch_index].im_idx, batch[batch_index].selected_input_index = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
                 batch[batch_index].ii, batch[batch_index].jj = 0, 0
 
             # if ii and jj are invalid then there is no data for this "top" image, so we can skip it
@@ -728,9 +725,9 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             if do_info:
                 print >> batch[batch_index].info_file, 1 if mt.is_spatial else 0, '%.6f' % mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
                 if mt.is_spatial:
-                    print >> batch[batch_index].info_file, '%d %d %d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
+                    print >> batch[batch_index].info_file, '%d %d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
                 else:
-                    print >> batch[batch_index].info_file, '%d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
+                    print >> batch[batch_index].info_file, '%d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
                 print >> batch[batch_index].info_file, batch[batch_index].filename
 
             if not (do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm):
diff --git a/misc.py b/misc.py
index 2a3916ba0..ba155d299 100644
--- a/misc.py
+++ b/misc.py
@@ -105,7 +105,7 @@ def get_files_from_siamese_image_list(settings):
     return available_files, labels
 
 
-def get_files_list(settings, should_convert_labels = False):
+def get_files_list(settings):
 
     print 'Getting image list...'
 
@@ -121,9 +121,6 @@ def get_files_list(settings, should_convert_labels = False):
         raise Exception(('Error: setting static_files_input_mode has invalid option (%s)' %
                          (settings.static_files_input_mode)))
 
-    if should_convert_labels and labels:
-        labels = [settings.convert_label_fn(label) for label in labels]
-
     print 'Getting image list... Done.'
 
     return available_files, labels
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index 8ccb6f29c..fb5539359 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -222,7 +222,7 @@ def _optimize(self, params, x0, prefix_template):
         results = [FindResults(batch_index) for batch_index in range(params.batch_size)]
 
         # check if all required outputs exist, in which case skip this optimization
-        all_outputs = [self.generate_output_names(batch_index, params, results, prefix_template) for batch_index in range(params.batch_size)]
+        all_outputs = [self.generate_output_names(batch_index, params, results, prefix_template, self.settings.caffevis_outputs_dir) for batch_index in range(params.batch_size)]
         relevant_outputs = [best_X_name for [best_X_name, best_Xpm_name, majority_X_name, majority_Xpm_name, info_name, info_pkl_name, info_big_pkl_name] in all_outputs]
         relevant_outputs_exist = [os.path.exists(best_X_name) for best_X_name in relevant_outputs]
         if all(relevant_outputs_exist):
@@ -454,12 +454,14 @@ def find_selected_input_index(self, layer_name):
 
         return -1
 
-    def generate_output_names(self, batch_index, params, results, prefix_template):
+    def generate_output_names(self, batch_index, params, results, prefix_template, output_dir):
 
         results_and_params = combine_dicts((('p.', params.__dict__),
                                             ('r.', results[batch_index].__dict__)))
         prefix = prefix_template % results_and_params
 
+        prefix = os.path.join(output_dir, prefix)
+
         if os.path.isdir(prefix):
             if prefix[-1] != '/':
                 prefix += '/'  # append slash for dir-only template
@@ -484,7 +486,7 @@ def save_results(self, params, results, prefix_template, brave = False, skipbig
         for batch_index in range(params.batch_size):
 
             [best_X_name, best_Xpm_name, majority_X_name, majority_Xpm_name, info_name, info_pkl_name, info_big_pkl_name] = \
-                self.generate_output_names(batch_index, params, results, prefix_template)
+                self.generate_output_names(batch_index, params, results, prefix_template, self.settings.caffevis_outputs_dir)
 
             # Don't overwrite previous results
             if os.path.exists(info_name) and not brave:
diff --git a/settings.py b/settings.py
index 7d806fedf..6c7cb0ad2 100644
--- a/settings.py
+++ b/settings.py
@@ -205,19 +205,11 @@
 # (when no neurons are selected). None to disable.
 caffevis_prob_layer = locals().get('caffevis_prob_layer', None)
 
-# String or None. Which directory to load pre-computed per-unit
-# visualizations from, if any. None to disable.
-caffevis_unit_jpg_dir = locals().get('caffevis_unit_jpg_dir', None)
-
 # what is the folder format for loading precomputed visualizations,
 # options are:
 #   "original_combined_single_image" - every unit has a single layer
 #   "max_tracker_output" - every unit has a list of images to be loaded
-caffevis_unit_jpg_dir_folder_format = locals().get('caffevis_unit_jpg_dir_folder_format', 'original_combined_single_image')
-
-# the location of the pickle data file which contains the information about the maximum activation histograms
-# usually named 'find_max_acts_output.pickle'
-caffevis_maximum_activation_histogram_data_file = locals().get('caffevis_maximum_activation_histogram_data_file', None)
+caffevis_outputs_dir_folder_format = locals().get('caffevis_outputs_dir_folder_format', 'max_tracker_output')
 
 # List. For which layers should jpgs be loaded for
 # visualization? If a layer name (full name, not prettified) is given
@@ -252,8 +244,8 @@
     caffevis_data_mean = caffevis_data_mean.replace('%DVT_ROOT%', dvt_root)
 if isinstance(caffevis_labels, basestring):
     caffevis_labels = caffevis_labels.replace('%DVT_ROOT%', dvt_root)
-if isinstance(caffevis_unit_jpg_dir, basestring):
-    caffevis_unit_jpg_dir = caffevis_unit_jpg_dir.replace('%DVT_ROOT%', dvt_root)
+if isinstance(caffevis_outputs_dir, basestring):
+    caffevis_outputs_dir = caffevis_outputs_dir.replace('%DVT_ROOT%', dvt_root)
 
 # Pause Caffe forward/backward computation for this many seconds after a keypress. This is to keep the processor free for a brief period after a keypress, which allow the interface to feel much more responsive. After this period has passed, Caffe resumes computation, in CPU mode often occupying all cores. Default: .1
 caffevis_pause_after_keys = locals().get('caffevis_pause_after_keys', .10)
@@ -347,11 +339,14 @@
 caffevis_score_thick = locals().get('caffevis_score_thick', 1)
 
 # how should histograms be loaded: 'calculate_in_realtime' or 'load_from_file'
-caffevis_histograms_format = locals().get('caffevis_histograms_format','calculate_in_realtime')
+caffevis_histograms_format = locals().get('caffevis_histograms_format','load_from_file')
 
 # should we black maximal input images with zero or negative activation score
 caffevis_clear_negative_activations = locals().get('caffevis_clear_negative_activations', False)
 
+# folder for generating and reading deep vis outputs
+caffevis_outputs_dir = locals().get('caffevis_outputs_dir', '.')
+
 # caffe net parameter - channel swap
 caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
 
@@ -368,15 +363,9 @@
 # caffe net parameter - image dims
 caffe_net_image_dims = locals().get('caffe_net_image_dims', None)
 
-# function used to convert a string label into something else, can be an integer
-convert_label_fn = locals().get('convert_label_fn', lambda label: int(label.strip()))
-
 # function used to check if a layer is a convolutional
 is_conv_fn = locals().get('is_conv_fn', lambda layer_name: 'conv' in layer_name)
 
-# location for max tracker output file
-find_max_acts_output_file = locals().get('find_max_acts_output_file', 'find_max_acts_output.pickled')
-
 # default value for do_maxes parameter in max_tracker
 max_tracker_do_maxes = locals().get('max_tracker_do_maxes', True)
 
@@ -398,30 +387,12 @@
 # default value for do_histograms parameter in max tracker
 max_tracker_do_histograms = locals().get('max_tracker_do_histograms', True)
 
-# default value for output_dir parameter in max tracker
-max_tracker_output_dir = locals().get('max_tracker_output_dir', './')
-
 # default batch size used in max_tracker
 max_tracker_batch_size = locals().get('max_tracker_batch_size', 1)
 
 # list of layers to output when using offlien scripts
 layers_to_output_in_offline_scripts = locals().get('layers_to_output_in_offline_scripts', [])
 
-# default value for list of layers used in max tracker
-# note the default is a hardcoded choice which was used in the original code,
-# for backward comparability we keep it as default, but you should change this parameter for each network
-default_max_tracker_layers_list = []
-# Description                           Name     Type           Input      Output   Filter   Stride  Pad
-default_max_tracker_layers_list.append(('data',  'Input',       None,      None,    None,    None,   None,))
-default_max_tracker_layers_list.append(('conv1', 'Convolution', (227,227), (55,55), (11,11), (4,4),  (0,0)))
-default_max_tracker_layers_list.append(('pool1', 'Pooling',     (55,55),   (27,27), (3,3),   (2,2),  (0,0)))
-default_max_tracker_layers_list.append(('conv2', 'Convolution', (27,27),   (27,27), (5,5),   (1,1),  (2,2)))
-default_max_tracker_layers_list.append(('pool2', 'Pooling',     (27,27),   (13,13), (3,3),   (2,2),  (0,0)))
-default_max_tracker_layers_list.append(('conv3', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
-default_max_tracker_layers_list.append(('conv4', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
-default_max_tracker_layers_list.append(('conv5', 'Convolution', (13,13),   (13,13), (3,3),   (1,1),  (1,1)))
-max_tracker_layers_list = locals().get('max_tracker_layers_list', default_max_tracker_layers_list)
-
 # list of siamese layers/blobs to show
 # note: if an item in the list is a pair of layers, then it is a siamese layer
 layers_list = locals().get('layers_list', [])
@@ -430,25 +401,25 @@
 optimize_image_rand_seed = locals().get('optimize_image_rand_seed', 0)
 
 # decay parameter for optimize_image.py
-optimize_image_decay = locals().get('optimize_image_decay', 0)
+optimize_image_decay = locals().get('optimize_image_decay', 0.0001)
 
 # blur-radius parameter for optimize_image.py
-optimize_image_blur_radius = locals().get('optimize_image_blur_radius', 0)
+optimize_image_blur_radius = locals().get('optimize_image_blur_radius', 1.0)
 
 # blur-every parameter for optimize_image.py
-optimize_image_blue_every = locals().get('optimize_image_blue_every', 0)
+optimize_image_blue_every = locals().get('optimize_image_blue_every', 4)
 
 # lr-policy parameter for optimize_image.py
 optimize_image_lr_policy = locals().get('optimize_image_lr_policy', 'constant')
 
 # lr-params parameter for optimize_image.py
-optimize_image_lr_params = locals().get('optimize_image_lr_params', '{"lr": 1}')
+optimize_image_lr_params = locals().get('optimize_image_lr_params', '{"lr": 100.0}')
 
 # max-iter parameter for optimize_image.py
-optimize_image_max_iters = locals().get('optimize_image_max_iters', [500])
+optimize_image_max_iters = locals().get('optimize_image_max_iters', [1000])
 
 # output-prefix parameter for optimize_image.py
-optimize_image_output_prefix = locals().get('optimize_image_output_prefix', 'outputs/%(p.push_layer)s/unit_%(p.push_channel)04d/opt_%(r.batch_index)03d')
+optimize_image_output_prefix = locals().get('optimize_image_output_prefix', '%(p.push_layer)s/unit_%(p.push_channel)04d/opt_%(r.batch_index)03d_seed%(p.rand_seed)d')
 
 # parameter which marks whether we should generate also the plus mean image of the optmized image
 optimize_image_generate_plus_mean = locals().get('optimize_image_generate_plus_mean', False)

From 162526174d795c19a6a5617a2ea02949b8533f0e Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Thu, 28 Sep 2017 23:29:03 +0300
Subject: [PATCH 086/106] - removed siamese_network_format, replaced with layer
 based information where needed

---
 find_maxes/crop_max_patches.py |  2 +-
 find_maxes/find_max_acts.py    |  2 +-
 find_maxes/max_tracker.py      | 17 +++++++++-------
 optimize/gradient_optimizer.py | 13 ++++++++----
 optimize_image.py              |  2 +-
 settings.py                    |  3 ---
 settings_misc.py               | 11 +++++++++++
 siamese_helper.py              | 36 +++++++++++++++++++++++++++++++++-
 8 files changed, 68 insertions(+), 18 deletions(-)

diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 4ba120c2a..490e077fe 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -47,7 +47,7 @@ def main():
     net, data_mean = load_network(settings)
 
     # validate batch size
-    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+    if settings.is_siamese and settings._calculated_siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
         # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
         assert (settings.max_tracker_batch_size == 1)
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index aa5527b23..daf21c047 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -52,7 +52,7 @@ def main():
     net, data_mean = load_network(settings)
 
     # validate batch size
-    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+    if settings.is_siamese and settings._calculated_siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
         # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
         assert (settings.max_tracker_batch_size == 1)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 111ab7c87..b2080a717 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -34,7 +34,7 @@ def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_fi
                  selected_input_index = None, ii = None, jj = None, recorded_val = None,
                  out_ii_start = None, out_ii_end = None, out_jj_start = None, out_jj_end = None, data_ii_start = None,
                  data_ii_end = None, data_jj_start = None, data_jj_end = None, im = None,
-                 denormalized_layer_name = None, denormalized_top_name = None):
+                 denormalized_layer_name = None, denormalized_top_name = None, layer_format = None):
         self.cc = cc
         self.channel_idx = channel_idx
         self.info_filename = info_filename
@@ -62,7 +62,7 @@ def __init__(self, cc = None, channel_idx = None, info_filename = None, maxim_fi
         self.im = im
         self.denormalized_layer_name = denormalized_layer_name
         self.denormalized_top_name = denormalized_top_name
-
+        self.layer_format = layer_format
 
 
 class InfoFileMetadata(object):
@@ -300,8 +300,10 @@ def update(self, net, image_idx, net_unique_input_source, batch_index):
 
             normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
 
+            layer_format = self.siamese_helper.get_layer_format_by_layer_name(layer_name)
+
             # in siamese network, implemented as pairs of layers, we might need to select one of the images from the siamese pair
-            if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_layer_pair':
+            if self.settings.is_siamese and layer_format == 'siamese_layer_pair':
                 selected_input_index = self.siamese_helper.get_index_of_saved_image_by_layer_name(layer_name)
 
                 if selected_input_index == 0:
@@ -320,7 +322,7 @@ def update(self, net, image_idx, net_unique_input_source, batch_index):
                                                                     net_unique_input_source, layer_name)
 
             # in siamese network, implemented as single layer with batch 2, we might need to select one of the images from the siamese pair
-            elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair':
+            elif self.settings.is_siamese and layer_format == 'siamese_batch_pair':
 
                 assert (self.settings.max_tracker_batch_size == 1)
 
@@ -792,16 +794,17 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                     # denormalized layer name, e.g. from "conv1_1" and selected_input_index=1 to "conv1_1_p"
                     batch[i].denormalized_layer_name = siamese_helper.denormalize_layer_name_for_max_tracker(layer_name, batch[i].selected_input_index)
                     batch[i].denormalized_top_name = layer_name_to_top_name(net, batch[i].denormalized_layer_name)
+                    batch[i].layer_format = siamese_helper.get_layer_format_by_layer_name(layer_name)
 
                     if len(net.blobs[batch[i].denormalized_top_name].data.shape) == 4:
-                        if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+                        if settings.is_siamese and batch[i].layer_format == 'siamese_batch_pair':
                             reproduced_val = net.blobs[batch[i].denormalized_top_name].data[batch[i].selected_input_index, batch[i].channel_idx, batch[i].ii, batch[i].jj]
 
                         else: # normal network, or siamese in siamese_layer_pair format
                             reproduced_val = net.blobs[batch[i].denormalized_top_name].data[i, batch[i].channel_idx, batch[i].ii, batch[i].jj]
 
                     else:
-                        if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+                        if settings.is_siamese and batch[i].layer_format == 'siamese_batch_pair':
                             reproduced_val = net.blobs[batch[i].denormalized_top_name].data[batch[i].selected_input_index, batch[i].channel_idx]
 
                         else:  # normal network, or siamese in siamese_layer_pair format
@@ -830,7 +833,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
                     for i in range(0, batch_index):
                         diffs = net.blobs[batch[i].denormalized_top_name].diff * 0
 
-                        if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+                        if settings.is_siamese and batch[i].layer_format == 'siamese_batch_pair':
                             if diffs.shape[0] == 2:
                                 if len(diffs.shape) == 4:
                                     diffs[batch[i].selected_input_index, batch[i].channel_idx, batch[i].ii, batch[i].jj] = 1.0
diff --git a/optimize/gradient_optimizer.py b/optimize/gradient_optimizer.py
index fb5539359..efe3247aa 100755
--- a/optimize/gradient_optimizer.py
+++ b/optimize/gradient_optimizer.py
@@ -17,6 +17,8 @@
 from caffe_misc import RegionComputer, get_max_data_extent, compute_data_layer_focus_area, extract_patch_from_image, \
     layer_name_to_top_name
 
+from siamese_helper import SiameseHelper
+
 
 class FindParams(object):
     def __init__(self, **kwargs):
@@ -166,6 +168,7 @@ def __init__(self, settings, net, batched_data_mean, labels = None, label_layers
         self.batched_data_mean = batched_data_mean
         self.labels = labels if labels else ['labels not provided' for ii in range(1000)]
         self.label_layers = label_layers if label_layers else list()
+        self.siamese_helper = SiameseHelper(self.settings.layers_list)
         if channel_swap_to_rgb:
             self.channel_swap_to_rgb = array(channel_swap_to_rgb)
         else:
@@ -268,8 +271,10 @@ def _optimize(self, params, x0, prefix_template):
             top_name = layer_name_to_top_name(self.net, params.push_layer)
             acts = self.net.blobs[top_name].data
 
+            layer_format = self.siamese_helper.get_layer_format_by_layer_name(params.push_layer)
+
             # note: no batch support in 'siamese_batch_pair'
-            if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 2:
+            if self.settings.is_siamese and layer_format == 'siamese_batch_pair' and acts.shape[0] == 2:
 
                 if not is_spatial:
                     # promote to 4D
@@ -282,7 +287,7 @@ def _optimize(self, params, x0, prefix_template):
                 # idxmax for conv layer will be like:        (batch,37, 4, 37)
                 obj[0] = acts[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]]
 
-            elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 1:
+            elif self.settings.is_siamese and layer_format == 'siamese_batch_pair' and acts.shape[0] == 1:
 
                 if not is_spatial:
                     # promote to 4D
@@ -337,9 +342,9 @@ def _optimize(self, params, x0, prefix_template):
                 # Promote bc -> bc01
                 diffs = diffs[:,:,np.newaxis,np.newaxis]
 
-            if self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 2:
+            if self.settings.is_siamese and layer_format == 'siamese_batch_pair' and acts.shape[0] == 2:
                 diffs[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
-            elif self.settings.is_siamese and self.settings.siamese_network_format == 'siamese_batch_pair' and acts.shape[0] == 1:
+            elif self.settings.is_siamese and layer_format == 'siamese_batch_pair' and acts.shape[0] == 1:
                 diffs[0, params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
             else:
                 diffs[np.arange(params.batch_size), params.push_unit[0], params.push_unit[1], params.push_unit[2]] = params.push_dir
diff --git a/optimize_image.py b/optimize_image.py
index be7c5c8c6..5a9292876 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -145,7 +145,7 @@ def main():
     net, data_mean = load_network(settings)
 
     # validate batch size
-    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
+    if settings.is_siamese and settings._calculated_siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
         # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
         assert (settings.max_tracker_batch_size == 1)
diff --git a/settings.py b/settings.py
index 6c7cb0ad2..86492b7f9 100644
--- a/settings.py
+++ b/settings.py
@@ -116,9 +116,6 @@
 # is the network loaded a siamese network
 is_siamese = locals().get('is_siamese', False)
 
-# siamese network format, can be either 'siamese_layer_pair' or 'siamese_batch_pair'
-siamese_network_format = locals().get('siamese_network_format', 'siamese_layer_pair')
-
 # siamese input mode, can be either 'concat_channelwise' or 'concat_along_width'
 siamese_input_mode = locals().get('siamese_input_mode', 'concat_channelwise')
 
diff --git a/settings_misc.py b/settings_misc.py
index ddd1007ca..74e10f5d0 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -9,6 +9,7 @@
 def deduce_calculated_settings(settings, net):
     set_calculated_is_gray_model(settings, net)
     set_calculated_image_dims(settings, net)
+    set_calculated_siamese_network_format(settings)
     read_network_dag(settings)
 
 
@@ -38,6 +39,16 @@ def set_calculated_image_dims(settings, net):
         settings._calculated_image_dims = input_shape[2:4]
 
 
+def set_calculated_siamese_network_format(settings):
+
+    settings._calculated_siamese_network_format = 'normal'
+
+    for layer_def in settings.layers_list:
+        if layer_def['format'] != 'normal':
+            settings._calculated_siamese_network_format = layer_def['format']
+            return
+
+
 def process_network_proto(settings):
 
     settings._processed_deploy_prototxt = settings.caffevis_deploy_prototxt + ".processed_by_deepvis"
diff --git a/siamese_helper.py b/siamese_helper.py
index d57dc600c..3b0d403d4 100644
--- a/siamese_helper.py
+++ b/siamese_helper.py
@@ -21,11 +21,13 @@ def __init__(self, layers_list):
         self.layer_name_to_normalized_layer_name = dict()
         self.normalized_layer_name_to_denormalized_layer_name = dict()
         self.layer_name_to_index_of_saved_image = dict()
+        self.layer_name_to_format = dict()
 
         # init dictionaries
         self._init_layer_name_to_normalized_layer_name()
         self._init_normalized_layer_name_to_denormalized_layer_name()
         self._init_layer_name_to_index_of_saved_image()
+        self._init_layer_name_to_format()
 
         return
 
@@ -94,11 +96,30 @@ def _init_layer_name_to_index_of_saved_image(self):
                 self.layer_name_to_index_of_saved_image[layer_names[1]] = 1
 
             elif layer_format == 'siamese_batch_pair':
-                # raise NotImplementedError()
                 self.layer_name_to_index_of_saved_image[layer_names] = -1
 
         return
 
+    def _init_layer_name_to_format(self):
+        '''
+        init layer_name_to_format dictionary
+        :return: none
+        '''
+
+        for layer_def in self.layers_list:
+            layer_format = layer_def['format']
+            layer_names = layer_def['name/s']
+
+            if layer_format == 'normal':
+                self.layer_name_to_format[layer_names] = layer_format
+
+            elif layer_format == 'siamese_layer_pair':
+                self.layer_name_to_format[layer_names[0]] = layer_format
+                self.layer_name_to_format[layer_names[1]] = layer_format
+
+            elif layer_format == 'siamese_batch_pair':
+                self.layer_name_to_format[layer_names] = layer_format
+
     def normalize_layer_name_for_max_tracker(self, layer_name):
         '''
         function used to normalize layer name, e.g. 'conv1' and 'conv1_p' will be normalized to 'conv1', given suitable
@@ -146,6 +167,19 @@ def get_index_of_saved_image_by_layer_name(self, layer_name):
         # this can happen for layer names which don't appear in the layers_list setting
         return -1
 
+    def get_layer_format_by_layer_name(self, layer_name):
+        '''
+        function which returns the layer format given the layer name
+        :param layer_name: layer name
+        :return: layer format
+        '''
+
+        if self.layer_name_to_format.has_key(layer_name):
+            return self.layer_name_to_format[layer_name]
+
+        # fallback to normal format
+        return 'normal'
+
     @staticmethod
     def get_header_from_layer_def(layer_def):
         '''

From 8a7188a29844767048ba4bc00ec4453cea92586f Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 00:00:04 +0300
Subject: [PATCH 087/106] - added layer size and receptive field to status line
 - added input file names to second status line - added model name to window
 title - changed inactivity graph for layer, to activity graph - rearrange
 help screen - added setting for gpu id - removed is_conv_fn - added helper
 scripts to run image generation in one line - added code to set batch size to
 1 in deepvis ui - show_maximal_score is now default on - deduce automatic
 default value for caffe_net_channel_swap from network proto - removed
 caffevis_score_layers setting, value is now deduce automatically

---
 app_base.py                    |  2 +-
 caffe_misc.py                  |  6 ++-
 caffevis/app.py                | 93 ++++++++++++++++++++++------------
 caffevis/caffevis_app_state.py | 24 ++++++++-
 find_maxes/crop_max_patches.py |  1 +
 find_maxes/find_max_acts.py    |  1 +
 find_maxes/max_tracker.py      | 10 ++--
 generate_max_input_images.sh   |  5 ++
 generate_synthesized_images.sh |  4 ++
 input_fetcher.py               |  2 +-
 live_vis.py                    | 15 ++----
 misc.py                        |  4 +-
 optimize_image.py              |  2 +-
 settings.py                    | 16 +++---
 settings_misc.py               | 57 ++++++++++++++++++---
 siamese_helper.py              | 15 ++++++
 16 files changed, 188 insertions(+), 69 deletions(-)
 create mode 100755 generate_max_input_images.sh
 create mode 100755 generate_synthesized_images.sh

diff --git a/app_base.py b/app_base.py
index 0e0fd8ac6..aefc9fcc7 100644
--- a/app_base.py
+++ b/app_base.py
@@ -6,7 +6,7 @@ class BaseApp(object):
     def __init__(self, settings, key_bindings):
         self.debug_level = 0
 
-    def handle_input(self, input_image, input_label, panes):
+    def handle_input(self, input_image, input_label, input_filename, panes):
         pass
         
     def handle_key(self, key, panes):
diff --git a/caffe_misc.py b/caffe_misc.py
index dcb01177e..ac9ef07bc 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -151,8 +151,12 @@ def save_caffe_image(img, filename, autoscale = True, autoscale_center = None):
 
 
 def layer_name_to_top_name(net, layer_name):
-    return net.top_names[layer_name][0]
 
+    if net.top_names.has_key(layer_name) and len(net.top_names[layer_name]) >= 1:
+        return net.top_names[layer_name][0]
+
+    else:
+        return None
 
 def get_max_data_extent(net, settings, layer_name, is_spatial):
     '''Gets the maximum size of the data layer that can influence a unit on layer.'''
diff --git a/caffevis/app.py b/caffevis/app.py
index 671811e8d..6d7581185 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -42,15 +42,21 @@ def __init__(self, settings, key_bindings):
         self.settings = settings
         self.bindings = key_bindings
 
-        self._net_channel_swap = settings.caffe_net_channel_swap
+        self.net, self._data_mean = load_network(settings)
+
+        # set network batch size to 1
+        current_input_shape = self.net.blobs[self.net.inputs[0]].shape
+        current_input_shape[0] = 1
+        self.net.blobs[self.net.inputs[0]].reshape(*current_input_shape)
+        self.net.reshape()
+
+        self._net_channel_swap = settings._calculated_channel_swap
 
         if self._net_channel_swap is None:
             self._net_channel_swap_inv = None
         else:
             self._net_channel_swap_inv = tuple([self._net_channel_swap.index(ii) for ii in range(len(self._net_channel_swap))])
 
-        self.net, self._data_mean = load_network(settings)
-
         check_force_backward_true(settings.caffevis_deploy_prototxt)
 
         self.labels = None
@@ -110,7 +116,7 @@ def quit(self):
     def _can_skip_all(self, panes):
         return ('caffevis_layers' not in panes.keys())
         
-    def handle_input(self, input_image, input_label, panes):
+    def handle_input(self, input_image, input_label, input_filename, panes):
         if self.debug_level > 1:
             print 'handle_input: frame number', self.handled_frames, 'is', 'None' if input_image is None else 'Available'
         self.handled_frames += 1
@@ -122,6 +128,7 @@ def handle_input(self, input_image, input_label, panes):
                 print 'CaffeVisApp.handle_input: pushed frame'
             self.state.next_frame = input_image
             self.state.next_label = input_label
+            self.state.next_filename = input_filename
             if self.debug_level > 1:
                 print 'CaffeVisApp.handle_input: caffe_net_state is:', self.state.caffe_net_state
 
@@ -245,6 +252,7 @@ def _draw_status_pane(self, pane):
         loc = self.settings.caffevis_status_loc[::-1]   # Reverse to OpenCV c,r order
 
         status = StringIO.StringIO()
+        status2 = StringIO.StringIO()
         fps = self.proc_thread.approx_fps()
         with self.state.lock:
             pattern_first_mode = "first" if self.state.pattern_first_only else "all"
@@ -279,13 +287,22 @@ def _draw_status_pane(self, pane):
 
             if self.state.extra_msg:
                 print >>status, '|', self.state.extra_msg
-                self.state.extra_msg = ''
 
-        strings = [FormattedString(line, defaults) for line in status.getvalue().split('\n')]
+            print >> status2, 'Layer size: %s' % (self.state.get_layer_output_size_string())
 
-        cv2_typeset_text(pane.data, strings, loc,
+            print >> status2, '| Receptive field:', '%s' % str(self.settings._receptive_field_per_layer[default_layer_name]) if self.settings._receptive_field_per_layer.has_key(default_layer_name) else 'N/A'
+
+            print >> status2, '| Input: %s' % (str(self.state.next_filename))
+
+        strings_line1 = [FormattedString(line, defaults) for line in status.getvalue().split('\n')]
+        strings_line2 = [FormattedString(line, defaults) for line in status2.getvalue().split('\n')]
+
+        locy = cv2_typeset_text(pane.data, strings_line1, (loc[0], loc[1] + 5),
                          line_spacing = self.settings.caffevis_status_line_spacing)
 
+        locy = cv2_typeset_text(pane.data, strings_line2, (loc[0], locy),
+                         line_spacing=self.settings.caffevis_status_line_spacing)
+
     def prepare_tile_image(self, display_3D, highlight_selected, n_tiles, tile_rows, tile_cols):
 
         if self.state.layers_show_back and self.state.pattern_mode == PatternMode.OFF:
@@ -487,9 +504,8 @@ def _display_pane_based_on_zoom_mode(self, display_2D, display_3D_highres, is_la
         pane.data[0:display_2D_resize.shape[0], 0:display_2D_resize.shape[1], :] = display_2D_resize
 
     def _add_label_or_score_overlay(self, default_layer_name, pane):
-        # check if label or score should be presented for the selected layer
-        if (default_layer_name in self.settings.caffevis_label_layers and self.state.cursor_area == 'bottom') or \
-                (default_layer_name in self.settings.caffevis_score_layers and self.state.cursor_area == 'bottom'):
+
+        if self.state.cursor_area == 'bottom':
 
             # Display label annotation atop layers pane (e.g. for fc8/prob)
             defaults = {'face': getattr(cv2, self.settings.caffevis_label_face),
@@ -502,27 +518,34 @@ def _add_label_or_score_overlay(self, default_layer_name, pane):
             if (self.labels) and (default_layer_name in self.settings.caffevis_label_layers):
                 text_to_display = self.labels[self.state.selected_unit] + " "
 
-            if (default_layer_name in self.settings.caffevis_score_layers):
-
+            if self.state.show_maximal_score:
                 if self.state.siamese_view_mode_has_two_images():
                     if self.state.layers_show_back:
                         blob1, blob2 = self.state.get_siamese_selected_diff_blobs(self.net)
-                        value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
-                        text_to_display += 'grad: ' + str(value1) + " " + str(value2)
+
+                        if len(blob1.shape) == 1:
+                            value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
+                            text_to_display += 'grad: ' + str(value1) + " " + str(value2)
                     else:
                         blob1, blob2 = self.state.get_siamese_selected_data_blobs(self.net)
-                        value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
-                        text_to_display += 'act: ' + str(value1) + " " + str(value2)
+
+                        if len(blob1.shape) == 1:
+                            value1, value2 = blob1[self.state.selected_unit], blob2[self.state.selected_unit]
+                            text_to_display += 'act: ' + str(value1) + " " + str(value2)
 
                 else:
                     if self.state.layers_show_back:
                         blob = self.state.get_single_selected_diff_blob(self.net)
-                        value = blob[self.state.selected_unit]
-                        text_to_display += 'grad: ' + str(value)
+
+                        if len(blob.shape) == 1:
+                            value = blob[self.state.selected_unit]
+                            text_to_display += 'grad: ' + str(value)
                     else:
                         blob = self.state.get_single_selected_data_blob(self.net)
-                        value = blob[self.state.selected_unit]
-                        text_to_display += 'act: ' + str(value)
+
+                        if len(blob.shape) == 1:
+                            value = blob[self.state.selected_unit]
+                            text_to_display += 'act: ' + str(value)
 
             lines = [FormattedString(text_to_display, defaults)]
             cv2_typeset_text(pane.data, lines, loc_base)
@@ -966,6 +989,9 @@ def get_processed_selected_unit(self, all_units, resize_shape, use_colored_data
             unit_data_resize = resize_without_fit(unit_data, resize_shape)
 
         if self.state.pattern_mode == PatternMode.OFF:
+            if self.state.last_frame is None:
+                pass
+
             input_image = SiameseHelper.get_image_from_frame(self.state.last_frame, self.state.settings.is_siamese,
                                                              resize_shape, self.state.siamese_view_mode)
             normalized_mask = unit_data_resize
@@ -1305,8 +1331,7 @@ def draw_help(self, help_pane, locy):
         locx = loc_base[0]
 
         lines = []
-        lines.append([FormattedString('', defaults)])
-        lines.append([FormattedString('Caffevis keys', defaults)])
+        lines.append([FormattedString('DeepVis keys', defaults)])
         
         kl,_ = self.bindings.get_key_help('sel_left')
         kr,_ = self.bindings.get_key_help('sel_right')
@@ -1326,15 +1351,21 @@ def draw_help(self, help_pane, locy):
         nav_string = 'Navigate with %s%s. Use %s to move faster.' % (keys_nav_0, keys_nav_1, keys_nav_f)
         lines.append([FormattedString('', defaults, width=120, align='right'),
                       FormattedString(nav_string, defaults)])
-            
-        for tag in ('sel_layer_left', 'sel_layer_right', 'zoom_mode', 'next_pattern_mode','pattern_first_only',
-                    'next_input_overlay', 'next_ez_back_mode_loop', 'next_back_view_option', 'next_color_map',
-                    'freeze_back_unit', 'show_back', 'boost_gamma', 'boost_individual', 'siamese_view_mode',
-                    'toggle_maximal_score', 'reset_state'):
-            key_strings, help_string = self.bindings.get_key_help(tag)
-            label = '%10s:' % (','.join(key_strings))
-            lines.append([FormattedString(label, defaults, width=120, align='right'),
-                          FormattedString(help_string, defaults)])
+
+        for tag in ('help_mode', 'static_file_increment', 'static_file_decrement', 'sel_layer_left', 'sel_layer_right',
+                    '', 'next_pattern_mode', 'pattern_first_only', '', 'next_input_overlay', 'next_ez_back_mode_loop',
+                    'next_back_view_option', 'next_color_map', '', 'freeze_back_unit', 'show_back', 'zoom_mode',
+                    'siamese_view_mode', 'toggle_maximal_score', 'boost_gamma', 'boost_individual', 'freeze_cam',
+                    'toggle_input_mode', 'stretch_mode', '', 'reset_state', 'quit'):
+
+            if (tag == ''):
+                lines.append([FormattedString('', defaults)])
+
+            else:
+                key_strings, help_string = self.bindings.get_key_help(tag)
+                label = '%10s:' % (','.join(key_strings))
+                lines.append([FormattedString(label, defaults, width=120, align='right'),
+                              FormattedString(help_string, defaults)])
 
         locy = cv2_typeset_text(help_pane.data, lines, (locx, locy),
                                 line_spacing = self.settings.help_line_spacing)
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 6d17e28b6..2edf089de 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -124,6 +124,7 @@ def __init__(self, net, settings, bindings):
         self.back_stale = True       # back becomes stale whenever the last back diffs were not computed using the current backprop unit and method (bprop or deconv)
         self.next_frame = None
         self.next_label = None
+        self.next_filename = None
         self.last_frame = None
         self.jpgvis_to_load_key = None
         self.last_key_at = 0
@@ -162,7 +163,7 @@ def get_headers(self):
         return headers
 
     def _reset_user_state(self):
-        self.show_maximal_score = False
+        self.show_maximal_score = True
         self.input_overlay_option = InputOverlayOption.OFF
         self.layer_idx = 0
         self.layer_boost_indiv_idx = self.settings.caffevis_boost_indiv_default_idx
@@ -495,3 +496,24 @@ def gray_to_colormap(self, gray_image):
 
     def convert_image_pair_to_network_input_format(self, settings, frame_pair, resize_shape):
         return SiameseHelper.convert_image_pair_to_network_input_format(frame_pair, resize_shape, settings.siamese_input_mode)
+
+    def get_layer_output_size(self, layer_def = None):
+
+        # if no layer specified, get current layer
+        if layer_def is None:
+            layer_def = self.get_current_layer_definition()
+
+        return SiameseHelper.get_layer_output_size(self.net, self.settings.is_siamese, layer_def, self.siamese_view_mode)
+
+    def get_layer_output_size_string(self, layer_def=None):
+
+        layer_output_size = self.get_layer_output_size(layer_def)
+
+        if len(layer_output_size) == 1:
+            return '(%d)' % (layer_output_size[0])
+        elif len(layer_output_size) == 2:
+            return '(%d,%d)' % (layer_output_size[0],layer_output_size[1])
+        elif len(layer_output_size) == 3:
+            return '(%d,%d,%d)' % (layer_output_size[0], layer_output_size[1], layer_output_size[2])
+        else:
+            return str(layer_output_size)
\ No newline at end of file
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 490e077fe..af7fb931c 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -56,6 +56,7 @@ def main():
     current_input_shape = net.blobs[net.inputs[0]].shape
     current_input_shape[0] = settings.max_tracker_batch_size
     net.blobs[net.inputs[0]].reshape(*current_input_shape)
+    net.reshape()
 
     assert args.do_maxes or args.do_deconv or args.do_deconv_norm or args.do_backprop or args.do_backprop_norm or args.do_info, 'Specify at least one do_* option to output.'
 
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index daf21c047..9ba5319d8 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -61,6 +61,7 @@ def main():
     current_input_shape = net.blobs[net.inputs[0]].shape
     current_input_shape[0] = settings.max_tracker_batch_size
     net.blobs[net.inputs[0]].reshape(*current_input_shape)
+    net.reshape()
 
     with WithTimer('Scanning images'):
         if settings.is_siamese:
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index b2080a717..bb51f2792 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -106,6 +106,7 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
 
                 break
 
+        fig.suptitle('max activations histgoram of layer %s channel %d' % (layer_name,channel_idx))
         ax.xaxis.label.set_text('max activation value')
         ax.yaxis.label.set_text('inputs count')
 
@@ -115,13 +116,13 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
 
     # generate histogram for layer
     num_bins = 20
-    hist, bin_edges = np.histogram(percent_dead, bins=num_bins, range=(0, 100))
+    hist, bin_edges = np.histogram(100 - percent_dead, bins=num_bins, range=(0, 100))
     width = 0.7 * (bin_edges[1] - bin_edges[0])
     center = (bin_edges[:-1] + bin_edges[1:]) / 2
 
     bar_colors = [None] * num_bins
-    begin_color = np.array([0, 1.0, 0])
-    end_color = np.array([1.0, 0, 0])
+    begin_color = np.array([1.0, 0, 0])
+    end_color = np.array([0, 1.0, 0])
     color_step = (end_color - begin_color) / (num_bins - 1)
     current_color = begin_color
     for i in range(num_bins):
@@ -130,7 +131,8 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
 
     ax.bar(center, hist, align='center', width=width, color=bar_colors)
 
-    ax.xaxis.label.set_text('percent of inactivity')
+    fig.suptitle('activity of layer %s' % (layer_name))
+    ax.xaxis.label.set_text('activity percent')
     ax.yaxis.label.set_text('channels count')
 
     process_layer_figure(fig)
diff --git a/generate_max_input_images.sh b/generate_max_input_images.sh
new file mode 100755
index 000000000..c52f51f6b
--- /dev/null
+++ b/generate_max_input_images.sh
@@ -0,0 +1,5 @@
+#!/usr/bin/env bash
+
+./find_maxes/find_max_acts.py
+./find_maxes/crop_max_patches.py
+
diff --git a/generate_synthesized_images.sh b/generate_synthesized_images.sh
new file mode 100755
index 000000000..831e9cbb8
--- /dev/null
+++ b/generate_synthesized_images.sh
@@ -0,0 +1,4 @@
+#!/usr/bin/env bash
+
+./optimize_image.py
+
diff --git a/input_fetcher.py b/input_fetcher.py
index 3ba18309b..fdd6b21cf 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -164,7 +164,7 @@ def get_frame(self):
         is not valid.
         '''
         with self.lock:
-            return (self.latest_frame_idx, self.latest_frame_data, self.latest_label)
+            return (self.latest_frame_idx, self.latest_frame_data, self.latest_label, self.latest_static_filename)
 
     def increment_static_file_idx(self, amount = 1):
         with self.lock:
diff --git a/live_vis.py b/live_vis.py
index 24b2b9233..e018c5629 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -61,7 +61,7 @@ def __init__(self, settings):
             app = app_class(settings, self.bindings)
             self.apps[app_name] = app
         self.help_mode = False
-        self.window_name = 'Deep Visualization Toolbox'
+        self.window_name = 'Deep Visualization Toolbox    |    Model: %s' % (settings.model_to_load)
         self.quit = False
         self.debug_level = 0
 
@@ -189,12 +189,13 @@ def run_loop(self):
                 redraw_needed |= app.redraw_needed()
 
             # Grab latest frame from input_updater thread
-            fr_idx,fr_data,fr_label = self.input_updater.get_frame()
+            fr_idx,fr_data,fr_label,fr_filename = self.input_updater.get_frame()
             is_new_frame = (fr_idx != latest_frame_idx and fr_data is not None)
             if is_new_frame:
                 latest_frame_idx = fr_idx
                 latest_frame_data = fr_data
                 latest_label = fr_label
+                latest_filename = fr_filename
                 frame_for_apps = fr_data
 
             if is_new_frame:
@@ -208,7 +209,7 @@ def run_loop(self):
                 # Pass frame to apps for processing
                 for app_name, app in self.apps.iteritems():
                     with WithTimer('%s:handle_input' % app_name, quiet = self.debug_level < 1):
-                        app.handle_input(latest_frame_data, latest_label, self.panes)
+                        app.handle_input(latest_frame_data, latest_label, latest_filename, self.panes)
                 frame_for_apps = None
 
             # Tell each app to draw
@@ -344,14 +345,6 @@ def draw_help(self):
         defaults = self.help_pane_defaults
         lines = []
         lines.append([FormattedString('~ ~ ~ Deep Visualization Toolbox ~ ~ ~', defaults, align='center', width=self.help_pane.j_size)])
-        lines.append([FormattedString('', defaults)])
-        lines.append([FormattedString('Base keys', defaults)])
-
-        for tag in ('help_mode', 'freeze_cam', 'toggle_input_mode', 'static_file_increment', 'static_file_decrement', 'stretch_mode', 'quit'):
-            key_strings, help_string = self.bindings.get_key_help(tag)
-            label = '%10s:' % (','.join(key_strings))
-            lines.append([FormattedString(label, defaults, width=120, align='right'),
-                          FormattedString(help_string, defaults)])
 
         locy = cv2_typeset_text(self.help_pane.data, lines, loc,
                                 line_spacing = self.settings.help_line_spacing)
diff --git a/misc.py b/misc.py
index ba155d299..eb60869f3 100644
--- a/misc.py
+++ b/misc.py
@@ -84,7 +84,7 @@ def get_files_from_image_list(settings):
         lines = image_list_file.readlines()
         # take first token from each line
         available_files = [tsplit(line, True, ' ', ',', '\t')[0] for line in lines if line.strip() != ""]
-        labels = [tsplit(line, True, ' ', ',', '\t')[1] for line in lines if line.strip() != ""]
+        labels = [tsplit(line, True, ' ', ',', '\t')[1].strip() for line in lines if line.strip() != ""]
 
     return available_files, labels
 
@@ -100,7 +100,7 @@ def get_files_from_siamese_image_list(settings):
         # take first and second tokens from each line
         available_files = [(tsplit(line, True, ' ', ',', '\t')[0], tsplit(line, True, ' ', ',', '\t')[1])
                            for line in lines if line.strip() != ""]
-        labels = [tsplit(line, True, ' ', ',', '\t')[2] for line in lines if line.strip() != ""]
+        labels = [tsplit(line, True, ' ', ',', '\t')[2].strip() for line in lines if line.strip() != ""]
 
     return available_files, labels
 
diff --git a/optimize_image.py b/optimize_image.py
index 5a9292876..be7c5c8c6 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -145,7 +145,7 @@ def main():
     net, data_mean = load_network(settings)
 
     # validate batch size
-    if settings.is_siamese and settings._calculated_siamese_network_format == 'siamese_batch_pair':
+    if settings.is_siamese and settings.siamese_network_format == 'siamese_batch_pair':
         # currently, no batch support for siamese_batch_pair networks
         # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
         assert (settings.max_tracker_batch_size == 1)
diff --git a/settings.py b/settings.py
index 86492b7f9..2ad2f6d32 100644
--- a/settings.py
+++ b/settings.py
@@ -76,7 +76,7 @@
     ('input',            (  0,    0,  300,   300)),    # This pane is required to show the input picture
     ('caffevis_aux',     (300,    0,  300,   300)),
     ('caffevis_back',    (600,    0,  300,   300)),
-    ('caffevis_status',  (900,    0,   30,  1500)),
+    ('caffevis_status',  (900,    0,  20*2 + 10,  1500)),
     ('caffevis_control', (  0,  300,   control_pane_height,   900)),
     ('caffevis_layers',  ( control_pane_height,  300,  900-control_pane_height,   900)),
     ('caffevis_jpgvis',  (  0, 1200,  900,   300)),
@@ -178,6 +178,9 @@
 # Whether to use GPU mode (if True) or CPU mode (if False)
 caffevis_mode_gpu = locals().get('caffevis_mode_gpu', True)
 
+# ID of GPU to use, default is 0
+caffevis_gpu_id = locals().get('caffevis_gpu_id', 0)
+
 # Data mean, if any, to be subtracted from input image file / webcam
 # image. Specify as string path to file or tuple of one value per
 # channel or None.
@@ -195,9 +198,6 @@
 # (when those neurons are selected). None to disable.
 caffevis_label_layers = locals().get('caffevis_label_layers', [])
 
-# Which layers should we print the channel score. makes sense for fully-connected layers. None to disable.
-caffevis_score_layers = locals().get('caffevis_score_layers', [])
-
 # Which layer to use for displaying class output numbers in left pane
 # (when no neurons are selected). None to disable.
 caffevis_prob_layer = locals().get('caffevis_prob_layer', None)
@@ -344,8 +344,9 @@
 # folder for generating and reading deep vis outputs
 caffevis_outputs_dir = locals().get('caffevis_outputs_dir', '.')
 
-# caffe net parameter - channel swap
-caffe_net_channel_swap = locals().get('caffe_net_channel_swap', (2,1,0))
+# caffe net parameter - channel swap, default is None which will make automatic decision according to other settings
+# the automatic setting is either (2,1,0) or (2,1,0,5,4,3) according to is_siamese value and siamese_input_mode
+caffe_net_channel_swap = locals().get('caffe_net_channel_swap', None)
 
 # caffe net parameter - transpose, used to convert HxWxK to KxHxW, when None uses caffe default which is (2,0,1)
 # this parameter should rarely change
@@ -360,9 +361,6 @@
 # caffe net parameter - image dims
 caffe_net_image_dims = locals().get('caffe_net_image_dims', None)
 
-# function used to check if a layer is a convolutional
-is_conv_fn = locals().get('is_conv_fn', lambda layer_name: 'conv' in layer_name)
-
 # default value for do_maxes parameter in max_tracker
 max_tracker_do_maxes = locals().get('max_tracker_do_maxes', True)
 
diff --git a/settings_misc.py b/settings_misc.py
index 74e10f5d0..1681317fd 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -4,13 +4,18 @@
 sys.path.insert(0,parentdir)
 
 from caffevis.caffevis_helper import set_mean
+from caffe_misc import layer_name_to_top_name, get_max_data_extent
+
+def deduce_calculated_settings_without_network(settings):
+    set_calculated_siamese_network_format(settings)
+    set_calculated_channel_swap(settings)
+    read_network_dag(settings)
 
 
-def deduce_calculated_settings(settings, net):
+def deduce_calculated_settings_with_network(settings, net):
     set_calculated_is_gray_model(settings, net)
     set_calculated_image_dims(settings, net)
-    set_calculated_siamese_network_format(settings)
-    read_network_dag(settings)
+    set_receptive_field_per_layer(settings, net)
 
 
 def set_calculated_is_gray_model(settings, net):
@@ -49,6 +54,39 @@ def set_calculated_siamese_network_format(settings):
             return
 
 
+def set_calculated_channel_swap(settings):
+
+    if settings.caffe_net_channel_swap is not None:
+        settings._calculated_channel_swap = settings.caffe_net_channel_swap
+
+    else:
+        if settings.is_siamese and settings.siamese_input_mode == 'concat_channelwise':
+            settings._calculated_channel_swap = (2, 1, 0, 5, 4, 3)
+
+        else:
+            settings._calculated_channel_swap = (2, 1, 0)
+
+
+def set_receptive_field_per_layer(settings, net):
+
+    print "Calculating receptive fields for all layers..."
+
+    receptive_field_per_layer = dict()
+
+    for layer_name in settings._layer_name_to_record.keys():
+
+        top_name = layer_name_to_top_name(net, layer_name)
+        if top_name is not None:
+            blob = net.blobs[top_name].data
+            is_spatial = (len(blob.shape) == 4)
+            layer_receptive_field = get_max_data_extent(net, settings, layer_name, is_spatial)
+            receptive_field_per_layer[layer_name] = layer_receptive_field
+
+    settings._receptive_field_per_layer = receptive_field_per_layer
+
+    print "Calculating receptive fields for all layers... Done."
+
+
 def process_network_proto(settings):
 
     settings._processed_deploy_prototxt = settings.caffevis_deploy_prototxt + ".processed_by_deepvis"
@@ -85,15 +123,20 @@ def load_network(settings):
     # main thread; it is also separately set in CaffeProcThread.
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
     import caffe
+
     if settings.caffevis_mode_gpu:
         caffe.set_mode_gpu()
-        print 'CaffeVisApp mode (in main thread):     GPU'
+        caffe.set_device(settings.caffevis_gpu_id)
+        print 'Loaded caffe in GPU mode, using device', settings.caffevis_gpu_id
+
     else:
         caffe.set_mode_cpu()
-        print 'CaffeVisApp mode (in main thread):     CPU'
+        print 'Loaded caffe in CPU mode'
 
     process_network_proto(settings)
 
+    deduce_calculated_settings_without_network(settings)
+
     net = caffe.Classifier(
         settings._processed_deploy_prototxt,
         settings.caffevis_network_weights,
@@ -101,9 +144,9 @@ def load_network(settings):
         mean=None,  # Set to None for now, assign later
         input_scale=settings.caffe_net_input_scale,
         raw_scale=settings.caffe_net_raw_scale,
-        channel_swap=settings.caffe_net_channel_swap)
+        channel_swap=settings._calculated_channel_swap)
 
-    deduce_calculated_settings(settings, net)
+    deduce_calculated_settings_with_network(settings, net)
 
     if settings.caffe_net_transpose:
         net.transformer.set_transpose(net.inputs[0], settings.caffe_net_transpose)
diff --git a/siamese_helper.py b/siamese_helper.py
index 3b0d403d4..ffc3aba40 100644
--- a/siamese_helper.py
+++ b/siamese_helper.py
@@ -586,3 +586,18 @@ def convert_image_pair_to_network_input_format(frame_pair, resize_shape, siamese
             im_small = np.concatenate((im_small1, im_small2), axis=1)
 
         return im_small
+
+    @staticmethod
+    def get_layer_output_size(net, is_siamese, layer_def, siamese_view_mode):
+
+        if (layer_def['format'] == 'siamese_batch_pair') or (layer_def['format'] == 'siamese_layer_pair' and siamese_view_mode == SiameseViewMode.BOTH_IMAGES):
+
+            data0, data1 = SiameseHelper.get_siamese_selected_data_blobs(net, layer_def, siamese_view_mode)
+            return data0.shape
+
+        else:
+
+            data = SiameseHelper.get_single_selected_data_blob(net, layer_def, siamese_view_mode)
+            return data.shape
+
+        pass
\ No newline at end of file

From d8b86fdc2cb63edcb6a73e8283d28e902c93823c Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:07:01 +0300
Subject: [PATCH 088/106] make control pane height be determined automatically

---
 caffevis/app.py  |  7 ++++++-
 live_vis.py      | 37 +++++++++++++++++++++++++++++++++----
 settings.py      | 17 +++++++++--------
 settings_misc.py |  2 ++
 4 files changed, 50 insertions(+), 13 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 6d7581185..3081757e4 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -238,10 +238,15 @@ def _draw_control_pane(self, pane):
                         fs.thick = self.settings.caffevis_control_thick_selected
             strings.append(fs)
 
-        cv2_typeset_text(pane.data, strings, loc,
+        locy = cv2_typeset_text(pane.data, strings, loc,
                          line_spacing = self.settings.caffevis_control_line_spacing,
                          wrap = True)
 
+        if hasattr(self.settings, 'control_pane_height'):
+            self.settings._calculated_control_pane_height = self.settings.control_pane_height
+        else:
+            self.settings._calculated_control_pane_height = locy - loc[1] + 4
+
     def _draw_status_pane(self, pane):
         pane.data[:] = to_255(self.settings.window_background)
 
diff --git a/live_vis.py b/live_vis.py
index e018c5629..04f38bc20 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -24,20 +24,20 @@ class ImproperlyConfigured(Exception):
     pass
 
 
-
-
 class Pane(object):
     '''Hold info about one window pane (rectangular region within the main window)'''
 
     def __init__(self, i_begin, j_begin, i_size, j_size):
+        self.reset(i_begin, j_begin, i_size, j_size)
+
+    def reset(self, i_begin, j_begin, i_size, j_size):
         self.i_begin = i_begin
         self.j_begin = j_begin
         self.i_size = i_size
         self.j_size = j_size
         self.i_end = i_begin + i_size
         self.j_end = j_begin + j_size
-        self.data = None    # eventually contains a slice of the window buffer
-
+        self.data = None  # eventually contains a slice of the window buffer
 
 
 class LiveVis(object):
@@ -114,6 +114,33 @@ def init_window(self):
         self.help_buffer = self.window_buffer.copy() # For rendering help mode
         self.help_pane.data = self.help_buffer[self.help_pane.i_begin:self.help_pane.i_end, self.help_pane.j_begin:self.help_pane.j_end]
 
+    def check_for_control_height_update(self):
+
+        if hasattr(self.settings, '_calculated_control_pane_height') and \
+           self.settings._calculated_control_pane_height != self.panes['caffevis_control'].i_size:
+
+            self.panes['caffevis_control'].reset(
+                self.settings.window_panes[4][1][0],
+                self.settings.window_panes[4][1][1],
+                self.settings._calculated_control_pane_height,
+                self.settings.window_panes[4][1][3])
+
+            self.panes['caffevis_layers'].reset(
+                self.settings._calculated_control_pane_height,
+                self.settings.window_panes[5][1][1],
+                self.settings.window_panes[5][1][2] + 3*20 - self.settings._calculated_control_pane_height,
+                self.settings.window_panes[5][1][3])
+
+            for _, pane in self.panes.iteritems():
+                pane.data = self.window_buffer[pane.i_begin:pane.i_end, pane.j_begin:pane.j_end]
+
+            return True
+
+        else:
+            return False
+
+        pass
+
     def run_loop(self):
         self.quit = False
         # Setup
@@ -188,6 +215,8 @@ def run_loop(self):
             for app_name, app in self.apps.iteritems():
                 redraw_needed |= app.redraw_needed()
 
+            redraw_needed |= self.check_for_control_height_update()
+
             # Grab latest frame from input_updater thread
             fr_idx,fr_data,fr_label,fr_filename = self.input_updater.get_frame()
             is_new_frame = (fr_idx != latest_frame_idx and fr_data is not None)
diff --git a/settings.py b/settings.py
index 2ad2f6d32..d7f8bf6ca 100644
--- a/settings.py
+++ b/settings.py
@@ -69,17 +69,18 @@
 if 'default_window_panes' in locals():
     raise Exception('Override window panes in settings_MODEL.py by defining window_panes, not default_window_panes')
 
-control_pane_height = locals().get('control_pane_height', 4*20)
+# set the following member to make control pane height constant, instead of automatic
+# control_pane_height = locals().get('control_pane_height', 3*20)
 
 default_window_panes = (
     # (i, j, i_size, j_size)
-    ('input',            (  0,    0,  300,   300)),    # This pane is required to show the input picture
-    ('caffevis_aux',     (300,    0,  300,   300)),
-    ('caffevis_back',    (600,    0,  300,   300)),
-    ('caffevis_status',  (900,    0,  20*2 + 10,  1500)),
-    ('caffevis_control', (  0,  300,   control_pane_height,   900)),
-    ('caffevis_layers',  ( control_pane_height,  300,  900-control_pane_height,   900)),
-    ('caffevis_jpgvis',  (  0, 1200,  900,   300)),
+    ('input',            (   0,    0,       300,   300)),    # This pane is required to show the input picture
+    ('caffevis_aux',     ( 300,    0,       300,   300)),
+    ('caffevis_back',    ( 600,    0,       300,   300)),
+    ('caffevis_status',  ( 900,    0, 2*20 + 10,  1500)),
+    ('caffevis_control', (   0,  300,      3*20,   900)),
+    ('caffevis_layers',  (3*20,  300,  900-3*20,   900)),
+    ('caffevis_jpgvis',  (   0, 1200,       900,   300)),
 )
 window_panes = locals().get('window_panes', default_window_panes)
 
diff --git a/settings_misc.py b/settings_misc.py
index 1681317fd..971b85362 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -75,6 +75,8 @@ def set_receptive_field_per_layer(settings, net):
 
     for layer_name in settings._layer_name_to_record.keys():
 
+        print "Calculating receptive field for layer %s..." % (layer_name)
+
         top_name = layer_name_to_top_name(net, layer_name)
         if top_name is not None:
             blob = net.blobs[top_name].data

From 9185e2c9eb956f3727e43c5ea0c38735c76a2b0b Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:10:29 +0300
Subject: [PATCH 089/106] - made get_receptive_field implementation lazy, to
 speed up loading of big networks (resnet, googlenet) - added limitation in
 load_image_per_unit() to avoid hanging for loading >1000 images - fixed bug
 in image_misc resize_without_fit and resize_to_fit

---
 caffe_misc.py    |  8 ++++----
 caffevis/app.py  | 32 ++++++++++++++++++++++++++++++--
 image_misc.py    | 16 ++++++++++------
 input_fetcher.py | 20 +++++++++-----------
 settings_misc.py | 24 ------------------------
 5 files changed, 53 insertions(+), 47 deletions(-)

diff --git a/caffe_misc.py b/caffe_misc.py
index ac9ef07bc..b0e5f586d 100644
--- a/caffe_misc.py
+++ b/caffe_misc.py
@@ -86,7 +86,7 @@ def merge_regions(region1, region2):
 
 
     @staticmethod
-    def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, crop_to_boundary=True):
+    def convert_region_dag(settings, from_layer, to_layer, region):
 
         step_region = None
 
@@ -120,7 +120,7 @@ def convert_region_dag(settings, from_layer, to_layer, region, verbose=False, cr
                     continue
 
                 # calculate convert_region_dag on each one
-                current_region = RegionComputer.convert_region_dag(settings, parent_layer.name, to_layer, step_region, verbose, crop_to_boundary)
+                current_region = RegionComputer.convert_region_dag(settings, parent_layer.name, to_layer, step_region)
 
                 # aggregate results
                 if total_region is None:
@@ -167,7 +167,7 @@ def get_max_data_extent(net, settings, layer_name, is_spatial):
         top_name = layer_name_to_top_name(net, layer_name)
         conv_size = net.blobs[top_name].data.shape[2:4]        # e.g. (13,13) for conv5
         layer_slice_middle = (conv_size[0]/2,conv_size[0]/2+1, conv_size[1]/2,conv_size[1]/2+1)   # e.g. (6,7,6,7,), the single center unit
-        data_slice = RegionComputer.convert_region_dag(settings, layer_name, 'input', layer_slice_middle, crop_to_boundary=False)
+        data_slice = RegionComputer.convert_region_dag(settings, layer_name, 'input', layer_slice_middle)
         data_slice_size = data_slice[1]-data_slice[0], data_slice[3]-data_slice[2]   # e.g. (163, 163) for conv5
         # crop data slice size to data size
         data_slice_size = min(data_slice_size[0], data_size[0]), min(data_slice_size[1], data_size[1])
@@ -184,7 +184,7 @@ def compute_data_layer_focus_area(is_spatial, ii, jj, settings, layer_name, size
         # Compute the focus area of the data layer
         layer_indices = (ii, ii + 1, jj, jj + 1)
 
-        data_indices = RegionComputer.convert_region_dag(settings, layer_name, 'input', layer_indices, crop_to_boundary=False)
+        data_indices = RegionComputer.convert_region_dag(settings, layer_name, 'input', layer_indices)
         data_ii_start, data_ii_end, data_jj_start, data_jj_end = data_indices
 
         # safe guard edges
diff --git a/caffevis/app.py b/caffevis/app.py
index 3081757e4..604c48b2d 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -27,7 +27,7 @@
     ColorMapOption, InputOverlayOption
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     check_force_backward_true, set_mean, get_image_from_files
-from caffe_misc import layer_name_to_top_name, save_caffe_image
+from caffe_misc import layer_name_to_top_name, save_caffe_image, get_max_data_extent
 from siamese_helper import SiameseHelper
 from settings_misc import load_network
 
@@ -295,7 +295,7 @@ def _draw_status_pane(self, pane):
 
             print >> status2, 'Layer size: %s' % (self.state.get_layer_output_size_string())
 
-            print >> status2, '| Receptive field:', '%s' % str(self.settings._receptive_field_per_layer[default_layer_name]) if self.settings._receptive_field_per_layer.has_key(default_layer_name) else 'N/A'
+            print >> status2, '| Receptive field:', '%s' % (str(self.get_receptive_field(default_layer_name)))
 
             print >> status2, '| Input: %s' % (str(self.state.next_filename))
 
@@ -308,6 +308,25 @@ def _draw_status_pane(self, pane):
         locy = cv2_typeset_text(pane.data, strings_line2, (loc[0], locy),
                          line_spacing=self.settings.caffevis_status_line_spacing)
 
+
+    def get_receptive_field(self, layer_name):
+
+        if not hasattr(self.settings, '_receptive_field_per_layer'):
+            self.settings._receptive_field_per_layer = dict()
+
+        # calculate lazy
+        if not self.settings._receptive_field_per_layer.has_key(layer_name):
+            print "Calculating receptive fields for layer %s" % (layer_name)
+            top_name = layer_name_to_top_name(self.net, layer_name)
+            if top_name is not None:
+                blob = self.net.blobs[top_name].data
+                is_spatial = (len(blob.shape) == 4)
+                layer_receptive_field = get_max_data_extent(self.net, self.settings, layer_name, is_spatial)
+                self.settings._receptive_field_per_layer[layer_name] = layer_receptive_field
+
+        return self.settings._receptive_field_per_layer[layer_name]
+
+
     def prepare_tile_image(self, display_3D, highlight_selected, n_tiles, tile_rows, tile_cols):
 
         if self.state.layers_show_back and self.state.pattern_mode == PatternMode.OFF:
@@ -346,6 +365,9 @@ def _draw_layer_pane(self, pane):
                 layer_dat_3D_1 = layer_dat_3D_1[:, np.newaxis, np.newaxis]
 
                 # we don't resize the images to half the size since there is no point in doing that in FC layers
+            elif layer_dat_3D_0.shape[2] == 1:
+                # we don't resize the images to half the size since it will crash
+                pass
             else:
                 # resize images to half the size
                 half_pane_shape = (layer_dat_3D_0.shape[1], layer_dat_3D_0.shape[2] / 2)
@@ -639,6 +661,12 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
         return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
     def load_image_per_unit(self, display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern):
+
+        # limit loading
+        if units_num > 1000:
+            print "WARNING: load_image_per_unit was asked to load %d units, aborted to avoid hang" % (units_num)
+            return None
+
         # for each neuron in layer
         for unit_id in range(0, units_num):
 
diff --git a/image_misc.py b/image_misc.py
index 16be9b3d9..85e1b0f5c 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -428,9 +428,11 @@ def resize_to_fit(img, out_max_shape,
 
     if convert_early:
         img = np.array(img, dtype=dtype_out)
-    out = cv2.resize(img,
-            (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))),   # in (c,r) order
-                     interpolation = grow_interpolation if scale > 1 else shrink_interpolation)
+
+    out = np.stack([cv2.resize(img[:,:,i],
+                               (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))),  # in (c,r) order
+                               interpolation = grow_interpolation if scale > 1 else shrink_interpolation)
+                    for i in range(img.shape[2])], axis=2)
 
     if convert_late:
         out = np.array(out, dtype=dtype_out)
@@ -473,9 +475,11 @@ def resize_without_fit(img, out_max_shape,
 
     if convert_early:
         img = np.array(img, dtype=dtype_out)
-    out = cv2.resize(img,
-            (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),   # in (c,r) order
-                     interpolation = grow_interpolation if min(scale_0,scale_1) > 1 else shrink_interpolation)
+
+    out = np.stack([cv2.resize(img[:,:,i],  # 0,0), fx=scale_1, fy=scale_0,
+                               (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),  # in (c,r) order
+                               interpolation=grow_interpolation if min(scale_0, scale_1) > 1 else shrink_interpolation)
+                    for i in range(img.shape[2])], axis=2)
 
     if convert_late:
         out = np.array(out, dtype=dtype_out)
diff --git a/input_fetcher.py b/input_fetcher.py
index fdd6b21cf..f881db608 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -54,7 +54,9 @@ def __init__(self, settings):
 
         # contains the requested number of increaments for file index
         self.static_file_idx_increment = 0
-        
+
+        self.available_files, self.labels = get_files_list(self.settings)
+
     def bind_camera(self):
         # Due to OpenCV limitations, this should be called from the main thread
         print 'InputImageFetcher: bind_camera starting'
@@ -177,8 +179,6 @@ def _increment_and_set_frame(self, frame, from_cam):
             self.latest_frame_data = frame
             self.latest_frame_is_from_cam = from_cam
 
-
-
     def check_increment_and_load_image(self):
         with self.lock:
             if (self.static_file_idx_increment == 0 and
@@ -188,16 +188,14 @@ def check_increment_and_load_image(self):
                 # Skip if a static frame is already loaded and there is no increment
                 return
 
-            available_files, labels = get_files_list(self.settings)
-
-            assert len(available_files) != 0, ('Error: No files found in %s matching %s (current working directory is %s)' %
+            assert len(self.available_files) != 0, ('Error: No files found in %s matching %s (current working directory is %s)' %
                                                (self.settings.static_files_dir, self.settings.static_files_regexp, os.getcwd()))
             if self.static_file_idx is None:
                 self.static_file_idx = 0
-            self.static_file_idx = (self.static_file_idx + self.static_file_idx_increment) % len(available_files)
+            self.static_file_idx = (self.static_file_idx + self.static_file_idx_increment) % len(self.available_files)
             self.static_file_idx_increment = 0
-            if self.latest_static_filename != available_files[self.static_file_idx] or self.latest_static_frame is None:
-                self.latest_static_filename = available_files[self.static_file_idx]
+            if self.latest_static_filename != self.available_files[self.static_file_idx] or self.latest_static_frame is None:
+                self.latest_static_filename = self.available_files[self.static_file_idx]
 
                 if self.settings.is_siamese:
                     # loading two images for siamese network
@@ -217,6 +215,6 @@ def check_increment_and_load_image(self):
                 self.latest_static_frame = im
 
                 # if we have labels, keep it
-                if labels:
-                    self.latest_label = labels[self.static_file_idx]
+                if self.labels:
+                    self.latest_label = self.labels[self.static_file_idx]
             self._increment_and_set_frame(self.latest_static_frame, False)
diff --git a/settings_misc.py b/settings_misc.py
index 971b85362..ab6a24a83 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -4,7 +4,6 @@
 sys.path.insert(0,parentdir)
 
 from caffevis.caffevis_helper import set_mean
-from caffe_misc import layer_name_to_top_name, get_max_data_extent
 
 def deduce_calculated_settings_without_network(settings):
     set_calculated_siamese_network_format(settings)
@@ -15,7 +14,6 @@ def deduce_calculated_settings_without_network(settings):
 def deduce_calculated_settings_with_network(settings, net):
     set_calculated_is_gray_model(settings, net)
     set_calculated_image_dims(settings, net)
-    set_receptive_field_per_layer(settings, net)
 
 
 def set_calculated_is_gray_model(settings, net):
@@ -67,28 +65,6 @@ def set_calculated_channel_swap(settings):
             settings._calculated_channel_swap = (2, 1, 0)
 
 
-def set_receptive_field_per_layer(settings, net):
-
-    print "Calculating receptive fields for all layers..."
-
-    receptive_field_per_layer = dict()
-
-    for layer_name in settings._layer_name_to_record.keys():
-
-        print "Calculating receptive field for layer %s..." % (layer_name)
-
-        top_name = layer_name_to_top_name(net, layer_name)
-        if top_name is not None:
-            blob = net.blobs[top_name].data
-            is_spatial = (len(blob.shape) == 4)
-            layer_receptive_field = get_max_data_extent(net, settings, layer_name, is_spatial)
-            receptive_field_per_layer[layer_name] = layer_receptive_field
-
-    settings._receptive_field_per_layer = receptive_field_per_layer
-
-    print "Calculating receptive fields for all layers... Done."
-
-
 def process_network_proto(settings):
 
     settings._processed_deploy_prototxt = settings.caffevis_deploy_prototxt + ".processed_by_deepvis"

From 683b0fade3810fd3d1a2e8da1ac981f64ac195e2 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:13:59 +0300
Subject: [PATCH 090/106] - removed use of check_force_backward_true - ignore
 NANs and added warning - fixed bugs

---
 caffevis/app.py           |  4 +---
 find_maxes/max_tracker.py | 16 ++++++++++++++--
 image_misc.py             | 26 ++++++++++++++++++--------
 optimize_image.py         |  4 +---
 4 files changed, 34 insertions(+), 16 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 604c48b2d..4529e7b80 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -26,7 +26,7 @@
 from caffevis_app_state import CaffeVisAppState, SiameseViewMode, PatternMode, BackpropMode, BackpropViewOption, \
     ColorMapOption, InputOverlayOption
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
-    check_force_backward_true, set_mean, get_image_from_files
+    set_mean, get_image_from_files
 from caffe_misc import layer_name_to_top_name, save_caffe_image, get_max_data_extent
 from siamese_helper import SiameseHelper
 from settings_misc import load_network
@@ -57,8 +57,6 @@ def __init__(self, settings, key_bindings):
         else:
             self._net_channel_swap_inv = tuple([self._net_channel_swap.index(ii) for ii in range(len(self._net_channel_swap))])
 
-        check_force_backward_true(settings.caffevis_deploy_prototxt)
-
         self.labels = None
         if self.settings.caffevis_labels:
             self.labels = read_label_file(self.settings.caffevis_labels)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index bb51f2792..515b795aa 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -200,18 +200,30 @@ def update(self, data, image_idx, selected_input_index, layer_unique_input_sourc
         if len(self.all_max_vals) < MAX_LIST_SIZE:
             self.all_max_vals.append(maxes)
 
+        produced_warning = False
+
         #insertion_idx = zeros((n_channels,))
         #pdb.set_trace()
         for ii in xrange(n_channels):
 
-            idx = np.searchsorted(self.max_vals[ii], data_unroll[ii, max_indexes[ii]])
+            max_value = data_unroll[ii, max_indexes[ii]]
+
+            # skip nan
+            if np.isnan(max_value):
+                # only warn once
+                if not produced_warning:
+                    print 'WARNING: got NAN activation on input', str(layer_unique_input_source)
+                    produced_warning = True
+                continue
+
+            idx = np.searchsorted(self.max_vals[ii], max_value)
             if idx == 0:
                 # Smaller than all 10
                 continue
             # Store new max in the proper order. Update both arrays:
             # self.max_vals:
             self.max_vals[ii,:idx-1] = self.max_vals[ii,1:idx]       # shift lower values
-            self.max_vals[ii,idx-1] = data_unroll[ii, max_indexes[ii]]     # store new max value
+            self.max_vals[ii,idx-1] = max_value                      # store new max value
             # self.max_locs
             self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
             # store new location
diff --git a/image_misc.py b/image_misc.py
index 85e1b0f5c..dcdbf0618 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -429,10 +429,15 @@ def resize_to_fit(img, out_max_shape,
     if convert_early:
         img = np.array(img, dtype=dtype_out)
 
-    out = np.stack([cv2.resize(img[:,:,i],
-                               (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))),  # in (c,r) order
-                               interpolation = grow_interpolation if scale > 1 else shrink_interpolation)
-                    for i in range(img.shape[2])], axis=2)
+    if len(img.shape) == 3:
+        out = np.stack([cv2.resize(img[:,:,i],
+                                   (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))),  # in (c,r) order
+                                   interpolation = grow_interpolation if scale > 1 else shrink_interpolation)
+                        for i in range(img.shape[2])], axis=2)
+    else:
+        out = cv2.resize(img,
+                         (int(round(img.shape[1] * scale)), int(round(img.shape[0] * scale))), # in (c,r) order
+                         interpolation=grow_interpolation if scale > 1 else shrink_interpolation)
 
     if convert_late:
         out = np.array(out, dtype=dtype_out)
@@ -476,10 +481,15 @@ def resize_without_fit(img, out_max_shape,
     if convert_early:
         img = np.array(img, dtype=dtype_out)
 
-    out = np.stack([cv2.resize(img[:,:,i],  # 0,0), fx=scale_1, fy=scale_0,
-                               (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),  # in (c,r) order
-                               interpolation=grow_interpolation if min(scale_0, scale_1) > 1 else shrink_interpolation)
-                    for i in range(img.shape[2])], axis=2)
+    if len(img.shape) == 3:
+        out = np.stack([cv2.resize(img[:,:,i],  # 0,0), fx=scale_1, fy=scale_0,
+                                   (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),  # in (c,r) order
+                                   interpolation=grow_interpolation if min(scale_0, scale_1) > 1 else shrink_interpolation)
+                        for i in range(img.shape[2])], axis=2)
+    else:
+        out = cv2.resize(img,  # 0,0), fx=scale_1, fy=scale_0,
+                         (int(round(img.shape[1] * scale_1)), int(round(img.shape[0] * scale_0))),  # in (c,r) order
+                         interpolation=grow_interpolation if min(scale_0, scale_1) > 1 else shrink_interpolation)
 
     if convert_late:
         out = np.array(out, dtype=dtype_out)
diff --git a/optimize_image.py b/optimize_image.py
index be7c5c8c6..e977f5538 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -7,7 +7,7 @@
 
 import settings
 from optimize.gradient_optimizer import GradientOptimizer, FindParams
-from caffevis.caffevis_helper import check_force_backward_true, read_label_file, set_mean
+from caffevis.caffevis_helper import read_label_file, set_mean
 from settings_misc import load_network
 from caffe_misc import layer_name_to_top_name
 
@@ -150,8 +150,6 @@ def main():
         # it can be added by simply handle the batch indexes properly, but it should be thoroughly tested
         assert (settings.max_tracker_batch_size == 1)
 
-    check_force_backward_true(settings.caffevis_deploy_prototxt)
-
     current_data_shape = net.blobs['data'].shape
     net.blobs['data'].reshape(args.batch_size, current_data_shape[1], current_data_shape[2], current_data_shape[3])
     net.reshape()

From 2c7bdbe10abe024cdae2663582dcd78677f6c928 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:23:02 +0300
Subject: [PATCH 091/106] - added visualization of activation correlation -
 added visualization of weights correlation - added #caffevis_gpu_id = 0 to
 settings_user.py.example

---
 bindings.py                    |   4 +-
 caffevis/app.py                | 175 ++++++++++++++++++++++++++++-----
 caffevis/caffevis_app_state.py |  16 ++-
 caffevis/caffevis_helper.py    |   5 +-
 find_maxes/crop_max_patches.py |   4 +
 find_maxes/find_max_acts.py    |  10 +-
 find_maxes/max_tracker.py      |  69 ++++++++++++-
 image_misc.py                  |  11 ++-
 optimize_image.py              |   4 +
 run_toolbox.py                 |   4 +
 settings.py                    |   3 +
 settings_user.py.example       |   2 +
 12 files changed, 266 insertions(+), 41 deletions(-)

diff --git a/bindings.py b/bindings.py
index 3442ef4b1..78f21028a 100644
--- a/bindings.py
+++ b/bindings.py
@@ -102,9 +102,9 @@ def get_key_help(self, tag):
 _.add('zoom_mode', 'z',
        'Cycle zooming through {currently selected unit, backprop results, none}')
 _.add('next_pattern_mode', 's',
-       'Cycle channels overlay (max opt, max input, weights hist, max hist, off)')
+       'Cycle channels overlay (max opt, max input, weights hist, max hist, correlation, off)')
 _.add('prev_pattern_mode', 'S',
-       'Cycle patterns overlay (max opt, max input, weights hist, max hist, off)')
+       'Cycle patterns overlay (max opt, max input, weights hist, max hist, correlation, off)')
 _.add('pattern_first_only', '1',
        'Toggle pattern loading first image only or loading all available images')
 
diff --git a/caffevis/app.py b/caffevis/app.py
index 4529e7b80..d7ed98688 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -267,6 +267,10 @@ def _draw_status_pane(self, pane):
                 print >> status, 'histogram(weights)'
             elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
                 print >> status, 'histogram(maximal activations)'
+            elif self.state.pattern_mode == PatternMode.ACTIVATIONS_CORRELATION:
+                print >> status, 'correlation(maximal activations)'
+            elif self.state.pattern_mode == PatternMode.WEIGHTS_CORRELATION:
+                print >> status, 'correlation(weights)'
             elif self.state.layers_show_back:
                 print >> status, 'back'
             else:
@@ -425,24 +429,34 @@ def _draw_layer_pane(self, pane):
                         self.state.pattern_first_only, file_search_pattern='maxim*.png')
 
             elif self.state.pattern_mode == PatternMode.WEIGHTS_HISTOGRAM:
-
-                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_weights_histograms(
-                        self.net, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
-                        show_layer_summary=self.state.cursor_area == 'top')
+                display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_weights_histograms(
+                    self.net, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                    show_layer_summary=self.state.cursor_area == 'top')
 
             elif self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM:
+                if self.settings.caffevis_histograms_format == 'load_from_file':
+                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
+                        default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, True,
+                        file_search_pattern='max_histogram.png',
+                        show_layer_summary=self.state.cursor_area == 'top',
+                        file_summary_pattern='layer_inactivity.png')
+
+                elif self.settings.caffevis_histograms_format == 'calculate_in_realtime':
+                    display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_maximal_activations_histograms(
+                        default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                        show_layer_summary=self.state.cursor_area == 'top')
 
-                    if self.settings.caffevis_histograms_format == 'load_from_file':
-                        display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
-                            default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, True,
-                            file_search_pattern='max_histogram.png',
-                            show_layer_summary=self.state.cursor_area == 'top',
-                            file_summary_pattern='layer_inactivity.png')
+            elif self.state.pattern_mode == PatternMode.ACTIVATIONS_CORRELATION:
+                display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_pattern_images_optimizer_format(
+                    default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, True,
+                    file_search_pattern=None,
+                    show_layer_summary=True,
+                    file_summary_pattern='channels_correlation.png')
 
-                    elif self.settings.caffevis_histograms_format == 'calculate_in_realtime':
-                        display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_maximal_activations_histograms(
-                            default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
-                            show_layer_summary=self.state.cursor_area == 'top')
+            elif self.state.pattern_mode == PatternMode.WEIGHTS_CORRELATION:
+                display_2D, display_3D, display_3D_highres, is_layer_summary_loaded = self.load_weights_correlation(
+                    self.net, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows,
+                    show_layer_summary=True)
 
             else:
                 raise Exception("Invalid value of pattern mode: %d" % self.state.pattern_mode)
@@ -638,9 +652,12 @@ def load_pattern_images_optimizer_format(self, default_layer_name, layer_dat_3D,
                             is_layer_summary_loaded = True
 
                         else:
-                            with WithTimer('CaffeVisApp:load_image_per_unit', quiet=self.debug_level < 1):
-                                # load all images
-                                display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
+                            if file_search_pattern is None:
+                                display_3D_highres = None
+                            else:
+                                with WithTimer('CaffeVisApp:load_image_per_unit', quiet=self.debug_level < 1):
+                                    # load all images
+                                    display_3D_highres = self.load_image_per_unit(display_3D_highres, load_layer, units_num, first_only, resize_shape, file_search_pattern)
 
                 except IOError:
                     # File does not exist, so just display disabled.
@@ -741,10 +758,10 @@ def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pan
             # 1. calculating size of results array
             # 2. generating weights histogram for selected unit
             # 3. generating weights histograms for all units
-            from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-            from matplotlib.figure import Figure
-            fig = Figure(figsize=(10, 10))
-            canvas = FigureCanvas(fig)
+
+            import matplotlib.pyplot as plt
+
+            fig = plt.figure(figsize=(10, 10))
             ax = fig.add_subplot(111)
 
             def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print):
@@ -791,6 +808,9 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
 
                         # load 2d image from cache file
                         display_2D = caffe_load_image(cache_layer_weights_histogram_image_path, color=True, as_uint=False)
+                        display_3D_highres = np.zeros(pane_shape)
+                        display_3D_highres = np.expand_dims(display_3D_highres, 0)
+                        display_3D_highres[0] = display_2D
 
                         is_layer_summary_loaded = True
 
@@ -873,7 +893,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
 
                         if folder_path:
                             mkdir_p(folder_path)
-                            save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
+                            save_caffe_image(display_2D[:,:,::-1].astype(np.float32).transpose((2,0,1)), cache_layer_weights_histogram_image_path)
                         else:
                             print "WARNING: unable to save weight histogram to cache since caffevis_outputs_dir is not set"
 
@@ -891,7 +911,7 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         if folder_path:
                             # save histogram image to cache
                             mkdir_p(folder_path)
-                            save_caffe_image(display_2D.astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
+                            save_caffe_image(display_2D[:,:,::-1].astype(np.float32).transpose((2,0,1)), cache_details_weights_histogram_image_path)
                         else:
                             print "WARNING: unable to save weight histogram to cache since caffevis_outputs_dir is not set"
 
@@ -910,6 +930,9 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                 self.img_cache.set(pattern_image_key_3d, display_3D_highres)
                 self.img_cache.set(pattern_image_key_2d, display_2D)
 
+            fig.clf()
+            plt.close(fig)
+
         else:
             # here we can safely assume that display_2D is not None, so we only need to check if show_layer_summary was requested
             if show_layer_summary:
@@ -922,6 +945,112 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
 
         return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
+    def load_weights_correlation(self, net, layer_name, layer_dat_3D, n_channels, pane, tile_cols, tile_rows, show_layer_summary):
+
+        is_layer_summary_loaded = False
+        display_2D = None
+        display_3D = None
+        empty_display_3D = np.zeros(layer_dat_3D.shape + (3,))
+
+        pattern_image_key_3d = (layer_name, "weights_correlation", show_layer_summary, self.state.selected_unit, "3D")
+        pattern_image_key_2d = (layer_name, "weights_correlation", show_layer_summary, self.state.selected_unit, "2D")
+
+        # Get highres version
+        display_3D_highres = self.img_cache.get(pattern_image_key_3d, None)
+        display_2D = self.img_cache.get(pattern_image_key_2d, None)
+
+        if display_3D_highres is None or display_2D is None:
+
+            pane_shape = pane.data.shape
+
+            if not self.settings.caffevis_outputs_dir:
+                folder_path = None
+                cache_layer_weights_correlation_image_path = None
+            else:
+                folder_path = os.path.join(self.settings.caffevis_outputs_dir, layer_name)
+                cache_layer_weights_correlation_image_path = os.path.join(folder_path, 'layer_weights_correlation.png')
+
+            try:
+
+                # try load cache file for layer weight correlation
+                if cache_layer_weights_correlation_image_path and os.path.exists(cache_layer_weights_correlation_image_path):
+
+                    # load 2d image from cache file
+                    display_2D = caffe_load_image(cache_layer_weights_correlation_image_path, color=True, as_uint=False)
+                    display_3D_highres = np.zeros(pane_shape)
+                    display_3D_highres = np.expand_dims(display_3D_highres, 0)
+                    display_3D_highres[0] = display_2D
+
+                    is_layer_summary_loaded = True
+
+                # if not loaded from cache, generate the data
+                if display_2D is None:
+
+                    # calculate weights correlation image
+
+                    # check if layer has weights at all
+                    if not net.params.has_key(layer_name):
+                        return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
+
+                    # skip layers with only one channel
+                    if n_channels == 1:
+                        return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
+
+                    data_unroll = net.params[layer_name][0].data.reshape((n_channels, -1))  # Note: no copy eg (96,3025). Does nothing if not is_spatial
+
+                    corr = np.corrcoef(data_unroll)
+
+                    # fix possible NANs
+                    corr = np.nan_to_num(corr)
+                    np.fill_diagonal(corr, 1)
+
+                    # sort correlation matrix
+                    indexes = np.lexsort(corr)
+                    sorted_corr = corr[indexes, :][:, indexes]
+
+                    # plot correlation matrix
+                    import matplotlib.pyplot as plt
+
+                    fig = plt.figure(figsize=(10, 10))
+                    plt.subplot(1, 1, 1)
+                    plt.imshow(sorted_corr, interpolation='nearest', vmin=-1, vmax=1)
+                    plt.colorbar()
+                    plt.title('channels weights correlation matrix for layer %s' % (layer_name))
+                    plt.tight_layout()
+                    figure_buffer = fig2data(fig)
+                    plt.close()
+
+                    display_3D_highres_summary = ensure_uint255_and_resize_without_fit(figure_buffer, pane_shape)
+                    display_3D_highres_summary = np.expand_dims(display_3D_highres_summary, 0)
+                    display_3D_highres = display_3D_highres_summary
+                    display_2D = display_3D_highres[0]
+                    is_layer_summary_loaded = True
+
+                    if folder_path:
+                        mkdir_p(folder_path)
+                        save_caffe_image(display_2D[:,:,::-1].astype(np.float32).transpose((2,0,1)), cache_layer_weights_correlation_image_path)
+                    else:
+                        print "WARNING: unable to save weight correlationto cache since caffevis_outputs_dir is not set"
+
+                self.img_cache.set(pattern_image_key_3d, display_3D_highres)
+                self.img_cache.set(pattern_image_key_2d, display_2D)
+
+            except IOError:
+                return display_2D, empty_display_3D, empty_display_3D, is_layer_summary_loaded
+                # File does not exist, so just display disabled.
+                pass
+
+        else:
+            # here we can safely assume that display_2D is not None, so we only need to check if show_layer_summary was requested
+            if show_layer_summary:
+                is_layer_summary_loaded = True
+
+            pass
+
+        if display_3D is None:
+            display_3D = self.downsample_display_3d(display_3D_highres, layer_dat_3D, pane, tile_cols, tile_rows)
+
+        return display_2D, display_3D, display_3D_highres, is_layer_summary_loaded
 
     def load_maximal_activations_histograms(self, default_layer_name, layer_dat_3D, n_tiles, pane, tile_cols, tile_rows, show_layer_summary):
 
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 2edf089de..001facc5b 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -11,7 +11,9 @@ class PatternMode:
     MAXIMAL_INPUT_IMAGE = 2
     WEIGHTS_HISTOGRAM = 3
     MAX_ACTIVATIONS_HISTOGRAM = 4
-    NUMBER_OF_MODES = 5
+    ACTIVATIONS_CORRELATION = 5
+    WEIGHTS_CORRELATION = 6
+    NUMBER_OF_MODES = 7
 
 class BackpropMode:
     OFF = 0
@@ -242,8 +244,12 @@ def handle_key(self, key):
                 self.layer_boost_gamma = self.layer_boost_gamma_choices[self.layer_boost_gamma_idx]
             elif tag == 'next_pattern_mode':
                 self.pattern_mode = (self.pattern_mode + 1) % PatternMode.NUMBER_OF_MODES
+                self.validate_state_for_summary_only_patterns()
+
             elif tag == 'prev_pattern_mode':
                 self.pattern_mode = (self.pattern_mode - 1 + PatternMode.NUMBER_OF_MODES) % PatternMode.NUMBER_OF_MODES
+                self.validate_state_for_summary_only_patterns()
+
             elif tag == 'pattern_first_only':
                 self.pattern_first_only = not self.pattern_first_only
             elif tag == 'show_back':
@@ -438,6 +444,8 @@ def move_selection(self, direction, dist = 1):
                     self.selected_unit += self.net_blob_info[default_top_name]['tile_cols']
                     self.cursor_area = 'top'
 
+        self.validate_state_for_summary_only_patterns()
+
     def _ensure_valid_selected(self):
 
         default_layer_name = self.get_default_layer_name()
@@ -516,4 +524,8 @@ def get_layer_output_size_string(self, layer_def=None):
         elif len(layer_output_size) == 3:
             return '(%d,%d,%d)' % (layer_output_size[0], layer_output_size[1], layer_output_size[2])
         else:
-            return str(layer_output_size)
\ No newline at end of file
+            return str(layer_output_size)
+
+    def validate_state_for_summary_only_patterns(self):
+        if self.pattern_mode in [PatternMode.ACTIVATIONS_CORRELATION, PatternMode.WEIGHTS_CORRELATION]:
+            self.cursor_area = 'top'
diff --git a/caffevis/caffevis_helper.py b/caffevis/caffevis_helper.py
index b514c541e..ee91e82a9 100644
--- a/caffevis/caffevis_helper.py
+++ b/caffevis/caffevis_helper.py
@@ -1,13 +1,12 @@
 #! /usr/bin/env python
-from sys import float_info
 
+from sys import float_info
 import numpy as np
 import os
+import glob
 
 from image_misc import get_tiles_height_width, caffe_load_image, ensure_uint255_and_resize_without_fit, FormattedString, \
     cv2_typeset_text, to_255
-import glob
-
 
 
 def net_preproc_forward(settings, net, img, data_hw):
diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index af7fb931c..6ecb734a8 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -1,5 +1,9 @@
 #! /usr/bin/env python
 
+# this import must comes first to make sure we use the non-display backend
+import matplotlib
+matplotlib.use('Agg')
+
 # add parent folder to search path, to enable import of core modules like settings
 import os,sys,inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 9ba5319d8..388356b24 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -1,5 +1,9 @@
 #! /usr/bin/env python
 
+# this import must comes first to make sure we use the non-display backend
+import matplotlib
+matplotlib.use('Agg')
+
 # add parent folder to search path, to enable import of core modules like settings
 import os,sys,inspect
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
@@ -7,7 +11,6 @@
 sys.path.insert(0,parentdir)
 
 import argparse
-#import ipdb as pdb
 import cPickle as pickle
 import numpy as np
 
@@ -43,6 +46,7 @@ def main():
     parser.add_argument('--outfile', type=str, default = os.path.join(settings.caffevis_outputs_dir, 'find_max_acts_output.pickled'), help='output filename for pkl')
     parser.add_argument('--outdir', type = str, default = settings.caffevis_outputs_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{max_histogram}.png')
     parser.add_argument('--do-histograms', action = 'store_true', default = settings.max_tracker_do_histograms, help = 'Output histogram image file containing histogrma of max values per channel')
+    parser.add_argument('--do-correlation', action = 'store_true', default = settings.max_tracker_do_correlation, help = 'Output correlation image file containing correlation of channels per layer')
 
     args = parser.parse_args()
 
@@ -71,11 +75,13 @@ def main():
 
     save_max_tracker_to_file(args.outfile, net_max_tracker)
 
+    if args.do_correlation:
+        net_max_tracker.calculate_correlation(args.outdir)
+
     if args.do_histograms:
         net_max_tracker.calculate_histograms(args.outdir)
 
 
-
 def save_max_tracker_to_file(filename, net_max_tracker):
 
     dir_name = os.path.dirname(filename)
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 515b795aa..31f69e117 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -74,11 +74,9 @@ def __init__(self, info_file = None, ref_count = None):
 
 def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, process_channel_figure, process_layer_figure):
 
-    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-    from matplotlib.figure import Figure
+    import matplotlib.pyplot as plt
 
-    fig = Figure(figsize=(10, 10))
-    canvas = FigureCanvas(fig)
+    fig = plt.figure(figsize=(10, 10))
     ax = fig.add_subplot(111)
 
     # for each channel
@@ -137,6 +135,9 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
 
     process_layer_figure(fig)
 
+    fig.clf()
+    plt.close(fig)
+
     pass
 
 
@@ -266,6 +267,52 @@ def process_layer_figure(fig):
 
         pass
 
+    def calculate_correlation(self, layer_name, outdir):
+
+        # convert list of arrays to numpy array
+        all_max_array = np.vstack(self.all_max_vals)
+
+        # skip layers with only one channel
+        if all_max_array.shape[1] == 1:
+            return
+
+        corr = np.corrcoef(all_max_array.transpose())
+
+        # fix possible NANs
+        corr = np.nan_to_num(corr)
+        np.fill_diagonal(corr, 1)
+
+        # sort correlation matrix
+        # import cPickle as pickle
+        #  with open('corr_%s.pickled' % layer_name, 'wb') as ff:
+        #     pickle.dump(corr, ff, protocol=2)
+
+        # alternative sorting
+        # values = np.dot(corr, np.arange(corr.shape[0]))
+        # indexes = np.argsort(values)
+
+        indexes = np.lexsort(corr)
+        sorted_corr = corr[indexes,:][:,indexes]
+
+        # plot correlation matrix
+        import matplotlib.pyplot as plt
+        fig = plt.figure(figsize=(10, 10))
+        plt.subplot(1, 1, 1)
+        plt.imshow(sorted_corr, interpolation='nearest', vmin=-1, vmax=1)
+        plt.colorbar()
+        plt.title('channels activations correlation matrix for layer %s' % (layer_name))
+        plt.tight_layout()
+
+        # save correlation matrix
+        layer_dir = os.path.join(outdir, layer_name)
+        mkdir_p(layer_dir)
+        filename = os.path.join(layer_dir, 'channels_correlation.png')
+        fig.savefig(filename, bbox_inches='tight')
+
+        plt.close()
+
+        return
+
 
 class NetMaxTracker(object):
     def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'float32'):
@@ -375,6 +422,20 @@ def calculate_histograms(self, outdir):
 
         pass
 
+    def calculate_correlation(self, outdir):
+
+        print "calculate_correlation on network"
+        for layer_name in self.layers:
+            print "calculate_correlation on layer %s" % layer_name
+
+            # normalize layer name, this is used for siamese networks where we want layers "conv_1" and "conv_1_p" to
+            # count as the same layer in terms of activations
+            normalized_layer_name = self.siamese_helper.normalize_layer_name_for_max_tracker(layer_name)
+
+            self.max_trackers[normalized_layer_name].calculate_correlation(layer_name, outdir)
+
+        pass
+
     def __getstate__(self):
         # Copy the object's state from self.__dict__ which contains
         # all our instance attributes. Always use the dict.copy()
diff --git a/image_misc.py b/image_misc.py
index dcdbf0618..72b358563 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -29,10 +29,7 @@ def fig2data(fig):
 
 def array_histogram(arr, histogram_pane_shape, title, xlabel, ylabel):
 
-    from matplotlib.backends.backend_agg import FigureCanvasAgg as FigureCanvas
-    from matplotlib.figure import Figure
-    fig = Figure(figsize=(10, 10))
-    canvas = FigureCanvas(fig)
+    fig = plt.figure(figsize=(10, 10))
     ax = fig.add_subplot(111)
 
     # generate histogram
@@ -48,7 +45,11 @@ def array_histogram(arr, histogram_pane_shape, title, xlabel, ylabel):
     ax.yaxis.label.set_text(ylabel)
 
     figure_buffer = fig2data(fig)
-    # result = ensure_uint255_and_resize_without_fit(figure_buffer, histogram_pane_shape)
+
+    ax.cla()
+    fig.clf()
+    plt.close(fig)
+
     return figure_buffer
 
 
diff --git a/optimize_image.py b/optimize_image.py
index e977f5538..93b8fe6a9 100755
--- a/optimize_image.py
+++ b/optimize_image.py
@@ -1,5 +1,9 @@
 #! /usr/bin/env python
 
+# this import must comes first to make sure we use the non-display backend
+import matplotlib
+matplotlib.use('Agg')
+
 import os
 import sys
 import argparse
diff --git a/run_toolbox.py b/run_toolbox.py
index 97ba4f2e8..7731fcbdd 100755
--- a/run_toolbox.py
+++ b/run_toolbox.py
@@ -1,5 +1,9 @@
 #! /usr/bin/env python
 
+# this import must comes first to make sure we use the non-display backend
+import matplotlib
+matplotlib.use('Agg')
+
 import os
 from live_vis import LiveVis
 from bindings import bindings
diff --git a/settings.py b/settings.py
index d7f8bf6ca..57ca6db37 100644
--- a/settings.py
+++ b/settings.py
@@ -383,6 +383,9 @@
 # default value for do_histograms parameter in max tracker
 max_tracker_do_histograms = locals().get('max_tracker_do_histograms', True)
 
+# default value for do_correlation parameter in max tracker
+max_tracker_do_correlation = locals().get('max_tracker_do_correlation', True)
+
 # default batch size used in max_tracker
 max_tracker_batch_size = locals().get('max_tracker_batch_size', 1)
 
diff --git a/settings_user.py.example b/settings_user.py.example
index 29c2ee3e0..deba7a157 100644
--- a/settings_user.py.example
+++ b/settings_user.py.example
@@ -9,6 +9,8 @@ base_folder = os.path.expanduser('~')
 # Use GPU? Default is True.
 #caffevis_mode_gpu = False
 
+#caffevis_gpu_id = 0
+
 # network selection, for network settings see file settings_local.py
 model_to_load = 'model'
 #model_to_load = 'caffenet_yos'

From fa1dd3d9c1654930d8b88ce82541739ca66c2d0a Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:25:37 +0300
Subject: [PATCH 092/106] - moved get_receptive_field to settings_misc - added
 caching to file of get_receptive_field results

---
 caffevis/app.py  | 21 +++-----------------
 settings_misc.py | 50 ++++++++++++++++++++++++++++++++++++++++++++++++
 2 files changed, 53 insertions(+), 18 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index d7ed98688..2202c489a 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -27,9 +27,9 @@
     ColorMapOption, InputOverlayOption
 from caffevis_helper import get_pretty_layer_name, read_label_file, load_sprite_image, load_square_sprite_image, \
     set_mean, get_image_from_files
-from caffe_misc import layer_name_to_top_name, save_caffe_image, get_max_data_extent
+from caffe_misc import layer_name_to_top_name, save_caffe_image
 from siamese_helper import SiameseHelper
-from settings_misc import load_network
+from settings_misc import load_network, get_receptive_field
 
 
 class CaffeVisApp(BaseApp):
@@ -297,7 +297,7 @@ def _draw_status_pane(self, pane):
 
             print >> status2, 'Layer size: %s' % (self.state.get_layer_output_size_string())
 
-            print >> status2, '| Receptive field:', '%s' % (str(self.get_receptive_field(default_layer_name)))
+            print >> status2, '| Receptive field:', '%s' % (str(get_receptive_field(self.settings, self.net, default_layer_name)))
 
             print >> status2, '| Input: %s' % (str(self.state.next_filename))
 
@@ -311,22 +311,7 @@ def _draw_status_pane(self, pane):
                          line_spacing=self.settings.caffevis_status_line_spacing)
 
 
-    def get_receptive_field(self, layer_name):
 
-        if not hasattr(self.settings, '_receptive_field_per_layer'):
-            self.settings._receptive_field_per_layer = dict()
-
-        # calculate lazy
-        if not self.settings._receptive_field_per_layer.has_key(layer_name):
-            print "Calculating receptive fields for layer %s" % (layer_name)
-            top_name = layer_name_to_top_name(self.net, layer_name)
-            if top_name is not None:
-                blob = self.net.blobs[top_name].data
-                is_spatial = (len(blob.shape) == 4)
-                layer_receptive_field = get_max_data_extent(self.net, self.settings, layer_name, is_spatial)
-                self.settings._receptive_field_per_layer[layer_name] = layer_receptive_field
-
-        return self.settings._receptive_field_per_layer[layer_name]
 
 
     def prepare_tile_image(self, display_3D, highlight_selected, n_tiles, tile_rows, tile_cols):
diff --git a/settings_misc.py b/settings_misc.py
index ab6a24a83..ea2afc6c0 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -3,7 +3,10 @@
 parentdir = os.path.dirname(currentdir)
 sys.path.insert(0,parentdir)
 
+import cPickle as pickle
+
 from caffevis.caffevis_helper import set_mean
+from caffe_misc import layer_name_to_top_name, get_max_data_extent
 
 def deduce_calculated_settings_without_network(settings):
     set_calculated_siamese_network_format(settings)
@@ -246,3 +249,50 @@ def read_network_dag(settings):
     settings._layer_name_to_record = layer_name_to_record
 
     return
+
+
+def _get_receptive_fields_cache_filename(settings):
+    return os.path.join(settings.caffevis_outputs_dir, 'receptive_fields_cache.pickled')
+
+def get_receptive_field(settings, net, layer_name):
+
+    # flag which indicates whether the dictionary was changed hence we need to write it to cache
+    should_save_to_cache = False
+
+    # check if dictionary exists
+    if not hasattr(settings, '_receptive_field_per_layer'):
+
+        # if it doesn't, try load it from file
+        receptive_fields_cache_filename = _get_receptive_fields_cache_filename(settings)
+        if os.path.isfile(receptive_fields_cache_filename):
+            try:
+                with open(receptive_fields_cache_filename, 'rb') as receptive_fields_cache_file:
+                    settings._receptive_field_per_layer = pickle.load(receptive_fields_cache_file)
+            except:
+                settings._receptive_field_per_layer = dict()
+                should_save_to_cache = True
+        else:
+            settings._receptive_field_per_layer = dict()
+            should_save_to_cache = True
+
+    # calculate lazy
+    if not settings._receptive_field_per_layer.has_key(layer_name):
+        print "Calculating receptive fields for layer %s" % (layer_name)
+        top_name = layer_name_to_top_name(net, layer_name)
+        if top_name is not None:
+            blob = net.blobs[top_name].data
+            is_spatial = (len(blob.shape) == 4)
+            layer_receptive_field = get_max_data_extent(net, settings, layer_name, is_spatial)
+            settings._receptive_field_per_layer[layer_name] = layer_receptive_field
+            should_save_to_cache = True
+
+    if should_save_to_cache:
+        try:
+            receptive_fields_cache_filename = _get_receptive_fields_cache_filename(settings)
+            with open(receptive_fields_cache_filename, 'wb') as receptive_fields_cache_file:
+                pickle.dump(settings._receptive_field_per_layer, receptive_fields_cache_file, -1)
+        except IOError:
+            # ignore problems in cache saving
+            pass
+
+    return settings._receptive_field_per_layer[layer_name]
\ No newline at end of file

From 5fbc347c75e23ee9fb97711cf67eacab41c48118 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:26:27 +0300
Subject: [PATCH 093/106] fixed dead relu count for maxout based networks

---
 find_maxes/max_tracker.py | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 31f69e117..682b2d040 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -95,7 +95,7 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
         barlist = ax.bar(center, hist, align='center', width=width, color='g')
 
         for i in range(len(hist)):
-            if 0 >= bin_edges[i] and 0 <= bin_edges[i+1]:
+            if 0 >= bin_edges[i] and 0 < bin_edges[i+1]:
                 # mark dead bar in red
                 barlist[i].set_color('r')
 

From 50b7a8f0c12b38a561148f23a831b0efb5b6e53e Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:26:46 +0300
Subject: [PATCH 094/106] fixed missing cache when folder doesn't exist

---
 settings_misc.py | 2 ++
 1 file changed, 2 insertions(+)

diff --git a/settings_misc.py b/settings_misc.py
index ea2afc6c0..e6e029238 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -7,6 +7,7 @@
 
 from caffevis.caffevis_helper import set_mean
 from caffe_misc import layer_name_to_top_name, get_max_data_extent
+from misc import mkdir_p
 
 def deduce_calculated_settings_without_network(settings):
     set_calculated_siamese_network_format(settings)
@@ -289,6 +290,7 @@ def get_receptive_field(settings, net, layer_name):
     if should_save_to_cache:
         try:
             receptive_fields_cache_filename = _get_receptive_fields_cache_filename(settings)
+            mkdir_p(settings.caffevis_outputs_dir)
             with open(receptive_fields_cache_filename, 'wb') as receptive_fields_cache_file:
                 pickle.dump(settings._receptive_field_per_layer, receptive_fields_cache_file, -1)
         except IOError:

From 98f107873c9855b0af87bc5619dc12e3cf6614d0 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:32:53 +0300
Subject: [PATCH 095/106] - added mouse support when clicking layer or channel
 - fixed white background in some plots

---
 app_base.py                    |  4 +++
 caffevis/app.py                | 30 +++++++++++---------
 caffevis/caffevis_app_state.py | 51 ++++++++++++++++++++++++++++++++++
 image_misc.py                  | 27 ++++++++++++------
 live_vis.py                    | 17 ++++++++++--
 5 files changed, 106 insertions(+), 23 deletions(-)

diff --git a/app_base.py b/app_base.py
index aefc9fcc7..1ebe13841 100644
--- a/app_base.py
+++ b/app_base.py
@@ -13,6 +13,10 @@ def handle_key(self, key, panes):
         '''Handle key and return either key (to let someone downstream handle it) or None (if this app handled it)'''
         pass
 
+    def handle_mouse_left_click(self, x, y, flags, param, panes):
+        '''Handle mouse events'''
+        pass
+
     def redraw_needed(self, key, panes):
         '''App should return whether or not its internal state has
         been updated (perhaps in response to handle_key, handle_input,
diff --git a/caffevis/app.py b/caffevis/app.py
index 2202c489a..0e7ce6a79 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -67,6 +67,8 @@ def __init__(self, settings, key_bindings):
             raise Exception('caffevis_jpg_cache_size must be at least 10MB for normal operation.')
         self.img_cache = FIFOLimitedArrayCache(settings.caffevis_jpg_cache_size)
 
+        self.header_boxes = []
+
     def start(self):
         from jpg_vis_loading_thread import JPGVisLoadingThread
 
@@ -236,9 +238,9 @@ def _draw_control_pane(self, pane):
                         fs.thick = self.settings.caffevis_control_thick_selected
             strings.append(fs)
 
-        locy = cv2_typeset_text(pane.data, strings, loc,
-                         line_spacing = self.settings.caffevis_control_line_spacing,
-                         wrap = True)
+        locy, self.header_boxes = cv2_typeset_text(pane.data, strings, loc,
+                                                   line_spacing = self.settings.caffevis_control_line_spacing,
+                                                   wrap = True)
 
         if hasattr(self.settings, 'control_pane_height'):
             self.settings._calculated_control_pane_height = self.settings.control_pane_height
@@ -304,11 +306,11 @@ def _draw_status_pane(self, pane):
         strings_line1 = [FormattedString(line, defaults) for line in status.getvalue().split('\n')]
         strings_line2 = [FormattedString(line, defaults) for line in status2.getvalue().split('\n')]
 
-        locy = cv2_typeset_text(pane.data, strings_line1, (loc[0], loc[1] + 5),
-                         line_spacing = self.settings.caffevis_status_line_spacing)
+        locy, boxes = cv2_typeset_text(pane.data, strings_line1, (loc[0], loc[1] + 5),
+                                       line_spacing = self.settings.caffevis_status_line_spacing)
 
-        locy = cv2_typeset_text(pane.data, strings_line2, (loc[0], locy),
-                         line_spacing=self.settings.caffevis_status_line_spacing)
+        locy, boxes = cv2_typeset_text(pane.data, strings_line2, (loc[0], locy),
+                                       line_spacing=self.settings.caffevis_status_line_spacing)
 
 
 
@@ -464,7 +466,7 @@ def _draw_layer_pane(self, pane):
                                                                 boost_indiv = self.state.layer_boost_indiv,
                                                                 boost_gamma = self.state.layer_boost_gamma)
 
-            display_3D         = layer_dat_3D_normalized
+            display_3D = layer_dat_3D_normalized
 
         # Convert to float if necessary:
         display_3D = ensure_float01(display_3D)
@@ -746,7 +748,7 @@ def load_weights_histograms(self, net, layer_name, layer_dat_3D, n_channels, pan
 
             import matplotlib.pyplot as plt
 
-            fig = plt.figure(figsize=(10, 10))
+            fig = plt.figure(figsize=(10, 10), facecolor='white', tight_layout=False)
             ax = fig.add_subplot(111)
 
             def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print):
@@ -996,12 +998,11 @@ def load_weights_correlation(self, net, layer_name, layer_dat_3D, n_channels, pa
                     # plot correlation matrix
                     import matplotlib.pyplot as plt
 
-                    fig = plt.figure(figsize=(10, 10))
+                    fig = plt.figure(figsize=(10, 10), facecolor='white', tight_layout=True)
                     plt.subplot(1, 1, 1)
                     plt.imshow(sorted_corr, interpolation='nearest', vmin=-1, vmax=1)
                     plt.colorbar()
                     plt.title('channels weights correlation matrix for layer %s' % (layer_name))
-                    plt.tight_layout()
                     figure_buffer = fig2data(fig)
                     plt.close()
 
@@ -1453,6 +1454,9 @@ def _draw_jpgvis_pane(self, pane):
     def handle_key(self, key, panes):
         return self.state.handle_key(key)
 
+    def handle_mouse_left_click(self, x, y, flags, param, panes):
+        self.state.handle_mouse_left_click(x, y, flags, param, panes, self.header_boxes)
+
     def get_back_what_to_disp(self):
         '''Whether to show back diff information or stale or disabled indicator'''
         if (self.state.cursor_area == 'top' and not self.state.backprop_selection_frozen) or not self.state.back_enabled:
@@ -1512,7 +1516,7 @@ def draw_help(self, help_pane, locy):
                 lines.append([FormattedString(label, defaults, width=120, align='right'),
                               FormattedString(help_string, defaults)])
 
-        locy = cv2_typeset_text(help_pane.data, lines, (locx, locy),
-                                line_spacing = self.settings.help_line_spacing)
+        locy, boxes = cv2_typeset_text(help_pane.data, lines, (locx, locy),
+                                       line_spacing = self.settings.help_line_spacing)
 
         return locy
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 001facc5b..8ae3fd990 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -328,6 +328,57 @@ def handle_key(self, key):
 
         return (None if key_handled else key)
 
+    def handle_mouse_left_click(self, x, y, flags, param, panes, header_boxes):
+
+        for pane_name, pane in panes.items():
+            if pane.j_begin <= x < pane.j_end and pane.i_begin <= y < pane.i_end:
+
+                if pane_name == 'caffevis_control': # layers list
+
+                    # search for layer clicked on
+                    for box_idx, box in enumerate(header_boxes):
+                        start_x, end_x, start_y, end_y = box
+                        if start_x <= x - pane.j_begin < end_x and start_y <= y - pane.i_begin <= end_y:
+                            # print 'layers list clicked on layer %d (%s,%s)' % (box_idx, x, y)
+                            self.layer_idx = box_idx
+                            self.cursor_area = 'top'
+                            self._ensure_valid_selected()
+                            self.drawing_stale = True  # Request redraw any time we handled the mouse
+                            return
+                    # print 'layers list clicked on (%s,%s)' % (x, y)
+
+                elif pane_name == 'caffevis_layers': # channels list
+                    # print 'channels list clicked on (%s,%s)' % (x, y)
+
+                    default_layer_name = self.get_default_layer_name()
+                    default_top_name = layer_name_to_top_name(self.net, default_layer_name)
+
+                    tile_rows, tile_cols = self.net_blob_info[default_top_name]['tiles_rc']
+
+                    dy_per_channel = (pane.data.shape[0] + 1) / float(tile_rows)
+                    dx_per_channel = (pane.data.shape[1] + 1) / float(tile_cols)
+
+                    tile_x = int(((x - pane.j_begin) / dx_per_channel) + 1)
+                    tile_y = int(((y - pane.i_begin) / dy_per_channel) + 1)
+
+                    channel_id = (tile_y-1) * tile_cols + (tile_x - 1)
+
+                    self.selected_unit = channel_id
+                    self.cursor_area = 'bottom'
+
+                    self.validate_state_for_summary_only_patterns()
+                    self._ensure_valid_selected()
+                    self.drawing_stale = True  # Request redraw any time we handled the mouse
+                    return
+
+
+                else:
+                    # print "Clicked: %s - %s" % (x, y)
+                    pass
+                break
+
+        pass
+
     def redraw_needed(self):
         with self.lock:
             return self.drawing_stale
diff --git a/image_misc.py b/image_misc.py
index 72b358563..0ed99081b 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -1,5 +1,6 @@
 #! /usr/bin/env python
 
+import io
 import cv2
 import numpy as np
 import skimage
@@ -16,20 +17,26 @@ def fig2data(fig):
     @param fig a matplotlib figure
     @return a numpy 3D array of RGB values
     """
-    # draw the renderer
-    fig.canvas.draw()
 
+    # alternative implementation - which might be slower
+    # buf = io.BytesIO()
+    # fig.savefig(buf, format='png')
+    # buf.seek(0)
+    # image = caffe_load_image(buf, color=True, as_uint=True)
+    # buf.close()
+    #return image
+    
     # Get the RGB buffer from the figure
+    fig.canvas.draw()
     w, h = fig.canvas.get_width_height()
     buf = np.fromstring(fig.canvas.tostring_rgb(), dtype=np.uint8)
     buf.shape = (w, h, 3)
-
     return buf
+    
 
 
 def array_histogram(arr, histogram_pane_shape, title, xlabel, ylabel):
-
-    fig = plt.figure(figsize=(10, 10))
+    fig = plt.figure(figsize=(10, 10), facecolor='white')
     ax = fig.add_subplot(111)
 
     # generate histogram
@@ -527,6 +534,7 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
 
     Returns:
     locy: new y location = loc[1] + y-offset resulting from lines of text
+    boxes: list of boxes, one for each line, in the format (start_x, end_x, start_y, end_y)
     '''
 
     data_width = data.shape[1]
@@ -538,13 +546,15 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
         lines = [lines]
     assert isinstance(lines, list), 'lines must be a list of lines or list of FormattedString objects or a single FormattedString object'
     if len(lines) == 0:
-        return loc[1]
+        return loc[1], []
     if not isinstance(lines[0], list):
         # If a single line of text is given as a list of strings, convert to multiline format
         lines = [lines]
     
     locy = loc[1]
 
+    boxes = list()
+
     line_num = 0
     while line_num < len(lines):
         line = lines[line_num]
@@ -574,7 +584,8 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
                 lines.insert(line_num+1, new_next_line)
                 break
                 ###line_num += 1
-                ###continue    
+                ###continue
+            boxes.append((locx, locx + boxsize[0], locy - boxsize[1], locy))
             cv2.putText(data, fs.string, (locx,locy), fs.face, fs.fsize, fs.clr, fs.thick)
             maxy = max(maxy, boxsize[1])
             if fs.width is not None:
@@ -590,7 +601,7 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
         line_num += 1
         locy += maxy + line_spacing
         
-    return locy
+    return locy, boxes
 
 
 def saveimage(filename, im):
diff --git a/live_vis.py b/live_vis.py
index 04f38bc20..6da5aaf38 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -114,6 +114,19 @@ def init_window(self):
         self.help_buffer = self.window_buffer.copy() # For rendering help mode
         self.help_pane.data = self.help_buffer[self.help_pane.i_begin:self.help_pane.i_end, self.help_pane.j_begin:self.help_pane.j_end]
 
+        # add listener for mouse clicks
+        cv2.setMouseCallback(self.window_name, self.on_mouse_click)
+
+    def on_mouse_click(self, event, x, y, flags, param):
+        '''
+        Handle all button presses.
+        '''
+
+        if event == cv2.EVENT_LBUTTONUP:
+            for app_name, app in self.apps.iteritems():
+                with WithTimer('%s:on_mouse_click' % app_name, quiet=self.debug_level < 1):
+                    key = app.handle_mouse_left_click(x, y, flags, param, self.panes)
+
     def check_for_control_height_update(self):
 
         if hasattr(self.settings, '_calculated_control_pane_height') and \
@@ -375,8 +388,8 @@ def draw_help(self):
         lines = []
         lines.append([FormattedString('~ ~ ~ Deep Visualization Toolbox ~ ~ ~', defaults, align='center', width=self.help_pane.j_size)])
 
-        locy = cv2_typeset_text(self.help_pane.data, lines, loc,
-                                line_spacing = self.settings.help_line_spacing)
+        locy, boxes = cv2_typeset_text(self.help_pane.data, lines, loc,
+                                       line_spacing = self.settings.help_line_spacing)
 
         for app_name, app in self.apps.iteritems():
             locy = app.draw_help(self.help_pane, locy)

From f2bf6aaf36c87b7be984f09bc711b657ee0f2377 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:33:56 +0300
Subject: [PATCH 096/106] added error handling in case of missing input

---
 input_fetcher.py | 45 ++++++++++++++++++++++++++-------------------
 1 file changed, 26 insertions(+), 19 deletions(-)

diff --git a/input_fetcher.py b/input_fetcher.py
index f881db608..ecd9faae4 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -197,24 +197,31 @@ def check_increment_and_load_image(self):
             if self.latest_static_filename != self.available_files[self.static_file_idx] or self.latest_static_frame is None:
                 self.latest_static_filename = self.available_files[self.static_file_idx]
 
-                if self.settings.is_siamese:
-                    # loading two images for siamese network
-                    im1 = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]), color=not self.settings._calculated_is_gray_model)
-                    im2 = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]), color=not self.settings._calculated_is_gray_model)
-                    if not self.static_file_stretch_mode:
-                        im1 = crop_to_square(im1)
-                        im2 = crop_to_square(im2)
+                failed = False
+                try:
+                    if self.settings.is_siamese:
+                        # loading two images for siamese network
+                        im1 = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename[0]), color=not self.settings._calculated_is_gray_model)
+                        im2 = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename[1]), color=not self.settings._calculated_is_gray_model)
+                        if not self.static_file_stretch_mode:
+                            im1 = crop_to_square(im1)
+                            im2 = crop_to_square(im2)
+
+                        im = (im1,im2)
+
+                    else:
+                        im = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename), color=not self.settings._calculated_is_gray_model)
+                        if not self.static_file_stretch_mode:
+                            im = crop_to_square(im)
+                except Exception as e:
+                    failed = True
+                    print 'Failed loading data'
+
+                if not failed:
+                    self.latest_static_frame = im
+
+                    # if we have labels, keep it
+                    if self.labels:
+                        self.latest_label = self.labels[self.static_file_idx]
 
-                    im = (im1,im2)
-
-                else:
-                    im = caffe.io.load_image(os.path.join(self.settings.static_files_dir, self.latest_static_filename), color=not self.settings._calculated_is_gray_model)
-                    if not self.static_file_stretch_mode:
-                        im = crop_to_square(im)
-
-                self.latest_static_frame = im
-
-                # if we have labels, keep it
-                if self.labels:
-                    self.latest_label = self.labels[self.static_file_idx]
             self._increment_and_set_frame(self.latest_static_frame, False)

From 8324b0374e1d210e7cbcbee9426e657610767c2d Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:35:19 +0300
Subject: [PATCH 097/106] added option to generate input images which minimize
 activations

---
 find_maxes/crop_max_patches.py       |   8 +-
 find_maxes/find_max_acts.py          |   5 +-
 find_maxes/max_tracker.py            | 114 +++++++++++++++++----------
 generate_min_and_max_input_images.sh |   6 ++
 4 files changed, 87 insertions(+), 46 deletions(-)
 create mode 100755 generate_min_and_max_input_images.sh

diff --git a/find_maxes/crop_max_patches.py b/find_maxes/crop_max_patches.py
index 6ecb734a8..b40393b67 100755
--- a/find_maxes/crop_max_patches.py
+++ b/find_maxes/crop_max_patches.py
@@ -43,6 +43,7 @@ def main():
     parser.add_argument('--datadir',      type = str, default = settings.static_files_dir, help = 'directory to look for files in')
     parser.add_argument('--filelist',     type = str, default = settings.static_files_input_file, help = 'List of image files to consider, one per line. Must be the same filelist used to produce the NetMaxTracker!')
     parser.add_argument('--outdir',       type = str, default = settings.caffevis_outputs_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{maxes,deconv,backprop}_%%03d.png')
+    parser.add_argument('--search-min',    action='store_true', default=False, help='Should we also search for minimal activations?')
     args = parser.parse_args()
 
     settings.caffevis_deploy_prototxt = args.net_prototxt
@@ -84,10 +85,13 @@ def main():
         with WithTimer('Saved %d images per unit for %s units %d:%d.' % (args.N, normalized_layer_name, idx_begin, idx_end)):
 
             output_max_patches(settings, mt, net, normalized_layer_name, idx_begin, idx_end,
-                               args.N, args.datadir, args.filelist, args.outdir,
+                               args.N, args.datadir, args.filelist, args.outdir, False,
                                (args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
 
-
+            if args.search_min:
+                output_max_patches(settings, mt, net, normalized_layer_name, idx_begin, idx_end,
+                                   args.N, args.datadir, args.filelist, args.outdir, True,
+                                   (args.do_maxes, args.do_deconv, args.do_deconv_norm, args.do_backprop, args.do_backprop_norm, args.do_info))
 
 if __name__ == '__main__':
     main()
diff --git a/find_maxes/find_max_acts.py b/find_maxes/find_max_acts.py
index 388356b24..9a4082db4 100755
--- a/find_maxes/find_max_acts.py
+++ b/find_maxes/find_max_acts.py
@@ -47,6 +47,7 @@ def main():
     parser.add_argument('--outdir', type = str, default = settings.caffevis_outputs_dir, help = 'Which output directory to use. Files are output into outdir/layer/unit_%%04d/{max_histogram}.png')
     parser.add_argument('--do-histograms', action = 'store_true', default = settings.max_tracker_do_histograms, help = 'Output histogram image file containing histogrma of max values per channel')
     parser.add_argument('--do-correlation', action = 'store_true', default = settings.max_tracker_do_correlation, help = 'Output correlation image file containing correlation of channels per layer')
+    parser.add_argument('--search-min', action='store_true', default=False, help='Should we also search for minimal activations?')
 
     args = parser.parse_args()
 
@@ -69,9 +70,9 @@ def main():
 
     with WithTimer('Scanning images'):
         if settings.is_siamese:
-            net_max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N, args.outdir)
+            net_max_tracker = scan_pairs_for_maxes(settings, net, args.datadir, args.N, args.outdir, args.search_min)
         else: # normal operation
-            net_max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N, args.outdir)
+            net_max_tracker = scan_images_for_maxes(settings, net, args.datadir, args.N, args.outdir, args.search_min)
 
     save_max_tracker_to_file(args.outfile, net_max_tracker)
 
diff --git a/find_maxes/max_tracker.py b/find_maxes/max_tracker.py
index 682b2d040..5b8dfbb56 100644
--- a/find_maxes/max_tracker.py
+++ b/find_maxes/max_tracker.py
@@ -143,15 +143,24 @@ def prepare_max_histogram(layer_name, n_channels, channel_to_histogram_values, p
 
 class MaxTracker(object):
 
-    def __init__(self, is_spatial, n_channels, n_top = 10, initial_val = -1e99, dtype = 'float32'):
+    def __init__(self, is_spatial, n_channels, n_top = 10, initial_val = -1e99, dtype = 'float32', search_min = False):
         self.is_spatial = is_spatial
-        self.max_vals = np.ones((n_channels, n_top), dtype = dtype) * initial_val
         self.n_top = n_top
+        self.search_min = search_min
+
+        self.max_vals = np.ones((n_channels, n_top), dtype=dtype) * initial_val
         if is_spatial:
             self.max_locs = -np.ones((n_channels, n_top, 4), dtype = 'int')   # image_idx, selected_input_index, i, j
         else:
             self.max_locs = -np.ones((n_channels, n_top, 2), dtype = 'int')   # image_idx, selected_input_index
 
+        if self.search_min:
+            self.min_vals = np.ones((n_channels, n_top), dtype=dtype) * (-initial_val)
+            if is_spatial:
+                self.min_locs = -np.ones((n_channels, n_top, 4), dtype='int')  # image_idx, selected_input_index, i, j
+            else:
+                self.min_locs = -np.ones((n_channels, n_top, 2), dtype='int')  # image_idx, selected_input_index
+
         # set of seen inputs, used to avoid updating on the same input twice
         self.seen_inputs = set()
 
@@ -218,20 +227,35 @@ def update(self, data, image_idx, selected_input_index, layer_unique_input_sourc
                 continue
 
             idx = np.searchsorted(self.max_vals[ii], max_value)
-            if idx == 0:
-                # Smaller than all 10
-                continue
-            # Store new max in the proper order. Update both arrays:
-            # self.max_vals:
-            self.max_vals[ii,:idx-1] = self.max_vals[ii,1:idx]       # shift lower values
-            self.max_vals[ii,idx-1] = max_value                      # store new max value
-            # self.max_locs
-            self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
-            # store new location
-            if self.is_spatial:
-                self.max_locs[ii,idx-1] = (image_idx, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
-            else:
-                self.max_locs[ii,idx-1] = (image_idx, selected_input_index)
+            # if not smaller than all elements
+            if idx != 0:
+                # Store new value in the proper order. Update both arrays:
+                # self.max_vals:
+                self.max_vals[ii,:idx-1] = self.max_vals[ii,1:idx]       # shift lower values
+                self.max_vals[ii,idx-1] = max_value                      # store new max value
+                # self.max_locs
+                self.max_locs[ii,:idx-1] = self.max_locs[ii,1:idx]       # shift lower location data
+                # store new location
+                if self.is_spatial:
+                    self.max_locs[ii,idx-1] = (image_idx, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
+                else:
+                    self.max_locs[ii,idx-1] = (image_idx, selected_input_index)
+
+            if self.search_min:
+                idx = np.searchsorted(self.min_vals[ii], max_value)
+                # if not bigger than all elements
+                if idx != self.n_top:
+                    # Store new value in the proper order. Update both arrays:
+                    # self.min_vals:
+                    self.min_vals[ii, (idx+1):(self.n_top)] = self.min_vals[ii, idx:(self.n_top-1)]  # shift upper values
+                    self.min_vals[ii, idx] = max_value  # store new value
+                    # self.min_locs
+                    self.min_locs[ii, (idx+1):(self.n_top)] = self.min_locs[ii, idx:(self.n_top-1)]  # shift upper location data
+                    # store new location
+                    if self.is_spatial:
+                        self.min_locs[ii, idx] = (image_idx, selected_input_index) + np.unravel_index(max_indexes[ii], data.shape[1:])
+                    else:
+                        self.min_locs[ii, idx] = (image_idx, selected_input_index)
 
     def calculate_histogram(self, layer_name, outdir):
 
@@ -315,10 +339,11 @@ def calculate_correlation(self, layer_name, outdir):
 
 
 class NetMaxTracker(object):
-    def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'float32'):
+    def __init__(self, settings, layers, n_top = 10, initial_val = -1e99, dtype = 'float32', search_min = False):
         self.layers = layers
         self.init_done = False
         self.n_top = n_top
+        self.search_min = search_min
         self.initial_val = initial_val
         self.settings = settings
         self.siamese_helper = SiameseHelper(self.settings.layers_list)
@@ -342,7 +367,7 @@ def _init_with_net(self, net):
             if normalized_layer_name not in self.max_trackers:
                 self.max_trackers[normalized_layer_name] = MaxTracker(is_spatial, blob.shape[1], n_top = self.n_top,
                                                                       initial_val = self.initial_val,
-                                                                      dtype = blob.dtype)
+                                                                      dtype = blob.dtype, search_min = self.search_min)
 
         self.init_done = True
 
@@ -454,7 +479,7 @@ def __setstate__(self, state):
         self.settings = None
         self.siamese_helper = None
 
-def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
+def scan_images_for_maxes(settings, net, datadir, n_top, outdir, search_min):
     image_filenames, image_labels = get_files_list(settings)
     print 'Scanning %d files' % len(image_filenames)
     print '  First file', os.path.join(datadir, image_filenames[0])
@@ -462,7 +487,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
     import caffe
 
-    tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_to_output_in_offline_scripts)
+    tracker = NetMaxTracker(settings, n_top = n_top, layers=settings.layers_to_output_in_offline_scripts, search_min=search_min)
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
 
@@ -515,7 +540,7 @@ def scan_images_for_maxes(settings, net, datadir, n_top, outdir):
     return tracker
 
 
-def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
+def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir, search_min):
     image_filenames, image_labels = get_files_list(settings)
     print 'Scanning %d pairs' % len(image_filenames)
     print '  First pair', image_filenames[0]
@@ -523,7 +548,7 @@ def scan_pairs_for_maxes(settings, net, datadir, n_top, outdir):
     sys.path.insert(0, os.path.join(settings.caffevis_caffe_root, 'python'))
     import caffe
 
-    tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_to_output_in_offline_scripts)
+    tracker = NetMaxTracker(settings, n_top=n_top, layers=settings.layers_to_output_in_offline_scripts, search_min=search_min)
 
     net_input_dims = net.blobs['data'].data.shape[2:4]
 
@@ -624,7 +649,7 @@ def save_representations(settings, net, datadir, filelist, layer_name, first_N =
     return indices,rep
 
 
-def generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm):
+def generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, search_min):
 
     # init values
     info_filename = []
@@ -634,29 +659,31 @@ def generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_de
     backprop_filenames = []
     backpropnorm_filenames = []
 
+    prefix = 'min_' if search_min else ''
+
     if do_info:
-        info_filename = [os.path.join(unit_dir, 'info.txt')]
+        info_filename = [os.path.join(unit_dir, prefix + 'info.txt')]
 
     for max_idx_0 in range(num_top):
         if do_maxes:
-            maxim_filenames.append(os.path.join(unit_dir, 'maxim_%03d.png' % max_idx_0))
+            maxim_filenames.append(os.path.join(unit_dir, prefix + 'maxim_%03d.png' % max_idx_0))
 
         if do_deconv:
-            deconv_filenames.append(os.path.join(unit_dir, 'deconv_%03d.png' % max_idx_0))
+            deconv_filenames.append(os.path.join(unit_dir, prefix + 'deconv_%03d.png' % max_idx_0))
 
         if  do_deconv_norm:
-            deconvnorm_filenames.append(os.path.join(unit_dir, 'deconvnorm_%03d.png' % max_idx_0))
+            deconvnorm_filenames.append(os.path.join(unit_dir, prefix + 'deconvnorm_%03d.png' % max_idx_0))
 
         if do_backprop:
-            backprop_filenames.append(os.path.join(unit_dir, 'backprop_%03d.png' % max_idx_0))
+            backprop_filenames.append(os.path.join(unit_dir, prefix + 'backprop_%03d.png' % max_idx_0))
 
         if do_backprop_norm:
-            backpropnorm_filenames.append(os.path.join(unit_dir, 'backpropnorm_%03d.png' % max_idx_0))
+            backpropnorm_filenames.append(os.path.join(unit_dir, prefix + 'backpropnorm_%03d.png' % max_idx_0))
 
     return (info_filename, maxim_filenames, deconv_filenames, deconvnorm_filenames, backprop_filenames, backpropnorm_filenames)
 
 
-def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_end, num_top, datadir, filelist, outdir, do_which):
+def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_end, num_top, datadir, filelist, outdir, search_min, do_which):
     do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, do_info = do_which
     assert do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm or do_info, 'nothing to do'
 
@@ -665,6 +692,9 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
 
     mt = max_tracker
 
+    locs = mt.min_locs if search_min else mt.max_locs
+    vals = mt.min_vals if search_min else mt.max_vals
+
     image_filenames, image_labels = get_files_list(settings)
 
     if settings.is_siamese:
@@ -676,7 +706,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
 
     siamese_helper = SiameseHelper(settings.layers_list)
 
-    num_top_in_mt = mt.max_locs.shape[1]
+    num_top_in_mt = locs.shape[1]
     assert num_top <= num_top_in_mt, 'Requested %d top images but MaxTracker contains only %d' % (num_top, num_top_in_mt)
     assert idx_end >= idx_begin, 'Range error'
 
@@ -710,7 +740,7 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
          deconv_filenames,
          deconvnorm_filenames,
          backprop_filenames,
-         backpropnorm_filenames] = generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm)
+         backpropnorm_filenames] = generate_output_names(unit_dir, num_top, do_info, do_maxes, do_deconv, do_deconv_norm, do_backprop, do_backprop_norm, search_min)
 
         relevant_outputs = info_filename + \
                            maxim_filenames + \
@@ -754,25 +784,25 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             if mt.is_spatial:
 
                 # fix for backward compatability
-                if mt.max_locs.shape[2] == 5:
+                if locs.shape[2] == 5:
                     # remove second column
-                    mt.max_locs = np.delete(mt.max_locs, 1, 2)
+                    locs = np.delete(locs, 1, 2)
 
-                batch[batch_index].im_idx, batch[batch_index].selected_input_index, batch[batch_index].ii, batch[batch_index].jj = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+                batch[batch_index].im_idx, batch[batch_index].selected_input_index, batch[batch_index].ii, batch[batch_index].jj = locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
             else:
                 # fix for backward compatability
-                if mt.max_locs.shape[2] == 3:
+                if locs.shape[2] == 3:
                     # remove second column
-                    mt.max_locs = np.delete(mt.max_locs, 1, 2)
+                    locs = np.delete(locs, 1, 2)
 
-                batch[batch_index].im_idx, batch[batch_index].selected_input_index = mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+                batch[batch_index].im_idx, batch[batch_index].selected_input_index = locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]
                 batch[batch_index].ii, batch[batch_index].jj = 0, 0
 
             # if ii and jj are invalid then there is no data for this "top" image, so we can skip it
             if (batch[batch_index].ii, batch[batch_index].jj) == (-1,-1):
                 continue
 
-            batch[batch_index].recorded_val = mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx]
+            batch[batch_index].recorded_val = vals[batch[batch_index].channel_idx, batch[batch_index].max_idx]
             batch[batch_index].filename = image_filenames[batch[batch_index].im_idx]
             do_print = (batch[batch_index].max_idx_0 == 0)
             if do_print:
@@ -800,11 +830,11 @@ def output_max_patches(settings, max_tracker, net, layer_name, idx_begin, idx_en
             #        batch[batch_index].data_jj_start, batch[batch_index].data_jj_end)
 
             if do_info:
-                print >> batch[batch_index].info_file, 1 if mt.is_spatial else 0, '%.6f' % mt.max_vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
+                print >> batch[batch_index].info_file, 1 if mt.is_spatial else 0, '%.6f' % vals[batch[batch_index].channel_idx, batch[batch_index].max_idx],
                 if mt.is_spatial:
-                    print >> batch[batch_index].info_file, '%d %d %d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
+                    print >> batch[batch_index].info_file, '%d %d %d %d' % tuple(locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
                 else:
-                    print >> batch[batch_index].info_file, '%d %d' % tuple(mt.max_locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
+                    print >> batch[batch_index].info_file, '%d %d' % tuple(locs[batch[batch_index].channel_idx, batch[batch_index].max_idx]),
                 print >> batch[batch_index].info_file, batch[batch_index].filename
 
             if not (do_maxes or do_deconv or do_deconv_norm or do_backprop or do_backprop_norm):
diff --git a/generate_min_and_max_input_images.sh b/generate_min_and_max_input_images.sh
new file mode 100755
index 000000000..e5f294f40
--- /dev/null
+++ b/generate_min_and_max_input_images.sh
@@ -0,0 +1,6 @@
+#!/usr/bin/env bash
+
+./find_maxes/find_max_acts.py --search-min --N=10
+./find_maxes/crop_max_patches.py --search-min --N=10
+
+

From 872aebbc75bf6e6d41734718a1544e0719b0686b Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Fri, 29 Sep 2017 14:57:55 +0300
Subject: [PATCH 098/106] - added caffe as submodule - remove need for
 base_folder in settings_user since caffe is now a submodule

---
 .gitmodules                | 3 +++
 build_default.sh           | 5 +++++
 settings.py                | 7 +++++++
 settings_model_selector.py | 2 +-
 settings_user.py.example   | 5 -----
 5 files changed, 16 insertions(+), 6 deletions(-)
 create mode 100644 .gitmodules
 create mode 100755 build_default.sh

diff --git a/.gitmodules b/.gitmodules
new file mode 100644
index 000000000..2371634f1
--- /dev/null
+++ b/.gitmodules
@@ -0,0 +1,3 @@
+[submodule "caffe"]
+	path = caffe
+	url = https://github.com/arikpoz/caffe.git
diff --git a/build_default.sh b/build_default.sh
new file mode 100755
index 000000000..34a2fe928
--- /dev/null
+++ b/build_default.sh
@@ -0,0 +1,5 @@
+cd caffe
+cp Makefile.config.example Makefile.config
+make all pycaffe
+cd ..
+cp settings_user.py.example settings_user.py
diff --git a/settings.py b/settings.py
index 57ca6db37..679533839 100644
--- a/settings.py
+++ b/settings.py
@@ -244,6 +244,13 @@
     caffevis_labels = caffevis_labels.replace('%DVT_ROOT%', dvt_root)
 if isinstance(caffevis_outputs_dir, basestring):
     caffevis_outputs_dir = caffevis_outputs_dir.replace('%DVT_ROOT%', dvt_root)
+if isinstance(static_files_input_file, basestring):
+    static_files_input_file = static_files_input_file.replace('%DVT_ROOT%', dvt_root)
+if isinstance(static_files_dir, basestring):
+    static_files_dir = static_files_dir.replace('%DVT_ROOT%', dvt_root)
+
+
+
 
 # Pause Caffe forward/backward computation for this many seconds after a keypress. This is to keep the processor free for a brief period after a keypress, which allow the interface to feel much more responsive. After this period has passed, Caffe resumes computation, in CPU mode often occupying all cores. Default: .1
 caffevis_pause_after_keys = locals().get('caffevis_pause_after_keys', .10)
diff --git a/settings_model_selector.py b/settings_model_selector.py
index d9e34e39e..698df5efb 100644
--- a/settings_model_selector.py
+++ b/settings_model_selector.py
@@ -13,7 +13,7 @@
 # Resume usual pyc creation
 sys.dont_write_bytecode = False
 
-caffevis_caffe_root = base_folder + '/caffe'
+caffevis_caffe_root = os.path.join(os.path.dirname(os.path.abspath(__file__)),'./caffe')
 
 # the following code runs dynamically the import command:
 # from models.YOUR_MODEL.settings_YOUR_MODEL import *
diff --git a/settings_user.py.example b/settings_user.py.example
index deba7a157..298ce2a0c 100644
--- a/settings_user.py.example
+++ b/settings_user.py.example
@@ -1,11 +1,6 @@
 
 ##################################### user related settings #####################################
 
-import os
-
-# base folder for all relative paths
-base_folder = os.path.expanduser('~')
-
 # Use GPU? Default is True.
 #caffevis_mode_gpu = False
 

From ec482fe807826c7b372ebf1cf589e62913e8c8ed Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 1 Oct 2017 22:20:48 +0300
Subject: [PATCH 099/106] moved model settings files into single model_settings
 folder organized settings for caffenet added files to ignore list removed ugb
 since it doesn't work

---
 .gitignore                              |   2 +
 README.md                               |  11 ++
 app_base.py                             |   2 +-
 bindings.py                             |   4 +-
 build_default.sh                        |   6 +-
 caffe                                   |   1 +
 caffevis/app.py                         | 124 ++++++++++--
 caffevis/caffe_proc_thread.py           |   4 -
 caffevis/caffevis_app_state.py          | 238 ++++++++++++++++++------
 clean_build.sh                          |   2 +
 image_misc.py                           |  10 +-
 input_fetcher.py                        |  12 ++
 live_vis.py                             |  20 +-
 model_settings/__init__.py              |   0
 model_settings/settings_caffenet_yos.py |  22 +++
 settings.py                             |  20 +-
 settings_misc.py                        |   2 +-
 settings_model_selector.py              |   2 +-
 settings_user.py.example                |   3 +-
 siamese_helper.py                       |  97 +---------
 20 files changed, 390 insertions(+), 192 deletions(-)
 create mode 160000 caffe
 create mode 100755 clean_build.sh
 create mode 100644 model_settings/__init__.py
 create mode 100644 model_settings/settings_caffenet_yos.py

diff --git a/.gitignore b/.gitignore
index b438bdc1b..04b1763ad 100644
--- a/.gitignore
+++ b/.gitignore
@@ -1,2 +1,4 @@
+*.pyc
+.idea
 settings_user.py
 
diff --git a/README.md b/README.md
index f7ca84ee2..3d687f078 100644
--- a/README.md
+++ b/README.md
@@ -1,5 +1,16 @@
 # Deep Visualization Toolbox
 
+## Installation 
+
+Following are installation instruction for the new improved version of Deepvis.
+
+    $ git clone --recursive https://github.com/arikpoz/deep-visualization-toolbox.git
+    $ cd deep-visualization-toolbox && ./build_default.sh
+    
+    
+    
+## Original Version Credit
+
 This is the code required to run the Deep Visualization Toolbox, as well as to generate the neuron-by-neuron visualizations using regularized optimization.
 The toolbox and methods are described casually [here](http://yosinski.com/deepvis) and more formally in this paper:
 
diff --git a/app_base.py b/app_base.py
index 1ebe13841..a119662e7 100644
--- a/app_base.py
+++ b/app_base.py
@@ -32,7 +32,7 @@ def draw_help(self, panes):
         '''Tells the app to draw its help screen in the given pane. No return necessary.'''
         pass
 
-    def start(self):
+    def start(self, live_vis):
         '''Notify app to start, possibly creating any necessary threads'''
         pass
 
diff --git a/bindings.py b/bindings.py
index 78f21028a..651e6efa3 100644
--- a/bindings.py
+++ b/bindings.py
@@ -117,9 +117,9 @@ def get_key_help(self, tag):
 _.add('show_back', 'a',
        'Toggle between showing forward activations and back/deconv diffs')
 _.add('next_back_view_option', 'n',
-       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, sum>0, histogram)')
+       'Cycle through backprop view options (raw, gray, norm, normblur, sum>0, histogram)')
 _.add('prev_back_view_option', 'N',
-       'Cycle through backprop view options (raw, raw>0, raw<0, gray, norm, normblur, sum>0, histogram)')
+       'Cycle through backprop view options (raw, gray, norm, normblur, sum>0, histogram)')
 _.add('next_color_map', 'm',
        'Cycle through colormaps options (grayscale, jet, plasma)')
 _.add('prev_color_map', 'M',
diff --git a/build_default.sh b/build_default.sh
index 34a2fe928..350108fa1 100755
--- a/build_default.sh
+++ b/build_default.sh
@@ -1,5 +1,9 @@
+#!/usr/bin/env bash
 cd caffe
-cp Makefile.config.example Makefile.config
+# if Makefile.config has already existed, then don't overwrite it
+if [ ! -e  Makefile.config ]; then
+    cp Makefile.config.example Makefile.config
+fi
 make all pycaffe
 cd ..
 cp settings_user.py.example settings_user.py
diff --git a/caffe b/caffe
new file mode 160000
index 000000000..7fec25bad
--- /dev/null
+++ b/caffe
@@ -0,0 +1 @@
+Subproject commit 7fec25bad4405fb97b9a48d7307d8a8bb8bacae9
diff --git a/caffevis/app.py b/caffevis/app.py
index 0e7ce6a79..9c52633aa 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -3,6 +3,9 @@
 
 # add parent folder to search path, to enable import of core modules like settings
 import os,sys,inspect
+
+from dask.array.random import normal
+
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(currentdir)
 sys.path.insert(0,parentdir)
@@ -68,11 +71,13 @@ def __init__(self, settings, key_bindings):
         self.img_cache = FIFOLimitedArrayCache(settings.caffevis_jpg_cache_size)
 
         self.header_boxes = []
+        self.buttons_boxes = []
 
-    def start(self):
+    def start(self, live_vis):
         from jpg_vis_loading_thread import JPGVisLoadingThread
 
-        self.state = CaffeVisAppState(self.net, self.settings, self.bindings)
+        self.live_vis = live_vis
+        self.state = CaffeVisAppState(self.net, self.settings, self.bindings, live_vis)
         self.state.drawing_stale = True
         self.header_print_names = [get_pretty_layer_name(self.settings, nn) for nn in self.state.get_headers()]
 
@@ -158,6 +163,8 @@ def draw(self, panes):
                 self._draw_control_pane(panes['caffevis_control'])
             if 'caffevis_status' in panes:
                 self._draw_status_pane(panes['caffevis_status'])
+            if 'caffevis_buttons' in panes:
+                self._draw_buttons_pane(panes['caffevis_buttons'])
             layer_data_3D_highres = None
             if 'caffevis_layers' in panes:
                 layer_data_3D_highres = self._draw_layer_pane(panes['caffevis_layers'])
@@ -312,8 +319,105 @@ def _draw_status_pane(self, pane):
         locy, boxes = cv2_typeset_text(pane.data, strings_line2, (loc[0], locy),
                                        line_spacing=self.settings.caffevis_status_line_spacing)
 
+    def _draw_buttons_pane(self, pane):
 
+        pane.data[:] = to_255(self.settings.window_background)
 
+        header_defaults = {'face': getattr(cv2, self.settings.caffevis_buttons_header_face),
+                           'fsize': self.settings.caffevis_buttons_header_fsize,
+                           'clr': to_255(self.settings.caffevis_buttons_header_clr),
+                           'thick': self.settings.caffevis_buttons_header_thick}
+        normal_defaults = {'face': getattr(cv2, self.settings.caffevis_buttons_normal_face),
+                           'fsize': self.settings.caffevis_buttons_normal_fsize,
+                           'clr': to_255(self.settings.caffevis_buttons_normal_clr),
+                           'thick': self.settings.caffevis_buttons_normal_thick}
+        selected_defaults = {'face': getattr(cv2, self.settings.caffevis_buttons_selected_face),
+                           'fsize': self.settings.caffevis_buttons_selected_fsize,
+                           'clr': to_255(self.settings.caffevis_buttons_selected_clr),
+                           'thick': self.settings.caffevis_buttons_selected_thick}
+
+        loc = self.settings.caffevis_buttons_loc[::-1]  # Reverse to OpenCV c,r order
+
+        text = StringIO.StringIO()
+        fps = self.proc_thread.approx_fps()
+
+        lines = list()
+
+        with self.state.lock:
+            lines.append([FormattedString('Input', header_defaults)])
+
+            file_defaults = selected_defaults if self.live_vis.input_updater.static_file_mode else normal_defaults
+            camera_defaults = selected_defaults if not self.live_vis.input_updater.static_file_mode else normal_defaults
+
+            lines.append([FormattedString('File', file_defaults), FormattedString('Prev', normal_defaults), FormattedString('Next', normal_defaults)])
+            lines.append([FormattedString('Camera', camera_defaults)])
+            lines.append([FormattedString('', normal_defaults)])
+
+            activations_defaults = selected_defaults if self.state.pattern_mode == PatternMode.OFF and not self.state.layers_show_back else normal_defaults
+            gradients_defaults = selected_defaults if self.state.pattern_mode == PatternMode.OFF and self.state.layers_show_back else normal_defaults
+            max_optimized_defaults = selected_defaults if self.state.pattern_mode == PatternMode.MAXIMAL_OPTIMIZED_IMAGE else normal_defaults
+            max_input_defaults = selected_defaults if self.state.pattern_mode == PatternMode.MAXIMAL_INPUT_IMAGE else normal_defaults
+            weights_hist_defaults = selected_defaults if self.state.pattern_mode == PatternMode.WEIGHTS_HISTOGRAM else normal_defaults
+            act_hist_defaults = selected_defaults if self.state.pattern_mode == PatternMode.MAX_ACTIVATIONS_HISTOGRAM else normal_defaults
+            weights_corr_defaults = selected_defaults if self.state.pattern_mode == PatternMode.WEIGHTS_CORRELATION else normal_defaults
+            act_corr_defaults = selected_defaults if self.state.pattern_mode == PatternMode.ACTIVATIONS_CORRELATION else normal_defaults
+            lines.append([FormattedString('Modes', header_defaults)])
+            lines.append([FormattedString('Activations', activations_defaults)])
+            lines.append([FormattedString('Gradients', gradients_defaults)])
+            lines.append([FormattedString('Maximal Optimized', max_optimized_defaults)])
+            lines.append([FormattedString('Maximal Input', max_input_defaults)])
+            lines.append([FormattedString('Weights Histogram', weights_hist_defaults)])
+            lines.append([FormattedString('Activations Histogram', act_hist_defaults)])
+            lines.append([FormattedString('Weights Correlation', weights_corr_defaults)])
+            lines.append([FormattedString('Activations Correlation', act_corr_defaults)])
+            lines.append([FormattedString('', normal_defaults)])
+
+            no_overlay_defaults = selected_defaults if self.state.input_overlay_option == InputOverlayOption.OFF else normal_defaults
+            over_active_defaults = selected_defaults if self.state.input_overlay_option == InputOverlayOption.OVER_ACTIVE else normal_defaults
+            over_inactive_defaults = selected_defaults if self.state.input_overlay_option == InputOverlayOption.OVER_INACTIVE else normal_defaults
+            lines.append([FormattedString('Input Overlay', header_defaults)])
+            lines.append([FormattedString('No Overlay', no_overlay_defaults)])
+            lines.append([FormattedString('Over Active', over_active_defaults)])
+            lines.append([FormattedString('Over Inactive', over_inactive_defaults)])
+            lines.append([FormattedString('', normal_defaults)])
+
+            backprop_no_defaults = selected_defaults if self.state.back_mode == BackpropMode.OFF else normal_defaults
+            backprop_gradients_defaults = selected_defaults if self.state.back_mode == BackpropMode.GRAD else normal_defaults
+            backprop_zf_defaults = selected_defaults if self.state.back_mode == BackpropMode.DECONV_ZF else normal_defaults
+            backprop_gb_defaults = selected_defaults if self.state.back_mode == BackpropMode.DECONV_GB else normal_defaults
+            backprop_frozen_defaults = selected_defaults if self.state.backprop_selection_frozen else normal_defaults
+            lines.append([FormattedString('Backprop Modes', header_defaults)])
+            lines.append([FormattedString('No Backprop', backprop_no_defaults)])
+            lines.append([FormattedString('Gradient', backprop_gradients_defaults)])
+            lines.append([FormattedString('ZF Deconv', backprop_zf_defaults)])
+            lines.append([FormattedString('Guided Backprop', backprop_gb_defaults)])
+            lines.append([FormattedString('Freeze Origin', backprop_frozen_defaults)])
+            lines.append([FormattedString('', normal_defaults)])
+
+            backview_raw_defaults = selected_defaults if self.state.back_view_option == BackpropViewOption.RAW else normal_defaults
+            backview_gray_defaults = selected_defaults if self.state.back_view_option == BackpropViewOption.GRAY else normal_defaults
+            backview_norm_defaults = selected_defaults if self.state.back_view_option == BackpropViewOption.NORM else normal_defaults
+            backview_normblur_defaults = selected_defaults if self.state.back_view_option == BackpropViewOption.NORM_BLUR else normal_defaults
+            backview_possum_defaults = selected_defaults if self.state.back_view_option == BackpropViewOption.POS_SUM else normal_defaults
+            backview_hist_defaults = selected_defaults if self.state.back_view_option == BackpropViewOption.HISTOGRAM else normal_defaults
+            lines.append([FormattedString('Backprop Views', header_defaults)])
+            lines.append([FormattedString('Raw', backview_raw_defaults)])
+            lines.append([FormattedString('Gray', backview_gray_defaults)])
+            lines.append([FormattedString('Norm', backview_norm_defaults)])
+            lines.append([FormattedString('Blurred Norm', backview_normblur_defaults)])
+            lines.append([FormattedString('Sum > 0', backview_possum_defaults)])
+            lines.append([FormattedString('Gradient Histogram', backview_hist_defaults)])
+            lines.append([FormattedString('', normal_defaults)])
+
+            lines.append([FormattedString('Help', normal_defaults)])
+            lines.append([FormattedString('Quit', normal_defaults)])
+
+        # strings_line1 = [[FormattedString(line, defaults)] for line in text.getvalue().split('\n')]
+
+        locy, self.buttons_boxes = cv2_typeset_text(pane.data, lines, (loc[0], loc[1] + 5),
+                                       line_spacing=self.settings.caffevis_buttons_line_spacing)
+
+        return
 
 
     def prepare_tile_image(self, display_3D, highlight_selected, n_tiles, tile_rows, tile_cols):
@@ -1358,20 +1462,6 @@ def do_raw(grad_blob, resize_shape, input_image):
                     return norm01c(grad_blob, 0)
                 grad_img = run_processing_once_or_twice(pane.data.shape, do_raw)
 
-            elif back_view_option == BackpropViewOption.RAW_POS:
-                def do_raw_pos(grad_blob, resize_shape, input_image):
-                    if len(grad_blob.shape) == 3 and grad_blob.shape[2] != 3:
-                        return np.zeros(resize_shape)
-                    return norm01c(grad_blob, 0) * (grad_blob > 0) + (0.5) * (grad_blob <= 0)
-                grad_img = run_processing_once_or_twice(pane.data.shape, do_raw_pos)
-
-            elif back_view_option == BackpropViewOption.RAW_NEG:
-                def do_raw_neg(grad_blob, resize_shape, input_image):
-                    if len(grad_blob.shape) == 3 and grad_blob.shape[2] != 3:
-                        return np.zeros(resize_shape)
-                    return norm01c(grad_blob, 0) * (grad_blob < 0) + (0.5) * (grad_blob >= 0)
-                grad_img = run_processing_once_or_twice(pane.data.shape, do_raw_neg)
-
             elif back_view_option == BackpropViewOption.GRAY:
                 def do_gray(grad_blob, resize_shape, input_image):
                     return norm01c(grad_blob.mean(axis=2), 0)
@@ -1455,7 +1545,7 @@ def handle_key(self, key, panes):
         return self.state.handle_key(key)
 
     def handle_mouse_left_click(self, x, y, flags, param, panes):
-        self.state.handle_mouse_left_click(x, y, flags, param, panes, self.header_boxes)
+        self.state.handle_mouse_left_click(x, y, flags, param, panes, self.header_boxes, self.buttons_boxes)
 
     def get_back_what_to_disp(self):
         '''Whether to show back diff information or stale or disabled indicator'''
diff --git a/caffevis/caffe_proc_thread.py b/caffevis/caffe_proc_thread.py
index f5808d9dc..97e5cea54 100644
--- a/caffevis/caffe_proc_thread.py
+++ b/caffevis/caffe_proc_thread.py
@@ -105,10 +105,6 @@ def run(self):
                     with WithTimer('CaffeProcThread:deconv', quiet=self.debug_level < 1):
                         self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit, 'Guided Backprop')
 
-                elif back_mode == BackpropMode.DECONV_UGB:
-                    with WithTimer('CaffeProcThread:deconv', quiet=self.debug_level < 1):
-                        self.state.deconv_from_layer(self.net, backprop_layer_def, backprop_unit, 'Guided Backprop', unique=True)
-
                 with self.state.lock:
                     self.state.back_stale = False
 
diff --git a/caffevis/caffevis_app_state.py b/caffevis/caffevis_app_state.py
index 8ae3fd990..84c41c79b 100644
--- a/caffevis/caffevis_app_state.py
+++ b/caffevis/caffevis_app_state.py
@@ -11,8 +11,8 @@ class PatternMode:
     MAXIMAL_INPUT_IMAGE = 2
     WEIGHTS_HISTOGRAM = 3
     MAX_ACTIVATIONS_HISTOGRAM = 4
-    ACTIVATIONS_CORRELATION = 5
-    WEIGHTS_CORRELATION = 6
+    WEIGHTS_CORRELATION = 5
+    ACTIVATIONS_CORRELATION = 6
     NUMBER_OF_MODES = 7
 
 class BackpropMode:
@@ -20,8 +20,7 @@ class BackpropMode:
     GRAD = 1
     DECONV_ZF = 2
     DECONV_GB = 3
-    DECONV_UGB = 4
-    NUMBER_OF_MODES = 5
+    NUMBER_OF_MODES = 4
 
     @staticmethod
     def to_string(back_mode):
@@ -33,30 +32,22 @@ def to_string(back_mode):
             return 'deconv zf'
         elif back_mode == BackpropMode.DECONV_GB:
             return 'deconv gb'
-        elif back_mode == BackpropMode.DECONV_UGB:
-            return 'deconv ugb'
 
         return 'n/a'
 
 class BackpropViewOption:
     RAW = 0
-    RAW_POS = 1
-    RAW_NEG = 2
-    GRAY = 3
-    NORM = 4
-    NORM_BLUR = 5
-    POS_SUM = 6
-    HISTOGRAM = 7
-    NUMBER_OF_OPTIONS = 8
+    GRAY = 1
+    NORM = 2
+    NORM_BLUR = 3
+    POS_SUM = 4
+    HISTOGRAM = 5
+    NUMBER_OF_OPTIONS = 6
 
     @staticmethod
     def to_string(back_view_option):
         if back_view_option == BackpropViewOption.RAW:
             return 'raw'
-        elif back_view_option == BackpropViewOption.RAW_POS:
-            return 'raw>0'
-        elif back_view_option == BackpropViewOption.RAW_NEG:
-            return 'raw<0'
         elif back_view_option == BackpropViewOption.GRAY:
             return 'gray'
         elif back_view_option == BackpropViewOption.NORM:
@@ -107,11 +98,12 @@ def to_string(input_overlay_option):
 class CaffeVisAppState(object):
     '''State of CaffeVis app.'''
 
-    def __init__(self, net, settings, bindings):
+    def __init__(self, net, settings, bindings, live_vis):
         self.lock = Lock()  # State is accessed in multiple threads
         self.settings = settings
         self.bindings = bindings
         self.net = net
+        self.live_vis = live_vis
 
         self.fill_layers_list(net)
 
@@ -184,7 +176,7 @@ def _reset_user_state(self):
         self.back_enabled = False
         self.back_mode = BackpropMode.OFF
         self.back_view_option = BackpropViewOption.RAW
-        self.color_map_option = ColorMapOption.GRAY
+        self.color_map_option = ColorMapOption.JET
         self.pattern_mode = PatternMode.OFF    # type of patterns to show instead of activations in layers pane: maximal optimized image, maximal input image, maximal histogram, off
         self.pattern_first_only = True         # should we load only the first pattern image for each neuron, or all the relevant images per neuron
         self.layers_pane_zoom_mode = 0       # 0: off, 1: zoom selected (and show pref in small pane), 2: zoom backprop
@@ -243,43 +235,28 @@ def handle_key(self, key):
                 self.layer_boost_gamma_idx = (self.layer_boost_gamma_idx + 1) % len(self.layer_boost_gamma_choices)
                 self.layer_boost_gamma = self.layer_boost_gamma_choices[self.layer_boost_gamma_idx]
             elif tag == 'next_pattern_mode':
-                self.pattern_mode = (self.pattern_mode + 1) % PatternMode.NUMBER_OF_MODES
-                self.validate_state_for_summary_only_patterns()
+                self.set_pattern_mode((self.pattern_mode + 1) % PatternMode.NUMBER_OF_MODES)
 
             elif tag == 'prev_pattern_mode':
-                self.pattern_mode = (self.pattern_mode - 1 + PatternMode.NUMBER_OF_MODES) % PatternMode.NUMBER_OF_MODES
-                self.validate_state_for_summary_only_patterns()
+                self.set_pattern_mode((self.pattern_mode - 1 + PatternMode.NUMBER_OF_MODES) % PatternMode.NUMBER_OF_MODES)
 
             elif tag == 'pattern_first_only':
                 self.pattern_first_only = not self.pattern_first_only
+
             elif tag == 'show_back':
-                # If in pattern mode: switch to fwd/back. Else toggle fwd/back mode
-                if self.pattern_mode != PatternMode.OFF:
-                    self.pattern_mode = PatternMode.OFF
-                else:
-                    self.layers_show_back = not self.layers_show_back
-                if self.layers_show_back:
-                    if not self.back_enabled:
-                        if self.back_mode == BackpropMode.OFF:
-                            self.back_mode = BackpropMode.GRAD
-                        self.back_enabled = True
-                        self.back_stale = True
+                self.set_show_back(not self.layers_show_back)
 
             elif tag == 'next_ez_back_mode_loop':
-                self.back_mode = (self.back_mode + 1) % BackpropMode.NUMBER_OF_MODES
-                self.back_enabled = (self.back_mode != BackpropMode.OFF)
-                self.back_stale = True
+                self.set_back_mode((self.back_mode + 1) % BackpropMode.NUMBER_OF_MODES)
 
             elif tag == 'prev_ez_back_mode_loop':
-                self.back_mode = (self.back_mode - 1 + BackpropMode.NUMBER_OF_MODES) % BackpropMode.NUMBER_OF_MODES
-                self.back_enabled = (self.back_mode != BackpropMode.OFF)
-                self.back_stale = True
+                self.set_back_mode((self.back_mode - 1 + BackpropMode.NUMBER_OF_MODES) % BackpropMode.NUMBER_OF_MODES)
 
             elif tag == 'next_back_view_option':
-                self.back_view_option = (self.back_view_option + 1) % BackpropViewOption.NUMBER_OF_OPTIONS
+                self.set_back_view_option((self.back_view_option + 1) % BackpropViewOption.NUMBER_OF_OPTIONS)
 
             elif tag == 'prev_back_view_option':
-                self.back_view_option = (self.back_view_option - 1 + BackpropViewOption.NUMBER_OF_OPTIONS) % BackpropViewOption.NUMBER_OF_OPTIONS
+                self.set_back_view_option((self.back_view_option - 1 + BackpropViewOption.NUMBER_OF_OPTIONS) % BackpropViewOption.NUMBER_OF_OPTIONS)
 
             elif tag == 'next_color_map':
                 self.color_map_option = (self.color_map_option  + 1) % ColorMapOption.NUMBER_OF_OPTIONS
@@ -288,13 +265,8 @@ def handle_key(self, key):
                 self.color_map_option  = (self.color_map_option  - 1 + ColorMapOption.NUMBER_OF_OPTIONS) % ColorMapOption.NUMBER_OF_OPTIONS
 
             elif tag == 'freeze_back_unit':
-                # Freeze selected layer/unit as backprop unit
-                self.backprop_selection_frozen = not self.backprop_selection_frozen
-                if self.backprop_selection_frozen:
-                    # Grap layer/selected_unit upon transition from non-frozen -> frozen
-                    self.backprop_layer_idx = self.layer_idx
-                    self.backprop_unit = self.selected_unit
-                    self.backprop_siamese_view_mode = self.siamese_view_mode
+                self.toggle_freeze_back_unit()
+
             elif tag == 'zoom_mode':
                 self.layers_pane_zoom_mode = (self.layers_pane_zoom_mode + 1) % 3
                 if self.layers_pane_zoom_mode == 2 and not self.back_enabled:
@@ -314,10 +286,10 @@ def handle_key(self, key):
                 self.show_maximal_score = not self.show_maximal_score
 
             elif tag == 'next_input_overlay':
-                self.input_overlay_option = (self.input_overlay_option + 1) % InputOverlayOption.NUMBER_OF_OPTIONS
+                self.set_input_overlay((self.input_overlay_option + 1) % InputOverlayOption.NUMBER_OF_OPTIONS)
 
             elif tag == 'prev_input_overlay':
-                self.input_overlay_option  = (self.input_overlay_option  - 1 + InputOverlayOption.NUMBER_OF_OPTIONS) % InputOverlayOption.NUMBER_OF_OPTIONS
+                self.set_input_overlay((self.input_overlay_option  - 1 + InputOverlayOption.NUMBER_OF_OPTIONS) % InputOverlayOption.NUMBER_OF_OPTIONS)
 
             else:
                 key_handled = False
@@ -328,7 +300,7 @@ def handle_key(self, key):
 
         return (None if key_handled else key)
 
-    def handle_mouse_left_click(self, x, y, flags, param, panes, header_boxes):
+    def handle_mouse_left_click(self, x, y, flags, param, panes, header_boxes, buttons_boxes):
 
         for pane_name, pane in panes.items():
             if pane.j_begin <= x < pane.j_end and pane.i_begin <= y < pane.i_end:
@@ -337,7 +309,7 @@ def handle_mouse_left_click(self, x, y, flags, param, panes, header_boxes):
 
                     # search for layer clicked on
                     for box_idx, box in enumerate(header_boxes):
-                        start_x, end_x, start_y, end_y = box
+                        start_x, end_x, start_y, end_y, text = box
                         if start_x <= x - pane.j_begin < end_x and start_y <= y - pane.i_begin <= end_y:
                             # print 'layers list clicked on layer %d (%s,%s)' % (box_idx, x, y)
                             self.layer_idx = box_idx
@@ -371,6 +343,123 @@ def handle_mouse_left_click(self, x, y, flags, param, panes, header_boxes):
                     self.drawing_stale = True  # Request redraw any time we handled the mouse
                     return
 
+                elif pane_name == 'caffevis_buttons':
+                    # print 'buttons!'
+
+                    # search for layer clicked on
+                    for box_idx, box in enumerate(buttons_boxes):
+                        start_x, end_x, start_y, end_y, text = box
+                        if start_x <= x - pane.j_begin < end_x and start_y <= y - pane.i_begin <= end_y:
+                            # print 'DEBUG: pressed %s' % text
+
+                            if text == 'File':
+                                self.live_vis.input_updater.set_mode_static()
+                                pass
+                            elif text == 'Prev':
+                                self.live_vis.input_updater.prev_image()
+                                pass
+                            elif text == 'Next':
+                                self.live_vis.input_updater.next_image()
+                                pass
+                            elif text == 'Camera':
+                                self.live_vis.input_updater.set_mode_cam()
+                                pass
+
+                            elif text == 'Modes':
+                                pass
+                            elif text == 'Activations':
+                                self.set_show_back(False)
+                                pass
+                            elif text == 'Gradients':
+                                self.set_show_back(True)
+                                pass
+                            elif text == 'Maximal Optimized':
+                                with self.lock:
+                                    self.set_pattern_mode(PatternMode.MAXIMAL_OPTIMIZED_IMAGE)
+                                pass
+                            elif text == 'Maximal Input':
+                                with self.lock:
+                                    self.set_pattern_mode(PatternMode.MAXIMAL_INPUT_IMAGE)
+                                pass
+                            elif text == 'Weights Histogram':
+                                with self.lock:
+                                    self.set_pattern_mode(PatternMode.WEIGHTS_HISTOGRAM)
+                                pass
+                            elif text == 'Activations Histogram':
+                                with self.lock:
+                                    self.set_pattern_mode(PatternMode.MAX_ACTIVATIONS_HISTOGRAM)
+                                pass
+                            elif text == 'Weights Correlation':
+                                with self.lock:
+                                    self.set_pattern_mode(PatternMode.WEIGHTS_CORRELATION)
+                                pass
+                            elif text == 'Activations Correlation':
+                                with self.lock:
+                                    self.set_pattern_mode(PatternMode.ACTIVATIONS_CORRELATION)
+                                pass
+
+                            elif text == 'Input Overlay':
+                                pass
+                            elif text == 'No Overlay':
+                                self.set_input_overlay(InputOverlayOption.OFF)
+                                pass
+                            elif text == 'Over Active':
+                                self.set_input_overlay(InputOverlayOption.OVER_ACTIVE)
+                                pass
+                            elif text == 'Over Inactive':
+                                self.set_input_overlay(InputOverlayOption.OVER_INACTIVE)
+                                pass
+
+                            elif text == 'Backprop Modes':
+                                pass
+                            elif text == 'No Backprop':
+                                self.set_back_mode(BackpropMode.OFF)
+                                pass
+                            elif text == 'Gradient':
+                                self.set_back_mode(BackpropMode.GRAD)
+                                pass
+                            elif text == 'ZF Deconv':
+                                self.set_back_mode(BackpropMode.DECONV_ZF)
+                                pass
+                            elif text == 'Guided Backprop':
+                                self.set_back_mode(BackpropMode.DECONV_GB)
+                                pass
+                            elif text == 'Freeze Origin':
+                                self.toggle_freeze_back_unit()
+                                pass
+
+                            elif text == 'Backprop Views':
+                                pass
+                            elif text == 'Raw':
+                                self.set_back_view_option(BackpropViewOption.RAW)
+                                pass
+                            elif text == 'Gray':
+                                self.set_back_view_option(BackpropViewOption.GRAY)
+                                pass
+                            elif text == 'Norm':
+                                self.set_back_view_option(BackpropViewOption.NORM)
+                                pass
+                            elif text == 'Blurred Norm':
+                                self.set_back_view_option(BackpropViewOption.NORM_BLUR)
+                                pass
+                            elif text == 'Sum > 0':
+                                self.set_back_view_option(BackpropViewOption.POS_SUM)
+                                pass
+                            elif text == 'Gradient Histogram':
+                                self.set_back_view_option(BackpropViewOption.HISTOGRAM)
+                                pass
+
+                            elif text == 'Help':
+                                self.live_vis.toggle_help_mode()
+                                pass
+
+                            elif text == 'Quit':
+                                self.live_vis.set_quit_flag()
+                                pass
+
+                            self._ensure_valid_selected()
+                            self.drawing_stale = True
+                            return
 
                 else:
                     # print "Clicked: %s - %s" % (x, y)
@@ -434,10 +523,10 @@ def backward_from_layer(self, net, backprop_layer_def, backprop_unit):
             with self.lock:
                 self.back_enabled = False
 
-    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit, deconv_type, unique=False):
+    def deconv_from_layer(self, net, backprop_layer_def, backprop_unit, deconv_type):
 
         try:
-            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_view_mode, deconv_type, unique)
+            return SiameseHelper.deconv_from_layer(net, backprop_layer_def, backprop_unit, self.siamese_view_mode, deconv_type)
         except AttributeError:
             print 'ERROR: required bindings (deconv_from_layer) not found! Try using the deconv-deep-vis-toolbox branch as described here: https://github.com/yosinski/deep-visualization-toolbox'
             raise
@@ -580,3 +669,40 @@ def get_layer_output_size_string(self, layer_def=None):
     def validate_state_for_summary_only_patterns(self):
         if self.pattern_mode in [PatternMode.ACTIVATIONS_CORRELATION, PatternMode.WEIGHTS_CORRELATION]:
             self.cursor_area = 'top'
+
+    def set_pattern_mode(self, pattern_mode):
+        self.pattern_mode = pattern_mode
+        self.validate_state_for_summary_only_patterns()
+
+    def set_show_back(self, show_back):
+        # If in pattern mode: switch to fwd/back. Else toggle fwd/back mode
+        if self.pattern_mode != PatternMode.OFF:
+            self.set_pattern_mode(PatternMode.OFF)
+
+        self.layers_show_back = show_back
+        if self.layers_show_back:
+            if not self.back_enabled:
+                if self.back_mode == BackpropMode.OFF:
+                    self.back_mode = BackpropMode.GRAD
+                self.back_enabled = True
+                self.back_stale = True
+
+    def set_input_overlay(self, input_overlay):
+        self.input_overlay_option = input_overlay
+
+    def set_back_mode(self, back_mode):
+        self.back_mode = back_mode
+        self.back_enabled = (self.back_mode != BackpropMode.OFF)
+        self.back_stale = True
+
+    def toggle_freeze_back_unit(self):
+        # Freeze selected layer/unit as backprop unit
+        self.backprop_selection_frozen = not self.backprop_selection_frozen
+        if self.backprop_selection_frozen:
+            # Grap layer/selected_unit upon transition from non-frozen -> frozen
+            self.backprop_layer_idx = self.layer_idx
+            self.backprop_unit = self.selected_unit
+            self.backprop_siamese_view_mode = self.siamese_view_mode
+
+    def set_back_view_option(self, back_view_option):
+        self.back_view_option = back_view_option
diff --git a/clean_build.sh b/clean_build.sh
new file mode 100755
index 000000000..f352ecd63
--- /dev/null
+++ b/clean_build.sh
@@ -0,0 +1,2 @@
+cd caffe
+make clean
diff --git a/image_misc.py b/image_misc.py
index 0ed99081b..e21057279 100644
--- a/image_misc.py
+++ b/image_misc.py
@@ -3,11 +3,16 @@
 import io
 import cv2
 import numpy as np
+
+# note: these horrible lines solves an unknown segmentation fault, presumably related to opencv / skimage version issues
+cv2.namedWindow('test')
+cv2.destroyWindow('test')
+
 import skimage
 import skimage.io
-from copy import deepcopy
 import matplotlib.pyplot as plt
 
+
 from misc import WithTimer
 
 
@@ -561,6 +566,7 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
         maxy = 0
         locx = loc[0]
         for ii,fs in enumerate(line):
+            text = fs.string
             last_on_line = (ii == len(line) - 1)
             if not last_on_line:
                 fs.string += between
@@ -585,7 +591,7 @@ def cv2_typeset_text(data, lines, loc, between = ' ', string_spacing = 0, line_s
                 break
                 ###line_num += 1
                 ###continue
-            boxes.append((locx, locx + boxsize[0], locy - boxsize[1], locy))
+            boxes.append((locx, locx + boxsize[0], locy - boxsize[1], locy, text))
             cv2.putText(data, fs.string, (locx,locy), fs.face, fs.fsize, fs.clr, fs.thick)
             maxy = max(maxy, boxsize[1])
             if fs.width is not None:
diff --git a/input_fetcher.py b/input_fetcher.py
index ecd9faae4..d489f8449 100644
--- a/input_fetcher.py
+++ b/input_fetcher.py
@@ -172,6 +172,18 @@ def increment_static_file_idx(self, amount = 1):
         with self.lock:
             self.static_file_idx_increment += amount
 
+    def next_image(self):
+        if self.static_file_mode:
+            self.increment_static_file_idx(1)
+        else:
+            self.static_file_mode = True
+
+    def prev_image(self):
+        if self.static_file_mode:
+            self.increment_static_file_idx(-1)
+        else:
+            self.static_file_mode = True
+
     def _increment_and_set_frame(self, frame, from_cam):
         assert frame is not None
         with self.lock:
diff --git a/live_vis.py b/live_vis.py
index 6da5aaf38..03eec9211 100644
--- a/live_vis.py
+++ b/live_vis.py
@@ -168,7 +168,7 @@ def run_loop(self):
         heartbeat_functions = [self.input_updater.heartbeat]
         for app_name, app in self.apps.iteritems():
             print 'Starting app:', app_name
-            app.start()
+            app.start(self)
             heartbeat_functions.extend(app.get_heartbeats())
 
         ii = 0
@@ -321,17 +321,11 @@ def handle_key_pre_apps(self, key):
         elif tag == 'toggle_input_mode':
             self.input_updater.toggle_input_mode()
         elif tag == 'static_file_increment':
-            if self.input_updater.static_file_mode:
-                self.input_updater.increment_static_file_idx(1)
-            else:
-                self.input_updater.static_file_mode = True
+            self.input_updater.next_image()
         elif tag == 'static_file_decrement':
-            if self.input_updater.static_file_mode:
-                self.input_updater.increment_static_file_idx(-1)
-            else:
-                self.input_updater.static_file_mode = True
+            self.input_updater.prev_image()
         elif tag == 'help_mode':
-            self.help_mode = not self.help_mode
+            self.toggle_help_mode()
         elif tag == 'stretch_mode':
             self.input_updater.toggle_stretch_mode()
             print 'Stretch mode is now', self.input_updater.static_file_stretch_mode
@@ -346,7 +340,7 @@ def handle_key_pre_apps(self, key):
     def handle_key_post_apps(self, key):
         tag = self.bindings.get_tag(key)
         if tag == 'quit':
-            self.quit = True
+            self.set_quit_flag()
         elif key == None:
             pass
         else:
@@ -394,7 +388,11 @@ def draw_help(self):
         for app_name, app in self.apps.iteritems():
             locy = app.draw_help(self.help_pane, locy)
 
+    def toggle_help_mode(self):
+        self.help_mode = not self.help_mode
 
+    def set_quit_flag(self):
+        self.quit = True
 
 if __name__ == '__main__':
     print 'You probably want to run ./run_toolbox.py instead.'
diff --git a/model_settings/__init__.py b/model_settings/__init__.py
new file mode 100644
index 000000000..e69de29bb
diff --git a/model_settings/settings_caffenet_yos.py b/model_settings/settings_caffenet_yos.py
new file mode 100644
index 000000000..ad113377d
--- /dev/null
+++ b/model_settings/settings_caffenet_yos.py
@@ -0,0 +1,22 @@
+
+# basic network configuration
+base_folder = '%DVT_ROOT%/'
+caffevis_deploy_prototxt = base_folder + './models/caffenet-yos/caffenet-yos-deploy.prototxt'
+caffevis_network_weights = base_folder + './models/caffenet-yos/caffenet-yos-weights'
+caffevis_data_mean       = base_folder + './models/caffenet-yos/ilsvrc_2012_mean.npy'
+
+# input images
+static_files_dir = base_folder + './input_images/'
+
+# UI customization
+caffevis_label_layers    = ['fc8', 'prob']
+caffevis_labels          = base_folder + './models/caffenet-yos/ilsvrc_2012_labels.txt'
+caffevis_prob_layer      = 'prob'
+
+def caffevis_layer_pretty_name_fn(name):
+    return name.replace('pool','p').replace('norm','n')
+
+# offline scripts configuration
+# caffevis_outputs_dir = base_folder + './models/caffenet-yos/unit_jpg_vis'
+caffevis_outputs_dir = base_folder + './models/caffenet-yos/outputs'
+layers_to_output_in_offline_scripts = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob']
diff --git a/settings.py b/settings.py
index 679533839..b8f7db7ce 100644
--- a/settings.py
+++ b/settings.py
@@ -81,6 +81,7 @@
     ('caffevis_control', (   0,  300,      3*20,   900)),
     ('caffevis_layers',  (3*20,  300,  900-3*20,   900)),
     ('caffevis_jpgvis',  (   0, 1200,       900,   300)),
+    ('caffevis_buttons', (   0, 1500,       900,   300)),
 )
 window_panes = locals().get('window_panes', default_window_panes)
 
@@ -320,6 +321,23 @@
 caffevis_status_thick = locals().get('caffevis_status_thick', 1)
 caffevis_jpgvis_stack_vert = locals().get('caffevis_jpgvis_stack_vert', True)
 
+# Font settings for buttons pane (left most pane)
+caffevis_buttons_header_face = locals().get('caffevis_buttons_header_face', 'FONT_HERSHEY_COMPLEX_SMALL')
+caffevis_buttons_header_fsize = locals().get('caffevis_buttons_header_fsize', 1.0 * global_font_size)
+caffevis_buttons_header_clr = locals().get('caffevis_buttons_header_clr', (.8,.8,.8))
+caffevis_buttons_header_thick = locals().get('caffevis_buttons_header_thick', 2)
+caffevis_buttons_normal_face = locals().get('caffevis_buttons_normal_face', 'FONT_HERSHEY_COMPLEX_SMALL')
+caffevis_buttons_normal_fsize = locals().get('caffevis_buttons_normal_fsize', 1.0 * global_font_size)
+caffevis_buttons_normal_clr = locals().get('caffevis_buttons_normal_clr', (.8,.8,.8))
+caffevis_buttons_normal_thick = locals().get('caffevis_buttons_normal_thick', 1)
+caffevis_buttons_selected_face = locals().get('caffevis_buttons_selected_face', 'FONT_HERSHEY_COMPLEX_SMALL')
+caffevis_buttons_selected_fsize = locals().get('caffevis_buttons_selected_fsize', 1.0 * global_font_size)
+caffevis_buttons_selected_clr = locals().get('caffevis_buttons_selected_clr', (.5,1,.5))
+caffevis_buttons_selected_thick = locals().get('caffevis_buttons_selected_thick', 1)
+
+caffevis_buttons_loc = locals().get('caffevis_buttons_loc', (15,10))   # r,c order
+caffevis_buttons_line_spacing = locals().get('caffevis_buttons_line_spacing', 10)     # extra pixel spacing between lines
+
 # Font settings for class prob output (top 5 classes listed on left)
 caffevis_class_face = locals().get('caffevis_class_face', 'FONT_HERSHEY_COMPLEX_SMALL')
 caffevis_class_loc = locals().get('caffevis_class_loc', (20,10))   # r,c order
@@ -361,7 +379,7 @@
 caffe_net_transpose = locals().get('caffe_net_transpose', None)
 
 # caffe net parameter - raw scale, multiplies input BEFORE mean subtraction
-caffe_net_raw_scale = locals().get('caffe_net_raw_scale', None)
+caffe_net_raw_scale = locals().get('caffe_net_raw_scale', 255.0)
 
 # caffe net parameter - input scale, multiplies input AFTER mean subtraction
 caffe_net_input_scale = locals().get('caffe_net_input_scale', None)
diff --git a/settings_misc.py b/settings_misc.py
index e6e029238..ebaba79ab 100644
--- a/settings_misc.py
+++ b/settings_misc.py
@@ -28,7 +28,7 @@ def set_calculated_is_gray_model(settings, net):
         channels = input_shape[1]
         if channels == 1:
             settings._calculated_is_gray_model = True
-        elif channels == 2 and setings.is_siamese:
+        elif channels == 2 and settings.is_siamese:
             settings._calculated_is_gray_model = True
         elif channels == 3:
             settings._calculated_is_gray_model = False
diff --git a/settings_model_selector.py b/settings_model_selector.py
index 698df5efb..b37cf17cb 100644
--- a/settings_model_selector.py
+++ b/settings_model_selector.py
@@ -17,5 +17,5 @@
 
 # the following code runs dynamically the import command:
 # from models.YOUR_MODEL.settings_YOUR_MODEL import *
-import_code = 'from models.' + model_to_load + '.settings_' + model_to_load + ' import *'
+import_code = 'from model_settings.settings_' + model_to_load + ' import *'
 exec (import_code, globals())
diff --git a/settings_user.py.example b/settings_user.py.example
index 298ce2a0c..ede8b2886 100644
--- a/settings_user.py.example
+++ b/settings_user.py.example
@@ -7,5 +7,4 @@
 #caffevis_gpu_id = 0
 
 # network selection, for network settings see file settings_local.py
-model_to_load = 'model'
-#model_to_load = 'caffenet_yos'
+model_to_load = 'caffenet_yos'
diff --git a/siamese_helper.py b/siamese_helper.py
index ffc3aba40..3ea78d0c5 100644
--- a/siamese_helper.py
+++ b/siamese_helper.py
@@ -419,7 +419,7 @@ def backward_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mod
         pass
 
     @staticmethod
-    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mode, deconv_type, unique=False):
+    def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mode, deconv_type):
 
         # if we are in siamese_batch_pair, we don't care of siamese_view_mode since we must do deconv on the 2-batch
         # otherwise, if we are in siamese_layer_pair, we do it on both layers only if both deconv are requested
@@ -436,21 +436,14 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mode,
             diffs1 = expand_dims(diffs1, 0)
 
             if backprop_layer_def['format'] == 'siamese_layer_pair':
-                if not unique:
-                    net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True, deconv_type=deconv_type)
-                    net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True, deconv_type=deconv_type)
-                else:
-                    SiameseHelper.siamese_unique_deconv_from_layer(net, backprop_layer_def['name/s'][0], backprop_layer_def['name/s'][1], diffs0, diffs1, deconv_type)
+                net.deconv_from_layer(backprop_layer_def['name/s'][0], diffs0, zero_higher=True, deconv_type=deconv_type)
+                net.deconv_from_layer(backprop_layer_def['name/s'][1], diffs1, zero_higher=True, deconv_type=deconv_type)
 
             elif backprop_layer_def['format'] == 'siamese_batch_pair':
                 # combine them to 2-batch and send once
                 diffs = concatenate((diffs0, diffs1), axis=0)
 
-                if not unique:
-                    net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True, deconv_type=deconv_type)
-
-                else:  # unique deconv
-                    SiameseHelper.unique_deconv_from_layer(net, backprop_layer_def['name/s'], diffs, deconv_type)
+                net.deconv_from_layer(backprop_layer_def['name/s'], diffs, zero_higher=True, deconv_type=deconv_type)
 
         else: # normal layer, or siamese layer but siamese input mode is 'first' or 'second'
 
@@ -464,89 +457,7 @@ def deconv_from_layer(net, backprop_layer_def, backprop_unit, siamese_view_mode,
 
             selected_backprop_layer_name = SiameseHelper.get_single_selected_layer_name(backprop_layer_def, siamese_view_mode)
 
-            if not unique:
-                net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
-
-            else: # unique deconv
-                SiameseHelper.unique_deconv_from_layer(net, selected_backprop_layer_name, diffs, deconv_type)
-
-    @staticmethod
-    def unique_deconv_from_layer(net, selected_backprop_layer_name, diffs, deconv_type):
-        try:
-            # invert weights of selected layer
-            net.params[selected_backprop_layer_name][0].data[...] *= -1
-            # run deconv using invereted weights on last layer
-            net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
-            # save gradients of first selected unit
-            first_dict = dict()
-            for key in net.blobs.keys():
-                # L1 normalize first signal
-                first = net.blobs[key].diff.copy()
-                sum_first = np.sum(np.abs(first))
-                first /= (sum_first + np.finfo(np.float32).eps)
-
-                # keep first signal
-                first_dict[key] = first
-
-            # invert weights back
-            net.params[selected_backprop_layer_name][0].data[...] *= -1
-            # run deconv using normal weights on last layer
             net.deconv_from_layer(selected_backprop_layer_name, diffs, zero_higher=True, deconv_type=deconv_type)
-            for key in net.blobs.keys():
-                # L1 normalize current signal
-                current = net.blobs[key].diff.copy()
-                sum_current = np.sum(np.abs(current))
-                current /= (sum_current + np.finfo(np.float32).eps)
-
-                # get normalized first signal
-                first = first_dict[key]
-
-                net.blobs[key].diff[...] = current - first
-        except Exception as err:
-            print "ERROR while running unique_deconv_from_layer(), error:", str(err)
-            for key in net.blobs.keys():
-                net.blobs[key].diff[...] = 0
-
-    @staticmethod
-    def siamese_unique_deconv_from_layer(net, backprop_layer_name1, backprop_layer_name2, diffs1, diffs2, deconv_type):
-        try:
-            # invert weights of selected layer
-            # note: we only need to inverse the weights of the first layer, since they are shared
-            net.params[backprop_layer_name1][0].data[...] *= -1
-            # run deconv using invereted weights on last layer
-            net.deconv_from_layer(backprop_layer_name1, diffs1, zero_higher=True, deconv_type=deconv_type)
-            net.deconv_from_layer(backprop_layer_name2, diffs2, zero_higher=True, deconv_type=deconv_type)
-            # save gradients of first selected unit
-            first_dict = dict()
-            for key in net.blobs.keys():
-                # L1 normalize first signal
-                first = net.blobs[key].diff.copy()
-                sum_first = np.sum(np.abs(first))
-                first /= (sum_first + np.finfo(np.float32).eps)
-
-                # keep first signal
-                first_dict[key] = first
-
-            # invert weights back
-            net.params[backprop_layer_name1][0].data[...] *= -1
-            # run deconv using normal weights on last layer
-            net.deconv_from_layer(backprop_layer_name1, diffs1, zero_higher=True, deconv_type=deconv_type)
-            net.deconv_from_layer(backprop_layer_name2, diffs2, zero_higher=True, deconv_type=deconv_type)
-            for key in net.blobs.keys():
-                # L1 normalize current signal
-                current = net.blobs[key].diff.copy()
-                sum_current = np.sum(np.abs(current))
-                current /= (sum_current + np.finfo(np.float32).eps)
-
-                # get normalized first signal
-                first = first_dict[key]
-
-                net.blobs[key].diff[...] = current - first
-
-        except Exception as err:
-            print "ERROR while running siamese_unique_deconv_from_layer(), error:", str(err)
-            for key in net.blobs.keys():
-                net.blobs[key].diff[...] = 0
 
     @staticmethod
     def get_image_from_frame(frame, is_siamese, image_shape, siamese_view_mode):

From b8ae04b35d1805358d19caa98c7c4846e65cabb4 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 1 Oct 2017 22:43:26 +0300
Subject: [PATCH 100/106] updated settings for googlenet and squeezenet

---
 model_settings/settings_bvlc_googlenet.py     | 43 ++++++++++++++++
 model_settings/settings_squeezenet.py         | 25 +++++++++
 models/.gitignore                             |  3 ++
 .../settings_local.template-bvlc-googlenet.py | 50 ------------------
 .../settings_local.template-caffenet-yos.py   | 35 -------------
 .../settings_local.template-squeezenet.py     | 51 -------------------
 settings_user.py.example                      |  4 +-
 7 files changed, 74 insertions(+), 137 deletions(-)
 create mode 100644 model_settings/settings_bvlc_googlenet.py
 create mode 100644 model_settings/settings_squeezenet.py
 delete mode 100644 models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
 delete mode 100644 models/caffenet-yos/settings_local.template-caffenet-yos.py
 delete mode 100644 models/squeezenet/settings_local.template-squeezenet.py

diff --git a/model_settings/settings_bvlc_googlenet.py b/model_settings/settings_bvlc_googlenet.py
new file mode 100644
index 000000000..5000578c8
--- /dev/null
+++ b/model_settings/settings_bvlc_googlenet.py
@@ -0,0 +1,43 @@
+
+# basic network configuration
+base_folder = '%DVT_ROOT%/'
+caffevis_deploy_prototxt = base_folder + './models/bvlc-googlenet/bvlc-googlenet-deploy.prototxt'
+caffevis_network_weights = base_folder + './models/bvlc-googlenet/bvlc_googlenet.caffemodel'
+caffevis_data_mean       = (104, 117, 123)
+
+# input images
+static_files_dir = base_folder + './input_images/'
+
+# UI customization
+caffevis_labels          = base_folder + './models/bvlc-googlenet/ilsvrc_2012_labels.txt'
+caffevis_prob_layer      = 'prob'
+caffevis_label_layers    = ['loss3/classifier', 'prob']
+
+layers_list = []
+layers_list.append({'name/s': 'conv1/7x7_s2', 'format': 'normal'})
+layers_list.append({'name/s': 'conv2/3x3_reduce', 'format': 'normal'})
+layers_list.append({'name/s': 'conv2/3x3', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_3a/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_3b/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_4a/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_4b/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_4c/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_4d/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_4e/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_5a/output', 'format': 'normal'})
+layers_list.append({'name/s': 'inception_5b/output', 'format': 'normal'})
+layers_list.append({'name/s': 'prob', 'format': 'normal'})
+
+def caffevis_layer_pretty_name_fn(name):
+    # Shorten many layer names to fit in control pane (full layer name visible in status bar)
+    name = name.replace('conv','c').replace('pool','p').replace('norm','n')
+    name = name.replace('inception_','i').replace('output','o').replace('reduce','r').replace('split_','s')
+    name = name.replace('__','_').replace('__','_')
+    return name
+
+# offline scripts configuration
+caffevis_outputs_dir = base_folder + './models/bvlc-googlenet/outputs'
+layers_to_output_in_offline_scripts = ['conv1/7x7_s2', 'conv2/3x3_reduce', 'conv2/3x3', 'inception_3a/output',
+                                       'inception_3b/output', 'inception_4a/output', 'inception_4b/output',
+                                       'inception_4c/output', 'inception_4d/output', 'inception_4e/output',
+                                       'inception_5a/output', 'inception_5b/output', 'prob']
diff --git a/model_settings/settings_squeezenet.py b/model_settings/settings_squeezenet.py
new file mode 100644
index 000000000..6318f620d
--- /dev/null
+++ b/model_settings/settings_squeezenet.py
@@ -0,0 +1,25 @@
+
+# basic network configuration
+base_folder = '%DVT_ROOT%/'
+caffevis_deploy_prototxt = base_folder + './models/squeezenet/deploy.prototxt'
+caffevis_network_weights = base_folder + './models/squeezenet/squeezenet_v1.0.caffemodel'
+caffevis_data_mean       = (104, 117, 123)
+
+# input images
+static_files_dir = base_folder + './input_images/'
+
+# UI customization
+caffevis_labels          = base_folder + './models/squeezenet/ilsvrc_2012_labels.txt'
+caffevis_prob_layer      = 'prob'
+caffevis_label_layers    = ['conv10', 'relu_conv10', 'pool10', 'prob']
+
+def caffevis_layer_pretty_name_fn(name):
+    name = name.replace('fire','f').replace('relu_expand','re').replace('expand','e').replace('concat','c').replace('squeeze','s')
+    name = name.replace('1x1_','').replace('1x1','')
+    return name
+
+# Don't display duplicate *_split_* layers
+caffevis_filter_layers = lambda name: '_split_' in name
+
+# offline scripts configuration
+caffevis_outputs_dir = base_folder + './models/squeezenet/outputs'
diff --git a/models/.gitignore b/models/.gitignore
index be7186603..10e685655 100644
--- a/models/.gitignore
+++ b/models/.gitignore
@@ -1 +1,4 @@
 *.caffemodel
+*.processed_by_deepvis
+receptive_fields_cache.pickled
+
diff --git a/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py b/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
deleted file mode 100644
index b81eaf949..000000000
--- a/models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py
+++ /dev/null
@@ -1,50 +0,0 @@
-# Define critical settings and/or override defaults specified in
-# settings.py. Any settings defined here will override those defined in settings.py
-
-# Set this to point to your compiled checkout of caffe
-caffevis_caffe_root      = '/path/to/caffe'
-
-# Load model: bvlc-googlenet
-# Path to caffe deploy prototxt file. Minibatch size should be 1.
-caffevis_deploy_prototxt = '%DVT_ROOT%/models/bvlc-googlenet/bvlc-googlenet-deploy.prototxt'
-
-# Path to network weights to load.
-caffevis_network_weights = '%DVT_ROOT%/models/bvlc-googlenet/bvlc-googlenet.caffemodel'
-
-# Other optional settings; see complete documentation for each in settings.py.
-caffevis_data_mean       = (104, 117, 123)   # per-channel mean
-caffevis_labels          = '%DVT_ROOT%/models/bvlc-googlenet/ilsvrc_2012_labels.txt'
-caffevis_jpgvis_layers   = []
-caffevis_prob_layer      = 'prob'
-caffevis_label_layers    = ['loss3/classifier', 'prob']
-def caffevis_layer_pretty_name_fn(name):
-    # Shorten many layer names to fit in control pane (full layer name visible in status bar)
-    name = name.replace('conv','c').replace('pool','p').replace('norm','n')
-    name = name.replace('inception_','i').replace('output','o').replace('reduce','r').replace('split_','s')
-    name = name.replace('__','_').replace('__','_')
-    return name
-# Don't display duplicate *_split_* layers
-caffevis_filter_layers = lambda name: '_split_' in name
-
-# Window panes for bvlc-googlenet (no caffevis_jpgvis pane, larger control pane to fit many layer names).
-_control_height = 125
-window_panes = (
-    # (i, j, i_size, j_size)
-    ('input',            (  0,    0,  300,   300)),
-    ('caffevis_aux',     (300,    0,  300,   300)),
-    ('caffevis_back',    (600,    0,  300,   300)),
-    ('caffevis_status',  (900,    0,   30,  1500)),
-    ('caffevis_control', (  0,  300,   _control_height, 1200)),
-    ('caffevis_layers',  ( _control_height,  300,  900 - _control_height, 1200)),
-)
-caffevis_layers_aspect_ratio = float(window_panes[-1][1][3])/window_panes[-1][1][2]  # Actual ratio from caffevis_layers
-caffevis_control_fsize = .85
-
-# Use GPU? Default is True.
-#caffevis_mode_gpu = True
-# Display tweaks.
-# Scale all window panes in UI by this factor
-#global_scale = 1.0
-# Scale all fonts by this factor    
-#global_font_size = 1.0
-
diff --git a/models/caffenet-yos/settings_local.template-caffenet-yos.py b/models/caffenet-yos/settings_local.template-caffenet-yos.py
deleted file mode 100644
index 9a240bf5d..000000000
--- a/models/caffenet-yos/settings_local.template-caffenet-yos.py
+++ /dev/null
@@ -1,35 +0,0 @@
-# Define critical settings and/or override defaults specified in
-# settings.py. Copy this file to settings_local.py in the same
-# directory as settings.py and edit. Any settings defined here
-# will override those defined in settings.py
-
-
-
-# Set this to point to your compiled checkout of caffe
-caffevis_caffe_root      = '/path/to/caffe'
-
-# Load model: caffenet-yos
-# Path to caffe deploy prototxt file. Minibatch size should be 1.
-caffevis_deploy_prototxt = '%DVT_ROOT%/models/caffenet-yos/caffenet-yos-deploy.prototxt'
-
-# Path to network weights to load.
-caffevis_network_weights = '%DVT_ROOT%/models/caffenet-yos/caffenet-yos-weights'
-
-# Other optional settings; see complete documentation for each in settings.py.
-caffevis_data_mean       = '%DVT_ROOT%/models/caffenet-yos/ilsvrc_2012_mean.npy'
-caffevis_labels          = '%DVT_ROOT%/models/caffenet-yos/ilsvrc_2012_labels.txt'
-caffevis_label_layers    = ('fc8', 'prob')
-caffevis_prob_layer      = 'prob'
-caffevis_unit_jpg_dir    = '%DVT_ROOT%/models/caffenet-yos/unit_jpg_vis'
-caffevis_jpgvis_layers   = ['conv1', 'conv2', 'conv3', 'conv4', 'conv5', 'fc6', 'fc7', 'fc8', 'prob']
-caffevis_jpgvis_remap    = {'pool1': 'conv1', 'pool2': 'conv2', 'pool5': 'conv5'}
-def caffevis_layer_pretty_name_fn(name):
-    return name.replace('pool','p').replace('norm','n')
-
-# Use GPU? Default is True.
-#caffevis_mode_gpu = True
-# Display tweaks.
-# Scale all window panes in UI by this factor
-#global_scale = 1.0
-# Scale all fonts by this factor    
-#global_font_size = 1.0
diff --git a/models/squeezenet/settings_local.template-squeezenet.py b/models/squeezenet/settings_local.template-squeezenet.py
deleted file mode 100644
index 00fa05f71..000000000
--- a/models/squeezenet/settings_local.template-squeezenet.py
+++ /dev/null
@@ -1,51 +0,0 @@
-# Define critical settings and/or override defaults specified in
-# settings.py. Any settings defined here will override those defined in settings.py
-
-# Set this to point to your compiled checkout of caffe
-caffevis_caffe_root      = '/path/to/caffe'
-
-# Load model: squeezenet
-# Path to caffe deploy prototxt file. Minibatch size should be 1.
-caffevis_deploy_prototxt = '%DVT_ROOT%/models/squeezenet/deploy.prototxt'
-
-# Path to network weights to load.
-caffevis_network_weights = '%DVT_ROOT%/models/squeezenet/squeezenet_v1.0.caffemodel'
-
-
-
-# Other optional settings; see complete documentation for each in settings.py.
-caffevis_data_mean       = (104, 117, 123)   # per-channel mean
-caffevis_labels          = '%DVT_ROOT%/models/squeezenet/ilsvrc_2012_labels.txt'
-caffevis_prob_layer      = 'prob'
-caffevis_label_layers    = ['conv10', 'relu_conv10', 'pool10', 'prob']
-def caffevis_layer_pretty_name_fn(name):
-    name = name.replace('fire','f').replace('relu_expand','re').replace('expand','e').replace('concat','c').replace('squeeze','s')
-    name = name.replace('1x1_','').replace('1x1','')
-    return name
-# Don't display duplicate *_split_* layers
-caffevis_filter_layers = lambda name: '_split_' in name
-
-# Window panes for squeezenet (no caffevis_jpgvis pane, larger control pane to fit many layer names).
-_control_height = 45
-window_panes = (
-    # (i, j, i_size, j_size)
-    ('input',            (  0,    0,  300,   300)),
-    ('caffevis_aux',     (300,    0,  300,   300)),
-    ('caffevis_back',    (600,    0,  300,   300)),
-    ('caffevis_status',  (900,    0,   30,  1500)),
-    ('caffevis_control', (  0,  300,   _control_height, 1200)),
-    ('caffevis_layers',  ( _control_height,  300,  900 - _control_height, 1200)),
-)
-caffevis_layers_aspect_ratio = float(window_panes[-1][1][3])/window_panes[-1][1][2]  # Actual ratio from caffevis_layers
-
-# Use GPU? Default is True.
-#caffevis_mode_gpu = True
-# Display tweaks.
-# Scale all window panes in UI by this factor
-#global_scale = 1.0
-# Scale all fonts by this factor    
-#global_font_size = 1.0
-
-# Wider spacing
-caffevis_control_line_spacing = 10
-
diff --git a/settings_user.py.example b/settings_user.py.example
index ede8b2886..0ecfe1ebb 100644
--- a/settings_user.py.example
+++ b/settings_user.py.example
@@ -7,4 +7,6 @@
 #caffevis_gpu_id = 0
 
 # network selection, for network settings see file settings_local.py
-model_to_load = 'caffenet_yos'
+model_to_load = 'caffenet_yos' # AlexNet
+# model_to_load = 'bvlc_googlenet'
+# model_to_load = 'squeezenet'

From c11f278f06a8bbe54124cd58432ef2da7472e12c Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Mon, 16 Oct 2017 10:10:52 +0300
Subject: [PATCH 101/106] updated installation instructions in README.md

---
 README.md | 4 +++-
 1 file changed, 3 insertions(+), 1 deletion(-)

diff --git a/README.md b/README.md
index 3d687f078..b771f8065 100644
--- a/README.md
+++ b/README.md
@@ -7,7 +7,9 @@ Following are installation instruction for the new improved version of Deepvis.
     $ git clone --recursive https://github.com/arikpoz/deep-visualization-toolbox.git
     $ cd deep-visualization-toolbox && ./build_default.sh
     
-    
+Note: there is no need to download Caffe separately, it is now a sub-module of this repository and will get downloaded and built using the above instructions.
+
+
     
 ## Original Version Credit
 

From d737a097082a76ab7b61e27e9c394eda067fea25 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sat, 18 Nov 2017 21:40:19 +0200
Subject: [PATCH 102/106] fixed bug: crash due to use of invalid cache

---
 caffevis/app.py | 8 ++++++--
 1 file changed, 6 insertions(+), 2 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index 9c52633aa..a91cc8c32 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -921,8 +921,12 @@ def calculate_weights_histogram_for_specific_unit(channel_idx, fig, ax, do_print
                         # generate empty highlights
                         display_2D_highlights_only = self.prepare_tile_image(display_3D * 0, True, n_channels, tile_rows, tile_cols)
 
-                        # mix highlights with cached image
-                        display_2D = (display_2D_highlights_only != 0) * display_2D_highlights_only + (display_2D_highlights_only == 0) * display_2D
+                        # if shapes are not equal, cache is invalid
+                        if display_2D_highlights_only.shape == display_2D.shape:
+                            # mix highlights with cached image
+                            display_2D = (display_2D_highlights_only != 0) * display_2D_highlights_only + (display_2D_highlights_only == 0) * display_2D
+                        else:
+                            display_2D = None
 
                 # if not loaded from cache, generate the data
                 if display_2D is None:

From b084d77c052e5db7335816a2a27d3d74cbbbcb6e Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 8 Apr 2018 00:33:30 +0300
Subject: [PATCH 103/106] fix caffe build to make install easier

---
 build_default.sh | 3 +++
 1 file changed, 3 insertions(+)

diff --git a/build_default.sh b/build_default.sh
index 350108fa1..7ee60c4af 100755
--- a/build_default.sh
+++ b/build_default.sh
@@ -1,5 +1,8 @@
 #!/usr/bin/env bash
 cd caffe
+# fix issue with caffe build from source in ubuntu, see more details here: https://github.com/BVLC/caffe/issues/2347
+find . -type f -exec sed -i -e 's^"hdf5.h"^"hdf5/serial/hdf5.h"^g' -e 's^"hdf5_hl.h"^"hdf5/serial/hdf5_hl.h"^g' '{}' \;
+
 # if Makefile.config has already existed, then don't overwrite it
 if [ ! -e  Makefile.config ]; then
     cp Makefile.config.example Makefile.config

From 286fecdb3f2cf9637f44bb1b0b84310cd4e7e421 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 8 Apr 2018 02:08:35 +0300
Subject: [PATCH 104/106] removed unneeded dependency

---
 caffevis/app.py | 2 --
 1 file changed, 2 deletions(-)

diff --git a/caffevis/app.py b/caffevis/app.py
index a91cc8c32..0c9fb63af 100644
--- a/caffevis/app.py
+++ b/caffevis/app.py
@@ -4,8 +4,6 @@
 # add parent folder to search path, to enable import of core modules like settings
 import os,sys,inspect
 
-from dask.array.random import normal
-
 currentdir = os.path.dirname(os.path.abspath(inspect.getfile(inspect.currentframe())))
 parentdir = os.path.dirname(currentdir)
 sys.path.insert(0,parentdir)

From 7d4dce0f5ea1ed3868c00796514694dbc80c75d2 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 8 Apr 2018 02:08:48 +0300
Subject: [PATCH 105/106] updated readme

---
 README.md                        | 205 ++++++++++++-------------------
 doc/activation-correlation.png   | Bin 0 -> 580769 bytes
 doc/basic-layout.png             | Bin 0 -> 838092 bytes
 doc/detect-inactive-channels.png | Bin 0 -> 54442 bytes
 doc/detect-inactive-layers.png   | Bin 0 -> 56148 bytes
 doc/guided-backprop.png          | Bin 0 -> 875575 bytes
 doc/maximal-input.png            | Bin 0 -> 304489 bytes
 doc/maximal-optimized.png        | Bin 0 -> 319195 bytes
 8 files changed, 78 insertions(+), 127 deletions(-)
 create mode 100644 doc/activation-correlation.png
 create mode 100644 doc/basic-layout.png
 create mode 100644 doc/detect-inactive-channels.png
 create mode 100644 doc/detect-inactive-layers.png
 create mode 100644 doc/guided-backprop.png
 create mode 100644 doc/maximal-input.png
 create mode 100644 doc/maximal-optimized.png

diff --git a/README.md b/README.md
index b771f8065..da712a49c 100644
--- a/README.md
+++ b/README.md
@@ -1,20 +1,6 @@
 # Deep Visualization Toolbox
 
-## Installation 
-
-Following are installation instruction for the new improved version of Deepvis.
-
-    $ git clone --recursive https://github.com/arikpoz/deep-visualization-toolbox.git
-    $ cd deep-visualization-toolbox && ./build_default.sh
-    
-Note: there is no need to download Caffe separately, it is now a sub-module of this repository and will get downloaded and built using the above instructions.
-
-
-    
-## Original Version Credit
-
-This is the code required to run the Deep Visualization Toolbox, as well as to generate the neuron-by-neuron visualizations using regularized optimization.
-The toolbox and methods are described casually [here](http://yosinski.com/deepvis) and more formally in this paper:
+The original version of the tool was first described [here](http://yosinski.com/deepvis) and more formally in this paper:
 
  * Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. [Understanding neural networks through deep visualization](http://arxiv.org/abs/1506.06579). Presented at the Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015.
 
@@ -25,155 +11,120 @@ If you find this paper or code useful, we encourage you to cite the paper. BibTe
     Booktitle = {Deep Learning Workshop, International Conference on Machine Learning (ICML)},
     Title = {Understanding Neural Networks Through Deep Visualization},
     Year = {2015}}
+    
+This repository contains an improved version of the tool, made by Arik Poznanski.
+The most notable improvements are:
+  * Added new visualizations: 
+    * Activation Histograms
+    * Activation Correlation
+  * Tool usage made easier
+    * Reduced number of user-tuned parameters
+    * Support for non-sequential networks like: Inception and ResNet
+    * Support for Siamese networks
+    * Enhanced UI (Input overlays, color maps, mouse support)
+    * Support input source (Directory, image list, siamese image list)
+  * Tested on all major network architectures, including: LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet and ResNet.  
+    
+## Installation 
 
+Following are installation instruction for the new improved version of Deepvis.
 
+    $ git clone --recursive https://github.com/arikpoz/deep-visualization-toolbox.git
+    $ cd deep-visualization-toolbox && ./build_default.sh
+    
+Note: there is no need to download Caffe separately, it is now a sub-module of this repository and will get downloaded and built using the above instructions.
 
-# Features
-
-The main toolbox window looks like this, here showing a convolutional unit that responds to automobile wheels:
-
-![DeepVis Toolbox Screenshot bus](doc/example_caffenet-yos_bus_wheel_unit.jpg?raw=true)
-
-For a quick tour of the toolbox features, including what each pane of the above interface is showing, watch this [4 min YouTube video](https://www.youtube.com/watch?v=AgkfIQ4IGaM). In addition to processing images files from disk, the toolbox can run off a webcam for live network visualization **(below left)**.
-The toolbox comes bundled with the default [caffenet-yos](models/caffenet-yos) model weights and pre-computed per-unit visualizations shown in the paper. Weights, but not per-unit visualizations, for [bvlc-googlenet](models/bvlc-googlenet) **(below right)** and [squeezenet](models/squeezenet) can be downloaded by scripts in their respective directories.
-
-[![DeepVis Toolbox Screenshot webcam](doc/example_caffenet-yos_webcam.300.jpg)](doc/example_caffenet-yos_webcam.jpg?raw=true)
-[![DeepVis Toolbox Screenshot bvlc-googlenet](doc/example_bvlc-googlenet_bus.300.jpg)](doc/example_bvlc-googlenet_bus.jpg?raw=true)
-
-You can visualize your own model as well. However, note that the toolbox provides two rather separate sets of features; the first is easy to use with your own model, and the second is more involved:
-
-1. **Forward/backward prop**: Images can be run forward through the network to visualize activations, and derivatives of any unit with respect to any other unit can be computed using backprop. In addition to traditional backprop, deconv from [Zeiler and Fergus (2014)](https://scholar.google.com/scholar?q=Zeiler+Visualizing+and+understanding+convolutional+networks) is supported as a way of flowing information backwards through the network. Doing forward and backward passes works for any model that can be run in Caffe (including yours!).
-
-2. **Per-unit visualizations**: Three types of per-unit visualizations can be computed for a network &mdash; max image, deconv of max image, activation maximization via regularized optimization &mdash; but these visualizations must be computed *outside* the toolbox and saved as jpg. The toolbox then loads these jpgs to display alongside units as they are selected. Visualizations must be pre-computed because they are far too expensive to run live. For example, going through the 1.3m image training set to find the images causing top-9 activations took 40 hours on our system (for all units). Per-unit visualization jpgs are provided for the caffenet-yos model, but not for the bvlc-googlenet or squeezenet models (and not for yours, but you can [compute them yourself](doc/computing_per_unit_visualizations.md)).
-
-Summary:
-
-| Model          | Forward/Backward prop | Per-unit visualizations |
-| -------------  | ---------------- | ----------------------- |
-| [caffenet-yos](models/caffenet-yos) | **easy**             | **included**            |
-| [bvlc-googlenet](bvlc-googlenet)    | **easy**             | not-included, [generate](doc/computing_per_unit_visualizations.md) if desired            |
-| [squeezenet](models/squeezenet)     | **easy**             | not-included, [generate](doc/computing_per_unit_visualizations.md) if desired            |
-| your network | **easy** (just point to your model in `settings_local.py`)  | not-included, [generate](doc/computing_per_unit_visualizations.md) if desired  |
-
-
-
-# Setting up and running the toolbox
-
-### Step 0: Compile master branch of caffe (optional but recommended)
-
-Checkout the master branch of [Caffe](http://caffe.berkeleyvision.org/) and compile it on your
-machine. If you've never used Caffe before, it can take a bit of time to get all the required libraries in place. Fortunately, the [installation process is well documented](http://caffe.berkeleyvision.org/installation.html). When you're installing the OpenCV dependency, install the Python bindings as well (see Step 2 below).
-
-Note: When compiling Caffe, you can set `CPU_ONLY := 1` in your `Makefile.config` to skip all the Cuda/GPU stuff. The Deep Visualization Toolbox can run with Caffe in either CPU or GPU mode, and it's simpler to get Caffe to compile for the first time in `CPU_ONLY` mode. If Caffe is compiled with GPU options enabled, CPU vs. GPU may be switched at runtime via a setting in `settings_local.py`. Also, cuDNN may be enabled or disabled by recompiling Caffe with or without cuDNN.
-
-
-### Step 1: Compile the deconv-deep-vis-toolbox branch of caffe
-
-Instead of using the master branch of Caffe, to use the demo
-you'll need the slightly modified [deconv-deep-vis-toolbox Caffe branch](https://github.com/yosinski/caffe/tree/deconv-deep-vis-toolbox) (supporting deconv and a few
-extra Python bindings). Getting the branch and switching to it is easy.
-Starting from your Caffe directory (that is, the directory where you've checked out Caffe, *not* the directory where you've checked out the DeepVis Toolbox), run:
-
-    $ git remote add yosinski https://github.com/yosinski/caffe.git
-    $ git fetch --all
-    $ git checkout --track -b deconv-deep-vis-toolbox yosinski/deconv-deep-vis-toolbox
-    $ < edit Makefile.config to suit your system if not already done in Step 0 >
-    $ make clean
-    $ make -j
-    $ make -j pycaffe
-
-As noted above, feel free to compile in `CPU_ONLY` mode.
-
-
-
-### Step 2: Install prerequisites
-
-The only prerequisites beyond those required for Caffe are `python-opencv`, `scipy`, and `scikit-image`, which may be installed as follows (other install options exist as well):
-
-#### Ubuntu:
-
-    $ sudo apt-get install python-opencv scipy python-skimage
-
-#### Mac using [homebrew](http://brew.sh/):
-
-Install `python-opencv` using one of the following two lines, depending on whether you want to compile using Intel TBB to enable parallel operations:
-
-    $ brew install opencv
-    $ brew install --with-tbb opencv
-
-Install `scipy` either with OpenBLAS...
-
-    $ brew install openblas
-    $ brew install --with-openblas scipy
+Run the tool:
 
-...or without it
+    $ ./run_toolbox.py
 
-    $ brew install scipy
+Once the toolbox is running, push 'h' to show a help screen.   
+   
+## Loading a New Model
 
-And install `scikit-image` using pip:
+1. Define a model settings file: settings_your_model.py
+```python
+# network definition
+caffevis_deploy_prototxt = '../your-model-deploy.prototxt'
+caffevis_network_weights = '../your-model-weights.caffemodel'
+caffevis_data_mean = '../your-model-mean.npy'
 
-    $ pip install scikit-image
+# input configuration
+static_files_dir = '../input_images_folder'
 
-You may have already installed the `python-opencv` bindings as part of the Caffe setup process. If `import cv2` works from Python, then you're all set. Similarly for `import scipy` and `import skimage`.
+# output configuration
+caffevis_outputs_dir = '../outputs'
+layers_to_output_in_offline_scripts = ['conv1','conv2', ..., 'fc1']
+```
 
+2. Define a user settings file: settings_user.py
 
+```python
+# GPU configuration
+#caffevis_mode_gpu = False
+caffevis_gpu_id = 2
 
-### Step 3: Download and configure Deep Visualization Toolbox code
+# select model to load
+model_to_load = 'your_model'
+#model_to_load = 'previous_model'
+```
 
-You can put it wherever you like:
+## Basic Layout
 
-    $ git clone https://github.com/yosinski/deep-visualization-toolbox
-    $ cd deep-visualization-toolbox
+![Basic layout](doc/basic-layout.png)
 
-The settings in the latest version of the toolbox (February 2016) work a bit differently than in earlier versions (April 2015). If you have the latest version (recommended!), 
-the minimal steps are to create a `settings_local.py` file using the template for the default `caffenet-yos` model:
 
-    $ cp models/caffenet-yos/settings_local.template-caffenet-yos.py settings_local.py
+## Activation Histograms
 
-And then edit the `settings_local.py` file to make the `caffevis_caffe_root` variable point to the directory where you've compiled caffe in Step 1:
+* Helps to study the activity of a channel over a set of inputs.
 
-    $ < edit settings_local.py >
+* Given a dataset of N input images, we compute the activation of each channel over the dataset and histogram the corresponding values.
 
-*Note on settings:* Settings are now split into two files: a versioned `settings.py` file that provides documentation and default values for all settings and an unversioned `settings_local.py` file. This latter file allows you to override any default setting to tailor the toolbox to your specific setup (Caffe path, CPU vs. GPU, webcam device, etc) and model (model weights, prototxt, sizes of the various panels shown in the toolbox, etc). This also makes it easy to distribute settings tweaks alongside models: for example, `models/bvlc-googlenet/settings_local.template-bvlc-googlenet.py` includes the appropriate window pane sizes and so on for the `bvlc-googlenet` model. To load a new model, just change the details in `settings_local.py`, perhaps by copying from the included template.
+1. Detect inactive channels: 
+![Detect inactive channels](doc/detect-inactive-channels.png)
 
-Finally, download the default model weights and corresponding top-9 visualizations saved as jpg (downloads a 230MB model and 1.1GB of jpgs to show as visualization):
+2. Detect inactive layers: 
+![Detect inactive layers](doc/detect-inactive-layers.png)
 
-    $ cd models/caffenet-yos/
-    $ ./fetch.sh
-    $ cd ../..
+* The behavior seen on the right, is clearly an indication of a problem in the training process. Since most of the channels are inactive and effectively the model capacity is reduced.
 
+* A few reasons for this behavior:
+  * A constant zero ReLU activation, a.k.a. “dead” ReLU.
+  * Poor network initialization.
+   
+## Activation Correlation
 
+* Seek correlations between activation values of different channels in the same layer to check network capacity usage.
 
-### Step 4: Run it!
+* On the left, there is a healthy correlation matrix, where the channels are completely uncorrelated. On the right, there is a correlation matrix with all the channels either highly or inversely correlated.
 
-Simple:
+![Activation correlation](doc/activation-correlation.png)
 
-    $ ./run_toolbox.py
+* The capacity utilization of the network is relatively low. Increase in number of parameters won't improve performance.
 
-Once the toolbox is running, push 'h' to show a help screen. You can also have a look at `bindings.py` to see what the various keys do. If the window is too large or too small for your screen, set the `global_scale` and `global_font_size` variables in `settings_local.py` to values smaller or larger than 1.0.
 
+## Maximal Input
 
+* For each channel we find the image patch from our dataset the has the highest activation.
 
-# Troubleshooting
+![Maximal input](doc/maximal-input.png)
 
-If you have any problems running the Deep Vis Toolbox, here are a few things to try:
 
- * Make sure you can compile the master branch of Caffe (Step 0 above)! If you can't, see the [detailed compilation instructions for Caffe](http://caffe.berkeleyvision.org/installation.html). If you encounter issues, the [caffe-users](https://groups.google.com/forum/#!forum/caffe-users) mailing list is a good place to look for solutions others have found.
- * Try using the `dev` branch of this toolbox instead of `master` (`git checkout dev`). Sometimes it's a little more up to date.
- * If you get an error (`AttributeError: 'Classifier' object has no attribute 'backward_from_layer'`) when switching to backprop or deconv modes, it's because your compiled branch of Caffe does not have the necessary Python bindings for backprop/deconv. Follow the directions in "Step 1: Compile the deconv-deep-vis-toolbox branch of caffe" above.
- * If the backprop pane in the lower left is just gray, it's probably because backprop and deconv are producing all zeros. By default, Caffe won't compute derivatives at the data layer, because they're not needed to update parameters. The fix is simple: just add `force_backward: true` to your network prototxt, [like this](https://github.com/yosinski/deep-visualization-toolbox/blob/master/models/caffenet-yos/caffenet-yos-deploy.prototxt#L7).
- * If the toolbox runs but the keys don't respond as expected, this may be because keys behave differently on different platforms. Run the `test_keys.py` script to test behavior on your system.
- * If none of that helps, feel free to [email me](http://yosinski.com/) or [submit an issue](https://github.com/yosinski/deep-visualization-toolbox/issues). I might have left out an important detail here or there :).
+## Maximal Optimized
 
+* Using a regularized optimization process we approximate for each channel the image that empirically has the highest activation 
 
+![Maximal optimized](doc/maximal-optimized.png)
 
-# Other ways of running the toolbox
 
-If running the toolbox on a local Mac or Linux machine isn't working for you, you might want to try one of these other options:
+## Backprop Modes
 
- * John Moeller has put together a [Docker container for the toolbox](https://github.com/fishcorn/dvtb-container). This should even work on Windows! (confirmation needed)
+* Backprop visualization is basically a regular backprop step that continues to the pixel level. It provides an easy way to study the influence of each pixel on the network decision.
 
- * If you're desperate, it's also possible to [run the toolbox on Amazon EC2](doc/deep-vis-on-aws.md), but display will be much slower and images can be loaded only from file (not from webcam).
+* Original toolbox supports only ZF-deconv and vanilla backprop.
 
+* Our enhanced version supports also guided backpropagation that provides better localization.
 
+* Guided backpropagation: Gradients are propagated back trough the ReLU  activation only if the forward  activation and the gradient are positive. 
 
+![Guided backprop](doc/guided-backprop.png)
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From f47371a9a7e29e95f8450ca6a487d183ad108932 Mon Sep 17 00:00:00 2001
From: arikpoz <arik.com@gmail.com>
Date: Sun, 8 Apr 2018 08:46:17 +0300
Subject: [PATCH 106/106] fixed typos in readme

---
 README.md | 27 ++++++++++++++-------------
 1 file changed, 14 insertions(+), 13 deletions(-)

diff --git a/README.md b/README.md
index da712a49c..a06075777 100644
--- a/README.md
+++ b/README.md
@@ -1,17 +1,5 @@
 # Deep Visualization Toolbox
 
-The original version of the tool was first described [here](http://yosinski.com/deepvis) and more formally in this paper:
-
- * Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. [Understanding neural networks through deep visualization](http://arxiv.org/abs/1506.06579). Presented at the Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015.
-
-If you find this paper or code useful, we encourage you to cite the paper. BibTeX:
-
-    @inproceedings{yosinski-2015-ICML-DL-understanding-neural-networks,
-    Author = {Jason Yosinski and Jeff Clune and Anh Nguyen and Thomas Fuchs and Hod Lipson},
-    Booktitle = {Deep Learning Workshop, International Conference on Machine Learning (ICML)},
-    Title = {Understanding Neural Networks Through Deep Visualization},
-    Year = {2015}}
-    
 This repository contains an improved version of the tool, made by Arik Poznanski.
 The most notable improvements are:
   * Added new visualizations: 
@@ -24,6 +12,19 @@ The most notable improvements are:
     * Enhanced UI (Input overlays, color maps, mouse support)
     * Support input source (Directory, image list, siamese image list)
   * Tested on all major network architectures, including: LeNet, AlexNet, ZFNet, GoogLeNet, VGGNet and ResNet.  
+
+The original version of the tool was first described [here](http://yosinski.com/deepvis) and more formally in this paper:
+
+ * Jason Yosinski, Jeff Clune, Anh Nguyen, Thomas Fuchs, and Hod Lipson. [Understanding neural networks through deep visualization](http://arxiv.org/abs/1506.06579). Presented at the Deep Learning Workshop, International Conference on Machine Learning (ICML), 2015.
+
+If you find this paper or code useful, we encourage you to cite the paper. BibTeX:
+
+    @inproceedings{yosinski-2015-ICML-DL-understanding-neural-networks,
+    Author = {Jason Yosinski and Jeff Clune and Anh Nguyen and Thomas Fuchs and Hod Lipson},
+    Booktitle = {Deep Learning Workshop, International Conference on Machine Learning (ICML)},
+    Title = {Understanding Neural Networks Through Deep Visualization},
+    Year = {2015}}
+    
     
 ## Installation 
 
@@ -105,7 +106,7 @@ model_to_load = 'your_model'
 
 ## Maximal Input
 
-* For each channel we find the image patch from our dataset the has the highest activation.
+* For each channel we find the image patch from our dataset that has the highest activation.
 
 ![Maximal input](doc/maximal-input.png)