From 02f75df520a3b3b5ae66be7bf5f67eed5ddcc318 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 19 Oct 2017 00:12:15 +0200
Subject: [PATCH 01/22] Alexnet prepared for new Aeon: call to Aeon wrapper and
 changes in config

---
 .../ILSVRC2012/Alexnet/alexnet_neon.py        | 54 ++++++++++++++-----
 .../ILSVRC2012/Alexnet/test.sh                |  2 +-
 2 files changed, 43 insertions(+), 13 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py b/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
index 057c951..d05b4a2 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
+++ b/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
@@ -27,16 +27,52 @@
     alexnet_neon.py -w <path-to-saved-batches> --test_only \
             --model_file <saved weights file>
 """
-
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.initializers import Constant, Gaussian
 from neon.layers import Conv, Dropout, Pooling, GeneralizedCost, Affine, LRN
 from neon.optimizers import GradientDescentMomentum, MultiOptimizer, Schedule
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
 from neon.models import Model
-from neon.data import ImageLoader
 from neon.callbacks.callbacks import Callbacks
 
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=1000)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def common_config(subset_pct, manifest_filename, manifest_root, batch_size):
+#    cache_root = get_data_cache_or_nothing('i1k-cache/')
+    image_config = {"type": "image",
+                    "height": 224,
+                    "width": 224}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "scale": [0.875, 0.875],
+                    "crop_enable": True}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_train_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    train_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size)
+    train_config['shuffle_enable'] = True
+    train_config['shuffle_manifest'] = True
+    return wrap_dataloader(AeonDataLoader(train_config))
+
+def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    val_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
 # parse the command line arguments (generates the backend)
 parser = NeonArgparser(__doc__)
 parser.add_argument('--subset_pct', type=float, default=100,
@@ -49,14 +85,8 @@
     if args.model_file is None:
         raise ValueError('To test model, trained weights need to be provided')
 
-# setup data provider
-img_set_options = dict(repo_dir=args.data_dir,
-                       inner_size=224,
-                       subset_pct=args.subset_pct)
-train = ImageLoader(set_name='train', scale_range=(256, 256),
-                    shuffle=True, **img_set_options)
-test = ImageLoader(set_name='validation', scale_range=(256, 256),
-                   do_transforms=False, **img_set_options)
+train = make_train_config(args.subset_pct, args.manifest["train"], args.manifest_root, batch_size=args.batch_size)
+val = make_val_config(args.subset_pct, args.manifest["val"], args.manifest_root, batch_size=args.batch_size)
 
 init_g1 = Gaussian(scale=0.01)
 init_g2 = Gaussian(scale=0.005)
@@ -105,7 +135,7 @@
 
 # configure callbacks
 valmetric = TopKMisclassification(k=5)
-callbacks = Callbacks(model, eval_set=test, metric=valmetric, **args.callback_args)
+callbacks = Callbacks(model, eval_set=val, metric=valmetric, **args.callback_args)
 
 if args.model_file is not None:
     model.load_params(args.model_file)
@@ -113,7 +143,7 @@
     cost = GeneralizedCost(costfunc=CrossEntropyMulti())
     model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
 
-mets = model.eval(test, metric=valmetric)
+mets = model.eval(val, metric=valmetric)
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
                                                                      (1.0-mets[1])*100,
diff --git a/ImageClassification/ILSVRC2012/Alexnet/test.sh b/ImageClassification/ILSVRC2012/Alexnet/test.sh
index e81fb41..6ede82e 100755
--- a/ImageClassification/ILSVRC2012/Alexnet/test.sh
+++ b/ImageClassification/ILSVRC2012/Alexnet/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u alexnet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /usr/local/data/I1K/macrobatches/ -vvv --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+python -u alexnet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted/ --manifest_root /data/i1k-extracted --manifest train:/data/i1k-extracted/train-index.csv --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar  -z 256 | tee output.dat 2>&1
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc

From 1fc4a105137d6547fb61a1737c9fd69e32e415c4 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 19 Oct 2017 00:16:27 +0200
Subject: [PATCH 02/22] Version changes in readme.md file

---
 ImageClassification/ILSVRC2012/Alexnet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/Alexnet/readme.md b/ImageClassification/ILSVRC2012/Alexnet/readme.md
index 10c3233..63bcdef 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/readme.md
+++ b/ImageClassification/ILSVRC2012/Alexnet/readme.md
@@ -33,7 +33,7 @@ Note there has been some changes to the format of the mean data subtraction;
 users with the old format may be prompted to run an update script before proceeding.
 
 
-This script was tested with the [neon release v1.4.0](https://github.com/NervanaSystems/neon/tree/v1.4.0)
+This script was tested with the [neon release v2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0)
 (commit SHA bc196cb).
 Make sure that your local repo is synced to this commit and run the
 [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html)

From 19926d4f05cf8faa6681cbbebbf67535303b5529 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 19 Oct 2017 01:11:01 +0200
Subject: [PATCH 03/22] Googlenet prepared for Aeon 1.0

---
 .../ILSVRC2012/Googlenet/googlenet_neon.py    | 44 +++++++++++++++----
 .../ILSVRC2012/Googlenet/test.sh              |  2 +-
 2 files changed, 36 insertions(+), 10 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py b/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
index fe815de..0298713 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
+++ b/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
@@ -18,6 +18,7 @@
 """
 
 import os
+import numpy as np
 
 from neon.util.argparser import NeonArgparser
 from neon.layers import Conv, Pooling, MergeBroadcast, BranchNode
@@ -28,7 +29,35 @@
 from neon.optimizers import GradientDescentMomentum, MultiOptimizer
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
 from neon.models import Model
-from neon.data import ImageLoader
+
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=1000)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def common_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    image_config = {"type": "image",
+                    "height": 224,
+                    "width": 224}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "scale": [0.875, 0.875]}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size):
+    val_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size)
+    return wrap_dataloader(AeonDataLoader(val_config))
 
 parser = NeonArgparser(__doc__)
 parser.add_argument('--subset_pct', type=float, default=100,
@@ -38,10 +67,7 @@
 args = parser.parse_args()
 
 # setup data provider
-img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
-                       subset_pct=args.subset_pct)
-test = ImageLoader(set_name='validation', scale_range=(256, 256),
-                   do_transforms=False, **img_set_options)
+val = make_val_config(args.subset_pct, args.manifest['val'], args.manifest_root, args.batch_size)
 
 init1 = Xavier(local=False)
 initx = Xavier(local=True)
@@ -123,15 +149,15 @@ def aux_branch(bnode):
 
 assert os.path.exists(args.model_file), 'script requires the trained weights file'
 model.load_params(args.model_file)
-model.initialize(test, cost)
+model.initialize(val, cost)
 
 
 print 'running speed benchmark...'
-model.benchmark(test, cost, opt)
+model.benchmark(val, cost, opt)
 
 print '\nCalculating performance on validation set...'
-test.reset()
-mets = model.eval(test, metric=valmetric)
+val.reset()
+mets = model.eval(val, metric=valmetric)
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
                                                                      (1.0-mets[1])*100,
diff --git a/ImageClassification/ILSVRC2012/Googlenet/test.sh b/ImageClassification/ILSVRC2012/Googlenet/test.sh
index 530a750..419b296 100755
--- a/ImageClassification/ILSVRC2012/Googlenet/test.sh
+++ b/ImageClassification/ILSVRC2012/Googlenet/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u googlenet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /usr/local/data/I1K/macrobatches/ -vvv --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+python -u googlenet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc

From 952048dae013d6f708f6f80b240641a30277f562 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 19 Oct 2017 01:15:30 +0200
Subject: [PATCH 04/22] Incorrect commit information in readme.md

---
 ImageClassification/ILSVRC2012/Alexnet/readme.md | 3 +--
 1 file changed, 1 insertion(+), 2 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/Alexnet/readme.md b/ImageClassification/ILSVRC2012/Alexnet/readme.md
index 63bcdef..e6d4387 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/readme.md
+++ b/ImageClassification/ILSVRC2012/Alexnet/readme.md
@@ -33,8 +33,7 @@ Note there has been some changes to the format of the mean data subtraction;
 users with the old format may be prompted to run an update script before proceeding.
 
 
-This script was tested with the [neon release v2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0)
-(commit SHA bc196cb).
+This script was tested with the [neon release v2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0).
 Make sure that your local repo is synced to this commit and run the
 [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html)
 before proceeding.

From d1aedf431f00a7f3d8a4118b53eb28f5aecad52d Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 19 Oct 2017 01:19:42 +0200
Subject: [PATCH 05/22] Changes in readme.md about Neon verion

---
 ImageClassification/ILSVRC2012/Googlenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/Googlenet/readme.md b/ImageClassification/ILSVRC2012/Googlenet/readme.md
index 66e30cd..2f0d62f 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/readme.md
+++ b/ImageClassification/ILSVRC2012/Googlenet/readme.md
@@ -26,7 +26,7 @@ During training, the images were randomly cropped and flipped horizontally but s
 To run the model, first the ImageNet data set needs to be uploaded and converted to the format compatible with neon (see  [instructions](http://neon.nervanasys.com/docs/latest/datasets.html#imagenet)).  Note there has been some changes to the format of the mean data subtraction; users with the old format may be prompted to run an update script before proceeding.
 
 
-This script works with the [neon commit SHA 66846b409](https://github.com/NervanaSystems/neon/commit/66846b4097d256e176cd76559dfa4e0bc54ab6dc).  Make sure that your local repo is synced to this commit and run the [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
+This script works with the [neon release v2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0).  Make sure that your local repo is synced to this commit and run the [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
 
 
 If neon is installed into a `virtualenv`, make sure that it is activated before running the commands below.  Also, the commands below use the GPU backend by default so add `-b cpu` if you are running on a system without a compatible GPU.

From ae6d4b06b6f335de08c2b44e5a1ff0077e39b7c7 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Fri, 20 Oct 2017 00:39:06 +0200
Subject: [PATCH 06/22] VGG changed for new Neon

---
 ImageClassification/ILSVRC2012/VGG/test.sh    |  2 +-
 .../ILSVRC2012/VGG/vgg_neon.py                | 61 ++++++++++++++++---
 2 files changed, 52 insertions(+), 11 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/VGG/test.sh b/ImageClassification/ILSVRC2012/VGG/test.sh
index 3778627..3d5ebb3 100755
--- a/ImageClassification/ILSVRC2012/VGG/test.sh
+++ b/ImageClassification/ILSVRC2012/VGG/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /usr/local/data/I1K/macrobatches/ -vvv --model_file $WEIGHTS_FILE --no_progress_bar  -z 64 --vgg_version E | tee output.dat 2>&1
+python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted -vvv --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv --model_file $WEIGHTS_FILE --no_progress_bar -z 16 --vgg_version E | tee output.dat 2>&1
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
diff --git a/ImageClassification/ILSVRC2012/VGG/vgg_neon.py b/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
index 6faf18f..f3ccf16 100644
--- a/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
+++ b/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
@@ -20,7 +20,7 @@
     K. Simonyan, A. Zisserman
     arXiv:1409.1556
 """
-
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.backends import gen_backend
 from neon.initializers import Constant, GlorotUniform, Xavier
@@ -28,9 +28,44 @@
 from neon.optimizers import GradientDescentMomentum, Schedule, MultiOptimizer
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, TopKMisclassification
 from neon.models import Model
-from neon.data import ImageLoader
 from neon.callbacks.callbacks import Callbacks
 
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=1000)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def common_config(subset_pct, manifest_filename, manifest_root, batch_size, scale):
+    image_config = {"type": "image",
+                    "height": 224,
+                    "width": 224}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "scale": scale,
+                    "crop_enable": True}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_train_config(subset_pct, manifest_filename, manifest_root, batch_size, scale):
+    train_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size, scale)
+    train_config['shuffle_enable'] = True
+    train_config['shuffle_manifest'] = True
+    return wrap_dataloader(AeonDataLoader(train_config))
+
+def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size, scale):
+    val_config = common_config(subset_pct, manifest_filename, manifest_root, batch_size, scale)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
 # parse the command line arguments
 parser = NeonArgparser(__doc__)
 parser.add_argument('--vgg_version', default='D', choices=['D', 'E'],
@@ -41,12 +76,18 @@
                     help='skip fitting - evaluate metrics on trained model weights')
 args = parser.parse_args()
 
-img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
-                       subset_pct=args.subset_pct)
-train = ImageLoader(set_name='train', scale_range=(256, 384),
-                    shuffle=True, **img_set_options)
-test = ImageLoader(set_name='validation', scale_range=(256, 256), do_transforms=False,
-                   shuffle=False, **img_set_options)
+# Training set has different scale configuration (256, 384) than testing set (256, 256). Why?
+# Commenting it out gives the folowing runtime error:
+# RuntimeError: Unable to create internal loader object: value for 'scale' out of range
+#train = make_train_config(args.subset_pct, args.manifest["train"], args.manifest_root, batch_size=args.batch_size, scale=[0.875, 0.583])
+val = make_val_config(args.subset_pct, args.manifest["val"], args.manifest_root, batch_size=args.batch_size, scale=[0.875, 0.875])
+
+#img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
+#                       subset_pct=args.subset_pct)
+#train = ImageLoader(set_name='train', scale_range=(256, 384),
+#                    shuffle=True, **img_set_options)
+#test = ImageLoader(set_name='validation', scale_range=(256, 256), do_transforms=False,
+#                   shuffle=False, **img_set_options)
 
 init1 = Xavier(local=True)
 initfc = GlorotUniform()
@@ -83,10 +124,10 @@
 
 # configure callbacks
 top5 = TopKMisclassification(k=5)
-callbacks = Callbacks(model, eval_set=test, metric=top5, **args.callback_args)
+callbacks = Callbacks(model, eval_set=val, metric=top5, **args.callback_args)
 
 model.load_params(args.model_file)
-mets=model.eval(test, metric=TopKMisclassification(k=5))
+mets=model.eval(val, metric=TopKMisclassification(k=5))
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],
                                                                     (1.0-mets[1])*100,

From ed06362d08d0823df98656f34fd7ec82d3fd7ee9 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Fri, 20 Oct 2017 00:40:06 +0200
Subject: [PATCH 07/22] VGG batch size changed to 64

---
 ImageClassification/ILSVRC2012/VGG/test.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/VGG/test.sh b/ImageClassification/ILSVRC2012/VGG/test.sh
index 3d5ebb3..34f0ace 100755
--- a/ImageClassification/ILSVRC2012/VGG/test.sh
+++ b/ImageClassification/ILSVRC2012/VGG/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted -vvv --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv --model_file $WEIGHTS_FILE --no_progress_bar -z 16 --vgg_version E | tee output.dat 2>&1
+python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted -vvv --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv --model_file $WEIGHTS_FILE --no_progress_bar -z 64 --vgg_version E | tee output.dat 2>&1
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc

From 4ad1a24a09ac5b522c67e2d47f6b9615770c85b0 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Fri, 20 Oct 2017 00:42:02 +0200
Subject: [PATCH 08/22] VGG readme.md updated

---
 ImageClassification/ILSVRC2012/VGG/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/VGG/readme.md b/ImageClassification/ILSVRC2012/VGG/readme.md
index 1c69957..c6d3dc5 100644
--- a/ImageClassification/ILSVRC2012/VGG/readme.md
+++ b/ImageClassification/ILSVRC2012/VGG/readme.md
@@ -95,7 +95,7 @@ The batch size is set to 64 in the examples above because with larger batch size
 
 ### Version compatibility
 
-Neon version: commit SHA [e7ab2c2e2](https://github.com/NervanaSystems/neon/commit/e7ab2c2e27f113a4d36d17ba8c79546faed7d916).
+Neon version: v2.2.0 (https://github.com/NervanaSystems/neon/tree/v2.2.0).
 
 ## Citation
 

From 3e7736f673cbdea55ad2c72a494d8c0aaa9ca769 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sat, 28 Oct 2017 22:11:53 +0200
Subject: [PATCH 09/22] Change in how output is stored in output.dat file

---
 ImageClassification/CIFAR10/All_CNN/test.sh | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/CIFAR10/All_CNN/test.sh b/ImageClassification/CIFAR10/All_CNN/test.sh
index af3c27f..a6dc2f8 100755
--- a/ImageClassification/CIFAR10/All_CNN/test.sh
+++ b/ImageClassification/CIFAR10/All_CNN/test.sh
@@ -24,7 +24,7 @@ echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
 python -u $TEST_SCRIPT -i ${EXECUTOR_NUMBER} -vvv \
-           --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+           --model_file $WEIGHTS_FILE --no_progress_bar 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc

From f3eb1001a6da4bf2fa3d49b1eab46c501f9d9a25 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sat, 28 Oct 2017 22:14:28 +0200
Subject: [PATCH 10/22] Changing version on Neon in readme file

---
 ImageClassification/CIFAR10/All_CNN/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/CIFAR10/All_CNN/readme.md b/ImageClassification/CIFAR10/All_CNN/readme.md
index 246acee..972c9b6 100644
--- a/ImageClassification/CIFAR10/All_CNN/readme.md
+++ b/ImageClassification/CIFAR10/All_CNN/readme.md
@@ -13,7 +13,7 @@ The trained weights file can be downloaded from AWS
 
 
 ### neon version
-The model weight file above has been generated using neon version tag [v1.4.0]((https://github.com/NervanaSystems/neon/releases/tag/v1.4.0).
+The model weight file above has been generated using neon version tag [v2.2.0]((https://github.com/NervanaSystems/neon/releases/tag/v2.2.0).
 It may not work with other versions.
 
 ### Performance

From 839eaf57cbe119b1fb43d7cffa8655098c77368c Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sun, 29 Oct 2017 22:43:20 +0100
Subject: [PATCH 11/22] CIFAR resnet corrected for neon 2.2

---
 .../CIFAR10/DeepResNet/resnet_eval.py         | 40 +++++++++++++++----
 .../CIFAR10/DeepResNet/test.sh                |  4 +-
 2 files changed, 35 insertions(+), 9 deletions(-)

diff --git a/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py b/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py
index 8528515..9c1687c 100644
--- a/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py
+++ b/ImageClassification/CIFAR10/DeepResNet/resnet_eval.py
@@ -15,24 +15,50 @@
 # ----------------------------------------------------------------------------
 import os
 
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.util.persist import load_obj
 from neon.transforms import Misclassification, CrossEntropyMulti
 from neon.optimizers import GradientDescentMomentum
 from neon.layers import GeneralizedCost
 from neon.models import Model
-from neon.data import DataLoader, ImageParams
+
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
 
 # parse the command line arguments (generates the backend)
 parser = NeonArgparser(__doc__)
 args = parser.parse_args()
 
-# setup data provider
-test_dir = os.path.join(args.data_dir, 'val')
-shape = dict(channel_count=3, height=32, width=32)
-test_params = ImageParams(center=True, flip=False, **shape)
-common = dict(target_size=1, nclasses=10)
-test_set = DataLoader(set_name='val', repo_dir=test_dir, media_params=test_params, **common)
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=10)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def config(manifest_filename, manifest_root, batch_size, subset_pct):
+    image_config = {"type": "image",
+                    "height": 32,
+                    "width": 32}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "crop_enable": True,
+                    "center": True,
+                    "flip_enable": False}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+def make_val_config(manifest_filename, manifest_root, batch_size, subset_pct=100):
+    val_config = config(manifest_filename, manifest_root, batch_size, subset_pct)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
+test_set = make_val_config(args.manifest["val"], args.manifest_root, batch_size=args.batch_size)
 
 model = Model(load_obj(args.model_file))
 cost = GeneralizedCost(costfunc=CrossEntropyMulti())
diff --git a/ImageClassification/CIFAR10/DeepResNet/test.sh b/ImageClassification/CIFAR10/DeepResNet/test.sh
index 8f0591c..48a78ac 100755
--- a/ImageClassification/CIFAR10/DeepResNet/test.sh
+++ b/ImageClassification/CIFAR10/DeepResNet/test.sh
@@ -23,14 +23,14 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u $TEST_SCRIPT  -i ${EXECUTOR_NUMBER} -vvv --model_file $WEIGHTS_FILE --no_progress_bar -w /usr/local/data/CIFAR10/macrobatches | tee output.dat 2>&1
+python -u $TEST_SCRIPT  -i ${EXECUTOR_NUMBER} -vvv --model_file $WEIGHTS_FILE --manifest val:/data/CIFAR/val-index.csv --manifest_root /data/CIFAR -b gpu -z 32 --no_progress_bar 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
 fi
 
 # get the top-1 misclass
-top1=`tail -n 1 output.dat | sed "s/.*Accuracy: //" | sed "s/ \% (Top-1).*//"`
+top1=`cat output.dat | sed -n "s/.*Accuracy: \(.*\) \% (Top-1).*/\1/p"`
 
 top1pass=0
 top1pass=`echo $top1'>'85 | bc -l`

From 4690ccf0cff7e7986ebf16025648a8000d60f8a2 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sun, 29 Oct 2017 23:51:05 +0100
Subject: [PATCH 12/22] Resnet prepared for new data loader

---
 .../CIFAR10/DeepResNet/resnet_cifar10.py      | 89 +++++++++----------
 1 file changed, 43 insertions(+), 46 deletions(-)

diff --git a/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py b/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py
index 133a651..91adec8 100644
--- a/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py
+++ b/ImageClassification/CIFAR10/DeepResNet/resnet_cifar10.py
@@ -13,6 +13,7 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ----------------------------------------------------------------------------
+import numpy as np
 from neon.util.argparser import NeonArgparser
 from neon.initializers import Kaiming, IdentityInit
 from neon.layers import Conv, Pooling, GeneralizedCost, Affine, Activation
@@ -20,63 +21,59 @@
 from neon.optimizers import GradientDescentMomentum, Schedule
 from neon.transforms import Rectlin, Softmax, CrossEntropyMulti, Misclassification
 from neon.models import Model
-from neon.data import ImageLoader, ImageParams, DataLoader
 from neon.callbacks.callbacks import Callbacks
 
 import os
 
+from neon.data.dataloader_transformers import OneHot, TypeCast, BGRMeanSubtract
+from neon.data.aeon_shim import AeonDataLoader
+
+def wrap_dataloader(dl, dtype=np.float32):
+    dl = OneHot(dl, index=1, nclasses=10)
+    dl = TypeCast(dl, index=0, dtype=dtype)
+    dl = BGRMeanSubtract(dl, index=0)
+    return dl
+
+def config(manifest_filename, manifest_root, batch_size, subset_pct):
+    image_config = {"type": "image",
+                    "height": 32,
+                    "width": 32}
+    label_config = {"type": "label",
+                    "binary": False}
+    augmentation = {"type": "image",
+                    "crop_enable": True}
+
+    return {'manifest_filename': manifest_filename,
+            'manifest_root': manifest_root,
+            'batch_size': batch_size,
+            'subset_fraction': float(subset_pct/100.0),
+            'etl': [image_config, label_config],
+            'augmentation': [augmentation]}
+
+
+def make_train_config(manifest_filename, manifest_root, batch_size, subset_pct=100):
+    train_config = config(manifest_filename, manifest_root, batch_size, subset_pct)
+    train_config['augmentation'][0]['center'] = False
+    train_config['augmentation'][0]['flip_enable'] = True
+    train_config['shuffle_enable'] = True
+    train_config['shuffle_manifest'] = True
+
+    return wrap_dataloader(AeonDataLoader(train_config))
+
+
+def make_val_config(manifest_filename, manifest_root, batch_size, subset_pct=100):
+    val_config = config(manifest_filename, manifest_root, batch_size, subset_pct)
+    return wrap_dataloader(AeonDataLoader(val_config))
+
 # parse the command line arguments (generates the backend)
 parser = NeonArgparser(__doc__)
 parser.add_argument('--depth', type=int, default=9,
                     help='depth of each stage (network depth will be 6n+2)')
 args = parser.parse_args()
 
-
-def extract_images(out_dir, padded_size):
-    '''
-    Save CIFAR-10 dataset as PNG files
-    '''
-    import numpy as np
-    from neon.data import load_cifar10
-    from PIL import Image
-    dataset = dict()
-    dataset['train'], dataset['val'], _ = load_cifar10(out_dir, normalize=False)
-    pad_size = (padded_size - 32) // 2 if padded_size > 32 else 0
-    pad_width = ((0, 0), (pad_size, pad_size), (pad_size, pad_size))
-
-    for setn in ('train', 'val'):
-        data, labels = dataset[setn]
-
-        img_dir = os.path.join(out_dir, setn)
-        ulabels = np.unique(labels)
-        for ulabel in ulabels:
-            subdir = os.path.join(img_dir, str(ulabel))
-            if not os.path.exists(subdir):
-                os.makedirs(subdir)
-
-        for idx in range(data.shape[0]):
-            im = np.pad(data[idx].reshape((3, 32, 32)), pad_width, mode='mean')
-            im = np.uint8(np.transpose(im, axes=[1, 2, 0]).copy())
-            im = Image.fromarray(im)
-            path = os.path.join(img_dir, str(labels[idx][0]), str(idx) + '.png')
-            im.save(path, format='PNG')
-
 # setup data provider
-train_dir = os.path.join(args.data_dir, 'train')
-test_dir = os.path.join(args.data_dir, 'val')
-if not (os.path.exists(train_dir) and os.path.exists(test_dir)):
-    extract_images(args.data_dir, 40)
-
-# setup data provider
-shape = dict(channel_count=3, height=32, width=32)
-train_params = ImageParams(center=False, flip=True, **shape)
-test_params = ImageParams(**shape)
-common = dict(target_size=1, nclasses=10)
-
-train = DataLoader(set_name='train', repo_dir=train_dir, media_params=train_params,
-                   shuffle=True, **common)
-test = DataLoader(set_name='val', repo_dir=test_dir, media_params=test_params, **common)
-
+train = make_train_config(args.manifest['train'], args.manifest_root, args.batch_size)
+test = make_val_config(args.manifest['val'], args.manifest_root, args.batch_size)
 
 def conv_params(fsize, nfm, stride=1, relu=True):
     return dict(fshape=(fsize, fsize, nfm), strides=stride, padding=(1 if fsize > 1 else 0),

From 0655d217392f746e5c2d199a66f43409c7030842 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sun, 29 Oct 2017 23:56:21 +0100
Subject: [PATCH 13/22] Corrected documentation for neon 2.2

---
 ImageClassification/CIFAR10/DeepResNet/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ImageClassification/CIFAR10/DeepResNet/readme.md b/ImageClassification/CIFAR10/DeepResNet/readme.md
index a7a2216..dcc80ff 100644
--- a/ImageClassification/CIFAR10/DeepResNet/readme.md
+++ b/ImageClassification/CIFAR10/DeepResNet/readme.md
@@ -24,10 +24,10 @@ Training this model with the options described below should be able to achieve a
 accuracy using only mean subtraction, random cropping, and random flips.
 
 ## Instructions
-This script was tested with [neon version 1.5.0](https://github.com/NervanaSystems/neon/tree/v1.5.0).
+This script was tested with [neon version 2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0).
 Make sure that your local repo is synced to this commit and run the [installation
 procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
-Commit SHA for v1.5.0 is  `8a5b2c45784499cd3aba3c322ea10b3661c2a2a9`
+Commit SHA for v2.2.0 is  `5843e7116d880dfc59c8fb558beb58dd2ef421d0`
 
 This example uses the `DataLoader` module to load the images for consumption while applying random
 cropping, flipping, and shuffling.  To use the DataLoader, the script will generate PNG files from

From f7e633798d1ea0362b886a6cc1df2af8bb154e36 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 2 Nov 2017 18:12:21 +0100
Subject: [PATCH 14/22] Alexnet checked for Neon v2.3

---
 ImageClassification/ILSVRC2012/Alexnet/readme.md | 2 +-
 ImageClassification/ILSVRC2012/Alexnet/test.sh   | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/Alexnet/readme.md b/ImageClassification/ILSVRC2012/Alexnet/readme.md
index e6d4387..d5181af 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/readme.md
+++ b/ImageClassification/ILSVRC2012/Alexnet/readme.md
@@ -33,7 +33,7 @@ Note there has been some changes to the format of the mean data subtraction;
 users with the old format may be prompted to run an update script before proceeding.
 
 
-This script was tested with the [neon release v2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0).
+This script was tested with the [neon release v2.3.0](https://github.com/NervanaSystems/neon/tree/v2.3.0).
 Make sure that your local repo is synced to this commit and run the
 [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html)
 before proceeding.
diff --git a/ImageClassification/ILSVRC2012/Alexnet/test.sh b/ImageClassification/ILSVRC2012/Alexnet/test.sh
index 6ede82e..f8113b7 100755
--- a/ImageClassification/ILSVRC2012/Alexnet/test.sh
+++ b/ImageClassification/ILSVRC2012/Alexnet/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u alexnet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted/ --manifest_root /data/i1k-extracted --manifest train:/data/i1k-extracted/train-index.csv --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar  -z 256 | tee output.dat 2>&1
+python -u alexnet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted/ --manifest_root /data/i1k-extracted --manifest train:/data/i1k-extracted/train-index.csv --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar  -z 256 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc

From ccd9daf5a11f5778c3d64bfa7fcf6d6510bc06e8 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 2 Nov 2017 18:38:15 +0100
Subject: [PATCH 15/22] Changes for neon 2.3: add load_states=False

---
 ImageClassification/ILSVRC2012/VGG/test.sh     | 3 ++-
 ImageClassification/ILSVRC2012/VGG/vgg_neon.py | 9 +--------
 2 files changed, 3 insertions(+), 9 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/VGG/test.sh b/ImageClassification/ILSVRC2012/VGG/test.sh
index 34f0ace..46f3c0b 100755
--- a/ImageClassification/ILSVRC2012/VGG/test.sh
+++ b/ImageClassification/ILSVRC2012/VGG/test.sh
@@ -22,7 +22,8 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted -vvv --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv --model_file $WEIGHTS_FILE --no_progress_bar -z 64 --vgg_version E | tee output.dat 2>&1
+python -u vgg_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted -vvv --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv --model_file $WEIGHTS_FILE --no_progress_bar -z 64 --vgg_version E 2>&1 | tee output.dat
+
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc
diff --git a/ImageClassification/ILSVRC2012/VGG/vgg_neon.py b/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
index f3ccf16..94a6bfd 100644
--- a/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
+++ b/ImageClassification/ILSVRC2012/VGG/vgg_neon.py
@@ -82,13 +82,6 @@ def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size, sc
 #train = make_train_config(args.subset_pct, args.manifest["train"], args.manifest_root, batch_size=args.batch_size, scale=[0.875, 0.583])
 val = make_val_config(args.subset_pct, args.manifest["val"], args.manifest_root, batch_size=args.batch_size, scale=[0.875, 0.875])
 
-#img_set_options = dict(repo_dir=args.data_dir, inner_size=224,
-#                       subset_pct=args.subset_pct)
-#train = ImageLoader(set_name='train', scale_range=(256, 384),
-#                    shuffle=True, **img_set_options)
-#test = ImageLoader(set_name='validation', scale_range=(256, 256), do_transforms=False,
-#                   shuffle=False, **img_set_options)
-
 init1 = Xavier(local=True)
 initfc = GlorotUniform()
 
@@ -126,7 +119,7 @@ def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size, sc
 top5 = TopKMisclassification(k=5)
 callbacks = Callbacks(model, eval_set=val, metric=top5, **args.callback_args)
 
-model.load_params(args.model_file)
+model.load_params(args.model_file, load_states=False)
 mets=model.eval(val, metric=TopKMisclassification(k=5))
 print 'Validation set metrics:'
 print 'LogLoss: %.2f, Accuracy: %.1f %% (Top-1), %.1f %% (Top-5)' % (mets[0],

From 14be62e3371d586570b4324d475f365b052777af Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 2 Nov 2017 18:39:14 +0100
Subject: [PATCH 16/22] Changes in neon version

---
 ImageClassification/ILSVRC2012/VGG/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/VGG/readme.md b/ImageClassification/ILSVRC2012/VGG/readme.md
index c6d3dc5..46e9329 100644
--- a/ImageClassification/ILSVRC2012/VGG/readme.md
+++ b/ImageClassification/ILSVRC2012/VGG/readme.md
@@ -95,7 +95,7 @@ The batch size is set to 64 in the examples above because with larger batch size
 
 ### Version compatibility
 
-Neon version: v2.2.0 (https://github.com/NervanaSystems/neon/tree/v2.2.0).
+Neon version: v2.3.0 (https://github.com/NervanaSystems/neon/tree/v2.3.0).
 
 ## Citation
 

From e58f7184fda75556cd37de354eb2599a981c028c Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 2 Nov 2017 18:42:57 +0100
Subject: [PATCH 17/22] Added load_states=False for neon 2.3

---
 ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py | 2 +-
 ImageClassification/ILSVRC2012/Googlenet/test.sh           | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py b/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
index 0298713..ee75662 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
+++ b/ImageClassification/ILSVRC2012/Googlenet/googlenet_neon.py
@@ -148,7 +148,7 @@ def aux_branch(bnode):
                  weights=[1, 0., 0.])  # We only want to consider the CE of the main path
 
 assert os.path.exists(args.model_file), 'script requires the trained weights file'
-model.load_params(args.model_file)
+model.load_params(args.model_file, load_states=False)
 model.initialize(val, cost)
 
 
diff --git a/ImageClassification/ILSVRC2012/Googlenet/test.sh b/ImageClassification/ILSVRC2012/Googlenet/test.sh
index 419b296..fbca2ed 100755
--- a/ImageClassification/ILSVRC2012/Googlenet/test.sh
+++ b/ImageClassification/ILSVRC2012/Googlenet/test.sh
@@ -22,7 +22,7 @@ WEIGHTS_FILE=${WEIGHTS_URL##*/}
 echo "Downloading weights file from ${WEIGHTS_URL}"
 curl -o $WEIGHTS_FILE $WEIGHTS_URL 2> /dev/null
 
-python -u googlenet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar | tee output.dat 2>&1
+python -u googlenet_neon.py --test_only -i ${EXECUTOR_NUMBER} -w /data/i1k-extracted --manifest_root /data/i1k-extracted --manifest val:/data/i1k-extracted/val-index.csv -vvv --model_file $WEIGHTS_FILE --no_progress_bar 2>&1 | tee output.dat
 rc=$?
 if [ $rc -ne 0 ];then
     exit $rc

From 70ff8df6fed3c4b8a809c7c78314237011499fd1 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 2 Nov 2017 18:43:42 +0100
Subject: [PATCH 18/22] Neon version changed to 2.3

---
 ImageClassification/ILSVRC2012/Googlenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/Googlenet/readme.md b/ImageClassification/ILSVRC2012/Googlenet/readme.md
index 2f0d62f..5768307 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/readme.md
+++ b/ImageClassification/ILSVRC2012/Googlenet/readme.md
@@ -26,7 +26,7 @@ During training, the images were randomly cropped and flipped horizontally but s
 To run the model, first the ImageNet data set needs to be uploaded and converted to the format compatible with neon (see  [instructions](http://neon.nervanasys.com/docs/latest/datasets.html#imagenet)).  Note there has been some changes to the format of the mean data subtraction; users with the old format may be prompted to run an update script before proceeding.
 
 
-This script works with the [neon release v2.2.0](https://github.com/NervanaSystems/neon/tree/v2.2.0).  Make sure that your local repo is synced to this commit and run the [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
+This script works with the [neon release v2.3.0](https://github.com/NervanaSystems/neon/tree/v2.3.0).  Make sure that your local repo is synced to this commit and run the [installation procedure](http://neon.nervanasys.com/docs/latest/installation.html) before proceeding.
 
 
 If neon is installed into a `virtualenv`, make sure that it is activated before running the commands below.  Also, the commands below use the GPU backend by default so add `-b cpu` if you are running on a system without a compatible GPU.

From 9e8c7a57d9c6573462587e5f06531172f3c6191f Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Thu, 2 Nov 2017 18:48:06 +0100
Subject: [PATCH 19/22] All_CNN: version of neon changed to 2.3

---
 ImageClassification/CIFAR10/All_CNN/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/CIFAR10/All_CNN/readme.md b/ImageClassification/CIFAR10/All_CNN/readme.md
index 972c9b6..002baec 100644
--- a/ImageClassification/CIFAR10/All_CNN/readme.md
+++ b/ImageClassification/CIFAR10/All_CNN/readme.md
@@ -13,7 +13,7 @@ The trained weights file can be downloaded from AWS
 
 
 ### neon version
-The model weight file above has been generated using neon version tag [v2.2.0]((https://github.com/NervanaSystems/neon/releases/tag/v2.2.0).
+The model weight file above has been generated using neon version tag [v2.3.0]((https://github.com/NervanaSystems/neon/releases/tag/v2.3.0).
 It may not work with other versions.
 
 ### Performance

From 5502ec3414b3aa324209103c93b982fdd0118fca Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sat, 9 Dec 2017 22:12:16 +0100
Subject: [PATCH 20/22] Changing links to weight files in format with fused
 conv+bias

---
 ImageClassification/ILSVRC2012/VGG/readme.md | 4 ++--
 1 file changed, 2 insertions(+), 2 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/VGG/readme.md b/ImageClassification/ILSVRC2012/VGG/readme.md
index 46e9329..5b54a2d 100644
--- a/ImageClassification/ILSVRC2012/VGG/readme.md
+++ b/ImageClassification/ILSVRC2012/VGG/readme.md
@@ -7,8 +7,8 @@ The model run script is included here [vgg_neon.py](./vgg_neon.py).  This script
 
 ### Trained weights
 The trained weights file can be downloaded from AWS using the following links:
-[VGG_D.p]( https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_D.p) and [VGG_E.p][S3_WEIGHTS_FILE].
-[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_E.p
+[VGG_D.p]( https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_D_fused_conv_bias.p) and [VGG_E.p][S3_WEIGHTS_FILE].
+[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/VGG/VGG_E_fused_conv_bias.p
 
 ## Performance
 

From 8b4be94c275acda7715a393c945f4c70cac983f6 Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sun, 10 Dec 2017 21:54:00 +0100
Subject: [PATCH 21/22] Alexnet converted to the new format with fused bias and
 convolution layers

---
 ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py | 2 +-
 ImageClassification/ILSVRC2012/Alexnet/readme.md       | 2 +-
 2 files changed, 2 insertions(+), 2 deletions(-)

diff --git a/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py b/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
index d05b4a2..e77b30a 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
+++ b/ImageClassification/ILSVRC2012/Alexnet/alexnet_neon.py
@@ -138,7 +138,7 @@ def make_val_config(subset_pct, manifest_filename, manifest_root, batch_size):
 callbacks = Callbacks(model, eval_set=val, metric=valmetric, **args.callback_args)
 
 if args.model_file is not None:
-    model.load_params(args.model_file)
+    model.load_params(args.model_file, load_states=False)
 if not args.test_only:
     cost = GeneralizedCost(costfunc=CrossEntropyMulti())
     model.fit(train, optimizer=opt, num_epochs=args.epochs, cost=cost, callbacks=callbacks)
diff --git a/ImageClassification/ILSVRC2012/Alexnet/readme.md b/ImageClassification/ILSVRC2012/Alexnet/readme.md
index d5181af..7175be7 100644
--- a/ImageClassification/ILSVRC2012/Alexnet/readme.md
+++ b/ImageClassification/ILSVRC2012/Alexnet/readme.md
@@ -16,7 +16,7 @@ The model run script is included below [alexnet_neon.py](./alexnet_neon.py).
 ### Trained weights
 The trained weights file can be downloaded from AWS using the following link:
 [trained Alexnet model weights][S3_WEIGHTS_FILE]
-[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/alexnet.p
+[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/alexnet/alexnet_fused_conv_bias.p
 
 ### Performance
 This model is acheiving 58.6% top-1 and 81.1% top-5 accuracy on the validation

From a428edb00206e46772e351b593c6326b8674ed0c Mon Sep 17 00:00:00 2001
From: Tomasz Patejko <tomasz.patejko@intel.com>
Date: Sun, 10 Dec 2017 23:15:59 +0100
Subject: [PATCH 22/22] Googlenet uses weights file in new format with fused
 conv+bias layers

---
 ImageClassification/ILSVRC2012/Googlenet/readme.md | 2 +-
 1 file changed, 1 insertion(+), 1 deletion(-)

diff --git a/ImageClassification/ILSVRC2012/Googlenet/readme.md b/ImageClassification/ILSVRC2012/Googlenet/readme.md
index 5768307..62665c6 100644
--- a/ImageClassification/ILSVRC2012/Googlenet/readme.md
+++ b/ImageClassification/ILSVRC2012/Googlenet/readme.md
@@ -14,7 +14,7 @@ The model run script is included here [googlenet_neon.py](./googlenet_neon.py).
 The trained weights file can be downloaded from AWS using the following link:
 [trained googlenet model weights][S3_WEIGHTS_FILE].
 
-[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/googlenet/googlenet.p
+[S3_WEIGHTS_FILE]: https://s3-us-west-1.amazonaws.com/nervana-modelzoo/googlenet/googlenet_fused_conv_bias.p
 
 ### Performance
 This model is acheiving 64% top-1 and 85.5% top-5 accuracy on the validation data set.