milestone v0.1

Author: yunlongdong

infCNN/LayerLib.py

@@ -1,5 +1,5 @@
 from .Core import Layer
-from .conv_s import conv
+from .conv import conv
 import numpy as np
 
 

infCNN/conv.py

@@ -1,6 +1,9 @@
 import numpy as np
 from time import time
-from numba import njit
+try: from numba import njit
+except: njit = None
+
+if njit is None: print('install numba can double speed!')
 
 def neighbors(shape, core):
     shp = [slice(0,i) for i in core]
@@ -12,8 +15,7 @@ def neighbors(shape, core):
     acc = np.cumprod((1,)+shape[::-1][:-1])
     return np.dot(idx.T, acc[::-1])
 
-@njit
-def fill_col(pdimg, idx, colimg):
+def jit_fill_col(pdimg, idx, colimg):
     s = 0
     for i in range(len(pdimg)):
         if pdimg[i]&1==0: continue
@@ -22,19 +24,25 @@ def fill_col(pdimg, idx, colimg):
             s += 1
     return colimg
 
+def fill_col(pdimg, idx, colimg):
+    rc = np.where(pdimg&1)[0]
+    rc = rc.reshape((-1,1))+idx
+    colimg[:] = pdimg[rc.ravel()]
+    return colimg
+
+if not njit is None: fill_col = njit(jit_fill_col)
+
 def conv(img, core, stride=(1,1), buf=[np.zeros(1, dtype=np.int32)]):
     # new the col_img, if needed
     strh, strw = stride
     cimg_w = np.cumprod(core.shape[1:])[-1]
     n,c,h,w = img.shape
     cimg_h = n*(h//strh)*(w//strw)
-    
     if len(buf[0])<cimg_h*cimg_w:
         buf[0] = col_img = np.zeros(cimg_h*cimg_w, dtype=np.int32)
     else:
         col_img = buf[0][:cimg_h*cimg_w]
         col_img[:] = 0
-    
     # mark where need
     iimg = img.view(dtype=np.int32)
     iimg &= 0xfffffffe
@@ -71,4 +79,4 @@ def conv(img, core, stride=(1,1), buf=[np.zeros(1, dtype=np.int32)]):
 
     start = time()
     rst2 = conv(img, core, (1,1))
-    print('jit cost:', time()-start)
+    print('numpy cost:', time()-start)

infCNN/numpy_extend.py

@@ -20,18 +20,19 @@ def _resize(img, k, ra, rs, _rs, ca, cs, _cs, out):
             rcab = rra * _cs[c] + rrb * cs[c]
             out[r,c] = rcab
 
-def upsample(img, k=2, out=None):
-    nc, (h, w), e = img.shape[:-2], img.shape[-2:], 1e-4
+def upsample(img, k, out=None):
+    nc, (h, w) = img.shape[:-2], img.shape[-2:]
     if out is None:
         out = np.zeros(nc+(h*k, w*k), dtype=img.dtype)
-    
-    rs = np.linspace(e,h-1-e, h*k, dtype=np.float32)
-    cs = np.linspace(e,w-1-e, w*k, dtype=np.float32)
+    rs = np.linspace(-0.5+0.5/k,h-0.5-0.5/k, h*k, dtype=np.float32)
+    cs = np.linspace(-0.5+0.5/k,w-0.5-0.5/k, w*k, dtype=np.float32)
+    np.clip(rs, 0, h-1, out=rs)
+    np.clip(cs, 0, w-1, out=cs)
     ra = np.floor(rs).astype(np.uint32)
     ca = np.floor(cs).astype(np.uint32)
-    rs -= ra
-    cs -= ca
-
+    np.clip(ra, 0, h-1.5, out=ra)
+    np.clip(ca, 0, w-1.5, out=ca)
+    rs -= ra; cs -= ca; 
     outcol = out.reshape((-1, h*k, w*k))
     imgcol = img.reshape((-1, h, w))
     for i, o in zip(imgcol, outcol):
@@ -40,11 +41,20 @@ def upsample(img, k=2, out=None):
 
 if __name__ == '__main__':
     from time import time
-    from skimage.data import astronaut
+    from skimage.data import astronaut, camera
     img = astronaut().transpose(2,0,1).astype(np.float32)
-
+    img = camera().astype(np.float32)
+    img = np.array([[1,2],[3,4]], dtype=np.float32)
     # firs time to jit
-    resize(img, 2)
+    resize(img, 4)
     start = time()
-    rst = resize(img, 2)
-    print('\nmine v1', time()-start)
+    for i in range(10):
+        rst1 = resize(img, 2)
+    print('mine v1', time()-start)
+
+    import cv2
+    start = time()
+    for i in range(10):
+        rst2 = cv2.resize(img, (4, 4))
+    print('cv', time()-start)
+    

infCNN/upsample.py

@@ -3,15 +3,9 @@
 # infCNN layer
 from infCNN import Conv2d, Dense
 # infCNN op 
-from infCNN import ReLU, Flatten, MaxPool, Softmax 
+from infCNN import relu, flatten, maxpool, softmax 
 from time import time
 
-# op
-relu = ReLU()
-flatten = Flatten()
-maxpool = MaxPool()
-softmax = Softmax()
-
 class LeNet():
     def __init__(self):
         self.conv1 = Conv2d(1, 6, 5, 1)

plgs/net.py

@@ -13,7 +13,7 @@ The training on pytorch is regular, which can be seen in ```train/``` folder. Th
 
 
 #### Plugins for ImagePy
-The plugin is in ```plgs/``` folder
+The plugin example for mnist is in ```plgs/``` folder
 
 #### References