modification about the op

Author: yunlongdong

infCNN/OpLib.py

@@ -82,3 +82,8 @@ def forward(self, x):
         return self._pool_forward(x, self.maxpool, self.size, self.stride)
 
 
+relu = ReLU()
+flatten = Flatten()
+sigmoid = Sigmoid()
+softmax = Softmax()
+maxpool = MaxPool()

infCNN/__init__.py

@@ -1,2 +1,3 @@
 from .LayerLib import * 
 from .OpLib import * 
+from .numpy_extend import *

infCNN/numpy_extend.py

@@ -0,0 +1,28 @@
+import numpy as np
+
+
+# up 2x featuremap, NxCxHxW -> NxCx2Hx2W
+def up2x(fm):
+    shape2x = np.array(fm.shape)
+    shape2x[-2:] *= 2
+
+    fm2x = np.zeros(shape2x)
+    fm2x[:, :, ::2, ::2] = fm
+    fm2x[:, :, 1::2, 1::2] = fm
+    
+    return fm2x
+
+
+# up 4x featuremap, NxCxHxW -> NxCx4Hx4W
+def up4x(fm):
+    fm4x = up2x(up2x(fm))
+    return fm4x
+
+if __name__ == "__main__":
+    img = np.random.randn(2, 3, 100, 100)
+    img2x = up4x(img)
+    print(img.shape, img2x.shape)
+
+    print(np.sum(img - img2x[:, :, ::4, ::4]))
+
+    

net.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)

readme.md

@@ -12,6 +12,8 @@ Example is shown in ```net.py```, which shows the inference of a LeNet CNN. Weig
 The training on pytorch is regular, which can be seen in ```train/``` folder. The weights of CNN are exported to ```.mat``` file.
 
 
+#### Plugins for ImagePy
+The plugin is in ```plgs/``` folder
 
 #### References