comment surgery helpers for clarity

surgery.py

@@ -3,6 +3,18 @@
 import numpy as np
 
 def transplant(new_net, net, suffix=''):
+    """
+    Transfer weights by copying matching parameters, coercing parameters of
+    incompatible shape, and dropping unmatched parameters.
+
+    The coercion is useful to convert fully connected layers to their
+    equivalent convolutional layers, since the weights are the same and only
+    the shapes are different.  In particular, equivalent fully connected and
+    convolution layers have shapes O x I and O x I x H x W respectively for O
+    outputs channels, I input channels, H kernel height, and W kernel width.
+
+    Both  `net` to `new_net` arguments must be instantiated `caffe.Net`s.
+    """
     for p in net.params:
         p_new = p + suffix
         if p_new not in new_net.params:
@@ -18,12 +30,10 @@ def transplant(new_net, net, suffix=''):
                 print 'copying', p, ' -> ', p_new, i
             new_net.params[p_new][i].data.flat = net.params[p][i].data.flat
 
-def expand_score(new_net, new_layer, net, layer):
-    old_cl = net.params[layer][0].num
-    new_net.params[new_layer][0].data[:old_cl][...] = net.params[layer][0].data
-    new_net.params[new_layer][1].data[0,0,0,:old_cl][...] = net.params[layer][1].data
-
 def upsample_filt(size):
+    """
+    Make a 2D bilinear kernel suitable for upsampling of the given (h, w) size.
+    """
     factor = (size + 1) // 2
     if size % 2 == 1:
         center = factor - 1
@@ -34,6 +44,9 @@ def upsample_filt(size):
            (1 - abs(og[1] - center) / factor)
 
 def interp(net, layers):
+    """
+    Set weights of each layer in layers to bilinear kernels for interpolation.
+    """
     for l in layers:
         m, k, h, w = net.params[l][0].data.shape
         if m != k and k != 1:
@@ -44,3 +57,12 @@ def interp(net, layers):
             raise
         filt = upsample_filt(h)
         net.params[l][0].data[range(m), range(k), :, :] = filt
+
+def expand_score(new_net, new_layer, net, layer):
+    """
+    Transplant an old score layer's parameters, with k < k' classes, into a new
+    score layer with k classes s.t. the first k' are the old classes.
+    """
+    old_cl = net.params[layer][0].num
+    new_net.params[new_layer][0].data[:old_cl][...] = net.params[layer][0].data
+    new_net.params[new_layer][1].data[0,0,0,:old_cl][...] = net.params[layer][1].data