update

theano/tensor/nnet/abstract_conv.py

@@ -11,8 +11,6 @@ from theano.gof import Apply, Op
 
 import numpy
 try:
-    # TODO: move these back out to global scope when they no longer
-    # cause an atexit error
     from scipy.signal.signaltools import _valfrommode, _bvalfromboundary
     from scipy.signal.sigtools import _convolve2d
     imported_scipy_signal = True
@@ -441,18 +439,15 @@ class BaseAbstractConv2d(Op):
         # This may change in the future.
         return False
 
-    def corr2d(self, img, kern, mode="valid"):
+    def conv2d(self, img, kern, mode="valid"):
         """
         Basic slow python implementatation for DebugMode
         """
 
         if not imported_scipy_signal:
-            raise theano.gof.utils.MethodNotDefined(
-                "c_headers", type(self), self.__class__.__name__,
-                "Need the python package for scipy.signal to be installed "
-                "for the python implementation. You can use the C"
-                " implementation instead.")
-
+            raise NotImplementedError(
+                "AbstractConv perform requires the python package"
+                " for scipy.signal to be installed.")
         if not (mode in ('valid', 'full')):
             raise ValueError(
                 'invalid mode {}, which must be either '
@@ -525,7 +520,7 @@ class AbstractConv2d(BaseAbstractConv2d):
             img = new_img
         if not self.filter_flip:
             kern = kern[:, :, ::-1, ::-1]
-        conv_out = self.corr2d(img, kern, mode)
+        conv_out = self.conv2d(img, kern, mode="valid")
         conv_out = conv_out[:, :, ::self.subsample[0], ::self.subsample[1]]
 
         o[0] = node.outputs[0].type.filter(conv_out)
@@ -654,7 +649,7 @@ class AbstractConv2d_gradWeights(BaseAbstractConv2d):
 
         topgrad = topgrad.transpose(1, 0, 2, 3)[:, :, ::-1, ::-1]
         img = img.transpose(1, 0, 2, 3)
-        kern = self.corr2d(img, topgrad, mode)
+        kern = self.conv2d(img, topgrad, mode="valid")
         if self.filter_flip:
             kern = kern.transpose(1, 0, 2, 3)[:, :, ::-1, ::-1]
         else:
@@ -762,7 +757,6 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
             pad_h, pad_w = (kern.shape[2] // 2, kern.shape[3] // 2)
         elif isinstance(mode, tuple):
             pad_h, pad_w = map(int, self.border_mode)
-        mode = "valid"
         if self.subsample[0] > 1 or self.subsample[1] > 1:
             new_shape = (topgrad.shape[0], topgrad.shape[1],
                          shape[0] + 2 * pad_h - kern.shape[2] + 1,
@@ -773,7 +767,7 @@ class AbstractConv2d_gradInputs(BaseAbstractConv2d):
         kern = kern.transpose(1, 0, 2, 3)
         if self.filter_flip:
             topgrad = topgrad[:, :, ::-1, ::-1]
-        img = self.corr2d(topgrad, kern, mode="full")
+        img = self.conv2d(topgrad, kern, mode="full")
         if self.filter_flip:
             img = img[:, :, ::-1, ::-1]
         if pad_h > 0 or pad_w > 0: