Don't add twice the Assert about shape in AbstractConv

theano/tensor/nnet/abstract_conv.py

@@ -1680,19 +1680,20 @@ class AbstractConv2d(AbstractConv):
     def grad(self, inp, grads):
         bottom, weights = inp
         top, = grads
+        # Don't add the assert again, as it was already added in the forward.
         d_bottom = AbstractConv2d_gradInputs(self.imshp, self.kshp,
                                              self.border_mode,
                                              self.subsample,
                                              self.filter_flip,
                                              self.filter_dilation)(
-            weights, top, bottom.shape[-2:])
+            weights, top, bottom.shape[-2:], add_assert_shape=False)
         d_weights = AbstractConv2d_gradWeights(self.imshp, self.kshp,
                                                self.border_mode,
                                                self.subsample,
                                                self.filter_flip,
                                                self.filter_dilation)(
 
-            bottom, top, weights.shape[-2:])
+            bottom, top, weights.shape[-2:], add_assert_shape=False)
 
         # Make sure that the broadcastable pattern of the inputs is used
         # for the gradients, even if the grad opts are not able to infer
@@ -1781,7 +1782,7 @@ class AbstractConv_gradWeights(BaseAbstractConv):
                                                        filter_dilation=filter_dilation)
 
     # Update shape/height_width
-    def make_node(self, img, topgrad, shape):
+    def make_node(self, img, topgrad, shape, add_assert_shape=True):
         # Make sure both inputs are Variables with the same Type
         if not isinstance(img, theano.Variable):
             img = as_tensor_variable(img)
@@ -1795,10 +1796,10 @@ class AbstractConv_gradWeights(BaseAbstractConv):
             raise TypeError('img must be %dD tensor' % (2 + self.convdim))
         if topgrad.type.ndim != 2 + self.convdim:
             raise TypeError('topgrad must be %dD tensor' % (2 + self.convdim))
-
-        img = assert_shape(img, self.imshp,
-                           'AbstractConv_gradWeights shape mismatch: shape of '
-                           'image does not match given imshp.')
+        if add_assert_shape:
+            img = assert_shape(img, self.imshp,
+                               'AbstractConv_gradWeights shape mismatch: shape of '
+                               'image does not match given imshp.')
 
         shape = as_tensor_variable(shape)
         broadcastable = [topgrad.broadcastable[1],
@@ -2020,7 +2021,7 @@ class AbstractConv_gradInputs(BaseAbstractConv):
                                                       filter_dilation=filter_dilation)
 
     # Update shape/height_width
-    def make_node(self, kern, topgrad, shape):
+    def make_node(self, kern, topgrad, shape, add_assert_shape=True):
         # Make sure both inputs are Variables with the same Type
         if not isinstance(kern, theano.Variable):
             kern = as_tensor_variable(kern)
@@ -2035,9 +2036,10 @@ class AbstractConv_gradInputs(BaseAbstractConv):
         if topgrad.type.ndim != 2 + self.convdim:
             raise TypeError('topgrad must be %dD tensor' % (2 + self.convdim))
 
-        kern = assert_shape(kern, self.kshp,
-                            'AbstractConv_gradInputs shape mismatch: shape of '
-                            'filters does not match given kshp.')
+        if add_assert_shape:
+            kern = assert_shape(kern, self.kshp,
+                                'AbstractConv_gradInputs shape mismatch: shape of '
+                                'filters does not match given kshp.')
 
         shape = as_tensor_variable(shape)
         broadcastable = [topgrad.type.broadcastable[0],
@@ -2158,8 +2160,9 @@ class AbstractConv2d_gradInputs(AbstractConv_gradInputs):
                                                self.border_mode,
                                                self.subsample,
                                                self.filter_flip,
-                                               self.filter_dilation)(bottom, top,
-                                                                     weights.shape[-2:])
+                                               self.filter_dilation)(
+                                                   bottom, top,
+                                                   weights.shape[-2:])
         d_top = AbstractConv2d(self.imshp, self.kshp,
                                self.border_mode,
                                self.subsample,