Also check shapes inside the ops.

theano/tensor/nnet/abstract_conv.py

@@ -523,14 +523,14 @@ def conv2d(input,
 
     input = as_tensor_variable(input)
     filters = as_tensor_variable(filters)
-    if input_shape is not None:
+
     input = assert_shape(input, input_shape,
                          'conv2d shape mismatch: shape of '
                          'input does not match given input_shape.')
-    if filter_shape is not None:
     filters = assert_shape(filters, filter_shape,
                            'conv2d shape mismatch: shape of '
                            'filters does not match given filter_shape.')
+
     conv_op = AbstractConv2d(imshp=input_shape,
                              kshp=filter_shape,
                              border_mode=border_mode,
@@ -630,14 +630,14 @@ def conv3d(input,
 
     input = as_tensor_variable(input)
     filters = as_tensor_variable(filters)
-    if input_shape is not None:
+
     input = assert_shape(input, input_shape,
                          'conv3d shape mismatch: shape of '
                          'input does not match given input_shape.')
-    if filter_shape is not None:
     filters = assert_shape(filters, filter_shape,
                            'conv3d shape mismatch: shape of '
                            'filters does not match given filter_shape.')
+
     conv_op = AbstractConv3d(imshp=input_shape,
                              kshp=filter_shape,
                              border_mode=border_mode,
@@ -1600,6 +1600,13 @@ class AbstractConv(BaseAbstractConv):
         if kern.type.ndim != 2 + self.convdim:
             raise TypeError('kern must be %dD tensor' % (2 + self.convdim))
 
+        img = assert_shape(img, self.imshp,
+                           'AbstractConv shape mismatch: shape of '
+                           'image does not match given imshp.')
+        kern = assert_shape(kern, self.kshp,
+                            'AbstractConv shape mismatch: shape of '
+                            'filters does not match given kshp.')
+
         broadcastable = [img.broadcastable[0],
                          kern.broadcastable[0]] + ([False] * self.convdim)
         output = img.type.clone(broadcastable=broadcastable)()
@@ -1811,6 +1818,10 @@ class AbstractConv_gradWeights(BaseAbstractConv):
         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.')
+
         shape = as_tensor_variable(shape)
         broadcastable = [topgrad.broadcastable[1],
                          img.broadcastable[1]] + ([False] * self.convdim)
@@ -2046,6 +2057,10 @@ 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.')
+
         shape = as_tensor_variable(shape)
         broadcastable = [topgrad.type.broadcastable[0],
                          kern.type.broadcastable[1]] + ([False] * self.convdim)