AbstractConv_gradInputs.perform should also check the shapes.

theano/tensor/nnet/abstract_conv.py

@@ -1966,6 +1966,21 @@ class AbstractConv_gradInputs(BaseAbstractConv):
                 '"valid", "full", "half", an integer or a tuple of'
                 ' integers'.format(mode))
 
+        imshp = self.imshp[:] if self.imshp is not None else [None] * (2 + self.convdim)
+        fallback_imshp = ([topgrad.shape[0], kern.shape[1]] +
+                          [shape[i] for i in range(self.convdim)])
+        imshp = [fallback_imshp[i] if imshp[i] is None else imshp[i]
+                 for i in range(2 + self.convdim)]
+        expected_topgrad_shape = get_conv_output_shape(
+            imshp, kern.shape,
+            self.border_mode, self.subsample, self.filter_dilation)
+        if not tuple(expected_topgrad_shape) == tuple(topgrad.shape):
+            raise ValueError(
+                'invalid input_shape for gradInputs: the given input_shape '
+                'would produce an output of shape {}, but the given topgrad '
+                'has shape {}'.format(tuple(expected_topgrad_shape),
+                                      tuple(topgrad.shape)))
+
         dil_kernshp = tuple((kern.shape[i + 2] - 1) * self.filter_dilation[i] + 1
                             for i in range(self.convdim))
         pad = (0,) * self.convdim