Better conv2d error message

theano/tensor/nnet/conv.py

@@ -93,16 +93,30 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
         image_shape = list(image_shape)
         for i in xrange(len(image_shape)):
             if image_shape[i] is not None:
+                try:
                     image_shape[i] = get_scalar_constant_value(
                         as_tensor_variable(image_shape[i]))
+                except NotScalarConstantError, e:
+                    raise NotScalarConstantError(
+                        "The convolution need that the shape"
+                        " information are constant values. We got"
+                        " %s for the image_shape parameter" %
+                        image_shape[i])
                 assert str(image_shape[i].dtype).startswith('int')
                 image_shape[i] = int(image_shape[i])
     if filter_shape is not None:
         filter_shape = list(filter_shape)
         for i in xrange(len(filter_shape)):
             if filter_shape[i] is not None:
+                try:
                     filter_shape[i] = get_scalar_constant_value(
                         as_tensor_variable(filter_shape[i]))
+                except NotScalarConstantError, e:
+                    raise NotScalarConstantError(
+                        "The convolution need that the shape"
+                        " information are constant values. We got"
+                        " %s for the filter_shape "
+                        "parameter" % filter_shape[i])
                 assert str(filter_shape[i].dtype).startswith('int')
                 filter_shape[i] = int(filter_shape[i])
 

theano/tensor/nnet/tests/test_conv.py

@@ -7,7 +7,7 @@ from theano.tests import unittest_tools as utt
 
 from theano.tensor.nnet import conv
 
-from theano.tensor.basic import _allclose
+from theano.tensor.basic import _allclose, NotScalarConstantError
 
 
 class TestConv2D(utt.InferShapeTester):
@@ -353,6 +353,20 @@ class TestConv2D(utt.InferShapeTester):
                       N_image_shape=(3, 2, 8, 8),
                       N_filter_shape=(4, 2, 5, 5))
 
+    def test_wrong_info(self):
+        """
+        Test convolutions when we don't give a constant as shape information
+        """
+        i = theano.scalar.basic.int32()
+        self.assertRaises(NotScalarConstantError, self.validate,
+                          (3, 2, 8, i), (4, 2, 5, 5),
+                          N_image_shape=(3, 2, 8, 8),
+                          N_filter_shape=(4, 2, 5, 5))
+        self.assertRaises(NotScalarConstantError, self.validate,
+                          (3, 2, 8, 8), (4, 2, 5, i),
+                          N_image_shape=(3, 2, 8, 8),
+                          N_filter_shape=(4, 2, 5, 5))
+
     def test_full_mode(self):
         """
         Tests basic convolution in full mode and case where filter