make convolution op accept Constant variable as shape.

theano/tensor/nnet/conv.py

@@ -69,6 +69,23 @@ def conv2d(input, filters, image_shape=None, filter_shape=None,
     :return: set of feature maps generated by convolutional layer. Tensor is of shape 
       (batch size, nb filters, output row, output col)
     """
+
+    #accept Constant value for image_shape and filter_shape.
+    if image_shape is not None:
+        image_shape = list(image_shape)
+        for i in range(len(image_shape)):
+            if image_shape[i] is not None:
+                image_shape[i] = tensor.get_constant_value(tensor.as_tensor_variable(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 range(len(filter_shape)):
+            if filter_shape[i] is not None:
+                filter_shape[i] = tensor.get_constant_value(tensor.as_tensor_variable(filter_shape[i]))
+                assert str(filter_shape[i].dtype).startswith('int')
+                filter_shape[i] = int(filter_shape[i])
+
     if image_shape and filter_shape:
       try:
         assert image_shape[1]==filter_shape[1]

theano/tensor/nnet/tests/test_conv.py

@@ -25,9 +25,9 @@ class TestConv2D(unittest.TestCase):
                  verify_grad=True, should_raise=False):
 
         if N_image_shape is None:
-            N_image_shape = image_shape
+            N_image_shape = [T.get_constant_value(T.as_tensor_variable(x)) for x in image_shape]
         if N_filter_shape is None:
-            N_filter_shape = filter_shape
+            N_filter_shape = [T.get_constant_value(T.as_tensor_variable(x)) for x in filter_shape]
     
         if not input:
             input = self.input
@@ -203,6 +203,19 @@ class TestConv2D(unittest.TestCase):
         self.validate((3,2,7,5), (5,2,2,3), 'full', subsample=(2,2))
         self.validate((3,2,7,5), (5,2,2,3), 'valid', subsample=(2,1))
 
+    def test_shape_Constant_tensor(self):
+        """
+        Tests convolution where the {image,filter}_shape is a Constant tensor.
+        """
+        as_t=T.as_tensor_variable
+        self.validate((as_t(3),as_t(2),as_t(7),as_t(5)), (5,2,2,3), 'valid')
+        self.validate(as_t([3,2,7,5]), (5,2,2,3), 'valid')
+        self.validate(as_t((3,2,7,5)), (5,2,2,3), 'valid')
+        self.validate((3,2,7,5), (as_t(5),as_t(2),as_t(2),as_t(3)), 'valid')
+        self.validate((3,2,7,5), as_t([5,2,2,3]), 'valid')
+        self.validate((3,2,7,5), as_t((5,2,2,3)), 'valid')
+        self.validate(as_t([3,2,7,5]), as_t([5,2,2,3]), 'full')
+
     def test_invalid_filter_shape(self):
         """
         Tests scenario where filter_shape[1] != input_shape[1]