Add shape test for conv3d gradi

theano/sandbox/cuda/tests/test_dnn.py

@@ -769,6 +769,51 @@ class TestDnnInferShapes(utt.InferShapeTester):
                 dnn.GpuDnnConvGradI
             )
 
+    def test_conv3d_gradi(self):
+        if not dnn.dnn_available():
+            raise SkipTest(dnn.dnn_available.msg)
+        ftensor5 = T.TensorType(dtype="float32", broadcastable=(False,) * 5)
+        img = ftensor5('img')
+        kerns = ftensor5('kerns')
+        out = ftensor5('out')
+        img_val = numpy.asarray(
+            numpy.random.rand(8, 4, 6, 7, 5),
+            dtype='float32'
+        )
+        kern_vals = numpy.asarray(
+            numpy.random.rand(9, 4, 5, 1, 2),
+            dtype='float32'
+        )
+
+        for params in product(
+            ['valid', 'full'],
+            [(1, 1, 1), (2, 2, 2)],
+            ['conv', 'cross']
+        ):
+            out_vals = numpy.zeros(
+                dnn.GpuDnnConv3d.get_out_shape(img_val.shape, kern_vals.shape,
+                                               border_mode=params[0],
+                                               subsample=params[1]),
+                dtype='float32')
+
+            desc = dnn.GpuDnnConvDesc(
+                border_mode=params[0],
+                subsample=params[1],
+                conv_mode=params[2]
+            )(kerns.shape, out.shape)
+            conv_grad_i = dnn.GpuDnnConv3dGradI()(
+                kerns,
+                out,
+                img,
+                desc,
+            )
+            self._compile_and_check(
+                [kerns, out, img],
+                [conv_grad_i],
+                [kern_vals, out_vals, img_val],
+                dnn.GpuDnnConv3dGradI
+            )
+
     def test_pool(self):
         if not dnn.dnn_available():
             raise SkipTest(dnn.dnn_available.msg)