* review comments

nnvm/tests/python/frontend/tensorflow/test_forward.py

@@ -225,8 +225,12 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
     with tf.Graph().as_default():
         in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
         in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
-        strides = [1] + strides + [1]
-        dilations = [1] + dilations + [1]
+        if data_format == 'NHWC':
+            strides = [1] + strides + [1]
+            dilations = [1] + dilations + [1]
+        else:
+            strides = [1, 1] + strides
+            dilations = [1, 1] + dilations
 
         nn_ops.conv2d(in_data,
                       in_filter,
@@ -240,7 +244,9 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
 def test_forward_convolution():
     if is_gpu_available():
         _test_convolution([4, 176, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NCHW')
+        _test_convolution([4, 19, 17, 17], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NCHW')
         _test_convolution([4, 124, 17, 17], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NCHW')
+        _test_convolution([4, 12, 17, 17], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NCHW')
 
     _test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
     _test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')

python/tvm/relay/frontend/tensorflow.py

@@ -778,7 +778,7 @@ def _transform_mask(stride_dim, ellipsis_mask):
                 pass
             else:
                 final_output.append(out_shape[gather_index])
-        # Prevent 0-dim tensors which are not accepted by nnvm
+        # Prevent 0-dim tensors which are not accepted by Relay
         if not final_output:
             final_output.append(1)
         return _op.reshape(out, newshape=tuple(final_output))
@@ -1444,7 +1444,7 @@ def from_tensorflow(self, graph, layout="NHWC", shape=None, outputs=None):
                             input_shape = self._output_shapes[node_name][0]
                         inputs.append(in_sym[0])
                         input_shapes[in_sym[0]] = input_shape
-                        # This means the node is 1d in NNVM and 0d in TF.
+                        # This means the node is 1d in Relay and 0d in TF.
                         # See `_expand_dims_0d_aware`.
                         if self._outputs_are_0d[node_name][tensor_slot] and input_shape:
                             input_0d_mismatch.add(in_sym)

tests/python/frontend/tensorflow/test_forward.py

@@ -228,8 +228,12 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
     with tf.Graph().as_default():
         in_data = array_ops.placeholder(shape=tensor_in_sizes, dtype='float32')
         in_filter = constant_op.constant(filter_array, shape=filter_in_sizes, dtype='float32')
-        strides = [1] + strides + [1]
-        dilations = [1] + dilations + [1]
+        if data_format == 'NHWC':
+            strides = [1] + strides + [1]
+            dilations = [1] + dilations + [1]
+        else:
+            strides = [1, 1] + strides
+            dilations = [1, 1] + dilations
 
         nn_ops.conv2d(in_data,
                       in_filter,
@@ -243,7 +247,9 @@ def _test_convolution(tensor_in_sizes, filter_in_sizes,
 def test_forward_convolution():
     if is_gpu_available():
         _test_convolution([4, 176, 8, 8], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NCHW')
+        _test_convolution([4, 19, 17, 17], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NCHW')
         _test_convolution([4, 124, 17, 17], [1, 1, 124, 19], [1, 1], [1, 1], 'SAME', 'NCHW')
+        _test_convolution([4, 12, 17, 17], [3, 3, 12, 32], [1, 1], [2, 2], 'VALID', 'NCHW')
 
     _test_convolution([4, 8, 8, 176], [1, 1, 176, 32], [1, 1], [1, 1], 'SAME', 'NHWC')
     _test_convolution([4, 17, 17, 19], [3, 3, 19, 19], [1, 1], [2, 2], 'VALID', 'NHWC')