Fix bug in depthwise conv dilations with padding=same (#1320)

Signed-off-by: Tom Wildenhain <[email protected]>

Author: TomWildenhain-Microsoft

tests/backend_test_base.py

@@ -182,10 +182,9 @@ def freeze_and_run_tf(self, func, feed_dict, outputs, as_session, premade_placeh
                 tf.import_graph_def(graph_def, name='')
                 graph_def = tf_optimize(list(feed_dict.keys()), outputs, graph_def, fold_constant=constant_fold)
 
-        if True or self.config.is_debug_mode:
-            model_path = os.path.join(self.test_data_directory, self._testMethodName + "_after_tf_optimize.pb")
-            utils.save_protobuf(model_path, graph_def)
-            self.logger.debug("created file  %s", model_path)
+        model_path = os.path.join(self.test_data_directory, self._testMethodName + "_after_tf_optimize.pb")
+        utils.save_protobuf(model_path, graph_def)
+        self.logger.debug("created file  %s", model_path)
         return result, graph_def, initialized_tables
 
     def convert_to_tflite(self, graph_def, feed_dict, outputs):

tests/test_backend.py

@@ -609,6 +609,20 @@ def func(x):
         # rtol is a bit high, 2 values have a bit high error. Maybe use different input data.
         self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=0.01)
 
+    @check_tf_min_version("1.14", "tf depthwise_conv2d dilations")
+    @check_opset_min_version(11, "non-const pads")
+    def test_depthwiseconv_dilations(self):
+        x_shape = [1, 32, 32, 1]
+        kernel_shape = [5, 5, 1, 1]
+        x_val = np.arange(1, 1 + np.prod(x_shape)).astype("float32").reshape(x_shape)
+        kernel_val = np.arange(1, 1 + np.prod(kernel_shape)).astype("float32").reshape(kernel_shape)
+        def func(x):
+            kernel = tf.constant(kernel_val, dtype=tf.float32, name='k')
+            conv = tf.nn.depthwise_conv2d(x, kernel, strides=[1, 1, 1, 1], padding='SAME', dilations=[3, 4])
+            return tf.identity(conv, name=_TFOUTPUT)
+        # rtol is a bit high, 2 values have a bit high error. Maybe use different input data.
+        self._run_test_case(func, [_OUTPUT], {_INPUT: x_val}, rtol=0.01)
+
     @check_tf_max_version("1.15", "not supported in tf-2.0")
     def test_dropout(self):
         x_val = np.ones([1, 24, 24, 3], dtype=np.float32)

tf2onnx/onnx_opset/nn.py

@@ -587,9 +587,9 @@ def version_1(cls, ctx, node, **kwargs):
 
         node.set_attr("kernel_shape", [k_h, k_w])
         strides = conv_dims_attr(node, "strides")
-        conv_dims_attr(node, "dilations")
+        dilations = conv_dims_attr(node, "dilations")
         node.set_attr("group", k_input_channels)
-        add_padding(ctx, node, kernel_shape, strides)
+        add_padding(ctx, node, kernel_shape, strides, dilations)
 
         new_kernel_shape = [k_h, k_w, 1, k_output_channels]
         conv_convert_inputs(ctx, node, with_kernel=True, new_kernel_shape=new_kernel_shape)