def run_and_compare(self, output_names_with_port, onnx_feed_dict, origin_proto, op_type,
remaining_op_num, debug=False, rtol=1e-07):
utils.make_sure(op_type is not None, "op_type should be specified")
utils.make_sure(remaining_op_num is not None, "remaining_op_num should be specified")
origin_model_path = self.save_onnx_model(origin_proto, onnx_feed_dict, postfix="_origin")
new_proto = GraphUtil.optimize_graph_with_model_proto(origin_proto)
self.assertTrue(new_proto, msg="model proto after optimizer should not be None")
new_model_path = self.save_onnx_model(new_proto, onnx_feed_dict, postfix="_opt")
current = GraphUtil.get_node_count_from_onnx_graph(new_proto.graph)
self.assertTrue(current[op_type] == remaining_op_num,
msg="Expect " + str(remaining_op_num) + " " + op_type + " ops left, but actually " +
str(current[op_type]) + " left")
if self.config.is_onnxruntime_backend:
expected = self.run_onnxruntime(origin_model_path, onnx_feed_dict, output_names_with_port)
actual = self.run_onnxruntime(new_model_path, onnx_feed_dict, output_names_with_port)
else:
raise ValueError("only onnxruntime is supported to test transpose optimizer")
for expected_val, actual_val in zip(expected, actual):
self.assertAllClose(expected_val, actual_val, rtol=rtol, atol=0.)
self.assertEqual(expected_val.dtype, actual_val.dtype)
self.assertEqual(expected_val.shape, actual_val.shape)
def run_and_compare(self,
output_names_with_port,
onnx_feed_dict,
origin_proto,
debug=False,
rtol=1e-07):
origin_model_path = self.save_onnx_model(origin_proto,
onnx_feed_dict,
postfix="_origin")
new_proto = GraphUtil.opt_transposes_with_model_proto(origin_proto)
self.assertTrue(new_proto,
msg="model proto after optimizer should not be None")
new_model_path = self.save_onnx_model(new_proto,
onnx_feed_dict,
postfix="_opt")
previous = GraphUtil.get_node_count_from_onnx_graph(origin_proto.graph)
current = GraphUtil.get_node_count_from_onnx_graph(new_proto.graph)
self.assertTrue(current["Transpose"] < previous["Transpose"],
msg="transpose ops count not changed")
if type(self).BACKEND == "onnxruntime":
expected = self.run_onnxruntime(origin_model_path, onnx_feed_dict,
output_names_with_port)
actual = self.run_onnxruntime(new_model_path, onnx_feed_dict,
output_names_with_port)
else:
raise ValueError(
"only onnxruntime is supported to test transpose optimizer")
for expected_val, actual_val in zip(expected, actual):
self.assertAllClose(expected_val, actual_val, rtol=rtol, atol=0.)
self.assertEqual(expected_val.dtype, actual_val.dtype)
self.assertEqual(expected_val.shape, actual_val.shape)
