def test_trans_output_as_graph_outputs(self):
"""
If transpose's output is graph's output, don't optimize it.
"""
trans = helper.make_node("Transpose", ["X"], ["Y"], name="trans", perm=[0, 2, 3, 1])
graph_proto = helper.make_graph(
[trans],
"trans-to-graph-output",
[helper.make_tensor_value_info("X", TensorProto.FLOAT, (2, 3, 4, 5))],
[helper.make_tensor_value_info("Y", TensorProto.FLOAT, (2, 4, 5, 3))],
)
graph = GraphUtil.create_graph_from_onnx_graph(graph_proto)
# remove identity to graph output
identity_op = graph.get_node_by_output(graph.outputs[0])
graph.outputs = [identity_op.input[0]]
graph.remove_node(identity_op.name)
optimized_graph = GraphUtil.optimize_graph(graph)
self.assertTrue(optimized_graph, msg="graph after optimizer should not be None")
trans_cnt = len(group_nodes_by_type(optimized_graph)["Transpose"])
self.assertTrue(trans_cnt == 1, msg="Expect 1 Transpose ops left, but actually " + str(trans_cnt) + " left")
def run_test(self,
name,
backend="caffe2",
debug=False,
onnx_file=None,
opset=None,
perf=None,
fold_const=None):
"""Run complete test against backend."""
print(name)
self.perf = perf
# get the model
if self.url:
_, dir_name = self.download_file()
model_path = os.path.join(dir_name, self.local)
else:
model_path = self.local
dir_name = os.path.dirname(self.local)
print("\tdownloaded", model_path)
inputs = list(self.input_names.keys())
outputs = self.output_names
if self.model_type in ["checkpoint"]:
graph_def, inputs, outputs = loader.from_checkpoint(
model_path, inputs, outputs)
elif self.model_type in ["saved_model"]:
graph_def, inputs, outputs = loader.from_saved_model(
model_path, inputs, outputs)
else:
graph_def, inputs, outputs = loader.from_graphdef(
model_path, inputs, outputs)
# create the input data
inputs = {}
for k, v in self.input_names.items():
if isinstance(v, six.text_type) and v.startswith("np."):
inputs[k] = eval(v) # pylint: disable=eval-used
else:
inputs[k] = self.make_input(v)
if self.more_inputs:
for k, v in self.more_inputs.items():
inputs[k] = v
graph_def = tf2onnx.tfonnx.tf_optimize(inputs.keys(),
self.output_names, graph_def,
fold_const)
shape_override = {}
g = tf.import_graph_def(graph_def, name='')
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True),
graph=g) as sess:
# fix inputs if needed
for k in inputs.keys(): # pylint: disable=consider-iterating-dictionary
t = sess.graph.get_tensor_by_name(k)
dtype = tf.as_dtype(t.dtype).name
if type != "float32":
v = inputs[k]
inputs[k] = v.astype(dtype)
if self.force_input_shape:
for k, v in inputs.items():
shape_override[k] = list(v.shape)
# run the model with tensorflow
if self.skip_tensorflow:
print("\ttensorflow", "SKIPPED")
else:
tf_results = self.run_tensorflow(sess, inputs)
print("\ttensorflow", "OK")
model_proto = None
try:
# convert model to onnx
onnx_graph = self.to_onnx(sess.graph,
opset=opset,
shape_override=shape_override,
input_names=inputs.keys())
model_proto = onnx_graph.make_model("converted from tf2onnx")
new_model_proto = GraphUtil.optimize_graph(onnx_graph,
"test",
debug=debug)
if new_model_proto:
model_proto = new_model_proto
else:
print(
"\tNON-CRITICAL, optimizers are not applied successfully"
)
print("\tto_onnx", "OK")
if debug:
onnx_graph.dump_graph()
if onnx_file:
self.create_onnx_file(name, model_proto, inputs, onnx_file)
except Exception as ex:
tb = traceback.format_exc()
print("\tto_onnx", "FAIL", ex, tb)
try:
onnx_results = None
if backend == "caffe2":
onnx_results = self.run_caffe2(name, model_proto, inputs)
elif backend == "onnxmsrtnext":
onnx_results = self.run_onnxmsrtnext(name, model_proto, inputs)
elif backend == "onnxruntime":
onnx_results = self.run_onnxruntime(name, model_proto, inputs)
else:
raise ValueError("unknown backend")
print("\trun_onnx OK")
try:
if self.skip_tensorflow:
print("\tResults: skipped tensorflow")
else:
if self.check_only_shape:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_array_equal(
tf_res.shape, onnx_res.shape)
else:
for tf_res, onnx_res in zip(tf_results, onnx_results):
np.testing.assert_allclose(tf_res,
onnx_res,
rtol=self.rtol,
atol=self.atol)
print("\tResults: OK")
return True
except Exception as ex:
print("\tResults: ", ex)
except Exception as ex:
print("\trun_onnx", "FAIL", ex)
return False
