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#!/usr/bin/env python3
# Copyright (c) 2020 Samsung Electronics Co., Ltd. All Rights Reserved
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import onnx
import os
import sys
from onnx import AttributeProto, TensorProto
from onnx import numpy_helper
from onnx import helper
def _data_type_str(data_type):
return TensorProto.DataType.Name(data_type)
def _get_attribute_value(attr):
if attr.type == AttributeProto.TENSOR:
return "{}, {}".format(
_data_type_str(attr.t.data_type), numpy_helper.to_array(attr.t))
if attr.type == AttributeProto.GRAPH:
# TODO revise when graph node is available
return "<graph>"
if attr.type == AttributeProto.TENSORS:
# TODO revise to see contents
return "<tensors>..."
if attr.type == AttributeProto.GRAPHS:
# TODO revise when graph node is available
return "<graphs>..."
return helper.get_attribute_value(attr)
def _dump_header(onnx_model):
print("[General] -----------------------------")
print("IR version =", onnx_model.ir_version)
print("Producer =", onnx_model.producer_name, onnx_model.producer_version)
print("")
def _dump_operators(onnx_model):
opcodes_dict = dict()
for node in onnx_model.graph.node:
if node.op_type in opcodes_dict:
opcodes_dict[node.op_type] = opcodes_dict[node.op_type] + 1
else:
opcodes_dict[node.op_type] = 1
print("[Operators] ---------------------------")
for opcode_key in opcodes_dict:
print("{:>5} {}".format(opcodes_dict[opcode_key], opcode_key))
print("")
def _dump_initializers(onnx_model):
print("[Initializers] ------------------------")
for initializer in onnx_model.graph.initializer:
init_name = '"{}"'.format(initializer.name)
dtstr = _data_type_str(initializer.data_type)
print('{:<15} {} {}'.format(init_name, dtstr, initializer.dims))
print("")
def _dump_nodes(onnx_model):
print("[Nodes] -------------------------------")
for node in onnx_model.graph.node:
print('{0}("{1}")'.format(node.op_type, node.name))
attribute = ''
for attr in node.attribute:
if attribute != '':
attribute += ', '
attribute += "{}: {}".format(attr.name, _get_attribute_value(attr))
if attribute != '':
print(' A {0}'.format(attribute))
for inp in node.input:
print(' I "{0}"'.format(inp))
for out in node.output:
print(' O "{0}"'.format(out))
print("")
def _dump_inputoutputs(onnx_model):
print("[Graph Input/Output]-------------------")
for mod_input in onnx_model.graph.input:
io_name = '"{}"'.format(mod_input.name)
dtstr = _data_type_str(mod_input.type.tensor_type.elem_type)
shape = mod_input.type.tensor_type.shape
input_shape = [dim.dim_value for dim in shape.dim]
print(' I: {:<15} {} {}'.format(io_name, dtstr, input_shape))
for mod_output in onnx_model.graph.output:
io_name = '"{}"'.format(mod_output.name)
dtstr = _data_type_str(mod_output.type.tensor_type.elem_type)
shape = mod_output.type.tensor_type.shape
output_shape = [dim.dim_value for dim in shape.dim]
print(' O: {:<15} {} {}'.format(io_name, dtstr, output_shape))
print("")
def _dump_graph(onnx_model):
_dump_header(onnx_model)
_dump_operators(onnx_model)
_dump_initializers(onnx_model)
_dump_nodes(onnx_model)
_dump_inputoutputs(onnx_model)
def _help_exit(cmd_name):
print('Dump ONNX model file Graph')
print('Usage: {0} [onnx_path]'.format(cmd_name))
print('')
exit()
def main():
if len(sys.argv) < 2:
_help_exit(os.path.basename(sys.argv[0]))
onnx_model = onnx.load(sys.argv[1])
onnx.checker.check_model(onnx_model)
_dump_graph(onnx_model)
if __name__ == "__main__":
main()
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