# # Copyright (c) 2021 Krai Ltd. # # SPDX-License-Identifier: BSD-3-Clause. # # Redistribution and use in source and binary forms, with or without # modification, are permitted provided that the following conditions are met: # # 1. Redistributions of source code must retain the above copyright notice, # this list of conditions and the following disclaimer. # # 2. Redistributions in binary form must reproduce the above copyright notice, # this list of conditions and the following disclaimer in the documentation # and/or other materials provided with the distribution. # # 3. Neither the name of the copyright holder nor the names of its contributors # may be used to endorse or promote products derived from this software without # specific prior written permission. # # THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" # AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE # IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE # ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE # LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR # CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF # SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS # INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN # CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) # ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE # POSSIBILITY OF SUCH DAMAGE. # # execution example in virtual env: # ck virtual env --tags=onnx,python-package --shell_cmd="python3 fix_unused_initializers_warnings.py resnet34.onnx" import sys import onnx from onnx import optimizer from onnx import helper from onnx import AttributeProto, TensorProto, GraphProto onnx_filename = sys.argv[1] try: input_names = ['image'] output_names = ['Concat_470', 'Transpose_661'] onnx.utils.extract_model(onnx_filename, onnx_filename, input_names, output_names) except: pass model_def = onnx.load(onnx_filename) del model_def.graph.output[1] del model_def.graph.output[0] concat = helper.make_tensor_value_info('Concat_470', TensorProto.FLOAT, [1,4,15130]) transpose = helper.make_tensor_value_info('Transpose_661', TensorProto.FLOAT, [1,81,15130]) model_def.graph.output.extend([concat]) model_def.graph.output.extend([transpose]) onnx.save(model_def, onnx_filename)