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6343b0db47
* Add notes for tensors * Optimize some apis * move some warnings * Support build with Paddle2ONNX * Add protobuf support * Fix compile on mac * add clearn package script * Add paddle2onnx code * remove submodule * Add onnx ocde * remove softlink * add onnx code * fix error * Add cmake file * fix patchelf * update paddle2onnx * Delete .gitmodules --------- Co-authored-by: PaddleCI <paddle_ci@example.com> Co-authored-by: pangyoki <pangyoki@126.com> Co-authored-by: jiangjiajun <jiangjiajun@baidu.lcom>
63 lines
2.1 KiB
Python
Executable File
63 lines
2.1 KiB
Python
Executable File
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from paddle2onnx.legacy.passes import PassManager
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def get_repeated_output(inputs, outputs):
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repeated_output = {}
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for idx in range(len(outputs)):
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opt = outputs[idx]
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if opt in inputs:
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repeated_output[opt] = idx
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return repeated_output
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@PassManager('inplace_node_pass')
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class InplaceNodePass(object):
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name_count = dict()
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@classmethod
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def generate_new_name(cls, name):
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if name in cls.name_count:
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cls.name_count[name] += 1
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else:
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cls.name_count[name] = 1
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new_name = name + '.' + str(cls.name_count[name])
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return new_name
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@classmethod
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def run_pass(cls, onnx_graph):
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node_map = list(onnx_graph.node_map.items())
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name_mapping = {}
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for idx in range(len(node_map)):
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name, node = node_map[idx]
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inputs = node.inputs
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outputs = node.outputs
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for idx in range(len(inputs)):
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ipt = inputs[idx]
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if ipt in name_mapping:
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inputs[idx] = name_mapping[ipt]
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repeated_output = get_repeated_output(inputs, outputs)
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if len(repeated_output) != 0:
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for opt, idx in repeated_output.items():
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name_mapping[opt] = cls.generate_new_name(opt)
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outputs[idx] = name_mapping[opt]
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node.set_inputs(inputs)
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node.set_outputs(outputs)
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onnx_graph.update_node(node)
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return onnx_graph
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