wehub-resource-sync
43 lines
1.6 KiB
Python
43 lines
1.6 KiB
Python
#!/usr/bin/env python3
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#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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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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#
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import onnx_graphsurgeon as gs
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import onnx
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graph = gs.import_onnx(onnx.load("model.onnx"))
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fake_node = [node for node in graph.nodes if node.op == "FakeNodeToRemove"][0]
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# Get the input node of the fake node
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# Node provides i() and o() functions that can optionally be provided an index (default is 0)
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# These serve as convenience functions for the alternative, which would be to fetch the input/output
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# tensor first, then fetch the input/output node of the tensor.
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# For example, node.i() is equivalent to node.inputs[0].inputs[0]
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inp_node = fake_node.i()
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# Reconnect the input node to the output tensors of the fake node, so that the first identity
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# node in the example graph now skips over the fake node.
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inp_node.outputs = fake_node.outputs
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fake_node.outputs.clear()
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# Remove the fake node from the graph completely
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graph.cleanup()
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model = onnx.shape_inference.infer_shapes(gs.export_onnx(graph))
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onnx.save(model, "removed.onnx")
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