wehub-resource-sync
63 lines
2.6 KiB
Python
63 lines
2.6 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 numpy as np
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import onnx
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# Though omitted in this example, in some cases, it may be useful to embed
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# shape information in the graph. We can use ONNX shape inference to do this:
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#
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# from onnx import shape_inference
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# model = shape_inference.infer_shapes(onnx.load("model.onnx"))
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#
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# IMPORTANT: In some cases, ONNX shape inference may not correctly infer shapes,
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# which will result in an invalid subgraph. To avoid this, you can instead modify
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# the tensors to include the shape information yourself.
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model = onnx.load("model.onnx")
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graph = gs.import_onnx(model)
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# Since we already know the names of the tensors we're interested in, we can
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# grab them directly from the tensor map.
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#
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# NOTE: If you do not know the tensor names you want, you can view the graph in
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# Netron to determine them, or use ONNX GraphSurgeon in an interactive shell
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# to print the graph.
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tensors = graph.tensors()
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# If you want to embed shape information, but cannot use ONNX shape inference,
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# you can manually modify the tensors at this point:
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#
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# graph.inputs = [tensors["x1"].to_variable(dtype=np.float32, shape=(1, 3, 224, 224))]
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# graph.outputs = [tensors["add_out"].to_variable(dtype=np.float32, shape=(1, 3, 224, 224))]
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#
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# IMPORTANT: You must include type information for input and output tensors if it is not already
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# present in the graph.
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#
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# NOTE: ONNX GraphSurgeon will also accept dynamic shapes - simply set the corresponding
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# dimension(s) to `gs.Tensor.DYNAMIC`, e.g. `shape=(gs.Tensor.DYNAMIC, 3, 224, 224)`
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graph.inputs = [tensors["x1"].to_variable(dtype=np.float32)]
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graph.outputs = [tensors["add_out"].to_variable(dtype=np.float32)]
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# Notice that we do not need to manually modify the rest of the graph. ONNX GraphSurgeon will
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# take care of removing any unnecessary nodes or tensors, so that we are left with only the subgraph.
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graph.cleanup()
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onnx.save(gs.export_onnx(graph), "subgraph.onnx")
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