wehub-resource-sync
50 lines
1.6 KiB
Python
50 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 numpy as np
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import onnx
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# Computes Y = x0 + (a * x1 + b)
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shape = (1, 3, 224, 224)
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# Inputs
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x0 = gs.Variable(name="x0", dtype=np.float32, shape=shape)
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x1 = gs.Variable(name="x1", dtype=np.float32, shape=shape)
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# Intermediate tensors
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a = gs.Constant("a", values=np.ones(shape=shape, dtype=np.float32))
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b = gs.Constant("b", values=np.ones(shape=shape, dtype=np.float32))
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mul_out = gs.Variable(name="mul_out")
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add_out = gs.Variable(name="add_out")
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# Outputs
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Y = gs.Variable(name="Y", dtype=np.float32, shape=shape)
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nodes = [
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# mul_out = a * x1
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gs.Node(op="Mul", inputs=[a, x1], outputs=[mul_out]),
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# add_out = mul_out + b
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gs.Node(op="Add", inputs=[mul_out, b], outputs=[add_out]),
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# Y = x0 + add
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gs.Node(op="Add", inputs=[x0, add_out], outputs=[Y]),
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]
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graph = gs.Graph(nodes=nodes, inputs=[x0, x1], outputs=[Y], ir_version=10)
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onnx.save(gs.export_onnx(graph), "model.onnx")
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