wehub-resource-sync
59 lines
1.7 KiB
Python
59 lines
1.7 KiB
Python
#
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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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# Register functions to make graph generation easier
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@gs.Graph.register()
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def min(self, *args):
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return self.layer(op="Min", inputs=args, outputs=["min_out"])[0]
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@gs.Graph.register()
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def max(self, *args):
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return self.layer(op="Max", inputs=args, outputs=["max_out"])[0]
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@gs.Graph.register()
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def identity(self, inp):
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return self.layer(op="Identity", inputs=[inp], outputs=["identity_out"])[0]
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# Generate the graph
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graph = gs.Graph(ir_version=10)
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graph.inputs = [gs.Variable("input", shape=(4, 4), dtype=np.float32)]
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# Clip values to [0, 6]
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MIN_VAL = np.array(0, np.float32)
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MAX_VAL = np.array(6, np.float32)
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# Add identity nodes to make the graph structure a bit more interesting
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inp = graph.identity(graph.inputs[0])
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max_out = graph.max(graph.min(inp, MAX_VAL), MIN_VAL)
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graph.outputs = [
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graph.identity(max_out),
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]
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# Graph outputs must include dtype information
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graph.outputs[0].to_variable(dtype=np.float32, shape=(4, 4))
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onnx.save(gs.export_onnx(graph), "model.onnx")
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