Asfiya Baig
83 lines
2.6 KiB
Python
83 lines
2.6 KiB
Python
#
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# SPDX-FileCopyrightText: Copyright (c) 1993-2023 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 argparse
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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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import ctypes
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import tensorrt as trt
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from polygraphy.backend.trt import (
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CreateConfig,
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EngineFromNetwork,
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NetworkFromOnnxPath,
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TrtRunner,
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)
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def parseArgs():
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parser = argparse.ArgumentParser(description="Options for Circular Padding plugin C++ example")
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parser.add_argument('--precision', type=str, default="fp32", choices=["fp32", "fp16"], help="Precision to use for plugin")
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parser.add_argument('--plugin-lib', type=str, help="Path to the Circular Padding plugin lib", required=True)
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return parser.parse_args()
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if __name__ == "__main__":
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args = parseArgs()
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handle = ctypes.CDLL(args.plugin_lib)
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if not handle:
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raise RuntimeError("Could not load Circular Padding plugin library")
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precision = np.float32 if args.precision == "fp32" else np.float16
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inp_shape = (10, 3, 32, 32)
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X = np.random.normal(size=inp_shape).astype(precision)
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pads = (1, 1, 1, 1)
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# create ONNX model
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onnx_path = "test_CircPadPlugin.onnx"
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inputA = gs.Variable(name="X", shape=inp_shape, dtype=precision)
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Y = gs.Variable(name="Y", dtype=precision)
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myPluginNode = gs.Node(
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name="CircPadPlugin",
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op="CircPadPlugin",
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inputs=[inputA],
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outputs=[Y],
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attrs={"pads": pads},
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)
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graph = gs.Graph(nodes=[myPluginNode], inputs=[inputA], outputs=[Y], opset=16)
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onnx.save(gs.export_onnx(graph), onnx_path)
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# build engine
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build_engine = EngineFromNetwork(
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NetworkFromOnnxPath(onnx_path), CreateConfig(fp16=precision==np.float16)
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)
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Y_ref = np.pad(X, [[0, 0], [0, 0], [pads[0], pads[1]], [pads[2], pads[3]]], "wrap")
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# Run
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with TrtRunner(build_engine, "trt_runner")as runner:
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outputs = runner.infer({"X": X})
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Y = outputs["Y"]
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if np.allclose(Y, Y_ref):
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print("Inference result correct!")
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else:
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print("Inference result incorrect!")
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