wehub-resource-sync
60 lines
1.9 KiB
Python
60 lines
1.9 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 tensorflow as tf
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import argparse
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from utils import create_efficientnet_model
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from tensorflow_quantization.quantize import quantize_model
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from tensorflow_quantization.custom_qdq_cases import EfficientNetQDQCase
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def export_saved_model(model_version="b0"):
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model = create_efficientnet_model(model_version=model_version)
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q_model = quantize_model(model, custom_qdq_cases=[EfficientNetQDQCase()])
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if args.ckpt:
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q_model.load_weights(args.ckpt).expect_partial()
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tf.keras.models.save_model(q_model, args.output)
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print("Exported the model to {}".format(args.output))
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if __name__ == "__main__":
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parser = argparse.ArgumentParser(
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description="Export saved model for efficientnet_b0"
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)
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parser.add_argument(
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"--ckpt",
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type=str,
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default="qat/checkpoints_best",
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help="Path to pretrained QAT efficientnet checkpoint.",
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)
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parser.add_argument(
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"--output",
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type=str,
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default="qat/saved_model",
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help="Path to pretrained QAT saved model.",
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)
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parser.add_argument(
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"--model_version",
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type=str,
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default="b0",
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help="EfficientNet model version, currently supports {'b0', 'b3'}.",
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)
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args = parser.parse_args()
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export_saved_model(args.model_version)
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