77 lines
2.6 KiB
Python
77 lines
2.6 KiB
Python
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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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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from __future__ import absolute_import, division, print_function
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import os
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import sys
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import numpy as np
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import paddle
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import paddleslim
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from paddle.jit import to_static
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from paddleslim.analysis import dygraph_flops as flops
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__dir__ = os.path.dirname(os.path.abspath(__file__))
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sys.path.append(os.path.abspath(os.path.join(__dir__, '../../')))
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from paddleslim.dygraph.quant import QAT
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from ppcls.data import build_dataloader
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from ppcls.utils import config as conf
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from ppcls.utils.logger import init_logger
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def main():
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args = conf.parse_args()
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config = conf.get_config(args.config, overrides=args.override, show=False)
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assert os.path.exists(
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os.path.join(config["Global"]["save_inference_dir"],
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'inference.pdmodel')) and os.path.exists(
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os.path.join(config["Global"]["save_inference_dir"],
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'inference.pdiparams'))
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config["DataLoader"]["Eval"]["sampler"]["batch_size"] = 1
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config["DataLoader"]["Eval"]["loader"]["num_workers"] = 0
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init_logger()
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device = paddle.set_device("cpu")
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train_dataloader = build_dataloader(config["DataLoader"], "Eval", device,
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False)
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def sample_generator(loader):
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def __reader__():
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for indx, data in enumerate(loader):
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images = np.array(data[0])
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yield images
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return __reader__
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paddle.enable_static()
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place = paddle.CPUPlace()
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exe = paddle.static.Executor(place)
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paddleslim.quant.quant_post_static(
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executor=exe,
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model_dir=config["Global"]["save_inference_dir"],
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model_filename='inference.pdmodel',
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params_filename='inference.pdiparams',
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quantize_model_path=os.path.join(
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config["Global"]["save_inference_dir"], "quant_post_static_model"),
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sample_generator=sample_generator(train_dataloader),
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batch_size=config["DataLoader"]["Eval"]["sampler"]["batch_size"],
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batch_nums=10)
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if __name__ == "__main__":
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main()
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