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138 lines
4.7 KiB
Python
138 lines
4.7 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
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from __future__ import division
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from __future__ import print_function
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import os
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import sys
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__dir__ = os.path.dirname(__file__)
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sys.path.append(__dir__)
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sys.path.append(os.path.join(__dir__, "..", "..", ".."))
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sys.path.append(os.path.join(__dir__, "..", "..", "..", "tools"))
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import paddle
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from ppocr.data import build_dataloader, set_signal_handlers
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from ppocr.modeling.architectures import build_model
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from ppocr.postprocess import build_post_process
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from ppocr.metrics import build_metric
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from ppocr.utils.save_load import load_model
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import tools.program as program
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def main(config, device, logger, vdl_writer):
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global_config = config["Global"]
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# build dataloader
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set_signal_handlers()
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valid_dataloader = build_dataloader(config, "Eval", device, logger)
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# build post process
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post_process_class = build_post_process(config["PostProcess"], global_config)
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# build model
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# for rec algorithm
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if hasattr(post_process_class, "character"):
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char_num = len(getattr(post_process_class, "character"))
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config["Architecture"]["Head"]["out_channels"] = char_num
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model = build_model(config["Architecture"])
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if config["Architecture"]["model_type"] == "det":
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input_shape = [1, 3, 640, 640]
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elif config["Architecture"]["model_type"] == "rec":
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input_shape = [1, 3, 32, 320]
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flops = paddle.flops(model, input_shape)
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logger.info("FLOPs before pruning: {}".format(flops))
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from paddleslim.dygraph import FPGMFilterPruner
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model.train()
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pruner = FPGMFilterPruner(model, input_shape)
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# build metric
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eval_class = build_metric(config["Metric"])
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def eval_fn():
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metric = program.eval(model, valid_dataloader, post_process_class, eval_class)
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if config["Architecture"]["model_type"] == "det":
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main_indicator = "hmean"
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else:
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main_indicator = "acc"
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logger.info("metric[{}]: {}".format(main_indicator, metric[main_indicator]))
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return metric[main_indicator]
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params_sensitive = pruner.sensitive(
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eval_func=eval_fn,
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sen_file="./sen.pickle",
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skip_vars=["conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0"],
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)
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logger.info(
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"The sensitivity analysis results of model parameters saved in sen.pickle"
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)
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# calculate pruned params's ratio
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params_sensitive = pruner._get_ratios_by_loss(params_sensitive, loss=0.02)
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for key in params_sensitive.keys():
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logger.info("{}, {}".format(key, params_sensitive[key]))
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plan = pruner.prune_vars(params_sensitive, [0])
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flops = paddle.flops(model, input_shape)
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logger.info("FLOPs after pruning: {}".format(flops))
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# load pretrain model
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load_model(config, model)
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metric = program.eval(model, valid_dataloader, post_process_class, eval_class)
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if config["Architecture"]["model_type"] == "det":
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main_indicator = "hmean"
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else:
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main_indicator = "acc"
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logger.info("metric['']: {}".format(main_indicator, metric[main_indicator]))
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# start export model
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from paddle.jit import to_static
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infer_shape = [3, -1, -1]
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if config["Architecture"]["model_type"] == "rec":
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infer_shape = [3, 32, -1] # for rec model, H must be 32
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if (
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"Transform" in config["Architecture"]
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and config["Architecture"]["Transform"] is not None
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and config["Architecture"]["Transform"]["name"] == "TPS"
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):
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logger.info(
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"When there is tps in the network, variable length input is not supported, and the input size needs to be the same as during training"
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)
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infer_shape[-1] = 100
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model = to_static(
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model,
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input_spec=[
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paddle.static.InputSpec(shape=[None] + infer_shape, dtype="float32")
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],
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)
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save_path = "{}/inference".format(config["Global"]["save_inference_dir"])
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paddle.jit.save(model, save_path)
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logger.info("inference model is saved to {}".format(save_path))
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if __name__ == "__main__":
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config, device, logger, vdl_writer = program.preprocess(is_train=True)
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main(config, device, logger, vdl_writer)
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