161 lines
5.3 KiB
Python
161 lines
5.3 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 json
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import cv2
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import paddle
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from paddle import fluid
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import paddleslim as slim
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from copy import deepcopy
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from tools import program
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import paddle
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import paddle.distributed as dist
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from ppocr.data import build_dataloader
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from ppocr.modeling.architectures import build_model
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from ppocr.losses import build_loss
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from ppocr.optimizer import build_optimizer
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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 init_model
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import tools.program as program
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dist.get_world_size()
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def get_pruned_params(parameters, mode="det"):
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if mode == "det":
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skip_prune_params = [
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"conv2d_56.w_0", "conv2d_54.w_0", "conv2d_51.w_0",
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"conv_last_weights", "conv14_linear_weights",
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"conv13_expand_weights", "conv12_linear_weights",
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"conv12_expand_weights", "conv7_expand_weights",
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"conv8_expand_weights", "conv8_linear_weights",
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"conv5_linear_weights", "conv5_expand_weights",
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"conv3_linear_weights"
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]
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skip_prune_params = skip_prune_params + ['conv2d_53.w_0']
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else:
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skip_prune_params = None
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params = []
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for param in parameters:
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if len(
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param.shape
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) == 4 and 'depthwise' not in param.name and 'transpose' not in param.name and "conv2d_57" not in param.name and "conv2d_56" not in param.name:
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if param.name not in skip_prune_params:
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params.append(param.name)
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return params
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def main(config, device, logger, vdl_writer):
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# init dist environment
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if config['Global']['distributed']:
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dist.init_parallel_env()
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global_config = config['Global']
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# build dataloader
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train_dataloader = build_dataloader(config, 'Train', device, logger)
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if config['Eval']:
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valid_dataloader = build_dataloader(config, 'Eval', device, logger)
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else:
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valid_dataloader = None
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# build post process
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post_process_class = build_post_process(config['PostProcess'],
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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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flops = paddle.flops(model, [1, 3, 640, 640])
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logger.info(f"FLOPs before pruning: {flops}")
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from paddleslim.dygraph import FPGMFilterPruner
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model.train()
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pruner = FPGMFilterPruner(model, [1, 3, 640, 640])
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# build loss
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loss_class = build_loss(config['Loss'])
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# build optim
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optimizer, lr_scheduler = build_optimizer(
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config['Optimizer'],
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epochs=config['Global']['epoch_num'],
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step_each_epoch=len(train_dataloader),
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parameters=model.parameters())
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# build metric
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eval_class = build_metric(config['Metric'])
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# load pretrain model
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pre_best_model_dict = init_model(config, model, logger, optimizer)
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logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
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format(len(train_dataloader), len(valid_dataloader)))
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def eval_fn():
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metric = program.eval(model, valid_dataloader, post_process_class,
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eval_class)
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logger.info(f"metric['hmean']: {metric['hmean']}")
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return metric['hmean']
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pruner.sensitive(
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eval_func=eval_fn,
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sen_file="./sen.pickle",
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skip_vars=[
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"conv2d_57.w_0", "conv2d_transpose_2.w_0", "conv2d_transpose_3.w_0"
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])
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params = get_pruned_params(model.parameters())
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ratios = {}
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# set the prune ratio is 0.2
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for param in params:
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ratios[param] = 0.2
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plan = pruner.prune_vars(ratios, [0])
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for param in model.parameters():
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if ("weights" in param.name and "conv" in param.name) or (
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"w_0" in param.name and "conv2d" in param.name):
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logger.info(f"{param.name}: {param.shape}")
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flops = paddle.flops(model, [1, 3, 640, 640])
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logger.info(f"FLOPs after pruning: {flops}")
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# start train
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program.train(config, train_dataloader, valid_dataloader, device, model,
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loss_class, optimizer, lr_scheduler, post_process_class,
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eval_class, pre_best_model_dict, logger, vdl_writer)
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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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