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- from __future__ import absolute_import
- from __future__ import division
- from __future__ import print_function
- import os
- import sys
- __dir__ = os.path.dirname(__file__)
- sys.path.append(__dir__)
- sys.path.append(os.path.join(__dir__, '..', '..', '..'))
- sys.path.append(os.path.join(__dir__, '..', '..', '..', 'tools'))
- import paddle
- import paddle.distributed as dist
- from ppocr.data import build_dataloader
- from ppocr.modeling.architectures import build_model
- from ppocr.losses import build_loss
- from ppocr.optimizer import build_optimizer
- from ppocr.postprocess import build_post_process
- from ppocr.metrics import build_metric
- from ppocr.utils.save_load import load_model
- import tools.program as program
- dist.get_world_size()
- def get_pruned_params(parameters):
- params = []
- for param in parameters:
- if len(
- param.shape
- ) == 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:
- params.append(param.name)
- return params
- def main(config, device, logger, vdl_writer):
-
- if config['Global']['distributed']:
- dist.init_parallel_env()
- global_config = config['Global']
-
- train_dataloader = build_dataloader(config, 'Train', device, logger)
- if config['Eval']:
- valid_dataloader = build_dataloader(config, 'Eval', device, logger)
- else:
- valid_dataloader = None
-
- post_process_class = build_post_process(config['PostProcess'],
- global_config)
-
-
- if hasattr(post_process_class, 'character'):
- char_num = len(getattr(post_process_class, 'character'))
- config['Architecture']["Head"]['out_channels'] = char_num
- model = build_model(config['Architecture'])
- if config['Architecture']['model_type'] == 'det':
- input_shape = [1, 3, 640, 640]
- elif config['Architecture']['model_type'] == 'rec':
- input_shape = [1, 3, 32, 320]
- flops = paddle.flops(model, input_shape)
- logger.info("FLOPs before pruning: {}".format(flops))
- from paddleslim.dygraph import FPGMFilterPruner
- model.train()
- pruner = FPGMFilterPruner(model, input_shape)
-
- loss_class = build_loss(config['Loss'])
-
- optimizer, lr_scheduler = build_optimizer(
- config['Optimizer'],
- epochs=config['Global']['epoch_num'],
- step_each_epoch=len(train_dataloader),
- model=model)
-
- eval_class = build_metric(config['Metric'])
-
- pre_best_model_dict = load_model(config, model, optimizer)
- logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
- format(len(train_dataloader), len(valid_dataloader)))
-
- eval_class = build_metric(config['Metric'])
- logger.info('train dataloader has {} iters, valid dataloader has {} iters'.
- format(len(train_dataloader), len(valid_dataloader)))
- def eval_fn():
- metric = program.eval(model, valid_dataloader, post_process_class,
- eval_class, False)
- if config['Architecture']['model_type'] == 'det':
- main_indicator = 'hmean'
- else:
- main_indicator = 'acc'
- logger.info("metric[{}]: {}".format(main_indicator, metric[
- main_indicator]))
- return metric[main_indicator]
- run_sensitive_analysis = False
- """
- run_sensitive_analysis=True:
- Automatically compute the sensitivities of convolutions in a model.
- The sensitivity of a convolution is the losses of accuracy on test dataset in
- differenct pruned ratios. The sensitivities can be used to get a group of best
- ratios with some condition.
-
- run_sensitive_analysis=False:
- Set prune trim ratio to a fixed value, such as 10%. The larger the value,
- the more convolution weights will be cropped.
- """
- if run_sensitive_analysis:
- params_sensitive = pruner.sensitive(
- eval_func=eval_fn,
- sen_file="./deploy/slim/prune/sen.pickle",
- skip_vars=[
- "conv2d_57.w_0", "conv2d_transpose_2.w_0",
- "conv2d_transpose_3.w_0"
- ])
- logger.info(
- "The sensitivity analysis results of model parameters saved in sen.pickle"
- )
-
- params_sensitive = pruner._get_ratios_by_loss(
- params_sensitive, loss=0.02)
- for key in params_sensitive.keys():
- logger.info("{}, {}".format(key, params_sensitive[key]))
- else:
- params_sensitive = {}
- for param in model.parameters():
- if 'transpose' not in param.name and 'linear' not in param.name:
-
- params_sensitive[param.name] = 0.1
- plan = pruner.prune_vars(params_sensitive, [0])
- flops = paddle.flops(model, input_shape)
- logger.info("FLOPs after pruning: {}".format(flops))
-
- program.train(config, train_dataloader, valid_dataloader, device, model,
- loss_class, optimizer, lr_scheduler, post_process_class,
- eval_class, pre_best_model_dict, logger, vdl_writer)
- if __name__ == '__main__':
- config, device, logger, vdl_writer = program.preprocess(is_train=True)
- main(config, device, logger, vdl_writer)
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