433 lines
16 KiB
Python
Executable File
433 lines
16 KiB
Python
Executable File
# 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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import platform
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import yaml
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import time
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import shutil
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import paddle
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import paddle.distributed as dist
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from tqdm import tqdm
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from argparse import ArgumentParser, RawDescriptionHelpFormatter
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from ppocr.utils.stats import TrainingStats
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from ppocr.utils.save_load import save_model
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from ppocr.utils.utility import print_dict
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from ppocr.utils.logging import get_logger
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from ppocr.data import build_dataloader
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import numpy as np
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class ArgsParser(ArgumentParser):
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def __init__(self):
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super(ArgsParser, self).__init__(
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formatter_class=RawDescriptionHelpFormatter)
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self.add_argument("-c", "--config", help="configuration file to use")
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self.add_argument(
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"-o", "--opt", nargs='+', help="set configuration options")
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def parse_args(self, argv=None):
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args = super(ArgsParser, self).parse_args(argv)
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assert args.config is not None, \
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"Please specify --config=configure_file_path."
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args.opt = self._parse_opt(args.opt)
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return args
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def _parse_opt(self, opts):
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config = {}
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if not opts:
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return config
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for s in opts:
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s = s.strip()
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k, v = s.split('=')
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config[k] = yaml.load(v, Loader=yaml.Loader)
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return config
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class AttrDict(dict):
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"""Single level attribute dict, NOT recursive"""
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def __init__(self, **kwargs):
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super(AttrDict, self).__init__()
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super(AttrDict, self).update(kwargs)
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def __getattr__(self, key):
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if key in self:
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return self[key]
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raise AttributeError("object has no attribute '{}'".format(key))
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global_config = AttrDict()
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default_config = {'Global': {'debug': False, }}
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def load_config(file_path):
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"""
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Load config from yml/yaml file.
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Args:
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file_path (str): Path of the config file to be loaded.
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Returns: global config
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"""
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merge_config(default_config)
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_, ext = os.path.splitext(file_path)
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assert ext in ['.yml', '.yaml'], "only support yaml files for now"
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merge_config(yaml.load(open(file_path, 'rb'), Loader=yaml.Loader))
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return global_config
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def merge_config(config):
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"""
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Merge config into global config.
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Args:
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config (dict): Config to be merged.
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Returns: global config
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"""
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for key, value in config.items():
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if "." not in key:
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if isinstance(value, dict) and key in global_config:
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global_config[key].update(value)
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else:
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global_config[key] = value
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else:
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sub_keys = key.split('.')
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assert (
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sub_keys[0] in global_config
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), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
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global_config.keys(), sub_keys[0])
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cur = global_config[sub_keys[0]]
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for idx, sub_key in enumerate(sub_keys[1:]):
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if idx == len(sub_keys) - 2:
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cur[sub_key] = value
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else:
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cur = cur[sub_key]
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def check_gpu(use_gpu):
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"""
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Log error and exit when set use_gpu=true in paddlepaddle
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cpu version.
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"""
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err = "Config use_gpu cannot be set as true while you are " \
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"using paddlepaddle cpu version ! \nPlease try: \n" \
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"\t1. Install paddlepaddle-gpu to run model on GPU \n" \
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"\t2. Set use_gpu as false in config file to run " \
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"model on CPU"
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try:
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if use_gpu and not paddle.is_compiled_with_cuda():
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print(err)
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sys.exit(1)
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except Exception as e:
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pass
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def train(config,
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train_dataloader,
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valid_dataloader,
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device,
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model,
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loss_class,
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optimizer,
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lr_scheduler,
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post_process_class,
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eval_class,
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pre_best_model_dict,
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logger,
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vdl_writer=None):
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cal_metric_during_train = config['Global'].get('cal_metric_during_train',
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False)
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log_smooth_window = config['Global']['log_smooth_window']
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epoch_num = config['Global']['epoch_num']
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print_batch_step = config['Global']['print_batch_step']
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eval_batch_step = config['Global']['eval_batch_step']
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global_step = 0
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if 'global_step' in pre_best_model_dict:
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global_step = pre_best_model_dict['global_step']
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start_eval_step = 0
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if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
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start_eval_step = eval_batch_step[0]
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eval_batch_step = eval_batch_step[1]
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if len(valid_dataloader) == 0:
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logger.info(
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'No Images in eval dataset, evaluation during training will be disabled'
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)
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start_eval_step = 1e111
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logger.info(
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"During the training process, after the {}th iteration, an evaluation is run every {} iterations".
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format(start_eval_step, eval_batch_step))
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save_epoch_step = config['Global']['save_epoch_step']
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save_model_dir = config['Global']['save_model_dir']
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if not os.path.exists(save_model_dir):
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os.makedirs(save_model_dir)
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main_indicator = eval_class.main_indicator
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best_model_dict = {main_indicator: 0}
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best_model_dict.update(pre_best_model_dict)
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train_stats = TrainingStats(log_smooth_window, ['lr'])
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model_average = False
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model.train()
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use_srn = config['Architecture']['algorithm'] == "SRN"
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use_nrtr = config['Architecture']['algorithm'] == "NRTR"
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try:
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model_type = config['Architecture']['model_type']
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except:
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model_type = None
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if 'start_epoch' in best_model_dict:
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start_epoch = best_model_dict['start_epoch']
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else:
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start_epoch = 1
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for epoch in range(start_epoch, epoch_num + 1):
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train_dataloader = build_dataloader(
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config, 'Train', device, logger, seed=epoch)
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train_batch_cost = 0.0
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train_reader_cost = 0.0
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batch_sum = 0
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batch_start = time.time()
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max_iter = len(train_dataloader) - 1 if platform.system(
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) == "Windows" else len(train_dataloader)
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for idx, batch in enumerate(train_dataloader):
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train_reader_cost += time.time() - batch_start
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if idx >= max_iter:
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break
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lr = optimizer.get_lr()
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images = batch[0]
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if use_srn:
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model_average = True
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if use_srn or model_type == 'table' or use_nrtr:
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preds = model(images, data=batch[1:])
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else:
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preds = model(images)
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loss = loss_class(preds, batch)
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avg_loss = loss['loss']
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avg_loss.backward()
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optimizer.step()
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optimizer.clear_grad()
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train_batch_cost += time.time() - batch_start
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batch_sum += len(images)
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if not isinstance(lr_scheduler, float):
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lr_scheduler.step()
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# logger and visualdl
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stats = {k: v.numpy().mean() for k, v in loss.items()}
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stats['lr'] = lr
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train_stats.update(stats)
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if cal_metric_during_train: # only rec and cls need
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batch = [item.numpy() for item in batch]
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if model_type == 'table':
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eval_class(preds, batch)
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else:
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post_result = post_process_class(preds, batch[1])
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eval_class(post_result, batch)
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metric = eval_class.get_metric()
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train_stats.update(metric)
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if vdl_writer is not None and dist.get_rank() == 0:
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for k, v in train_stats.get().items():
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vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
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vdl_writer.add_scalar('TRAIN/lr', lr, global_step)
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if dist.get_rank() == 0 and (
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(global_step > 0 and global_step % print_batch_step == 0) or
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(idx >= len(train_dataloader) - 1)):
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logs = train_stats.log()
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strs = 'epoch: [{}/{}], iter: {}, {}, reader_cost: {:.5f} s, batch_cost: {:.5f} s, samples: {}, ips: {:.5f}'.format(
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epoch, epoch_num, global_step, logs, train_reader_cost /
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print_batch_step, train_batch_cost / print_batch_step,
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batch_sum, batch_sum / train_batch_cost)
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logger.info(strs)
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train_batch_cost = 0.0
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train_reader_cost = 0.0
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batch_sum = 0
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# eval
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if global_step > start_eval_step and \
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(global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
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if model_average:
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Model_Average = paddle.incubate.optimizer.ModelAverage(
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0.15,
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parameters=model.parameters(),
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min_average_window=10000,
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max_average_window=15625)
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Model_Average.apply()
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cur_metric = eval(
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model,
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valid_dataloader,
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post_process_class,
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eval_class,
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model_type,
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use_srn=use_srn)
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cur_metric_str = 'cur metric, {}'.format(', '.join(
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['{}: {}'.format(k, v) for k, v in cur_metric.items()]))
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logger.info(cur_metric_str)
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# logger metric
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if vdl_writer is not None:
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for k, v in cur_metric.items():
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if isinstance(v, (float, int)):
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vdl_writer.add_scalar('EVAL/{}'.format(k),
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cur_metric[k], global_step)
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if cur_metric[main_indicator] >= best_model_dict[
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main_indicator]:
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best_model_dict.update(cur_metric)
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best_model_dict['best_epoch'] = epoch
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save_model(
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model,
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optimizer,
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save_model_dir,
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logger,
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is_best=True,
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prefix='best_accuracy',
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best_model_dict=best_model_dict,
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epoch=epoch,
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global_step=global_step)
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best_str = 'best metric, {}'.format(', '.join([
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'{}: {}'.format(k, v) for k, v in best_model_dict.items()
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]))
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logger.info(best_str)
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# logger best metric
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if vdl_writer is not None:
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vdl_writer.add_scalar('EVAL/best_{}'.format(main_indicator),
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best_model_dict[main_indicator],
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global_step)
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global_step += 1
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optimizer.clear_grad()
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batch_start = time.time()
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if dist.get_rank() == 0:
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save_model(
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model,
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optimizer,
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save_model_dir,
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logger,
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is_best=False,
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prefix='latest',
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best_model_dict=best_model_dict,
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epoch=epoch,
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global_step=global_step)
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if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
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save_model(
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model,
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optimizer,
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save_model_dir,
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logger,
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is_best=False,
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prefix='iter_epoch_{}'.format(epoch),
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best_model_dict=best_model_dict,
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epoch=epoch,
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global_step=global_step)
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best_str = 'best metric, {}'.format(', '.join(
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['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
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logger.info(best_str)
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if dist.get_rank() == 0 and vdl_writer is not None:
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vdl_writer.close()
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return
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def eval(model,
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valid_dataloader,
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post_process_class,
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eval_class,
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model_type,
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use_srn=False):
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model.eval()
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with paddle.no_grad():
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total_frame = 0.0
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total_time = 0.0
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pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
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max_iter = len(valid_dataloader) - 1 if platform.system(
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) == "Windows" else len(valid_dataloader)
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for idx, batch in enumerate(valid_dataloader):
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if idx >= max_iter:
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break
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images = batch[0]
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start = time.time()
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if use_srn or model_type == 'table':
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preds = model(images, data=batch[1:])
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else:
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preds = model(images)
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batch = [item.numpy() for item in batch]
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# Obtain usable results from post-processing methods
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total_time += time.time() - start
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# Evaluate the results of the current batch
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if model_type == 'table':
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eval_class(preds, batch)
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else:
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post_result = post_process_class(preds, batch[1])
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eval_class(post_result, batch)
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pbar.update(1)
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total_frame += len(images)
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# Get final metric，eg. acc or hmean
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metric = eval_class.get_metric()
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pbar.close()
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model.train()
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metric['fps'] = total_frame / total_time
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return metric
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def preprocess(is_train=False):
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FLAGS = ArgsParser().parse_args()
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config = load_config(FLAGS.config)
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merge_config(FLAGS.opt)
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# check if set use_gpu=True in paddlepaddle cpu version
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use_gpu = config['Global']['use_gpu']
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check_gpu(use_gpu)
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alg = config['Architecture']['algorithm']
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assert alg in [
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'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN',
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'CLS', 'PGNet', 'Distillation', 'NRTR', 'TableAttn'
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]
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device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
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device = paddle.set_device(device)
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config['Global']['distributed'] = dist.get_world_size() != 1
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if is_train:
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# save_config
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save_model_dir = config['Global']['save_model_dir']
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os.makedirs(save_model_dir, exist_ok=True)
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with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
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yaml.dump(
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dict(config), f, default_flow_style=False, sort_keys=False)
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log_file = '{}/train.log'.format(save_model_dir)
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else:
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log_file = None
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logger = get_logger(name='root', log_file=log_file)
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if config['Global']['use_visualdl']:
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from visualdl import LogWriter
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save_model_dir = config['Global']['save_model_dir']
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vdl_writer_path = '{}/vdl/'.format(save_model_dir)
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os.makedirs(vdl_writer_path, exist_ok=True)
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vdl_writer = LogWriter(logdir=vdl_writer_path)
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else:
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vdl_writer = None
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print_dict(config, logger)
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logger.info('train with paddle {} and device {}'.format(paddle.__version__,
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device))
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return config, device, logger, vdl_writer
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