program.py 13.4 KB
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# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

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import os
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import sys
import yaml
import time
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import shutil
import paddle
import paddle.distributed as dist
from tqdm import tqdm
from argparse import ArgumentParser, RawDescriptionHelpFormatter

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from ppocr.utils.stats import TrainingStats
from ppocr.utils.save_load import save_model
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from ppocr.utils.utility import print_dict
from ppocr.utils.logging import get_logger
from ppocr.data import build_dataloader
import numpy as np
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class ArgsParser(ArgumentParser):
    def __init__(self):
        super(ArgsParser, self).__init__(
            formatter_class=RawDescriptionHelpFormatter)
        self.add_argument("-c", "--config", help="configuration file to use")
        self.add_argument(
            "-o", "--opt", nargs='+', help="set configuration options")

    def parse_args(self, argv=None):
        args = super(ArgsParser, self).parse_args(argv)
        assert args.config is not None, \
            "Please specify --config=configure_file_path."
        args.opt = self._parse_opt(args.opt)
        return args

    def _parse_opt(self, opts):
        config = {}
        if not opts:
            return config
        for s in opts:
            s = s.strip()
            k, v = s.split('=')
            config[k] = yaml.load(v, Loader=yaml.Loader)
        return config


class AttrDict(dict):
    """Single level attribute dict, NOT recursive"""

    def __init__(self, **kwargs):
        super(AttrDict, self).__init__()
        super(AttrDict, self).update(kwargs)

    def __getattr__(self, key):
        if key in self:
            return self[key]
        raise AttributeError("object has no attribute '{}'".format(key))


global_config = AttrDict()

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default_config = {'Global': {'debug': False, }}

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def load_config(file_path):
    """
    Load config from yml/yaml file.
    Args:
        file_path (str): Path of the config file to be loaded.
    Returns: global config
    """
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    merge_config(default_config)
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    _, ext = os.path.splitext(file_path)
    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


def merge_config(config):
    """
    Merge config into global config.
    Args:
        config (dict): Config to be merged.
    Returns: global config
    """
    for key, value in config.items():
        if "." not in key:
            if isinstance(value, dict) and key in global_config:
                global_config[key].update(value)
            else:
                global_config[key] = value
        else:
            sub_keys = key.split('.')
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            assert (
                sub_keys[0] in global_config
            ), "the sub_keys can only be one of global_config: {}, but get: {}, please check your running command".format(
                global_config.keys(), sub_keys[0])
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            cur = global_config[sub_keys[0]]
            for idx, sub_key in enumerate(sub_keys[1:]):
                if idx == len(sub_keys) - 2:
                    cur[sub_key] = value
                else:
                    cur = cur[sub_key]


def check_gpu(use_gpu):
    """
    Log error and exit when set use_gpu=true in paddlepaddle
    cpu version.
    """
    err = "Config use_gpu cannot be set as true while you are " \
          "using paddlepaddle cpu version ! \nPlease try: \n" \
          "\t1. Install paddlepaddle-gpu to run model on GPU \n" \
          "\t2. Set use_gpu as false in config file to run " \
          "model on CPU"

    try:
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        if use_gpu and not paddle.fluid.is_compiled_with_cuda():
            print(err)
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            sys.exit(1)
    except Exception as e:
        pass


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def train(config,
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          train_dataloader,
          valid_dataloader,
          device,
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          model,
          loss_class,
          optimizer,
          lr_scheduler,
          post_process_class,
          eval_class,
          pre_best_model_dict,
          logger,
          vdl_writer=None):
    cal_metric_during_train = config['Global'].get('cal_metric_during_train',
                                                   False)
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    log_smooth_window = config['Global']['log_smooth_window']
    epoch_num = config['Global']['epoch_num']
    print_batch_step = config['Global']['print_batch_step']
    eval_batch_step = config['Global']['eval_batch_step']
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    global_step = 0
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    start_eval_step = 0
    if type(eval_batch_step) == list and len(eval_batch_step) >= 2:
        start_eval_step = eval_batch_step[0]
        eval_batch_step = eval_batch_step[1]
        logger.info(
            "During the training process, after the {}th iteration, an evaluation is run every {} iterations".
            format(start_eval_step, eval_batch_step))
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    save_epoch_step = config['Global']['save_epoch_step']
    save_model_dir = config['Global']['save_model_dir']
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    if not os.path.exists(save_model_dir):
        os.makedirs(save_model_dir)
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    main_indicator = eval_class.main_indicator
    best_model_dict = {main_indicator: 0}
    best_model_dict.update(pre_best_model_dict)
    train_stats = TrainingStats(log_smooth_window, ['lr'])
    model.train()

    if 'start_epoch' in best_model_dict:
        start_epoch = best_model_dict['start_epoch']
    else:
        start_epoch = 0

    for epoch in range(start_epoch, epoch_num):
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        if epoch > 0:
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            train_dataloader = build_dataloader(config, 'Train', device, logger)
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        train_batch_cost = 0.0
        train_reader_cost = 0.0
        batch_sum = 0
        batch_start = time.time()
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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 >= len(train_dataloader):
                break
            lr = optimizer.get_lr()
            images = batch[0]
            preds = model(images)
            loss = loss_class(preds, batch)
            avg_loss = loss['loss']
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            avg_loss.backward()
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            optimizer.step()
            optimizer.clear_grad()
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            train_batch_cost += time.time() - batch_start
            batch_sum += len(images)

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            if not isinstance(lr_scheduler, float):
                lr_scheduler.step()
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            # logger and visualdl
            stats = {k: v.numpy().mean() for k, v in loss.items()}
            stats['lr'] = lr
            train_stats.update(stats)

            if cal_metric_during_train:  # onlt rec and cls need
                batch = [item.numpy() for item in batch]
                post_result = post_process_class(preds, batch[1])
                eval_class(post_result, batch)
                metirc = eval_class.get_metric()
                train_stats.update(metirc)

            if vdl_writer is not None and dist.get_rank() == 0:
                for k, v in train_stats.get().items():
                    vdl_writer.add_scalar('TRAIN/{}'.format(k), v, global_step)
                vdl_writer.add_scalar('TRAIN/lr', lr, global_step)

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            if dist.get_rank(
            ) == 0 and global_step > 0 and global_step % print_batch_step == 0:
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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 /
                    print_batch_step, train_batch_cost / print_batch_step,
                    batch_sum, batch_sum / train_batch_cost)
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                logger.info(strs)
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                train_batch_cost = 0.0
                train_reader_cost = 0.0
                batch_sum = 0
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            # eval
            if global_step > start_eval_step and \
                    (global_step - start_eval_step) % eval_batch_step == 0 and dist.get_rank() == 0:
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                cur_metirc = eval(model, valid_dataloader, post_process_class,
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                                  eval_class)
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                cur_metirc_str = 'cur metirc, {}'.format(', '.join(
                    ['{}: {}'.format(k, v) for k, v in cur_metirc.items()]))
                logger.info(cur_metirc_str)

                # logger metric
                if vdl_writer is not None:
                    for k, v in cur_metirc.items():
                        if isinstance(v, (float, int)):
                            vdl_writer.add_scalar('EVAL/{}'.format(k),
                                                  cur_metirc[k], global_step)
                if cur_metirc[main_indicator] >= best_model_dict[
                        main_indicator]:
                    best_model_dict.update(cur_metirc)
                    best_model_dict['best_epoch'] = epoch
                    save_model(
                        model,
                        optimizer,
                        save_model_dir,
                        logger,
                        is_best=True,
                        prefix='best_accuracy',
                        best_model_dict=best_model_dict,
                        epoch=epoch)
                best_str = 'best metirc, {}'.format(', '.join([
                    '{}: {}'.format(k, v) for k, v in best_model_dict.items()
                ]))
                logger.info(best_str)
                # logger best metric
                if vdl_writer is not None:
                    vdl_writer.add_scalar('EVAL/best_{}'.format(main_indicator),
                                          best_model_dict[main_indicator],
                                          global_step)
            global_step += 1
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            batch_start = time.time()
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        if dist.get_rank() == 0:
            save_model(
                model,
                optimizer,
                save_model_dir,
                logger,
                is_best=False,
                prefix='latest',
                best_model_dict=best_model_dict,
                epoch=epoch)
        if dist.get_rank() == 0 and epoch > 0 and epoch % save_epoch_step == 0:
            save_model(
                model,
                optimizer,
                save_model_dir,
                logger,
                is_best=False,
                prefix='iter_epoch_{}'.format(epoch),
                best_model_dict=best_model_dict,
                epoch=epoch)
    best_str = 'best metirc, {}'.format(', '.join(
        ['{}: {}'.format(k, v) for k, v in best_model_dict.items()]))
    logger.info(best_str)
    if dist.get_rank() == 0 and vdl_writer is not None:
        vdl_writer.close()
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    return


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def eval(model, valid_dataloader, post_process_class, eval_class):
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    model.eval()
    with paddle.no_grad():
        total_frame = 0.0
        total_time = 0.0
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        pbar = tqdm(total=len(valid_dataloader), desc='eval model:')
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        for idx, batch in enumerate(valid_dataloader):
            if idx >= len(valid_dataloader):
                break
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            images = batch[0]
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            start = time.time()
            preds = model(images)

            batch = [item.numpy() for item in batch]
            # Obtain usable results from post-processing methods
            post_result = post_process_class(preds, batch[1])
            total_time += time.time() - start
            # Evaluate the results of the current batch
            eval_class(post_result, batch)
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            pbar.update(1)
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            total_frame += len(images)
        # Get final metirc,eg. acc or hmean
        metirc = eval_class.get_metric()
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    pbar.close()
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    model.train()
    metirc['fps'] = total_frame / total_time
    return metirc
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def preprocess(is_train=False):
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    FLAGS = ArgsParser().parse_args()
    config = load_config(FLAGS.config)
    merge_config(FLAGS.opt)

    # check if set use_gpu=True in paddlepaddle cpu version
    use_gpu = config['Global']['use_gpu']
    check_gpu(use_gpu)

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    alg = config['Architecture']['algorithm']
    assert alg in [
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        'EAST', 'DB', 'SAST', 'Rosetta', 'CRNN', 'STARNet', 'RARE', 'SRN', 'CLS'
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    ]
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    device = 'gpu:{}'.format(dist.ParallelEnv().dev_id) if use_gpu else 'cpu'
    device = paddle.set_device(device)
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    config['Global']['distributed'] = dist.get_world_size() != 1
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    if is_train:
        # save_config
        save_model_dir = config['Global']['save_model_dir']
        os.makedirs(save_model_dir, exist_ok=True)
        with open(os.path.join(save_model_dir, 'config.yml'), 'w') as f:
            yaml.dump(
                dict(config), f, default_flow_style=False, sort_keys=False)
        log_file = '{}/train.log'.format(save_model_dir)
    else:
        log_file = None
    logger = get_logger(name='root', log_file=log_file)
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    if config['Global']['use_visualdl']:
        from visualdl import LogWriter
        vdl_writer_path = '{}/vdl/'.format(save_model_dir)
        os.makedirs(vdl_writer_path, exist_ok=True)
        vdl_writer = LogWriter(logdir=vdl_writer_path)
    else:
        vdl_writer = None
    print_dict(config, logger)
    logger.info('train with paddle {} and device {}'.format(paddle.__version__,
                                                            device))
    return config, device, logger, vdl_writer