Python utils.setup_logger() Examples

The following are 2 code examples of utils.setup_logger(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module utils , or try the search function .
Example #1
Source File: main.py    From reid_baseline_with_syncbn with MIT License 6 votes vote down vote up 
def train(args):
    if args.config_file != "":
        cfg.merge_from_file(args.config_file)
    cfg.merge_from_list(args.opts)
    cfg.freeze()

    output_dir = cfg.OUTPUT_DIR
    if output_dir and not os.path.exists(output_dir):
        os.makedirs(output_dir)
    shutil.copy(args.config_file, cfg.OUTPUT_DIR)

    num_gpus = torch.cuda.device_count()

    logger = setup_logger('reid_baseline', output_dir, 0)
    logger.info('Using {} GPUS'.format(num_gpus))
    logger.info(args)
    logger.info('Running with config:\n{}'.format(cfg))

    train_dl, val_dl, num_query, num_classes = make_dataloader(cfg, num_gpus) 

    model = build_model(cfg, num_classes)

    loss_func = make_loss(cfg, num_classes)

    trainer = BaseTrainer(cfg, model, train_dl, val_dl,
                          loss_func, num_query, num_gpus)

    for epoch in range(trainer.epochs):
        for batch in trainer.train_dl:
            trainer.step(batch)
            trainer.handle_new_batch()
        trainer.handle_new_epoch()
Example #2
Source File: base_trainer.py    From crnn.gluon with Apache License 2.0 4 votes vote down vote up 
def __init__(self, config, model, criterion, ctx, sample_input):
        config['trainer']['output_dir'] = os.path.join(str(pathlib.Path(os.path.abspath(__name__)).parent),
                                                       config['trainer']['output_dir'])
        config['name'] = config['name'] + '_' + model.model_name
        self.save_dir = os.path.join(config['trainer']['output_dir'], config['name'])
        self.checkpoint_dir = os.path.join(self.save_dir, 'checkpoint')
        self.alphabet = config['dataset']['alphabet']

        if config['trainer']['resume_checkpoint'] == '' and config['trainer']['finetune_checkpoint'] == '':
            shutil.rmtree(self.save_dir, ignore_errors=True)
        if not os.path.exists(self.checkpoint_dir):
            os.makedirs(self.checkpoint_dir)
        # 保存本次实验的alphabet 到模型保存的地方
        save(list(self.alphabet), os.path.join(self.save_dir, 'dict.txt'))
        self.global_step = 0
        self.start_epoch = 0
        self.config = config

        self.model = model
        self.criterion = criterion
        # logger and tensorboard
        self.tensorboard_enable = self.config['trainer']['tensorboard']
        self.epochs = self.config['trainer']['epochs']
        self.display_interval = self.config['trainer']['display_interval']
        if self.tensorboard_enable:
            from mxboard import SummaryWriter
            self.writer = SummaryWriter(self.save_dir, verbose=False)

        self.logger = setup_logger(os.path.join(self.save_dir, 'train.log'))
        self.logger.info(pformat(self.config))
        self.logger.info(self.model)
        # device set
        self.ctx = ctx
        mx.random.seed(2)  # 设置随机种子

        self.logger.info('train with mxnet: {} and device: {}'.format(mx.__version__, self.ctx))
        self.metrics = {'val_acc': 0, 'train_loss': float('inf'), 'best_model': ''}

        schedule = self._initialize('lr_scheduler', mx.lr_scheduler)
        optimizer = self._initialize('optimizer', mx.optimizer, lr_scheduler=schedule)
        self.trainer = gluon.Trainer(self.model.collect_params(), optimizer=optimizer)

        if self.config['trainer']['resume_checkpoint'] != '':
            self._laod_checkpoint(self.config['trainer']['resume_checkpoint'], resume=True)
        elif self.config['trainer']['finetune_checkpoint'] != '':
            self._laod_checkpoint(self.config['trainer']['finetune_checkpoint'], resume=False)

        if self.tensorboard_enable:
            try:
                # add graph
                from mxnet.gluon import utils as gutils
                self.model(sample_input)
                self.writer.add_graph(model)
            except:
                self.logger.error(traceback.format_exc())
                self.logger.warn('add graph to tensorboard failed')