Пример #1
0
def _dist_train(model,
                dataset,
                cfg,
                validate=False,
                logger=None,
                timestamp=None,
                meta=None):
    """Distributed training function.

    Args:
        model (nn.Module): The model to be trained.
        dataset (:obj:`Dataset`): Train dataset.
        cfg (dict): The config dict for training.
        validate (bool): Whether to do evaluation. Default: False.
        logger (logging.Logger | None): Logger for training. Default: None.
        timestamp (str | None): Local time for runner. Default: None.
        meta (dict | None): Meta dict to record some important information.
            Default: None.
    """
    # prepare data loaders
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]
    data_loaders = [
        build_dataloader(ds,
                         cfg.data.samples_per_gpu,
                         cfg.data.workers_per_gpu,
                         dist=True,
                         drop_last=cfg.data.get('drop_last', False),
                         seed=cfg.seed) for ds in dataset
    ]
    # put model on gpus
    find_unused_parameters = cfg.get('find_unused_parameters', False)
    model = DistributedDataParallelWrapper(
        model,
        device_ids=[torch.cuda.current_device()],
        broadcast_buffers=False,
        find_unused_parameters=find_unused_parameters)

    # build runner
    optimizer = build_optimizers(model, cfg.optimizers)
    runner = IterBasedRunner(model,
                             optimizer=optimizer,
                             work_dir=cfg.work_dir,
                             logger=logger,
                             meta=meta)
    # an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp

    # register hooks
    runner.register_training_hooks(cfg.lr_config,
                                   checkpoint_config=cfg.checkpoint_config,
                                   log_config=cfg.log_config)

    # visual hook
    if cfg.get('visual_config', None) is not None:
        cfg.visual_config['output_dir'] = os.path.join(
            cfg.work_dir, cfg.visual_config['output_dir'])
        runner.register_hook(mmcv.build_from_cfg(cfg.visual_config, HOOKS))

    # evaluation hook
    if validate and cfg.get('evaluation', None) is not None:
        dataset = build_dataset(cfg.data.val)
        samples_per_gpu = cfg.data.get('val_samples_per_gpu',
                                       cfg.data.samples_per_gpu)
        workers_per_gpu = cfg.data.get('val_workers_per_gpu',
                                       cfg.data.workers_per_gpu)
        data_loader = build_dataloader(dataset,
                                       samples_per_gpu=samples_per_gpu,
                                       workers_per_gpu=workers_per_gpu,
                                       dist=True,
                                       shuffle=False)
        save_path = osp.join(cfg.work_dir, 'val_visuals')
        runner.register_hook(
            DistEvalIterHook(data_loader,
                             save_path=save_path,
                             **cfg.evaluation))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_iters)
Пример #2
0
def _dist_train(model,
                dataset,
                cfg,
                validate=False,
                logger=None,
                timestamp=None,
                meta=None):
    """Distributed training function.

    Args:
        model (nn.Module): The model to be trained.
        dataset (:obj:`Dataset`): Train dataset.
        cfg (dict): The config dict for training.
        validate (bool): Whether to do evaluation. Default: False.
        logger (logging.Logger | None): Logger for training. Default: None.
        timestamp (str | None): Local time for runner. Default: None.
        meta (dict | None): Meta dict to record some important information.
            Default: None.
    """
    dataset = dataset if isinstance(dataset, (list, tuple)) else [dataset]

    # step 1: give default values and override (if exist) from cfg.data
    loader_cfg = dict(
        seed=cfg.get('seed'),
        drop_last=False,
        dist=True,
        **({} if torch.__version__ != 'parrots' else dict(
            prefetch_num=2,
            pin_memory=False,
        )),
        **dict((k, cfg.data[k]) for k in [
            'samples_per_gpu',
            'workers_per_gpu',
            'shuffle',
            'seed',
            'drop_last',
            'prefetch_num',
            'pin_memory',
        ] if k in cfg.data))

    # step 2: cfg.data.train_dataloader has highest priority
    train_loader_cfg = dict(loader_cfg, **cfg.data.get('train_dataloader', {}))

    data_loaders = [build_dataloader(ds, **train_loader_cfg) for ds in dataset]

    # put model on gpus
    find_unused_parameters = cfg.get('find_unused_parameters', False)
    model = DistributedDataParallelWrapper(
        model,
        device_ids=[torch.cuda.current_device()],
        broadcast_buffers=False,
        find_unused_parameters=find_unused_parameters)

    # build runner
    optimizer = build_optimizers(model, cfg.optimizers)
    runner = IterBasedRunner(
        model,
        optimizer=optimizer,
        work_dir=cfg.work_dir,
        logger=logger,
        meta=meta)
    # an ugly walkaround to make the .log and .log.json filenames the same
    runner.timestamp = timestamp

    # register hooks
    runner.register_training_hooks(
        cfg.lr_config,
        checkpoint_config=cfg.checkpoint_config,
        log_config=cfg.log_config)

    # visual hook
    if cfg.get('visual_config', None) is not None:
        cfg.visual_config['output_dir'] = os.path.join(
            cfg.work_dir, cfg.visual_config['output_dir'])
        runner.register_hook(mmcv.build_from_cfg(cfg.visual_config, HOOKS))

    # evaluation hook
    if validate and cfg.get('evaluation', None) is not None:
        dataset = build_dataset(cfg.data.val)

        if ('val_samples_per_gpu' in cfg.data
                or 'val_workers_per_gpu' in cfg.data):
            warnings.warn('"val_samples_per_gpu/val_workers_per_gpu" have '
                          'been deprecated. Please use '
                          '"val_dataloader=dict(samples_per_gpu=1)" instead. '
                          'Details see '
                          'https://github.com/open-mmlab/mmediting/pull/201')

        val_loader_cfg = dict(
            loader_cfg,
            shuffle=False,
            drop_last=False,
            **dict((newk, cfg.data[oldk]) for oldk, newk in [
                ('val_samples_per_gpu', 'samples_per_gpu'),
                ('val_workers_per_gpu', 'workers_per_gpu'),
            ] if oldk in cfg.data),
            **cfg.data.get('val_dataloader', {}))

        data_loader = build_dataloader(dataset, **val_loader_cfg)
        save_path = osp.join(cfg.work_dir, 'val_visuals')
        runner.register_hook(
            DistEvalIterHook(
                data_loader, save_path=save_path, **cfg.evaluation))

    if cfg.resume_from:
        runner.resume(cfg.resume_from)
    elif cfg.load_from:
        runner.load_checkpoint(cfg.load_from)
    runner.run(data_loaders, cfg.workflow, cfg.total_iters)