Python utils.create_logger方法代码示例

# 需要导入模块: from utils import utils [as 别名]
# 或者: from utils.utils import create_logger [as 别名]
def main():
    args = cfg.parse_args()
    torch.cuda.manual_seed(args.random_seed)
    assert args.exp_name
    assert args.load_path.endswith('.pth')
    assert os.path.exists(args.load_path)
    args.path_helper = set_log_dir('logs_eval', args.exp_name)
    logger = create_logger(args.path_helper['log_path'], phase='test')

    # set tf env
    _init_inception()
    inception_path = check_or_download_inception(None)
    create_inception_graph(inception_path)

    # import network
    gen_net = eval('models.'+args.gen_model+'.Generator')(args=args).cuda()

    # fid stat
    if args.dataset.lower() == 'cifar10':
        fid_stat = 'fid_stat/fid_stats_cifar10_train.npz'
    elif args.dataset.lower() == 'stl10':
        fid_stat = 'fid_stat/stl10_train_unlabeled_fid_stats_48.npz'
    else:
        raise NotImplementedError(f'no fid stat for {args.dataset.lower()}')
    assert os.path.exists(fid_stat)

    # initial
    fixed_z = torch.cuda.FloatTensor(np.random.normal(0, 1, (25, args.latent_dim)))

    # set writer
    logger.info(f'=> resuming from {args.load_path}')
    checkpoint_file = args.load_path
    assert os.path.exists(checkpoint_file)
    checkpoint = torch.load(checkpoint_file)

    if 'avg_gen_state_dict' in checkpoint:
        gen_net.load_state_dict(checkpoint['avg_gen_state_dict'])
        epoch = checkpoint['epoch']
        logger.info(f'=> loaded checkpoint {checkpoint_file} (epoch {epoch})')
    else:
        gen_net.load_state_dict(checkpoint)
        logger.info(f'=> loaded checkpoint {checkpoint_file}')

    logger.info(args)
    writer_dict = {
        'writer': SummaryWriter(args.path_helper['log_path']),
        'valid_global_steps': 0,
    }
    inception_score, fid_score = validate(args, fixed_z, fid_stat, gen_net, writer_dict, clean_dir=False)
    logger.info(f'Inception score: {inception_score}, FID score: {fid_score}.') 

# 需要导入模块: from utils import utils [as 别名]
# 或者: from utils.utils import create_logger [as 别名]
def main():
    args = parse_args()
    update_config(cfg, args)

    cfg.defrost()
    cfg.RANK = args.rank
    cfg.freeze()

    logger, final_output_dir, tb_log_dir = create_logger(
        cfg, args.cfg, 'train'
    )

    logger.info(pprint.pformat(args))
    logger.info(cfg)

    if args.gpu is not None:
        warnings.warn('You have chosen a specific GPU. This will completely '
                      'disable data parallelism.')

    if args.dist_url == "env://" and args.world_size == -1:
        args.world_size = int(os.environ["WORLD_SIZE"])

    args.distributed = args.world_size > 1 or cfg.MULTIPROCESSING_DISTRIBUTED

    ngpus_per_node = torch.cuda.device_count()
    if cfg.MULTIPROCESSING_DISTRIBUTED:
        # Since we have ngpus_per_node processes per node, the total world_size
        # needs to be adjusted accordingly
        args.world_size = ngpus_per_node * args.world_size
        # Use torch.multiprocessing.spawn to launch distributed processes: the
        # main_worker process function
        mp.spawn(
            main_worker,
            nprocs=ngpus_per_node,
            args=(ngpus_per_node, args, final_output_dir, tb_log_dir)
        )
    else:
        # Simply call main_worker function
        main_worker(
            ','.join([str(i) for i in cfg.GPUS]),
            ngpus_per_node,
            args,
            final_output_dir,
            tb_log_dir
        ) 

# 需要导入模块: from utils import utils [as 别名]
# 或者: from utils.utils import create_logger [as 别名]
def main():
    args = parse_args()

    logger, final_output_dir, tb_log_dir = create_logger(
        config, args.cfg, 'valid')

    logger.info(pprint.pformat(args))
    logger.info(pprint.pformat(config))

    # cudnn related setting
    cudnn.benchmark = config.CUDNN.BENCHMARK
    torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
    torch.backends.cudnn.enabled = config.CUDNN.ENABLED

    model = eval('models.'+config.MODEL.NAME+'.get_cls_net')(
        config)

    dump_input = torch.rand(
        (1, 3, config.MODEL.IMAGE_SIZE[1], config.MODEL.IMAGE_SIZE[0])
    )
    logger.info(get_model_summary(model, dump_input))

    if config.TEST.MODEL_FILE:
        logger.info('=> loading model from {}'.format(config.TEST.MODEL_FILE))
        model.load_state_dict(torch.load(config.TEST.MODEL_FILE))
    else:
        model_state_file = os.path.join(final_output_dir,
                                        'final_state.pth.tar')
        logger.info('=> loading model from {}'.format(model_state_file))
        model.load_state_dict(torch.load(model_state_file))

    gpus = list(config.GPUS)
    model = torch.nn.DataParallel(model, device_ids=gpus).cuda()

    # define loss function (criterion) and optimizer
    criterion = torch.nn.CrossEntropyLoss().cuda()

    # Data loading code
    valdir = os.path.join(config.DATASET.ROOT,
                          config.DATASET.TEST_SET)
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])

    valid_loader = torch.utils.data.DataLoader(
        datasets.ImageFolder(valdir, transforms.Compose([
            transforms.Resize(int(config.MODEL.IMAGE_SIZE[0] / 0.875)),
            transforms.CenterCrop(config.MODEL.IMAGE_SIZE[0]),
            transforms.ToTensor(),
            normalize,
        ])),
        batch_size=config.TEST.BATCH_SIZE_PER_GPU*len(gpus),
        shuffle=False,
        num_workers=config.WORKERS,
        pin_memory=True
    )

    # evaluate on validation set
    validate(config, valid_loader, model, criterion, final_output_dir,
             tb_log_dir, None) 

# 需要导入模块: from utils import utils [as 别名]
# 或者: from utils.utils import create_logger [as 别名]
def main():
    args = parse_args()
    reset_config(config, args)

    logger, final_output_dir, tb_log_dir = create_logger(
        config, args.cfg, 'valid')

    logger.info(pprint.pformat(args))
    logger.info(pprint.pformat(config))

    # cudnn related setting
    cudnn.benchmark = config.CUDNN.BENCHMARK
    torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
    torch.backends.cudnn.enabled = config.CUDNN.ENABLED

    backbone_model = eval('models.' + config.BACKBONE_MODEL + '.get_pose_net')(
        config, is_train=False)

    model = eval('models.' + config.MODEL + '.get_multiview_pose_net')(
        backbone_model, config)

    if config.TEST.MODEL_FILE:
        logger.info('=> loading model from {}'.format(config.TEST.MODEL_FILE))
        model.load_state_dict(torch.load(config.TEST.MODEL_FILE))
    else:
        model_path = 'model_best.pth.tar' if config.TEST.STATE == 'best' else 'final_state.pth.tar'
        model_state_file = os.path.join(final_output_dir, model_path)
        logger.info('=> loading model from {}'.format(model_state_file))
        model.load_state_dict(torch.load(model_state_file))

    gpus = [int(i) for i in config.GPUS.split(',')]
    model = torch.nn.DataParallel(model, device_ids=gpus).cuda()

    # define loss function (criterion) and optimizer
    criterion = JointsMSELoss(
        use_target_weight=config.LOSS.USE_TARGET_WEIGHT).cuda()

    # Data loading code
    normalize = transforms.Normalize(
        mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
    valid_dataset = eval('dataset.' + config.DATASET.TEST_DATASET)(
        config, config.DATASET.TEST_SUBSET, False,
        transforms.Compose([
            transforms.ToTensor(),
            normalize,
        ]))
    valid_loader = torch.utils.data.DataLoader(
        valid_dataset,
        batch_size=config.TEST.BATCH_SIZE * len(gpus),
        shuffle=False,
        num_workers=config.WORKERS,
        pin_memory=True)

    # evaluate on validation set
    validate(config, valid_loader, valid_dataset, model, criterion,
             final_output_dir, tb_log_dir) 

# 需要导入模块: from utils import utils [as 别名]
# 或者: from utils.utils import create_logger [as 别名]
def main():
    args = parse_args()
    logger, final_output_dir, tb_log_dir = create_logger(
        config, args.cfg, 'test3d')

    prediction_path = os.path.join(final_output_dir,
                                   config.TEST.HEATMAP_LOCATION_FILE)
    test_dataset = eval('dataset.' + config.DATASET.TEST_DATASET)(
        config, config.DATASET.TEST_SUBSET, False)

    all_heatmaps = h5py.File(prediction_path)['heatmaps']

    pairwise_file = config.PICT_STRUCT.PAIRWISE_FILE
    with open(pairwise_file, 'rb') as f:
        pairwise = pickle.load(f)['pairwise_constrain']

    cnt = 0
    grouping = test_dataset.grouping
    mpjpes = []
    for items in grouping:
        heatmaps = []
        boxes = []
        poses = []
        cameras = []

        for idx in items:
            datum = test_dataset.db[idx]
            camera = datum['camera']
            cameras.append(camera)

            poses.append(
                camera_to_world_frame(datum['joints_3d_camera'], camera['R'],
                                      camera['T']))

            box = {}
            box['scale'] = np.array(datum['scale'])
            box['center'] = np.array(datum['center'])
            boxes.append(box)

            heatmaps.append(all_heatmaps[cnt])
            cnt += 1
        heatmaps = np.array(heatmaps)

        # This demo uses GT root locations and limb length; but can be replaced by statistics
        grid_center = poses[0][0]
        body = HumanBody()
        limb_length = compute_limb_length(body, poses[0])
        prediction = rpsm(cameras, heatmaps, boxes, grid_center, limb_length,
                          pairwise, config)
        mpjpe = np.mean(np.sqrt(np.sum((prediction - poses[0])**2, axis=1)))
        mpjpes.append(mpjpe)
    print(np.mean(mpjpes)) 

# 需要导入模块: from utils import utils [as 别名]
# 或者: from utils.utils import create_logger [as 别名]
def main():
    args = parse_args()
    reset_config(config, args)

    logger, final_output_dir, tb_log_dir = create_logger(
        config, args.cfg, 'valid')

    logger.info(pprint.pformat(args))
    logger.info(pprint.pformat(config))

    # cudnn related setting
    cudnn.benchmark = config.CUDNN.BENCHMARK
    torch.backends.cudnn.deterministic = config.CUDNN.DETERMINISTIC
    torch.backends.cudnn.enabled = config.CUDNN.ENABLED

    model = eval('models.'+config.MODEL.NAME+'.get_pose_net')(
        config, is_train=False
    )

    if config.TEST.MODEL_FILE:
        logger.info('=> loading model from {}'.format(config.TEST.MODEL_FILE))
        model.load_state_dict(torch.load(config.TEST.MODEL_FILE))
    else:
        model_state_file = os.path.join(final_output_dir,
                                        'final_state.pth.tar')
        logger.info('=> loading model from {}'.format(model_state_file))
        model.load_state_dict(torch.load(model_state_file))

    gpus = [int(i) for i in config.GPUS.split(',')]
    model = torch.nn.DataParallel(model, device_ids=gpus).cuda()

    # define loss function (criterion) and optimizer
    criterion = JointsMSELoss(
        use_target_weight=config.LOSS.USE_TARGET_WEIGHT
    ).cuda()

    # Data loading code
    normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                     std=[0.229, 0.224, 0.225])
    valid_dataset = eval('dataset.'+config.DATASET.DATASET)(
        config,
        config.DATASET.ROOT,
        config.DATASET.TEST_SET,
        False,
        transforms.Compose([
            transforms.ToTensor(),
            normalize,
        ])
    )
    valid_loader = torch.utils.data.DataLoader(
        valid_dataset,
        batch_size=config.TEST.BATCH_SIZE*len(gpus),
        shuffle=False,
        num_workers=config.WORKERS,
        pin_memory=True
    )

    # evaluate on validation set
    validate(config, valid_loader, valid_dataset, model, criterion,
             final_output_dir, tb_log_dir)