Python logging.Logger方法代码示例

# 需要导入模块: from reid.utils import logging [as 别名]
# 或者: from reid.utils.logging import Logger [as 别名]
def main(args):
    cudnn.benchmark = True
    cudnn.enabled = True
    
    save_path = args.logs_dir
    sys.stdout = Logger(osp.join(args.logs_dir, 'log'+ str(args.merge_percent)+ time.strftime(".%m_%d_%H:%M:%S") + '.txt'))

    # get all unlabeled data for training
    dataset_all = datasets.create(args.dataset, osp.join(args.data_dir, args.dataset))
    new_train_data, cluster_id_labels = change_to_unlabel(dataset_all)

    num_train_ids = len(np.unique(np.array(cluster_id_labels)))
    nums_to_merge = int(num_train_ids * args.merge_percent)

    BuMain = Bottom_up(model_name=args.arch, batch_size=args.batch_size, 
            num_classes=num_train_ids,
            dataset=dataset_all,
            u_data=new_train_data, save_path=args.logs_dir, max_frames=args.max_frames,
            embeding_fea_size=args.fea)


    for step in range(int(1/args.merge_percent)-1):
        print('step: ',step)

        BuMain.train(new_train_data, step, loss=args.loss) 

        
        BuMain.evaluate(dataset_all.query, dataset_all.gallery)

        # get new train data for the next iteration
        print('----------------------------------------bottom-up clustering------------------------------------------------')
        cluster_id_labels, new_train_data = BuMain.get_new_train_data_v2(cluster_id_labels, nums_to_merge, step, penalty=args.size_penalty)
        print('\n\n') 

# 需要导入模块: from reid.utils import logging [as 别名]
# 或者: from reid.utils.logging import Logger [as 别名]
def main(args):
    cudnn.benchmark = True
    # Redirect print to both console and log file
    if not args.evaluate:
        sys.stdout = Logger(osp.join(args.logs_dir, 'log.txt'))

    train_loader = get_loader(args.train_path, args.height, args.width, relabel=True,
                                   batch_size=args.batch_size, mode='train', num_workers=args.workers, name_pattern = args.name_pattern)

    gallery_loader = get_loader(args.gallery_path, args.height, args.width, relabel=False,
                                   batch_size=args.batch_size, mode='test', num_workers=args.workers, name_pattern = args.name_pattern)

    query_loader = get_loader(args.query_path, args.height, args.width, relabel=False,
                                   batch_size=args.batch_size, mode='test', num_workers=args.workers, name_pattern = args.name_pattern)

    # Create model
    model = DenseNet(num_feature=args.num_feature, num_classes=args.true_class, num_iteration = args.num_iteration)

    # Load from checkpoint
    start_epoch = args.start_epoch
    if args.resume:
        checkpoint = load_checkpoint(args.resume)
        model.load_state_dict(checkpoint['state_dict'])

    model = nn.DataParallel(model).cuda()

    # Evaluator
    if args.evaluate:
        evaluator = Evaluator(model)
        print("Test:")
        evaluator.evaluate(query_loader, gallery_loader, query_loader.dataset.ret, gallery_loader.dataset.ret, args.output_feature)
        return

    # Start training
    model= train(args, model, train_loader, start_epoch)
    save_checkpoint({'state_dict': model.module.state_dict()}, fpath=osp.join(args.logs_dir, 'model.pth.tar'))

    evaluator = Evaluator(model)
    print("Test:")
    evaluator.evaluate(query_loader, gallery_loader, query_loader.dataset.ret, gallery_loader.dataset.ret, args.output_feature) 

# 需要导入模块: from reid.utils import logging [as 别名]
# 或者: from reid.utils.logging import Logger [as 别名]
def main(args):
    np.random.seed(args.seed)
    torch.manual_seed(args.seed)
    torch.cuda.manual_seed_all(args.seed)
    cudnn.benchmark = True

    sys.stdout = Logger(osp.join(args.log_dir, 'log_test.txt'))

    args.num_classes = 1
    
    # Create data loaders
    dataset = {}
    dataset['dataset'] = datasets.create(args.name, args.data_dir)
    dataset['train_loader'], dataset['query_loader'], dataset['gallery_loader'] \
        = create_test_data_loader(args, args.name, dataset['dataset'])
       
    if args.evaluate:
        cls_params = None
        trainer = PCBTrainer(args, cls_params=cls_params)
        evaluator = trainer.test()
        scores = {}

        scores['cmc_scores'], scores['mAP'], q_f, g_f, _ = \
            evaluator.evaluate(args.name, dataset['query_loader'], dataset['gallery_loader'],
                               dataset['dataset'].query, dataset['dataset'].gallery, isevaluate=True)
        
        print('Cross Ddomain CMC Scores')
        print('Source\t Target\t Top1\t Top5\t Top10\t MAP')
        print('{}->{}: {:6.2%} {:6.2%} {:6.2%} ({:.2%})'.format(args.s_name, args.name,
                                                                scores['cmc_scores'][0],
                                                                scores['cmc_scores'][1],
                                                                scores['cmc_scores'][2],
                                                                scores['mAP']))

        ################## whether rerank test ############
        if args.rerank:
            rerankor = Rerankor()
            rerankor.rerank(q_f, g_f,
                               savepath=os.path.join(args.save_dir, 'rerank'),
                               save=False, isevaluate=True,
                               dataset=dataset['dataset']) 

# 需要导入模块: from reid.utils import logging [as 别名]
# 或者: from reid.utils.logging import Logger [as 别名]
def main(args):
    cudnn.benchmark = True
    cudnn.enabled = True
    
    save_path = args.logs_dir
    sys.stdout = Logger(osp.join(args.logs_dir, 'log'+ str(args.merge_percent)+ time.strftime(".%m_%d_%H:%M:%S") + '.txt'))

    # get all unlabeled data for training
    dataset_all = datasets.create(args.dataset, osp.join(args.data_dir, args.dataset))
    new_train_data, cluster_id_labels = change_to_unlabel(dataset_all)

    num_train_ids = len(np.unique(np.array(cluster_id_labels)))
    nums_to_merge = int(num_train_ids * args.merge_percent)

    BuMain = Bottom_up(model_name=args.arch, batch_size=args.batch_size, 
            num_classes=num_train_ids,
            dataset=dataset_all,
            u_data=new_train_data, save_path=args.logs_dir, max_frames=args.max_frames,
            embeding_fea_size=args.fea)


    for step in range(int(1/args.merge_percent)-1):
        print('step: ',step)

        BuMain.train(new_train_data, step, loss=args.loss) 

        BuMain.evaluate(dataset_all.query, dataset_all.gallery)

        # get new train data for the next iteration
        print('----------------------------------------bottom-up clustering------------------------------------------------')
        cluster_id_labels, new_train_data = BuMain.get_new_train_data(cluster_id_labels, nums_to_merge, size_penalty=args.size_penalty)
        print('\n\n') 

# 需要导入模块: from reid.utils import logging [as 别名]
# 或者: from reid.utils.logging import Logger [as 别名]
def main(args):
    cudnn.benchmark = True
    cudnn.enabled = True
    save_path = args.logs_dir
    total_step = 100//args.EF + 1
    sys.stdout = Logger(osp.join(args.logs_dir, 'log'+ str(args.EF)+ time.strftime(".%m_%d_%H:%M:%S") + '.txt'))

    # get all the labeled and unlabeled data for training
    dataset_all = datasets.create(args.dataset, osp.join(args.data_dir, args.dataset))
    num_all_examples = len(dataset_all.train)
    l_data, u_data = get_one_shot_in_cam1(dataset_all, load_path="./examples/oneshot_{}_used_in_paper.pickle".format(dataset_all.name))
    
    resume_step, ckpt_file = -1, ''
    if args.resume:
        resume_step, ckpt_file = resume(args) 

    # initial the EUG algorithm 
    eug = EUG(model_name=args.arch, batch_size=args.batch_size, mode=args.mode, num_classes=dataset_all.num_train_ids, 
            data_dir=dataset_all.images_dir, l_data=l_data, u_data=u_data, save_path=args.logs_dir, max_frames=args.max_frames)


    new_train_data = l_data 
    for step in range(total_step):
        # for resume
        if step < resume_step: 
            continue

        nums_to_select = min(int( len(u_data) * (step+1) * args.EF / 100 ),  len(u_data))
        print("This is running {} with EF={}%, step {}:\t Nums_to_be_select {}, \t Logs-dir {}".format(
                args.mode, args.EF, step,  nums_to_select, save_path))

        # train the model or load ckpt        
        eug.train(new_train_data, step, epochs=70, step_size=55, init_lr=0.1) if step != resume_step else eug.resume(ckpt_file, step)

        # pseudo-label and confidence score
        pred_y, pred_score = eug.estimate_label()

        # select data
        selected_idx = eug.select_top_data(pred_score, nums_to_select)

        # add new data
        new_train_data = eug.generate_new_train_data(selected_idx, pred_y)

        # evluate
        eug.evaluate(dataset_all.query, dataset_all.gallery)