validate.py 文件源码

def validate(args):

    # Setup Dataloader
    data_loader = get_loader(args.dataset)
    data_path = get_data_path(args.dataset)
    loader = data_loader(data_path, split=args.split, is_transform=True, img_size=(args.img_rows, args.img_cols))
    n_classes = loader.n_classes
    valloader = data.DataLoader(loader, batch_size=args.batch_size, num_workers=4)
    running_metrics = runningScore(n_classes)

    # Setup Model
    model = get_model(args.model_path[:args.model_path.find('_')], n_classes)
    state = convert_state_dict(torch.load(args.model_path)['model_state'])
    model.load_state_dict(state)
    model.eval()

    for i, (images, labels) in tqdm(enumerate(valloader)):
        model.cuda()
        images = Variable(images.cuda(), volatile=True)
        labels = Variable(labels.cuda(), volatile=True)

        outputs = model(images)
        pred = outputs.data.max(1)[1].cpu().numpy()
        gt = labels.data.cpu().numpy()

        running_metrics.update(gt, pred)

    score, class_iou = running_metrics.get_scores()

    for k, v in score.items():
        print(k, v)

    for i in range(n_classes):
        print(i, class_iou[i])