Python torchvision.transforms 模块，RandomSizedCrop() 实例源码

def load_labels(data_dir,resize=(224,224)):

    data_transforms = {
        'train': transforms.Compose([
            transforms.RandomSizedCrop(max(resize)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
    }
    dsets = {x: datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms[x])
             for x in ['train']}  
    return (dsets['train'].classes)

def load_labels(data_dir,resize=(224,224)):

    data_transforms = {
        'train': transforms.Compose([
            transforms.RandomSizedCrop(max(resize)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ])
    }
    dsets = {x: datasets.ImageFolder(os.path.join(data_dir, 'train'), data_transforms[x])
             for x in ['train']}  
    return (dsets['train'].classes)

def load_data(resize):

    data_transforms = {
        'train': transforms.Compose([
            transforms.RandomSizedCrop(max(resize)),
            transforms.RandomHorizontalFlip(),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
        'val': transforms.Compose([
            #Higher scale-up for inception
            transforms.Scale(int(max(resize)/224*256)),
            transforms.CenterCrop(max(resize)),
            transforms.ToTensor(),
            transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
        ]),
    }

    data_dir = 'PlantVillage'
    dsets = {x: datasets.ImageFolder(os.path.join(data_dir, x), data_transforms[x])
             for x in ['train', 'val']}
    dset_loaders = {x: torch.utils.data.DataLoader(dsets[x], batch_size=batch_size,
                                                   shuffle=True)
                    for x in ['train', 'val']}
    dset_sizes = {x: len(dsets[x]) for x in ['train', 'val']}
    dset_classes = dsets['train'].classes

    return dset_loaders['train'], dset_loaders['val']

def inception_preproccess(input_size, normalize=__imagenet_stats):
    return transforms.Compose([
        transforms.RandomSizedCrop(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(**normalize)
    ])

def inception_color_preproccess(input_size, normalize=__imagenet_stats):
    return transforms.Compose([
        transforms.RandomSizedCrop(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        ColorJitter(
            brightness=0.4,
            contrast=0.4,
            saturation=0.4,
        ),
        Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']),
        transforms.Normalize(**normalize)
    ])

def inception_preproccess(input_size, normalize=__imagenet_stats):
    return transforms.Compose([
        transforms.RandomSizedCrop(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        transforms.Normalize(**normalize)
    ])

def inception_color_preproccess(input_size, normalize=__imagenet_stats):
    return transforms.Compose([
        transforms.RandomSizedCrop(input_size),
        transforms.RandomHorizontalFlip(),
        transforms.ToTensor(),
        ColorJitter(
            brightness=0.4,
            contrast=0.4,
            saturation=0.4,
        ),
        Lighting(0.1, __imagenet_pca['eigval'], __imagenet_pca['eigvec']),
        transforms.Normalize(**normalize)
    ])

def imagenet_like():

    crop_size = 299#224

    train_transformations = transforms.Compose([
        transforms.RandomSizedCrop(crop_size),
        transforms.RandomHorizontalFlip(),
        lambda img: img if random.random() < 0.5 else img.transpose(Image.FLIP_TOP_BOTTOM),
        transforms.ToTensor(),
        ColorJitter(brightness=0.4, contrast=0.4, saturation=0.4),
        normalize,
    ])

    val_transformations = transforms.Compose([
        transforms.CenterCrop(crop_size),
        transforms.ToTensor(),
        normalize,
    ])

    test_transformation = transforms.Compose([
        #TenCropPick(224),
        SpatialPick(),
        #transforms.CenterCrop(crop_size),
        transforms.ToTensor(),
        normalize,
    ])

    return {'train': train_transformations, 'val': val_transformations, 'test': test_transformation}

def get_transform(data_name, split_name, opt):
    normalizer = transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                      std=[0.229, 0.224, 0.225])
    t_list = []
    if split_name == 'train':
        t_list = [transforms.RandomSizedCrop(opt.crop_size),
                  transforms.RandomHorizontalFlip()]
    elif split_name == 'val':
        t_list = [transforms.Scale(256), transforms.CenterCrop(224)]
    elif split_name == 'test':
        t_list = [transforms.Scale(256), transforms.CenterCrop(224)]

    t_end = [transforms.ToTensor(), normalizer]
    transform = transforms.Compose(t_list + t_end)
    return transform