Python transforms.Compose() Examples

The following are 5 code examples of transforms.Compose(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module transforms , or try the search function .
Example #1
Source File: __init__.py    From Pytorch_Lightweight_Network with MIT License 6 votes vote down vote up 
def SSDTransform(size, color_jitter=True, scale=(0.1, 1), expand=(1, 4), min_area_frac=0.25):
    transforms = []
    if color_jitter:
        transforms.append(
            InputTransform(
                ColorJitter(
                    brightness=0.1, contrast=0.5,
                    saturation=0.5, hue=0.05,
                )
            )
        )
    transforms += [
        RandomApply([
            RandomExpand(expand),
        ]),
        RandomChoice([
            UseOriginal(),
            RandomSampleCrop(),
            RandomResizedCrop(size, scale=scale, ratio=(1/2, 2/1), min_area_frac=min_area_frac),
        ]),
        RandomHorizontalFlip(),
        Resize(size)
    ]
    return Compose(transforms)
Example #2
Source File: train_img_model_xent.py    From ReXCam with MIT License 6 votes vote down vote up 
def read_image(img_path):
    """Keep reading image until succeed.
    This can avoid IOError incurred by heavy IO process."""
    got_img = False
    if not osp.exists(img_path):
        raise IOError("{} does not exist".format(img_path))
    while not got_img:
        try:
            img = Image.open(img_path).convert('RGB')
            got_img = True
        except IOError:
            print("IOError incurred when reading '{}'. Will redo. Don't worry. Just chill.".format(img_path))
            pass

    transform_test = T.Compose([
        T.Resize((args.height, args.width)),
        T.ToTensor(),
        T.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
    ])
    img = transform_test(img)
    return img
Example #3
Source File: coco_utils.py    From deconvolution with GNU General Public License v3.0 6 votes vote down vote up 
def get_coco(root, image_set, transforms):
    PATHS = {
        "train": ("train2017", os.path.join("annotations", "instances_train2017.json")),
        "val": ("val2017", os.path.join("annotations", "instances_val2017.json")),
        # "train": ("val2017", os.path.join("annotations", "instances_val2017.json"))
    }
    CAT_LIST = [0, 5, 2, 16, 9, 44, 6, 3, 17, 62, 21, 67, 18, 19, 4,
                1, 64, 20, 63, 7, 72]

    transforms = Compose([
        FilterAndRemapCocoCategories(CAT_LIST, remap=True),
        ConvertCocoPolysToMask(),
        transforms
    ])

    img_folder, ann_file = PATHS[image_set]
    img_folder = os.path.join(root, img_folder)
    ann_file = os.path.join(root, ann_file)

    dataset = torchvision.datasets.CocoDetection(img_folder, ann_file, transforms=transforms)

    if image_set == "train":
        dataset = _coco_remove_images_without_annotations(dataset, CAT_LIST)

    return dataset
Example #4
Source File: train.py    From deconvolution with GNU General Public License v3.0 6 votes vote down vote up 
def get_transform(mode,base_size):
    #base_size = 520
    #crop_size = 480
    crop_size=int(480*base_size/520)

    min_size = int((0.5 if mode=='train' else 1.0) * base_size)
    max_size = int((2.0 if mode=='train' else 1.0) * base_size)
    transforms = []
    transforms.append(T.RandomResize(min_size, max_size))
    if mode=='train':
        transforms.append(T.RandomHorizontalFlip(0.5))
        transforms.append(T.RandomCrop(crop_size))
    transforms.append(T.ToTensor())
    transforms.append(T.Normalize(mean=[0.485, 0.456, 0.406],
                                std=[0.229, 0.224, 0.225]))

    return T.Compose(transforms)
Example #5
Source File: datasets.py    From pytorch-center-loss with MIT License 6 votes vote down vote up 
def __init__(self, batch_size, use_gpu, num_workers):
        transform = transforms.Compose([
            transforms.ToTensor(),
            transforms.Normalize((0.1307,), (0.3081,))
        ])

        pin_memory = True if use_gpu else False

        trainset = torchvision.datasets.MNIST(root='./data/mnist', train=True, download=True, transform=transform)
        
        trainloader = torch.utils.data.DataLoader(
            trainset, batch_size=batch_size, shuffle=True,
            num_workers=num_workers, pin_memory=pin_memory,
        )
        
        testset = torchvision.datasets.MNIST(root='./data/mnist', train=False, download=True, transform=transform)
        
        testloader = torch.utils.data.DataLoader(
            testset, batch_size=batch_size, shuffle=False,
            num_workers=num_workers, pin_memory=pin_memory,
        )

        self.trainloader = trainloader
        self.testloader = testloader
        self.num_classes = 10