Python torchvision.transforms.transforms.RandomResizedCrop() Examples

The following are 2 code examples of torchvision.transforms.transforms.RandomResizedCrop(). 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 torchvision.transforms.transforms , or try the search function .
Example #1
Source File: CelebA.py    From cortex with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
def handle(self, source, copy_to_local=False, normalize=True,
               split=None, classification_mode=False, **transform_args):
        """

        Args:
            source:
            copy_to_local:
            normalize:
            **transform_args:

        Returns:

        """
        Dataset = self.make_indexing(CelebA)
        data_path = self.get_path(source)

        if copy_to_local:
            data_path = self.copy_to_local_path(data_path)

        if normalize and isinstance(normalize, bool):
            normalize = [(0.5, 0.5, 0.5), (0.5, 0.5, 0.5)]

        if classification_mode:
            train_transform = transforms.Compose([
                transforms.RandomResizedCrop(64),
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                transforms.Normalize(*normalize),
            ])
            test_transform = transforms.Compose([
                transforms.Resize(64),
                transforms.CenterCrop(64),
                transforms.ToTensor(),
                transforms.Normalize(*normalize),
            ])
        else:
            train_transform = build_transforms(normalize=normalize,
                                               **transform_args)
            test_transform = train_transform

        if split is None:
            train_set = Dataset(root=data_path, transform=train_transform,
                                download=True)
            test_set = Dataset(root=data_path, transform=test_transform)
        else:
            train_set, test_set = self.make_split(
                data_path, split, Dataset, train_transform, test_transform)
        input_names = ['images', 'labels', 'attributes']

        dim_c, dim_x, dim_y = train_set[0][0].size()
        dim_l = len(train_set.classes)
        dim_a = train_set.attributes[0].shape[0]

        dims = dict(x=dim_x, y=dim_y, c=dim_c, labels=dim_l, attributes=dim_a)
        self.add_dataset('train', train_set)
        self.add_dataset('test', test_set)
        self.set_input_names(input_names)
        self.set_dims(**dims)

        self.set_scale((-1, 1))
Example #2
Source File: torchvision_datasets.py    From cortex with BSD 3-Clause "New" or "Revised" License 4 votes vote down vote up 
def _handle_STL(self, Dataset, data_path, transform=None,
                    labeled_only=False, stl_center_crop=False,
                    stl_resize_only=False, stl_no_resize=False):
        normalize = transforms.Normalize((0.5, 0.5, 0.5), (0.5, 0.5, 0.5))

        if stl_no_resize:
            train_transform = transforms.Compose([
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ])
            test_transform = transforms.Compose([
                transforms.ToTensor(),
                normalize,
            ])
        else:
            if stl_center_crop:
                tr_trans = transforms.CenterCrop(64)
                te_trans = transforms.CenterCrop(64)
            elif stl_resize_only:
                tr_trans = transforms.Resize(64)
                te_trans = transforms.Resize(64)
            elif stl_no_resize:
                pass
            else:
                tr_trans = transforms.RandomResizedCrop(64)
                te_trans = transforms.Resize(64)

            train_transform = transforms.Compose([
                tr_trans,
                transforms.RandomHorizontalFlip(),
                transforms.ToTensor(),
                normalize,
            ])
            test_transform = transforms.Compose([
                te_trans,
                transforms.ToTensor(),
                normalize,
            ])
        if labeled_only:
            split = 'train'
        else:
            split = 'train+unlabeled'
        train_set = Dataset(
            data_path, split=split, transform=train_transform, download=True)
        test_set = Dataset(
            data_path, split='test', transform=test_transform, download=True)
        return train_set, test_set