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tensorplay.vision.datasets

Classes

class DatasetFolder [source]

DatasetFolder(root, loader, extensions=None, transform=None, target_transform=None, is_valid_file=None)

Bases: VisionDataset

Methods

__init__(self, root, loader, extensions=None, transform=None, target_transform=None, is_valid_file=None) [source]

Initialize self. See help(type(self)) for accurate signature.

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extra_repr(self) -> str [source]

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class ImageFolder [source]

ImageFolder(root, transform=None, target_transform=None, loader=<function default_loader at 0x0000021B12D3DA80>, is_valid_file=None)

Bases: DatasetFolder

A generic data loader where the images are arranged in this way:

root/dog/xxx.png
root/dog/xxy.png
root/dog/xxz.png

root/cat/123.png
root/cat/nsdf3.png
root/cat/asd932_.png

Args

• root (string): Root directory path.
• transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, transforms.RandomCrop
• target_transform (callable, optional): A function/transform that takes in the target and transforms it.
• loader (callable, optional): A function to load an image given its path.
• is_valid_file (callable, optional): A function that takes path of an Image file and check if the file is a valid_file (used to check of corrupt files)

Methods

__init__(self, root, transform=None, target_transform=None, loader=<function default_loader at 0x0000021B12D3DA80>, is_valid_file=None) [source]

Initialize self. See help(type(self)) for accurate signature.

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extra_repr(self) -> str [source]

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class MNIST [source]

MNIST(root=None, train=True, transform=None, target_transform=None, download=False)

Bases: VisionDataset

MNIST <http://yann.lecun.com/exdb/mnist/>_ Dataset.

Args

• root (string, optional): Root directory of dataset where MNIST/processed/training.pt and MNIST/processed/test.pt exist. If None, uses default cache directory.
• train (bool, optional): If True, creates dataset from training.pt, otherwise from test.pt.
• download (bool, optional): If true, downloads the dataset from the internet and puts it in root directory. If dataset is already downloaded, it is not downloaded again.
• transform (callable, optional): A function/transform that takes in an PIL image and returns a transformed version. E.g, transforms.RandomCrop
• target_transform (callable, optional): A function/transform that takes in the target and transforms it.

Methods

__init__(self, root=None, train=True, transform=None, target_transform=None, download=False) [source]

Initialize self. See help(type(self)) for accurate signature.

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download(self) [source]

Download the MNIST data if it doesn't exist in processed_folder already.

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extra_repr(self) -> str [source]

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class VisionDataset [source]

VisionDataset(root, transform=None, target_transform=None)

Bases: Dataset

Methods

__init__(self, root, transform=None, target_transform=None) [source]

Initialize self. See help(type(self)) for accurate signature.

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extra_repr(self) -> str [source]

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Functions

default_loader() [source]

default_loader(path)

make_dataset() [source]

make_dataset(directory: str, class_to_idx: Dict[str, int], extensions: Optional[Iterable[str]] = None, is_valid_file: Optional[Callable[[str], bool]] = None) -> List[Tuple[str, int]]

Generates a list of samples in the form (path_to_sample, class_index).

Args

• directory (str): root directory path
• class_to_idx (Dict[str, int]): A mapping from class name to class index.
• extensions (Optional[Iterable[str]]): A list of allowed extensions (case-insensitive).
• is_valid_file (Optional[Callable[[str], bool]]): A function that takes a file path and returns True if the file is valid.

Returns

List[Tuple[str, int]]: includes (path_to_sample, class_index) for valid samples.