Source code for torchvision.datasets.usps

import os
from pathlib import Path
from typing import Any, Callable, Optional, Tuple, Union

import numpy as np
from PIL import Image

from .utils import download_url
from .vision import VisionDataset

[docs]class USPS(VisionDataset): """`USPS <>`_ Dataset. The data-format is : [label [index:value ]*256 \\n] * num_lines, where ``label`` lies in ``[1, 10]``. The value for each pixel lies in ``[-1, 1]``. Here we transform the ``label`` into ``[0, 9]`` and make pixel values in ``[0, 255]``. Args: root (str or ``pathlib.Path``): Root directory of dataset to store``USPS`` data files. train (bool, optional): If True, creates dataset from ``usps.bz2``, otherwise from ``usps.t.bz2``. transform (callable, optional): A function/transform that takes in a 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. 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. """ split_list = { "train": [ "", "usps.bz2", "ec16c51db3855ca6c91edd34d0e9b197", ], "test": [ "", "usps.t.bz2", "8ea070ee2aca1ac39742fdd1ef5ed118", ], } def __init__( self, root: Union[str, Path], train: bool = True, transform: Optional[Callable] = None, target_transform: Optional[Callable] = None, download: bool = False, ) -> None: super().__init__(root, transform=transform, target_transform=target_transform) split = "train" if train else "test" url, filename, checksum = self.split_list[split] full_path = os.path.join(self.root, filename) if download and not os.path.exists(full_path): download_url(url, self.root, filename, md5=checksum) import bz2 with as fp: raw_data = [line.decode().split() for line in fp.readlines()] tmp_list = [[x.split(":")[-1] for x in data[1:]] for data in raw_data] imgs = np.asarray(tmp_list, dtype=np.float32).reshape((-1, 16, 16)) imgs = ((imgs + 1) / 2 * 255).astype(dtype=np.uint8) targets = [int(d[0]) - 1 for d in raw_data] = imgs self.targets = targets
[docs] def __getitem__(self, index: int) -> Tuple[Any, Any]: """ Args: index (int): Index Returns: tuple: (image, target) where target is index of the target class. """ img, target =[index], int(self.targets[index]) # doing this so that it is consistent with all other datasets # to return a PIL Image img = Image.fromarray(img, mode="L") if self.transform is not None: img = self.transform(img) if self.target_transform is not None: target = self.target_transform(target) return img, target
def __len__(self) -> int: return len(


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