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