131 lines
4.7 KiB
Python
131 lines
4.7 KiB
Python
import os.path
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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 check_integrity, download_url, verify_str_arg
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from .vision import VisionDataset
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class SVHN(VisionDataset):
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"""`SVHN <http://ufldl.stanford.edu/housenumbers/>`_ Dataset.
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Note: The SVHN dataset assigns the label `10` to the digit `0`. However, in this Dataset,
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we assign the label `0` to the digit `0` to be compatible with PyTorch loss functions which
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expect the class labels to be in the range `[0, C-1]`
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.. warning::
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This class needs `scipy <https://docs.scipy.org/doc/>`_ to load data from `.mat` format.
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Args:
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root (str or ``pathlib.Path``): Root directory of the dataset where the data is stored.
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split (string): One of {'train', 'test', 'extra'}.
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Accordingly dataset is selected. 'extra' is Extra training set.
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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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"http://ufldl.stanford.edu/housenumbers/train_32x32.mat",
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"train_32x32.mat",
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"e26dedcc434d2e4c54c9b2d4a06d8373",
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],
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"test": [
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"http://ufldl.stanford.edu/housenumbers/test_32x32.mat",
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"test_32x32.mat",
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"eb5a983be6a315427106f1b164d9cef3",
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],
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"extra": [
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"http://ufldl.stanford.edu/housenumbers/extra_32x32.mat",
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"extra_32x32.mat",
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"a93ce644f1a588dc4d68dda5feec44a7",
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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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split: str = "train",
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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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self.split = verify_str_arg(split, "split", tuple(self.split_list.keys()))
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self.url = self.split_list[split][0]
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self.filename = self.split_list[split][1]
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self.file_md5 = self.split_list[split][2]
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if download:
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self.download()
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if not self._check_integrity():
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raise RuntimeError("Dataset not found or corrupted. You can use download=True to download it")
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# import here rather than at top of file because this is
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# an optional dependency for torchvision
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import scipy.io as sio
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# reading(loading) mat file as array
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loaded_mat = sio.loadmat(os.path.join(self.root, self.filename))
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self.data = loaded_mat["X"]
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# loading from the .mat file gives an np.ndarray of type np.uint8
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# converting to np.int64, so that we have a LongTensor after
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# the conversion from the numpy array
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# the squeeze is needed to obtain a 1D tensor
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self.labels = loaded_mat["y"].astype(np.int64).squeeze()
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# the svhn dataset assigns the class label "10" to the digit 0
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# this makes it inconsistent with several loss functions
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# which expect the class labels to be in the range [0, C-1]
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np.place(self.labels, self.labels == 10, 0)
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self.data = np.transpose(self.data, (3, 2, 0, 1))
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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.labels[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(np.transpose(img, (1, 2, 0)))
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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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def _check_integrity(self) -> bool:
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root = self.root
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md5 = self.split_list[self.split][2]
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fpath = os.path.join(root, self.filename)
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return check_integrity(fpath, md5)
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def download(self) -> None:
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md5 = self.split_list[self.split][2]
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download_url(self.url, self.root, self.filename, md5)
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def extra_repr(self) -> str:
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return "Split: {split}".format(**self.__dict__)
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