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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 scipy.io as sio |
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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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self.labels = loaded_mat["y"].astype(np.int64).squeeze() |
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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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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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