[docs]classSVHN(VisionDataset):"""`SVHN <http://ufldl.stanford.edu/housenumbers/>`_ Dataset. Note: The SVHN dataset assigns the label `10` to the digit `0`. However, in this Dataset, we assign the label `0` to the digit `0` to be compatible with PyTorch loss functions which expect the class labels to be in the range `[0, C-1]` .. warning:: This class needs `scipy <https://docs.scipy.org/doc/>`_ to load data from `.mat` format. Args: root (str or ``pathlib.Path``): Root directory of the dataset where the data is stored. split (string): One of {'train', 'test', 'extra'}. Accordingly dataset is selected. 'extra' is Extra training set. 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":["http://ufldl.stanford.edu/housenumbers/train_32x32.mat","train_32x32.mat","e26dedcc434d2e4c54c9b2d4a06d8373",],"test":["http://ufldl.stanford.edu/housenumbers/test_32x32.mat","test_32x32.mat","eb5a983be6a315427106f1b164d9cef3",],"extra":["http://ufldl.stanford.edu/housenumbers/extra_32x32.mat","extra_32x32.mat","a93ce644f1a588dc4d68dda5feec44a7",],}def__init__(self,root:Union[str,Path],split:str="train",transform:Optional[Callable]=None,target_transform:Optional[Callable]=None,download:bool=False,)->None:super().__init__(root,transform=transform,target_transform=target_transform)self.split=verify_str_arg(split,"split",tuple(self.split_list.keys()))self.url=self.split_list[split][0]self.filename=self.split_list[split][1]self.file_md5=self.split_list[split][2]ifdownload:self.download()ifnotself._check_integrity():raiseRuntimeError("Dataset not found or corrupted. You can use download=True to download it")# import here rather than at top of file because this is# an optional dependency for torchvisionimportscipy.ioassio# reading(loading) mat file as arrayloaded_mat=sio.loadmat(os.path.join(self.root,self.filename))self.data=loaded_mat["X"]# loading from the .mat file gives an np.ndarray of type np.uint8# converting to np.int64, so that we have a LongTensor after# the conversion from the numpy array# the squeeze is needed to obtain a 1D tensorself.labels=loaded_mat["y"].astype(np.int64).squeeze()# the svhn dataset assigns the class label "10" to the digit 0# this makes it inconsistent with several loss functions# which expect the class labels to be in the range [0, C-1]np.place(self.labels,self.labels==10,0)self.data=np.transpose(self.data,(3,2,0,1))
[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=self.data[index],int(self.labels[index])# doing this so that it is consistent with all other datasets# to return a PIL Imageimg=Image.fromarray(np.transpose(img,(1,2,0)))ifself.transformisnotNone:img=self.transform(img)ifself.target_transformisnotNone:target=self.target_transform(target)returnimg,target
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