o
    Vh                     @   sx   d dl Zd dlmZ d dlmZmZmZmZm	Z	 d dl
Zd dlmZ ddlmZmZmZ ddlmZ G dd	 d	eZdS )
    N)Path)AnyCallableOptionalTupleUnion)Image   )check_integritydownload_urlverify_str_arg)VisionDatasetc                       s   e Zd ZdZg dg dg ddZ				dd	eeef d
edee	 dee	 de
ddf fddZdedeeef fddZdefddZde
fddZdddZdefddZ  ZS )SVHNa  `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.

    )z6http://ufldl.stanford.edu/housenumbers/train_32x32.matztrain_32x32.mat e26dedcc434d2e4c54c9b2d4a06d8373)z5http://ufldl.stanford.edu/housenumbers/test_32x32.matztest_32x32.mat eb5a983be6a315427106f1b164d9cef3)z6http://ufldl.stanford.edu/housenumbers/extra_32x32.matzextra_32x32.mat a93ce644f1a588dc4d68dda5feec44a7)traintestextrar   NFrootsplit	transformtarget_transformdownloadreturnc                    s   t  j|||d t|dt| j | _| j| d | _| j| d | _| j| d | _	|r3| 
  |  s;tddd lm} |tj| j| j}|d | _|d tj | _t| j| jd	kd t| jd
| _d S )N)r   r   r   r   r	      zHDataset not found or corrupted. You can use download=True to download itXy
   )   r   r   r	   )super__init__r   tuple
split_listkeysr   urlfilenamefile_md5r   _check_integrityRuntimeErrorscipy.ioioloadmatospathjoinr   dataastypenpint64squeezelabelsplace	transpose)selfr   r   r   r   r   sio
loaded_mat	__class__ M/var/www/vscode/kcb/lib/python3.10/site-packages/torchvision/datasets/svhn.pyr!   6   s   
zSVHN.__init__indexc                 C   s\   | j | t| j| }}tt|d}| jdur | |}| jdur*| |}||fS )z
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        )r	   r   r   N)	r0   intr5   r   	fromarrayr2   r7   r   r   )r8   r?   imgtargetr=   r=   r>   __getitem__^   s   



zSVHN.__getitem__c                 C   s
   t | jS )N)lenr0   r8   r=   r=   r>   __len__t   s   
zSVHN.__len__c                 C   s0   | j }| j| j d }tj|| j}t||S Nr   )r   r#   r   r-   r.   r/   r&   r
   )r8   r   md5fpathr=   r=   r>   r(   w   s   
zSVHN._check_integrityc                 C   s(   | j | j d }t| j| j| j| d S rH   )r#   r   r   r%   r   r&   )r8   rI   r=   r=   r>   r   }   s   zSVHN.downloadc                 C   s   dj di | jS )NzSplit: {split}r=   )format__dict__rF   r=   r=   r>   
extra_repr   s   zSVHN.extra_repr)r   NNF)r   N)__name__
__module____qualname____doc__r#   r   strr   r   r   boolr!   r@   r   r   rD   rG   r(   r   rM   __classcell__r=   r=   r;   r>   r      s8    
(
r   )os.pathr-   pathlibr   typingr   r   r   r   r   numpyr2   PILr   utilsr
   r   r   visionr   r   r=   r=   r=   r>   <module>   s    