
    ڧg                         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	e          ZdS )
    N)Path)AnyCallableOptionalTupleUnion)Image   )check_integritydownload_urlverify_str_arg)VisionDatasetc                        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Zdedeeef         fdZdefdZde
fdZddZdefdZ x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                 d   t                                          |||           t          |dt          | j                                                            | _        | j        |         d         | _        | j        |         d         | _        | j        |         d         | _	        |r| 
                                 |                                 st          d          dd lm} |                    t           j                            | j        | j                            }|d         | _        |d                             t,          j                                                  | _        t-          j        | j        | j        d	k    d           t-          j        | 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__s	           U/var/www/html/ai-engine/env/lib/python3.11/site-packages/torchvision/datasets/svhn.pyr$   zSVHN.__init__6   sg    	EUVVV#E7E$/:N:N:P:P4Q4QRR
?5)!,.q1.q1 	MMOOO$$&& 	kijjj 	 [[di!G!GHH
sO	
 !o,,RX66>>@@
 	dkR/333LL99			    indexc                    | j         |         t          | j        |                   }}t          j        t          j        |d                    }| j        |                     |          }| j        |                     |          }||fS )z
        Args:
            index (int): Index

        Returns:
            tuple: (image, target) where target is index of the target class.
        )r
   r   r   )	r3   intr8   r	   	fromarrayr5   r:   r   r   )r;   rA   imgtargets       r?   __getitem__zSVHN.__getitem__^   s     i&DK,>(?(?V obl3	::;;>%..%%C ,**622FF{r@   c                 *    t          | j                  S )N)lenr3   r;   s    r?   __len__zSVHN.__len__t   s    49~~r@   c                     | j         }| j        | j                 d         }t          j                            || j                  }t          ||          S Nr   )r   r&   r   r0   r1   r2   r)   r   )r;   r   md5fpaths       r?   r+   zSVHN._check_integrityw   sC    yodj)!,T4=11uc***r@   c                 x    | j         | j                 d         }t          | j        | j        | j        |           d S rM   )r&   r   r   r(   r   r)   )r;   rN   s     r?   r   zSVHN.download}   s5    odj)!,TXty$-=====r@   c                 &     dj         di | j        S )NzSplit: {split} )format__dict__rJ   s    r?   
extra_reprzSVHN.extra_repr   s    &&77777r@   )r   NNF)r   N)__name__
__module____qualname____doc__r&   r   strr   r   r   boolr$   rC   r   r   rG   rK   r+   r   rU   __classcell__)r>   s   @r?   r   r      sp        0
 
 


 
 


 
 
 J* (,/3&: &:CI&: &: H%	&:
 #8,&: &: 
&: &: &: &: &: &:P sCx    ,    +$ + + + +> > > >8C 8 8 8 8 8 8 8 8r@   r   )os.pathr0   pathlibr   typingr   r   r   r   r   numpyr5   PILr	   utilsr   r   r   visionr   r   rR   r@   r?   <module>rd      s           8 8 8 8 8 8 8 8 8 8 8 8 8 8           @ @ @ @ @ @ @ @ @ @ ! ! ! ! ! !v8 v8 v8 v8 v8= v8 v8 v8 v8 v8r@   