a
    PSic                     @   sd   d dl mZ d dlmZmZmZmZ d dlZddl	m
Z
mZmZmZ ddlmZ G dd deZdS )	    )Path)AnyTupleCallableOptionalN   )check_integritydownload_and_extract_archivedownload_urlverify_str_arg)VisionDatasetc                       s   e Zd ZdZdZddddZddd	d
Zdeeee	 ee	 e
dd fddZedddZeeef dddZedddZdd Zdd Z  ZS )
Flowers102a  `Oxford 102 Flower <https://www.robots.ox.ac.uk/~vgg/data/flowers/102/>`_ Dataset.

    .. warning::

        This class needs `scipy <https://docs.scipy.org/doc/>`_ to load target files from `.mat` format.

    Oxford 102 Flower is an image classification dataset consisting of 102 flower categories. The
    flowers were chosen to be flowers commonly occurring in the United Kingdom. Each class consists of
    between 40 and 258 images.

    The images have large scale, pose and light variations. In addition, there are categories that
    have large variations within the category, and several very similar categories.

    Args:
        root (string): Root directory of the dataset.
        split (string, optional): The dataset split, supports ``"train"`` (default), ``"val"``, or ``"test"``.
        transform (callable, optional): A function/transform that takes in an 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.
    z2https://www.robots.ox.ac.uk/~vgg/data/flowers/102/)z102flowers.tgzZ 52808999861908f626f3c1f4e79d11fa)zimagelabels.matZ e0620be6f572b9609742df49c70aed4d)z	setid.matZ a5357ecc9cb78c4bef273ce3793fc85c)imagelabelsetidZtrnidvalidZtstidtrainvaltestr   NF)rootsplit	transformtarget_transformdownloadreturnc                    s  t  j|||d t|dd| _t| jd | _| jd | _|rH|   | 	 sXt
dddlm} || j| jd	 d  d
d}|| j| j   }|| j| jd d  d
d}	tt|	d d  d}
g | _g | _|D ]0}| j|
|  | j| jd|dd  qd S )N)r   r   r   r   zflowers-102jpgzHDataset not found or corrupted. You can use download=True to download itr   )loadmatr   T)Z
squeeze_mer   labelsr   image_Z05dz.jpg)super__init__r   _splitr   r   _base_folder_images_folderr   _check_integrityRuntimeErrorscipy.ior   
_file_dict_splits_maptolistdict	enumerate_labels_image_filesappend)selfr   r   r   r   r   r   Zset_idsZ	image_idsr   Zimage_id_to_labelZimage_id	__class__ [/var/www/html/django/DPS/env/lib/python3.9/site-packages/torchvision/datasets/flowers102.pyr!   +   s$    zFlowers102.__init__)r   c                 C   s
   t | jS )N)lenr.   r0   r3   r3   r4   __len__L   s    zFlowers102.__len__c                 C   sP   | j | | j|  }}tj|d}| jr8| |}| jrH| |}||fS )NRGB)r.   r-   PILImageopenconvertr   r   )r0   idx
image_filer   r   r3   r3   r4   __getitem__O   s    

zFlowers102.__getitem__c                 C   s   d| j  S )Nzsplit=)r"   r6   r3   r3   r4   
extra_repr[   s    zFlowers102.extra_reprc                 C   sN   | j  r| j  sdS dD ],}| j| \}}tt| j| |s dS qdS )NFr   r   T)r$   existsis_dirr(   r   strr#   r0   idfilenamemd5r3   r3   r4   r%   ^   s    zFlowers102._check_integrityc                 C   st   |   rd S t| j | jd d  t| j| jd d d dD ],}| j| \}}t| j| t| j|d qBd S )Nr   r   r   )rH   rA   )r%   r	   _download_url_prefixr(   rD   r#   r
   rE   r3   r3   r4   r   h   s    zFlowers102.download)r   NNF)__name__
__module____qualname____doc__rI   r(   r)   rD   r   r   boolr!   intr7   r   r   r?   r@   r%   r   __classcell__r3   r3   r1   r4   r   
   s0       !
r   )pathlibr   typingr   r   r   r   	PIL.Imager9   utilsr   r	   r
   r   visionr   r   r3   r3   r3   r4   <module>   s
   