U
h @ sh d dl Z d dlmZ d dlmZmZmZmZmZ d dl Z
ddl m Z m
Z
ddlmZ G dd deZdS ) N)Path)AnyCallableOptionalTupleUnion ) download_and_extract_archiveverify_str_arg)
VisionDatasetc s e Zd ZdZdZdZdeeef ee e
e e
e dd fdd
Z e
d
d d Ze
eeef d
ddZed
ddZe d
ddZdd
ddZ ZS )Food101a `The Food-101 Data Set `_.
The Food-101 is a challenging data set of 101 food categories with 101,000 images.
For each class, 250 manually reviewed test images are provided as well as 750 training images.
On purpose, the training images were not cleaned, and thus still contain some amount of noise.
This comes mostly in the form of intense colors and sometimes wrong labels. All images were
rescaled to have a maximum side length of 512 pixels.
Args:
root (str or ``pathlib.Path``): Root directory of the dataset.
split (string, optional): The dataset split, supports ``"train"`` (default) and ``"test"``.
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. Default is False.
z1http://data.vision.ee.ethz.ch/cvl/food-101.tar.gzZ 85eeb15f3717b99a5da872d97d918f87trainNF)rootsplit transformtarget_transformdownloadreturnc
s t j|||d t|dd _t jd _ jd _ jd _|rT
sdt dg _ g _
t j| d }t| }W 5 Q R X t| _tt jtt j _| D ]B\}} j j| gt| 7 _ j
fd d
| D 7 _
qd S ) N)r r r )r
testzfood-101metaZimagesz;Dataset not found. You can use download=True to download itz.jsonc s$ g | ] } j j| d d qS )z.jpg/)_images_folderjoinpathr ).0Z im_rel_pathself N/var/www/html/venv/lib/python3.8/site-packages/torchvision/datasets/food101.py
A s z$Food101.__init__..)super__init__r
_splitr r Z _base_folder _meta_folderr _download
_check_exists RuntimeError_labels _image_filesopenjsonloadsreadsortedkeysclassesdictziprangelenZ class_to_idxitems)
r r r r r r fmetadataZ class_labelZ im_rel_paths __class__r r r # s( zFood101.__init__)r c C s
t | jS N)r2 r' r r r r __len__E s zFood101.__len__)idxr c C sP | j | | j| }}tj|d}| jr8| |}| jrH| |}||fS )NRGB)r' r& PILZImager( convertr r )r r: Z
image_filelabelimager r r __getitem__H s
zFood101.__getitem__c C s d| j S )Nzsplit=)r! r r r r
extra_reprT s zFood101.extra_reprc C s t dd | j| jfD S )Nc s s | ]}| o| V qd S r8 )existsis_dir)r folderr r r X s z(Food101._check_exists..)allr" r r r r r r$ W s zFood101._check_existsc C s$ | r d S t| j| j| jd d S )N)Z
download_rootmd5)r$ r _URLr _MD5r r r r r# Z s zFood101._download)r
NNF)__name__
__module__ __qualname____doc__rH rI r strr r r boolr intr9 r r r@ rA r$ r#
__classcell__r r r6 r r s(
" r )r) pathlibr typingr r r r r Z PIL.Imager< utilsr r
Zvisionr r r r r r s