^j!dZddlZddlmZddlmZmZmZmZm Z m Z  ddlm Z dZ ddlmZdZ dd lmZdZ dd lmZdZd d lmZmZeeeeee ZeddZedddddZdZ d dedeededededededededeededededefdZy#e $rdZ YwxYw#e $rdZYwxYw#e $rdZYwxYw#e $rdZYwxYw)!zE Dataset Factory Hacked together by / Copyright 2021, Ross Wightman N)Optional)CIFAR100CIFAR10MNISTKMNIST FashionMNIST ImageFolder) Places365TF) INaturalist)QMNIST)ImageNet)IterableImageDataset ImageDataset)cifar10cifar100mnistkmnist fashion_mnist)traintraining)valvalid validationeval evaluationc|jdd}tjj|}tjj |r|Sfd}|t vr|t S|t vr |t S)N[rc|D]E}tjj|}tjj|sC|cSS)N)ospathjoinexists)synstry_rootroots d/var/www/ramen.bs-engineer-server.com/venv/lib/python3.12/site-packages/timm/data/dataset_factory.py_tryz_search_split.._try2sB Aww||D!,Hww~~h'  )splitr r!r"r#_TRAIN_SYNONYM _EVAL_SYNONYM)r'r+ split_namer&r)s` r( _search_splitr/+sxS!!$Jww||D*-H ww~~h ^#N# K } $M" Kr*namer'r+ search_split class_map load_bytes is_trainingdownload batch_size num_samplesseedrepeatsinput_img_modetrust_remote_codec |jDcic] \}}| || }}}|j}|jdr|jddd}t d'||d|}|t vrt |}|t v}|d'd|i|}|S|dk(s|dk(rtsJd d }|jd}t|d kDr,|d jd }t|d k(r|d }|d}|t vrd}n |tvrd}td'||d|}|S|dk(r1tsJd|t vrd}n |tvrd}td'd|i|}|S|dk(r$tsJd|t v}td'd|i|}|S|dk(r7|jdt sJd|tvrd}t#d'd|i|}|S|dk(s|dk(r;|r+t$j&j)|r t+||}t-|fi|}|SJd||jdrt/|f|||| | d|}|S|jd rt1|f||||||| | | | | d! |}|S|jd"rt1|f||||||| | | | d# |}|S|jd$rt1|f|||||| | | | d% |}|S|r+t$j&j)|r t+||}t/|f|||| d&|}|Scc}}w)(a  Dataset factory method In parentheses after each arg are the type of dataset supported for each arg, one of: * Folder - default, timm folder (or tar) based ImageDataset * Torch - torchvision based datasets * HFDS - Hugging Face Datasets * HFIDS - Hugging Face Datasets Iterable (streaming mode, with IterableDataset) * TFDS - Tensorflow-datasets wrapper in IterabeDataset interface via IterableImageDataset * WDS - Webdataset * All - any of the above Args: name: Dataset name, empty is okay for folder based datasets root: Root folder of dataset (All) split: Dataset split (All) search_split: Search for split specific child fold from root so one can specify `imagenet/` instead of `/imagenet/val`, etc on cmd line / config. (Folder, Torch) class_map: Specify class -> index mapping via text file or dict (Folder) load_bytes: Load data, return images as undecoded bytes (Folder) download: Download dataset if not present and supported (HFIDS, TFDS, Torch) is_training: Create dataset in train mode, this is different from the split. For Iterable / TDFS it enables shuffle, ignored for other datasets. (TFDS, WDS, HFIDS) batch_size: Batch size hint for iterable datasets (TFDS, WDS, HFIDS) seed: Seed for iterable datasets (TFDS, WDS, HFIDS) repeats: Dataset repeats per iteration i.e. epoch (TFDS, WDS, HFIDS) input_img_mode: Input image color conversion mode e.g. 'RGB', 'L' (folder, TFDS, WDS, HFDS, HFIDS) trust_remote_code: Trust remote code in Hugging Face Datasets if True (HFDS, HFIDS) **kwargs: Other args to pass through to underlying Dataset and/or Reader classes Returns: Dataset object ztorch//)r'r5r inaturalistinatz@Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalistfullrr_ 2021_train 2021_valid)version target_type places365zGPlease update to a newer PyTorch and torchvision for Places365 dataset.ztrain-standardrr+qmnistzDPlease update to a newer PyTorch and torchvision for QMNIST dataset.imagenetr5zFPlease update to a newer PyTorch and torchvision for ImageNet dataset. image_folderfolderzUnknown torchvision dataset zhfds/)readerr+r2r:r;zhfids/) rMr+r2r4r5r6r7r9r8r:r;ztfds/) rMr+r2r4r5r6r7r9r8r:zwds/) rMr+r2r4r6r7r9r8r:)rMr2r3r:)itemslower startswithr+dict_TORCH_BASIC_DSr,has_inaturalistlenr-r has_places365r has_qmnistr pop has_imagenetr r r!isdirr/r rr)r0r'r+r1r2r3r4r5r6r7r8r9r:r;kwargskv torch_kwargsds_class use_traindsrG split_splits r(create_datasetrc?sb &||~ ?tq!ad ?F ? ::rns \\.M0O+J-L8     D40TdRVW ,#!! !%)#"'ggsmgg g  g  ggggc]gggg ggM O J LsDB%B2B?C %B/.B/2B<;B<?C C  CC