RЦi!SrSSKrSSKJr SSKJrJrJrJrJ r J r SSKJ r Sr SSKJr SrSS KJr SrSS KJr SrS S KJrJr \"\\\\\ S 9r\"SSS9r\"SSSSSS9rSrS S\S\\S\S\S\S\S\S\S\S\\S\S\S\S\4Sjjrg!\ a Sr Nf=f!\ a SrNf=f!\ a SrNf=f!\ a SrNf=f)!zDDataset 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$^URS5Sn[RRTU5n[RR U5(aU$U4SjnU[ ;aU"[ 5mT$U[ ;a U"[ 5mT$)N[rc>UHKn[RRTU5n[RRU5(dMIUs $ T$)N)ospathjoinexists)synstry_rootroots X/var/www/html/ai-image-ml/venv/lib/python3.13/site-packages/timm/data/dataset_factory.py_try_search_split.._try2s@Aww||D!,Hww~~h'' )splitr r!r"r#_TRAIN_SYNONYM _EVAL_SYNONYM)r'r, split_namer&r)s` r( _search_splitr0+s{S!!$Jww||D*-H ww~~h ^#N# K } $M" Kr+namer'r, search_split class_map load_bytes is_trainingdownload batch_size num_samplesseedrepeatsinput_img_modetrust_remote_codec UR5VVs0sHunnUcM UU_M nnnUR5nURS5(GaURSS5Sn[ S'XS.UD6nU[ ;a[ UnU[ ;nU"S'SU0UD6nU$US:XdUS:Xa[(dS 5eS nURS5n[U5S :a-US RS 5n[U5S :XaUS nUSnU[ ;aSnO U[;aSn[S'UUS.UD6nU$US:Xa:[(dS5eU[ ;aSnO U[;aSn[S'SU0UD6nU$US:Xa*[(dS5eU[ ;n[S'SU0UD6nU$US:Xa>URS5 [ (dS5eU[;aSn[#S'SU0UD6nU$US:XdUS:XaDU(a/[$R&R)U5(a [+X5n[-U40UD6nU$SU35eURS5(a[/U4UUUU U S.UD6nU$URS 5(a[1U4UUUUUUU U U U U S!. UD6nU$URS"5(a[1U4UUUUUUU U U U S#. UD6nU$URS$5(a[1U4UUUUUU U U U S%. UD6nU$U(a/[$R&R)U5(a [+X5n[/U4UUUU S&.UD6nU$s snnf)(aDataset 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'r6r 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.imagenetr6zFPlease update to a newer PyTorch and torchvision for ImageNet dataset. image_folderfolderzUnknown torchvision dataset zhfds/)readerr,r3r;r<zhfids/) rNr,r3r5r6r7r8r:r9r;r<ztfds/) rNr,r3r5r6r7r8r:r9r;zwds/) rNr,r3r5r7r8r:r9r;)rNr3r4r;)itemslower startswithr,dict_TORCH_BASIC_DSr-has_inaturalistlenr.r has_places365r has_qmnistr pop has_imagenetr r r!isdirr0r rr)r1r'r,r2r3r4r5r6r7r8r9r:r;r<kwargskv torch_kwargsds_class use_traindsrH split_splits r(create_datasetrd?sb &||~ ?~tq!dad~F ? ::ros \\.M0O+J-L8     D40TdRVW ,#!! !%)#"'ggsmgg g  g  ggggc]gggg ggM O J LsDBB,B:CB)(B),B76B7:CCCC