U ʹj@sldZddlZddlmZddlmZmZmZmZm Z m Z zddlm Z dZ Wne k rddZ YnXzddlmZdZWne k rdZYnXzdd lmZdZWne k rdZYnXzdd lmZdZWne k rdZYnXd d lmZmZeeeeee d ZedddZeddddddZddZdeeeeeeeeeeeeeeed ddZdS)zE Dataset Factory Hacked together by / Copyright 2021, Ross Wightman N)Optional)CIFAR100CIFAR10MNISTKMNIST FashionMNIST ImageFolder) Places365TF) INaturalist)QMNIST)ImageNet)IterableImageDataset ImageDataset)Zcifar10Zcifar100ZmnistZkmnistZ fashion_mnist)trainZtraining)valZvalid validationevalZ evaluationcs^|dd}tj|}tj|r,|Sfdd}|tkrJ|tn|tkrZ|tS)N[rcs0|D]&}tj|}tj|r|SqSN)ospathjoinexists)Zsynstry_rootrootX/var/www/html/bwave/python/venv/lib/python3.8/site-packages/timm/data/dataset_factory.py_try2s   z_search_split.._try)splitrrrr_TRAIN_SYNONYM _EVAL_SYNONYM)rr!Z split_namerr rrr _search_split+s   r$r*RGB) namerr! search_split class_map load_bytes is_trainingdownload batch_size num_samplesseedrepeatsinput_img_modec  Ksdd| D} |}|dr|ddd}tf||d| }|tkrxt|}|tk}|fd|i|}q|d ks|d kr tstd d }|d}t |d kr|dd}t |d kr|d}|d}|tkrd}n |t krd}t f||d|}q|dkrVt s$td|tkr4d}n|t krBd}t fd|i|}n|dkrtsntd|tk}tfd|i|}n|dkrtstd|t krd}tfd|i|}nT|dks|dkr|rtj|rt||}t|f| }ndstd|n|d rBt|f|||| d!| }n|d"rvt|f||||||| | | | d# | }n|d$rt|f||||||| | | | d# | }nj|d%rt|f|||||| | | | d& | }n8|rtj|rt||}t|f|||| d'| }|S)(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 * 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/folder) 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 (HFDS, 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) batch_size: batch size hint for (TFDS, WDS) seed: seed for iterable datasets (TFDS, WDS) repeats: dataset repeats per iteration i.e. epoch (TFDS, WDS) input_img_mode: Input image color conversion mode e.g. 'RGB', 'L' (folder, TFDS, WDS, HFDS) **kwargs: other args to pass to dataset Returns: Dataset object cSsi|]\}}|dk r||qSrr).0kvrrr msz"create_dataset..ztorch//)rr,rZ inaturalistZinatz@Please update to PyTorch 1.10, torchvision 0.11+ for Inaturalistfullr r_Z 2021_trainZ 2021_valid)version target_typeZ places365zGPlease update to a newer PyTorch and torchvision for Places365 dataset.ztrain-standardrr!ZqmnistzDPlease update to a newer PyTorch and torchvision for QMNIST dataset.ZimagenetzFPlease update to a newer PyTorch and torchvision for ImageNet dataset.Z image_folderfolderFzUnknown torchvision dataset zhfds/)readerr!r)r1zhfids/) r>r!r)r+r,r-r.r0r/r1ztfds/zwds/) r>r!r)r+r-r.r0r/r1)r>r)r*r1)itemslower startswithr!dict_TORCH_BASIC_DSr"has_inaturalistAssertionErrorlenr#r has_places365r has_qmnistr has_imagenetr rrisdirr$rrr)r'rr!r(r)r*r+r,r-r.r/r0r1kwargsZ torch_kwargsZds_classZ use_trainZdsr<Z split_splitrrrcreate_dataset?s.                    rL) NrTNFFFr Nr%rr&) __doc__rtypingrZtorchvision.datasetsrrrrrrr rG ImportErrorr rDr rHr rIZdatasetrrrBrCr"r#r$strboolintrLrrrrsz