NgEdZddlZddlZddlZddlmZddlZddlmZ ddl m Z  ddl Z e jgdddlZ ejddd dZn#e$red d ZYnwxYwn%#e$rZeeed edZ[wwxYwdd lmZddlmZddlmZeej ddZ!eej ddZ"eej ddZ#ej$%ddZ&dZ'dZ(GddeZ)dS)a  Dataset reader that wraps TFDS datasets Wraps many (most?) TFDS image-classification datasets from https://github.com/tensorflow/datasets https://www.tensorflow.org/datasets/catalog/overview#image_classification Hacked together by / Copyright 2020 Ross Wightman N)Optional)ImageGPUF)drop_remainderzWarning: This version of tfds doesn't have the latest even_splits impl. Please update or use tfds-nightly for better fine-grained split behaviour.TzMPlease install tensorflow_datasets package `pip install tensorflow-datasets`.)load_class_map)Reader) SharedCount TFDS_TP_SIZETFDS_SHUFFLE_SIZEi TFDS_PREFETCH_SIZEiINTEGER_ACCURATEcFtj|||S)N)channels dct_method)tfimage decode_jpeg)serialized_imagefeaturerrs Y/var/www/html/ai-engine/env/lib/python3.11/site-packages/timm/data/readers/reader_tfds.pydecode_exampler0s* 8     cfdtdzDfdtDS)Nc:g|]}t|zz S)round).0in num_sampless r z&even_split_indices..:s*CCC%K!+,,CCCrrc Dg|]}d|d|dzdS)[:r]r)r!r" partitionssplits rr%z&even_split_indices..;s? N N Nu ; ;z!} ; ;z!a%'8 ; ; ; N N Nr)range)r+r#r$r*s```@reven_split_indicesr-9sOCCCCCeAEllCCCJ N N N N NU1XX N N NNrc^d|jvriS|jdfdjD}|S)Nlabelc<i|]}||Sr)str2int)r!r# class_labels r z$get_class_labels..Bs)III!A{**1--IIIr)featuresnames)info class_to_idxr2s @rget_class_labelsr8>sCdm## -(KIIII{7HIIIL rceZdZdZ dfd Zd Z ddeefdZdZ dZ dZ dZ ddZ ddZxZS) ReaderTfdsa Wrap Tensorflow Datasets for use in PyTorch There several things to be aware of: * To prevent excessive samples being dropped per epoch w/ distributed training or multiplicity of dataloader workers, the train iterator wraps to avoid returning partial batches that trigger drop_last https://github.com/pytorch/pytorch/issues/33413 * With PyTorch IterableDatasets, each worker in each replica operates in isolation, the final batch from each worker could be a different size. For training this is worked around by option above, for validation extra samples are inserted iff distributed mode is enabled so that the batches being reduced across replicas are of same size. This will slightly alter the results, distributed validation will not be 100% correct. This is similar to common handling in DistributedSampler for normal Datasets but a bit worse since there are up to N * J extra samples with IterableDatasets. * The sharding (splitting of dataset into TFRecord) files imposes limitations on the number of replicas and dataloader workers you can use. For really small datasets that only contain a few shards you may have to train non-distributed w/ 1-2 dataloader workers. This is likely not a huge concern as the benefit of distributed training or fast dataloading should be much less for small datasets. * This wrapper is currently configured to return individual, decompressed image samples from the TFDS dataset. The augmentation (transforms) and batching is still done in PyTorch. It would be possible to specify TF augmentation fn and return augmented batches w/ some modifications to other downstream components. NtrainFrr*rRGBr/rc$t||_||_||_||_||_| |_|pt|_ |pt|_ |pt|_ | |_| |_| |_| |_t%j|||_|r|jd|_|rt-||_d|_n+|jdkrt1|jjni|_|jjj||_|jj|_d|_d|_tAj!rZtAj"rGtAj#dkr0tAj$|_tAj#|_d|_%d|_&d|_'d|_(d|_)d|_*d|_+tY|_-|j|_.dS)a Tensorflow-datasets Wrapper Args: root: root data dir (ie your TFDS_DATA_DIR. not dataset specific sub-dir) name: tfds dataset name (eg `imagenet2012`) split: tfds dataset split (can use all TFDS split strings eg `train[:10%]`) is_training: training mode, shuffle enabled, dataset len rounded by batch_size batch_size: batch_size to use to unsure total samples % batch_size == 0 in training across all dis nodes download: download and build TFDS dataset if set, otherwise must use tfds CLI repeats: iterate through (repeat) the dataset this many times per iteration (once if 0 or 1) seed: common seed for shard shuffle across all distributed/worker instances input_key: name of Feature to return as data (input) input_img_mode: image mode if input is an image (currently PIL mode string) target_key: name of Feature to return as target (label) target_img_mode: image mode if target is an image (currently PIL mode string) prefetch_size: override default tf.data prefetch buffer size shuffle_size: override default tf.data shuffle buffer size max_threadpool_size: override default threadpool size for tf.data )data_dirFTr/rrN)/super__init__rootr+ is_training batch_sizerepeats common_seed PREFETCH_SIZE prefetch_size SHUFFLE_SIZE shuffle_size MAX_TP_SIZEmax_threadpool_size input_keyinput_img_mode target_keytarget_img_modetfdsbuilderdownload_and_prepare remap_classr r7r8r6splits split_info num_examplesr$ dist_rankdist_num_replicasdist is_availableis_initializedget_world_sizeget_rankglobal_num_workers num_workers worker_info worker_seedsubsplitds init_countr epoch_countreinit_each_iter)selfnamerBr+ class_maprCrDdownloadrEseedrMrNrOrPrHrJrL __class__s rrAzReaderTfds.__init__^sL   &$ +;m(8L#6#E+ #,$.|D4888  0 L - - / / /   j .y 9 9D #D  GKZaGaGa 01B C C CgiD ,+259?7!"     ;4#6#8#8 ;T=P=R=RUV=V=V!]__DN%)%8%:%:D "#$ &==!% 0rc||j_dSN)rfvalue)rhcounts r set_epochzReaderTfds.set_epochs!&rr`cV|jdS|||_|j|jz|_dSdSro)rdr`rYr_)rhr`s rset_loader_cfgzReaderTfds.set_loader_cfgs@ 7  F  "*D &*&Q  #59bO.1HH`PTP`L`  @)@%doty7G7TUUD$6tz4CZ\`\l$m$m (12B(C $ 0T=T U UI$-.>$?DM  "Q & &4=+@M66$($;"2%)%;7M o)D,<,BB-1'   \ $ $--4:*T=PTW=W=W]^ _ _ _```# %  '//##'.w 'D'Db Jb BEaIaeieuIuBvBv''?CD''@ __W % %   t|a//B   xC 0$2CDDH__fjfvwwB [[T-1HH$J\]] ^ ^-## 1rc2td|j|jzt|j|jz }|js |jdkrt j|}|jr$t j||jz |jz}t|S)Nr) rrEr$r_rYrCmathceilrDint)rhnum_worker_sampless r_num_samples_per_workerz"ReaderTfds._num_samples_per_workers 4< 4#3 3c$:QSWSi6j6j j    ?t599!%+=!>!>    c!%+=+O!P!PSWSb!b %&&&rc#K|j|jr||}d}|jD]}||j}|jr@|jdkr|jdkr|dddddf}tj||j}||j }|j rtj||j }n|j r |j |}||fV|dz }|j r||krn|j s<|jdkr3|j.d|cxkr|kr#ndS||kr||fV|dz }||kdSdSdSdSdSdS)Nrr|r)moder)rdrgrrrMrNndimr fromarrayrOrPrTr7rCrYrc)rhtarget_sample_count sample_countsample input_data target_datas r__iter__zReaderTfds.__iter__s 7?d3? 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LL     r)Nr;NFrFrr<rr=r/rNNNro)FF)__name__ __module__ __qualname____doc__rArrrrrtrrrrrr __classcell__)rms@rr:r:Fs%4  $#W1W1W1W1W1W1r''' *.PP!#PPPPPPPd'''*"*"*"X&&&&rr:)rr)*rrossystypingrrtorch.distributed distributedrZPILr tensorflowrconfigset_visible_devicestensorflow_datasetsrQrr TypeErrorprint ImportErrorerjr readerr shared_countr renvirongetrKrIrGdecode make_decoderrr-r8r:rrrrsx   I!!"e,,,&&&&%Qu5555 % %%% [ \ \ \ $%    E!HHH E YZZZ G &%%%%%%%%%%%c"*..3344 s2:>>"5t<<== BJNN#7>>?? OOO ]]]]]]]]]]s;#A9 A A9 A52A94A55A99B>BB