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* add support for train/val datasets for kits19 * split dataset into train and val sets * add tests for kits19 dataloader * add MLPerf dataset tests to CI * update unet3d model_eval script * fix linting * add nibabel * fix how mock dataset gets created * update ref implementation with permalink and no edits * clean up test and update rand_flip implementation * cleanups
Each model should be a clean single file. They are imported from the top level `models` directory It should be capable of loading weights from the reference imp. We will focus on these 5 models: # Resnet50-v1.5 (classic) -- 8.2 GOPS/input # Retinanet # 3D UNET (upconvs) # RNNT # BERT-large (transformer) They are used in both the training and inference benchmark: https://mlcommons.org/en/training-normal-21/ https://mlcommons.org/en/inference-edge-30/ And we will submit to both. NOTE: we are Edge since we don't have ECC RAM