forked from tinygrad/tinygrad
3644077a42
* add DICE loss and metrics * update dice to include reference implementation's link * remove unused imports * remove unnecessary test file and update pred + label for metrics and losses test * add tests to CI + add exclusion of mlperf_unet3d --------- Co-authored-by: chenyu <[email protected]>
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