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* MaskRCNN weights loading * backbone maybe works * backbone works, but resnet body atol 1e-3 * RPN Call, but veryy wrong output * fixed topk * RPN maybe works, not sure about nms * Fix cursed modules * add back editorconfig * Full call, wrong output * Full call works * fix mask * use NMS from retinanet * Removing extra funcs * refactor * readable * Add example to run model * remove filter * Fix split, batched inference is worse * Fix image sizes * Matching reference * merge master * add filter on top detections * cuda backend fixed * add model eval and spec * convert images to rgb * fix eval * simplify examples code * remove extra code * meshgrid using tinygrad * removing numpy * roi align, floor, ceil * remove numpy from level_mapper * remove numpy from pooler * Revert "Merge branch 'master' of github.com:kunwar31/tinygrad into mrcnn-inference" This reverts commit4b95a3cb49, reversing changes made to98f2b1fa2e. * roi align gather * fix master merge * revert to old floor, ceil as ints present in domain * use log2 op * fix indexes * weird bug with ints and gpu * weird bug with ints and gpu * refactors, add env var for gather * floor with contiguous, where * refactor topk, sort * remove staticmethod * refactor stride * remove log2 mlop * realize -> contiguous * refactor forward * remove num_classes, stride_in_1x1 from state * refactor forward * refactoring * flake8 * removing numpy in anchor gen, use numpy for gather, nonzero, optimize topk * keep using tinygrad for smaller gathers * fix empty tensors * comms * move from tensor.py * resnet test passing * add coco dataset back * fix spaces * add test for log2 * no need to create Tensors * no need to create Tensors --------- Co-authored-by: Kunwar Raj Singh <kunwar31@pop-os.localdomain>
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