* 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 commit 4b95a3cb49, reversing
changes made to 98f2b1fa2e.
* 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>
* add retinanet with resnet backbone
* adds resnext to support loading retinanet pretrained on openimages
* object detection post processing with numpy
* data is downloaded and converted to coco format with fiftyone
* data loading and mAP evaluation with pycocotools
* remove fiftyone dep
* * eval freq
* fix model timing
* del jit for last batch
* faster accumulate
* New unittest for utils.py
Unit test fetch in basic ways. Would have tested more fetches, but
downloading stuff for tests is annoying and mocking is more
dependencies.
* Remove unused imports
* added resnets
* fix minor
* fix minor
* resnet in models
* added resnet test
* added resnet train test
* added linear, conv2d nn tests
* fix minor in extra/training
* resnet in models
* fix minor
* fix tolerance for linear in nn test
* fix eval, this causes cpu and gpu UT failing
* revert transformer test
* fix minor for CPU test
* improved model get_params for sequential layer
* fix minor for params counting
* commented broken ops tests
* improved train for resnet
