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add FCOS #4961
add FCOS #4961
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💊 CI failures summary and remediationsAs of commit 6840a7d (more details on the Dr. CI page):
🕵️ 1 new failure recognized by patternsThe following CI failures do not appear to be due to upstream breakages: unittest_linux_gpu_py3.8 (1/1)Step: "Run tests" (full log | diagnosis details | 🔁 rerun)
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Fixing lint
Fixing lint in giou_loss.py
Fixing python lint, docstrings and add typing annotations
Apply ufmt format
Fixing docs format in GIoU Loss
Minor fixes for the docs
Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com>
Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com>
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Thank you for all your patience and commitment. Looks great to me now and I think its ready to merge, but I will just wait for @datumbox approval to make sure I didn't miss any changes suggested by him.
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LGTM! Thanks a lot @xiaohu2015 and @zhiqwang for your amazing work.
We are going to base our new model contribution process on this collaboration. @jdsgomes is currently leading this and we will ping you once this is drafted to get your input.
Looking forward working with you again, soon. Perhaps next target is EfficientNetV2? 😃
Hey @jdsgomes! You merged this PR, but no labels were added. The list of valid labels is available at https://github.com/pytorch/vision/blob/main/.github/process_commit.py |
I have opened a ticket to add FCOS model in Pytorch Hub: pytorch/hub#265 |
Summary: * add fcos * update fcos * add giou_loss * add BoxLinearCoder for FCOS * add full code for FCOS * add giou loss * add fcos * add __all__ * Fixing lint * Fixing lint in giou_loss.py * Add typing annotation to fcos * Add trained checkpoints * Use partial to replace lambda * Minor fixes to docstrings * Apply ufmt format * Fixing docstrings * Fixing jit scripting * Minor fixes to docstrings * Fixing jit scripting * Ignore mypy in fcos * Fixing trained checkpoints * Fixing unit-test of jit script * Fixing docstrings * Add test/expect/ModelTester.test_fcos_resnet50_fpn_expect.pkl * Fixing test_detection_model_trainable_backbone_layers * Update test_fcos_resnet50_fpn_expect.pkl * rename stride to box size * remove TODO and fix some typo * merge some code for better * impove the comments * remove decode and encode of BoxLinearCoder * remove some unnecessary hints * use the default value in detectron2. * update doc * Add unittest for BoxLinearCoder * Add types in FCOS * Add docstring for BoxLinearCoder * Minor fix for the docstring * update doc * Update fcos_resnet50_fpn_coco pretained weights url * Update torchvision/models/detection/fcos.py * Update torchvision/models/detection/fcos.py * Update torchvision/models/detection/fcos.py * Update torchvision/models/detection/fcos.py * Add FCOS model documentation * Fix typo in FCOS documentation * Add fcos to the prototype builder * Capitalize COCO_V1 * Fix params of fcos * fix bug for partial * Fixing docs indentation * Fixing docs format in giou_loss * Adopt Reference for GIoU Loss * Rename giou_loss to generalized_box_iou_loss * remove overwrite_eps * Update AP test values * Minor fixes for the docs * Minor fixes for the docs * Update torchvision/models/detection/fcos.py * Update torchvision/prototype/models/detection/fcos.py Reviewed By: jdsgomes, prabhat00155 Differential Revision: D33739385 fbshipit-source-id: 7dab616adfd0c34fe21f0153c1da51f97ef43b95 Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com> Co-authored-by: Zhiqiang Wang <zhiqwang@foxmail.com> Co-authored-by: zhiqiang <zhiqwang@foxmail.com> Co-authored-by: Joao Gomes <jdsgomes@fb.com> Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com> Co-authored-by: Joao Gomes <joaopsgomes@gmail.com>
The pr is about #2707
We (@xiaohu2015 and @zhiqwang ) implemented torchvision-style FCOS by referencing to detectron2 and AdelaiDet repo.
if train with the retinanet training command:
you can get:
the AP result is better to the paper (38.6).
if train with training command (1x, 12 epoch, the dafult setting in the FCOS paper):
you can get:
the AP result is smilar to the paper (38.6).
cc @jdsgomes