GitLab

Summary:
Add new backbone
Experimental results are https://fburl.com/7fyecmrc

Reviewed By: bichenwu09

Differential Revision: D26877909

fbshipit-source-id: ba3f97a1e4d84bec22d6a345f1fca06c741010cc

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/96

In `DETRRunner`, the method `build_optimizer` customized the following logics, which are actually redundant to parent class implementation and can be removed.
- Discount LR for certain modules, such as those with name `reference_points`, `backbone`, and `sampling_offsets`.
  - Those can be achieved by `SOLVER.LR_MULTIPLIER_OVERWRITE` after we update `get_default_optimizer_params` in `mobile-vision/d2go/d2go/optimizer/build.py`.
- Full model gradient clipping
  - This is also implemented in `mobile-vision/d2go/d2go/optimizer/build.py`

It also has minor issues
- It ignores `SOLVER.WEIGHT_DECAY_NORM` which can set a different weight decay for affine parameters in the norm modules.

Reviewed By: zhanghang1989

Differential Revision: D29420642

fbshipit-source-id: deeb9348c9d282231c540dde6161acedd8e3a119

Summary:
In D29048363 (https://github.com/facebookresearch/d2go/commit/c480d4e4e213a850cced7758f7b62c20caad8820) we make the detaching of `reference_points` earlier in the hope of allowing more gradient flow to update weights in `self.bbox_embed`.
In this diff, we revert the changes as i) it does not improve box AP ii) it may potential cause in-stable optimization when iterative box refinement is turned on.

Reviewed By: zhanghang1989

Differential Revision: D29530735

fbshipit-source-id: 3217c863343836e129d53e07c0eedb2db8164fe6

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/97

Major changes
- Fix a bug within `inference()` function
- Refactor code to remove redundant code between `SetCriterion` and `FocalLossSetCriterion`.

Reviewed By: zhanghang1989

Differential Revision: D29481067

fbshipit-source-id: 64788f1ff331177db964eb36d380430799d1d2f2

Summary:
Major changes
- As described in details in appendix A.4 in deformable DETR paper (https://arxiv.org/abs/2010.04159), the gradient back-propagation is blocked at inverse_sigmoid(bounding box x/y/w/h from last decoder layer). This can be implemented by detaching tensor from compute graph in pytorch. However, currently we detach at an incorrect tensor, preventing update the layers which predicts delta x/y/w/h. Fix this bug.
- Add more comments to annotate data types and tensor shape in the code. This should NOT affect the actual implementation.

Reviewed By: zhanghang1989

Differential Revision: D29048363

fbshipit-source-id: c5b5e89793c86d530b077a7b999769881f441b69

Summary: Add create and train unit tests to OSS runner

Reviewed By: zhanghang1989

Differential Revision: D29254417

fbshipit-source-id: f7c52b90b2bc7afa83a204895be149664c675e52

Summary:
Major changes
- Add a new runner `EgoDETRRunner` which inherit from existing `DETRRunner` in D2 (https://github.com/facebookresearch/d2go/commit/62c21f252ad314961cf0157ee8f37cc4f7835e1d)GO repo.
- Add a new data mapper `EgoDETRDatasetMapper` which has custom crop transform generator and supports generic data augmentation.

Reviewed By: zhanghang1989

Differential Revision: D28895225

fbshipit-source-id: 4181ff8fce81df22a01d355fdff7e81e83d69e64

Summary: TorchVision recently upgrade their version to 0.10.0 which causes issues in the version check in detr.

Reviewed By: wat3rBro

Differential Revision: D27575085

fbshipit-source-id: 75f459fe7a711161e908609fcf2f2d28a01a6c74

fbshipit-source-id: f4a8ba78691d8cf46e003ef0bd2e95f170932778