GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/199

- `create_fake_detection_data_loader` currently doesn't take `cfg` as input, sometimes we need to test the augmentation that needs more complicated different cfg.
- name is a bit bad, rename it to `create_detection_data_loader_on_toy_dataset`.
- width/height were the resized size previously, we want to change it to the size of data source (image files) and use `cfg` to control resized size.

Update V3:
In V2 there're some test failures, the reason is that V2 is building data loader (via GeneralizedRCNN runner) using actual test config instead of default config before this diff + dataset name change. In V3 we uses the test's runner instead of default runner for the consistency. This reveals some real bugs that we didn't test before.

Reviewed By: omkar-fb

Differential Revision: D35238890

fbshipit-source-id: 28a6037374e74f452f91b494bd455b38d3a48433

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/191

When exporting model to torchscript (using `MODEL.DEVICE = "cpu"`), mean/std are constant instead of model parameters. Therefore after casting the torchscript to CUDA, the mean/std remains on cpu. This will cause problem when running inference on GPU.

The fix is exporting the model with `MODEL.DEVICE = "cuda"`. However D2 (https://github.com/facebookresearch/d2go/commit/87374efb134e539090e0b5c476809dc35bf6aedb)Go internally uses "cpu" during export (via cli: https://fburl.com/code/4mpk153i, via workflow: https://fburl.com/code/zcj5ud4u) by default. For CLI, user can manually set `--device`, but for workflow it's hard to do so. Further more it's hard to support mixed model using single `--device` option. So this diff adds a special handling in the RCNN's `default_prepare_for_export` to bypass the `--device` option.

Reviewed By: zhanghang1989

Differential Revision: D35097613

fbshipit-source-id: df9f44f49af1f0fd4baf3d7ccae6c31e341f3ef6

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/163

Make quantizing FPN work, note that this is not a proper fix, which might be making pytorch picking the D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)'s Conv2d, and we need to revert this diff if it's supported.

Differential Revision: D33523917

fbshipit-source-id: 3d00f540a9fcb75a34125c244d86263d517a359f

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/152

Reviewed By: zhanghang1989

Differential Revision: D31591900

fbshipit-source-id: 6ee8124419d535caf03532eda4f729e707b6dda7

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/154

Reviewed By: zhanghang1989

Differential Revision: D33352204

fbshipit-source-id: e1a9ac6eb2574dfe6931435275e27c9508f66352

Reviewed By: sstsai-adl

Differential Revision: D32216605

fbshipit-source-id: bebee1edae85e940c7dcc6a64dbe341a2fde36a2

Summary: this utility function was added in D30272112 (https://github.com/facebookresearch/d2go/commit/737d099b0a8b0fb1f548435e73f95e1252442827) and is useful to all D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8) users as well

Differential Revision: D31833523

fbshipit-source-id: 0adfc612adb8b448fa7f3dbec1b1278c309554c5

Summary:
Supported specifying customized parameter groups from model.
* Allow model to specify customized parameter groups by implementing a function `model.get_optimizer_param_groups(cfg)`
* Supported model with ddp.

Reviewed By: zhanghang1989

Differential Revision: D31289315

fbshipit-source-id: c91ba8014508e9fd5f172601b9c1c83c188338fd

Summary:
Refactor for get_optimizer_param_groups.
* Split `get_default_optimizer_params()` into multiple functions:
  * `get_optimizer_param_groups_default()`
  * `get_optimizer_param_groups_lr()`
  * `get_optimizer_param_groups_weight_decay()`
* Regroup the parameters to create the minimal amount of groups.
* Print all parameter groups when the optimizer is created.
    Param group 0: {amsgrad: False, betas: (0.9, 0.999), eps: 1e-08, lr: 10.0, params: 1, weight_decay: 1.0}
    Param group 1: {amsgrad: False, betas: (0.9, 0.999), eps: 1e-08, lr: 1.0, params: 1, weight_decay: 1.0}
    Param group 2: {amsgrad: False, betas: (0.9, 0.999), eps: 1e-08, lr: 1.0, params: 2, weight_decay: 0.0}
* Add some unit tests.

Reviewed By: zhanghang1989

Differential Revision: D31287783

fbshipit-source-id: e87df0ae0e67343bb2130db945d8faced44d7411

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/117

Fix github ci failure due to lack of coco datset. It was cased by D31134064 (https://github.com/facebookresearch/d2go/commit/f018d4a7ceef437d8fc3ca8b2bba4b7321917e06)

Reviewed By: mattcyu1, wat3rBro

Differential Revision: D31179666

fbshipit-source-id: fe25129d167afcdcb577e5c8d82f3432ba939ca9

Reviewed By: zhanghang1989

Differential Revision: D31134064

fbshipit-source-id: 825ca14477243a53f84b8521f4430a2b080324bd

Reviewed By: stephenyan1231, zhanghang1989

Differential Revision: D30903817

fbshipit-source-id: 578e6b02a1bd59b1bd841399fc60111d320ae9aa

Summary:
https://fb.workplace.com/groups/pythonfoundation/posts/2990917737888352

Remove `mobile-vision` from opt-out list; leaving `mobile-vision/SNPE` opted out because of 3rd-party code.

arc lint --take BLACK --apply-patches --paths-cmd 'hg files mobile-vision'

allow-large-files

Reviewed By: sstsai-adl

Differential Revision: D30721093

fbshipit-source-id: 9e5c16d988b315b93a28038443ecfb92efd18ef8

Summary:
Enable the inference for boltnn (via running torchscript).
- merge rcnn's boltnn test with other export types.
- misc fixes.

Differential Revision: D30610386

fbshipit-source-id: 7b78136f8ca640b5fc179cb47e3218e709418d71

Summary:
A torch version which is batched allows us to avoid CPU <--> GPU copy which
gets us ~200ms per iteration saving. This new version of generating boundary
weight mask produces identical masks.

Reviewed By: wat3rBro

Differential Revision: D30176412

fbshipit-source-id: 877f4c6337e7870d3bafd8eb9157ac166ddd588a

Summary:
Added multi-tensor optimizer implementation for SGD, from `torch.optim._multi_tensor`. It can potentially provide ~5% QPS improvement by using `foreach` API to speed up the optimizer step.

Using it is optional, from the configuration file, by specifying `SGD_MT` in the `SOLVER.OPTIMIZER` setting.

Reviewed By: zhanghang1989

Differential Revision: D30377761

fbshipit-source-id: 06107f1b91e9807c1db5d1b0ca6be09fcbb13e67

Summary: The uint8 cast means that the floating point non_bd_weight is never assigned

Reviewed By: wat3rBro

Differential Revision: D30176377

fbshipit-source-id: 013602bb4313393f220ee0f1510bf1ff83bd56fc

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/83

- Implement `prepare_for_export` for `SemanticSegmentor`
- Add unit test comparing numerical matching

Reviewed By: zhanghang1989

Differential Revision: D29088421

fbshipit-source-id: ccb86ac4b4b90a63eeebdbf76b2bf31c1da65a8b

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/75

Refactor the base test case
- make test_dir valid throughout the test (rather than under local context), so individual test can load back the export model
- refactor the `custom_setup_test` for easier override.
- move parameterized into base class to avoid copying naming function

Reviewed By: zhanghang1989

Differential Revision: D28651067

fbshipit-source-id: c59a311564f6114039e20ed3a23e5dd9c84f4ae4

Reviewed By: newstzpz

Differential Revision: D27747996

fbshipit-source-id: 6ae3b89c3944098828e246e5a4a89209b8e171a1

Reviewed By: zhanghang1989

Differential Revision: D27783989

fbshipit-source-id: f05c11e396a2f62366721b365929b29f05d5bc02

Summary: Separate unit tests into individual folder based on functionality.

Reviewed By: wat3rBro

Differential Revision: D27132567

fbshipit-source-id: 9a8200be530ca14c7ef42191d59795b05b9800cc