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/154

Reviewed By: zhanghang1989

Differential Revision: D33352204

fbshipit-source-id: e1a9ac6eb2574dfe6931435275e27c9508f66352

Reviewed By: sstsai-adl

Differential Revision: D32216605

fbshipit-source-id: bebee1edae85e940c7dcc6a64dbe341a2fde36a2

Reviewed By: zhanghang1989

Differential Revision: D31134064

fbshipit-source-id: 825ca14477243a53f84b8521f4430a2b080324bd

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:
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