GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/200

Currently when exporting the RCNN model, we call it with `self.model.inference(inputs, do_postprocess=False)[0]`, therefore the output of exported model is not post-processed, eg. the mask is in the squared shape. This diff adds the option to include postprocess in the exported model.

Worth noting that since the input is a single tensor, the post-process doesn't resize the output to original resolution, and we can't apply the post-process twice to further resize it in the Predictor's PostProcessFunc, add an assertion to raise error in this case. But this is fine for most production use cases where the input is not resized.

Set `RCNN_EXPORT.INCLUDE_POSTPROCESS` to `True` to enable this.

Reviewed By: tglik

Differential Revision: D34904058

fbshipit-source-id: 65f120eadc9747e9918d26ce0bd7dd265931cfb5

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