GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/166

Pickling of transform functions seems to have changed (did not dig into it) in December, breaking the support for this augmentation. This error happens when training with multiple dataloaders. Using partial functions instead.

Differential Revision: D33665177

fbshipit-source-id: 4dfd41b92f3a6fea549b6e7a79bf0bf14a3cceaa

Summary: The type signature of create_runner is not accurate. We expect lightning runners to follow DefaultTask. Also change setup.py to not import directly, which was causing circular dependencies together with the change.

Reviewed By: wat3rBro

Differential Revision: D32792069

fbshipit-source-id: 0fbb55eb269dd681dbc8df49d71c9635f56293b8

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/160

If the returned object of visualize_train_input is a dictionary, use the key as tag suffix and the values as separate output images.

Reviewed By: zhanghang1989, wat3rBro

Differential Revision: D33468573

fbshipit-source-id: b0a47ba312ff59700534e917c62af1dfa83dd5be

Summary:
Add support in the default lightning task to run a custom training step from Meta Arch if exists.
The goal is to allow custom training step without the need to inherit from the default lightning task class and override it. This will allow us to use a signle lightning task and still allow users to customize the training step. In the long run this will be further encapsulated in modeling hook, making it more modular and compositable with other custom code.

This change is a follow up from discussion in  https://fburl.com/diff/yqlsypys

Reviewed By: wat3rBro

Differential Revision: D33534624

fbshipit-source-id: 560f06da03f218e77ad46832be9d741417882c56

Summary:
Add option to train Person Instance Segmentation using lightning instead of D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8).
This is needed because we want to try PIS with SuperNet and our SuperNet based training is implemented in d2go lightning task

Reviewed By: zhanghang1989

Differential Revision: D33281437

fbshipit-source-id: e1b6567f3c77ce51240fb50d81350bc97735713a

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/161

Updated scaling rules for base_lr_end and quantization.

Reviewed By: zhanghang1989, wat3rBro

Differential Revision: D33292860

fbshipit-source-id: c7a8747c8fb1f894d3c5508bbd607b3d1ef3d400

Summary:
### New commit log messages
  4eede7c30 Add deprecation path for renamed training type plugins (#11227)

Reviewed By: edward-io, daniellepintz

Differential Revision: D33409991

fbshipit-source-id: 373e48767e992d67db3c85e436648481ad16c9d0

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/158

Add unit tests for visualization wrapper and dataloader visualization wrapper.

Reviewed By: zhanghang1989, wat3rBro

Differential Revision: D33457734

fbshipit-source-id: e5f946ae4ee711a0914d8ac65b96cac40e7ab13b

Summary:
Current implementation of d2go lightning default task fails when running a model training with EMA.
The error is :
```
RuntimeError: Expected to have finished reduction in the prior iteration before starting a new one. This error indicates that your module has parameters that were not used in producing loss.
```
The error is due the fact the d2go lightning task create a copy of the ema model for evaluation that does not included in the training, which raise the error that there are unused params.
This is solved by moving the copy creation to after training and to when evaluation starts.

Reviewed By: kazhang

Differential Revision: D33442690

fbshipit-source-id: e9e469e33811de0b4171a64293cc16a8157af08c

Summary:
### New commit log messages
  b64dea9dc Rename `DDPPlugin` to `DDPStrategy` (#11142)

Reviewed By: jjenniferdai

Differential Revision: D33259306

fbshipit-source-id: b4608c6b96b4a7977eaa4ed3f03c4b824882aef0

Summary: Try LSJ with Faster RCNN with FBNet backbone

Reviewed By: newstzpz

Differential Revision: D32054932

fbshipit-source-id: 4fdb30e7b1258d6f167f2c2fd331209aad1b599a

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/155

- remove tracing, which shouldn't affect anything.
- create the model in cpu mode, since it might have issue casting model.

Reviewed By: zhanghang1989

Differential Revision: D33357269

fbshipit-source-id: 27a0330ebb12b993744dee47151c3056cd584ccf

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

Reviewed By: zhanghang1989

Differential Revision: D33352204

fbshipit-source-id: e1a9ac6eb2574dfe6931435275e27c9508f66352

Summary: DDPPlugin has been renamed to DDPStrategy (as part of https://github.com/PyTorchLightning/pytorch-lightning/issues/10549), causing oss CI to fail. Simply skipping the import to unblock CI since DDP feature is not used in test.

Reviewed By: kazhang

Differential Revision: D33351636

fbshipit-source-id: 7a1881c8cd48d9ff17edd41137d27a976103fdde

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/153

Fix the error:
'''
ImportError: cannot import name 'Detectron2GoRunner' from 'd2go.runner' (/home/runner/work/d2go/d2go/d2go/runner/__init__.py)
'''

Reviewed By: zhanghang1989

Differential Revision: D33306196

fbshipit-source-id: 6c773260855498ed498bd43f998bc4de891ff9e2

Summary:
1. Add registry for coco injection to allow for easier overriding of cococ injections
2. Coco loading currently is limited to certain keys. Adding option to allow for copying certain keys from the outputs.

Reviewed By: zhanghang1989

Differential Revision: D33132517

fbshipit-source-id: 57ac4994a66f9c75457cada7e85fb15da4818f3e

Summary: Dataset size check happened at https://fburl.com/code/ey18d081 . Adding this assertion to break and detect errors earlier.

Differential Revision: D33108979

fbshipit-source-id: c931a2167fce8ec0ce764365778d879280bd5af4

Summary:
Currently the lightning task rely on the default runner for the vis wrapper logic. This does not allow to overload the get_data_loader_vis_wrapper is subclasses of the lightning task class.
This diff fixes this issue and properly take the vis wrapper given by the overloaded get_data_loader_vis_wrapper functions in the runner.

Reviewed By: zhanghang1989

Differential Revision: D33190410

fbshipit-source-id: 48cb3a8fa4b11df41d025d115d21002991549ced

Summary:
With this diff, we add support for Pseudo-GT / machine-generated labels on a D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go dataset.

The idea is to generate pseudo-GT early on a project and on a rolling basis replace these labels with human-GT as annotation progress occurs (which we know is costly).

## how to use

To add pseudo-GT labels for a class (segmentation only), all that needs to be done is add the mask folder (with machine-generated labels) just like you would for human-generated labels **but with the "_pseudo-label" postfix**.

After this, you'd have to register the dataset following the instructions in D32298220.

## example

For example, say you're adding an **eyebrows** class to a dataset located in: `manifold://pai_mobile/tree/datasets/some_dataset/batch1`.

You'd then add your `eyebrows` folder with all your machine-generated .PNGs as `eyebrows_pseudo-label` in `manifold://pai_mobile/tree/datasets/some_dataset/batch1/mask/eyebrows_pseudo-label`.

After this, you'd have to register the dataset following the instructions in D32298220.

Reviewed By: wenliangzhao2018

Differential Revision: D32298221

fbshipit-source-id: 230a862e6be69306fb5c119b778e14e12d1280e0

Summary: will help debugging

Reviewed By: tglik

Differential Revision: D32771358

fbshipit-source-id: 659c0edc79354ca8688b13058f784f653c0cff37

Summary:
This tool mimics how the e2e_workflow initializes the runner+config pair, which is the only way to obtain the dataset information from the catalog.

This is very early and mostly helps debug. In the near future, this tool is meant to help users understand whether their current setup is compatible with the AIM lineage automation D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)GO provides, ideally giving clear signal on what to change.

Differential Revision: D32497618

fbshipit-source-id: 83f1d6ae1dca48ce37a05c84b460ea4bb7a1fd79

Reviewed By: zhanghang1989

Differential Revision: D32680045

fbshipit-source-id: ad21fd81a496a10d4d0499de83ff2469b4fcbf00

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/146

OSSpytorch package removed caffe2. This causes https://github.com/facebookresearch/d2go/issues/137

This diff only makes d2go importable without caffe2. But export is still broken when caffe2 is not available.

Reviewed By: zhanghang1989

Differential Revision: D32690938

fbshipit-source-id: d345687bd720b4b1376494478f1fa44f4c591ccf

Summary:
Experimental models from Xiaoliang [D31749820]

Pretrained weights:
fbnet_vit_tiny_v3_lepe n/a
fbnet_deit_v0 f298782311

Reviewed By: XiaoliangDai

Differential Revision: D32054949

fbshipit-source-id: 7c2aa0679a545ed814ba1db421408a5f9a59a2c8

Summary: make it an option

Differential Revision: D32601981

fbshipit-source-id: 308a0c49939531d840914aa8e256aae6db463929

Summary: Pull Request resolved: https://github.com/facebookresearch/mobile-vision/pull/56

Reviewed By: ppwwyyxx

Differential Revision: D32576986

fbshipit-source-id: 1b20d1927a36ac80e33b51ff971b54767f647d43

Summary:
for sythetic data, we want to enable having different transforms for different dataloaders.

To do that, we need to be able to construct different kinds of transforms.

This means that using the cfg's hard-coded location isn't convenient - we'd have to edit the cfg during run time and call the build function multiple times

Differential Revision: D32486576

fbshipit-source-id: 767b63c5c787e31a67dbf8710ab9bab84a0651db

Summary:
### New commit log messages
  fa0ed17f8 remove deprecated train_loop (#10482)

Reviewed By: kandluis

Differential Revision: D32454980

fbshipit-source-id: a35237dde06cc9ddac5373b75992ce88a6771c76

Reviewed By: newstzpz

Differential Revision: D32301322

fbshipit-source-id: a9e951b9de600012125b8b94c0c1ace929b491b8

Summary:
fvcore flops calculator throws on this error:

KeyError: 'Only support flattening dictionaries if keys are str.'

Setting flops to some value so it doesn't enter pdb mode.

Reviewed By: stephenyan1231

Differential Revision: D32144492

fbshipit-source-id: 604cd4660cea9ffbfb3f1da35d32e06ccf607a50

Reviewed By: newstzpz

Differential Revision: D31209906

fbshipit-source-id: 0be4e3c1db623e3c1fba8ba4259840d34192a77e

Summary: Extended Pix2Pix to allow for input extra data

Reviewed By: tax313

Differential Revision: D31469054

fbshipit-source-id: 790543f214ea9fa0158e509acb27193916bf17ce

Reviewed By: zertosh

Differential Revision: D32270982

fbshipit-source-id: 8767b469fe5404a882257c0c5209b34ed0c327dc

Summary: code was kept for short term to support loading old training jobs during the period when the default config is polluted; now it should be safe to remove this BC support and dead code

Differential Revision: D32218217

fbshipit-source-id: 3772477653151ccbcb4ae7098b9414853b581ad1

Reviewed By: sstsai-adl

Differential Revision: D32216605

fbshipit-source-id: bebee1edae85e940c7dcc6a64dbe341a2fde36a2

Reviewed By: jreese, ppwwyyxx

Differential Revision: D32191010

fbshipit-source-id: 1e40b7a090be3a0e25b930fb908ec177719fce50

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/134

Updating `all_steps_qat` example config to use learnable QAT method.

And add logic in `GeneralizedRCNNPatch.prepare_for_quant` to call the new `d2go.utils.qat_utils.get_qat_qconfig` to properly support QAT in D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go training workflow

Differential Revision: D32147216

fbshipit-source-id: 32831c6156bc5c0775196ad8edc890a5292d204f

Summary: Allow reading `.npy` format binary masks shaped (H, W,) in addition to `.png` image masks shaped (H, W, C).

Reviewed By: wat3rBro

Differential Revision: D30136542

fbshipit-source-id: 56df5a766ab15b6808a1327815857e5d38eac910