GitLab

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/180

Distributed backend is deprecated. Switching to use "use_ddp" instead.

Reviewed By: kazhang

Differential Revision: D34394993

fbshipit-source-id: a5bfb22f8952d20c9a8d86322cd740534c25c689

Summary:
Currently, the exporter method takes in a compare_accuracy parameter, which after all the compute (exporting etc.) raises an exception if it is set to True.

This looks like an antipattern, and causes a waste of compute.

Therefore, I am proposing to raise the exception at the very beginning of method call to let the client know in advance that this argument's functionality isn't implemented yet.

NOTE: We might also choose to get rid of the entire parameter. I am open for suggestions.

Differential Revision: D34186578

fbshipit-source-id: d7fbe7589dfe2d2f688b870885ca61e6829c9329

Reviewed By: Maninae

Differential Revision: D34097529

fbshipit-source-id: e3c860bb2374e694fd6ae54651a479c2398b2462

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/175

D33833203 adds `is_qat` argument to the fuser method, more details in https://fb.workplace.com/groups/2322282031156145/permalink/5026297484087906/. As results, MV's `fuse_utils.fuse_model` then becomes two functions: the original one is for non-qat; a new one `fuse_utils.fuse_model_qat` is for qat.

For D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go in most cases, `is_qat` can be inferred from `cfg.QUANTIZATION.QAT.ENABLED`, therefore we can extend the `fuse_model` to also take `is_qat` as parameter, and set it accordingly.

This diff updates all the call sites which is covered by unit tests. Those call sites include:
- default quantization APIs in d2go/modeling/quantization.py
- customized quantization APIs from individual meta-arch
- unit test itself

Reviewed By: tglik, jerryzh168

Differential Revision: D34112650

fbshipit-source-id: 026c309f603bee71d887e39aa4efee6477db731b

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/169

Make d2go DETR exportable (torchscript compatible)
Move generating masks to preprocessing

Reviewed By: sstsai-adl

Differential Revision: D33798073

fbshipit-source-id: d629b0c9cbdb67060982be717c7138a0e7e9adbc

Summary:
### New commit log messages
- [115a5d08e Decouple utilities from `LightningLoggerBase` (#11484)](https://github.com/PyTorchLightning/pytorch-lightning/pull/11484)

Reviewed By: tangbinh, wat3rBro

Differential Revision: D33960185

fbshipit-source-id: 6be72ad49f8433be6f238b36aa82d3f1b655e6f0

Summary:
Convert type comments in fbcode/mobile-vision

Produced by running:
```
python -m  libcst.tool codemod convert_type_comments.ConvertTypeComment fbcode/mobile-vision
```
from fbsource.

See
https://fb.workplace.com/groups/pythonfoundation/permalink/3106231549690303/

Reviewed By: grievejia

Differential Revision: D33897026

fbshipit-source-id: e7666555e47a9abc769975f6db6b2e6eda792d72

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/168

Add a hook in D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go config for custom parsing so we can support custom objects in D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go config like the search space objects.
Then adding SuperNet custom config processing to parse search space from arch_def when supernet is enabled, so it can be used in D2 (https://github.com/facebookresearch/d2go/commit/7992f91324aee6ae59795063a007c6837e60cdb8)Go SuperNet training.

This is an alternative approach to D33191150. In this approach we parse the entire architecture as a search space which will not have the limitations that we have in parsing only the dynamic blocks parts.

Reviewed By: zhanghang1989

Differential Revision: D33793423

fbshipit-source-id: 8acf5c5afb3c5c0005bdb0ca16847026e1b45e2c

Summary: As in the tittle

Reviewed By: XiaoliangDai

Differential Revision: D33413849

fbshipit-source-id: b891849c175edc7b8916bff2fcc40c76c4658f14

Summary: Learnable query doesn't improve the results, but it helps DETR with reference points in D33420993

Reviewed By: XiaoliangDai

Differential Revision: D33401417

fbshipit-source-id: 5296f2f969c04df18df292d61a7cf57107bc9b74

Summary: Add DETR_MODEL_REGISTRY registry to better support different variant of DETR (in later diff).

Reviewed By: newstzpz

Differential Revision: D32874194

fbshipit-source-id: f8e9a61417ec66bec9f2d98631260a2f4e2af4cf

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