GitLab

Reviewed By: zhanghang1989

Differential Revision: D27783989

fbshipit-source-id: f05c11e396a2f62366721b365929b29f05d5bc02

Summary:
Fix typos in exporter

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/45

Reviewed By: wat3rBro

Differential Revision: D27779963

Pulled By: zhanghang1989

fbshipit-source-id: bcf7922afe6d4cccc074615069538eb5a6098b98

Synchronize PyTorchLightning/pytorch-lightning (revision 0b843848@master) to github/third-party/PyTorchLightning/pytorch-lightning

Summary:
### New commit log messages
## [UnReleased] - 2021-MM-DD

### Added

- Added more explicit exception message when trying to execute `trainer.test()` or `trainer.validate()` with `fast_dev_run=True` ([#6667](https://github.com/PyTorchLightning/pytorch-lightning/pull/6667))

- Added `LightningCLI` class to provide simple reproducibility with minimum boilerplate training cli. ([#4492](https://github.com/PyTorchLightning/pytorch-lightning/pull/4492))

- Trigger warning when non-metric logged value with multi processes hasn't been reduced ([#6417](https://github.com/PyTorchLightning/pytorch-lightning/pull/6417))

- Added `gradient_clip_algorithm` argument to Trainer for gradient clipping by value ([#6123](https://github.com/PyTorchLightning/pytorch-lightning/pull/6123)).

- Added a way to print to terminal without breaking up the progress bar ([#5470](https://github.com/PyTorchLightning/pytorch-lightning/pull/5470))

- Added support to checkpoint after training steps in `ModelCheckpoint` callback ([#6146](https://github.com/PyTorchLightning/pytorch-lightning/pull/6146))

- Added `checkpoint` parameter to callback's `on_save_checkpoint` hook ([#6072](https://github.com/PyTorchLightning/pytorch-lightning/pull/6072))

- Added `RunningStage.SANITY_CHECKING` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Added `TrainerState.{FITTING,VALIDATING,TESTING,PREDICTING,TUNING}` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Added `Trainer.validate()` method to perform one evaluation epoch over the validation set ([#4948](https://github.com/PyTorchLightning/pytorch-lightning/pull/4948))

- Added `LightningEnvironment` for Lightning-specific DDP ([#5915](https://github.com/PyTorchLightning/pytorch-lightning/pull/5915))

- Added `teardown()` hook to LightningDataModule ([#4673](https://github.com/PyTorchLightning/pytorch-lightning/pull/4673))

- Added `auto_insert_metric_name` parameter to `ModelCheckpoint` ([#6277](https://github.com/PyTorchLightning/pytorch-lightning/pull/6277))

- Added arg to `self.log` that enables users to give custom names when dealing with multiple dataloaders ([#6274](https://github.com/PyTorchLightning/pytorch-lightning/pull/6274))

- Added `teardown` method to `BaseProfiler` to enable subclasses defining post-profiling steps outside of `__del__` ([#6370](https://github.com/PyTorchLightning/pytorch-lightning/pull/6370))

- Added `setup` method to `BaseProfiler` to enable subclasses defining pre-profiling steps for every process ([#6633](https://github.com/PyTorchLightning/pytorch-lightning/pull/6633))

- Added no return warning to predict ([#6139](https://github.com/PyTorchLightning/pytorch-lightning/pull/6139))

- Added `Trainer.predict` config validation ([#6543](https://github.com/PyTorchLightning/pytorch-lightning/pull/6543))

- Added `AbstractProfiler` interface ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))

- Added support for including module names for forward in the autograd trace of `PyTorchProfiler` ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))

- Added support for the PyTorch 1.8.1 autograd profiler ([#6618](https://github.com/PyTorchLightning/pytorch-lightning/pull/6618))

- Added `outputs` parameter to callback's `on_validation_epoch_end` & `on_test_epoch_end` hooks ([#6120](https://github.com/PyTorchLightning/pytorch-lightning/pull/6120))

- Added `configure_sharded_model` hook ([#6679](https://github.com/PyTorchLightning/pytorch-lightning/pull/6679))

- Added support for `precision=64`, enabling training with double precision ([#6595](https://github.com/PyTorchLightning/pytorch-lightning/pull/6595))

- Added support for DDP communication hooks ([#6736](https://github.com/PyTorchLightning/pytorch-lightning/issues/6736))

- Added `artifact_location` argument to `MLFlowLogger` which will be passed to the `MlflowClient.create_experiment` call ([#6677](https://github.com/PyTorchLightning/pytorch-lightning/pull/6677))

- Added `model` parameter to precision plugins' `clip_gradients` signature ([#6764](https://github.com/PyTorchLightning/pytorch-lightning/pull/6764))

### Changed

- Renamed `pytorch_lightning.callbacks.swa` to `pytorch_lightning.callbacks.stochastic_weight_avg` ([#6259](https://github.com/PyTorchLightning/pytorch-lightning/pull/6259))

- Refactor `RunningStage` and `TrainerState` usage ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Changed `trainer.evaluating` to return `True` if validating or testing ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Changed `setup()` and `teardown()` stage argument to take any of `{fit,validate,test,predict}` ([#6386](https://github.com/PyTorchLightning/pytorch-lightning/pull/6386))

- Changed profilers to save separate report files per state and rank ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))

- Changed `PyTorchProfiler` to use `torch.autograd.profiler.record_function` to record functions ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))

### Deprecated

- `period` has been deprecated in favor of `every_n_val_epochs` in the `ModelCheckpoint` callback ([#6146](https://github.com/PyTorchLightning/pytorch-lightning/pull/6146))

- Deprecated `trainer.running_sanity_check` in favor of `trainer.sanity_checking` ([#4945](https://github.com/PyTorchLightning/pytorch-lightning/pull/4945))

- Deprecated `Profiler(output_filename)` in favor of `dirpath` and `filename` ([#6621](https://github.com/PyTorchLightning/pytorch-lightning/pull/6621))

- Deprecated `PytorchProfiler(profiled_functions)` in favor of `record_functions` ([#6349](https://github.com/PyTorchLightning/pytorch-lightning/pull/6349))

- Deprecated metrics in favor of `torchmetrics` ([#6505](https://github.com/PyTorchLightning/pytorch-lightning/pull/6505),
    [#6530](https://github.com/PyTorchLightning/pytorch-lightning/pull/6530),
    [#6540](https://github.com/PyTorchLightning/pytorch-lightning/pull/6540),
    [#6547](https://github.com/PyTorchLightning/pytorch-lightning/pull/6547),
    [#6515](https://github.com/PyTorchLightning/pytorch-lightning/pull/6515),
    [#6572](https://github.com/PyTorchLightning/pytorch-lightning/pull/6572),
    [#6573](https://github.com/PyTorchLightning/pytorch-lightning/pull/6573),
    [#6584](https://github.com/PyTorchLightning/pytorch-lightning/pull/6584),
    [#6636](https://github.com/PyTorchLightning/pytorch-lightning/pull/6636),
    [#6637](https://github.com/PyTorchLightning/pytorch-lightning/pull/6637),
    [#6649](https://github.com/PyTorchLightning/pytorch-lightning/pull/6649),
    [#6659](https://github.com/PyTorchLightning/pytorch-lightning/pull/6659),
)

### Removed

- Removed support for passing a bool value to `profiler` argument of Trainer ([#6164](https://github.com/PyTorchLightning/pytorch-lightning/pull/6164))

- Removed no return warning from val/test step ([#6139](https://github.com/PyTorchLightning/pytorch-lightning/pull/6139))

- Removed passing a `ModelCheckpoint` instance to `Trainer(checkpoint_callback)` ([#6166](https://github.com/PyTorchLightning/pytorch-lightning/pull/6166))

- Removed deprecated Trainer argument `enable_pl_optimizer` and `automatic_optimization` ([#6163](https://github.com/PyTorchLightning/pytorch-lightning/pull/6163))

- Removed deprecated metrics ([#6161](https://github.com/PyTorchLightning/pytorch-lightning/pull/6161))
    * from `pytorch_lightning.metrics.functional.classification` removed `to_onehot`, `to_categorical`, `get_num_classes`, `roc`, `multiclass_roc`, `average_precision`, `precision_recall_curve`, `multiclass_precision_recall_curve`
    * from `pytorch_lightning.metrics.functional.reduction` removed `reduce`, `class_reduce`

- Removed deprecated `ModelCheckpoint` arguments `prefix`, `mode="auto"` ([#6162](https://github.com/PyTorchLightning/pytorch-lightning/pull/6162))

- Removed `mode='auto'` from `EarlyStopping` ([#6167](https://github.com/PyTorchLightning/pytorch-lightning/pull/6167))

- Removed legacy references for magic keys in the `Result` object ([#6016](https://github.com/PyTorchLightning/pytorch-lightning/pull/6016))

- Removed deprecated `LightningModule` `hparams` setter ([#6207](https://github.com/PyTorchLightning/pytorch-lightning/pull/6207))

- Removed legacy code to log or include metrics in the progress bar by returning them in a dict with the `"log"/"progress_bar"` magic keys. Use `self.log` instead ([#6734](https://github.com/PyTorchLightning/pytorch-lightning/pull/6734))

- Removed `optimizer_idx` argument from `training_step` in manual optimization ([#6093](https://github.com/PyTorchLightning/pytorch-lightning/pull/6093))

### Fixed

- Set better defaults for `rank_zero_only.rank` when training is launched with SLURM and torchelastic ([#6802](https://github.com/PyTorchLightning/pytorch-lightning/pull/6802/))

- Made the `Plugin.reduce` method more consistent across all Plugins to reflect a mean-reduction by default ([#6011](https://github.com/PyTorchLightning/pytorch-lightning/pull/6011))

- Move lightning module to correct device type when using LightningDistributedWrapper ([#6070](https://github.com/PyTorchLightning/pytorch-lightning/pull/6070))

- Do not print top-k verbose log with `ModelCheckpoint(monitor=None)` ([#6109](https://github.com/PyTorchLightning/pytorch-lightning/pull/6109))

- Fixed csv extension check ([#6436](https://github.com/PyTorchLightning/pytorch-lightning/pull/6436))

- Fixed `ModelCheckpoint(monitor=None, save_last=True)` not saving checkpoints ([#6136](https://github.com/PyTorchLightning/pytorch-lightning/pull/6136))

- Fixed `ModelCheckpoint(save_top_k=0, save_last=True)` not saving the `last` checkpoint ([#6136](https://github.com/PyTorchLightning/pytorch-lightning/pull/6136))

- Fixed `.teardown(stage='fit')` getting called during `trainer.test` ([#6386](https://github.com/PyTorchLightning/pytorch-lightning/pull/6386))

- Fixed `.on_fit_{start,end}()` getting called during `trainer.test` ([#6386](https://github.com/PyTorchLightning/pytorch-lightning/pull/6386))

- Fixed LightningModule `all_gather` on cpu tensors ([#6416](https://github.com/PyTorchLightning/pytorch-lightning/pull/6416))

- Fixed torch distributed not available in setup hook for DDP ([#6506](https://github.com/PyTorchLightning/pytorch-lightning/pull/6506))

- Fixed `EarlyStopping` logic when `min_epochs` or `min_steps` requirement is not met ([#6705](https://github.com/PyTorchLightning/pytorch-lightning/pull/6705))

## [1.2.7] - 2021-04-06

### Fixed

- Fixed resolve a bug with omegaconf and xm.save ([#6741](https://github.com/PyTorchLightning/pytorch-lightning/pull/6741))
- Fixed an issue with IterableDataset when __len__ is not defined ([#6828](https://github.com/PyTorchLightning/pytorch-lightning/pull/6828))
- Sanitize None params during pruning ([#6836](https://github.com/PyTorchLightning/pytorch-lightning/pull/6836))
- Enforce an epoch scheduler interval when using SWA ([#6588](https://github.com/PyTorchLightning/pytorch-lightning/pull/6588))
- Fixed TPU Colab hang issue, post training ([#6816](https://github.com/PyTorchLightning/pytorch-lightning/pull/6816))
- Fixed a bug where `TensorBoardLogger` would give a warning and not log correctly to a symbolic link `save_dir` ([#6730](https://github.com/PyTorchLightning/pytorch-lightning/pull/6730))

## [1.2.6] - 2021-03-30

### Changed

- Changed the behavior of `on_epoch_start` to run at the beginning of validation & test epoch ([#6498](https://github.com/PyTorchLightning/pytorch-lightning/pull/6498))

### Removed

- Removed legacy code to include `step` dictionary returns in `callback_metrics`. Use `self.log_dict` instead. ([#6682](https://github.com/PyTorchLightning/pytorch-lightning/pull/6682))

### Fixed

- Fixed `DummyLogger.log_hyperparams` raising a `TypeError` when running with `fast_dev_run=True` ([#6398](https://github.com/PyTorchLightning/pytorch-lightning/pull/6398))
- Fixed error on TPUs when there was no `ModelCheckpoint` ([#6654](https://github.com/PyTorchLightning/pytorch-lightning/pull/6654))
- Fixed `trainer.test` freeze on TPUs ([#6654](https://github.com/PyTorchLightning/pytorch-lightning/pull/6654))
- Fixed a bug where gradients were disabled after calling `Trainer.predict` ([#6657](https://github.com/PyTorchLightning/pytorch-lightning/pull/6657))
- Fixed bug where no TPUs were detected in a TPU pod env ([#6719](https://github.com/PyTorchLightning/pytorch-lightning/pull/6719))

## [1.2.5] - 2021-03-23

### Changed

- Update Gradient Clipping for the TPU Accelerator ([#6576](https://github.com/PyTorchLightning/pytorch-lightning/pull/6576))
- Refactored setup for typing friendly ([#6590](https://github.com/PyTorchLightning/pytorch-lightning/pull/6590))

### Fixed

- Fixed a bug where `all_gather` would not work correctly with `tpu_cores=8` ([#6587](https://github.com/PyTorchLightning/pytorch-lightning/pull/6587))
- Fixed comparing required versions ([#6434](https://github.com/PyTorchLightning/pytorch-lightning/pull/6434))
- Fixed duplicate logs appearing in console when using the python logging module ([#6275](https://github.com/PyTorchLightning/pytorch-lightning/pull/6275))
- Added Autocast in validation, test and predict modes for Native AMP ([#6565](https://github.com/PyTorchLightning/pytorch-lightning/pull/6565))

Reviewed By: shuyingsunshine21

Differential Revision: D27528929

fbshipit-source-id: 311c88f71461c2c79bbf185e28d7a6d683ccc26f

Summary:
Pull Request resolved: https://github.com/facebookresearch/d2go/pull/43

- move some of `test_meta_arch_rcnn.py` to oss
- `create_fake_detection_data_loader` doesn't give correct resolution, fix it
- set pooler resolution for faster test

Reviewed By: zhanghang1989

Differential Revision: D27726476

fbshipit-source-id: 063c0a01e95df10f91b58b0692da0546e21c115c

Summary:
- store expiration in meta data when loading dat

- use before_train_hook to rebuild data loader when expiration condition is met.

Reviewed By: zisting

Differential Revision: D27683164

fbshipit-source-id: e3e3c6c15eee7c02c7a1bfed5f4d4d0e67d61a4f

Summary:
1. Add changes to support variation of datasets
2. Fix runner to support torchscript export

Reviewed By: wat3rBro

Differential Revision: D26871461

fbshipit-source-id: ec46f7e0d2c14c9b802aec22d78b2a089e962a2f

Summary: `checkpoint_callback` now only accepts boolean values: https://github.com/PyTorchLightning/pytorch-lightning/blob/19e67d18c472c3a03dec4dd9bfcef031e9ca8719/pytorch_lightning/trainer/connectors/callback_connector.py#L65-L73

Reviewed By: shuyingsunshine21

Differential Revision: D27682178

fbshipit-source-id: 9e863aad7a23a76dee8ae5df9f5a78e7a94bfe8a

Summary:
Before: this test would assume only 2 checkpoints were stored: `last.ckpt`, and `FINAL_MODEL_CKPT`
Now: this test asserts that at least these 2 checkpoints are stored. In case the config specifies `save_top_k=-1` for instance, we'd save more checkpoints, causing this test to fail

Since this test is only loading the last and the final outputs, I'm changing the behavior to assert that these checkpoints must be saved and ignoring other checkpoint files that could be generated.

Reviewed By: kazhang

Differential Revision: D27671284

fbshipit-source-id: 0419fb46856d048e7b6eba3ff1dc65b7280a9a90

Summary: fbgs register_uri_image_loader, UniversalResourceLoader, _IMAGE_LOADER_REGISTRY returns no results other than this file

Reviewed By: newstzpz

Differential Revision: D27639902

fbshipit-source-id: 52e3bb77dbb547334938b8537d6e1c173405d12d

Summary:
we need to load the [config file](https://github.com/facebookresearch/d2go/blob/master/tests/misc/test_configs.py#L35) during the unittest.

The `get_resource_path` need to use `d2go.tests`
https://fburl.com/whn41ma0

The test fail at
https://github.com/facebookresearch/d2go/runs/2272258836

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/35

Reviewed By: wat3rBro

Differential Revision: D27578008

Pulled By: zhanghang1989

fbshipit-source-id: 5fa24c7f74bc3e59ffee98d57f02a2a558c2a4b0

Summary: TorchVision recently upgrade their version to 0.10.0 which causes issues in the version check in detr.

Reviewed By: wat3rBro

Differential Revision: D27575085

fbshipit-source-id: 75f459fe7a711161e908609fcf2f2d28a01a6c74

Summary: Prediction count evaluator needs to gather it's state before computing metrics, otherwise when parallelized across N GPUs, we only get metrics computed from 1/N of the dataset, increasing our eval signal's variance.

Reviewed By: wat3rBro

Differential Revision: D27416864

fbshipit-source-id: b2c5334cd5a38bebcd06c6ace1627a6b71645fdd

Summary: Add typing to transform.

Reviewed By: wat3rBro

Differential Revision: D27145140

fbshipit-source-id: 8556427b421bf91a05692a590db175c68c4d6890

Summary:
Make data and evaluation visualization optional.
* could return None.

Reviewed By: zhanghang1989, wat3rBro

Differential Revision: D27316632

fbshipit-source-id: 2a85db4815cbf3407a20a74c125dcd52d75167fa

Summary:
Format changes.
* [Option] + [Shift] + [F]

Reviewed By: mattcyu1, zhanghang1989, wat3rBro

Differential Revision: D27316555

fbshipit-source-id: 0fc3396eb34d964478cb3551dc73b47412089ccb

Summary:
#Facebook:

`build_d2go_train_loader` will replace `runner.build_detection_train_loader`, currently we call `build_d2go_train_loader` from `runner.build_detection_train_loader` since some runner has there own implementation, we will solve those cases and remove the `runner.build_detection_train_loader` API.

Currently `build_d2go_train_loader` uses `_MAPPED_TRAIN_LOADER_BUILDER_REGISTRY` to support different versions between OSS and FB, not sure if this is a good pattern or not, please comment in the diff if you have better idea.

Reviewed By: zhanghang1989

Differential Revision: D27505681

fbshipit-source-id: b5caf7280a88c2ebccb498097c0b7af51c966fc6

Reviewed By: newstzpz

Differential Revision: D27255960

fbshipit-source-id: 1699ff23d2bc610dffc0215a90a7c1c17e3783c3

Summary: Fixing unit test that was not listed due to rebase error.

Reviewed By: newstzpz, wat3rBro

Differential Revision: D27456322

fbshipit-source-id: 519c5c086adfb19104ed99234f4f476eb34a79bc

Summary: Train a pix2pix model on the paired dataset. During inference, it can transfer an source image to the target image.

Reviewed By: newstzpz

Differential Revision: D27371290

fbshipit-source-id: 3141bc6d9e4fe0013f6ea3de3cf998163d286168

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

Reviewed By: wat3rBro

Differential Revision: D27132567

fbshipit-source-id: 9a8200be530ca14c7ef42191d59795b05b9800cc

Summary:
fixes https://github.com/facebookresearch/d2go/issues/27

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/28

Reviewed By: newstzpz

Differential Revision: D27214440

Pulled By: zhanghang1989

fbshipit-source-id: da538ad1e29faa9c36065db89138b1cc97045a28

Summary: On datasets that don't contain the dataset name / mapping, initialization using the parent visualizer class breaks. Split this into it's own function so that the functionality can be overridden in a subclass.

Reviewed By: wat3rBro

Differential Revision: D27412314

fbshipit-source-id: a91db47615b14ba982285ce819901b8db27e5693

Summary:
In 9d238344, the test utils were moved to the core library, but the import for the create_fake_detection_data_loader inside the d2go_beginner.ipynb wasn't updated.

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/29

Reviewed By: newstzpz

Differential Revision: D27239846

Pulled By: zhanghang1989

fbshipit-source-id: e39df32746b1d1081026f9969bda84e73ac7df55

Summary: all utils code are moved to d2go.utils.testing

Reviewed By: newstzpz

Differential Revision: D27209943

fbshipit-source-id: 6c5cb14858155a8ed13478d65ee8e02ef74616d7

Summary: - Added support for running evaluation for models where the number of subclasses in the model output is less than the number of subclases in the annotated dataset

Reviewed By: vivekn

Differential Revision: D27090466

fbshipit-source-id: 704c438c1bbca333648c0477c412bf3ed79f04e7

Summary: Add `build_auto_stream_train_loader`

Reviewed By: newstzpz

Differential Revision: D27343030

fbshipit-source-id: a79d3ed1ac41fc159d10bb6ff1db74549b645a1c

Summary: The default mapper may load "file_name" and "sem_seg_file_name" from `dataset_dict`, when prefetching them from manifold, we no longer need to load them because they're already fetched. This diff adds two more fields for holding those pre-fetched data, and make the mapper work in both cases.

Reviewed By: newstzpz

Differential Revision: D26972340

fbshipit-source-id: 63f6dc809d321e149aa5adf9f92c3ace07cbf2a7

Summary:
Evaluate the predictor generated by previous step.
This diff modify the lightning_train_net to reuse the evaluation logic by adding a `predictor_path` param.
This diff also makes Lightning training backend depends on `cfg.MODEL.DEVICE` so that in evaluate_predictor step, user could set backend by changing model device. This is useful for evaluating int8 quantized model.

Reviewed By: newstzpz

Differential Revision: D27150609

fbshipit-source-id: fb72da3e81db932c0fa479350150720143e09a3e

Summary: As titled.

Reviewed By: newstzpz

Differential Revision: D27074737

fbshipit-source-id: 72f2535fc730a37f5ea8f58aaff88005c28ffc5b

Summary:
Given that the way to create D2 (https://github.com/facebookresearch/d2go/commit/465cdb842513eb910aa20fcedea1d2edd15dc7b7)go runner and Lightning task are different, get_class was introduced so that in application we could do:
```
if is Lightning:
    task_cls = get_class(classname)
    task = task_cls(cfg)
else:
    runner = create_runner(classname)
```
It turns out that we could need to do that in many places: workflow, binaries.
This diff revert `get_class` and return class in `create_runner` if the class is a Lightning module.

Reviewed By: newstzpz

Differential Revision: D26676595

fbshipit-source-id: c3ce2016d09fe073af4c2dd9f98eea4e59ca621b

Summary: Prepare the launch script for IG, which support registering new dataset for GANs on the fly

Reviewed By: newstzpz

Differential Revision: D27211763

fbshipit-source-id: f79978ceae246ab4f27a8083d25dd50c62dcefab

Summary: Not d2go.tests is not a library for oss, move utils code to d2go.utils.testing

Reviewed By: zhanghang1989

Differential Revision: D26706933

fbshipit-source-id: 85767b66bbb6c67db05e11823beb4840220b2aa3

Summary:
`checkpoint_callback` is being phased out. Initially, it was a special way to configure checkpoints, but it makes more sense for those callbacks to be included in the general `callbacks` trainer argument. In 1.2.X, `checkpoint_callback` is expected to be a boolean value only.

If `checkpoint_callback=False` **and** an instance of `ModelCheckpoint` is passed in the trainer's `callbacks` arguments, Lightning raises a [misconfiguration error](https://github.com/PyTorchLightning/pytorch-lightning/blob/2f6ce1ae7fff34d16d3707571f6a9a7b0fb0c50a/pytorch_lightning/trainer/connectors/callback_connector.py#L66-L70)

Reviewed By: newstzpz

Differential Revision: D27139315

fbshipit-source-id: 07ad5ea520583a2e46a9cb2a938f98968265c932

Summary: Add option to specify a custom subclass id mapping. Allows for flexibility when training models with different outputs needed.

Reviewed By: sanjeevk42

Differential Revision: D26826986

fbshipit-source-id: 9dba4f0f2f2afebd2f152ddd9aebd46cf4c86a0d

Summary: Pull Request resolved: https://github.com/facebookresearch/d2go/pull/24

Reviewed By: wat3rBro

Differential Revision: D27127642

Pulled By: zhanghang1989

fbshipit-source-id: 18bc3c2fa05232cacc778925db6b7dcea99b108c

Summary:
As titled.
The logs will be stored in scuba_caffe2_pytorch_usage_stats and can be queried like
```
WITH events AS (
    SELECT
        DISTINCT workflow_run_id,
        REGEXP_EXTRACT(event, 'D2 (https://github.com/facebookresearch/d2go/commit/465cdb842513eb910aa20fcedea1d2edd15dc7b7)Go.Runner\.([a-zA-Z0-9_]*)', 1)
            AS runner_name,
        ds
    FROM scuba_caffe2_pytorch_usage_stats
    WHERE
        ds BETWEEN '$START_DATE$' AND '$END_DATE$'
        AND event LIKE '%D2 (https://github.com/facebookresearch/d2go/commit/465cdb842513eb910aa20fcedea1d2edd15dc7b7)Go.Runner%'
        AND flow_is_test = 0
        AND flow_is_local_run = 0
        AND workflow_run_id > 0

)
SELECT
    COUNT(1) AS total_runs,
    runner_name,
    ds
FROM events
GROUP BY
    ds, runner_name
ORDER BY
    total_runs DESC
```

Reviewed By: colin2328

Differential Revision: D26032225

fbshipit-source-id: ab1e06f3b1af200baf530506be9b3894ddf77126

Summary: Extend conversion to support ids beyond cocotext format where ids are strings.

Reviewed By: newstzpz

Differential Revision: D27018211

fbshipit-source-id: 7282fd4b9a7e9cd19323235ed1a3c3e7b33cb6b4

Reviewed By: sanjeevk42

Differential Revision: D26922320

fbshipit-source-id: 8d6b4bc709d931020483284febede8cf8904d90f

Summary:
# context

Right now, when we register Person or Hair data we have a mask directory with .PNGs inside and when we register MCS data we have multiple directories <class>_mask. These are 2 separate file structures to maintain.

# this diff

**This diff is to add support for the following file structure for *both* Multi-Class and Single-Class Semantic Segmentation:**

```
mask/
  person/
  hair/
  skin/
  beard/
  clothes/
```

**without breaking the previous format for Single-Class Semantic Segmentation (`mask/` with .PNGs).**

# why?

With this change, we'll be able to prepare data once (create the file structure and d2go coco jsons) and make it ready-to-be-registered for Single-Class Semantic Segmentation (Person or Hair) and Multi-Class Segmentation.

We'll save on having to prepare the dataset 3 times and managing multiple copies of the same images and annotations. There will be one source of truth. For UGC, we'll be able to enforce privacy deletions with less complications.

Also, this is the format used in the Video Eval tool. So data prepared for training can be used for video evaluation if needed and data prepared for video evaluation can easily be prepared for training.

# to dos:

- ~~Add the v2 path support on the Single-Class Segmentation's `SemSegEvaluator` so that the v2 format can be used for evaluation (sync with wat3rBro for best way to do this)~~
  - **Done.** This happens automatically given the the Single-Class case in the `MultiSemSegEvaluator` class doesn't re-register the datasets. See the first job for an example.

Reviewed By: wat3rBro

Differential Revision: D26795469

fbshipit-source-id: 6ebbb1a430c049dad361d56842a587c76926ee23

Summary:
**Added a Issue template**

Issue : https://github.com/facebookresearch/d2go/issues/8

Pull Request resolved: https://github.com/facebookresearch/d2go/pull/15

Reviewed By: newstzpz

Differential Revision: D26933233

Pulled By: zhanghang1989

fbshipit-source-id: af4af309fae3432021f15a6aecbbfb62e0c58be2