GitLab

Summary: Remove the dataset's need to provide the task type.

Reviewed By: davnov134, kjchalup

Differential Revision: D38314000

fbshipit-source-id: 3805d885b5d4528abdc78c0da03247edb9abf3f7

Summary: Currently, seeds are set only inside the train loop. But this does not ensure that the model weights are initialized the same way everywhere which makes all experiments irreproducible. This diff fixes it.

Reviewed By: bottler

Differential Revision: D38315840

fbshipit-source-id: 3d2ecebbc36072c2b68dd3cd8c5e30708e7dd808

Summary:
This large diff rewrites a significant portion of Implicitron's config hierarchy. The new hierarchy, and some of the default implementation classes, are as follows:
```
Experiment
    data_source: ImplicitronDataSource
        dataset_map_provider
        data_loader_map_provider
    model_factory: ImplicitronModelFactory
        model: GenericModel
    optimizer_factory: ImplicitronOptimizerFactory
    training_loop: ImplicitronTrainingLoop
        evaluator: ImplicitronEvaluator
```

1) Experiment (used to be ExperimentConfig) is now a top-level Configurable and contains as members mainly (mostly new) high-level factory Configurables.
2) Experiment's job is to run factories, do some accelerate setup and then pass the results to the main training loop.
3) ImplicitronOptimizerFactory and ImplicitronModelFactory are new high-level factories that create the optimizer, scheduler, model, and stats objects.
4) TrainingLoop is a new configurable that runs the main training loop and the inner train-validate step.
5) Evaluator is a new configurable that TrainingLoop uses to run validation/test steps.
6) GenericModel is not the only model choice anymore. Instead, ImplicitronModelBase (by default instantiated with GenericModel) is a member of Experiment and can be easily replaced by a custom implementation by the user.

All the new Configurables are children of ReplaceableBase, and can be easily replaced with custom implementations.

In addition, I added support for the exponential LR schedule, updated the config files and the test, as well as added a config file that reproduces NERF results and a test to run the repro experiment.

Reviewed By: bottler

Differential Revision: D37723227

fbshipit-source-id: b36bee880d6aa53efdd2abfaae4489d8ab1e8a27

Summary: Avoid calculating all_train_cameras before it is needed, because it is slow in some datasets.

Reviewed By: shapovalov

Differential Revision: D38037157

fbshipit-source-id: 95461226655cde2626b680661951ab17ebb0ec75

Summary: For debugging, introduce PYTORCH3D_NO_ACCELERATE env var.

Reviewed By: shapovalov

Differential Revision: D37885393

fbshipit-source-id: de080080c0aa4b6d874028937083a0113bb97c23

Summary:
1. Respecting `visdom_show_preds` parameter when it is False.
2. Clipping the images pre-visualisation, which is important for methods like SRN that are not arare of pixel value range.

Reviewed By: bottler

Differential Revision: D37786439

fbshipit-source-id: 8dbb5104290bcc5c2829716b663cae17edc911bd

Summary:
## Changes:
- Added Accelerate Library and refactored experiment.py to use it
- Needed to move `init_optimizer` and `ExperimentConfig` to a separate file to be compatible with submitit/hydra
- Needed to make some modifications to data loaders etc to work well with the accelerate ddp wrappers
- Loading/saving checkpoints incorporates an unwrapping step so remove the ddp wrapped model

## Tests

Tested with both `torchrun` and `submitit/hydra` on two gpus locally. Here are the commands:

**Torchrun**

Modules loaded:
```sh
1) anaconda3/2021.05   2) cuda/11.3   3) NCCL/2.9.8-3-cuda.11.3   4) gcc/5.2.0. (but unload gcc when using submit)
```

```sh
torchrun --nnodes=1 --nproc_per_node=2 experiment.py --config-path ./configs --config-name repro_singleseq_nerf_test
```

**Submitit/Hydra Local test**

```sh
~/pytorch3d/projects/implicitron_trainer$ HYDRA_FULL_ERROR=1 python3.9 experiment.py --config-name repro_singleseq_nerf_test --multirun --config-path ./configs  hydra/launcher=submitit_local hydra.launcher.gpus_per_node=2 hydra.launcher.tasks_per_node=2 hydra.launcher.nodes=1
```

**Submitit/Hydra distributed test**

```sh
~/implicitron/pytorch3d$ python3.9 experiment.py --config-name repro_singleseq_nerf_test --multirun --config-path ./configs  hydra/launcher=submitit_slurm hydra.launcher.gpus_per_node=8 hydra.launcher.tasks_per_node=8 hydra.launcher.nodes=1 hydra.launcher.partition=learnlab hydra.launcher.timeout_min=4320
```

## TODOS:
- Fix distributed evaluation: currently this doesn't work as the input format to the evaluation function is not suitable for gathering across gpus (needs to be nested list/tuple/dicts of objects that satisfy `is_torch_tensor`) and currently `frame_data`  contains `Cameras` type.
- Refactor the `accelerator` object to be accessible by all functions instead of needing to pass it around everywhere? Maybe have a `Trainer` class and add it as a method?
- Update readme with installation instructions for accelerate and also commands for running jobs with torchrun and submitit/hydra

X-link: https://github.com/fairinternal/pytorch3d/pull/37

Reviewed By: davnov134, kjchalup

Differential Revision: D37543870

Pulled By: bottler

fbshipit-source-id: be9eb4e91244d4fe3740d87dafec622ae1e0cf76

Summary: As part of removing Task, move camera difficulty bin breaks from hard code to the top level.

Reviewed By: davnov134

Differential Revision: D37491040

fbshipit-source-id: f2d6775ebc490f6f75020d13f37f6b588cc07a0b

Summary: Enable pyre checking of the trainer code.

Reviewed By: shapovalov

Differential Revision: D36545438

fbshipit-source-id: db1ea8d1ade2da79a2956964eb0c7ba302fa40d1

Summary: As part of removing Task, make the dataset code generate the source cameras for itself. There's a small optimization available here, in that the JsonIndexDataset could avoid loading images.

Reviewed By: shapovalov

Differential Revision: D37313423

fbshipit-source-id: 3e5e0b2aabbf9cc51f10547a3523e98c72ad8755

Summary: Copy code from NeRF for loading LLFF data and blender synthetic data, and create dataset objects for them

Reviewed By: shapovalov

Differential Revision: D35581039

fbshipit-source-id: af7a6f3e9a42499700693381b5b147c991f57e5d

Summary: Add test that the yaml files deserialize.

Reviewed By: davnov134

Differential Revision: D36830673

fbshipit-source-id: b785d8db97b676686036760bfa2dd3fa638bda57

Summary: Preparing for pluggables in experiment.py

Reviewed By: davnov134

Differential Revision: D36830674

fbshipit-source-id: eab499d1bc19c690798fbf7da547544df7e88fa5

Summary: Add simple interactive testrunner for experiment.py

Reviewed By: shapovalov

Differential Revision: D35316221

fbshipit-source-id: d424bcba632eef89eefb56e18e536edb58ec6f85

Summary: The ImplicitronDataset class corresponds to JsonIndexDatasetMapProvider

Reviewed By: shapovalov

Differential Revision: D36661396

fbshipit-source-id: 80ca2ff81ef9ecc2e3d1f4e1cd14b6f66a7ec34d

Summary: replace dataloader_zoo with a pluggable DataLoaderMapProvider.

Reviewed By: shapovalov

Differential Revision: D36475441

fbshipit-source-id: d16abb190d876940434329928f2e3f2794a25416

Summary: replace dataset_zoo with a pluggable DatasetMapProvider. The logic is now in annotated_file_dataset_map_provider.

Reviewed By: shapovalov

Differential Revision: D36443965

fbshipit-source-id: 9087649802810055e150b2fbfcc3c197a761f28a

Summary: Separate ImplicitronDatasetBase and FrameData (to be used by all data sources) from ImplicitronDataset (which is specific).

Reviewed By: shapovalov

Differential Revision: D36413111

fbshipit-source-id: 3725744cde2e08baa11aff4048237ba10c7efbc6

Summary:
Move dataset_args and dataloader_args from ExperimentConfig into a new member called datasource so that it can contain replaceables.

Also add enum Task for task type.

Reviewed By: shapovalov

Differential Revision: D36201719

fbshipit-source-id: 47d6967bfea3b7b146b6bbd1572e0457c9365871

Summary: Stronger typing for these functions

Reviewed By: shapovalov

Differential Revision: D36170489

fbshipit-source-id: a2104b29dbbbcfcf91ae1d076cd6b0e3d2030c0b

Summary:
To avoid model_zoo, we need to make GenericModel pluggable.
I also align creation APIs for convenience.

Reviewed By: bottler, davnov134

Differential Revision: D35933093

fbshipit-source-id: 8228926528eb41a795fbfbe32304b8019197e2b1

Summary:
Try again to solve https://github.com/facebookresearch/pytorch3d/issues/1144 pickling problem.
D35258561 (https://github.com/facebookresearch/pytorch3d/commit/24260130ce382640a89e978cd671298488ec6451) didn't work.

When writing a function or vanilla class C which you want people to be able to call get_default_args on, you must add the line enable_get_default_args(C) to it. This causes autogeneration of a hidden dataclass in the module.

Reviewed By: davnov134

Differential Revision: D35364410

fbshipit-source-id: 53f6e6fff43e7142ae18ca3b06de7d0c849ef965

Summary:
ListConfig and DictConfig members of get_default_args(X) when X is a callable will contain references to a temporary dataclass and therefore be unpicklable. Avoid this in a few cases.

Fixes https://github.com/facebookresearch/pytorch3d/issues/1144

Reviewed By: shapovalov

Differential Revision: D35258561

fbshipit-source-id: e52186825f52accee9a899e466967a4ff71b3d25

Summary:
Before the fix, running get_default_args(C: Callable) returns an unstructured DictConfig which causes Enums to be handled incorrectly. This is a fix.

WIP update: Currently tests still fail whenever a function signature contains an untyped argument: This needs to be somehow fixed.

Reviewed By: bottler

Differential Revision: D34932124

fbshipit-source-id: ecdc45c738633cfea5caa7480ba4f790ece931e8

Co-authored-by: Jeremy Francis Reizenstein <bottler@users.noreply.github.com>