Summary:
Stats are logically connected to the training loop, not to the model. Hence, moving to the training loop.
Also removing resume_epoch from OptimizerFactory in favor of a single place - ModelFactory. This removes the need for config consistency checks etc.
Reviewed By: kjchalup
Differential Revision: D38313475
fbshipit-source-id: a1d188a63e28459df381ff98ad8acdcdb14887b7
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:
## 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: Make dataset type and args configurable on JsonIndexDatasetMapProvider.
Reviewed By: davnov134
Differential Revision: D36666705
fbshipit-source-id: 4d0a3781d9a956504f51f1c7134c04edf1eb2846
