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Summary: Updates for recent replaceables.

Reviewed By: kjchalup

Differential Revision: D38437370

fbshipit-source-id: 00d600aa451e5849ba48107cd7a4319e9fc8549f
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Jeremy Reizenstein 2022-08-09 20:48:51 -07:00 committed by Facebook GitHub Bot
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projects/implicitron_trainer/README.md
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@ -203,14 +203,29 @@ to replace the implementation and potentially override the parameters.
# Code and config structure
As per above, the config structure is parsed automatically from the module hierarchy.
In particular, model parameters are contained in `generic_model_args` node, and dataset parameters in `data_source_args` node.
The main object for this trainer loop is `Experiment`. It has four top-level replaceable components.
Here is the class structure (single-line edges show aggregation, while double lines show available implementations):
* `data_source`: This is a `DataSourceBase` which defaults to `ImplicitronDataSource`.
It constructs the data sets and dataloaders.
* `model_factory`: This is a `ModelFactoryBase` which defaults to `ImplicitronModelFactory`.
It constructs the model, which is usually an instance of implicitron's main `GenericModel` class, and can load its weights from a checkpoint.
* `optimizer_factory`: This is an `OptimizerFactoryBase` which defaults to `ImplicitronOptimizerFactory`.
It constructs the optimizer and can load its weights from a checkpoint.
* `training_loop`: This is a `TrainingLoopBase` which defaults to `ImplicitronTrainingLoop` and defines the main training loop.
As per above, the config structure is parsed automatically from the module hierarchy.
In particular, for ImplicitronModelFactory with generic model, model parameters are contained in the `model_factory_ImplicitronModelFactory_args.model_GenericModel_args` node, and dataset parameters in `data_source_ImplicitronDataSource_args` node.
Here is the class structure of GenericModel (single-line edges show aggregation, while double lines show available implementations):
```
generic_model_args: GenericModel
└-- sequence_autodecoder_args: Autodecoder
└-- raysampler_args: RaySampler
model_GenericModel_args: GenericModel
└-- global_encoder_*_args: GlobalEncoderBase
╘== SequenceAutodecoder
└-- autodecoder_args: Autodecoder
╘== HarmonicTimeEncoder
└-- raysampler_*_args: RaySampler
╘== AdaptiveRaysampler
╘== NearFarRaysampler
└-- renderer_*_args: BaseRenderer
╘== MultiPassEmissionAbsorptionRenderer
╘== LSTMRenderer
@ -228,19 +243,16 @@ generic_model_args: GenericModel
╘== IdrFeatureField
└-- image_feature_extractor_*_args: FeatureExtractorBase
╘== ResNetFeatureExtractor
└-- view_sampler_args: ViewSampler
└-- feature_aggregator_*_args: FeatureAggregatorBase
╘== IdentityFeatureAggregator
╘== AngleWeightedIdentityFeatureAggregator
╘== AngleWeightedReductionFeatureAggregator
╘== ReductionFeatureAggregator
solver_args: init_optimizer
data_source_args: ImplicitronDataSource
└-- dataset_map_provider_*_args
└-- data_loader_map_provider_*_args
└-- view_pooler_args: ViewPooler
└-- view_sampler_args: ViewSampler
└-- feature_aggregator_*_args: FeatureAggregatorBase
╘== IdentityFeatureAggregator
╘== AngleWeightedIdentityFeatureAggregator
╘== AngleWeightedReductionFeatureAggregator
╘== ReductionFeatureAggregator
```
Please look at the annotations of the respective classes or functions for the lists of hyperparameters.
Please look at the annotations of the respective classes or functions for the lists of hyperparameters. `tests/experiment.yaml` shows every possible option if you have no user-defined classes.
# Reproducing CO3D experiments