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data_source

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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
This commit is contained in:
Jeremy Reizenstein
2022-05-20 07:50:30 -07:00
committed by Facebook GitHub Bot
parent 9ec9d057cc
commit 73dc109dba
10 changed files with 194 additions and 124 deletions
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13
projects/implicitron_trainer/README.md
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@@ -66,7 +66,8 @@ If you have a custom `experiment.py` script (as in the Option 2 above), replace
To run training, pass a yaml config file, followed by a list of overridden arguments.
For example, to train NeRF on the first skateboard sequence from CO3D dataset, you can run:
```shell
pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf dataset_args.dataset_root=<DATASET_ROOT> dataset_args.category='skateboard' dataset_args.test_restrict_sequence_id=0 test_when_finished=True exp_dir=<CHECKPOINT_DIR>
dataset_args=data_source_args.dataset_args
pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf $dataset_args.dataset_root=<DATASET_ROOT> $dataset_args.category='skateboard' $dataset_args.test_restrict_sequence_id=0 test_when_finished=True exp_dir=<CHECKPOINT_DIR>
```
Here, `--config-path` points to the config path relative to `pytorch3d_implicitron_runner` location;
@@ -84,7 +85,8 @@ To run evaluation on the latest checkpoint after (or during) training, simply ad
E.g. for executing the evaluation on the NeRF skateboard sequence, you can run:
```shell
pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf dataset_args.dataset_root=<CO3D_DATASET_ROOT> dataset_args.category='skateboard' dataset_args.test_restrict_sequence_id=0 exp_dir=<CHECKPOINT_DIR> eval_only=True
dataset_args=data_source_args.dataset_args
pytorch3d_implicitron_runner --config-path ./configs/ --config-name repro_singleseq_nerf $dataset_args.dataset_root=<CO3D_DATASET_ROOT> $dataset_args.category='skateboard' $dataset_args.test_restrict_sequence_id=0 exp_dir=<CHECKPOINT_DIR> eval_only=True
```
Evaluation prints the metrics to `stdout` and dumps them to a json file in `exp_dir`.
@@ -202,7 +204,7 @@ 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 `dataset_args` node.
In particular, model parameters are contained in `generic_model_args` node, and dataset parameters in `data_source_args` node.
Here is the class structure (single-line edges show aggregation, while double lines show available implementations):
```
@@ -233,8 +235,9 @@ generic_model_args: GenericModel
╘== AngleWeightedReductionFeatureAggregator
╘== ReductionFeatureAggregator
solver_args: init_optimizer
dataset_args: dataset_zoo
dataloader_args: dataloader_zoo
data_source_args: ImplicitronDataSource
└-- dataset_args
└-- dataloader_args
```
Please look at the annotations of the respective classes or functions for the lists of hyperparameters.