Add the OverfitModel

Summary:
Introduces the OverfitModel for NeRF-style training with overfitting to one scene.
It is a specific case of GenericModel. It has been disentangle to ease usage.

## General modification

1. Modularize a minimum GenericModel to introduce OverfitModel
2. Introduce OverfitModel and ensure through unit testing that it behaves like GenericModel.

## Modularization

The following methods have been extracted from GenericModel to allow modularity with ManyViewModel:
- get_objective is now a call to weighted_sum_losses
- log_loss_weights
- prepare_inputs

The generic methods have been moved to an utils.py file.

Simplify the code to introduce OverfitModel.

Private methods like chunk_generator are now public and can now be used by ManyViewModel.

Reviewed By: shapovalov

Differential Revision: D43771992

fbshipit-source-id: 6102aeb21c7fdd56aa2ff9cd1dd23fd9fbf26315

projects/implicitron_trainer/tests/test_experiment.py

@@ -141,7 +141,11 @@ class TestExperiment(unittest.TestCase):
         # Check that all the pre-prepared configs are valid.
         config_files = []
 
-        for pattern in ("repro_singleseq*.yaml", "repro_multiseq*.yaml"):
+        for pattern in (
+            "repro_singleseq*.yaml",
+            "repro_multiseq*.yaml",
+            "overfit_singleseq*.yaml",
+        ):
             config_files.extend(
                 [
                     f