TrainerConfig inherit from InstantiateConfig (#1175)

Making TrainerConfig an InstantiateConfig so it can be swapped in and out with different methods. Also made the TrainerConfig a subclass of ExperimentConfig (which was changed from a printableconfig to an instantiateconfig), since we were already instantiating the Trainer class with the ExperimentConfig

Removed the "config" field from the TrainerCallbackAttributes struct to eliminate circular dependencies when types are checked.

Minor scripts and docs changes to accommodate the update to the config struct, meaning the flags that were associated with the TrainerConfig were moved up to the top level of the config.

nerfstudio/utils/eval_utils.py

@@ -27,8 +27,7 @@ import yaml
 from rich.console import Console
 from typing_extensions import Literal
 
-from nerfstudio.configs.base_config import TrainerConfig
-from nerfstudio.configs.experiment_config import ExperimentConfig
+from nerfstudio.engine.trainer import TrainerConfig
 from nerfstudio.pipelines.base_pipeline import Pipeline
 
 CONSOLE = Console(width=120)
@@ -69,7 +68,7 @@ def eval_setup(
     config_path: Path,
     eval_num_rays_per_chunk: Optional[int] = None,
     test_mode: Literal["test", "val", "inference"] = "test",
-) -> Tuple[ExperimentConfig, Pipeline, Path]:
+) -> Tuple[TrainerConfig, Pipeline, Path]:
     """Shared setup for loading a saved pipeline for evaluation.
 
     Args:
@@ -86,14 +85,14 @@ def eval_setup(
     """
     # load save config
     config = yaml.load(config_path.read_text(), Loader=yaml.Loader)
-    assert isinstance(config, ExperimentConfig)
+    assert isinstance(config, TrainerConfig)
 
     if eval_num_rays_per_chunk:
         config.pipeline.model.eval_num_rays_per_chunk = eval_num_rays_per_chunk
 
     # load checkpoints from wherever they were saved
     # TODO: expose the ability to choose an arbitrary checkpoint
-    config.trainer.load_dir = config.get_checkpoint_dir()
+    config.load_dir = config.get_checkpoint_dir()
     config.pipeline.datamanager.eval_image_indices = None
 
     # setup pipeline (which includes the DataManager)
@@ -103,6 +102,6 @@ def eval_setup(
     pipeline.eval()
 
     # load checkpointed information
-    checkpoint_path = eval_load_checkpoint(config.trainer, pipeline)
+    checkpoint_path = eval_load_checkpoint(config, pipeline)
 
     return config, pipeline, checkpoint_path