move no input data flag from pipeline to datamanager

nerfstudio/configs/method_configs.py

@@ -455,7 +455,6 @@ method_configs["dreamfusion"] = TrainerConfig(
     max_num_iterations=30000,
     mixed_precision=True,
     pipeline=VanillaPipelineConfig(
-        no_input_data=True,
         datamanager=DreamFusionDataManagerConfig(
             horizontal_rotation_warmup=2000,
         ),

nerfstudio/data/datamanagers/base_datamanager.py

@@ -409,6 +409,7 @@ class VanillaDataManager(DataManager, Generic[TDataset]):  # pylint: disable=abs
         self.test_mode = test_mode
         self.test_split = "test" if test_mode in ["test", "inference"] else "val"
         self.dataparser_config = self.config.dataparser
+        self.input_data = True
         if self.config.data is not None:
             self.config.dataparser.data = Path(self.config.data)
         else:

nerfstudio/data/datamanagers/dreamfusion_datamanager.py

@@ -212,6 +212,7 @@ class DreamFusionDataManager(DataManager):  # pylint: disable=abstract-method
         self.sampler = None
         self.test_mode = test_mode
         self.test_split = "test" if test_mode in ["test", "inference"] else "val"
+        self.input_data = False
 
         if self.config.data is not None:
             CONSOLE.print("[red] --data should not be used with the DreamFusionDataManager[/red]")

nerfstudio/engine/trainer.py

@@ -223,7 +223,7 @@ class Trainer:
         """Train the model."""
         assert self.pipeline.datamanager.train_dataset is not None, "Missing DatsetInputs"
 
-        if not self.pipeline.no_input_data:
+        if self.pipeline.datamanager.input_data:
             self.pipeline.datamanager.train_dataparser_outputs.save_dataparser_transform(
                 self.base_dir / "dataparser_transforms.json"
             )

nerfstudio/pipelines/base_pipeline.py

@@ -95,7 +95,6 @@ class Pipeline(nn.Module):
 
     datamanager: DataManager
     _model: Model
-    generative: bool
     world_size: int
 
     @property
@@ -211,8 +210,6 @@ class VanillaPipelineConfig(cfg.InstantiateConfig):
     """specifies the datamanager config"""
     model: ModelConfig = ModelConfig()
     """specifies the model config"""
-    no_input_data: bool = False
-    """specifies whether the pipeline is expects an input dataset"""
 
 
 class VanillaPipeline(Pipeline):
@@ -248,7 +245,6 @@ class VanillaPipeline(Pipeline):
         self.datamanager: VanillaDataManager = config.datamanager.setup(
             device=device, test_mode=test_mode, world_size=world_size, local_rank=local_rank
         )
-        self.no_input_data = config.no_input_data
         self.datamanager.to(device)
         # TODO(ethan): get rid of scene_bounds from the model
         assert self.datamanager.train_dataset is not None, "Missing input dataset"