rename dreamfusion datamanager to random camras datamanager, check datamanager typing in pipeline and trainer

nerfstudio/configs/method_configs.py

@@ -30,7 +30,7 @@ from nerfstudio.data.datamanagers.base_datamanager import (
     VanillaDataManager,
     VanillaDataManagerConfig,
 )
-from nerfstudio.data.datamanagers.dreamfusion_datamanager import DreamFusionDataManagerConfig
+from nerfstudio.data.datamanagers.random_cameras_datamanager import RandomCamerasDataManagerConfig
 from nerfstudio.data.dataparsers.blender_dataparser import BlenderDataParserConfig
 from nerfstudio.data.dataparsers.dnerf_dataparser import DNeRFDataParserConfig
 from nerfstudio.data.dataparsers.dycheck_dataparser import DycheckDataParserConfig
@@ -458,7 +458,7 @@ method_configs["dreamfusion"] = TrainerConfig(
     max_num_iterations=30000,
     mixed_precision=True,
     pipeline=VanillaPipelineConfig(
-        datamanager=DreamFusionDataManagerConfig(
+        datamanager=RandomCamerasDataManagerConfig(
             horizontal_rotation_warmup=2000,
         ),
         model=DreamFusionModelConfig(

nerfstudio/data/datamanagers/random_cameras_datamanager.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 
 """
-Data manager for dreamfusion
+Data manager without input images, only random camera poses
 """
 
 from __future__ import annotations
@@ -150,10 +150,10 @@ def random_train_pose(
 
 
 @dataclass
-class DreamFusionDataManagerConfig(DataManagerConfig):
+class RandomCamerasDataManagerConfig(DataManagerConfig):
     """Configuration for data manager that does not load from a dataset. Instead, it generates random poses."""
 
-    _target: Type = field(default_factory=lambda: DreamFusionDataManager)
+    _target: Type = field(default_factory=lambda: RandomCamerasDataManager)
     train_resolution: int = 64
     """Training resolution"""
     eval_resolution: int = 64
@@ -180,7 +180,7 @@ class DreamFusionDataManagerConfig(DataManagerConfig):
     """How many steps until the full horizontal rotation range is used"""
 
 
-class DreamFusionDataManager(DataManager):  # pylint: disable=abstract-method
+class RandomCamerasDataManager(DataManager):  # pylint: disable=abstract-method
     """Basic stored data manager implementation.
 
     This is pretty much a port over from our old dataloading utilities, and is a little jank
@@ -193,12 +193,12 @@ class DreamFusionDataManager(DataManager):  # pylint: disable=abstract-method
         config: the DataManagerConfig used to instantiate class
     """
 
-    config: DreamFusionDataManagerConfig
+    config: RandomCamerasDataManagerConfig
 
     # pylint: disable=super-init-not-called
     def __init__(
         self,
-        config: DreamFusionDataManagerConfig,
+        config: RandomCamerasDataManagerConfig,
         device: Union[torch.device, str] = "cpu",
         test_mode: Literal["test", "val", "inference"] = "val",
         world_size: int = 1,
@@ -215,7 +215,7 @@ class DreamFusionDataManager(DataManager):  # pylint: disable=abstract-method
         self.input_data = False
 
         if self.config.data is not None:
-            CONSOLE.print("[red] --data should not be used with the DreamFusionDataManager[/red]")
+            CONSOLE.print("[red] --data should not be used with the RandomCamerasDataManager[/red]")
             sys.exit(1)
 
         print(type(config))

nerfstudio/engine/trainer.py

@@ -38,6 +38,7 @@ from nerfstudio.engine.callbacks import (
     TrainingCallbackAttributes,
     TrainingCallbackLocation,
 )
+from nerfstudio.data.datamanagers.base_datamanager import VanillaDataManager
 from nerfstudio.engine.optimizers import Optimizers
 from nerfstudio.pipelines.base_pipeline import VanillaPipeline
 from nerfstudio.utils import profiler, writer
@@ -231,6 +232,7 @@ class Trainer:
         assert self.pipeline.datamanager.train_dataset is not None, "Missing DatsetInputs"
 
         if self.pipeline.datamanager.input_data:
+            assert isinstance(self.pipeline.datamanager, VanillaDataManager)
             self.pipeline.datamanager.train_dataparser_outputs.save_dataparser_transform(
                 self.base_dir / "dataparser_transforms.json"
             )

nerfstudio/pipelines/base_pipeline.py

@@ -41,6 +41,7 @@ from nerfstudio.configs import base_config as cfg
 from nerfstudio.data.datamanagers.base_datamanager import (
     DataManager,
     DataManagerConfig,
+    VanillaDataManager,
 )
 from nerfstudio.engine.callbacks import TrainingCallback, TrainingCallbackAttributes
 from nerfstudio.models.base_model import Model, ModelConfig
@@ -346,6 +347,7 @@ class VanillaPipeline(Pipeline):
         """
         self.eval()
         metrics_dict_list = []
+        assert isinstance(self.datamanager, VanillaDataManager)
         num_images = len(self.datamanager.fixed_indices_eval_dataloader)
         with Progress(
             TextColumn("[progress.description]{task.description}"),