fix tests

docs/nerfology/methods/cleanerf.md

@@ -0,0 +1,14 @@
+# CleaNeRF
+ 
+### Running the Method
+ 
+```bash
+pip install cleanerf
+ns-train cleanerf --help
+```
+ 
+## Method
+ 
+### Overview
+
+TODO

docs/nerfology/methods/index.md

@@ -31,4 +31,5 @@ In addition to their implementations, we have provided guides that walk through
     Nerfacto<nerfacto.md>
     Instant-NGP<instant_ngp.md>
     Semantic NeRF-W<semantic_nerfw.md>
+    CleaNeRF<cleanerf.md>
 ```

nerfstudio/data/utils/dataloaders.py

@@ -230,14 +230,6 @@ class RandIndicesEvalDataloader(EvalDataloader):
         device: Device to load data to
     """
 
-    def __init__(
-        self,
-        input_dataset: InputDataset,
-        device: Union[torch.device, str] = "cpu",
-        **kwargs,
-    ):
-        super().__init__(input_dataset, device, **kwargs)
-
     def __iter__(self):
         return self
 

scripts/render.py

@@ -36,7 +36,6 @@ from nerfstudio.cameras.camera_paths import (
 from nerfstudio.cameras.cameras import Cameras, CameraType
 from nerfstudio.data.scene_box import SceneBox
 from nerfstudio.model_components import renderers
-from nerfstudio.model_components.renderers import DepthRenderer
 from nerfstudio.pipelines.base_pipeline import Pipeline
 from nerfstudio.utils import colormaps, install_checks
 from nerfstudio.utils.eval_utils import eval_setup
@@ -57,6 +56,7 @@ def _render_trajectory_video(
     camera_type: CameraType = CameraType.PERSPECTIVE,
     depth_near_plane: Optional[float] = None,
     depth_far_plane: Optional[float] = None,
+    using_eval_images: bool = False,
 ) -> None:
     """Helper function to create a video of the spiral trajectory.
 
@@ -72,16 +72,15 @@ def _render_trajectory_video(
         camera_type: Camera projection format type.
         depth_near_plane: Closest depth to consider. If None, use min image value.
         depth_far_plane: Furthest depth to consider. If None, use max image value.
+        using_eval_images: Whether the eval images are being used or not.
+        depth_method: Method to use for depth rendering. If None, use model default.
     """
+    # pylint: disable=too-many-statements
     CONSOLE.print("[bold green]Creating trajectory " + output_format)
     cameras.rescale_output_resolution(rendered_resolution_scaling_factor)
     cameras = cameras.to(pipeline.device)
     fps = len(cameras) / seconds
 
-    # set the pipeline to use median depth
-    # TODO(ethan): don't hardcode this...
-    pipeline.model.renderer_depth = DepthRenderer(method="median")
-
     progress = Progress(
         TextColumn(":movie_camera: Rendering :movie_camera:"),
         BarColumn(),
@@ -153,9 +152,8 @@ def _render_trajectory_video(
                         output_image = np.concatenate((output_image,) * 3, axis=-1)
                     render_image.append(output_image)
 
-                # TODO: check for eval-images mode
-                # if, so then save the GT image as well!
-                if True:
+                if using_eval_images:
+                    # if in the eval-images mode, then save the GT images as well
                     batch = pipeline.datamanager.eval_dataloader.input_dataset[camera_idx]
                     rgb_gt = batch["image"]
                     # resize to match the rendered image
@@ -390,6 +388,7 @@ class RenderTrajectory:
             camera_type=camera_type,
             depth_near_plane=self.depth_near_plane,
             depth_far_plane=self.depth_far_plane,
+            using_eval_images=self.traj == "eval-images",
         )