some fixes to eval pipeline

Author: kerrj

nerfstudio/data/datamanagers/full_images_datamanager.py

@@ -352,7 +352,7 @@ def setup_eval(self):
             self.eval_image_dataloader = DataLoader(
                 self.eval_imagebatch_stream,
                 batch_size=1,
-                num_workers=0,
+                num_workers=0, # This must be 0 otherwise there is a crash when trying to pickle custom_view_processor
                 collate_fn=identity_collate,
             )
             self.iter_eval_image_dataloader = iter(self.eval_image_dataloader)
@@ -434,6 +434,9 @@ def next_eval_image(self, step: int) -> Tuple[Cameras, Dict]:
         """Returns the next evaluation batch
         Returns a Camera instead of raybundle
         TODO: Make sure this logic is consistent with the vanilladatamanager"""
+        if self.config.cache_images == "disk":
+            camera, data = next(self.iter_eval_image_dataloader)[0]
+            return camera, data
         image_idx = self.eval_unseen_cameras.pop(random.randint(0, len(self.eval_unseen_cameras) - 1))
         # Make sure to re-populate the unseen cameras list if we have exhausted it
         if len(self.eval_unseen_cameras) == 0:

nerfstudio/data/datamanagers/parallel_datamanager.py

@@ -174,7 +174,7 @@ def setup_eval(self):
         self.eval_ray_dataloader = DataLoader(
             self.eval_raybatchstream,
             batch_size=1,
-            num_workers=0,
+            num_workers=0, # This must be 0 otherwise there is a crash when trying to pickle custom_view_processor
             shuffle=False,
             collate_fn=identity_collate,  # Our dataset handles batching / collation of rays
         )