PixelSampler Fixes (#2298)

* monodepth implemented

* device

* pair pixel sampler

* docstring

* removing unused imports

* fix bugs

* cache in cache dir, no longer edit json file

* moving to depth nerfacto

* fixed bug using median depth by accident

* removing depthdataset from nerfacto

* added pixelsamplerconfig

* added equirectangular warning back

* bug fix

* delete sample_config.py + small fixes

* rename to pixel_sampler

* cleaning

* test

* passing num_rays_per_batch properly

* fixed transforms typo

* renaming in nerfacto

* remove depth files from test data

* removed defaulting to base pixel sampler with mask

* adding patch_size back

* Warning for notimplemented error and patch_size fix

* fix config.pixel_sampler check

---------

Co-authored-by: Matthew Tancik <[email protected]>
Co-authored-by: Ethan Weber <[email protected]>
Co-authored-by: Ethan Weber <[email protected]>

nerfstudio/data/datamanagers/base_datamanager.py

@@ -461,7 +461,7 @@ def create_eval_dataset(self) -> TDataset:
 
     def _get_pixel_sampler(self, dataset: TDataset, num_rays_per_batch: int) -> PixelSampler:
         """Infer pixel sampler to use."""
-        if self.config.patch_size > 1:
+        if self.config.patch_size > 1 and type(self.config.pixel_sampler) is PixelSamplerConfig:
             return PatchPixelSamplerConfig().setup(
                 patch_size=self.config.patch_size, num_rays_per_batch=num_rays_per_batch
             )

nerfstudio/data/pixel_samplers.py

@@ -318,8 +318,9 @@ def sample_method(
         device: Union[torch.device, str] = "cpu",
     ) -> Int[Tensor, "batch_size 3"]:
         if isinstance(mask, Tensor):
-            # Note: if there is a mask, should switch to the base PixelSampler class
-            raise NotImplementedError()
+            raise NotImplementedError(
+                "Masked sampling not implemented for PairPixelSampler. Change Config to PixelSamplerConfig instead."
+            )
         else:
             sub_bs = batch_size // (self.config.patch_size**2)
             indices = torch.rand((sub_bs, 3), device=device) * torch.tensor(
@@ -379,10 +380,10 @@ def sample_method(  # pylint: disable=no-self-use
         mask: Optional[Tensor] = None,
         device: Union[torch.device, str] = "cpu",
     ) -> Int[Tensor, "batch_size 3"]:
-        if mask:
-            # Note: if there is a mask, should switch to the base PixelSampler class
-
-            raise NotImplementedError()
+        if isinstance(mask, Tensor):
+            raise NotImplementedError(
+                "Masked sampling not implemented for PairPixelSampler. Change Config to PixelSamplerConfig instead."
+            )
         else:
             s = (self.rays_to_sample, 1)
             ns = torch.randint(0, num_images, s, dtype=torch.long, device=device)