fix linting

nerfstudio/cameras/camera_utils.py

@@ -366,7 +366,8 @@ def radial_and_tangential_undistort(
 
     if distortion_params.shape[-1] == 0:
         return coords
-    elif distortion_params.shape[-1] < 8:
+
+    if distortion_params.shape[-1] < 8:
         distortion_params = F.pad(distortion_params, (0, 8 - distortion_params.shape[-1]), "constant", 0.0)
     assert distortion_params.shape[-1] == 8
 

nerfstudio/cameras/cameras.py

@@ -17,7 +17,6 @@ Camera Models
 """
 import base64
 import math
-import os
 from dataclasses import dataclass
 from enum import Enum, auto
 from typing import Dict, List, Optional, Tuple, Union
@@ -35,7 +34,6 @@ from nerfstudio.cameras import camera_utils
 from nerfstudio.cameras.rays import RayBundle
 from nerfstudio.data.scene_box import SceneBox
 from nerfstudio.utils import profiler
-from nerfstudio.utils.misc import strtobool
 from nerfstudio.utils.tensor_dataclass import TensorDataclass
 
 TORCH_DEVICE = Union[torch.device, str]  # pylint: disable=invalid-name
@@ -659,7 +657,7 @@ class Cameras(TensorDataclass):
                                 coord_stack[coord_mask, :].reshape(3, -1, 2),
                                 distortion_params[mask, :],
                             ).reshape(-1, 2)
-                        except:
+                        except ImportError:
                             coord_stack[coord_mask, :] = camera_utils.radial_and_tangential_undistort(
                                 coord_stack[coord_mask, :].reshape(3, -1, 2),
                                 distortion_params[mask, :],
@@ -670,12 +668,11 @@ class Cameras(TensorDataclass):
                 if mask.any():
                     if distortion_params_delta is not None:
                         raise NotImplementedError("Fisheye distortion optimization is not implemented")
-                    else:
-                        # use nerfacc with do not provide gradient
-                        coord_stack[coord_mask, :] = opencv_lens_undistortion_fisheye(
-                            coord_stack[coord_mask, :].reshape(3, -1, 2),
-                            distortion_params[mask, :],
-                        ).reshape(-1, 2)
+                    # use nerfacc which does not provide gradient
+                    coord_stack[coord_mask, :] = opencv_lens_undistortion_fisheye(
+                        coord_stack[coord_mask, :].reshape(3, -1, 2),
+                        distortion_params[mask, :],
+                    ).reshape(-1, 2)
 
         # Make sure after we have undistorted our images, the shapes are still correct
         assert coord_stack.shape == (3,) + num_rays_shape + (2,)

nerfstudio/data/datamanagers/base_datamanager.py

@@ -548,14 +548,12 @@ class VanillaDataManager(DataManager):  # pylint: disable=abstract-method
     def get_train_rays_per_batch(self) -> int:
         if self.train_pixel_sampler is None:
             return self.config.train_num_rays_per_batch
-        else:
-            return self.train_pixel_sampler.num_rays_per_batch
+        return self.train_pixel_sampler.num_rays_per_batch
 
     def get_eval_rays_per_batch(self) -> int:
         if self.eval_pixel_sampler is None:
             return self.config.eval_num_rays_per_batch
-        else:
-            return self.eval_pixel_sampler.num_rays_per_batch
+        return self.eval_pixel_sampler.num_rays_per_batch
 
     def get_datapath(self) -> Path:
         return self.config.dataparser.data

nerfstudio/data/dataparsers/instant_ngp_dataparser.py

@@ -25,7 +25,6 @@ import numpy as np
 import torch
 from rich.console import Console
 
-from nerfstudio.cameras import camera_utils
 from nerfstudio.cameras.cameras import Cameras, CameraType
 from nerfstudio.data.dataparsers.base_dataparser import (
     DataParser,

nerfstudio/model_components/ray_generators.py

@@ -17,7 +17,6 @@ Ray generator.
 """
 from typing import Optional
 
-import torch
 from torch import nn
 from torchtyping import TensorType
 

nerfstudio/models/nerfplayer_ngp.py

@@ -105,7 +105,7 @@ class NerfplayerNGPModel(NGPModel):
 
         self.field = NerfplayerNGPField(
             aabb=self.scene_box.aabb,
-            use_appearance_embedding=self.config.use_appearance_embedding,
+            use_appearance_embedding=False,
             num_images=self.num_train_data,
             temporal_dim=self.config.temporal_dim,
             num_levels=self.config.num_levels,
@@ -119,9 +119,8 @@ class NerfplayerNGPModel(NGPModel):
         self.scene_aabb = Parameter(self.scene_box.aabb.flatten(), requires_grad=False)
 
         # Occupancy Grid
-        grid_scale = 1 << (self.config.grid_levels - 1)
         self.occupancy_grid = nerfacc.OccGridEstimator(
-            roi_aabb=nerfacc.grid._enlarge_aabb(self.scene_aabb, 1.0 / grid_scale),
+            roi_aabb=self.scene_aabb,
             resolution=self.config.grid_resolution,
             levels=self.config.grid_levels,
         )