fix core tests

.github/workflows/core_code_checks.yml

@@ -15,10 +15,10 @@ jobs:
 
     steps:
       - uses: actions/checkout@v3
-      - name: Set up Python 3.8.13
+      - name: Set up Python 3.11.13
         uses: actions/setup-python@v4
         with:
-          python-version: '3.8.13'
+          python-version: '3.11.13'
       - uses: actions/cache@v3
         with:
           path: ${{ env.pythonLocation }}

nerfstudio/cameras/camera_utils.py

@@ -172,7 +172,7 @@ def get_interpolated_poses(pose_a: NDArray, pose_b: NDArray, steps: int = 10) ->
     quat_b = quaternion_from_matrix(pose_b[:3, :3])
 
     ts = np.linspace(0, 1, steps)
-    quats = [quaternion_slerp(quat_a, quat_b, t) for t in ts]
+    quats = [quaternion_slerp(quat_a, quat_b, float(t)) for t in ts]
     trans = [(1 - t) * pose_a[:3, 3] + t * pose_b[:3, 3] for t in ts]
 
     poses_ab = []
@@ -199,7 +199,7 @@ def get_interpolated_k(
         List of interpolated camera poses
     """
     Ks: List[Float[Tensor, "3 3"]] = []
-    ts = np.linspace(0, 1, steps)
+    ts = torch.linspace(0, 1, steps, dtype=k_a.dtype, device=k_a.device)
     for t in ts:
         new_k = k_a * (1.0 - t) + k_b * t
         Ks.append(new_k)
@@ -218,7 +218,7 @@ def get_interpolated_time(
         steps: number of steps the interpolated pose path should contain
     """
     times: List[Float[Tensor, "1"]] = []
-    ts = np.linspace(0, 1, steps)
+    ts = torch.linspace(0, 1, steps, dtype=time_a.dtype, device=time_a.device)
     for t in ts:
         new_t = time_a * (1.0 - t) + time_b * t
         times.append(new_t)

nerfstudio/cameras/rays.py

@@ -136,6 +136,7 @@ def get_weights(self, densities: Float[Tensor, "*batch num_samples 1"]) -> Float
             Weights for each sample
         """
 
+        assert self.deltas is not None, "Deltas must be set to compute weights"
         delta_density = self.deltas * densities
         alphas = 1 - torch.exp(-delta_density)
 

nerfstudio/data/dataparsers/colmap_dataparser.py

@@ -455,6 +455,7 @@ def _load_3D_points(self, colmap_path: Path, transform_matrix: torch.Tensor, sca
                 points3D_image_ids.append(
                     torch.cat((nids, torch.full((max_num_points - len(nids),), -1, dtype=torch.int64)))
                 )
+                assert downscale_factor is not None
                 points3D_image_xy.append(
                     torch.cat((nxy, torch.full((max_num_points - len(nxy), nxy.shape[-1]), 0, dtype=torch.float32)))
                     / downscale_factor

nerfstudio/data/dataparsers/dycheck_dataparser.py

@@ -289,8 +289,8 @@ def process_frames(self, frame_names: List[str], time_ids: np.ndarray) -> Tuple[
             cam_json = load_from_json(self.data / f"camera/{frame}.json")
             c2w = torch.as_tensor(cam_json["orientation"]).T
             position = torch.as_tensor(cam_json["position"])
-            position -= self._center  # some scenes look weird (wheel)
-            position *= self._scale * self.config.scale_factor
+            position -= torch.as_tensor(self._center)  # some scenes look weird (wheel)
+            position *= torch.as_tensor(self._scale) * self.config.scale_factor
             pose = torch.zeros([3, 4])
             pose[:3, :3] = c2w
             pose[:3, 3] = position

nerfstudio/data/dataparsers/nerfstudio_dataparser.py

@@ -484,6 +484,7 @@ def _get_fname(self, filepath: Path, data_dir: Path, downsample_folder_prefix="i
                 CONSOLE.log(f"Auto image downscale factor of {self.downscale_factor}")
             else:
                 self.downscale_factor = self.config.downscale_factor
+            assert self.downscale_factor is not None
 
         if self.downscale_factor > 1:
             return data_dir / f"{downsample_folder_prefix}{self.downscale_factor}" / filepath.name

nerfstudio/data/pixel_samplers.py

@@ -227,6 +227,7 @@ def sample_method_fisheye(
 
                 rand_samples = torch.rand((samples_needed, 2), device=device)
                 # Convert random samples to radius and theta.
+                assert self.config.fisheye_crop_radius is not None
                 radii = self.config.fisheye_crop_radius * torch.sqrt(rand_samples[:, 0])
                 theta = 2.0 * torch.pi * rand_samples[:, 1]
 

nerfstudio/data/utils/data_utils.py

@@ -116,7 +116,7 @@ def get_depth_image_from_path(
     else:
         image = cv2.imread(str(filepath.absolute()), cv2.IMREAD_ANYDEPTH)
         image = image.astype(np.float32) * scale_factor
-        image = cv2.resize(image, (width, height), interpolation=interpolation)
+        image = cv2.resize(image, (width, height), interpolation=interpolation)  # type: ignore
     return torch.from_numpy(image[:, :, np.newaxis])
 
 

nerfstudio/exporter/exporter_utils.py

@@ -345,6 +345,7 @@ def collect_camera_poses(pipeline: VanillaPipeline) -> Tuple[List[Dict[str, Any]
     camera_optimizer = None
     if hasattr(pipeline.model, "camera_optimizer"):
         camera_optimizer = pipeline.model.camera_optimizer
+        assert isinstance(camera_optimizer, CameraOptimizer)
 
     train_frames = collect_camera_poses_for_dataset(train_dataset, camera_optimizer)
     # Note: returning original poses, even if --eval-mode=all

nerfstudio/field_components/encodings.py

@@ -45,11 +45,6 @@ def __init__(self, in_dim: int) -> None:
             raise ValueError("Input dimension should be greater than zero")
         super().__init__(in_dim=in_dim)
 
-    @classmethod
-    def get_tcnn_encoding_config(cls) -> dict:
-        """Get the encoding configuration for tcnn if implemented"""
-        raise NotImplementedError("Encoding does not have a TCNN implementation")
-
     @abstractmethod
     def forward(self, in_tensor: Shaped[Tensor, "*bs input_dim"]) -> Shaped[Tensor, "*bs output_dim"]:
         """Call forward and returns and processed tensor
@@ -217,6 +212,7 @@ def __init__(
         self.min_freq = min_freq_exp
         self.max_freq = max_freq_exp
         self.register_buffer(name="b_matrix", tensor=basis)
+        self.b_matrix: Tensor
         self.include_input = include_input
 
     def get_out_dim(self) -> int:

nerfstudio/fields/sdf_field.py

@@ -328,6 +328,7 @@ def get_alpha(
         )  # always non-positive
 
         # Estimate signed distances at section points
+        assert ray_samples.deltas is not None, "Ray samples must have deltas for alpha computation."
         estimated_next_sdf = sdf + iter_cos * ray_samples.deltas * 0.5
         estimated_prev_sdf = sdf - iter_cos * ray_samples.deltas * 0.5
 

nerfstudio/models/generfacto.py

@@ -19,7 +19,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Dict, List, Optional, Tuple, Type
+from typing import Dict, List, Optional, Tuple, Type, cast
 
 import numpy as np
 import torch
@@ -444,7 +444,7 @@ def get_loss_dict(self, outputs, batch, metrics_dict=None) -> Dict[str, torch.Te
         loss_dict = misc.scale_dict(loss_dict, self.config.loss_coefficients)
         if self.train_normals:
             # orientation loss for computed normals
-            loss_dict["orientation_loss"] = self.orientation_loss_mult * torch.mean(
+            loss_dict["orientation_loss"] = cast(float, self.orientation_loss_mult) * torch.mean(
                 outputs["rendered_orientation_loss"]
             )
         else:

nerfstudio/models/instant_ngp.py

@@ -19,7 +19,7 @@
 from __future__ import annotations
 
 from dataclasses import dataclass, field
-from typing import Dict, List, Literal, Optional, Tuple, Type, Union
+from typing import Dict, List, Literal, Optional, Tuple, Type, Union, cast
 
 import nerfacc
 import torch
@@ -152,7 +152,7 @@ def get_training_callbacks(
         def update_occupancy_grid(step: int):
             self.occupancy_grid.update_every_n_steps(
                 step=step,
-                occ_eval_fn=lambda x: self.field.density_fn(x) * self.config.render_step_size,
+                occ_eval_fn=lambda x: self.field.density_fn(x) * cast(float, self.config.render_step_size),
             )
 
         return [
@@ -170,7 +170,7 @@ def get_param_groups(self) -> Dict[str, List[Parameter]]:
         param_groups["fields"] = list(self.field.parameters())
         return param_groups
 
-    def get_outputs(self, ray_bundle: RayBundle):
+    def get_outputs(self, ray_bundle: RayBundle):  # type: ignore
         assert self.field is not None
         num_rays = len(ray_bundle)
 

nerfstudio/models/splatfacto.py

@@ -291,7 +291,7 @@ def populate_modules(self):
             )
             self.strategy_state = self.strategy.initialize_state()
         else:
-            raise ValueError(f"""Splatfacto does not support strategy {self.config.strategy} 
+            raise ValueError(f"""Splatfacto does not support strategy {self.config.strategy}
                              Currently, the supported strategies include default and mcmc.""")
 
     @property
@@ -552,7 +552,7 @@ def get_outputs(self, camera: Cameras) -> Dict[str, Union[torch.Tensor, List]]:
             colors_crop = torch.sigmoid(colors_crop).squeeze(1)  # [N, 1, 3] -> [N, 3]
             sh_degree_to_use = None
 
-        render, alpha, self.info = rasterization(
+        render, alpha, self.info = rasterization(  # type: ignore[reportPossiblyUnboundVariable]
             means=means_crop,
             quats=quats_crop,  # rasterization does normalization internally
             scales=torch.exp(scales_crop),

nerfstudio/process_data/process_data_utils.py

@@ -21,7 +21,7 @@
 import sys
 from enum import Enum
 from pathlib import Path
-from typing import List, Literal, Optional, OrderedDict, Tuple, Union
+from typing import List, Literal, Optional, OrderedDict, Tuple, Union, cast
 
 import cv2
 import imageio
@@ -95,7 +95,7 @@ def get_image_filenames(directory: Path, max_num_images: int = -1) -> Tuple[List
     else:
         idx = np.arange(num_orig_images)
 
-    image_filenames = list(np.array(image_paths)[idx])
+    image_filenames = cast(List[Path], list(np.array(image_paths)[idx]))
 
     return image_filenames, num_orig_images
 
@@ -596,7 +596,7 @@ def generate_circle_mask(height: int, width: int, percent_radius) -> Optional[np
     mask = np.zeros((height, width), dtype=np.uint8)
     center = (width // 2, height // 2)
     radius = int(percent_radius * np.sqrt(width**2 + height**2) / 2.0)
-    cv2.circle(mask, center, radius, 1, -1)
+    cv2.circle(mask, center, radius, 1, -1)  # type: ignore
     return mask
 
 

nerfstudio/scripts/downloads/eyeful_tower.py

@@ -120,7 +120,7 @@ def scale_metashape_transform(xml_tree: ET.ElementTree, target_width: int, targe
 
         root = transformed.getroot()
         assert len(root) == 1
-        chunk = root[0]
+        chunk = root[0]  # type: ignore[reportOptionalSubscript]
         sensors = chunk.find("sensors")
         assert sensors is not None
 

nerfstudio/scripts/process_data.py

@@ -19,7 +19,7 @@
 import zipfile
 from dataclasses import dataclass
 from pathlib import Path
-from typing import Optional, Union
+from typing import List, Optional, Union, cast
 
 import numpy as np
 import tyro
@@ -90,7 +90,7 @@ def main(self) -> None:
         record3d_image_filenames = list(np.array(record3d_image_filenames)[idx])
         # Copy images to output directory
         copied_image_paths = process_data_utils.copy_images_list(
-            record3d_image_filenames,
+            cast(List[Path], record3d_image_filenames),
             image_dir=image_dir,
             verbose=self.verbose,
             num_downscales=self.num_downscales,

nerfstudio/scripts/train.py

@@ -215,6 +215,7 @@ def launch(
             process_context.join()
         except KeyboardInterrupt:
             for i, process in enumerate(process_context.processes):
+                assert process is not None
                 if process.is_alive():
                     CONSOLE.log(f"Terminating process {i}...")
                     process.terminate()

nerfstudio/utils/colormaps.py

@@ -111,7 +111,10 @@ def apply_float_colormap(image: Float[Tensor, "*bs 1"], colormap: Colormaps = "v
     image_long_max = torch.max(image_long)
     assert image_long_min >= 0, f"the min value is {image_long_min}"
     assert image_long_max <= 255, f"the max value is {image_long_max}"
-    return torch.tensor(matplotlib.colormaps[colormap].colors, device=image.device)[image_long[..., 0]]
+    return torch.tensor(
+        matplotlib.colormaps[colormap].colors,  # type: ignore
+        device=image.device,
+    )[image_long[..., 0]]
 
 
 def apply_depth_colormap(

nerfstudio/utils/misc.py

@@ -44,7 +44,7 @@ def get_dict_to_torch(stuff: T, device: Union[torch.device, str] = "cpu", exclud
                 stuff[k] = get_dict_to_torch(v, device)
         return stuff
     if isinstance(stuff, torch.Tensor):
-        return stuff.to(device)
+        return stuff.to(device)  # type: ignore[reportReturnType]
     return stuff
 
 
@@ -59,7 +59,7 @@ def get_dict_to_cpu(stuff: T) -> T:
             stuff[k] = get_dict_to_cpu(v)
         return stuff
     if isinstance(stuff, torch.Tensor):
-        return stuff.detach().cpu()
+        return stuff.detach().cpu()  # type: ignore[reportReturnType]
     return stuff
 
 

nerfstudio/utils/profiler.py

@@ -58,7 +58,7 @@ def time_function(name_or_func: Union[CallableT, str]) -> Union[CallableT, Conte
     Returns:
         A wrapped function or context to use in a `with` statement.
     """
-    return _TimeFunction(name_or_func)
+    return _TimeFunction(name_or_func)  # type: ignore[reportReturnType]
 
 
 class _TimeFunction(ContextDecorator):

nerfstudio/viewer/render_panel.py

@@ -367,7 +367,9 @@ def update_spline(self) -> None:
         points_array = self._position_spline.evaluate(
             self.spline_t_from_t_sec(np.linspace(0, transition_times_cumsum[-1], num_frames))
         )
-        colors_array = np.array([colorsys.hls_to_rgb(h, 0.5, 1.0) for h in np.linspace(0.0, 1.0, len(points_array))])
+        colors_array = np.array(
+            [colorsys.hls_to_rgb(float(h), 0.5, 1.0) for h in np.linspace(0.0, 1.0, len(points_array))],
+        )
 
         # Clear prior spline nodes.
         for node in self._spline_nodes:
@@ -377,7 +379,7 @@ def update_spline(self) -> None:
         self._spline_nodes.append(
             self._server.scene.add_spline_catmull_rom(
                 "/render_camera_spline",
-                positions=points_array,
+                points=points_array,
                 color=(220, 220, 220),
                 closed=self.loop,
                 line_width=1.0,

nerfstudio/viewer/render_state_machine.py

@@ -147,10 +147,9 @@ def _render_img(self, camera_state: CameraState):
                         [color[0] / 255.0, color[1] / 255.0, color[2] / 255.0],
                         device=self.viewer.get_model().device,
                     )
-                    self.viewer.get_model().set_background(background_color)
+                    self.viewer.get_model().set_background(background_color)  # type: ignore[reportCallIssue]
                 was_training = self.viewer.get_model().training
                 self.viewer.get_model().eval()
-                step = self.viewer.step
                 try:
                     if self.viewer.control_panel.crop_viewport:
                         color = self.viewer.control_panel.background_color
@@ -201,7 +200,10 @@ def _render_img(self, camera_state: CameraState):
         render_time = vis_t.duration
         if writer.is_initialized() and render_time != 0:
             writer.put_time(
-                name=EventName.VIS_RAYS_PER_SEC, duration=num_rays / render_time, step=step, avg_over_steps=True
+                name=EventName.VIS_RAYS_PER_SEC,
+                duration=num_rays / render_time,
+                step=self.viewer.step,
+                avg_over_steps=True,
             )
         return outputs
 

pyproject.toml

@@ -58,7 +58,7 @@ dependencies = [
     "torchvision>=0.14.1",
     "torchmetrics[image]>=1.0.1",
     "typing_extensions>=4.4.0",
-    "viser==0.2.7",
+    "viser==1.0.0",
     "nuscenes-devkit>=1.1.1",
     "wandb>=0.13.3",
     "xatlas",
@@ -93,7 +93,7 @@ dev = [
     "pre-commit==3.3.2",
     "pytest==7.1.2",
     "pytest-xdist==2.5.0",
-    "ruff>=0.6.1",
+    "ruff==0.12.2",
     "sshconf==0.2.5",
     "pycolmap>=0.3.0",  # NOTE: pycolmap==0.3.0 is not available on newer python versions
     "diffusers==0.16.1",
@@ -103,8 +103,7 @@ dev = [
     # NOTE: Disabling projectaria-tools because it doesn't have prebuilt windows wheels
     # Syntax comes from here: https://pip.pypa.io/en/stable/reference/requirement-specifiers/
     "projectaria-tools>=1.3.1; sys_platform != 'win32'",
-    # pin torch to <=2.1 to fix https://github.com/pytorch/pytorch/issues/118736
-    "torch>=1.13.1,<2.2",
+    "torch==2.7.1",
     "awscli==1.33.18"
 ]
 
@@ -161,7 +160,6 @@ reportMissingImports = "warning"
 reportMissingTypeStubs = false
 reportPrivateImportUsage = false
 
-pythonVersion = "3.8"
 pythonPlatform = "Linux"
 
 [tool.ruff]
@@ -185,6 +183,7 @@ lint.ignore = [
     "PLR0915",  # Too many statements.
     "PLR0913",  # Too many arguments.
     "PLC0414",  # Import alias does not rename variable. (this is used for exporting names)
+    "PLC0415",  # `import` should be at the top-level of a file
     "PLC1901",  # Use falsey strings.
     "PLR5501",  # Use `elif` instead of `else if`.
     "PLR0911",  # Too many return statements.