Fix CQS signal readability-braces-around-statements in fbcode/vision/fair

Reviewed By: bottler

Differential Revision: D94068738

fbshipit-source-id: cd47c67d4269ac7461acb73da6de9e4373da9d4c

docs/conf.py

@@ -19,7 +19,6 @@
 #
 import os
 import sys
-
 import unittest.mock as mock
 
 from recommonmark.parser import CommonMarkParser

projects/implicitron_trainer/experiment.py

@@ -48,22 +48,18 @@ The outputs of the experiment are saved and logged in multiple ways:
 import logging
 import os
 import warnings
-
 from dataclasses import field
 
 import hydra
-
 import torch
 from accelerate import Accelerator
 from omegaconf import DictConfig, OmegaConf
 from packaging import version
-
 from pytorch3d.implicitron.dataset.data_source import (
     DataSourceBase,
     ImplicitronDataSource,
 )
 from pytorch3d.implicitron.models.base_model import ImplicitronModelBase
-
 from pytorch3d.implicitron.models.renderer.multipass_ea import (
     MultiPassEmissionAbsorptionRenderer,
 )

projects/implicitron_trainer/impl/model_factory.py

@@ -11,7 +11,6 @@ import os
 from typing import Optional
 
 import torch.optim
-
 from accelerate import Accelerator
 from pytorch3d.implicitron.models.base_model import ImplicitronModelBase
 from pytorch3d.implicitron.tools import model_io

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -14,9 +14,7 @@ from dataclasses import field
 from typing import Any, Dict, List, Optional, Tuple
 
 import torch.optim
-
 from accelerate import Accelerator
-
 from pytorch3d.implicitron.models.base_model import ImplicitronModelBase
 from pytorch3d.implicitron.tools import model_io
 from pytorch3d.implicitron.tools.config import (

projects/implicitron_trainer/tests/test_experiment.py

@@ -12,7 +12,6 @@ import unittest
 from pathlib import Path
 
 import torch
-
 from hydra import compose, initialize_config_dir
 from omegaconf import OmegaConf
 from projects.implicitron_trainer.impl.optimizer_factory import (

pytorch3d/csrc/pulsar/include/renderer.destruct.device.h

@@ -18,68 +18,89 @@ namespace Renderer {
 
 template <bool DEV>
 HOST void destruct(Renderer* self) {
-  if (self->result_d != NULL)
+  if (self->result_d != NULL) {
     FREE(self->result_d);
+  }
   self->result_d = NULL;
-  if (self->min_depth_d != NULL)
+  if (self->min_depth_d != NULL) {
     FREE(self->min_depth_d);
+  }
   self->min_depth_d = NULL;
-  if (self->min_depth_sorted_d != NULL)
+  if (self->min_depth_sorted_d != NULL) {
     FREE(self->min_depth_sorted_d);
+  }
   self->min_depth_sorted_d = NULL;
-  if (self->ii_d != NULL)
+  if (self->ii_d != NULL) {
     FREE(self->ii_d);
+  }
   self->ii_d = NULL;
-  if (self->ii_sorted_d != NULL)
+  if (self->ii_sorted_d != NULL) {
     FREE(self->ii_sorted_d);
+  }
   self->ii_sorted_d = NULL;
-  if (self->ids_d != NULL)
+  if (self->ids_d != NULL) {
     FREE(self->ids_d);
+  }
   self->ids_d = NULL;
-  if (self->ids_sorted_d != NULL)
+  if (self->ids_sorted_d != NULL) {
     FREE(self->ids_sorted_d);
+  }
   self->ids_sorted_d = NULL;
-  if (self->workspace_d != NULL)
+  if (self->workspace_d != NULL) {
     FREE(self->workspace_d);
+  }
   self->workspace_d = NULL;
-  if (self->di_d != NULL)
+  if (self->di_d != NULL) {
     FREE(self->di_d);
+  }
   self->di_d = NULL;
-  if (self->di_sorted_d != NULL)
+  if (self->di_sorted_d != NULL) {
     FREE(self->di_sorted_d);
+  }
   self->di_sorted_d = NULL;
-  if (self->region_flags_d != NULL)
+  if (self->region_flags_d != NULL) {
     FREE(self->region_flags_d);
+  }
   self->region_flags_d = NULL;
-  if (self->num_selected_d != NULL)
+  if (self->num_selected_d != NULL) {
     FREE(self->num_selected_d);
+  }
   self->num_selected_d = NULL;
-  if (self->forw_info_d != NULL)
+  if (self->forw_info_d != NULL) {
     FREE(self->forw_info_d);
+  }
   self->forw_info_d = NULL;
-  if (self->min_max_pixels_d != NULL)
+  if (self->min_max_pixels_d != NULL) {
     FREE(self->min_max_pixels_d);
+  }
   self->min_max_pixels_d = NULL;
-  if (self->grad_pos_d != NULL)
+  if (self->grad_pos_d != NULL) {
     FREE(self->grad_pos_d);
+  }
   self->grad_pos_d = NULL;
-  if (self->grad_col_d != NULL)
+  if (self->grad_col_d != NULL) {
     FREE(self->grad_col_d);
+  }
   self->grad_col_d = NULL;
-  if (self->grad_rad_d != NULL)
+  if (self->grad_rad_d != NULL) {
     FREE(self->grad_rad_d);
+  }
   self->grad_rad_d = NULL;
-  if (self->grad_cam_d != NULL)
+  if (self->grad_cam_d != NULL) {
     FREE(self->grad_cam_d);
+  }
   self->grad_cam_d = NULL;
-  if (self->grad_cam_buf_d != NULL)
+  if (self->grad_cam_buf_d != NULL) {
     FREE(self->grad_cam_buf_d);
+  }
   self->grad_cam_buf_d = NULL;
-  if (self->grad_opy_d != NULL)
+  if (self->grad_opy_d != NULL) {
     FREE(self->grad_opy_d);
+  }
   self->grad_opy_d = NULL;
-  if (self->n_grad_contributions_d != NULL)
+  if (self->n_grad_contributions_d != NULL) {
     FREE(self->n_grad_contributions_d);
+  }
   self->n_grad_contributions_d = NULL;
 }
 

pytorch3d/csrc/pulsar/include/renderer.norm_sphere_gradients.device.h

@@ -64,8 +64,9 @@ GLOBAL void norm_sphere_gradients(Renderer renderer, const int num_balls) {
   // The sphere only contributes to the camera gradients if it is
   // large enough in screen space.
   if (renderer.ids_sorted_d[idx] > 0 && ii.max.x >= ii.min.x + 3 &&
-      ii.max.y >= ii.min.y + 3)
+      ii.max.y >= ii.min.y + 3) {
     renderer.ids_sorted_d[idx] = 1;
+  }
   END_PARALLEL_NORET();
 };
 

pytorch3d/csrc/pulsar/include/renderer.render.device.h

@@ -139,8 +139,9 @@ GLOBAL void render(
       coord_y < cam_norm.film_border_top + cam_norm.film_height) {
     // Initialize the result.
     if (mode == 0u) {
-      for (uint c_id = 0; c_id < cam_norm.n_channels; ++c_id)
+      for (uint c_id = 0; c_id < cam_norm.n_channels; ++c_id) {
         result[c_id] = bg_col[c_id];
+      }
     } else {
       result[0] = 0.f;
     }
@@ -190,20 +191,22 @@ GLOBAL void render(
             "render|found intersection with sphere %u.\n",
             sphere_id_l[write_idx]);
       }
-      if (ii.min.x == MAX_USHORT)
+      if (ii.min.x == MAX_USHORT) {
         // This is an invalid sphere (out of image). These spheres have
         // maximum depth. Since we ordered the spheres by earliest possible
         // intersection depth we re certain that there will no other sphere
         // that is relevant after this one.
         loading_done = true;
+      }
     }
     // Reset n_pixels_done.
     n_pixels_done = 0;
     thread_block.sync(); // Make sure n_loaded is updated.
     if (n_loaded > RENDER_BUFFER_LOAD_THRESH) {
       // The load buffer is full enough. Draw.
-      if (thread_block.thread_rank() == 0)
+      if (thread_block.thread_rank() == 0) {
         n_balls_loaded += n_loaded;
+      }
       max_closest_possible_intersection = 0.f;
       // This excludes threads outside of the image boundary. Also, it reduces
       // block artifacts.
@@ -290,8 +293,9 @@ GLOBAL void render(
       uint warp_done = thread_warp.ballot(done);
       int warp_done_bit_cnt = POPC(warp_done);
 #endif //__CUDACC__ && __HIP_PLATFORM_AMD__
-      if (thread_warp.thread_rank() == 0)
+      if (thread_warp.thread_rank() == 0) {
         ATOMICADD_B(&n_pixels_done, warp_done_bit_cnt);
+      }
       // This sync is necessary to keep n_loaded until all threads are done with
       // painting.
       thread_block.sync();
@@ -299,8 +303,9 @@ GLOBAL void render(
     }
     thread_block.sync();
   }
-  if (thread_block.thread_rank() == 0)
+  if (thread_block.thread_rank() == 0) {
     n_balls_loaded += n_loaded;
+  }
   PULSAR_LOG_DEV_PIX(
       PULSAR_LOG_RENDER_PIX,
       "render|loaded %d balls in total.\n",
@@ -386,8 +391,9 @@ GLOBAL void render(
             static_cast<float>(tracker.get_n_hits());
   } else {
     float sm_d_normfac = FRCP(FMAX(sm_d, FEPS));
-    for (uint c_id = 0; c_id < cam_norm.n_channels; ++c_id)
+    for (uint c_id = 0; c_id < cam_norm.n_channels; ++c_id) {
       result[c_id] *= sm_d_normfac;
+    }
     int write_loc = (coord_y - cam_norm.film_border_top) * cam_norm.film_width *
             (3 + 2 * n_track) +
         (coord_x - cam_norm.film_border_left) * (3 + 2 * n_track);

pytorch3d/csrc/pulsar/pytorch/renderer.cpp

@@ -860,8 +860,9 @@ std::tuple<torch::Tensor, torch::Tensor> Renderer::forward(
             ? (cudaStream_t) nullptr
 #endif
             : (cudaStream_t) nullptr);
-    if (mode == 1)
+    if (mode == 1) {
       results[batch_i] = results[batch_i].slice(2, 0, 1, 1);
+    }
     forw_infos[batch_i] = from_blob(
         this->renderer_vec[batch_i].forw_info_d,
         {this->renderer_vec[0].cam.film_height,

pytorch3d/csrc/pulsar/pytorch/renderer.h

@@ -128,8 +128,9 @@ struct Renderer {
     stream << "pulsar::Renderer[";
     // Device info.
     stream << self.device_type;
-    if (self.device_index != -1)
+    if (self.device_index != -1) {
       stream << ", ID " << self.device_index;
+    }
     stream << "]";
     return stream;
   }

pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp

@@ -106,6 +106,8 @@ auto ComputeFaceAreas(const torch::Tensor& face_verts) {
   return face_areas;
 }
 
+namespace {
+
 // Helper function to use with std::find_if to find the index of any
 // values in the top k struct which match a given idx.
 struct IsNeighbor {
@@ -118,7 +120,6 @@ struct IsNeighbor {
   int neighbor_idx;
 };
 
-namespace {
 void RasterizeMeshesNaiveCpu_worker(
     const int start_yi,
     const int end_yi,

pytorch3d/implicitron/dataset/dataset_base.py

@@ -21,7 +21,6 @@ from typing import (
 )
 
 import torch
-
 from pytorch3d.implicitron.dataset.frame_data import FrameData
 from pytorch3d.implicitron.dataset.utils import GenericWorkaround
 

pytorch3d/implicitron/dataset/frame_data.py

@@ -25,7 +25,6 @@ from typing import (
 
 import numpy as np
 import torch
-
 from pytorch3d.implicitron.dataset import orm_types, types
 from pytorch3d.implicitron.dataset.utils import (
     adjust_camera_to_bbox_crop_,

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -38,7 +38,6 @@ from pytorch3d.implicitron.dataset.utils import is_known_frame_scalar
 from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
 from pytorch3d.renderer.camera_utils import join_cameras_as_batch
 from pytorch3d.renderer.cameras import CamerasBase
-
 from tqdm import tqdm
 
 
@@ -327,9 +326,9 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
                 assert os.path.normpath(
                     # pyre-ignore[16]
                     self.frame_annots[idx]["frame_annotation"].image.path
-                ) == os.path.normpath(
-                    path
-                ), f"Inconsistent frame indices {seq_name, frame_no, path}."
+                ) == os.path.normpath(path), (
+                    f"Inconsistent frame indices {seq_name, frame_no, path}."
+                )
             return idx
 
         dataset_idx = [

pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py

@@ -21,7 +21,6 @@ from pytorch3d.renderer.cameras import CamerasBase
 
 from .dataset_map_provider import DatasetMap, DatasetMapProviderBase, PathManagerFactory
 from .json_index_dataset import JsonIndexDataset
-
 from .utils import (
     DATASET_TYPE_KNOWN,
     DATASET_TYPE_TEST,

pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py

@@ -18,7 +18,6 @@ from typing import Dict, List, Optional, Tuple, Type, Union
 
 import numpy as np
 from iopath.common.file_io import PathManager
-
 from omegaconf import DictConfig
 from pytorch3d.implicitron.dataset.dataset_map_provider import (
     DatasetMap,
@@ -31,7 +30,6 @@ from pytorch3d.implicitron.tools.config import (
     registry,
     run_auto_creation,
 )
-
 from pytorch3d.renderer.cameras import CamerasBase
 from tqdm import tqdm
 

pytorch3d/implicitron/dataset/llff_dataset_map_provider.py

@@ -12,7 +12,6 @@ import torch
 from pytorch3d.implicitron.tools.config import registry
 
 from .load_llff import load_llff_data
-
 from .single_sequence_dataset import (
     _interpret_blender_cameras,
     SingleSceneDatasetMapProviderBase,

pytorch3d/implicitron/dataset/load_llff.py

@@ -8,7 +8,6 @@ import os
 import warnings
 
 import numpy as np
-
 from PIL import Image
 
 

pytorch3d/implicitron/dataset/orm_types.py

@@ -13,7 +13,6 @@ import struct
 from typing import Optional, Tuple
 
 import numpy as np
-
 from pytorch3d.implicitron.dataset.types import (
     DepthAnnotation,
     ImageAnnotation,
@@ -22,7 +21,6 @@ from pytorch3d.implicitron.dataset.types import (
     VideoAnnotation,
     ViewpointAnnotation,
 )
-
 from sqlalchemy import LargeBinary
 from sqlalchemy.orm import (
     composite,

pytorch3d/implicitron/dataset/sql_dataset.py

@@ -10,7 +10,6 @@ import hashlib
 import json
 import logging
 import os
-
 import urllib
 from dataclasses import dataclass, Field, field
 from typing import (
@@ -32,13 +31,11 @@ import pandas as pd
 import sqlalchemy as sa
 import torch
 from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
-
 from pytorch3d.implicitron.dataset.frame_data import (
     FrameData,
     FrameDataBuilder,  # noqa
     FrameDataBuilderBase,
 )
-
 from pytorch3d.implicitron.tools.config import (
     registry,
     ReplaceableBase,
@@ -486,9 +483,10 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
             *self._get_pick_filters(),
             *self._get_exclude_filters(),
         ]
-        if self.pick_sequences_sql_clause:
+        if pick_sequences_sql_clause := self.pick_sequences_sql_clause:
             print("Applying the custom SQL clause.")
-            where_conditions.append(sa.text(self.pick_sequences_sql_clause))
+            # pyre-ignore[6]: TextClause is compatible with where conditions
+            where_conditions.append(sa.text(pick_sequences_sql_clause))
 
         def add_where(stmt):
             return stmt.where(*where_conditions) if where_conditions else stmt
@@ -508,6 +506,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
 
             subquery = add_where(subquery).subquery()
             stmt = sa.select(subquery.c.sequence_name).where(
+                # pyre-ignore[6]: SQLAlchemy column comparison returns ColumnElement, not bool
                 subquery.c.row_number <= self.limit_sequences_per_category_to
             )
 
@@ -636,9 +635,10 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
                     )
                 )
 
-        if self.pick_frames_sql_clause:
+        if pick_frames_sql_clause := self.pick_frames_sql_clause:
             logger.info("Applying the custom SQL clause.")
-            pick_frames_criteria.append(sa.text(self.pick_frames_sql_clause))
+            # pyre-ignore[6]: TextClause is compatible with where conditions
+            pick_frames_criteria.append(sa.text(pick_frames_sql_clause))
 
         if pick_frames_criteria:
             index = self._pick_frames_by_criteria(index, pick_frames_criteria)
@@ -701,9 +701,10 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):
                 )
             )
 
-        if self.pick_frames_sql_clause:
+        if pick_frames_sql_clause := self.pick_frames_sql_clause:
             logger.info("  applying custom SQL clause")
-            where_conditions.append(sa.text(self.pick_frames_sql_clause))
+            # pyre-ignore[6]: TextClause is compatible with where conditions
+            where_conditions.append(sa.text(pick_frames_sql_clause))
 
         if where_conditions:
             stmt = stmt.where(*where_conditions)

pytorch3d/implicitron/dataset/sql_dataset_provider.py

@@ -12,9 +12,7 @@ import os
 from typing import List, Optional, Tuple, Type
 
 import numpy as np
-
 from omegaconf import DictConfig, OmegaConf
-
 from pytorch3d.implicitron.dataset.dataset_map_provider import (
     DatasetMap,
     DatasetMapProviderBase,

pytorch3d/implicitron/dataset/train_eval_data_loader_provider.py

@@ -18,7 +18,6 @@ from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
 from pytorch3d.implicitron.dataset.dataset_map_provider import DatasetMap
 from pytorch3d.implicitron.dataset.frame_data import FrameData
 from pytorch3d.implicitron.tools.config import registry, run_auto_creation
-
 from torch.utils.data import DataLoader
 
 logger = logging.getLogger(__name__)

pytorch3d/implicitron/dataset/utils.py

@@ -15,7 +15,6 @@ from typing import List, Optional, Tuple, TypeVar, Union
 import numpy as np
 import torch
 from PIL import Image
-
 from pytorch3d.io import IO
 from pytorch3d.renderer.cameras import PerspectiveCameras
 from pytorch3d.structures.pointclouds import Pointclouds

pytorch3d/implicitron/evaluation/evaluator.py

@@ -14,7 +14,6 @@ import warnings
 from typing import Any, Dict, List, Optional, Tuple
 
 import torch
-
 import tqdm
 from pytorch3d.implicitron.evaluation import evaluate_new_view_synthesis as evaluate
 from pytorch3d.implicitron.models.base_model import EvaluationMode, ImplicitronModelBase

pytorch3d/implicitron/models/base_model.py

@@ -10,7 +10,6 @@ from dataclasses import dataclass, field
 from typing import Any, Dict, List, Optional
 
 import torch
-
 from pytorch3d.implicitron.models.renderer.base import EvaluationMode
 from pytorch3d.implicitron.tools.config import ReplaceableBase
 from pytorch3d.renderer.cameras import CamerasBase

pytorch3d/implicitron/models/generic_model.py

@@ -16,7 +16,6 @@ from typing import Any, Dict, List, Optional, Tuple, TYPE_CHECKING, Union
 
 import torch
 from omegaconf import DictConfig
-
 from pytorch3d.implicitron.models.base_model import (
     ImplicitronModelBase,
     ImplicitronRender,
@@ -28,7 +27,6 @@ from pytorch3d.implicitron.models.metrics import (
     RegularizationMetricsBase,
     ViewMetricsBase,
 )
-
 from pytorch3d.implicitron.models.renderer.base import (
     BaseRenderer,
     EvaluationMode,
@@ -38,7 +36,6 @@ from pytorch3d.implicitron.models.renderer.base import (
     RenderSamplingMode,
 )
 from pytorch3d.implicitron.models.renderer.ray_sampler import RaySamplerBase
-
 from pytorch3d.implicitron.models.utils import (
     apply_chunked,
     chunk_generator,
@@ -53,7 +50,6 @@ from pytorch3d.implicitron.tools.config import (
     registry,
     run_auto_creation,
 )
-
 from pytorch3d.implicitron.tools.rasterize_mc import rasterize_sparse_ray_bundle
 from pytorch3d.renderer import utils as rend_utils
 from pytorch3d.renderer.cameras import CamerasBase

pytorch3d/implicitron/models/implicit_function/base.py

@@ -10,7 +10,6 @@ from abc import ABC, abstractmethod
 from typing import Optional
 
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
-
 from pytorch3d.implicitron.tools.config import ReplaceableBase
 from pytorch3d.renderer.cameras import CamerasBase
 

pytorch3d/implicitron/models/implicit_function/decoding_functions.py

@@ -16,14 +16,11 @@ This file contains
 
 import logging
 from dataclasses import field
-
 from enum import Enum
 from typing import Dict, Optional, Tuple
 
 import torch
-
 from omegaconf import DictConfig
-
 from pytorch3d.implicitron.tools.config import (
     Configurable,
     registry,

pytorch3d/implicitron/models/implicit_function/idr_feature_field.py

@@ -11,7 +11,6 @@ import torch
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
 from pytorch3d.implicitron.tools.config import registry
 from pytorch3d.renderer.implicit import HarmonicEmbedding
-
 from torch import nn
 
 from .base import ImplicitFunctionBase

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -21,7 +21,6 @@ from pytorch3d.renderer.implicit import HarmonicEmbedding
 from pytorch3d.renderer.implicit.utils import ray_bundle_to_ray_points
 
 from .base import ImplicitFunctionBase
-
 from .decoding_functions import (  # noqa
     _xavier_init,
     MLPWithInputSkips,

pytorch3d/implicitron/models/implicit_function/utils.py

@@ -9,7 +9,6 @@
 from typing import Callable, Optional
 
 import torch
-
 import torch.nn.functional as F
 from pytorch3d.common.compat import prod
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle

pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py

@@ -13,9 +13,7 @@ from dataclasses import fields
 from typing import Callable, Dict, Optional, Tuple
 
 import torch
-
 from omegaconf import DictConfig
-
 from pytorch3d.implicitron.models.implicit_function.base import ImplicitFunctionBase
 from pytorch3d.implicitron.models.implicit_function.decoding_functions import (
     DecoderFunctionBase,

pytorch3d/implicitron/models/overfit_model.py

@@ -17,7 +17,6 @@ from typing import Any, Callable, Dict, List, Optional, Tuple, TYPE_CHECKING, Un
 
 import torch
 from omegaconf import DictConfig
-
 from pytorch3d.implicitron.models.base_model import (
     ImplicitronModelBase,
     ImplicitronRender,
@@ -28,7 +27,6 @@ from pytorch3d.implicitron.models.metrics import (
     RegularizationMetricsBase,
     ViewMetricsBase,
 )
-
 from pytorch3d.implicitron.models.renderer.base import (
     BaseRenderer,
     EvaluationMode,
@@ -50,7 +48,6 @@ from pytorch3d.implicitron.tools.config import (
     registry,
     run_auto_creation,
 )
-
 from pytorch3d.implicitron.tools.rasterize_mc import rasterize_sparse_ray_bundle
 from pytorch3d.renderer import utils as rend_utils
 from pytorch3d.renderer.cameras import CamerasBase

pytorch3d/implicitron/models/renderer/ray_point_refiner.py

@@ -11,7 +11,6 @@ import copy
 import torch
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
 from pytorch3d.implicitron.tools.config import Configurable, expand_args_fields
-
 from pytorch3d.renderer.implicit.sample_pdf import sample_pdf
 
 

pytorch3d/implicitron/models/renderer/rgb_net.py

@@ -12,7 +12,6 @@ import torch
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
 from pytorch3d.implicitron.tools.config import enable_get_default_args
 from pytorch3d.renderer.implicit import HarmonicEmbedding
-
 from torch import nn
 
 

pytorch3d/implicitron/models/utils.py

@@ -17,11 +17,8 @@ from typing import Any, Dict, Optional, Tuple
 import torch
 import tqdm
 from pytorch3d.common.compat import prod
-
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
-
 from pytorch3d.implicitron.tools import image_utils
-
 from pytorch3d.implicitron.tools.utils import cat_dataclass
 
 
@@ -83,9 +80,9 @@ def preprocess_input(
 
     if mask_depths and fg_mask is not None and depth_map is not None:
         # mask the depths
-        assert (
-            mask_threshold > 0.0
-        ), "Depths should be masked only with thresholded masks"
+        assert mask_threshold > 0.0, (
+            "Depths should be masked only with thresholded masks"
+        )
         warnings.warn("Masking depths!")
         depth_map = depth_map * fg_mask
 

pytorch3d/implicitron/tools/rasterize_mc.py

@@ -10,7 +10,6 @@ import math
 from typing import Optional, Tuple
 
 import pytorch3d
-
 import torch
 from pytorch3d.ops import packed_to_padded
 from pytorch3d.renderer import PerspectiveCameras

pytorch3d/implicitron/tools/stats.py

@@ -499,7 +499,7 @@ class StatsJSONEncoder(json.JSONEncoder):
             return enc
         else:
             raise TypeError(
-                f"Object of type {o.__class__.__name__} " f"is not JSON serializable"
+                f"Object of type {o.__class__.__name__} is not JSON serializable"
             )
 
 

pytorch3d/implicitron/tools/video_writer.py

@@ -17,7 +17,6 @@ import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-
 from PIL import Image
 
 _NO_TORCHVISION = False

pytorch3d/io/obj_io.py

@@ -796,7 +796,7 @@ def save_obj(
         # Create .mtl file with the material name and texture map filename
         # TODO: enable material properties to also be saved.
         with _open_file(mtl_path, path_manager, "w") as f_mtl:
-            lines = f"newmtl mesh\n" f"map_Kd {output_path.stem}.png\n"
+            lines = f"newmtl mesh\nmap_Kd {output_path.stem}.png\n"
             f_mtl.write(lines)
 
 

pytorch3d/loss/__init__.py

@@ -8,11 +8,8 @@
 
 
 from .chamfer import chamfer_distance
-
 from .mesh_edge_loss import mesh_edge_loss
-
 from .mesh_laplacian_smoothing import mesh_laplacian_smoothing
-
 from .mesh_normal_consistency import mesh_normal_consistency
 from .point_mesh_distance import point_mesh_edge_distance, point_mesh_face_distance
 

pytorch3d/loss/mesh_laplacian_smoothing.py

@@ -114,9 +114,7 @@ def mesh_laplacian_smoothing(meshes, method: str = "uniform"):
             if method == "cot":
                 norm_w = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1)
                 idx = norm_w > 0
-                # pyre-fixme[58]: `/` is not supported for operand types `float` and
-                #  `Tensor`.
-                norm_w[idx] = 1.0 / norm_w[idx]
+                norm_w[idx] = torch.reciprocal(norm_w[idx])
             else:
                 L_sum = torch.sparse.sum(L, dim=1).to_dense().view(-1, 1)
                 norm_w = 0.25 * inv_areas

pytorch3d/loss/point_mesh_distance.py

@@ -6,6 +6,7 @@
 
 # pyre-unsafe
 
+import torch
 from pytorch3d import _C
 from pytorch3d.structures import Meshes, Pointclouds
 from torch.autograd import Function
@@ -302,8 +303,7 @@ def point_mesh_edge_distance(meshes: Meshes, pcls: Pointclouds):
     point_to_cloud_idx = pcls.packed_to_cloud_idx()  # (sum(P_i), )
     num_points_per_cloud = pcls.num_points_per_cloud()  # (N,)
     weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    weights_p = 1.0 / weights_p.float()
+    weights_p = torch.reciprocal(weights_p.float())
     point_to_edge = point_to_edge * weights_p
     point_dist = point_to_edge.sum() / N
 
@@ -377,8 +377,7 @@ def point_mesh_face_distance(
     point_to_cloud_idx = pcls.packed_to_cloud_idx()  # (sum(P_i),)
     num_points_per_cloud = pcls.num_points_per_cloud()  # (N,)
     weights_p = num_points_per_cloud.gather(0, point_to_cloud_idx)
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    weights_p = 1.0 / weights_p.float()
+    weights_p = torch.reciprocal(weights_p.float())
     point_to_face = point_to_face * weights_p
     point_dist = point_to_face.sum() / N
 

pytorch3d/ops/__init__.py

@@ -8,17 +8,14 @@
 
 from .ball_query import ball_query
 from .cameras_alignment import corresponding_cameras_alignment
-
 from .cubify import cubify
 from .graph_conv import GraphConv
 from .interp_face_attrs import interpolate_face_attributes
 from .iou_box3d import box3d_overlap
 from .knn import knn_gather, knn_points
 from .laplacian_matrices import cot_laplacian, laplacian, norm_laplacian
-
 from .mesh_face_areas_normals import mesh_face_areas_normals
 from .mesh_filtering import taubin_smoothing
-
 from .packed_to_padded import packed_to_padded, padded_to_packed
 from .perspective_n_points import efficient_pnp
 from .points_alignment import corresponding_points_alignment, iterative_closest_point
@@ -30,9 +27,7 @@ from .points_to_volumes import (
     add_pointclouds_to_volumes,
     add_points_features_to_volume_densities_features,
 )
-
 from .sample_farthest_points import sample_farthest_points
-
 from .sample_points_from_meshes import sample_points_from_meshes
 from .subdivide_meshes import SubdivideMeshes
 from .utils import (
@@ -42,7 +37,6 @@ from .utils import (
     is_pointclouds,
     wmean,
 )
-
 from .vert_align import vert_align
 
 

pytorch3d/ops/cubify.py

@@ -11,9 +11,7 @@ from typing import Optional
 
 import torch
 import torch.nn.functional as F
-
 from pytorch3d.common.compat import meshgrid_ij
-
 from pytorch3d.structures import Meshes
 
 

pytorch3d/ops/laplacian_matrices.py

@@ -55,11 +55,9 @@ def laplacian(verts: torch.Tensor, edges: torch.Tensor) -> torch.Tensor:
     # We construct the Laplacian matrix by adding the non diagonal values
     # i.e. L[i, j] = 1 ./ deg(i) if (i, j) is an edge
     deg0 = deg[e0]
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    deg0 = torch.where(deg0 > 0.0, 1.0 / deg0, deg0)
+    deg0 = torch.where(deg0 > 0.0, torch.reciprocal(deg0), deg0)
     deg1 = deg[e1]
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    deg1 = torch.where(deg1 > 0.0, 1.0 / deg1, deg1)
+    deg1 = torch.where(deg1 > 0.0, torch.reciprocal(deg1), deg1)
     val = torch.cat([deg0, deg1])
     L = torch.sparse_coo_tensor(idx, val, (V, V), dtype=torch.float32)
 
@@ -137,8 +135,7 @@ def cot_laplacian(
     val = torch.stack([area] * 3, dim=1).view(-1)
     inv_areas.scatter_add_(0, idx, val)
     idx = inv_areas > 0
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    inv_areas[idx] = 1.0 / inv_areas[idx]
+    inv_areas[idx] = torch.reciprocal(inv_areas[idx])
     inv_areas = inv_areas.view(-1, 1)
 
     return L, inv_areas

pytorch3d/ops/sample_points_from_meshes.py

@@ -16,9 +16,7 @@ import sys
 from typing import Tuple, Union
 
 import torch
-
 from pytorch3d.ops.mesh_face_areas_normals import mesh_face_areas_normals
-
 from pytorch3d.ops.packed_to_padded import packed_to_padded
 from pytorch3d.renderer.mesh.rasterizer import Fragments as MeshFragments
 

pytorch3d/renderer/__init__.py

@@ -69,7 +69,6 @@ from .mesh import (
     TexturesUV,
     TexturesVertex,
 )
-
 from .points import (
     AlphaCompositor,
     NormWeightedCompositor,

pytorch3d/renderer/camera_conversions.py

@@ -153,12 +153,12 @@ def _pulsar_from_opencv_projection(
     # Check image sizes.
     image_w = image_size_wh[0, 0]
     image_h = image_size_wh[0, 1]
-    assert torch.all(
-        image_size_wh[:, 0] == image_w
-    ), "All images in a batch must have the same width!"
-    assert torch.all(
-        image_size_wh[:, 1] == image_h
-    ), "All images in a batch must have the same height!"
+    assert torch.all(image_size_wh[:, 0] == image_w), (
+        "All images in a batch must have the same width!"
+    )
+    assert torch.all(image_size_wh[:, 1] == image_h), (
+        "All images in a batch must have the same height!"
+    )
     # Focal length.
     fx = camera_matrix[:, 0, 0].unsqueeze(1)
     fy = camera_matrix[:, 1, 1].unsqueeze(1)

pytorch3d/renderer/cameras.py

@@ -629,10 +629,8 @@ class FoVPerspectiveCameras(CamerasBase):
         # so the so the z sign is 1.0.
         z_sign = 1.0
 
-        # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-        K[:, 0, 0] = 2.0 * znear / (max_x - min_x)
-        # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-        K[:, 1, 1] = 2.0 * znear / (max_y - min_y)
+        K[:, 0, 0] = torch.div(2.0 * znear, max_x - min_x)
+        K[:, 1, 1] = torch.div(2.0 * znear, max_y - min_y)
         K[:, 0, 2] = (max_x + min_x) / (max_x - min_x)
         K[:, 1, 2] = (max_y + min_y) / (max_y - min_y)
         K[:, 3, 2] = z_sign * ones
@@ -1178,9 +1176,7 @@ class PerspectiveCameras(CamerasBase):
         xy_inv_depth = torch.cat(
             # pyre-fixme[6]: For 1st argument expected `Union[List[Tensor],
             #  tuple[Tensor, ...]]` but got `Tuple[Tensor, float]`.
-            # pyre-fixme[58]: `/` is not supported for operand types `float` and
-            #  `Tensor`.
-            (xy_depth[..., :2], 1.0 / xy_depth[..., 2:3]),
+            (xy_depth[..., :2], torch.reciprocal(xy_depth[..., 2:3])),
             dim=-1,  # type: ignore
         )
         return unprojection_transform.transform_points(xy_inv_depth)

pytorch3d/renderer/mesh/__init__.py

@@ -12,7 +12,6 @@ from .clip import (
     ClippedFaces,
     convert_clipped_rasterization_to_original_faces,
 )
-
 from .rasterize_meshes import rasterize_meshes
 from .rasterizer import MeshRasterizer, RasterizationSettings
 from .renderer import MeshRenderer, MeshRendererWithFragments

pytorch3d/renderer/mesh/clip.py

@@ -434,13 +434,7 @@ def clip_faces(
     # These will then be filled in for each case.
     ###########################################
     F_clipped = (
-        F
-        # pyre-fixme[58]: `+` is not supported for operand types `int` and
-        #  `Union[bool, float, int]`.
-        + faces_delta_cum[-1].item()
-        # pyre-fixme[58]: `+` is not supported for operand types `int` and
-        #  `Union[bool, float, int]`.
-        + faces_delta[-1].item()
+        F + int(faces_delta_cum[-1].item()) + int(faces_delta[-1].item())
     )  # Total number of faces in the new Meshes
     face_verts_clipped = torch.zeros(
         (F_clipped, 3, 3), dtype=face_verts_unclipped.dtype, device=device

pytorch3d/renderer/mesh/rasterize_meshes.py

@@ -14,7 +14,6 @@ import torch
 from pytorch3d import _C
 
 from ..utils import parse_image_size
-
 from .clip import (
     clip_faces,
     ClipFrustum,

pytorch3d/renderer/mesh/textures.py

@@ -71,9 +71,7 @@ def _list_to_padded_wrapper(
         # pyre-fixme[6]: For 2nd param expected `int` but got `Union[bool, float, int]`.
         x_reshaped.append(y.reshape(-1, D))
     x_padded = list_to_padded(x_reshaped, pad_size=pad_size, pad_value=pad_value)
-    # pyre-fixme[58]: `+` is not supported for operand types `Tuple[int, int]` and
-    #  `Size`.
-    return x_padded.reshape((N, -1) + reshape_dims)
+    return x_padded.reshape((N, -1) + tuple(reshape_dims))
 
 
 def _padded_to_list_wrapper(
@@ -104,9 +102,7 @@ def _padded_to_list_wrapper(
     # pyre-fixme[6]: For 3rd param expected `int` but got `Union[bool, float, int]`.
     x_reshaped = x.reshape(N, M, D)
     x_list = padded_to_list(x_reshaped, split_size=split_size)
-    # pyre-fixme[58]: `+` is not supported for operand types `Tuple[typing.Any]` and
-    #  `Size`.
-    x_list = [xl.reshape((xl.shape[0],) + reshape_dims) for xl in x_list]
+    x_list = [xl.reshape((xl.shape[0],) + tuple(reshape_dims)) for xl in x_list]
     return x_list
 
 
@@ -625,9 +621,7 @@ class TexturesAtlas(TexturesBase):
         of length `k`.
         """
         if len(faces_ids_list) != len(self.atlas_list()):
-            raise IndexError(
-                "faces_ids_list must be of " "the same length as atlas_list."
-            )
+            raise IndexError("faces_ids_list must be of the same length as atlas_list.")
 
         sub_features = []
         for atlas, faces_ids in zip(self.atlas_list(), faces_ids_list):
@@ -1657,7 +1651,7 @@ class TexturesUV(TexturesBase):
             raise NotImplementedError("This function does not support multiple maps.")
         if len(faces_ids_list) != len(self.faces_uvs_padded()):
             raise IndexError(
-                "faces_uvs_padded must be of " "the same length as face_ids_list."
+                "faces_uvs_padded must be of the same length as face_ids_list."
             )
 
         sub_faces_uvs, sub_verts_uvs, sub_maps = [], [], []
@@ -1871,7 +1865,7 @@ class TexturesVertex(TexturesBase):
         """
         if len(vertex_ids_list) != len(self.verts_features_list()):
             raise IndexError(
-                "verts_features_list must be of " "the same length as vertex_ids_list."
+                "verts_features_list must be of the same length as vertex_ids_list."
             )
 
         sub_features = []

pytorch3d/renderer/opengl/opengl_utils.py

@@ -24,7 +24,6 @@ from typing import Any, Dict
 
 os.environ["PYOPENGL_PLATFORM"] = "egl"
 import OpenGL.EGL as egl  # noqa
-
 import pycuda.driver as cuda  # noqa
 from OpenGL._opaque import opaque_pointer_cls  # noqa
 from OpenGL.raw.EGL._errors import EGLError  # noqa

pytorch3d/renderer/opengl/rasterizer_opengl.py

@@ -17,15 +17,12 @@ import numpy as np
 import OpenGL.GL as gl
 import pycuda.gl
 import torch
-
 import torch.nn as nn
-
 from pytorch3d.structures.meshes import Meshes
 
 from ..cameras import FoVOrthographicCameras, FoVPerspectiveCameras
 from ..mesh.rasterizer import Fragments, RasterizationSettings
 from ..utils import parse_image_size
-
 from .opengl_utils import _torch_to_opengl, global_device_context_store
 
 # Shader strings, used below to compile an OpenGL program.

pytorch3d/renderer/points/__init__.py

@@ -9,9 +9,7 @@
 import torch
 
 from .compositor import AlphaCompositor, NormWeightedCompositor
-
 from .pulsar.unified import PulsarPointsRenderer
-
 from .rasterize_points import rasterize_points
 from .rasterizer import PointsRasterizationSettings, PointsRasterizer
 from .renderer import PointsRenderer

pytorch3d/renderer/points/rasterize_points.py

@@ -11,7 +11,6 @@ from typing import List, Optional, Tuple, Union
 import numpy as np
 import torch
 from pytorch3d import _C
-
 from pytorch3d.renderer.mesh.rasterize_meshes import pix_to_non_square_ndc
 
 from ..utils import parse_image_size

pytorch3d/renderer/utils.py

@@ -269,9 +269,7 @@ class TensorProperties(nn.Module):
                         # to have the same shape as the input tensor.
                         new_dims = len(tensor_dims) - len(idx_dims)
                         new_shape = idx_dims + (1,) * new_dims
-                        # pyre-fixme[58]: `+` is not supported for operand types
-                        # `Tuple[int]` and `torch._C.Size`
-                        expand_dims = (-1,) + tensor_dims[1:]
+                        expand_dims = (-1,) + tuple(tensor_dims[1:])
                         _batch_idx = _batch_idx.view(*new_shape)
                         _batch_idx = _batch_idx.expand(*expand_dims)
 

pytorch3d/structures/meshes.py

@@ -531,9 +531,9 @@ class Meshes:
             list of tensors of vertices of shape (V_n, 3).
         """
         if self._verts_list is None:
-            assert (
-                self._verts_padded is not None
-            ), "verts_padded is required to compute verts_list."
+            assert self._verts_padded is not None, (
+                "verts_padded is required to compute verts_list."
+            )
             self._verts_list = struct_utils.padded_to_list(
                 self._verts_padded, self.num_verts_per_mesh().tolist()
             )
@@ -547,9 +547,9 @@ class Meshes:
             list of tensors of faces of shape (F_n, 3).
         """
         if self._faces_list is None:
-            assert (
-                self._faces_padded is not None
-            ), "faces_padded is required to compute faces_list."
+            assert self._faces_padded is not None, (
+                "faces_padded is required to compute faces_list."
+            )
             self._faces_list = struct_utils.padded_to_list(
                 self._faces_padded, self.num_faces_per_mesh().tolist()
             )
@@ -925,9 +925,9 @@ class Meshes:
 
         verts_list = self.verts_list()
         faces_list = self.faces_list()
-        assert (
-            faces_list is not None and verts_list is not None
-        ), "faces_list and verts_list arguments are required"
+        assert faces_list is not None and verts_list is not None, (
+            "faces_list and verts_list arguments are required"
+        )
 
         if self.isempty():
             self._faces_padded = torch.zeros(

pytorch3d/structures/pointclouds.py

@@ -433,9 +433,9 @@ class Pointclouds:
             list of tensors of points of shape (P_n, 3).
         """
         if self._points_list is None:
-            assert (
-                self._points_padded is not None
-            ), "points_padded is required to compute points_list."
+            assert self._points_padded is not None, (
+                "points_padded is required to compute points_list."
+            )
             points_list = []
             for i in range(self._N):
                 points_list.append(

pytorch3d/transforms/rotation_conversions.py

@@ -52,8 +52,7 @@ def quaternion_to_matrix(quaternions: torch.Tensor) -> torch.Tensor:
         Rotation matrices as tensor of shape (..., 3, 3).
     """
     r, i, j, k = torch.unbind(quaternions, -1)
-    # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-    two_s = 2.0 / (quaternions * quaternions).sum(-1)
+    two_s = torch.div(2.0, (quaternions * quaternions).sum(-1))
 
     o = torch.stack(
         (
@@ -137,18 +136,18 @@ def matrix_to_quaternion(matrix: torch.Tensor) -> torch.Tensor:
     # we produce the desired quaternion multiplied by each of r, i, j, k
     quat_by_rijk = torch.stack(
         [
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([q_abs[..., 0] ** 2, m21 - m12, m02 - m20, m10 - m01], dim=-1),
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([m21 - m12, q_abs[..., 1] ** 2, m10 + m01, m02 + m20], dim=-1),
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([m02 - m20, m10 + m01, q_abs[..., 2] ** 2, m12 + m21], dim=-1),
-            # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and
-            #  `int`.
-            torch.stack([m10 - m01, m20 + m02, m21 + m12, q_abs[..., 3] ** 2], dim=-1),
+            torch.stack(
+                [torch.square(q_abs[..., 0]), m21 - m12, m02 - m20, m10 - m01], dim=-1
+            ),
+            torch.stack(
+                [m21 - m12, torch.square(q_abs[..., 1]), m10 + m01, m02 + m20], dim=-1
+            ),
+            torch.stack(
+                [m02 - m20, m10 + m01, torch.square(q_abs[..., 2]), m12 + m21], dim=-1
+            ),
+            torch.stack(
+                [m10 - m01, m20 + m02, m21 + m12, torch.square(q_abs[..., 3])], dim=-1
+            ),
         ],
         dim=-2,
     )

pytorch3d/transforms/transform3d.py

@@ -623,9 +623,7 @@ class Scale(Transform3d):
         Return the inverse of self._matrix.
         """
         xyz = torch.stack([self._matrix[:, i, i] for i in range(4)], dim=1)
-        # pyre-fixme[58]: `/` is not supported for operand types `float` and `Tensor`.
-        ixyz = 1.0 / xyz
-        # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
+        ixyz = torch.reciprocal(xyz)
         imat = torch.diag_embed(ixyz, dim1=1, dim2=2)
         return imat
 

pytorch3d/utils/__init__.py

@@ -12,11 +12,8 @@ from .camera_conversions import (
     pulsar_from_cameras_projection,
     pulsar_from_opencv_projection,
 )
-
 from .checkerboard import checkerboard
-
 from .ico_sphere import ico_sphere
-
 from .torus import torus
 
 

setup.py

@@ -75,6 +75,21 @@ def get_extensions():
         ]
         if os.name != "nt":
             nvcc_args.append("-std=c++17")
+
+        # CUDA 13.0+ compatibility flags for pulsar.
+        # Starting with CUDA 13, __global__ function visibility changed.
+        # See: https://developer.nvidia.com/blog/
+        #      cuda-c-compiler-updates-impacting-elf-visibility-and-linkage/
+        cuda_version = torch.version.cuda
+        if cuda_version is not None:
+            major = int(cuda_version.split(".")[0])
+            if major >= 13:
+                nvcc_args.extend(
+                    [
+                        "--device-entity-has-hidden-visibility=false",
+                        "-static-global-template-stub=false",
+                    ]
+                )
         if cub_home is None:
             prefix = os.environ.get("CONDA_PREFIX", None)
             if prefix is not None and os.path.isdir(prefix + "/include/cub"):

tests/benchmarks/bm_ball_query_large.py

@@ -8,7 +8,6 @@ from itertools import product
 
 import torch
 from fvcore.common.benchmark import benchmark
-
 from pytorch3d.ops.ball_query import ball_query
 
 

tests/implicitron/models/test_utils.py

@@ -8,7 +8,6 @@
 import unittest
 
 import torch
-
 from pytorch3d.implicitron.models.utils import preprocess_input, weighted_sum_losses
 
 

tests/implicitron/test_batch_sampler.py

@@ -11,12 +11,10 @@ from dataclasses import dataclass
 from itertools import product
 
 import numpy as np
-
 import torch
 from pytorch3d.implicitron.dataset.data_loader_map_provider import (
     DoublePoolBatchSampler,
 )
-
 from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
 from pytorch3d.implicitron.dataset.frame_data import FrameData
 from pytorch3d.implicitron.dataset.scene_batch_sampler import SceneBatchSampler

tests/implicitron/test_bbox.py

@@ -7,9 +7,7 @@
 import unittest
 
 import numpy as np
-
 import torch
-
 from pytorch3d.implicitron.dataset.utils import (
     bbox_xywh_to_xyxy,
     bbox_xyxy_to_xywh,
@@ -21,7 +19,6 @@ from pytorch3d.implicitron.dataset.utils import (
     rescale_bbox,
     resize_image,
 )
-
 from tests.common_testing import TestCaseMixin
 
 

tests/implicitron/test_co3d_sql.py

@@ -9,7 +9,6 @@ import os
 import unittest
 
 import torch
-
 from pytorch3d.implicitron.dataset.data_loader_map_provider import (  # noqa
     SequenceDataLoaderMapProvider,
     SimpleDataLoaderMapProvider,

tests/implicitron/test_eval_demo.py

@@ -8,7 +8,6 @@ import os
 import unittest
 
 from pytorch3d.implicitron import eval_demo
-
 from tests.common_testing import interactive_testing_requested
 
 from .common_resources import CO3D_MANIFOLD_PATH

tests/implicitron/test_evaluation.py

@@ -15,7 +15,6 @@ import unittest
 import lpips
 import numpy as np
 import torch
-
 from pytorch3d.implicitron.dataset.frame_data import FrameData
 from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
 from pytorch3d.implicitron.evaluation.evaluate_new_view_synthesis import eval_batch

tests/implicitron/test_extending_orm_types.py

@@ -14,7 +14,6 @@ from typing import ClassVar, Optional, Type
 import pandas as pd
 import pkg_resources
 import sqlalchemy as sa
-
 from pytorch3d.implicitron.dataset import types
 from pytorch3d.implicitron.dataset.frame_data import FrameData, GenericFrameDataBuilder
 from pytorch3d.implicitron.dataset.orm_types import (

tests/implicitron/test_frame_data_builder.py

@@ -12,7 +12,6 @@ from typing import List
 
 import numpy as np
 import torch
-
 from pytorch3d.implicitron.dataset import types
 from pytorch3d.implicitron.dataset.dataset_base import FrameData
 from pytorch3d.implicitron.dataset.frame_data import FrameDataBuilder
@@ -29,7 +28,6 @@ from pytorch3d.implicitron.dataset.utils import (
 )
 from pytorch3d.implicitron.tools.config import get_default_args
 from pytorch3d.renderer.cameras import PerspectiveCameras
-
 from tests.common_testing import TestCaseMixin
 from tests.implicitron.common_resources import get_skateboard_data
 

tests/implicitron/test_json_index_dataset_provider_v2.py

@@ -13,7 +13,6 @@ import unittest
 from typing import List
 
 import numpy as np
-
 import torch
 import torchvision
 from PIL import Image

tests/implicitron/test_model_visualize.py

@@ -13,7 +13,6 @@ from typing import Tuple
 import torch
 from pytorch3d.implicitron.dataset.json_index_dataset import JsonIndexDataset
 from pytorch3d.implicitron.dataset.visualize import get_implicitron_sequence_pointcloud
-
 from pytorch3d.implicitron.models.visualization.render_flyaround import render_flyaround
 from pytorch3d.implicitron.tools.config import expand_args_fields
 from pytorch3d.implicitron.tools.point_cloud_utils import render_point_cloud_pytorch3d

tests/implicitron/test_models_renderer_base.py

@@ -8,9 +8,7 @@
 import unittest
 
 import numpy as np
-
 import torch
-
 from pytorch3d.implicitron.models.renderer.base import (
     approximate_conical_frustum_as_gaussians,
     compute_3d_diagonal_covariance_gaussian,
@@ -18,7 +16,6 @@ from pytorch3d.implicitron.models.renderer.base import (
     ImplicitronRayBundle,
 )
 from pytorch3d.implicitron.models.renderer.ray_sampler import AbstractMaskRaySampler
-
 from tests.common_testing import TestCaseMixin
 
 

tests/implicitron/test_models_renderer_ray_sampler.py

@@ -7,7 +7,6 @@
 import unittest
 from itertools import product
 from typing import Tuple
-
 from unittest.mock import patch
 
 import torch
@@ -18,7 +17,6 @@ from pytorch3d.implicitron.models.renderer.ray_sampler import (
     compute_radii,
     NearFarRaySampler,
 )
-
 from pytorch3d.renderer.cameras import (
     CamerasBase,
     FoVOrthographicCameras,
@@ -28,7 +26,6 @@ from pytorch3d.renderer.cameras import (
 )
 from pytorch3d.renderer.implicit.utils import HeterogeneousRayBundle
 from tests.common_camera_utils import init_random_cameras
-
 from tests.common_testing import TestCaseMixin
 
 CAMERA_TYPES = (

tests/implicitron/test_orm_types.py

@@ -7,7 +7,6 @@
 import unittest
 
 import numpy as np
-
 from pytorch3d.implicitron.dataset.orm_types import ArrayTypeFactory, TupleTypeFactory
 
 

tests/implicitron/test_pointcloud_utils.py

@@ -8,7 +8,6 @@ import unittest
 
 import torch
 from pytorch3d.implicitron.tools.point_cloud_utils import get_rgbd_point_cloud
-
 from pytorch3d.renderer.cameras import PerspectiveCameras
 from tests.common_testing import TestCaseMixin
 

tests/implicitron/test_ray_point_refiner.py

@@ -8,7 +8,6 @@ import unittest
 from itertools import product
 
 import torch
-
 from pytorch3d.implicitron.models.renderer.ray_point_refiner import (
     apply_blurpool_on_weights,
     RayPointRefiner,

tests/implicitron/test_sql_dataset.py

@@ -10,9 +10,7 @@ import unittest
 from collections import Counter
 
 import pkg_resources
-
 import torch
-
 from pytorch3d.implicitron.dataset.sql_dataset import SqlIndexDataset
 
 NO_BLOBS_KWARGS = {

tests/implicitron/test_srn.py

@@ -16,7 +16,6 @@ from pytorch3d.implicitron.models.implicit_function.scene_representation_network
 from pytorch3d.implicitron.models.renderer.ray_sampler import ImplicitronRayBundle
 from pytorch3d.implicitron.tools.config import get_default_args
 from pytorch3d.renderer import PerspectiveCameras
-
 from tests.common_testing import TestCaseMixin
 
 _BATCH_SIZE: int = 3

tests/implicitron/test_voxel_grid_implicit_function.py

@@ -8,13 +8,11 @@
 import unittest
 
 import torch
-
 from omegaconf import DictConfig, OmegaConf
 from pytorch3d.implicitron.models.implicit_function.voxel_grid_implicit_function import (
     VoxelGridImplicitFunction,
 )
 from pytorch3d.implicitron.models.renderer.base import ImplicitronRayBundle
-
 from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
 from pytorch3d.renderer import ray_bundle_to_ray_points
 from tests.common_testing import TestCaseMixin

tests/implicitron/test_voxel_grids.py

@@ -10,7 +10,6 @@ from typing import Optional, Tuple
 
 import torch
 from omegaconf import DictConfig, OmegaConf
-
 from pytorch3d.implicitron.models.implicit_function.utils import (
     interpolate_line,
     interpolate_plane,
@@ -22,7 +21,6 @@ from pytorch3d.implicitron.models.implicit_function.voxel_grid import (
     VMFactorizedVoxelGrid,
     VoxelGridModule,
 )
-
 from pytorch3d.implicitron.tools.config import expand_args_fields, get_default_args
 from tests.common_testing import TestCaseMixin
 

tests/test_cameras.py

@@ -60,7 +60,6 @@ from pytorch3d.transforms.rotation_conversions import random_rotations
 from pytorch3d.transforms.so3 import so3_exp_map
 
 from .common_camera_utils import init_random_cameras
-
 from .common_testing import TestCaseMixin
 
 

tests/test_io_ply.py

@@ -673,9 +673,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
 
     def test_load_simple_binary(self):
         for big_endian in [True, False]:
-            verts = (
-                "0 0 0 " "0 0 1 " "0 1 1 " "0 1 0 " "1 0 0 " "1 0 1 " "1 1 1 " "1 1 0"
-            ).split()
+            verts = ("0 0 0 0 0 1 0 1 1 0 1 0 1 0 0 1 0 1 1 1 1 1 1 0").split()
             faces = (
                 "4 0 1 2 3 "
                 "4 7 6 5 4 "
@@ -688,7 +686,7 @@ class TestMeshPlyIO(TestCaseMixin, unittest.TestCase):
                 "3 4 5 1"
             ).split()
             short_one = b"\00\01" if big_endian else b"\01\00"
-            mixed_data = b"\00\00" b"\03\03" + (short_one + b"\00\01\01\01" b"\00\02")
+            mixed_data = b"\00\00\03\03" + (short_one + b"\00\01\01\01\00\02")
             minus_one_data = b"\xff" * 14
             endian_char = ">" if big_endian else "<"
             format = (

tests/test_raysampling.py

@@ -604,9 +604,9 @@ class TestRaysampling(TestCaseMixin, unittest.TestCase):
                     # test weather they are of the correct shape
                     for attr in ("origins", "directions", "lengths", "xys"):
                         tensor = getattr(ray_bundle, attr)
-                        assert tensor.shape[:2] == torch.Size(
-                            (n_rays_total, 1)
-                        ), tensor.shape
+                        assert tensor.shape[:2] == torch.Size((n_rays_total, 1)), (
+                            tensor.shape
+                        )
 
                     # if two camera ids are same than origins should also be the same
                     # directions and xys are always different and lengths equal

tests/test_render_meshes.py

@@ -12,7 +12,6 @@ Sanity checks for output images from the renderer.
 import os
 import unittest
 from collections import namedtuple
-
 from itertools import product
 
 import numpy as np

tests/test_transforms.py

@@ -148,14 +148,14 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         for t_pair in ((t1, t2), (t1, t3), (t2, t3)):
             matrix1 = t_pair[0].get_matrix()
             matrix2 = t_pair[1].get_matrix()
-            self.assertTrue(torch.allclose(matrix1, matrix2))
+            self.assertClose(matrix1, matrix2)
 
     def test_init_with_custom_matrix(self):
         for matrix in (torch.randn(10, 4, 4), torch.randn(4, 4)):
             t = Transform3d(matrix=matrix)
             self.assertTrue(t.device == matrix.device)
             self.assertTrue(t._matrix.dtype == matrix.dtype)
-            self.assertTrue(torch.allclose(t._matrix, matrix.view(t._matrix.shape)))
+            self.assertClose(t._matrix, matrix.view(t._matrix.shape))
 
     def test_init_with_custom_matrix_errors(self):
         bad_shapes = [[10, 5, 4], [3, 4], [10, 4, 4, 1], [10, 4, 4, 2], [4, 4, 4, 3]]
@@ -189,8 +189,8 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         normals_out_expected = torch.tensor(
             [[1.0, 0.0, 0.0], [0.0, 1.0, 0.0], [1.0, 1.0, 0.0]]
         ).view(1, 3, 3)
-        self.assertTrue(torch.allclose(points_out, points_out_expected))
-        self.assertTrue(torch.allclose(normals_out, normals_out_expected))
+        self.assertClose(points_out, points_out_expected)
+        self.assertClose(normals_out, normals_out_expected)
 
     @mock.patch.dict(os.environ, {"PYTORCH3D_CHECK_ROTATION_MATRICES": "1"}, clear=True)
     def test_rotate_check_rot_valid_on(self):
@@ -206,8 +206,8 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         normals_out = t.transform_normals(normals)
         points_out_expected = torch.bmm(points, R)
         normals_out_expected = torch.bmm(normals, R)
-        self.assertTrue(torch.allclose(points_out, points_out_expected))
-        self.assertTrue(torch.allclose(normals_out, normals_out_expected))
+        self.assertClose(points_out, points_out_expected)
+        self.assertClose(normals_out, normals_out_expected)
 
     @mock.patch.dict(os.environ, {"PYTORCH3D_CHECK_ROTATION_MATRICES": "0"}, clear=True)
     def test_rotate_check_rot_valid_off(self):
@@ -223,8 +223,8 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         normals_out = t.transform_normals(normals)
         points_out_expected = torch.bmm(points, R)
         normals_out_expected = torch.bmm(normals, R)
-        self.assertTrue(torch.allclose(points_out, points_out_expected))
-        self.assertTrue(torch.allclose(normals_out, normals_out_expected))
+        self.assertClose(points_out, points_out_expected)
+        self.assertClose(normals_out, normals_out_expected)
 
     def test_scale(self):
         t = Transform3d().scale(2.0).scale(0.5, 0.25, 1.0)
@@ -242,8 +242,8 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         normals_out_expected = torch.tensor(
             [[1.0, 0.0, 0.0], [0.0, 2.0, 0.0], [1.0, 2.0, 0.0]]
         ).view(1, 3, 3)
-        self.assertTrue(torch.allclose(points_out, points_out_expected))
-        self.assertTrue(torch.allclose(normals_out, normals_out_expected))
+        self.assertClose(points_out, points_out_expected)
+        self.assertClose(normals_out, normals_out_expected)
 
     def test_scale_translate(self):
         t = Transform3d().scale(2, 1, 3).translate(1, 2, 3)
@@ -261,8 +261,8 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         normals_out_expected = torch.tensor(
             [[0.5, 0.0, 0.0], [0.0, 1.0, 0.0], [0.5, 1.0, 0.0]]
         ).view(1, 3, 3)
-        self.assertTrue(torch.allclose(points_out, points_out_expected))
-        self.assertTrue(torch.allclose(normals_out, normals_out_expected))
+        self.assertClose(points_out, points_out_expected)
+        self.assertClose(normals_out, normals_out_expected)
 
     def test_rotate_axis_angle(self):
         t = Transform3d().rotate_axis_angle(90.0, axis="Z")
@@ -280,8 +280,8 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         normals_out_expected = torch.tensor(
             [[0.0, 1.0, 0.0], [0.0, 1.0, 0.0], [0.0, 1.0, 0.0]]
         ).view(1, 3, 3)
-        self.assertTrue(torch.allclose(points_out, points_out_expected, atol=1e-7))
-        self.assertTrue(torch.allclose(normals_out, normals_out_expected, atol=1e-7))
+        self.assertClose(points_out, points_out_expected, atol=1e-7)
+        self.assertClose(normals_out, normals_out_expected, atol=1e-7)
 
     def test_transform_points_fail(self):
         t1 = Scale(0.1, 0.1, 0.1)
@@ -369,7 +369,7 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
 
             # assert all same
             for m in (m1, m2, m3, m4):
-                self.assertTrue(torch.allclose(m, m5, atol=1e-3))
+                self.assertClose(m, m5, atol=1e-3)
 
     def _check_indexed_transforms(self, t3d, t3d_selected, indices):
         t3d_matrix = t3d.get_matrix()
@@ -488,7 +488,7 @@ class TestTransform(TestCaseMixin, unittest.TestCase):
         self.assertClose(new_points, new_points_expect)
 
 
-class TestTranslate(unittest.TestCase):
+class TestTranslate(TestCaseMixin, unittest.TestCase):
     def test_python_scalar(self):
         t = Translate(0.2, 0.3, 0.4)
         matrix = torch.tensor(
@@ -502,7 +502,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_torch_scalar(self):
         x = torch.tensor(0.2)
@@ -520,7 +520,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_mixed_scalars(self):
         x = 0.2
@@ -538,7 +538,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_torch_scalar_grads(self):
         # Make sure backprop works if we give torch scalars
@@ -549,8 +549,8 @@ class TestTranslate(unittest.TestCase):
         t._matrix.sum().backward()
         self.assertTrue(hasattr(x, "grad"))
         self.assertTrue(hasattr(y, "grad"))
-        self.assertTrue(torch.allclose(x.grad, x.new_ones(x.shape)))
-        self.assertTrue(torch.allclose(y.grad, y.new_ones(y.shape)))
+        self.assertClose(x.grad, x.new_ones(x.shape))
+        self.assertClose(y.grad, y.new_ones(y.shape))
 
     def test_torch_vectors(self):
         x = torch.tensor([0.2, 2.0])
@@ -574,7 +574,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_vector_broadcast(self):
         x = torch.tensor([0.2, 2.0])
@@ -598,7 +598,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_bad_broadcast(self):
         x = torch.tensor([0.2, 2.0, 20.0])
@@ -629,7 +629,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_mixed_broadcast_grad(self):
         x = 0.2
@@ -643,8 +643,8 @@ class TestTranslate(unittest.TestCase):
         z_grad = torch.tensor([1.0, 1.0])
         self.assertEqual(y.grad.shape, y_grad.shape)
         self.assertEqual(z.grad.shape, z_grad.shape)
-        self.assertTrue(torch.allclose(y.grad, y_grad))
-        self.assertTrue(torch.allclose(z.grad, z_grad))
+        self.assertClose(y.grad, y_grad)
+        self.assertClose(z.grad, z_grad)
 
     def test_matrix(self):
         xyz = torch.tensor([[0.2, 0.3, 0.4], [2.0, 3.0, 4.0]])
@@ -666,7 +666,7 @@ class TestTranslate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_matrix_extra_args(self):
         xyz = torch.tensor([[0.2, 0.3, 0.4], [2.0, 3.0, 4.0]])
@@ -679,8 +679,8 @@ class TestTranslate(unittest.TestCase):
         im = t.inverse()._matrix
         im_2 = t._matrix.inverse()
         im_comp = t.get_matrix().inverse()
-        self.assertTrue(torch.allclose(im, im_comp))
-        self.assertTrue(torch.allclose(im, im_2))
+        self.assertClose(im, im_comp, atol=1e-4)
+        self.assertClose(im, im_2, atol=1e-4)
 
     def test_get_item(self, batch_size=5):
         device = torch.device("cuda:0")
@@ -692,7 +692,7 @@ class TestTranslate(unittest.TestCase):
         self.assertIsInstance(t3d_selected, Translate)
 
 
-class TestScale(unittest.TestCase):
+class TestScale(TestCaseMixin, unittest.TestCase):
     def test_single_python_scalar(self):
         t = Scale(0.1)
         matrix = torch.tensor(
@@ -706,7 +706,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_single_torch_scalar(self):
         t = Scale(torch.tensor(0.1))
@@ -721,7 +721,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_single_vector(self):
         t = Scale(torch.tensor([0.1, 0.2]))
@@ -742,7 +742,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_single_matrix(self):
         xyz = torch.tensor([[0.1, 0.2, 0.3], [1.0, 2.0, 3.0]])
@@ -764,7 +764,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_three_python_scalar(self):
         t = Scale(0.1, 0.2, 0.3)
@@ -779,7 +779,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_three_torch_scalar(self):
         t = Scale(torch.tensor(0.1), torch.tensor(0.2), torch.tensor(0.3))
@@ -794,7 +794,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_three_mixed_scalar(self):
         t = Scale(torch.tensor(0.1), 0.2, torch.tensor(0.3))
@@ -809,7 +809,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_three_vector_broadcast(self):
         x = torch.tensor([0.1])
@@ -833,7 +833,7 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_three_mixed_broadcast_grad(self):
         x = 0.1
@@ -857,14 +857,14 @@ class TestScale(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
         t._matrix.sum().backward()
         self.assertTrue(hasattr(y, "grad"))
         self.assertTrue(hasattr(z, "grad"))
         y_grad = torch.tensor(2.0)
         z_grad = torch.tensor([1.0, 1.0])
-        self.assertTrue(torch.allclose(y.grad, y_grad))
-        self.assertTrue(torch.allclose(z.grad, z_grad))
+        self.assertClose(y.grad, y_grad)
+        self.assertClose(z.grad, z_grad)
 
     def test_inverse(self):
         x = torch.tensor([0.1])
@@ -874,8 +874,8 @@ class TestScale(unittest.TestCase):
         im = t.inverse()._matrix
         im_2 = t._matrix.inverse()
         im_comp = t.get_matrix().inverse()
-        self.assertTrue(torch.allclose(im, im_comp))
-        self.assertTrue(torch.allclose(im, im_2))
+        self.assertClose(im, im_comp)
+        self.assertClose(im, im_2)
 
     def test_get_item(self, batch_size=5):
         device = torch.device("cuda:0")
@@ -887,7 +887,7 @@ class TestScale(unittest.TestCase):
         self.assertIsInstance(t3d_selected, Scale)
 
 
-class TestTransformBroadcast(unittest.TestCase):
+class TestTransformBroadcast(TestCaseMixin, unittest.TestCase):
     def test_broadcast_transform_points(self):
         t1 = Scale(0.1, 0.1, 0.1)
         N = 10
@@ -965,10 +965,10 @@ class TestTransformBroadcast(unittest.TestCase):
         composed_mat = t1N2.get_matrix()
         self.assertTrue(composed_mat.shape == (N, 4, 4))
         expected_mat = torch.eye(3, dtype=torch.float32) * 0.3 * 0.2 * 0.1
-        self.assertTrue(torch.allclose(composed_mat[0, :3, :3], expected_mat))
+        self.assertClose(composed_mat[0, :3, :3], expected_mat)
 
 
-class TestRotate(unittest.TestCase):
+class TestRotate(TestCaseMixin, unittest.TestCase):
     def test_single_matrix(self):
         R = torch.eye(3)
         t = Rotate(R)
@@ -983,7 +983,7 @@ class TestRotate(unittest.TestCase):
             ],
             dtype=torch.float32,
         )
-        self.assertTrue(torch.allclose(t._matrix, matrix))
+        self.assertClose(t._matrix, matrix)
 
     def test_invalid_dimensions(self):
         R = torch.eye(4)
@@ -998,8 +998,8 @@ class TestRotate(unittest.TestCase):
         im = t.inverse()._matrix
         im_2 = t._matrix.inverse()
         im_comp = t.get_matrix().inverse()
-        self.assertTrue(torch.allclose(im, im_comp, atol=1e-4))
-        self.assertTrue(torch.allclose(im, im_2, atol=1e-4))
+        self.assertClose(im, im_comp, atol=1e-4)
+        self.assertClose(im, im_2, atol=1e-4)
 
     def test_get_item(self, batch_size=5):
         device = torch.device("cuda:0")
@@ -1011,7 +1011,7 @@ class TestRotate(unittest.TestCase):
         self.assertIsInstance(t3d_selected, Rotate)
 
 
-class TestRotateAxisAngle(unittest.TestCase):
+class TestRotateAxisAngle(TestCaseMixin, unittest.TestCase):
     def test_rotate_x_python_scalar(self):
         t = RotateAxisAngle(angle=90, axis="X")
         # fmt: off
@@ -1030,10 +1030,8 @@ class TestRotateAxisAngle(unittest.TestCase):
         points = torch.tensor([0.0, 1.0, 0.0])[None, None, :]  # (1, 1, 3)
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, 1.0])
-        self.assertTrue(
-            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
-        )
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(transformed_points.squeeze(), expected_points, atol=1e-7)
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_x_torch_scalar(self):
         angle = torch.tensor(90.0)
@@ -1054,10 +1052,8 @@ class TestRotateAxisAngle(unittest.TestCase):
         points = torch.tensor([0.0, 1.0, 0.0])[None, None, :]  # (1, 1, 3)
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, 1.0])
-        self.assertTrue(
-            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
-        )
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(transformed_points.squeeze(), expected_points, atol=1e-7)
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_x_torch_tensor(self):
         angle = torch.tensor([0, 45.0, 90.0])  # (N)
@@ -1089,10 +1085,10 @@ class TestRotateAxisAngle(unittest.TestCase):
             dtype=torch.float32,
         )
         # fmt: on
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(t._matrix, matrix, atol=1e-7)
         angle = angle
         t = RotateAxisAngle(angle=angle, axis="X")
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_y_python_scalar(self):
         t = RotateAxisAngle(angle=90, axis="Y")
@@ -1112,10 +1108,8 @@ class TestRotateAxisAngle(unittest.TestCase):
         points = torch.tensor([1.0, 0.0, 0.0])[None, None, :]  # (1, 1, 3)
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, -1.0])
-        self.assertTrue(
-            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
-        )
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(transformed_points.squeeze(), expected_points, atol=1e-7)
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_y_torch_scalar(self):
         """
@@ -1141,10 +1135,8 @@ class TestRotateAxisAngle(unittest.TestCase):
         points = torch.tensor([1.0, 0.0, 0.0])[None, None, :]  # (1, 1, 3)
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 0.0, -1.0])
-        self.assertTrue(
-            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
-        )
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(transformed_points.squeeze(), expected_points, atol=1e-7)
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_y_torch_tensor(self):
         angle = torch.tensor([0, 45.0, 90.0])
@@ -1176,7 +1168,7 @@ class TestRotateAxisAngle(unittest.TestCase):
             dtype=torch.float32,
         )
         # fmt: on
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_z_python_scalar(self):
         t = RotateAxisAngle(angle=90, axis="Z")
@@ -1196,10 +1188,8 @@ class TestRotateAxisAngle(unittest.TestCase):
         points = torch.tensor([1.0, 0.0, 0.0])[None, None, :]  # (1, 1, 3)
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 1.0, 0.0])
-        self.assertTrue(
-            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
-        )
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(transformed_points.squeeze(), expected_points, atol=1e-7)
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_z_torch_scalar(self):
         angle = torch.tensor(90.0)
@@ -1220,10 +1210,8 @@ class TestRotateAxisAngle(unittest.TestCase):
         points = torch.tensor([1.0, 0.0, 0.0])[None, None, :]  # (1, 1, 3)
         transformed_points = t.transform_points(points)
         expected_points = torch.tensor([0.0, 1.0, 0.0])
-        self.assertTrue(
-            torch.allclose(transformed_points.squeeze(), expected_points, atol=1e-7)
-        )
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(transformed_points.squeeze(), expected_points, atol=1e-7)
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_z_torch_tensor(self):
         angle = torch.tensor([0, 45.0, 90.0])
@@ -1255,7 +1243,7 @@ class TestRotateAxisAngle(unittest.TestCase):
             dtype=torch.float32,
         )
         # fmt: on
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_rotate_compose_x_y_z(self):
         angle = torch.tensor(90.0)
@@ -1301,7 +1289,7 @@ class TestRotateAxisAngle(unittest.TestCase):
         # order of transforms is t1 -> t2
         matrix = torch.matmul(matrix1, torch.matmul(matrix2, matrix3))
         composed_matrix = t.get_matrix()
-        self.assertTrue(torch.allclose(composed_matrix, matrix, atol=1e-7))
+        self.assertClose(composed_matrix, matrix, atol=1e-7)
 
     def test_rotate_angle_radians(self):
         t = RotateAxisAngle(angle=math.pi / 2, degrees=False, axis="Z")
@@ -1318,7 +1306,7 @@ class TestRotateAxisAngle(unittest.TestCase):
             dtype=torch.float32,
         )
         # fmt: on
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_lower_case_axis(self):
         t = RotateAxisAngle(angle=90.0, axis="z")
@@ -1335,7 +1323,7 @@ class TestRotateAxisAngle(unittest.TestCase):
             dtype=torch.float32,
         )
         # fmt: on
-        self.assertTrue(torch.allclose(t._matrix, matrix, atol=1e-7))
+        self.assertClose(t._matrix, matrix, atol=1e-7)
 
     def test_axis_fail(self):
         with self.assertRaises(ValueError):