Run tests in github action not circleci (#1896)

Summary: Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1896

Differential Revision: D65272512

Pulled By: bottler

.circleci/regenerate.py

@@ -88,6 +88,7 @@ def workflow_pair(
     upload=False,
     filter_branch,
 ):
+
     w = []
     py = python_version.replace(".", "")
     pyt = pytorch_version.replace(".", "")
@@ -126,6 +127,7 @@ def generate_base_workflow(
     btype,
     filter_branch=None,
 ):
+
     d = {
         "name": base_workflow_name,
         "python_version": python_version,

.github/workflows/build.yml

@@ -3,9 +3,6 @@ on:
   pull_request:
     branches:
       - main
-  push:
-    branches:
-      - main
 jobs:
   binary_linux_conda_cuda:
     runs-on: 4-core-ubuntu-gpu-t4

dev/linter.sh

@@ -10,7 +10,7 @@
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
 DIR=$(dirname "${DIR}")
 
-if [[ -f "${DIR}/BUCK" ]]
+if [[ -f "${DIR}/TARGETS" ]]
 then
   pyfmt "${DIR}"
 else
@@ -36,5 +36,5 @@ then
 
   echo "Running pyre..."
   echo "To restart/kill pyre server, run 'pyre restart' or 'pyre kill' in fbcode/"
-  ( cd ~/fbsource/fbcode; arc pyre check //vision/fair/pytorch3d/... )
+  ( cd ~/fbsource/fbcode; pyre -l vision/fair/pytorch3d/ )
 fi

docs/conf.py

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

docs/examples/pulsar_basic.py

@@ -10,7 +10,6 @@ This example demonstrates the most trivial, direct interface of the pulsar
 sphere renderer. It renders and saves an image with 10 random spheres.
 Output: basic.png.
 """
-
 import logging
 import math
 from os import path

docs/examples/pulsar_basic_unified.py

@@ -11,7 +11,6 @@ interface for sphere renderering. It renders and saves an image with
 10 random spheres.
 Output: basic-pt3d.png.
 """
-
 import logging
 from os import path
 

docs/examples/pulsar_cam.py

@@ -14,7 +14,6 @@ distorted. Gradient-based optimization is used to converge towards the
 original camera parameters.
 Output: cam.gif.
 """
-
 import logging
 import math
 from os import path

docs/examples/pulsar_cam_unified.py

@@ -14,7 +14,6 @@ distorted. Gradient-based optimization is used to converge towards the
 original camera parameters.
 Output: cam-pt3d.gif
 """
-
 import logging
 from os import path
 

docs/examples/pulsar_multiview.py

@@ -18,7 +18,6 @@ This example is not available yet through the 'unified' interface,
 because opacity support has not landed in PyTorch3D for general data
 structures yet.
 """
-
 import logging
 import math
 from os import path

docs/examples/pulsar_optimization.py

@@ -13,7 +13,6 @@ The scene is initialized with random spheres. Gradient-based
 optimization is used to converge towards a faithful
 scene representation.
 """
-
 import logging
 import math
 

docs/examples/pulsar_optimization_unified.py

@@ -13,7 +13,6 @@ The scene is initialized with random spheres. Gradient-based
 optimization is used to converge towards a faithful
 scene representation.
 """
-
 import logging
 import math
 

docs/modules/implicitron/datasets.rst

@@ -3,6 +3,11 @@ pytorch3d.implicitron.dataset specific datasets
 
 specific datasets
 
+.. automodule:: pytorch3d.implicitron.dataset.blender_dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 .. automodule:: pytorch3d.implicitron.dataset.json_index_dataset_map_provider
     :members:
     :undoc-members:
@@ -13,6 +18,11 @@ specific datasets
     :undoc-members:
     :show-inheritance:
 
+.. automodule:: pytorch3d.implicitron.dataset.llff_dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
 .. automodule:: pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider
     :members:
     :undoc-members:

packaging/pytorch3d/meta.yaml

@@ -32,6 +32,7 @@ requirements:
 
 build:
   string: py{{py}}_{{ environ['CU_VERSION'] }}_pyt{{ environ['PYTORCH_VERSION_NODOT']}}
+  # script: LD_LIBRARY_PATH=$PREFIX/lib:$BUILD_PREFIX/lib:$LD_LIBRARY_PATH python setup.py install --single-version-externally-managed --record=record.txt # [not win]
   script: python setup.py install --single-version-externally-managed --record=record.txt # [not win]
   script_env:
     - CUDA_HOME
@@ -56,6 +57,7 @@ test:
     - pandas
     - sqlalchemy
   commands:
+    #pytest .
     python -m unittest discover -v -s tests -t .
 
 

projects/implicitron_trainer/configs/overfit_singleseq_nerf_blender.yaml

@@ -0,0 +1,56 @@
+defaults:
+- overfit_singleseq_base
+- _self_
+exp_dir: "./data/overfit_nerf_blender_repro/${oc.env:BLENDER_SINGLESEQ_CLASS}"
+data_source_ImplicitronDataSource_args:
+  data_loader_map_provider_SequenceDataLoaderMapProvider_args:
+    dataset_length_train: 100
+  dataset_map_provider_class_type: BlenderDatasetMapProvider
+  dataset_map_provider_BlenderDatasetMapProvider_args:
+    base_dir: ${oc.env:BLENDER_DATASET_ROOT}/${oc.env:BLENDER_SINGLESEQ_CLASS}
+    n_known_frames_for_test: null
+    object_name: ${oc.env:BLENDER_SINGLESEQ_CLASS}
+    path_manager_factory_class_type: PathManagerFactory
+    path_manager_factory_PathManagerFactory_args:
+      silence_logs: true
+
+model_factory_ImplicitronModelFactory_args:
+  model_class_type: "OverfitModel"
+  model_OverfitModel_args:
+    mask_images: false
+    raysampler_class_type: AdaptiveRaySampler
+    raysampler_AdaptiveRaySampler_args:
+      n_pts_per_ray_training: 64
+      n_pts_per_ray_evaluation: 64
+      n_rays_per_image_sampled_from_mask: 4096
+      stratified_point_sampling_training: true
+      stratified_point_sampling_evaluation: false
+      scene_extent: 2.0
+      scene_center:
+      - 0.0
+      - 0.0
+      - 0.0
+    renderer_MultiPassEmissionAbsorptionRenderer_args:
+      density_noise_std_train: 0.0
+      n_pts_per_ray_fine_training: 128
+      n_pts_per_ray_fine_evaluation: 128
+      raymarcher_EmissionAbsorptionRaymarcher_args:
+        blend_output: false
+    loss_weights:
+      loss_rgb_mse: 1.0
+      loss_prev_stage_rgb_mse: 1.0
+      loss_mask_bce: 0.0
+      loss_prev_stage_mask_bce: 0.0
+      loss_autodecoder_norm: 0.00
+
+optimizer_factory_ImplicitronOptimizerFactory_args:
+  exponential_lr_step_size: 3001
+  lr_policy: LinearExponential
+  linear_exponential_lr_milestone: 200
+
+training_loop_ImplicitronTrainingLoop_args:
+  max_epochs: 6000
+  metric_print_interval: 10
+  store_checkpoints_purge: 3
+  test_when_finished: true
+  validation_interval: 100

projects/implicitron_trainer/configs/repro_singleseq_nerf_blender.yaml

@@ -0,0 +1,55 @@
+defaults:
+- repro_singleseq_base
+- _self_
+exp_dir: "./data/nerf_blender_repro/${oc.env:BLENDER_SINGLESEQ_CLASS}"
+data_source_ImplicitronDataSource_args:
+  data_loader_map_provider_SequenceDataLoaderMapProvider_args:
+    dataset_length_train: 100
+  dataset_map_provider_class_type: BlenderDatasetMapProvider
+  dataset_map_provider_BlenderDatasetMapProvider_args:
+    base_dir: ${oc.env:BLENDER_DATASET_ROOT}/${oc.env:BLENDER_SINGLESEQ_CLASS}
+    n_known_frames_for_test: null
+    object_name: ${oc.env:BLENDER_SINGLESEQ_CLASS}
+    path_manager_factory_class_type: PathManagerFactory
+    path_manager_factory_PathManagerFactory_args:
+      silence_logs: true
+
+model_factory_ImplicitronModelFactory_args:
+  model_GenericModel_args:
+    mask_images: false
+    raysampler_class_type: AdaptiveRaySampler
+    raysampler_AdaptiveRaySampler_args:
+      n_pts_per_ray_training: 64
+      n_pts_per_ray_evaluation: 64
+      n_rays_per_image_sampled_from_mask: 4096
+      stratified_point_sampling_training: true
+      stratified_point_sampling_evaluation: false
+      scene_extent: 2.0
+      scene_center:
+      - 0.0
+      - 0.0
+      - 0.0
+    renderer_MultiPassEmissionAbsorptionRenderer_args:
+      density_noise_std_train: 0.0
+      n_pts_per_ray_fine_training: 128
+      n_pts_per_ray_fine_evaluation: 128
+      raymarcher_EmissionAbsorptionRaymarcher_args:
+        blend_output: false
+    loss_weights:
+      loss_rgb_mse: 1.0
+      loss_prev_stage_rgb_mse: 1.0
+      loss_mask_bce: 0.0
+      loss_prev_stage_mask_bce: 0.0
+      loss_autodecoder_norm: 0.00
+
+optimizer_factory_ImplicitronOptimizerFactory_args:
+  exponential_lr_step_size: 3001
+  lr_policy: LinearExponential
+  linear_exponential_lr_milestone: 200
+
+training_loop_ImplicitronTrainingLoop_args:
+  max_epochs: 6000
+  metric_print_interval: 10
+  store_checkpoints_purge: 3
+  test_when_finished: true
+  validation_interval: 100

projects/implicitron_trainer/experiment.py

@@ -7,7 +7,7 @@
 
 # pyre-unsafe
 
-""" "
+""""
 This file is the entry point for launching experiments with Implicitron.
 
 Launch Training
@@ -44,22 +44,25 @@ The outputs of the experiment are saved and logged in multiple ways:
         config file.
 
 """
-
 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

@@ -11,6 +11,7 @@ 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
@@ -25,6 +26,7 @@ logger = logging.getLogger(__name__)
 
 
 class ModelFactoryBase(ReplaceableBase):
+
     resume: bool = True  # resume from the last checkpoint
 
     def __call__(self, **kwargs) -> ImplicitronModelBase:
@@ -114,9 +116,7 @@ class ImplicitronModelFactory(ModelFactoryBase):
                         "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                     }
                 model_state_dict = torch.load(
-                    model_io.get_model_path(model_path),
+                    model_io.get_model_path(model_path), map_location=map_location
-                    map_location=map_location,
-                    weights_only=True,
                 )
 
                 try:

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -14,7 +14,9 @@ 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 (
@@ -121,7 +123,6 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
         """
         # Get the parameters to optimize
         if hasattr(model, "_get_param_groups"):  # use the model function
-            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             p_groups = model._get_param_groups(self.lr, wd=self.weight_decay)
         else:
             p_groups = [
@@ -240,7 +241,7 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
                     map_location = {
                         "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                     }
-                optimizer_state = torch.load(opt_path, map_location, weights_only=True)
+                optimizer_state = torch.load(opt_path, map_location)
             else:
                 raise FileNotFoundError(f"Optimizer state {opt_path} does not exist.")
         return optimizer_state

projects/implicitron_trainer/impl/training_loop.py

@@ -161,6 +161,7 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
         for epoch in range(start_epoch, self.max_epochs):
             # automatic new_epoch and plotting of stats at every epoch start
             with stats:
+
                 # Make sure to re-seed random generators to ensure reproducibility
                 # even after restart.
                 seed_all_random_engines(seed + epoch)
@@ -394,7 +395,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
             ):
                 prefix = f"e{stats.epoch}_it{stats.it[trainmode]}"
                 if hasattr(model, "visualize"):
-                    # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
                     model.visualize(
                         viz,
                         visdom_env_imgs,

projects/implicitron_trainer/tests/experiment.yaml

@@ -13,6 +13,13 @@ hydra:
 data_source_ImplicitronDataSource_args:
   dataset_map_provider_class_type: ???
   data_loader_map_provider_class_type: SequenceDataLoaderMapProvider
+  dataset_map_provider_BlenderDatasetMapProvider_args:
+    base_dir: ???
+    object_name: ???
+    path_manager_factory_class_type: PathManagerFactory
+    n_known_frames_for_test: null
+    path_manager_factory_PathManagerFactory_args:
+      silence_logs: true
   dataset_map_provider_JsonIndexDatasetMapProvider_args:
     category: ???
     task_str: singlesequence
@@ -84,6 +91,14 @@ data_source_ImplicitronDataSource_args:
       sort_frames: false
     path_manager_factory_PathManagerFactory_args:
       silence_logs: true
+  dataset_map_provider_LlffDatasetMapProvider_args:
+    base_dir: ???
+    object_name: ???
+    path_manager_factory_class_type: PathManagerFactory
+    n_known_frames_for_test: null
+    path_manager_factory_PathManagerFactory_args:
+      silence_logs: true
+    downscale_factor: 4
   dataset_map_provider_RenderedMeshDatasetMapProvider_args:
     num_views: 40
     data_file: null

projects/implicitron_trainer/tests/test_experiment.py

@@ -12,6 +12,7 @@ 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 (
@@ -52,8 +53,12 @@ class TestExperiment(unittest.TestCase):
         cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_class_type = (
             "JsonIndexDatasetMapProvider"
         )
-        dataset_args = cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        dataset_args = (
-        dataloader_args = cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
+            cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        )
+        dataloader_args = (
+            cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
+        )
         dataset_args.category = "skateboard"
         dataset_args.test_restrict_sequence_id = 0
         dataset_args.dataset_root = "manifold://co3d/tree/extracted"
@@ -89,8 +94,12 @@ class TestExperiment(unittest.TestCase):
         cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_class_type = (
             "JsonIndexDatasetMapProvider"
         )
-        dataset_args = cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        dataset_args = (
-        dataloader_args = cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
+            cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        )
+        dataloader_args = (
+            cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_args
+        )
         dataset_args.category = "skateboard"
         dataset_args.test_restrict_sequence_id = 0
         dataset_args.dataset_root = "manifold://co3d/tree/extracted"
@@ -102,7 +111,9 @@ class TestExperiment(unittest.TestCase):
         cfg.training_loop_ImplicitronTrainingLoop_args.max_epochs = 2
         cfg.training_loop_ImplicitronTrainingLoop_args.store_checkpoints = False
         cfg.optimizer_factory_ImplicitronOptimizerFactory_args.lr_policy = "Exponential"
-        cfg.optimizer_factory_ImplicitronOptimizerFactory_args.exponential_lr_step_size = 2
+        cfg.optimizer_factory_ImplicitronOptimizerFactory_args.exponential_lr_step_size = (
+            2
+        )
 
         if DEBUG:
             experiment.dump_cfg(cfg)

projects/implicitron_trainer/tests/test_optimizer_factory.py

@@ -81,9 +81,8 @@ class TestOptimizerFactory(unittest.TestCase):
 
     def test_param_overrides_self_param_group_assignment(self):
         pa, pb, pc = [torch.nn.Parameter(data=torch.tensor(i * 1.0)) for i in range(3)]
-        na, nb = (
+        na, nb = Node(params=[pa]), Node(
-            Node(params=[pa]),
+            params=[pb], param_groups={"self": "pb_self", "p1": "pb_param"}
-            Node(params=[pb], param_groups={"self": "pb_self", "p1": "pb_param"}),
         )
         root = Node(children=[na, nb], params=[pc], param_groups={"m1": "pb_member"})
         param_groups = self._get_param_groups(root)

projects/nerf/nerf/dataset.py

@@ -84,9 +84,9 @@ def get_nerf_datasets(
 
     if autodownload and any(not os.path.isfile(p) for p in (cameras_path, image_path)):
         # Automatically download the data files if missing.
-        download_data([dataset_name], data_root=data_root)
+        download_data((dataset_name,), data_root=data_root)
 
-    train_data = torch.load(cameras_path, weights_only=True)
+    train_data = torch.load(cameras_path)
     n_cameras = train_data["cameras"]["R"].shape[0]
 
     _image_max_image_pixels = Image.MAX_IMAGE_PIXELS

projects/nerf/nerf/stats.py

@@ -194,6 +194,7 @@ class Stats:
         it = self.it[stat_set]
 
         for stat in self.log_vars:
+
             if stat not in self.stats[stat_set]:
                 self.stats[stat_set][stat] = AverageMeter()
 

projects/nerf/test_nerf.py

@@ -24,6 +24,7 @@ CONFIG_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs"
 
 @hydra.main(config_path=CONFIG_DIR, config_name="lego")
 def main(cfg: DictConfig):
+
     # Device on which to run.
     if torch.cuda.is_available():
         device = "cuda"
@@ -62,7 +63,7 @@ def main(cfg: DictConfig):
         raise ValueError(f"Model checkpoint {checkpoint_path} does not exist!")
 
     print(f"Loading checkpoint {checkpoint_path}.")
-    loaded_data = torch.load(checkpoint_path, weights_only=True)
+    loaded_data = torch.load(checkpoint_path)
     # Do not load the cached xy grid.
     # - this allows setting an arbitrary evaluation image size.
     state_dict = {

projects/nerf/tests/test_raysampler.py

@@ -42,6 +42,7 @@ class TestRaysampler(unittest.TestCase):
         cameras, rays = [], []
 
         for _ in range(batch_size):
+
             R = random_rotations(1)
             T = torch.randn(1, 3)
             focal_length = torch.rand(1, 2) + 0.5

projects/nerf/train_nerf.py

@@ -25,6 +25,7 @@ CONFIG_DIR = os.path.join(os.path.dirname(os.path.realpath(__file__)), "configs"
 
 @hydra.main(config_path=CONFIG_DIR, config_name="lego")
 def main(cfg: DictConfig):
+
     # Set the relevant seeds for reproducibility.
     np.random.seed(cfg.seed)
     torch.manual_seed(cfg.seed)
@@ -76,7 +77,7 @@ def main(cfg: DictConfig):
         # Resume training if requested.
         if cfg.resume and os.path.isfile(checkpoint_path):
             print(f"Resuming from checkpoint {checkpoint_path}.")
-            loaded_data = torch.load(checkpoint_path, weights_only=True)
+            loaded_data = torch.load(checkpoint_path)
             model.load_state_dict(loaded_data["model"])
             stats = pickle.loads(loaded_data["stats"])
             print(f"   => resuming from epoch {stats.epoch}.")
@@ -218,6 +219,7 @@ def main(cfg: DictConfig):
 
         # Validation
         if epoch % cfg.validation_epoch_interval == 0 and epoch > 0:
+
             # Sample a validation camera/image.
             val_batch = next(val_dataloader.__iter__())
             val_image, val_camera, camera_idx = val_batch[0].values()

pytorch3d/__init__.py

@@ -6,4 +6,4 @@
 
 # pyre-unsafe
 
-__version__ = "0.7.9"
+__version__ = "0.7.8"

pytorch3d/common/compat.py

@@ -17,7 +17,7 @@ Some functions which depend on PyTorch or Python versions.
 
 
 def meshgrid_ij(
-    *A: Union[torch.Tensor, Sequence[torch.Tensor]],
+    *A: Union[torch.Tensor, Sequence[torch.Tensor]]
 ) -> Tuple[torch.Tensor, ...]:  # pragma: no cover
     """
     Like torch.meshgrid was before PyTorch 1.10.0, i.e. with indexing set to ij

pytorch3d/common/workaround/symeig3x3.py

@@ -82,12 +82,10 @@ class _SymEig3x3(nn.Module):
         q = inputs_trace / 3.0
 
         # Calculate squared sum of elements outside the main diagonal / 2
-        p1 = (
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
-            torch.square(inputs).sum(dim=(-1, -2)) - torch.square(inputs_diag).sum(-1)
+        p1 = ((inputs**2).sum(dim=(-1, -2)) - (inputs_diag**2).sum(-1)) / 2
-        ) / 2
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
-        p2 = torch.square(inputs_diag - q[..., None]).sum(dim=-1) + 2.0 * p1.clamp(
+        p2 = ((inputs_diag - q[..., None]) ** 2).sum(dim=-1) + 2.0 * p1.clamp(self._eps)
-            self._eps
-        )
 
         p = torch.sqrt(p2 / 6.0)
         B = (inputs - q[..., None, None] * self._identity) / p[..., None, None]
@@ -106,9 +104,7 @@ class _SymEig3x3(nn.Module):
         # Soft dispatch between the degenerate case (diagonal A) and general.
         # diag_soft_cond -> 1.0 when p1 < 6 * eps and diag_soft_cond -> 0.0 otherwise.
         # We use 6 * eps to take into account the error accumulated during the p1 summation
-        diag_soft_cond = torch.exp(-torch.square(p1 / (6 * self._eps))).detach()[
+        diag_soft_cond = torch.exp(-((p1 / (6 * self._eps)) ** 2)).detach()[..., None]
-            ..., None
-        ]
 
         # Eigenvalues are the ordered elements of main diagonal in the degenerate case
         diag_eigenvals, _ = torch.sort(inputs_diag, dim=-1)
@@ -203,7 +199,8 @@ class _SymEig3x3(nn.Module):
             cross_products[..., :1, :]
         )
 
-        norms_sq = torch.square(cross_products).sum(dim=-1)
+        # pyre-fixme[58]: `**` is not supported for operand types `Tensor` and `int`.
+        norms_sq = (cross_products**2).sum(dim=-1)
         max_norms_index = norms_sq.argmax(dim=-1)
 
         # Pick only the cross-product with highest squared norm for each input

pytorch3d/csrc/ball_query/ball_query.cu

@@ -32,9 +32,7 @@ __global__ void BallQueryKernel(
     at::PackedTensorAccessor64<int64_t, 3, at::RestrictPtrTraits> idxs,
     at::PackedTensorAccessor64<scalar_t, 3, at::RestrictPtrTraits> dists,
     const int64_t K,
-    const float radius,
+    const float radius2) {
-    const float radius2,
-    const bool skip_points_outside_cube) {
   const int64_t N = p1.size(0);
   const int64_t chunks_per_cloud = (1 + (p1.size(1) - 1) / blockDim.x);
   const int64_t chunks_to_do = N * chunks_per_cloud;
@@ -53,19 +51,7 @@ __global__ void BallQueryKernel(
     // Iterate over points in p2 until desired count is reached or
     // all points have been considered
     for (int64_t j = 0, count = 0; j < lengths2[n] && count < K; ++j) {
-      if (skip_points_outside_cube) {
+      // Calculate the distance between the points
-        bool is_within_radius = true;
-        // Filter when any one coordinate is already outside the radius
-        for (int d = 0; is_within_radius && d < D; ++d) {
-          scalar_t abs_diff = fabs(p1[n][i][d] - p2[n][j][d]);
-          is_within_radius = (abs_diff <= radius);
-        }
-        if (!is_within_radius) {
-          continue;
-        }
-      }
-
-      // Else, calculate the distance between the points and compare
       scalar_t dist2 = 0.0;
       for (int d = 0; d < D; ++d) {
         scalar_t diff = p1[n][i][d] - p2[n][j][d];
@@ -91,8 +77,7 @@ std::tuple<at::Tensor, at::Tensor> BallQueryCuda(
     const at::Tensor& lengths1, // (N,)
     const at::Tensor& lengths2, // (N,)
     int K,
-    float radius,
+    float radius) {
-    bool skip_points_outside_cube) {
   // Check inputs are on the same device
   at::TensorArg p1_t{p1, "p1", 1}, p2_t{p2, "p2", 2},
       lengths1_t{lengths1, "lengths1", 3}, lengths2_t{lengths2, "lengths2", 4};
@@ -135,9 +120,7 @@ std::tuple<at::Tensor, at::Tensor> BallQueryCuda(
             idxs.packed_accessor64<int64_t, 3, at::RestrictPtrTraits>(),
             dists.packed_accessor64<float, 3, at::RestrictPtrTraits>(),
             K_64,
-            radius,
+            radius2);
-            radius2,
-            skip_points_outside_cube);
       }));
 
   AT_CUDA_CHECK(cudaGetLastError());

pytorch3d/csrc/ball_query/ball_query.h

@@ -25,9 +25,6 @@
 //      within the radius
 //    radius: the radius around each point within which the neighbors need to be
 //      located
-//    skip_points_outside_cube: If true, reduce multiplications of float values
-//      by not explicitly calculating distances to points that fall outside the
-//      D-cube with side length (2*radius) centered at each point in p1.
 //
 // Returns:
 //    p1_neighbor_idx: LongTensor of shape (N, P1, K), where
@@ -49,8 +46,7 @@ std::tuple<at::Tensor, at::Tensor> BallQueryCpu(
     const at::Tensor& lengths1,
     const at::Tensor& lengths2,
     const int K,
-    const float radius,
+    const float radius);
-    const bool skip_points_outside_cube);
 
 // CUDA implementation
 std::tuple<at::Tensor, at::Tensor> BallQueryCuda(
@@ -59,8 +55,7 @@ std::tuple<at::Tensor, at::Tensor> BallQueryCuda(
     const at::Tensor& lengths1,
     const at::Tensor& lengths2,
     const int K,
-    const float radius,
+    const float radius);
-    const bool skip_points_outside_cube);
 
 // Implementation which is exposed
 // Note: the backward pass reuses the KNearestNeighborBackward kernel
@@ -70,8 +65,7 @@ inline std::tuple<at::Tensor, at::Tensor> BallQuery(
     const at::Tensor& lengths1,
     const at::Tensor& lengths2,
     int K,
-    float radius,
+    float radius) {
-    bool skip_points_outside_cube) {
   if (p1.is_cuda() || p2.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CUDA(p1);
@@ -82,20 +76,16 @@ inline std::tuple<at::Tensor, at::Tensor> BallQuery(
         lengths1.contiguous(),
         lengths2.contiguous(),
         K,
-        radius,
+        radius);
-        skip_points_outside_cube);
 #else
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(p1);
-  CHECK_CPU(p2);
   return BallQueryCpu(
       p1.contiguous(),
       p2.contiguous(),
       lengths1.contiguous(),
       lengths2.contiguous(),
       K,
-      radius,
+      radius);
-      skip_points_outside_cube);
 }

pytorch3d/csrc/ball_query/ball_query_cpu.cpp

@@ -6,8 +6,8 @@
  * LICENSE file in the root directory of this source tree.
  */
 
-#include <math.h>
 #include <torch/extension.h>
+#include <queue>
 #include <tuple>
 
 std::tuple<at::Tensor, at::Tensor> BallQueryCpu(
@@ -16,8 +16,7 @@ std::tuple<at::Tensor, at::Tensor> BallQueryCpu(
     const at::Tensor& lengths1,
     const at::Tensor& lengths2,
     int K,
-    float radius,
+    float radius) {
-    bool skip_points_outside_cube) {
   const int N = p1.size(0);
   const int P1 = p1.size(1);
   const int D = p1.size(2);
@@ -39,16 +38,6 @@ std::tuple<at::Tensor, at::Tensor> BallQueryCpu(
     const int64_t length2 = lengths2_a[n];
     for (int64_t i = 0; i < length1; ++i) {
       for (int64_t j = 0, count = 0; j < length2 && count < K; ++j) {
-        if (skip_points_outside_cube) {
-          bool is_within_radius = true;
-          for (int d = 0; is_within_radius && d < D; ++d) {
-            float abs_diff = fabs(p1_a[n][i][d] - p2_a[n][j][d]);
-            is_within_radius = (abs_diff <= radius);
-          }
-          if (!is_within_radius) {
-            continue;
-          }
-        }
         float dist2 = 0;
         for (int d = 0; d < D; ++d) {
           float diff = p1_a[n][i][d] - p2_a[n][j][d];

pytorch3d/csrc/blending/sigmoid_alpha_blend.h

@@ -98,11 +98,6 @@ at::Tensor SigmoidAlphaBlendBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(distances);
-  CHECK_CPU(pix_to_face);
-  CHECK_CPU(alphas);
-  CHECK_CPU(grad_alphas);
-
   return SigmoidAlphaBlendBackwardCpu(
       grad_alphas, alphas, distances, pix_to_face, sigma);
 }

pytorch3d/csrc/compositing/alpha_composite.cu

@@ -28,16 +28,17 @@ __global__ void alphaCompositeCudaForwardKernel(
     const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
     const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
   // clang-format on
+  const int64_t batch_size = result.size(0);
   const int64_t C = features.size(0);
   const int64_t H = points_idx.size(2);
   const int64_t W = points_idx.size(3);
 
   // Get the batch and index
-  const auto batch = blockIdx.x;
+  const int batch = blockIdx.x;
 
   const int num_pixels = C * H * W;
-  const auto num_threads = gridDim.y * blockDim.x;
+  const int num_threads = gridDim.y * blockDim.x;
-  const auto tid = blockIdx.y * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
 
   // Iterate over each feature in each pixel
   for (int pid = tid; pid < num_pixels; pid += num_threads) {
@@ -78,16 +79,17 @@ __global__ void alphaCompositeCudaBackwardKernel(
     const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
     const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
   // clang-format on
+  const int64_t batch_size = points_idx.size(0);
   const int64_t C = features.size(0);
   const int64_t H = points_idx.size(2);
   const int64_t W = points_idx.size(3);
 
   // Get the batch and index
-  const auto batch = blockIdx.x;
+  const int batch = blockIdx.x;
 
   const int num_pixels = C * H * W;
-  const auto num_threads = gridDim.y * blockDim.x;
+  const int num_threads = gridDim.y * blockDim.x;
-  const auto tid = blockIdx.y * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
 
   // Parallelize over each feature in each pixel in images of size H * W,
   // for each image in the batch of size batch_size

pytorch3d/csrc/compositing/alpha_composite.h

@@ -74,9 +74,6 @@ torch::Tensor alphaCompositeForward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(features);
-    CHECK_CPU(alphas);
-    CHECK_CPU(points_idx);
     return alphaCompositeCpuForward(features, alphas, points_idx);
   }
 }
@@ -104,11 +101,6 @@ std::tuple<torch::Tensor, torch::Tensor> alphaCompositeBackward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(grad_outputs);
-    CHECK_CPU(features);
-    CHECK_CPU(alphas);
-    CHECK_CPU(points_idx);
-
     return alphaCompositeCpuBackward(
         grad_outputs, features, alphas, points_idx);
   }

pytorch3d/csrc/compositing/norm_weighted_sum.cu

@@ -28,16 +28,17 @@ __global__ void weightedSumNormCudaForwardKernel(
     const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
     const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
   // clang-format on
+  const int64_t batch_size = result.size(0);
   const int64_t C = features.size(0);
   const int64_t H = points_idx.size(2);
   const int64_t W = points_idx.size(3);
 
   // Get the batch and index
-  const auto batch = blockIdx.x;
+  const int batch = blockIdx.x;
 
   const int num_pixels = C * H * W;
-  const auto num_threads = gridDim.y * blockDim.x;
+  const int num_threads = gridDim.y * blockDim.x;
-  const auto tid = blockIdx.y * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
 
   // Parallelize over each feature in each pixel in images of size H * W,
   // for each image in the batch of size batch_size
@@ -91,16 +92,17 @@ __global__ void weightedSumNormCudaBackwardKernel(
     const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
     const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
   // clang-format on
+  const int64_t batch_size = points_idx.size(0);
   const int64_t C = features.size(0);
   const int64_t H = points_idx.size(2);
   const int64_t W = points_idx.size(3);
 
   // Get the batch and index
-  const auto batch = blockIdx.x;
+  const int batch = blockIdx.x;
 
   const int num_pixels = C * W * H;
-  const auto num_threads = gridDim.y * blockDim.x;
+  const int num_threads = gridDim.y * blockDim.x;
-  const auto tid = blockIdx.y * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
 
   // Parallelize over each feature in each pixel in images of size H * W,
   // for each image in the batch of size batch_size

pytorch3d/csrc/compositing/norm_weighted_sum.h

@@ -73,10 +73,6 @@ torch::Tensor weightedSumNormForward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(features);
-    CHECK_CPU(alphas);
-    CHECK_CPU(points_idx);
-
     return weightedSumNormCpuForward(features, alphas, points_idx);
   }
 }
@@ -104,11 +100,6 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumNormBackward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(grad_outputs);
-    CHECK_CPU(features);
-    CHECK_CPU(alphas);
-    CHECK_CPU(points_idx);
-
     return weightedSumNormCpuBackward(
         grad_outputs, features, alphas, points_idx);
   }

pytorch3d/csrc/compositing/weighted_sum.cu

@@ -26,16 +26,17 @@ __global__ void weightedSumCudaForwardKernel(
     const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
     const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
   // clang-format on
+  const int64_t batch_size = result.size(0);
   const int64_t C = features.size(0);
   const int64_t H = points_idx.size(2);
   const int64_t W = points_idx.size(3);
 
   // Get the batch and index
-  const auto batch = blockIdx.x;
+  const int batch = blockIdx.x;
 
   const int num_pixels = C * H * W;
-  const auto num_threads = gridDim.y * blockDim.x;
+  const int num_threads = gridDim.y * blockDim.x;
-  const auto tid = blockIdx.y * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
 
   // Parallelize over each feature in each pixel in images of size H * W,
   // for each image in the batch of size batch_size
@@ -73,16 +74,17 @@ __global__ void weightedSumCudaBackwardKernel(
     const at::PackedTensorAccessor64<float, 4, at::RestrictPtrTraits> alphas,
     const at::PackedTensorAccessor64<int64_t, 4, at::RestrictPtrTraits> points_idx) {
   // clang-format on
+  const int64_t batch_size = points_idx.size(0);
   const int64_t C = features.size(0);
   const int64_t H = points_idx.size(2);
   const int64_t W = points_idx.size(3);
 
   // Get the batch and index
-  const auto batch = blockIdx.x;
+  const int batch = blockIdx.x;
 
   const int num_pixels = C * H * W;
-  const auto num_threads = gridDim.y * blockDim.x;
+  const int num_threads = gridDim.y * blockDim.x;
-  const auto tid = blockIdx.y * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.y * blockDim.x + threadIdx.x;
 
   // Iterate over each pixel to compute the contribution to the
   // gradient for the features and weights

pytorch3d/csrc/compositing/weighted_sum.h

@@ -72,9 +72,6 @@ torch::Tensor weightedSumForward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(features);
-    CHECK_CPU(alphas);
-    CHECK_CPU(points_idx);
     return weightedSumCpuForward(features, alphas, points_idx);
   }
 }
@@ -101,11 +98,6 @@ std::tuple<torch::Tensor, torch::Tensor> weightedSumBackward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(grad_outputs);
-    CHECK_CPU(features);
-    CHECK_CPU(alphas);
-    CHECK_CPU(points_idx);
-
     return weightedSumCpuBackward(grad_outputs, features, alphas, points_idx);
   }
 }

pytorch3d/csrc/ext.cpp

@@ -8,6 +8,7 @@
 
 // clang-format off
 #include "./pulsar/global.h" // Include before <torch/extension.h>.
+#include <torch/extension.h>
 // clang-format on
 #include "./pulsar/pytorch/renderer.h"
 #include "./pulsar/pytorch/tensor_util.h"
@@ -105,16 +106,15 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   py::class_<
       pulsar::pytorch::Renderer,
       std::shared_ptr<pulsar::pytorch::Renderer>>(m, "PulsarRenderer")
-      .def(
+      .def(py::init<
-          py::init<
+           const uint&,
-              const uint&,
+           const uint&,
-              const uint&,
+           const uint&,
-              const uint&,
+           const bool&,
-              const bool&,
+           const bool&,
-              const bool&,
+           const float&,
-              const float&,
+           const uint&,
-              const uint&,
+           const uint&>())
-              const uint&>())
       .def(
           "__eq__",
           [](const pulsar::pytorch::Renderer& a,
@@ -149,10 +149,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
           py::arg("gamma"),
           py::arg("max_depth"),
           py::arg("min_depth") /* = 0.f*/,
-          py::arg("bg_col") /* = std::nullopt not exposed properly in
+          py::arg(
-                               pytorch 1.1. */
+              "bg_col") /* = at::nullopt not exposed properly in pytorch 1.1. */
           ,
-          py::arg("opacity") /* = std::nullopt ... */,
+          py::arg("opacity") /* = at::nullopt ... */,
           py::arg("percent_allowed_difference") = 0.01f,
           py::arg("max_n_hits") = MAX_UINT,
           py::arg("mode") = 0)

pytorch3d/csrc/face_areas_normals/face_areas_normals.h

@@ -60,8 +60,6 @@ std::tuple<at::Tensor, at::Tensor> FaceAreasNormalsForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(verts);
-  CHECK_CPU(faces);
   return FaceAreasNormalsForwardCpu(verts, faces);
 }
 
@@ -82,9 +80,5 @@ at::Tensor FaceAreasNormalsBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(grad_areas);
-  CHECK_CPU(grad_normals);
-  CHECK_CPU(verts);
-  CHECK_CPU(faces);
   return FaceAreasNormalsBackwardCpu(grad_areas, grad_normals, verts, faces);
 }

pytorch3d/csrc/gather_scatter/gather_scatter.cu

@@ -20,14 +20,14 @@ __global__ void GatherScatterCudaKernel(
     const size_t V,
     const size_t D,
     const size_t E) {
-  const auto tid = threadIdx.x;
+  const int tid = threadIdx.x;
 
   // Reverse the vertex order if backward.
   const int v0_idx = backward ? 1 : 0;
   const int v1_idx = backward ? 0 : 1;
 
   // Edges are split evenly across the blocks.
-  for (auto e = blockIdx.x; e < E; e += gridDim.x) {
+  for (int e = blockIdx.x; e < E; e += gridDim.x) {
     // Get indices of vertices which form the edge.
     const int64_t v0 = edges[2 * e + v0_idx];
     const int64_t v1 = edges[2 * e + v1_idx];
@@ -35,7 +35,7 @@ __global__ void GatherScatterCudaKernel(
     // Split vertex features evenly across threads.
     // This implementation will be quite wasteful when D<128 since there will be
     // a lot of threads doing nothing.
-    for (auto d = tid; d < D; d += blockDim.x) {
+    for (int d = tid; d < D; d += blockDim.x) {
       const float val = input[v1 * D + d];
       float* address = output + v0 * D + d;
       atomicAdd(address, val);

pytorch3d/csrc/gather_scatter/gather_scatter.h

@@ -53,7 +53,5 @@ at::Tensor GatherScatter(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(input);
-  CHECK_CPU(edges);
   return GatherScatterCpu(input, edges, directed, backward);
 }

pytorch3d/csrc/interp_face_attrs/interp_face_attrs.cu

@@ -20,8 +20,8 @@ __global__ void InterpFaceAttrsForwardKernel(
     const size_t P,
     const size_t F,
     const size_t D) {
-  const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  const int tid = threadIdx.x + blockIdx.x * blockDim.x;
-  const auto num_threads = blockDim.x * gridDim.x;
+  const int num_threads = blockDim.x * gridDim.x;
   for (int pd = tid; pd < P * D; pd += num_threads) {
     const int p = pd / D;
     const int d = pd % D;
@@ -93,8 +93,8 @@ __global__ void InterpFaceAttrsBackwardKernel(
     const size_t P,
     const size_t F,
     const size_t D) {
-  const auto tid = threadIdx.x + blockIdx.x * blockDim.x;
+  const int tid = threadIdx.x + blockIdx.x * blockDim.x;
-  const auto num_threads = blockDim.x * gridDim.x;
+  const int num_threads = blockDim.x * gridDim.x;
   for (int pd = tid; pd < P * D; pd += num_threads) {
     const int p = pd / D;
     const int d = pd % D;

pytorch3d/csrc/interp_face_attrs/interp_face_attrs.h

@@ -57,8 +57,6 @@ at::Tensor InterpFaceAttrsForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(face_attrs);
-  CHECK_CPU(barycentric_coords);
   return InterpFaceAttrsForwardCpu(pix_to_face, barycentric_coords, face_attrs);
 }
 
@@ -108,9 +106,6 @@ std::tuple<at::Tensor, at::Tensor> InterpFaceAttrsBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(face_attrs);
-  CHECK_CPU(barycentric_coords);
-  CHECK_CPU(grad_pix_attrs);
   return InterpFaceAttrsBackwardCpu(
       pix_to_face, barycentric_coords, face_attrs, grad_pix_attrs);
 }

pytorch3d/csrc/iou_box3d/iou_box3d.h

@@ -44,7 +44,5 @@ inline std::tuple<at::Tensor, at::Tensor> IoUBox3D(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(boxes1);
-  CHECK_CPU(boxes2);
   return IoUBox3DCpu(boxes1.contiguous(), boxes2.contiguous());
 }

pytorch3d/csrc/iou_box3d/iou_box3d_cpu.cpp

@@ -7,7 +7,10 @@
  */
 
 #include <torch/extension.h>
+#include <torch/torch.h>
 #include <list>
+#include <numeric>
+#include <queue>
 #include <tuple>
 #include "iou_box3d/iou_utils.h"
 

pytorch3d/csrc/iou_box3d/iou_utils.cuh

@@ -461,8 +461,10 @@ __device__ inline std::tuple<float3, float3> ArgMaxVerts(
 __device__ inline bool IsCoplanarTriTri(
     const FaceVerts& tri1,
     const FaceVerts& tri2) {
+  const float3 tri1_ctr = FaceCenter({tri1.v0, tri1.v1, tri1.v2});
   const float3 tri1_n = FaceNormal({tri1.v0, tri1.v1, tri1.v2});
 
+  const float3 tri2_ctr = FaceCenter({tri2.v0, tri2.v1, tri2.v2});
   const float3 tri2_n = FaceNormal({tri2.v0, tri2.v1, tri2.v2});
 
   // Check if parallel
@@ -498,6 +500,7 @@ __device__ inline bool IsCoplanarTriPlane(
     const FaceVerts& tri,
     const FaceVerts& plane,
     const float3& normal) {
+  const float3 tri_ctr = FaceCenter({tri.v0, tri.v1, tri.v2});
   const float3 nt = FaceNormal({tri.v0, tri.v1, tri.v2});
 
   // check if parallel
@@ -725,7 +728,7 @@ __device__ inline int BoxIntersections(
       }
     }
     // Update the face_verts_out tris
-    num_tris = min(MAX_TRIS, offset);
+    num_tris = offset;
     for (int j = 0; j < num_tris; ++j) {
       face_verts_out[j] = tri_verts_updated[j];
     }

pytorch3d/csrc/knn/knn.h

@@ -74,8 +74,6 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdx(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(p1);
-  CHECK_CPU(p2);
   return KNearestNeighborIdxCpu(p1, p2, lengths1, lengths2, norm, K);
 }
 
@@ -142,8 +140,6 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(p1);
-  CHECK_CPU(p2);
   return KNearestNeighborBackwardCpu(
       p1, p2, lengths1, lengths2, idxs, norm, grad_dists);
 }

pytorch3d/csrc/marching_cubes/marching_cubes.h

@@ -58,6 +58,5 @@ inline std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubes(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(vol);
   return MarchingCubesCpu(vol.contiguous(), isolevel);
 }

pytorch3d/csrc/packed_to_padded_tensor/packed_to_padded_tensor.h

@@ -88,8 +88,6 @@ at::Tensor PackedToPadded(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(inputs_packed);
-  CHECK_CPU(first_idxs);
   return PackedToPaddedCpu(inputs_packed, first_idxs, max_size);
 }
 
@@ -107,7 +105,5 @@ at::Tensor PaddedToPacked(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(inputs_padded);
-  CHECK_CPU(first_idxs);
   return PaddedToPackedCpu(inputs_padded, first_idxs, num_inputs);
 }

pytorch3d/csrc/point_mesh/point_mesh_cpu.cpp

@@ -174,8 +174,8 @@ std::tuple<at::Tensor, at::Tensor> HullHullDistanceForwardCpu(
   at::Tensor idxs = at::zeros({A_N,}, as_first_idx.options());
   // clang-format on
 
-  auto as_a = as.accessor<float, H1 == 1 ? 2 : 3>();
+  auto as_a = as.accessor < float, H1 == 1 ? 2 : 3 > ();
-  auto bs_a = bs.accessor<float, H2 == 1 ? 2 : 3>();
+  auto bs_a = bs.accessor < float, H2 == 1 ? 2 : 3 > ();
   auto as_first_idx_a = as_first_idx.accessor<int64_t, 1>();
   auto bs_first_idx_a = bs_first_idx.accessor<int64_t, 1>();
   auto dists_a = dists.accessor<float, 1>();
@@ -230,10 +230,10 @@ std::tuple<at::Tensor, at::Tensor> HullHullDistanceBackwardCpu(
   at::Tensor grad_as = at::zeros_like(as);
   at::Tensor grad_bs = at::zeros_like(bs);
 
-  auto as_a = as.accessor<float, H1 == 1 ? 2 : 3>();
+  auto as_a = as.accessor < float, H1 == 1 ? 2 : 3 > ();
-  auto bs_a = bs.accessor<float, H2 == 1 ? 2 : 3>();
+  auto bs_a = bs.accessor < float, H2 == 1 ? 2 : 3 > ();
-  auto grad_as_a = grad_as.accessor<float, H1 == 1 ? 2 : 3>();
+  auto grad_as_a = grad_as.accessor < float, H1 == 1 ? 2 : 3 > ();
-  auto grad_bs_a = grad_bs.accessor<float, H2 == 1 ? 2 : 3>();
+  auto grad_bs_a = grad_bs.accessor < float, H2 == 1 ? 2 : 3 > ();
   auto idx_bs_a = idx_bs.accessor<int64_t, 1>();
   auto grad_dists_a = grad_dists.accessor<float, 1>();
 

pytorch3d/csrc/point_mesh/point_mesh_cuda.cu

@@ -110,7 +110,7 @@ __global__ void DistanceForwardKernel(
     __syncthreads();
 
     // Perform reduction in shared memory.
-    for (auto s = blockDim.x / 2; s > 32; s >>= 1) {
+    for (int s = blockDim.x / 2; s > 32; s >>= 1) {
       if (tid < s) {
         if (min_dists[tid] > min_dists[tid + s]) {
           min_dists[tid] = min_dists[tid + s];
@@ -502,8 +502,8 @@ __global__ void PointFaceArrayForwardKernel(
   const float3* tris_f3 = (float3*)tris;
 
   // Parallelize over P * S computations
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int t_i = tid; t_i < P * T; t_i += num_threads) {
     const int t = t_i / P; // segment index.
@@ -576,8 +576,8 @@ __global__ void PointFaceArrayBackwardKernel(
   const float3* tris_f3 = (float3*)tris;
 
   // Parallelize over P * S computations
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int t_i = tid; t_i < P * T; t_i += num_threads) {
     const int t = t_i / P; // triangle index.
@@ -683,8 +683,8 @@ __global__ void PointEdgeArrayForwardKernel(
   float3* segms_f3 = (float3*)segms;
 
   // Parallelize over P * S computations
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int t_i = tid; t_i < P * S; t_i += num_threads) {
     const int s = t_i / P; // segment index.
@@ -752,8 +752,8 @@ __global__ void PointEdgeArrayBackwardKernel(
   float3* segms_f3 = (float3*)segms;
 
   // Parallelize over P * S computations
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int t_i = tid; t_i < P * S; t_i += num_threads) {
     const int s = t_i / P; // segment index.

pytorch3d/csrc/point_mesh/point_mesh_cuda.h

@@ -88,10 +88,6 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceDistanceForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(points_first_idx);
-  CHECK_CPU(tris);
-  CHECK_CPU(tris_first_idx);
   return PointFaceDistanceForwardCpu(
       points, points_first_idx, tris, tris_first_idx, min_triangle_area);
 }
@@ -147,10 +143,6 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceDistanceBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(tris);
-  CHECK_CPU(idx_points);
-  CHECK_CPU(grad_dists);
   return PointFaceDistanceBackwardCpu(
       points, tris, idx_points, grad_dists, min_triangle_area);
 }
@@ -229,10 +221,6 @@ std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(points_first_idx);
-  CHECK_CPU(tris);
-  CHECK_CPU(tris_first_idx);
   return FacePointDistanceForwardCpu(
       points, points_first_idx, tris, tris_first_idx, min_triangle_area);
 }
@@ -289,10 +277,6 @@ std::tuple<torch::Tensor, torch::Tensor> FacePointDistanceBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(tris);
-  CHECK_CPU(idx_tris);
-  CHECK_CPU(grad_dists);
   return FacePointDistanceBackwardCpu(
       points, tris, idx_tris, grad_dists, min_triangle_area);
 }
@@ -362,10 +346,6 @@ std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(points_first_idx);
-  CHECK_CPU(segms);
-  CHECK_CPU(segms_first_idx);
   return PointEdgeDistanceForwardCpu(
       points, points_first_idx, segms, segms_first_idx, max_points);
 }
@@ -416,10 +396,6 @@ std::tuple<torch::Tensor, torch::Tensor> PointEdgeDistanceBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(segms);
-  CHECK_CPU(idx_points);
-  CHECK_CPU(grad_dists);
   return PointEdgeDistanceBackwardCpu(points, segms, idx_points, grad_dists);
 }
 
@@ -488,10 +464,6 @@ std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(points_first_idx);
-  CHECK_CPU(segms);
-  CHECK_CPU(segms_first_idx);
   return EdgePointDistanceForwardCpu(
       points, points_first_idx, segms, segms_first_idx, max_segms);
 }
@@ -542,10 +514,6 @@ std::tuple<torch::Tensor, torch::Tensor> EdgePointDistanceBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(segms);
-  CHECK_CPU(idx_segms);
-  CHECK_CPU(grad_dists);
   return EdgePointDistanceBackwardCpu(points, segms, idx_segms, grad_dists);
 }
 
@@ -599,8 +567,6 @@ torch::Tensor PointFaceArrayDistanceForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(tris);
   return PointFaceArrayDistanceForwardCpu(points, tris, min_triangle_area);
 }
 
@@ -647,9 +613,6 @@ std::tuple<torch::Tensor, torch::Tensor> PointFaceArrayDistanceBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(tris);
-  CHECK_CPU(grad_dists);
   return PointFaceArrayDistanceBackwardCpu(
       points, tris, grad_dists, min_triangle_area);
 }
@@ -698,8 +661,6 @@ torch::Tensor PointEdgeArrayDistanceForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(segms);
   return PointEdgeArrayDistanceForwardCpu(points, segms);
 }
 
@@ -742,8 +703,5 @@ std::tuple<torch::Tensor, torch::Tensor> PointEdgeArrayDistanceBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(segms);
-  CHECK_CPU(grad_dists);
   return PointEdgeArrayDistanceBackwardCpu(points, segms, grad_dists);
 }

pytorch3d/csrc/points_to_volumes/points_to_volumes.h

@@ -104,12 +104,6 @@ inline void PointsToVolumesForward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points_3d);
-  CHECK_CPU(points_features);
-  CHECK_CPU(volume_densities);
-  CHECK_CPU(volume_features);
-  CHECK_CPU(grid_sizes);
-  CHECK_CPU(mask);
   PointsToVolumesForwardCpu(
       points_3d,
       points_features,
@@ -189,14 +183,6 @@ inline void PointsToVolumesBackward(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points_3d);
-  CHECK_CPU(points_features);
-  CHECK_CPU(grid_sizes);
-  CHECK_CPU(mask);
-  CHECK_CPU(grad_volume_densities);
-  CHECK_CPU(grad_volume_features);
-  CHECK_CPU(grad_points_3d);
-  CHECK_CPU(grad_points_features);
   PointsToVolumesBackwardCpu(
       points_3d,
       points_features,

pytorch3d/csrc/points_to_volumes/points_to_volumes_cpu.cpp

@@ -8,7 +8,9 @@
 
 #include <torch/csrc/autograd/VariableTypeUtils.h>
 #include <torch/extension.h>
+#include <algorithm>
 #include <cmath>
+#include <thread>
 #include <vector>
 
 // In the x direction, the location {0, ..., grid_size_x - 1} correspond to

pytorch3d/csrc/pulsar/global.h

@@ -15,8 +15,8 @@
 #endif
 
 #if defined(_WIN64) || defined(_WIN32)
-using uint = unsigned int;
+#define uint unsigned int
-using ushort = unsigned short;
+#define ushort unsigned short
 #endif
 
 #include "./logging.h" // <- include before torch/extension.h

pytorch3d/csrc/pulsar/gpu/commands.h

@@ -417,7 +417,7 @@ __device__ static float atomicMin(float* address, float val) {
       (OUT_PTR),              \
       (NUM_SELECTED_PTR),     \
       (NUM_ITEMS),            \
-      (STREAM));
+      stream = (STREAM));
 
 #define COPY_HOST_DEV(PTR_D, PTR_H, TYPE, SIZE) \
   HANDLECUDA(cudaMemcpy(                        \

pytorch3d/csrc/pulsar/host/commands.h

@@ -357,11 +357,11 @@ void MAX_WS(
 //
 //
 #define END_PARALLEL() \
-  end_parallel:;       \
+  end_parallel :;      \
   }
 #define END_PARALLEL_NORET() }
 #define END_PARALLEL_2D() \
-  end_parallel:;          \
+  end_parallel :;         \
   }                       \
   }
 #define END_PARALLEL_2D_NORET() \

pytorch3d/csrc/pulsar/include/camera.h

@@ -70,6 +70,11 @@ struct CamGradInfo {
   float3 pixel_dir_y;
 };
 
+// TODO: remove once https://github.com/NVlabs/cub/issues/172 is resolved.
+struct IntWrapper {
+  int val;
+};
+
 } // namespace pulsar
 
 #endif

pytorch3d/csrc/pulsar/include/math.h

@@ -149,6 +149,11 @@ IHD CamGradInfo operator*(const CamGradInfo& a, const float& b) {
   return res;
 }
 
+IHD IntWrapper operator+(const IntWrapper& a, const IntWrapper& b) {
+  IntWrapper res;
+  res.val = a.val + b.val;
+  return res;
+}
 } // namespace pulsar
 
 #endif

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

@@ -155,8 +155,8 @@ void backward(
         stream);
     CHECKLAUNCH();
     SUM_WS(
-        self->ids_sorted_d,
+        (IntWrapper*)(self->ids_sorted_d),
-        self->n_grad_contributions_d,
+        (IntWrapper*)(self->n_grad_contributions_d),
         static_cast<int>(num_balls),
         self->workspace_d,
         self->workspace_size,

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

@@ -52,7 +52,7 @@ HOST void construct(
   self->cam.film_width = width;
   self->cam.film_height = height;
   self->max_num_balls = max_num_balls;
-  MALLOC(self->result_d, float, width * height * n_channels);
+  MALLOC(self->result_d, float, width* height* n_channels);
   self->cam.orthogonal_projection = orthogonal_projection;
   self->cam.right_handed = right_handed_system;
   self->cam.background_normalization_depth = background_normalization_depth;
@@ -93,7 +93,7 @@ HOST void construct(
   MALLOC(self->di_sorted_d, DrawInfo, max_num_balls);
   MALLOC(self->region_flags_d, char, max_num_balls);
   MALLOC(self->num_selected_d, size_t, 1);
-  MALLOC(self->forw_info_d, float, width * height * (3 + 2 * n_track));
+  MALLOC(self->forw_info_d, float, width* height * (3 + 2 * n_track));
   MALLOC(self->min_max_pixels_d, IntersectInfo, 1);
   MALLOC(self->grad_pos_d, float3, max_num_balls);
   MALLOC(self->grad_col_d, float, max_num_balls* n_channels);

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

@@ -18,89 +18,68 @@ 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.h

@@ -255,7 +255,7 @@ GLOBAL void calc_signature(
  * for every iteration through the loading loop every thread could add a
  * 'hit' to the buffer.
  */
-#define RENDER_BUFFER_SIZE RENDER_BLOCK_SIZE * RENDER_BLOCK_SIZE * 2
+#define RENDER_BUFFER_SIZE RENDER_BLOCK_SIZE* RENDER_BLOCK_SIZE * 2
 /**
  * The threshold after which the spheres that are in the render buffer
  * are rendered and the buffer is flushed.

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

@@ -64,9 +64,8 @@ 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

@@ -139,9 +139,8 @@ 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;
     }
@@ -191,22 +190,20 @@ 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.
@@ -293,9 +290,8 @@ 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();
@@ -303,9 +299,8 @@ 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",
@@ -391,9 +386,8 @@ 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

@@ -860,9 +860,8 @@ 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

@@ -128,9 +128,8 @@ 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/pulsar/pytorch/tensor_util.cpp

@@ -8,7 +8,6 @@
 
 #ifdef WITH_CUDA
 #include <ATen/cuda/CUDAContext.h>
-#include <c10/cuda/CUDAException.h>
 #include <cuda_runtime_api.h>
 #endif
 #include <torch/extension.h>
@@ -34,13 +33,13 @@ torch::Tensor sphere_ids_from_result_info_nograd(
           .contiguous();
   if (forw_info.device().type() == c10::DeviceType::CUDA) {
 #ifdef WITH_CUDA
-    C10_CUDA_CHECK(cudaMemcpyAsync(
+    cudaMemcpyAsync(
         result.data_ptr(),
         tmp.data_ptr(),
         sizeof(uint32_t) * tmp.size(0) * tmp.size(1) * tmp.size(2) *
             tmp.size(3),
         cudaMemcpyDeviceToDevice,
-        at::cuda::getCurrentCUDAStream()));
+        at::cuda::getCurrentCUDAStream());
 #else
     throw std::runtime_error(
         "Copy on CUDA device initiated but built "

pytorch3d/csrc/pulsar/pytorch/util.cpp

@@ -7,7 +7,6 @@
  */
 
 #ifdef WITH_CUDA
-#include <c10/cuda/CUDAException.h>
 #include <cuda_runtime_api.h>
 
 namespace pulsar {
@@ -18,8 +17,7 @@ void cudaDevToDev(
     const void* src,
     const int& size,
     const cudaStream_t& stream) {
-  C10_CUDA_CHECK(
+  cudaMemcpyAsync(trg, src, size, cudaMemcpyDeviceToDevice, stream);
-      cudaMemcpyAsync(trg, src, size, cudaMemcpyDeviceToDevice, stream));
 }
 
 void cudaDevToHost(
@@ -27,8 +25,7 @@ void cudaDevToHost(
     const void* src,
     const int& size,
     const cudaStream_t& stream) {
-  C10_CUDA_CHECK(
+  cudaMemcpyAsync(trg, src, size, cudaMemcpyDeviceToHost, stream);
-      cudaMemcpyAsync(trg, src, size, cudaMemcpyDeviceToHost, stream));
 }
 
 } // namespace pytorch

pytorch3d/csrc/pulsar/warnings.cpp

@@ -6,6 +6,9 @@
  * LICENSE file in the root directory of this source tree.
  */
 
+#include "./global.h"
+#include "./logging.h"
+
 /**
  * A compilation unit to provide warnings about the code and avoid
  * repeated messages.

pytorch3d/csrc/rasterize_coarse/bitmask.cuh

@@ -25,7 +25,7 @@ class BitMask {
 
   // Use all threads in the current block to clear all bits of this BitMask
   __device__ void block_clear() {
-    for (auto i = threadIdx.x; i < H * W * D; i += blockDim.x) {
+    for (int i = threadIdx.x; i < H * W * D; i += blockDim.x) {
       data[i] = 0;
     }
     __syncthreads();

pytorch3d/csrc/rasterize_coarse/rasterize_coarse.cu

@@ -23,8 +23,8 @@ __global__ void TriangleBoundingBoxKernel(
     const float blur_radius,
     float* bboxes, // (4, F)
     bool* skip_face) { // (F,)
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
-  const auto num_threads = blockDim.x * gridDim.x;
+  const int num_threads = blockDim.x * gridDim.x;
   const float sqrt_radius = sqrt(blur_radius);
   for (int f = tid; f < F; f += num_threads) {
     const float v0x = face_verts[f * 9 + 0 * 3 + 0];
@@ -56,8 +56,8 @@ __global__ void PointBoundingBoxKernel(
     const int P,
     float* bboxes, // (4, P)
     bool* skip_points) {
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
-  const auto num_threads = blockDim.x * gridDim.x;
+  const int num_threads = blockDim.x * gridDim.x;
   for (int p = tid; p < P; p += num_threads) {
     const float x = points[p * 3 + 0];
     const float y = points[p * 3 + 1];
@@ -113,7 +113,7 @@ __global__ void RasterizeCoarseCudaKernel(
   const int chunks_per_batch = 1 + (E - 1) / chunk_size;
   const int num_chunks = N * chunks_per_batch;
 
-  for (auto chunk = blockIdx.x; chunk < num_chunks; chunk += gridDim.x) {
+  for (int chunk = blockIdx.x; chunk < num_chunks; chunk += gridDim.x) {
     const int batch_idx = chunk / chunks_per_batch; // batch index
     const int chunk_idx = chunk % chunks_per_batch;
     const int elem_chunk_start_idx = chunk_idx * chunk_size;
@@ -123,7 +123,7 @@ __global__ void RasterizeCoarseCudaKernel(
     const int64_t elem_stop_idx = elem_start_idx + elems_per_batch[batch_idx];
 
     // Have each thread handle a different face within the chunk
-    for (auto e = threadIdx.x; e < chunk_size; e += blockDim.x) {
+    for (int e = threadIdx.x; e < chunk_size; e += blockDim.x) {
       const int e_idx = elem_chunk_start_idx + e;
 
       // Check that we are still within the same element of the batch
@@ -170,7 +170,7 @@ __global__ void RasterizeCoarseCudaKernel(
     // Now we have processed every elem in the current chunk. We need to
     // count the number of elems in each bin so we can write the indices
     // out to global memory. We have each thread handle a different bin.
-    for (auto byx = threadIdx.x; byx < num_bins_y * num_bins_x;
+    for (int byx = threadIdx.x; byx < num_bins_y * num_bins_x;
          byx += blockDim.x) {
       const int by = byx / num_bins_x;
       const int bx = byx % num_bins_x;

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu

@@ -260,8 +260,8 @@ __global__ void RasterizeMeshesNaiveCudaKernel(
     float* pix_dists,
     float* bary) {
   // Simple version: One thread per output pixel
-  auto num_threads = gridDim.x * blockDim.x;
+  int num_threads = gridDim.x * blockDim.x;
-  auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  int tid = blockDim.x * blockIdx.x + threadIdx.x;
 
   for (int i = tid; i < N * H * W; i += num_threads) {
     // Convert linear index to 3D index
@@ -446,8 +446,8 @@ __global__ void RasterizeMeshesBackwardCudaKernel(
 
   // Parallelize over each pixel in images of
   // size H * W, for each image in the batch of size N.
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int t_i = tid; t_i < N * H * W; t_i += num_threads) {
     // Convert linear index to 3D index
@@ -650,8 +650,8 @@ __global__ void RasterizeMeshesFineCudaKernel(
 ) {
   // This can be more than H * W if H or W are not divisible by bin_size.
   int num_pixels = N * BH * BW * bin_size * bin_size;
-  auto num_threads = gridDim.x * blockDim.x;
+  int num_threads = gridDim.x * blockDim.x;
-  auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int pid = tid; pid < num_pixels; pid += num_threads) {
     // Convert linear index into bin and pixel indices. We make the within

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.h

@@ -138,9 +138,6 @@ RasterizeMeshesNaive(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(face_verts);
-    CHECK_CPU(mesh_to_face_first_idx);
-    CHECK_CPU(num_faces_per_mesh);
     return RasterizeMeshesNaiveCpu(
         face_verts,
         mesh_to_face_first_idx,
@@ -235,11 +232,6 @@ torch::Tensor RasterizeMeshesBackward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(face_verts);
-    CHECK_CPU(pix_to_face);
-    CHECK_CPU(grad_zbuf);
-    CHECK_CPU(grad_bary);
-    CHECK_CPU(grad_dists);
     return RasterizeMeshesBackwardCpu(
         face_verts,
         pix_to_face,
@@ -314,9 +306,6 @@ torch::Tensor RasterizeMeshesCoarse(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(face_verts);
-    CHECK_CPU(mesh_to_face_first_idx);
-    CHECK_CPU(num_faces_per_mesh);
     return RasterizeMeshesCoarseCpu(
         face_verts,
         mesh_to_face_first_idx,
@@ -434,8 +423,6 @@ RasterizeMeshesFine(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(face_verts);
-    CHECK_CPU(bin_faces);
     AT_ERROR("NOT IMPLEMENTED");
   }
 }

pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp

@@ -9,6 +9,7 @@
 #include <torch/extension.h>
 #include <algorithm>
 #include <list>
+#include <queue>
 #include <thread>
 #include <tuple>
 #include "ATen/core/TensorAccessor.h"
@@ -106,8 +107,6 @@ 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 {
@@ -120,6 +119,7 @@ struct IsNeighbor {
   int neighbor_idx;
 };
 
+namespace {
 void RasterizeMeshesNaiveCpu_worker(
     const int start_yi,
     const int end_yi,

pytorch3d/csrc/rasterize_points/rasterize_points.cu

@@ -97,8 +97,8 @@ __global__ void RasterizePointsNaiveCudaKernel(
     float* zbuf, // (N, H, W, K)
     float* pix_dists) { // (N, H, W, K)
   // Simple version: One thread per output pixel
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockDim.x * blockIdx.x + threadIdx.x;
+  const int tid = blockDim.x * blockIdx.x + threadIdx.x;
   for (int i = tid; i < N * H * W; i += num_threads) {
     // Convert linear index to 3D index
     const int n = i / (H * W); // Batch index
@@ -237,8 +237,8 @@ __global__ void RasterizePointsFineCudaKernel(
     float* pix_dists) { // (N, H, W, K)
   // This can be more than H * W if H or W are not divisible by bin_size.
   const int num_pixels = N * BH * BW * bin_size * bin_size;
-  const auto num_threads = gridDim.x * blockDim.x;
+  const int num_threads = gridDim.x * blockDim.x;
-  const auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  const int tid = blockIdx.x * blockDim.x + threadIdx.x;
 
   for (int pid = tid; pid < num_pixels; pid += num_threads) {
     // Convert linear index into bin and pixel indices. We make the within
@@ -376,8 +376,8 @@ __global__ void RasterizePointsBackwardCudaKernel(
     float* grad_points) { // (P, 3)
   // Parallelized over each of K points per pixel, for each pixel in images of
   // size H * W, for each image in the batch of size N.
-  auto num_threads = gridDim.x * blockDim.x;
+  int num_threads = gridDim.x * blockDim.x;
-  auto tid = blockIdx.x * blockDim.x + threadIdx.x;
+  int tid = blockIdx.x * blockDim.x + threadIdx.x;
   for (int i = tid; i < N * H * W * K; i += num_threads) {
     // const int n = i / (H * W * K); // batch index (not needed).
     const int yxk = i % (H * W * K);

pytorch3d/csrc/rasterize_points/rasterize_points.h

@@ -91,10 +91,6 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsNaive(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(points);
-    CHECK_CPU(cloud_to_packed_first_idx);
-    CHECK_CPU(num_points_per_cloud);
-    CHECK_CPU(radius);
     return RasterizePointsNaiveCpu(
         points,
         cloud_to_packed_first_idx,
@@ -170,10 +166,6 @@ torch::Tensor RasterizePointsCoarse(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(points);
-    CHECK_CPU(cloud_to_packed_first_idx);
-    CHECK_CPU(num_points_per_cloud);
-    CHECK_CPU(radius);
     return RasterizePointsCoarseCpu(
         points,
         cloud_to_packed_first_idx,
@@ -240,8 +232,6 @@ std::tuple<torch::Tensor, torch::Tensor, torch::Tensor> RasterizePointsFine(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(points);
-    CHECK_CPU(bin_points);
     AT_ERROR("NOT IMPLEMENTED");
   }
 }
@@ -294,10 +284,6 @@ torch::Tensor RasterizePointsBackward(
     AT_ERROR("Not compiled with GPU support");
 #endif
   } else {
-    CHECK_CPU(points);
-    CHECK_CPU(idxs);
-    CHECK_CPU(grad_zbuf);
-    CHECK_CPU(grad_dists);
     return RasterizePointsBackwardCpu(points, idxs, grad_zbuf, grad_dists);
   }
 }

pytorch3d/csrc/sample_farthest_points/sample_farthest_points.cu

@@ -35,6 +35,8 @@ __global__ void FarthestPointSamplingKernel(
   __shared__ int64_t selected_store;
 
   // Get constants
+  const int64_t N = points.size(0);
+  const int64_t P = points.size(1);
   const int64_t D = points.size(2);
 
   // Get batch index and thread index
@@ -107,8 +109,7 @@ at::Tensor FarthestPointSamplingCuda(
     const at::Tensor& points, // (N, P, 3)
     const at::Tensor& lengths, // (N,)
     const at::Tensor& K, // (N,)
-    const at::Tensor& start_idxs,
+    const at::Tensor& start_idxs) {
-    const int64_t max_K_known = -1) {
   // Check inputs are on the same device
   at::TensorArg p_t{points, "points", 1}, lengths_t{lengths, "lengths", 2},
       k_t{K, "K", 3}, start_idxs_t{start_idxs, "start_idxs", 4};
@@ -130,12 +131,7 @@ at::Tensor FarthestPointSamplingCuda(
 
   const int64_t N = points.size(0);
   const int64_t P = points.size(1);
-  int64_t max_K;
+  const int64_t max_K = at::max(K).item<int64_t>();
-  if (max_K_known > 0) {
-    max_K = max_K_known;
-  } else {
-    max_K = at::max(K).item<int64_t>();
-  }
 
   // Initialize the output tensor with the sampled indices
   auto idxs = at::full({N, max_K}, -1, lengths.options());

pytorch3d/csrc/sample_farthest_points/sample_farthest_points.h

@@ -43,8 +43,7 @@ at::Tensor FarthestPointSamplingCuda(
     const at::Tensor& points,
     const at::Tensor& lengths,
     const at::Tensor& K,
-    const at::Tensor& start_idxs,
+    const at::Tensor& start_idxs);
-    const int64_t max_K_known = -1);
 
 at::Tensor FarthestPointSamplingCpu(
     const at::Tensor& points,
@@ -57,23 +56,17 @@ at::Tensor FarthestPointSampling(
     const at::Tensor& points,
     const at::Tensor& lengths,
     const at::Tensor& K,
-    const at::Tensor& start_idxs,
+    const at::Tensor& start_idxs) {
-    const int64_t max_K_known = -1) {
   if (points.is_cuda() || lengths.is_cuda() || K.is_cuda()) {
 #ifdef WITH_CUDA
     CHECK_CUDA(points);
     CHECK_CUDA(lengths);
     CHECK_CUDA(K);
     CHECK_CUDA(start_idxs);
-    return FarthestPointSamplingCuda(
+    return FarthestPointSamplingCuda(points, lengths, K, start_idxs);
-        points, lengths, K, start_idxs, max_K_known);
 #else
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(points);
-  CHECK_CPU(lengths);
-  CHECK_CPU(K);
-  CHECK_CPU(start_idxs);
   return FarthestPointSamplingCpu(points, lengths, K, start_idxs);
 }

pytorch3d/csrc/sample_pdf/sample_pdf.h

@@ -71,8 +71,6 @@ inline void SamplePdf(
     AT_ERROR("Not compiled with GPU support.");
 #endif
   }
-  CHECK_CPU(weights);
-  CHECK_CPU(outputs);
   CHECK_CONTIGUOUS(outputs);
   SamplePdfCpu(bins, weights, outputs, eps);
 }

pytorch3d/csrc/utils/dispatch.cuh

@@ -99,7 +99,8 @@ namespace {
 // and increment it via template recursion until it is equal to the run-time
 // argument N.
 template <
-    template <typename, int64_t> class Kernel,
+    template <typename, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -123,7 +124,8 @@ struct DispatchKernelHelper1D {
 // 1D dispatch: Specialization when curN == maxN
 // We need this base case to avoid infinite template recursion.
 template <
-    template <typename, int64_t> class Kernel,
+    template <typename, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -143,7 +145,8 @@ struct DispatchKernelHelper1D<Kernel, T, minN, maxN, maxN, Args...> {
 // the run-time values of N and M, at which point we dispatch to the run
 // method of the kernel.
 template <
-    template <typename, int64_t, int64_t> class Kernel,
+    template <typename, int64_t, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -200,7 +203,8 @@ struct DispatchKernelHelper2D {
 
 // 2D dispatch, specialization for curN == maxN
 template <
-    template <typename, int64_t, int64_t> class Kernel,
+    template <typename, int64_t, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -239,7 +243,8 @@ struct DispatchKernelHelper2D<
 
 // 2D dispatch, specialization for curM == maxM
 template <
-    template <typename, int64_t, int64_t> class Kernel,
+    template <typename, int64_t, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -278,7 +283,8 @@ struct DispatchKernelHelper2D<
 
 // 2D dispatch, specialization for curN == maxN, curM == maxM
 template <
-    template <typename, int64_t, int64_t> class Kernel,
+    template <typename, int64_t, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -307,7 +313,8 @@ struct DispatchKernelHelper2D<
 
 // This is the function we expect users to call to dispatch to 1D functions
 template <
-    template <typename, int64_t> class Kernel,
+    template <typename, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,
@@ -323,7 +330,8 @@ void DispatchKernel1D(const int64_t N, Args... args) {
 
 // This is the function we expect users to call to dispatch to 2D functions
 template <
-    template <typename, int64_t, int64_t> class Kernel,
+    template <typename, int64_t, int64_t>
+    class Kernel,
     typename T,
     int64_t minN,
     int64_t maxN,

pytorch3d/csrc/utils/geometry_utils.cuh

@@ -376,6 +376,8 @@ PointLineDistanceBackward(
   float tt = t_top / t_bot;
   tt = __saturatef(tt);
   const float2 p_proj = (1.0f - tt) * v0 + tt * v1;
+  const float2 d = p - p_proj;
+  const float dist = sqrt(dot(d, d));
 
   const float2 grad_p = -1.0f * grad_dist * 2.0f * (p_proj - p);
   const float2 grad_v0 = grad_dist * (1.0f - tt) * 2.0f * (p_proj - p);

pytorch3d/csrc/utils/pytorch3d_cutils.h

@@ -15,7 +15,3 @@
 #define CHECK_CONTIGUOUS_CUDA(x) \
   CHECK_CUDA(x);                 \
   CHECK_CONTIGUOUS(x)
-#define CHECK_CPU(x)                    \
-  TORCH_CHECK(                          \
-      x.device().type() == torch::kCPU, \
-      "Cannot use CPU implementation: " #x " not on CPU.")

pytorch3d/csrc/utils/vec2.h

@@ -19,7 +19,7 @@ template <
         std::is_same<T, double>::value || std::is_same<T, float>::value>>
 struct vec2 {
   T x, y;
-  using scalar_t = T;
+  typedef T scalar_t;
   vec2(T x, T y) : x(x), y(y) {}
 };
 

pytorch3d/csrc/utils/vec3.h

@@ -18,7 +18,7 @@ template <
         std::is_same<T, double>::value || std::is_same<T, float>::value>>
 struct vec3 {
   T x, y, z;
-  using scalar_t = T;
+  typedef T scalar_t;
   vec3(T x, T y, T z) : x(x), y(y), z(z) {}
 };
 

pytorch3d/datasets/shapenet/shapenet_core.py

@@ -83,7 +83,7 @@ class ShapeNetCore(ShapeNetBase):  # pragma: no cover
                 ):
                     synset_set.add(synset)
                 elif (synset in self.synset_inv.keys()) and (
-                    path.isdir(path.join(data_dir, self.synset_inv[synset]))
+                    (path.isdir(path.join(data_dir, self.synset_inv[synset])))
                 ):
                     synset_set.add(self.synset_inv[synset])
                 else:

pytorch3d/datasets/utils.py

@@ -36,6 +36,7 @@ def collate_batched_meshes(batch: List[Dict]):  # pragma: no cover
 
     collated_dict["mesh"] = None
     if {"verts", "faces"}.issubset(collated_dict.keys()):
+
         textures = None
         if "textures" in collated_dict:
             textures = TexturesAtlas(atlas=collated_dict["textures"])

pytorch3d/implicitron/dataset/blender_dataset_map_provider.py

@@ -0,0 +1,55 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+
+import torch
+from pytorch3d.implicitron.tools.config import registry
+
+from .load_blender import load_blender_data
+from .single_sequence_dataset import (
+    _interpret_blender_cameras,
+    SingleSceneDatasetMapProviderBase,
+)
+
+
+@registry.register
+class BlenderDatasetMapProvider(SingleSceneDatasetMapProviderBase):
+    """
+    Provides data for one scene from Blender synthetic dataset.
+    Uses the code in load_blender.py
+
+    Members:
+        base_dir: directory holding the data for the scene.
+        object_name: The name of the scene (e.g. "lego"). This is just used as a label.
+            It will typically be equal to the name of the directory self.base_dir.
+        path_manager_factory: Creates path manager which may be used for
+            interpreting paths.
+        n_known_frames_for_test: If set, training frames are included in the val
+            and test datasets, and this many random training frames are added to
+            each test batch. If not set, test batches each contain just a single
+            testing frame.
+    """
+
+    def _load_data(self) -> None:
+        path_manager = self.path_manager_factory.get()
+        images, poses, _, hwf, i_split = load_blender_data(
+            self.base_dir,
+            testskip=1,
+            path_manager=path_manager,
+        )
+        H, W, focal = hwf
+        images_masks = torch.from_numpy(images).permute(0, 3, 1, 2)
+
+        # pyre-ignore[16]
+        self.poses = _interpret_blender_cameras(poses, focal)
+        # pyre-ignore[16]
+        self.images = images_masks[:, :3]
+        # pyre-ignore[16]
+        self.fg_probabilities = images_masks[:, 3:4]
+        # pyre-ignore[16]
+        self.i_split = i_split

pytorch3d/implicitron/dataset/data_source.py

@@ -64,12 +64,16 @@ class ImplicitronDataSource(DataSourceBase):
     def pre_expand(cls) -> None:
         # use try/finally to bypass cinder's lazy imports
         try:
+            from .blender_dataset_map_provider import (  # noqa: F401
+                BlenderDatasetMapProvider,
+            )
             from .json_index_dataset_map_provider import (  # noqa: F401
                 JsonIndexDatasetMapProvider,
             )
             from .json_index_dataset_map_provider_v2 import (  # noqa: F401
                 JsonIndexDatasetMapProviderV2,
             )
+            from .llff_dataset_map_provider import LlffDatasetMapProvider  # noqa: F401
             from .rendered_mesh_dataset_map_provider import (  # noqa: F401
                 RenderedMeshDatasetMapProvider,
             )

pytorch3d/implicitron/dataset/dataset_base.py

@@ -21,6 +21,7 @@ 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 +25,8 @@ from typing import (
 
 import numpy as np
 import torch
-from pytorch3d.implicitron.dataset import orm_types, types
+
+from pytorch3d.implicitron.dataset import types
 from pytorch3d.implicitron.dataset.utils import (
     adjust_camera_to_bbox_crop_,
     adjust_camera_to_image_scale_,
@@ -47,12 +48,8 @@ from pytorch3d.implicitron.dataset.utils import (
 from pytorch3d.implicitron.tools.config import registry, ReplaceableBase
 from pytorch3d.renderer.camera_utils import join_cameras_as_batch
 from pytorch3d.renderer.cameras import CamerasBase, PerspectiveCameras
-from pytorch3d.structures.meshes import join_meshes_as_batch, Meshes
 from pytorch3d.structures.pointclouds import join_pointclouds_as_batch, Pointclouds
 
-FrameAnnotationT = types.FrameAnnotation | orm_types.SqlFrameAnnotation
-SequenceAnnotationT = types.SequenceAnnotation | orm_types.SqlSequenceAnnotation
-
 
 @dataclass
 class FrameData(Mapping[str, Any]):
@@ -125,9 +122,9 @@ class FrameData(Mapping[str, Any]):
         meta: A dict for storing additional frame information.
     """
 
-    frame_number: Optional[torch.LongTensor] = None
+    frame_number: Optional[torch.LongTensor]
-    sequence_name: Union[str, List[str]] = ""
+    sequence_name: Union[str, List[str]]
-    sequence_category: Union[str, List[str]] = ""
+    sequence_category: Union[str, List[str]]
     frame_timestamp: Optional[torch.Tensor] = None
     image_size_hw: Optional[torch.LongTensor] = None
     effective_image_size_hw: Optional[torch.LongTensor] = None
@@ -158,7 +155,7 @@ class FrameData(Mapping[str, Any]):
         new_params = {}
         for field_name in iter(self):
             value = getattr(self, field_name)
-            if isinstance(value, (torch.Tensor, Pointclouds, CamerasBase, Meshes)):
+            if isinstance(value, (torch.Tensor, Pointclouds, CamerasBase)):
                 new_params[field_name] = value.to(*args, **kwargs)
             else:
                 new_params[field_name] = value
@@ -420,6 +417,7 @@ class FrameData(Mapping[str, Any]):
             for f in fields(elem):
                 if not f.init:
                     continue
+
                 list_values = override_fields.get(
                     f.name, [getattr(d, f.name) for d in batch]
                 )
@@ -428,7 +426,7 @@ class FrameData(Mapping[str, Any]):
                     if all(list_value is not None for list_value in list_values)
                     else None
                 )
-            return type(elem)(**collated)
+            return cls(**collated)
 
         elif isinstance(elem, Pointclouds):
             return join_pointclouds_as_batch(batch)
@@ -436,8 +434,6 @@ class FrameData(Mapping[str, Any]):
         elif isinstance(elem, CamerasBase):
             # TODO: don't store K; enforce working in NDC space
             return join_cameras_as_batch(batch)
-        elif isinstance(elem, Meshes):
-            return join_meshes_as_batch(batch)
         else:
             return torch.utils.data.dataloader.default_collate(batch)
 
@@ -458,8 +454,8 @@ class FrameDataBuilderBase(ReplaceableBase, Generic[FrameDataSubtype], ABC):
     @abstractmethod
     def build(
         self,
-        frame_annotation: FrameAnnotationT,
+        frame_annotation: types.FrameAnnotation,
-        sequence_annotation: SequenceAnnotationT,
+        sequence_annotation: types.SequenceAnnotation,
         *,
         load_blobs: bool = True,
         **kwargs,
@@ -545,8 +541,8 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
     def build(
         self,
-        frame_annotation: FrameAnnotationT,
+        frame_annotation: types.FrameAnnotation,
-        sequence_annotation: SequenceAnnotationT,
+        sequence_annotation: types.SequenceAnnotation,
         *,
         load_blobs: bool = True,
         **kwargs,
@@ -590,81 +586,58 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
             ),
         )
 
-        dataset_root = self.dataset_root
+        fg_mask_np: Optional[np.ndarray] = None
         mask_annotation = frame_annotation.mask
-        depth_annotation = frame_annotation.depth
-        image_path: str | None = None
-        mask_path: str | None = None
-        depth_path: str | None = None
-        pcl_path: str | None = None
-        if dataset_root is not None:  # set all paths even if we won’t load blobs
-            if frame_annotation.image.path is not None:
-                image_path = os.path.join(dataset_root, frame_annotation.image.path)
-                frame_data.image_path = image_path
-
-            if mask_annotation is not None and mask_annotation.path:
-                mask_path = os.path.join(dataset_root, mask_annotation.path)
-                frame_data.mask_path = mask_path
-
-            if depth_annotation is not None and depth_annotation.path is not None:
-                depth_path = os.path.join(dataset_root, depth_annotation.path)
-                frame_data.depth_path = depth_path
-
-            if point_cloud is not None:
-                pcl_path = os.path.join(dataset_root, point_cloud.path)
-                frame_data.sequence_point_cloud_path = pcl_path
-
-        fg_mask_np: np.ndarray | None = None
-        bbox_xywh: tuple[float, float, float, float] | None = None
-
         if mask_annotation is not None:
-            if load_blobs and self.load_masks and mask_path:
+            if load_blobs and self.load_masks:
-                fg_mask_np = self._load_fg_probability(frame_annotation, mask_path)
+                fg_mask_np, mask_path = self._load_fg_probability(frame_annotation)
+                frame_data.mask_path = mask_path
                 frame_data.fg_probability = safe_as_tensor(fg_mask_np, torch.float)
 
             bbox_xywh = mask_annotation.bounding_box_xywh
+            if bbox_xywh is None and fg_mask_np is not None:
+                bbox_xywh = get_bbox_from_mask(fg_mask_np, self.box_crop_mask_thr)
+
+            frame_data.bbox_xywh = safe_as_tensor(bbox_xywh, torch.float)
 
         if frame_annotation.image is not None:
             image_size_hw = safe_as_tensor(frame_annotation.image.size, torch.long)
             frame_data.image_size_hw = image_size_hw  # original image size
             # image size after crop/resize
             frame_data.effective_image_size_hw = image_size_hw
+            image_path = None
+            dataset_root = self.dataset_root
+            if frame_annotation.image.path is not None and dataset_root is not None:
+                image_path = os.path.join(dataset_root, frame_annotation.image.path)
+                frame_data.image_path = image_path
 
             if load_blobs and self.load_images:
                 if image_path is None:
                     raise ValueError("Image path is required to load images.")
 
-                no_mask = fg_mask_np is None  # didn’t read the mask file
+                image_np = load_image(self._local_path(image_path))
-                image_np = load_image(
-                    self._local_path(image_path), try_read_alpha=no_mask
-                )
-                if image_np.shape[0] == 4:  # RGBA image
-                    if no_mask:
-                        fg_mask_np = image_np[3:]
-                        frame_data.fg_probability = safe_as_tensor(
-                            fg_mask_np, torch.float
-                        )
-
-                    image_np = image_np[:3]
-
                 frame_data.image_rgb = self._postprocess_image(
                     image_np, frame_annotation.image.size, frame_data.fg_probability
                 )
 
-        if bbox_xywh is None and fg_mask_np is not None:
+        if (
-            bbox_xywh = get_bbox_from_mask(fg_mask_np, self.box_crop_mask_thr)
+            load_blobs
-        frame_data.bbox_xywh = safe_as_tensor(bbox_xywh, torch.float)
+            and self.load_depths
-
+            and frame_annotation.depth is not None
-        if load_blobs and self.load_depths and depth_path is not None:
+            and frame_annotation.depth.path is not None
-            frame_data.depth_map, frame_data.depth_mask = self._load_mask_depth(
+        ):
-                frame_annotation, depth_path, fg_mask_np
+            (
-            )
+                frame_data.depth_map,
+                frame_data.depth_path,
+                frame_data.depth_mask,
+            ) = self._load_mask_depth(frame_annotation, fg_mask_np)
 
         if load_blobs and self.load_point_clouds and point_cloud is not None:
-            assert pcl_path is not None
+            pcl_path = self._fix_point_cloud_path(point_cloud.path)
             frame_data.sequence_point_cloud = load_pointcloud(
                 self._local_path(pcl_path), max_points=self.max_points
             )
+            frame_data.sequence_point_cloud_path = pcl_path
 
         if frame_annotation.viewpoint is not None:
             frame_data.camera = self._get_pytorch3d_camera(frame_annotation)
@@ -680,14 +653,18 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
         return frame_data
 
-    def _load_fg_probability(self, entry: FrameAnnotationT, path: str) -> np.ndarray:
+    def _load_fg_probability(
-        fg_probability = load_mask(self._local_path(path))
+        self, entry: types.FrameAnnotation
+    ) -> Tuple[np.ndarray, str]:
+        assert self.dataset_root is not None and entry.mask is not None
+        full_path = os.path.join(self.dataset_root, entry.mask.path)
+        fg_probability = load_mask(self._local_path(full_path))
         if fg_probability.shape[-2:] != entry.image.size:
             raise ValueError(
                 f"bad mask size: {fg_probability.shape[-2:]} vs {entry.image.size}!"
             )
 
-        return fg_probability
+        return fg_probability, full_path
 
     def _postprocess_image(
         self,
@@ -708,14 +685,14 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
     def _load_mask_depth(
         self,
-        entry: FrameAnnotationT,
+        entry: types.FrameAnnotation,
-        path: str,
         fg_mask: Optional[np.ndarray],
-    ) -> tuple[torch.Tensor, torch.Tensor]:
+    ) -> Tuple[torch.Tensor, str, torch.Tensor]:
         entry_depth = entry.depth
         dataset_root = self.dataset_root
         assert dataset_root is not None
-        assert entry_depth is not None
+        assert entry_depth is not None and entry_depth.path is not None
+        path = os.path.join(dataset_root, entry_depth.path)
         depth_map = load_depth(self._local_path(path), entry_depth.scale_adjustment)
 
         if self.mask_depths:
@@ -729,11 +706,11 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
         else:
             depth_mask = (depth_map > 0.0).astype(np.float32)
 
-        return torch.tensor(depth_map), torch.tensor(depth_mask)
+        return torch.tensor(depth_map), path, torch.tensor(depth_mask)
 
     def _get_pytorch3d_camera(
         self,
-        entry: FrameAnnotationT,
+        entry: types.FrameAnnotation,
     ) -> PerspectiveCameras:
         entry_viewpoint = entry.viewpoint
         assert entry_viewpoint is not None
@@ -762,6 +739,19 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
             T=torch.tensor(entry_viewpoint.T, dtype=torch.float)[None],
         )
 
+    def _fix_point_cloud_path(self, path: str) -> str:
+        """
+        Fix up a point cloud path from the dataset.
+        Some files in Co3Dv2 have an accidental absolute path stored.
+        """
+        unwanted_prefix = (
+            "/large_experiments/p3/replay/datasets/co3d/co3d45k_220512/export_v23/"
+        )
+        if path.startswith(unwanted_prefix):
+            path = path[len(unwanted_prefix) :]
+        assert self.dataset_root is not None
+        return os.path.join(self.dataset_root, path)
+
     def _local_path(self, path: str) -> str:
         if self.path_manager is None:
             return path

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -38,6 +38,7 @@ 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
 
 
@@ -326,9 +327,9 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
                 assert os.path.normpath(
                     # pyre-ignore[16]
                     self.frame_annots[idx]["frame_annotation"].image.path
-                ) == os.path.normpath(path), (
+                ) == os.path.normpath(
-                    f"Inconsistent frame indices {seq_name, frame_no, path}."
+                    path
-                )
+                ), f"Inconsistent frame indices {seq_name, frame_no, path}."
             return idx
 
         dataset_idx = [

pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py

@@ -21,6 +21,7 @@ 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

@@ -18,6 +18,7 @@ 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,
@@ -30,6 +31,7 @@ from pytorch3d.implicitron.tools.config import (
     registry,
     run_auto_creation,
 )
+
 from pytorch3d.renderer.cameras import CamerasBase
 from tqdm import tqdm
 
@@ -220,6 +222,7 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
         self.dataset_map = dataset_map
 
     def _load_category(self, category: str) -> DatasetMap:
+
         frame_file = os.path.join(self.dataset_root, category, "frame_annotations.jgz")
         sequence_file = os.path.join(
             self.dataset_root, category, "sequence_annotations.jgz"

pytorch3d/implicitron/dataset/llff_dataset_map_provider.py

@@ -0,0 +1,69 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe
+
+
+import numpy as np
+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,
+)
+
+
+@registry.register
+class LlffDatasetMapProvider(SingleSceneDatasetMapProviderBase):
+    """
+    Provides data for one scene from the LLFF dataset.
+
+    Members:
+        base_dir: directory holding the data for the scene.
+        object_name: The name of the scene (e.g. "fern"). This is just used as a label.
+            It will typically be equal to the name of the directory self.base_dir.
+        path_manager_factory: Creates path manager which may be used for
+            interpreting paths.
+        n_known_frames_for_test: If set, training frames are included in the val
+            and test datasets, and this many random training frames are added to
+            each test batch. If not set, test batches each contain just a single
+            testing frame.
+        downscale_factor: determines image sizes.
+    """
+
+    downscale_factor: int = 4
+
+    def _load_data(self) -> None:
+        path_manager = self.path_manager_factory.get()
+        images, poses, _ = load_llff_data(
+            self.base_dir, factor=self.downscale_factor, path_manager=path_manager
+        )
+        hwf = poses[0, :3, -1]
+        poses = poses[:, :3, :4]
+
+        llffhold = 8
+        i_test = np.arange(images.shape[0])[::llffhold]
+        i_test_index = set(i_test.tolist())
+        i_train = np.array(
+            [i for i in np.arange(images.shape[0]) if i not in i_test_index]
+        )
+        i_split = (i_train, i_test, i_test)
+        H, W, focal = hwf
+        focal_ndc = 2 * focal / min(H, W)
+        images = torch.from_numpy(images).permute(0, 3, 1, 2)
+        poses = torch.from_numpy(poses)
+
+        # pyre-ignore[16]
+        self.poses = _interpret_blender_cameras(poses, focal_ndc)
+        # pyre-ignore[16]
+        self.images = images
+        # pyre-ignore[16]
+        self.fg_probabilities = None
+        # pyre-ignore[16]
+        self.i_split = i_split

pytorch3d/implicitron/dataset/load_blender.py

@@ -0,0 +1,143 @@
+# @lint-ignore-every LICENSELINT
+# Adapted from https://github.com/bmild/nerf/blob/master/load_blender.py
+# Copyright (c) 2020 bmild
+
+# pyre-unsafe
+import json
+import os
+
+import numpy as np
+import torch
+from PIL import Image
+
+
+def translate_by_t_along_z(t):
+    tform = np.eye(4).astype(np.float32)
+    tform[2][3] = t
+    return tform
+
+
+def rotate_by_phi_along_x(phi):
+    tform = np.eye(4).astype(np.float32)
+    tform[1, 1] = tform[2, 2] = np.cos(phi)
+    tform[1, 2] = -np.sin(phi)
+    tform[2, 1] = -tform[1, 2]
+    return tform
+
+
+def rotate_by_theta_along_y(theta):
+    tform = np.eye(4).astype(np.float32)
+    tform[0, 0] = tform[2, 2] = np.cos(theta)
+    tform[0, 2] = -np.sin(theta)
+    tform[2, 0] = -tform[0, 2]
+    return tform
+
+
+def pose_spherical(theta, phi, radius):
+    c2w = translate_by_t_along_z(radius)
+    c2w = rotate_by_phi_along_x(phi / 180.0 * np.pi) @ c2w
+    c2w = rotate_by_theta_along_y(theta / 180 * np.pi) @ c2w
+    c2w = np.array([[-1, 0, 0, 0], [0, 0, 1, 0], [0, 1, 0, 0], [0, 0, 0, 1]]) @ c2w
+    return c2w
+
+
+def _local_path(path_manager, path):
+    if path_manager is None:
+        return path
+    return path_manager.get_local_path(path)
+
+
+def load_blender_data(
+    basedir,
+    half_res=False,
+    testskip=1,
+    debug=False,
+    path_manager=None,
+    focal_length_in_screen_space=False,
+):
+    splits = ["train", "val", "test"]
+    metas = {}
+    for s in splits:
+        path = os.path.join(basedir, f"transforms_{s}.json")
+        with open(_local_path(path_manager, path)) as fp:
+            metas[s] = json.load(fp)
+
+    all_imgs = []
+    all_poses = []
+    counts = [0]
+    for s in splits:
+        meta = metas[s]
+        imgs = []
+        poses = []
+        if s == "train" or testskip == 0:
+            skip = 1
+        else:
+            skip = testskip
+
+        for frame in meta["frames"][::skip]:
+            fname = os.path.join(basedir, frame["file_path"] + ".png")
+            imgs.append(np.array(Image.open(_local_path(path_manager, fname))))
+            poses.append(np.array(frame["transform_matrix"]))
+        imgs = (np.array(imgs) / 255.0).astype(np.float32)
+        poses = np.array(poses).astype(np.float32)
+        counts.append(counts[-1] + imgs.shape[0])
+        all_imgs.append(imgs)
+        all_poses.append(poses)
+
+    i_split = [np.arange(counts[i], counts[i + 1]) for i in range(3)]
+
+    imgs = np.concatenate(all_imgs, 0)
+    poses = np.concatenate(all_poses, 0)
+
+    H, W = imgs[0].shape[:2]
+    camera_angle_x = float(meta["camera_angle_x"])
+    if focal_length_in_screen_space:
+        focal = 0.5 * W / np.tan(0.5 * camera_angle_x)
+    else:
+        focal = 1 / np.tan(0.5 * camera_angle_x)
+
+    render_poses = torch.stack(
+        [
+            torch.from_numpy(pose_spherical(angle, -30.0, 4.0))
+            for angle in np.linspace(-180, 180, 40 + 1)[:-1]
+        ],
+        0,
+    )
+
+    # In debug mode, return extremely tiny images
+    if debug:
+        import cv2
+
+        H = H // 32
+        W = W // 32
+        if focal_length_in_screen_space:
+            focal = focal / 32.0
+        imgs = [
+            torch.from_numpy(
+                cv2.resize(imgs[i], dsize=(25, 25), interpolation=cv2.INTER_AREA)
+            )
+            for i in range(imgs.shape[0])
+        ]
+        imgs = torch.stack(imgs, 0)
+        poses = torch.from_numpy(poses)
+        return imgs, poses, render_poses, [H, W, focal], i_split
+
+    if half_res:
+        import cv2
+
+        # TODO: resize images using INTER_AREA (cv2)
+        H = H // 2
+        W = W // 2
+        if focal_length_in_screen_space:
+            focal = focal / 2.0
+        imgs = [
+            torch.from_numpy(
+                cv2.resize(imgs[i], dsize=(400, 400), interpolation=cv2.INTER_AREA)
+            )
+            for i in range(imgs.shape[0])
+        ]
+        imgs = torch.stack(imgs, 0)
+
+    poses = torch.from_numpy(poses)
+
+    return imgs, poses, render_poses, [H, W, focal], i_split

pytorch3d/implicitron/dataset/load_llff.py

@@ -0,0 +1,341 @@
+# @lint-ignore-every LICENSELINT
+# Adapted from https://github.com/bmild/nerf/blob/master/load_llff.py
+# Copyright (c) 2020 bmild
+
+# pyre-unsafe
+import logging
+import os
+import warnings
+
+import numpy as np
+
+from PIL import Image
+
+
+# Slightly modified version of LLFF data loading code
+#  see https://github.com/Fyusion/LLFF for original
+
+logger = logging.getLogger(__name__)
+
+
+def _minify(basedir, path_manager, factors=(), resolutions=()):
+    needtoload = False
+    for r in factors:
+        imgdir = os.path.join(basedir, "images_{}".format(r))
+        if not _exists(path_manager, imgdir):
+            needtoload = True
+    for r in resolutions:
+        imgdir = os.path.join(basedir, "images_{}x{}".format(r[1], r[0]))
+        if not _exists(path_manager, imgdir):
+            needtoload = True
+    if not needtoload:
+        return
+    assert path_manager is None
+
+    from subprocess import check_output
+
+    imgdir = os.path.join(basedir, "images")
+    imgs = [os.path.join(imgdir, f) for f in sorted(_ls(path_manager, imgdir))]
+    imgs = [f for f in imgs if f.endswith("JPG", "jpg", "png", "jpeg", "PNG")]
+    imgdir_orig = imgdir
+
+    wd = os.getcwd()
+
+    for r in factors + resolutions:
+        if isinstance(r, int):
+            name = "images_{}".format(r)
+            resizearg = "{}%".format(100.0 / r)
+        else:
+            name = "images_{}x{}".format(r[1], r[0])
+            resizearg = "{}x{}".format(r[1], r[0])
+        imgdir = os.path.join(basedir, name)
+        if os.path.exists(imgdir):
+            continue
+
+        logger.info(f"Minifying {r}, {basedir}")
+
+        os.makedirs(imgdir)
+        check_output("cp {}/* {}".format(imgdir_orig, imgdir), shell=True)
+
+        ext = imgs[0].split(".")[-1]
+        args = " ".join(
+            ["mogrify", "-resize", resizearg, "-format", "png", "*.{}".format(ext)]
+        )
+        logger.info(args)
+        os.chdir(imgdir)
+        check_output(args, shell=True)
+        os.chdir(wd)
+
+        if ext != "png":
+            check_output("rm {}/*.{}".format(imgdir, ext), shell=True)
+            logger.info("Removed duplicates")
+        logger.info("Done")
+
+
+def _load_data(
+    basedir, factor=None, width=None, height=None, load_imgs=True, path_manager=None
+):
+
+    poses_arr = np.load(
+        _local_path(path_manager, os.path.join(basedir, "poses_bounds.npy"))
+    )
+    poses = poses_arr[:, :-2].reshape([-1, 3, 5]).transpose([1, 2, 0])
+    bds = poses_arr[:, -2:].transpose([1, 0])
+
+    img0 = [
+        os.path.join(basedir, "images", f)
+        for f in sorted(_ls(path_manager, os.path.join(basedir, "images")))
+        if f.endswith("JPG") or f.endswith("jpg") or f.endswith("png")
+    ][0]
+
+    def imread(f):
+        return np.array(Image.open(f))
+
+    sh = imread(_local_path(path_manager, img0)).shape
+
+    sfx = ""
+
+    if factor is not None:
+        sfx = "_{}".format(factor)
+        _minify(basedir, path_manager, factors=[factor])
+        factor = factor
+    elif height is not None:
+        factor = sh[0] / float(height)
+        width = int(sh[1] / factor)
+        _minify(basedir, path_manager, resolutions=[[height, width]])
+        sfx = "_{}x{}".format(width, height)
+    elif width is not None:
+        factor = sh[1] / float(width)
+        height = int(sh[0] / factor)
+        _minify(basedir, path_manager, resolutions=[[height, width]])
+        sfx = "_{}x{}".format(width, height)
+    else:
+        factor = 1
+
+    imgdir = os.path.join(basedir, "images" + sfx)
+    if not _exists(path_manager, imgdir):
+        raise ValueError(f"{imgdir} does not exist, returning")
+
+    imgfiles = [
+        _local_path(path_manager, os.path.join(imgdir, f))
+        for f in sorted(_ls(path_manager, imgdir))
+        if f.endswith("JPG") or f.endswith("jpg") or f.endswith("png")
+    ]
+    if poses.shape[-1] != len(imgfiles):
+        raise ValueError(
+            "Mismatch between imgs {} and poses {} !!!!".format(
+                len(imgfiles), poses.shape[-1]
+            )
+        )
+
+    sh = imread(imgfiles[0]).shape
+    poses[:2, 4, :] = np.array(sh[:2]).reshape([2, 1])
+    poses[2, 4, :] = poses[2, 4, :] * 1.0 / factor
+
+    if not load_imgs:
+        return poses, bds
+
+    imgs = imgs = [imread(f)[..., :3] / 255.0 for f in imgfiles]
+    imgs = np.stack(imgs, -1)
+
+    logger.info(f"Loaded image data, shape {imgs.shape}")
+    return poses, bds, imgs
+
+
+def normalize(x):
+    denom = np.linalg.norm(x)
+    if denom < 0.001:
+        warnings.warn("unsafe normalize()")
+    return x / denom
+
+
+def viewmatrix(z, up, pos):
+    vec2 = normalize(z)
+    vec1_avg = up
+    vec0 = normalize(np.cross(vec1_avg, vec2))
+    vec1 = normalize(np.cross(vec2, vec0))
+    m = np.stack([vec0, vec1, vec2, pos], 1)
+    return m
+
+
+def ptstocam(pts, c2w):
+    tt = np.matmul(c2w[:3, :3].T, (pts - c2w[:3, 3])[..., np.newaxis])[..., 0]
+    return tt
+
+
+def poses_avg(poses):
+
+    hwf = poses[0, :3, -1:]
+
+    center = poses[:, :3, 3].mean(0)
+    vec2 = normalize(poses[:, :3, 2].sum(0))
+    up = poses[:, :3, 1].sum(0)
+    c2w = np.concatenate([viewmatrix(vec2, up, center), hwf], 1)
+
+    return c2w
+
+
+def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
+    render_poses = []
+    rads = np.array(list(rads) + [1.0])
+    hwf = c2w[:, 4:5]
+
+    for theta in np.linspace(0.0, 2.0 * np.pi * rots, N + 1)[:-1]:
+        c = np.dot(
+            c2w[:3, :4],
+            np.array([np.cos(theta), -np.sin(theta), -np.sin(theta * zrate), 1.0])
+            * rads,
+        )
+        z = normalize(c - np.dot(c2w[:3, :4], np.array([0, 0, -focal, 1.0])))
+        render_poses.append(np.concatenate([viewmatrix(z, up, c), hwf], 1))
+    return render_poses
+
+
+def recenter_poses(poses):
+
+    poses_ = poses + 0
+    bottom = np.reshape([0, 0, 0, 1.0], [1, 4])
+    c2w = poses_avg(poses)
+    c2w = np.concatenate([c2w[:3, :4], bottom], -2)
+    bottom = np.tile(np.reshape(bottom, [1, 1, 4]), [poses.shape[0], 1, 1])
+    poses = np.concatenate([poses[:, :3, :4], bottom], -2)
+
+    poses = np.linalg.inv(c2w) @ poses
+    poses_[:, :3, :4] = poses[:, :3, :4]
+    poses = poses_
+    return poses
+
+
+def spherify_poses(poses, bds):
+    def add_row_to_homogenize_transform(p):
+        r"""Add the last row to homogenize 3 x 4 transformation matrices."""
+        return np.concatenate(
+            [p, np.tile(np.reshape(np.eye(4)[-1, :], [1, 1, 4]), [p.shape[0], 1, 1])], 1
+        )
+
+    # p34_to_44 = lambda p: np.concatenate(
+    #     [p, np.tile(np.reshape(np.eye(4)[-1, :], [1, 1, 4]), [p.shape[0], 1, 1])], 1
+    # )
+
+    p34_to_44 = add_row_to_homogenize_transform
+
+    rays_d = poses[:, :3, 2:3]
+    rays_o = poses[:, :3, 3:4]
+
+    def min_line_dist(rays_o, rays_d):
+        A_i = np.eye(3) - rays_d * np.transpose(rays_d, [0, 2, 1])
+        b_i = -A_i @ rays_o
+        pt_mindist = np.squeeze(
+            -np.linalg.inv((np.transpose(A_i, [0, 2, 1]) @ A_i).mean(0)) @ (b_i).mean(0)
+        )
+        return pt_mindist
+
+    pt_mindist = min_line_dist(rays_o, rays_d)
+
+    center = pt_mindist
+    up = (poses[:, :3, 3] - center).mean(0)
+
+    vec0 = normalize(up)
+    vec1 = normalize(np.cross([0.1, 0.2, 0.3], vec0))
+    vec2 = normalize(np.cross(vec0, vec1))
+    pos = center
+    c2w = np.stack([vec1, vec2, vec0, pos], 1)
+
+    poses_reset = np.linalg.inv(p34_to_44(c2w[None])) @ p34_to_44(poses[:, :3, :4])
+
+    rad = np.sqrt(np.mean(np.sum(np.square(poses_reset[:, :3, 3]), -1)))
+
+    sc = 1.0 / rad
+    poses_reset[:, :3, 3] *= sc
+    bds *= sc
+    rad *= sc
+
+    centroid = np.mean(poses_reset[:, :3, 3], 0)
+    zh = centroid[2]
+    radcircle = np.sqrt(rad**2 - zh**2)
+    new_poses = []
+
+    for th in np.linspace(0.0, 2.0 * np.pi, 120):
+
+        camorigin = np.array([radcircle * np.cos(th), radcircle * np.sin(th), zh])
+        up = np.array([0, 0, -1.0])
+
+        vec2 = normalize(camorigin)
+        vec0 = normalize(np.cross(vec2, up))
+        vec1 = normalize(np.cross(vec2, vec0))
+        pos = camorigin
+        p = np.stack([vec0, vec1, vec2, pos], 1)
+
+        new_poses.append(p)
+
+    new_poses = np.stack(new_poses, 0)
+
+    new_poses = np.concatenate(
+        [new_poses, np.broadcast_to(poses[0, :3, -1:], new_poses[:, :3, -1:].shape)], -1
+    )
+    poses_reset = np.concatenate(
+        [
+            poses_reset[:, :3, :4],
+            np.broadcast_to(poses[0, :3, -1:], poses_reset[:, :3, -1:].shape),
+        ],
+        -1,
+    )
+
+    return poses_reset, new_poses, bds
+
+
+def _local_path(path_manager, path):
+    if path_manager is None:
+        return path
+    return path_manager.get_local_path(path)
+
+
+def _ls(path_manager, path):
+    if path_manager is None:
+        return os.listdir(path)
+    return path_manager.ls(path)
+
+
+def _exists(path_manager, path):
+    if path_manager is None:
+        return os.path.exists(path)
+    return path_manager.exists(path)
+
+
+def load_llff_data(
+    basedir,
+    factor=8,
+    recenter=True,
+    bd_factor=0.75,
+    spherify=False,
+    path_zflat=False,
+    path_manager=None,
+):
+
+    poses, bds, imgs = _load_data(
+        basedir, factor=factor, path_manager=path_manager
+    )  # factor=8 downsamples original imgs by 8x
+    logger.info(f"Loaded {basedir}, {bds.min()}, {bds.max()}")
+
+    # Correct rotation matrix ordering and move variable dim to axis 0
+    poses = np.concatenate([poses[:, 1:2, :], -poses[:, 0:1, :], poses[:, 2:, :]], 1)
+    poses = np.moveaxis(poses, -1, 0).astype(np.float32)
+    imgs = np.moveaxis(imgs, -1, 0).astype(np.float32)
+    images = imgs
+    bds = np.moveaxis(bds, -1, 0).astype(np.float32)
+
+    # Rescale if bd_factor is provided
+    sc = 1.0 if bd_factor is None else 1.0 / (bds.min() * bd_factor)
+    poses[:, :3, 3] *= sc
+    bds *= sc
+
+    if recenter:
+        poses = recenter_poses(poses)
+
+    if spherify:
+        poses, render_poses, bds = spherify_poses(poses, bds)
+
+    images = images.astype(np.float32)
+    poses = poses.astype(np.float32)
+
+    return images, poses, bds