Revert "Fix CUDA kernel index data type in vision/fair/pytorch3d/pytorch3d/csrc/compositing/alpha_composite.cu +10"

This reverts commit 3987612062.

.circleci/regenerate.py

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

.github/workflows/build.yml

@@ -0,0 +1,23 @@
+name: facebookresearch/pytorch3d/build_and_test
+on:
+  pull_request:
+    branches:
+      - main
+  push:
+    branches:
+      - main
+jobs:
+  binary_linux_conda_cuda:
+    runs-on: 4-core-ubuntu-gpu-t4
+    env:
+      PYTHON_VERSION: "3.12"
+      BUILD_VERSION: "${{ github.run_number }}"
+      PYTORCH_VERSION: "2.4.1"
+      CU_VERSION: "cu121"
+      JUST_TESTRUN: 1
+    steps:
+    - uses: actions/checkout@v4
+    - name: Build and run tests
+      run: |-
+        conda create --name env --yes --quiet conda-build
+        conda run --no-capture-output --name env python3 ./packaging/build_conda.py --use-conda-cuda

dev/linter.sh

@@ -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; pyre -l vision/fair/pytorch3d/ )
+  ( cd ~/fbsource/fbcode; arc pyre check //vision/fair/pytorch3d/... )
 fi

docs/examples/pulsar_basic.py

@@ -10,6 +10,7 @@ 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

@@ -11,6 +11,7 @@ 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

@@ -14,6 +14,7 @@ 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

@@ -14,6 +14,7 @@ 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

@@ -18,6 +18,7 @@ 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

@@ -13,6 +13,7 @@ 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

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

packaging/build_conda.py

@@ -4,10 +4,11 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+import argparse
 import os.path
 import runpy
 import subprocess
-from typing import List
+from typing import List, Tuple
 
 # required env vars:
 # CU_VERSION: E.g. cu112
@@ -23,7 +24,7 @@ pytorch_major_minor = tuple(int(i) for i in PYTORCH_VERSION.split(".")[:2])
 source_root_dir = os.environ["PWD"]
 
 
-def version_constraint(version):
+def version_constraint(version) -> str:
     """
     Given version "11.3" returns " >=11.3,<11.4"
     """
@@ -32,7 +33,7 @@ def version_constraint(version):
     return f" >={version},<{upper}"
 
 
-def get_cuda_major_minor():
+def get_cuda_major_minor() -> Tuple[str, str]:
     if CU_VERSION == "cpu":
         raise ValueError("fn only for cuda builds")
     if len(CU_VERSION) != 5 or CU_VERSION[:2] != "cu":
@@ -42,11 +43,10 @@ def get_cuda_major_minor():
     return major, minor
 
 
-def setup_cuda():
+def setup_cuda(use_conda_cuda: bool) -> List[str]:
     if CU_VERSION == "cpu":
-        return
+        return []
     major, minor = get_cuda_major_minor()
-    os.environ["CUDA_HOME"] = f"/usr/local/cuda-{major}.{minor}/"
     os.environ["FORCE_CUDA"] = "1"
 
     basic_nvcc_flags = (
@@ -75,6 +75,15 @@ def setup_cuda():
 
     if os.environ.get("JUST_TESTRUN", "0") != "1":
         os.environ["NVCC_FLAGS"] = nvcc_flags
+    if use_conda_cuda:
+        os.environ["CONDA_CUDA_TOOLKIT_BUILD_CONSTRAINT1"] = "- cuda-toolkit"
+        os.environ["CONDA_CUDA_TOOLKIT_BUILD_CONSTRAINT2"] = (
+            f"- cuda-version={major}.{minor}"
+        )
+        return ["-c", f"nvidia/label/cuda-{major}.{minor}.0"]
+    else:
+        os.environ["CUDA_HOME"] = f"/usr/local/cuda-{major}.{minor}/"
+        return []
 
 
 def setup_conda_pytorch_constraint() -> List[str]:
@@ -95,7 +104,7 @@ def setup_conda_pytorch_constraint() -> List[str]:
         return ["-c", "pytorch", "-c", "nvidia"]
 
 
-def setup_conda_cudatoolkit_constraint():
+def setup_conda_cudatoolkit_constraint() -> None:
     if CU_VERSION == "cpu":
         os.environ["CONDA_CPUONLY_FEATURE"] = "- cpuonly"
         os.environ["CONDA_CUDATOOLKIT_CONSTRAINT"] = ""
@@ -116,7 +125,7 @@ def setup_conda_cudatoolkit_constraint():
     os.environ["CONDA_CUDATOOLKIT_CONSTRAINT"] = toolkit
 
 
-def do_build(start_args: List[str]):
+def do_build(start_args: List[str]) -> None:
     args = start_args.copy()
 
     test_flag = os.environ.get("TEST_FLAG")
@@ -132,8 +141,16 @@ def do_build(start_args: List[str]):
 
 
 if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="Build the conda package.")
+    parser.add_argument(
+        "--use-conda-cuda",
+        action="store_true",
+        help="get cuda from conda ignoring local cuda",
+    )
+    our_args = parser.parse_args()
+
     args = ["conda", "build"]
-    setup_cuda()
+    args += setup_cuda(use_conda_cuda=our_args.use_conda_cuda)
 
     init_path = source_root_dir + "/pytorch3d/__init__.py"
     build_version = runpy.run_path(init_path)["__version__"]

packaging/pytorch3d/meta.yaml

@@ -8,10 +8,13 @@ source:
 requirements:
   build:
     - {{ compiler('c') }} # [win]
+    {{ environ.get('CONDA_CUDA_TOOLKIT_BUILD_CONSTRAINT1', '') }}
+    {{ environ.get('CONDA_CUDA_TOOLKIT_BUILD_CONSTRAINT2', '') }}
     {{ environ.get('CONDA_CUB_CONSTRAINT') }}
 
   host:
     - python
+    - mkl =2023  # [x86_64]
     {{ environ.get('SETUPTOOLS_CONSTRAINT') }}
     {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }}
     {{ environ.get('CONDA_PYTORCH_MKL_CONSTRAINT') }}
@@ -22,6 +25,7 @@ requirements:
     - python
     - numpy >=1.11
     - torchvision >=0.5
+    - mkl =2023  # [x86_64]
     - iopath
     {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }}
     {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
@@ -47,8 +51,11 @@ test:
     - imageio
     - hydra-core
     - accelerate
+    - matplotlib
+    - tabulate
+    - pandas
+    - sqlalchemy
   commands:
-    #pytest .
     python -m unittest discover -v -s tests -t .
 
 

projects/implicitron_trainer/experiment.py

@@ -7,7 +7,7 @@
 
 # pyre-unsafe
 
-""""
+""" "
 This file is the entry point for launching experiments with Implicitron.
 
 Launch Training
@@ -44,6 +44,7 @@ The outputs of the experiment are saved and logged in multiple ways:
         config file.
 
 """
+
 import logging
 import os
 import warnings

projects/implicitron_trainer/impl/model_factory.py

@@ -26,7 +26,6 @@ logger = logging.getLogger(__name__)
 
 
 class ModelFactoryBase(ReplaceableBase):
-
     resume: bool = True  # resume from the last checkpoint
 
     def __call__(self, **kwargs) -> ImplicitronModelBase:
@@ -116,7 +115,9 @@ class ImplicitronModelFactory(ModelFactoryBase):
                         "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                     }
                 model_state_dict = torch.load(
-                    model_io.get_model_path(model_path), map_location=map_location
+                    model_io.get_model_path(model_path),
+                    map_location=map_location,
+                    weights_only=True,
                 )
 
                 try:

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -123,6 +123,7 @@ 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 = [
@@ -241,7 +242,7 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
                     map_location = {
                         "cuda:%d" % 0: "cuda:%d" % accelerator.local_process_index
                     }
-                optimizer_state = torch.load(opt_path, map_location)
+                optimizer_state = torch.load(opt_path, map_location, weights_only=True)
             else:
                 raise FileNotFoundError(f"Optimizer state {opt_path} does not exist.")
         return optimizer_state

projects/implicitron_trainer/impl/training_loop.py

@@ -161,7 +161,6 @@ 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)
@@ -395,6 +394,7 @@ 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/test_experiment.py

@@ -53,12 +53,8 @@ class TestExperiment(unittest.TestCase):
         cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_class_type = (
             "JsonIndexDatasetMapProvider"
         )
-        dataset_args = (
+        dataset_args = cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
-            cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        dataloader_args = cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_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"
@@ -94,12 +90,8 @@ class TestExperiment(unittest.TestCase):
         cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_class_type = (
             "JsonIndexDatasetMapProvider"
         )
-        dataset_args = (
+        dataset_args = cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
-            cfg.data_source_ImplicitronDataSource_args.dataset_map_provider_JsonIndexDatasetMapProvider_args
+        dataloader_args = cfg.data_source_ImplicitronDataSource_args.data_loader_map_provider_SequenceDataLoaderMapProvider_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"
@@ -111,9 +103,7 @@ 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 = (
+        cfg.optimizer_factory_ImplicitronOptimizerFactory_args.exponential_lr_step_size = 2
-            2
-        )
 
         if DEBUG:
             experiment.dump_cfg(cfg)

projects/implicitron_trainer/tests/test_optimizer_factory.py

@@ -81,8 +81,9 @@ 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 = Node(params=[pa]), Node(
+        na, nb = (
-            params=[pb], param_groups={"self": "pb_self", "p1": "pb_param"}
+            Node(params=[pa]),
+            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)
+    train_data = torch.load(cameras_path, weights_only=True)
     n_cameras = train_data["cameras"]["R"].shape[0]
 
     _image_max_image_pixels = Image.MAX_IMAGE_PIXELS

projects/nerf/nerf/stats.py

@@ -194,7 +194,6 @@ 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

@@ -24,7 +24,6 @@ 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"
@@ -63,7 +62,7 @@ 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)
+    loaded_data = torch.load(checkpoint_path, weights_only=True)
     # Do not load the cached xy grid.
     # - this allows setting an arbitrary evaluation image size.
     state_dict = {

projects/nerf/tests/test_raysampler.py

@@ -42,7 +42,6 @@ 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

@@ -25,7 +25,6 @@ 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)
@@ -77,7 +76,7 @@ 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)
+            loaded_data = torch.load(checkpoint_path, weights_only=True)
             model.load_state_dict(loaded_data["model"])
             stats = pickle.loads(loaded_data["stats"])
             print(f"   => resuming from epoch {stats.epoch}.")
@@ -219,7 +218,6 @@ 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/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/csrc/ball_query/ball_query_cpu.cpp

@@ -7,7 +7,6 @@
  */
 
 #include <torch/extension.h>
-#include <queue>
 #include <tuple>
 
 std::tuple<at::Tensor, at::Tensor> BallQueryCpu(

pytorch3d/csrc/compositing/alpha_composite.cu

@@ -28,7 +28,6 @@ __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);
@@ -79,7 +78,6 @@ __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);

pytorch3d/csrc/compositing/norm_weighted_sum.cu

@@ -28,7 +28,6 @@ __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);
@@ -92,7 +91,6 @@ __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);

pytorch3d/csrc/compositing/weighted_sum.cu

@@ -26,7 +26,6 @@ __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);
@@ -74,7 +73,6 @@ __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);

pytorch3d/csrc/ext.cpp

@@ -7,15 +7,11 @@
  */
 
 // clang-format off
-#if !defined(USE_ROCM)
 #include "./pulsar/global.h" // Include before <torch/extension.h>.
-#endif
 #include <torch/extension.h>
 // clang-format on
-#if !defined(USE_ROCM)
 #include "./pulsar/pytorch/renderer.h"
 #include "./pulsar/pytorch/tensor_util.h"
-#endif
 #include "ball_query/ball_query.h"
 #include "blending/sigmoid_alpha_blend.h"
 #include "compositing/alpha_composite.h"
@@ -104,7 +100,6 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
 
   // Pulsar.
   // Pulsar not enabled on AMD.
-#if !defined(USE_ROCM)
 #ifdef PULSAR_LOGGING_ENABLED
   c10::ShowLogInfoToStderr();
 #endif
@@ -154,10 +149,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
           py::arg("gamma"),
           py::arg("max_depth"),
           py::arg("min_depth") /* = 0.f*/,
-          py::arg(
+          py::arg("bg_col") /* = std::nullopt not exposed properly in
-              "bg_col") /* = at::nullopt not exposed properly in pytorch 1.1. */
+                               pytorch 1.1. */
           ,
-          py::arg("opacity") /* = at::nullopt ... */,
+          py::arg("opacity") /* = std::nullopt ... */,
           py::arg("percent_allowed_difference") = 0.01f,
           py::arg("max_n_hits") = MAX_UINT,
           py::arg("mode") = 0)
@@ -189,5 +184,4 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.attr("MAX_UINT") = py::int_(MAX_UINT);
   m.attr("MAX_USHORT") = py::int_(MAX_USHORT);
   m.attr("PULSAR_MAX_GRAD_SPHERES") = py::int_(MAX_GRAD_SPHERES);
-#endif
 }

pytorch3d/csrc/iou_box3d/iou_box3d_cpu.cpp

@@ -7,10 +7,7 @@
  */
 
 #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

@@ -461,10 +461,8 @@ __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
@@ -500,7 +498,6 @@ __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
@@ -728,7 +725,7 @@ __device__ inline int BoxIntersections(
       }
     }
     // Update the face_verts_out tris
-    num_tris = offset;
+    num_tris = min(MAX_TRIS, offset);
     for (int j = 0; j < num_tris; ++j) {
       face_verts_out[j] = tri_verts_updated[j];
     }

pytorch3d/csrc/points_to_volumes/points_to_volumes_cpu.cpp

@@ -8,9 +8,7 @@
 
 #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

@@ -36,11 +36,13 @@
 #pragma nv_diag_suppress 2951
 #pragma nv_diag_suppress 2967
 #else
+#if !defined(USE_ROCM)
 #pragma diag_suppress = attribute_not_allowed
 #pragma diag_suppress = 1866
 #pragma diag_suppress = 2941
 #pragma diag_suppress = 2951
 #pragma diag_suppress = 2967
+#endif //! USE_ROCM
 #endif
 #else // __CUDACC__
 #define INLINE inline
@@ -56,7 +58,9 @@
 #pragma clang diagnostic pop
 #ifdef WITH_CUDA
 #include <ATen/cuda/CUDAContext.h>
+#if !defined(USE_ROCM)
 #include <vector_functions.h>
+#endif //! USE_ROCM
 #else
 #ifndef cudaStream_t
 typedef void* cudaStream_t;

pytorch3d/csrc/pulsar/gpu/commands.h

@@ -59,6 +59,11 @@ getLastCudaError(const char* errorMessage, const char* file, const int line) {
 #define SHARED __shared__
 #define ACTIVEMASK() __activemask()
 #define BALLOT(mask, val) __ballot_sync((mask), val)
+
+/* TODO (ROCM-6.2): None of the WARP_* are used anywhere and ROCM-6.2 natively
+ * supports __shfl_*. Disabling until the move to ROCM-6.2.
+ */
+#if !defined(USE_ROCM)
 /**
  * Find the cumulative sum within a warp up to the current
  * thread lane, with each mask thread contributing base.
@@ -115,6 +120,7 @@ INLINE DEVICE float3 WARP_SUM_FLOAT3(
   ret.z = WARP_SUM(group, mask, base.z);
   return ret;
 }
+#endif //! USE_ROCM
 
 // Floating point.
 // #define FMUL(a, b) __fmul_rn((a), (b))
@@ -142,6 +148,7 @@ INLINE DEVICE float3 WARP_SUM_FLOAT3(
 #define FMA(x, y, z) __fmaf_rn((x), (y), (z))
 #define I2F(a) __int2float_rn(a)
 #define FRCP(x) __frcp_rn(x)
+#if !defined(USE_ROCM)
 __device__ static float atomicMax(float* address, float val) {
   int* address_as_i = (int*)address;
   int old = *address_as_i, assumed;
@@ -166,6 +173,7 @@ __device__ static float atomicMin(float* address, float val) {
   } while (assumed != old);
   return __int_as_float(old);
 }
+#endif //! USE_ROCM
 #define DMAX(a, b) FMAX(a, b)
 #define DMIN(a, b) FMIN(a, b)
 #define DSQRT(a) sqrt(a)

pytorch3d/csrc/pulsar/include/camera.device.h

@@ -14,7 +14,7 @@
 #include "./commands.h"
 
 namespace pulsar {
-IHD CamGradInfo::CamGradInfo() {
+IHD CamGradInfo::CamGradInfo(int x) {
   cam_pos = make_float3(0.f, 0.f, 0.f);
   pixel_0_0_center = make_float3(0.f, 0.f, 0.f);
   pixel_dir_x = make_float3(0.f, 0.f, 0.f);

pytorch3d/csrc/pulsar/include/camera.h

@@ -63,7 +63,7 @@ inline bool operator==(const CamInfo& a, const CamInfo& b) {
 };
 
 struct CamGradInfo {
-  HOST DEVICE CamGradInfo();
+  HOST DEVICE CamGradInfo(int = 0);
   float3 cam_pos;
   float3 pixel_0_0_center;
   float3 pixel_dir_x;

pytorch3d/csrc/pulsar/include/commands.h

@@ -24,7 +24,7 @@
 // #pragma diag_suppress = 68
 #include <ATen/cuda/CUDAContext.h>
 // #pragma pop
-#include "../cuda/commands.h"
+#include "../gpu/commands.h"
 #else
 #pragma clang diagnostic push
 #pragma clang diagnostic ignored "-Weverything"

pytorch3d/csrc/pulsar/include/math.h

@@ -46,6 +46,7 @@ IHD float3 outer_product_sum(const float3& a) {
 }
 
 // TODO: put intrinsics here.
+#if !defined(USE_ROCM)
 IHD float3 operator+(const float3& a, const float3& b) {
   return make_float3(a.x + b.x, a.y + b.y, a.z + b.z);
 }
@@ -93,6 +94,7 @@ IHD float3 operator*(const float3& a, const float3& b) {
 IHD float3 operator*(const float& a, const float3& b) {
   return b * a;
 }
+#endif //! USE_ROCM
 
 INLINE DEVICE float length(const float3& v) {
   // TODO: benchmark what's faster.

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

@@ -283,9 +283,15 @@ GLOBAL void render(
           (percent_allowed_difference > 0.f &&
            max_closest_possible_intersection > depth_threshold) ||
           tracker.get_n_hits() >= max_n_hits;
+#if defined(__CUDACC__) && defined(__HIP_PLATFORM_AMD__)
+      unsigned long long warp_done = __ballot(done);
+      int warp_done_bit_cnt = __popcll(warp_done);
+#else
       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)
-        ATOMICADD_B(&n_pixels_done, POPC(warp_done));
+        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();

pytorch3d/csrc/pulsar/pytorch/renderer.cpp

@@ -213,8 +213,8 @@ std::tuple<size_t, size_t, bool, torch::Tensor> Renderer::arg_check(
     const float& gamma,
     const float& max_depth,
     float& min_depth,
-    const c10::optional<torch::Tensor>& bg_col,
+    const std::optional<torch::Tensor>& bg_col,
-    const c10::optional<torch::Tensor>& opacity,
+    const std::optional<torch::Tensor>& opacity,
     const float& percent_allowed_difference,
     const uint& max_n_hits,
     const uint& mode) {
@@ -668,8 +668,8 @@ std::tuple<torch::Tensor, torch::Tensor> Renderer::forward(
     const float& gamma,
     const float& max_depth,
     float min_depth,
-    const c10::optional<torch::Tensor>& bg_col,
+    const std::optional<torch::Tensor>& bg_col,
-    const c10::optional<torch::Tensor>& opacity,
+    const std::optional<torch::Tensor>& opacity,
     const float& percent_allowed_difference,
     const uint& max_n_hits,
     const uint& mode) {
@@ -888,14 +888,14 @@ std::tuple<torch::Tensor, torch::Tensor> Renderer::forward(
 };
 
 std::tuple<
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>,
+    std::optional<torch::Tensor>,
-    at::optional<torch::Tensor>>
+    std::optional<torch::Tensor>>
 Renderer::backward(
     const torch::Tensor& grad_im,
     const torch::Tensor& image,
@@ -912,8 +912,8 @@ Renderer::backward(
     const float& gamma,
     const float& max_depth,
     float min_depth,
-    const c10::optional<torch::Tensor>& bg_col,
+    const std::optional<torch::Tensor>& bg_col,
-    const c10::optional<torch::Tensor>& opacity,
+    const std::optional<torch::Tensor>& opacity,
     const float& percent_allowed_difference,
     const uint& max_n_hits,
     const uint& mode,
@@ -922,7 +922,7 @@ Renderer::backward(
     const bool& dif_rad,
     const bool& dif_cam,
     const bool& dif_opy,
-    const at::optional<std::pair<uint, uint>>& dbg_pos) {
+    const std::optional<std::pair<uint, uint>>& dbg_pos) {
   this->ensure_on_device(this->device_tracker.device());
   size_t batch_size;
   size_t n_points;
@@ -1045,14 +1045,14 @@ Renderer::backward(
   }
   // Prepare the return value.
   std::tuple<
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>>
+      std::optional<torch::Tensor>>
       ret;
   if (mode == 1 || (!dif_pos && !dif_col && !dif_rad && !dif_cam && !dif_opy)) {
     return ret;

pytorch3d/csrc/pulsar/pytorch/renderer.h

@@ -44,21 +44,21 @@ struct Renderer {
       const float& gamma,
       const float& max_depth,
       float min_depth,
-      const c10::optional<torch::Tensor>& bg_col,
+      const std::optional<torch::Tensor>& bg_col,
-      const c10::optional<torch::Tensor>& opacity,
+      const std::optional<torch::Tensor>& opacity,
       const float& percent_allowed_difference,
       const uint& max_n_hits,
       const uint& mode);
 
   std::tuple<
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>,
+      std::optional<torch::Tensor>,
-      at::optional<torch::Tensor>>
+      std::optional<torch::Tensor>>
   backward(
       const torch::Tensor& grad_im,
       const torch::Tensor& image,
@@ -75,8 +75,8 @@ struct Renderer {
       const float& gamma,
       const float& max_depth,
       float min_depth,
-      const c10::optional<torch::Tensor>& bg_col,
+      const std::optional<torch::Tensor>& bg_col,
-      const c10::optional<torch::Tensor>& opacity,
+      const std::optional<torch::Tensor>& opacity,
       const float& percent_allowed_difference,
       const uint& max_n_hits,
       const uint& mode,
@@ -85,7 +85,7 @@ struct Renderer {
       const bool& dif_rad,
       const bool& dif_cam,
       const bool& dif_opy,
-      const at::optional<std::pair<uint, uint>>& dbg_pos);
+      const std::optional<std::pair<uint, uint>>& dbg_pos);
 
   // Infrastructure.
   /**
@@ -115,8 +115,8 @@ struct Renderer {
       const float& gamma,
       const float& max_depth,
       float& min_depth,
-      const c10::optional<torch::Tensor>& bg_col,
+      const std::optional<torch::Tensor>& bg_col,
-      const c10::optional<torch::Tensor>& opacity,
+      const std::optional<torch::Tensor>& opacity,
       const float& percent_allowed_difference,
       const uint& max_n_hits,
       const uint& mode);

pytorch3d/csrc/pulsar/pytorch/tensor_util.cpp

@@ -8,6 +8,7 @@
 
 #ifdef WITH_CUDA
 #include <ATen/cuda/CUDAContext.h>
+#include <c10/cuda/CUDAException.h>
 #include <cuda_runtime_api.h>
 #endif
 #include <torch/extension.h>
@@ -33,13 +34,13 @@ torch::Tensor sphere_ids_from_result_info_nograd(
           .contiguous();
   if (forw_info.device().type() == c10::DeviceType::CUDA) {
 #ifdef WITH_CUDA
-    cudaMemcpyAsync(
+    C10_CUDA_CHECK(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

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

pytorch3d/csrc/rasterize_meshes/rasterize_meshes_cpu.cpp

@@ -9,7 +9,6 @@
 #include <torch/extension.h>
 #include <algorithm>
 #include <list>
-#include <queue>
 #include <thread>
 #include <tuple>
 #include "ATen/core/TensorAccessor.h"

pytorch3d/csrc/sample_farthest_points/sample_farthest_points.cu

@@ -35,8 +35,6 @@ __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

pytorch3d/csrc/utils/geometry_utils.cuh

@@ -376,8 +376,6 @@ 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/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

@@ -36,7 +36,6 @@ 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/frame_data.py

@@ -26,7 +26,7 @@ from typing import (
 import numpy as np
 import torch
 
-from pytorch3d.implicitron.dataset import types
+from pytorch3d.implicitron.dataset import orm_types, types
 from pytorch3d.implicitron.dataset.utils import (
     adjust_camera_to_bbox_crop_,
     adjust_camera_to_image_scale_,
@@ -48,8 +48,12 @@ 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]):
@@ -122,9 +126,9 @@ class FrameData(Mapping[str, Any]):
         meta: A dict for storing additional frame information.
     """
 
-    frame_number: Optional[torch.LongTensor]
+    frame_number: Optional[torch.LongTensor] = None
-    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
@@ -155,7 +159,7 @@ 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)):
+            if isinstance(value, (torch.Tensor, Pointclouds, CamerasBase, Meshes)):
                 new_params[field_name] = value.to(*args, **kwargs)
             else:
                 new_params[field_name] = value
@@ -417,7 +421,6 @@ 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]
                 )
@@ -426,7 +429,7 @@ class FrameData(Mapping[str, Any]):
                     if all(list_value is not None for list_value in list_values)
                     else None
                 )
-            return cls(**collated)
+            return type(elem)(**collated)
 
         elif isinstance(elem, Pointclouds):
             return join_pointclouds_as_batch(batch)
@@ -434,6 +437,8 @@ 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)
 
@@ -454,8 +459,8 @@ class FrameDataBuilderBase(ReplaceableBase, Generic[FrameDataSubtype], ABC):
     @abstractmethod
     def build(
         self,
-        frame_annotation: types.FrameAnnotation,
+        frame_annotation: FrameAnnotationT,
-        sequence_annotation: types.SequenceAnnotation,
+        sequence_annotation: SequenceAnnotationT,
         *,
         load_blobs: bool = True,
         **kwargs,
@@ -541,8 +546,8 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
     def build(
         self,
-        frame_annotation: types.FrameAnnotation,
+        frame_annotation: FrameAnnotationT,
-        sequence_annotation: types.SequenceAnnotation,
+        sequence_annotation: SequenceAnnotationT,
         *,
         load_blobs: bool = True,
         **kwargs,
@@ -586,58 +591,81 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
             ),
         )
 
-        fg_mask_np: Optional[np.ndarray] = None
+        dataset_root = self.dataset_root
         mask_annotation = frame_annotation.mask
-        if mask_annotation is not None:
+        depth_annotation = frame_annotation.depth
-            if load_blobs and self.load_masks:
+        image_path: str | None = None
-                fg_mask_np, mask_path = self._load_fg_probability(frame_annotation)
+        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:
+                fg_mask_np = self._load_fg_probability(frame_annotation, 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.")
 
-                image_np = load_image(self._local_path(image_path))
+                no_mask = fg_mask_np is None  # didn’t read the mask file
+                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 (
+        if bbox_xywh is None and fg_mask_np is not None:
-            load_blobs
+            bbox_xywh = get_bbox_from_mask(fg_mask_np, self.box_crop_mask_thr)
-            and self.load_depths
+        frame_data.bbox_xywh = safe_as_tensor(bbox_xywh, torch.float)
-            and frame_annotation.depth is not None
+
-            and frame_annotation.depth.path is not None
+        if load_blobs and self.load_depths and 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:
-            pcl_path = self._fix_point_cloud_path(point_cloud.path)
+            assert pcl_path is not None
             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)
@@ -653,18 +681,14 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
         return frame_data
 
-    def _load_fg_probability(
+    def _load_fg_probability(self, entry: FrameAnnotationT, path: str) -> np.ndarray:
-        self, entry: types.FrameAnnotation
+        fg_probability = load_mask(self._local_path(path))
-    ) -> 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, full_path
+        return fg_probability
 
     def _postprocess_image(
         self,
@@ -685,14 +709,14 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
     def _load_mask_depth(
         self,
-        entry: types.FrameAnnotation,
+        entry: FrameAnnotationT,
+        path: str,
         fg_mask: Optional[np.ndarray],
-    ) -> Tuple[torch.Tensor, str, torch.Tensor]:
+    ) -> tuple[torch.Tensor, torch.Tensor]:
         entry_depth = entry.depth
         dataset_root = self.dataset_root
         assert dataset_root is not None
-        assert entry_depth is not None and entry_depth.path is not None
+        assert entry_depth 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:
@@ -706,11 +730,11 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
         else:
             depth_mask = (depth_map > 0.0).astype(np.float32)
 
-        return torch.tensor(depth_map), path, torch.tensor(depth_mask)
+        return torch.tensor(depth_map), torch.tensor(depth_mask)
 
     def _get_pytorch3d_camera(
         self,
-        entry: types.FrameAnnotation,
+        entry: FrameAnnotationT,
     ) -> PerspectiveCameras:
         entry_viewpoint = entry.viewpoint
         assert entry_viewpoint is not None
@@ -739,19 +763,6 @@ 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_map_provider_v2.py

@@ -222,7 +222,6 @@ 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/load_llff.py

@@ -75,7 +75,6 @@ def _minify(basedir, path_manager, factors=(), resolutions=()):
 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"))
     )
@@ -164,7 +163,6 @@ def ptstocam(pts, c2w):
 
 
 def poses_avg(poses):
-
     hwf = poses[0, :3, -1:]
 
     center = poses[:, :3, 3].mean(0)
@@ -192,7 +190,6 @@ def render_path_spiral(c2w, up, rads, focal, zdelta, zrate, rots, N):
 
 
 def recenter_poses(poses):
-
     poses_ = poses + 0
     bottom = np.reshape([0, 0, 0, 1.0], [1, 4])
     c2w = poses_avg(poses)
@@ -256,7 +253,6 @@ def spherify_poses(poses, bds):
     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])
 
@@ -311,7 +307,6 @@ def load_llff_data(
     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

pytorch3d/implicitron/dataset/orm_types.py

@@ -4,6 +4,8 @@
 # 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
+
 # This functionality requires SQLAlchemy 2.0 or later.
 
 import math

pytorch3d/implicitron/dataset/sql_dataset.py

@@ -4,11 +4,15 @@
 # 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 hashlib
 import json
 import logging
 import os
-from dataclasses import dataclass
+
+import urllib
+from dataclasses import dataclass, Field, field
 from typing import (
     Any,
     ClassVar,
@@ -29,17 +33,18 @@ import sqlalchemy as sa
 import torch
 from pytorch3d.implicitron.dataset.dataset_base import DatasetBase
 
-from pytorch3d.implicitron.dataset.frame_data import (  # noqa
+from pytorch3d.implicitron.dataset.frame_data import (
     FrameData,
-    FrameDataBuilder,
+    FrameDataBuilder,  # noqa
     FrameDataBuilderBase,
 )
+
 from pytorch3d.implicitron.tools.config import (
     registry,
     ReplaceableBase,
     run_auto_creation,
 )
-from sqlalchemy.orm import Session
+from sqlalchemy.orm import scoped_session, Session, sessionmaker
 
 from .orm_types import SqlFrameAnnotation, SqlSequenceAnnotation
 
@@ -51,7 +56,7 @@ _SET_LISTS_TABLE: str = "set_lists"
 
 
 @registry.register
-class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
+class SqlIndexDataset(DatasetBase, ReplaceableBase):
     """
     A dataset with annotations stored as SQLite tables. This is an index-based dataset.
     The length is returned after all sequence and frame filters are applied (see param
@@ -88,6 +93,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             engine verbatim. Don’t expose it to end users of your application!
         pick_categories: Restrict the dataset to the given list of categories.
         pick_sequences: A Sequence of sequence names to restrict the dataset to.
+        pick_sequences_sql_clause: Custom SQL WHERE clause to constrain sequence annotations.
         exclude_sequences: A Sequence of the names of the sequences to exclude.
         limit_sequences_per_category_to: Limit the dataset to the first up to N
             sequences within each category (applies after all other sequence filters
@@ -102,9 +108,16 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             more frames than that; applied after other frame-level filters.
         seed: The seed of the random generator sampling `n_frames_per_sequence`
             random frames per sequence.
+        preload_metadata: If True, the metadata is preloaded into memory.
+        precompute_seq_to_idx: If True, precomputes the mapping from sequence name to indices.
+        scoped_session: If True, allows different parts of the code to share
+            a global session to access the database.
     """
 
     frame_annotations_type: ClassVar[Type[SqlFrameAnnotation]] = SqlFrameAnnotation
+    sequence_annotations_type: ClassVar[Type[SqlSequenceAnnotation]] = (
+        SqlSequenceAnnotation
+    )
 
     sqlite_metadata_file: str = ""
     dataset_root: Optional[str] = None
@@ -117,6 +130,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
     pick_categories: Tuple[str, ...] = ()
 
     pick_sequences: Tuple[str, ...] = ()
+    pick_sequences_sql_clause: Optional[str] = None
     exclude_sequences: Tuple[str, ...] = ()
     limit_sequences_per_category_to: int = 0
     limit_sequences_to: int = 0
@@ -124,12 +138,22 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
     n_frames_per_sequence: int = -1
     seed: int = 0
     remove_empty_masks_poll_whole_table_threshold: int = 300_000
+    preload_metadata: bool = False
+    precompute_seq_to_idx: bool = False
     # we set it manually in the constructor
-    # _index: pd.DataFrame = field(init=False)
+    _index: pd.DataFrame = field(init=False, metadata={"omegaconf_ignore": True})
+    _sql_engine: sa.engine.Engine = field(
+        init=False, metadata={"omegaconf_ignore": True}
+    )
+    eval_batches: Optional[List[Any]] = field(
+        init=False, metadata={"omegaconf_ignore": True}
+    )
 
-    frame_data_builder: FrameDataBuilderBase
+    frame_data_builder: FrameDataBuilderBase  # pyre-ignore[13]
     frame_data_builder_class_type: str = "FrameDataBuilder"
 
+    scoped_session: bool = False
+
     def __post_init__(self) -> None:
         if sa.__version__ < "2.0":
             raise ImportError("This class requires SQL Alchemy 2.0 or later")
@@ -138,19 +162,28 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             raise ValueError("sqlite_metadata_file must be set")
 
         if self.dataset_root:
-            frame_builder_type = self.frame_data_builder_class_type
+            frame_args = f"frame_data_builder_{self.frame_data_builder_class_type}_args"
-            getattr(self, f"frame_data_builder_{frame_builder_type}_args")[
+            getattr(self, frame_args)["dataset_root"] = self.dataset_root
-                "dataset_root"
+            getattr(self, frame_args)["path_manager"] = self.path_manager
-            ] = self.dataset_root
 
         run_auto_creation(self)
-        self.frame_data_builder.path_manager = self.path_manager
 
-        # pyre-ignore  # NOTE: sqlite-specific args (read-only mode).
+        if self.path_manager is not None:
+            self.sqlite_metadata_file = self.path_manager.get_local_path(
+                self.sqlite_metadata_file
+            )
+            self.subset_lists_file = self.path_manager.get_local_path(
+                self.subset_lists_file
+            )
+
+        # NOTE: sqlite-specific args (read-only mode).
         self._sql_engine = sa.create_engine(
-            f"sqlite:///file:{self.sqlite_metadata_file}?mode=ro&uri=true"
+            f"sqlite:///file:{urllib.parse.quote(self.sqlite_metadata_file)}?mode=ro&uri=true"
         )
 
+        if self.preload_metadata:
+            self._sql_engine = self._preload_database(self._sql_engine)
+
         sequences = self._get_filtered_sequences_if_any()
 
         if self.subsets:
@@ -166,16 +199,29 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         if len(index) == 0:
             raise ValueError(f"There are no frames in the subsets: {self.subsets}!")
 
-        self._index = index.set_index(["sequence_name", "frame_number"])  # pyre-ignore
+        self._index = index.set_index(["sequence_name", "frame_number"])
 
-        self.eval_batches = None  # pyre-ignore
+        self.eval_batches = None
         if self.eval_batches_file:
             self.eval_batches = self._load_filter_eval_batches()
 
         logger.info(str(self))
 
+        if self.scoped_session:
+            self._session_factory = sessionmaker(bind=self._sql_engine)  # pyre-ignore
+
+        if self.precompute_seq_to_idx:
+            # This is deprecated and will be removed in the future.
+            # After we backport https://github.com/facebookresearch/uco3d/pull/3
+            logger.warning(
+                "Using precompute_seq_to_idx is deprecated and will be removed in the future."
+            )
+            self._index["rowid"] = np.arange(len(self._index))
+            groupby = self._index.groupby("sequence_name", sort=False)["rowid"]
+            self._seq_to_indices = dict(groupby.apply(list))  # pyre-ignore
+            del self._index["rowid"]
+
     def __len__(self) -> int:
-        # pyre-ignore[16]
         return len(self._index)
 
     def __getitem__(self, frame_idx: Union[int, Tuple[str, int]]) -> FrameData:
@@ -232,12 +278,18 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             self.frame_annotations_type.frame_number
             == int(frame),  # cast from np.int64
         )
-        seq_stmt = sa.select(SqlSequenceAnnotation).where(
+        seq_stmt = sa.select(self.sequence_annotations_type).where(
-            SqlSequenceAnnotation.sequence_name == seq
+            self.sequence_annotations_type.sequence_name == seq
         )
-        with Session(self._sql_engine) as session:
+        if self.scoped_session:
-            entry = session.scalars(stmt).one()
+            # pyre-ignore
-            seq_metadata = session.scalars(seq_stmt).one()
+            with scoped_session(self._session_factory)() as session:
+                entry = session.scalars(stmt).one()
+                seq_metadata = session.scalars(seq_stmt).one()
+        else:
+            with Session(self._sql_engine) as session:
+                entry = session.scalars(stmt).one()
+                seq_metadata = session.scalars(seq_stmt).one()
 
         assert entry.image.path == self._index.loc[(seq, frame), "_image_path"]
 
@@ -250,7 +302,6 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         return frame_data
 
     def __str__(self) -> str:
-        # pyre-ignore[16]
         return f"SqlIndexDataset #frames={len(self._index)}"
 
     def sequence_names(self) -> Iterable[str]:
@@ -260,9 +311,10 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
     # override
     def category_to_sequence_names(self) -> Dict[str, List[str]]:
         stmt = sa.select(
-            SqlSequenceAnnotation.category, SqlSequenceAnnotation.sequence_name
+            self.sequence_annotations_type.category,
+            self.sequence_annotations_type.sequence_name,
         ).where(  # we limit results to sequences that have frames after all filters
-            SqlSequenceAnnotation.sequence_name.in_(self.sequence_names())
+            self.sequence_annotations_type.sequence_name.in_(self.sequence_names())
         )
         with self._sql_engine.connect() as connection:
             cat_to_seqs = pd.read_sql(stmt, connection)
@@ -335,17 +387,31 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         rows = self._index.index.get_loc(seq_name)
         if isinstance(rows, slice):
             assert rows.stop is not None, "Unexpected result from pandas"
-            rows = range(rows.start or 0, rows.stop, rows.step or 1)
+            rows_seq = range(rows.start or 0, rows.stop, rows.step or 1)
         else:
-            rows = np.where(rows)[0]
+            rows_seq = list(np.where(rows)[0])
 
         index_slice, idx = self._get_frame_no_coalesced_ts_by_row_indices(
-            rows, seq_name, subset_filter
+            rows_seq, seq_name, subset_filter
         )
         index_slice["idx"] = idx
 
         yield from index_slice.itertuples(index=False)
 
+    # override
+    def sequence_indices_in_order(
+        self, seq_name: str, subset_filter: Optional[Sequence[str]] = None
+    ) -> Iterator[int]:
+        """Same as `sequence_frames_in_order` but returns the iterator over
+        only dataset indices.
+        """
+        if self.precompute_seq_to_idx and subset_filter is None:
+            # pyre-ignore
+            yield from self._seq_to_indices[seq_name]
+        else:
+            for _, _, idx in self.sequence_frames_in_order(seq_name, subset_filter):
+                yield idx
+
     # override
     def get_eval_batches(self) -> Optional[List[Any]]:
         """
@@ -379,11 +445,35 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             or self.limit_sequences_to > 0
             or self.limit_sequences_per_category_to > 0
             or len(self.pick_sequences) > 0
+            or self.pick_sequences_sql_clause is not None
             or len(self.exclude_sequences) > 0
             or len(self.pick_categories) > 0
             or self.n_frames_per_sequence > 0
         )
 
+    def _preload_database(
+        self, source_engine: sa.engine.base.Engine
+    ) -> sa.engine.base.Engine:
+        destination_engine = sa.create_engine("sqlite:///:memory:")
+        metadata = sa.MetaData()
+        metadata.reflect(bind=source_engine)
+        metadata.create_all(bind=destination_engine)
+
+        with source_engine.connect() as source_conn:
+            with destination_engine.connect() as destination_conn:
+                for table_obj in metadata.tables.values():
+                    # Select all rows from the source table
+                    source_rows = source_conn.execute(table_obj.select())
+
+                    # Insert rows into the destination table
+                    for row in source_rows:
+                        destination_conn.execute(table_obj.insert().values(row))
+
+                    # Commit the changes for each table
+                    destination_conn.commit()
+
+        return destination_engine
+
     def _get_filtered_sequences_if_any(self) -> Optional[pd.Series]:
         # maximum possible filter (if limit_sequences_per_category_to == 0):
         # WHERE category IN 'self.pick_categories'
@@ -396,19 +486,22 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             *self._get_pick_filters(),
             *self._get_exclude_filters(),
         ]
+        if self.pick_sequences_sql_clause:
+            print("Applying the custom SQL clause.")
+            where_conditions.append(sa.text(self.pick_sequences_sql_clause))
 
         def add_where(stmt):
             return stmt.where(*where_conditions) if where_conditions else stmt
 
         if self.limit_sequences_per_category_to <= 0:
-            stmt = add_where(sa.select(SqlSequenceAnnotation.sequence_name))
+            stmt = add_where(sa.select(self.sequence_annotations_type.sequence_name))
         else:
             subquery = sa.select(
-                SqlSequenceAnnotation.sequence_name,
+                self.sequence_annotations_type.sequence_name,
                 sa.func.row_number()
                 .over(
                     order_by=sa.text("ROWID"),  # NOTE: ROWID is SQLite-specific
-                    partition_by=SqlSequenceAnnotation.category,
+                    partition_by=self.sequence_annotations_type.category,
                 )
                 .label("row_number"),
             )
@@ -444,31 +537,34 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             return []
 
         logger.info(f"Limiting dataset to categories: {self.pick_categories}")
-        return [SqlSequenceAnnotation.category.in_(self.pick_categories)]
+        return [self.sequence_annotations_type.category.in_(self.pick_categories)]
 
     def _get_pick_filters(self) -> List[sa.ColumnElement]:
         if not self.pick_sequences:
             return []
 
         logger.info(f"Limiting dataset to sequences: {self.pick_sequences}")
-        return [SqlSequenceAnnotation.sequence_name.in_(self.pick_sequences)]
+        return [self.sequence_annotations_type.sequence_name.in_(self.pick_sequences)]
 
     def _get_exclude_filters(self) -> List[sa.ColumnOperators]:
         if not self.exclude_sequences:
             return []
 
         logger.info(f"Removing sequences from the dataset: {self.exclude_sequences}")
-        return [SqlSequenceAnnotation.sequence_name.notin_(self.exclude_sequences)]
+        return [
+            self.sequence_annotations_type.sequence_name.notin_(self.exclude_sequences)
+        ]
 
     def _load_subsets_from_json(self, subset_lists_path: str) -> pd.DataFrame:
-        assert self.subsets is not None
+        subsets = self.subsets
+        assert subsets is not None
         with open(subset_lists_path, "r") as f:
             subset_to_seq_frame = json.load(f)
 
         seq_frame_list = sum(
             (
                 [(*row, subset) for row in subset_to_seq_frame[subset]]
-                for subset in self.subsets
+                for subset in subsets
             ),
             [],
         )
@@ -522,7 +618,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
                 stmt = sa.select(
                     self.frame_annotations_type.sequence_name,
                     self.frame_annotations_type.frame_number,
-                ).where(self.frame_annotations_type._mask_mass == 0)
+                ).where(self.frame_annotations_type._mask_mass == 0)  # pyre-ignore[16]
                 with Session(self._sql_engine) as session:
                     to_remove = session.execute(stmt).all()
 
@@ -586,7 +682,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         stmt = sa.select(
             self.frame_annotations_type.sequence_name,
             self.frame_annotations_type.frame_number,
-            self.frame_annotations_type._image_path,
+            self.frame_annotations_type._image_path,  # pyre-ignore[16]
             sa.null().label("subset"),
         )
         where_conditions = []
@@ -600,7 +696,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             logger.info("  excluding samples with empty masks")
             where_conditions.append(
                 sa.or_(
-                    self.frame_annotations_type._mask_mass.is_(None),
+                    self.frame_annotations_type._mask_mass.is_(None),  # pyre-ignore[16]
                     self.frame_annotations_type._mask_mass != 0,
                 )
             )
@@ -634,7 +730,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         assert self.eval_batches_file
         logger.info(f"Loading eval batches from {self.eval_batches_file}")
 
-        if not os.path.isfile(self.eval_batches_file):
+        if (
+            self.path_manager and not self.path_manager.isfile(self.eval_batches_file)
+        ) or (not self.path_manager and not os.path.isfile(self.eval_batches_file)):
             # The batch indices file does not exist.
             # Most probably the user has not specified the root folder.
             raise ValueError(
@@ -642,7 +740,8 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
                 + "Please specify a correct dataset_root folder."
             )
 
-        with open(self.eval_batches_file, "r") as f:
+        eval_batches_file = self._local_path(self.eval_batches_file)
+        with open(eval_batches_file, "r") as f:
             eval_batches = json.load(f)
 
         # limit the dataset to sequences to allow multiple evaluations in one file
@@ -726,9 +825,15 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             self.frame_annotations_type.sequence_name == seq_name,
             self.frame_annotations_type.frame_number.in_(frames),
         )
+        frame_no_ts = None
 
-        with self._sql_engine.connect() as connection:
+        if self.scoped_session:
-            frame_no_ts = pd.read_sql_query(stmt, connection)
+            stmt_text = str(stmt.compile(compile_kwargs={"literal_binds": True}))
+            with scoped_session(self._session_factory)() as session:  # pyre-ignore
+                frame_no_ts = pd.read_sql_query(stmt_text, session.connection())
+        else:
+            with self._sql_engine.connect() as connection:
+                frame_no_ts = pd.read_sql_query(stmt, connection)
 
         if len(frame_no_ts) != len(index_slice):
             raise ValueError(
@@ -758,11 +863,18 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             prefixes=["TEMP"],  # NOTE SQLite specific!
         )
 
+    @classmethod
+    def pre_expand(cls) -> None:
+        # remove dataclass annotations that are not meant to be init params
+        # because they cause troubles for OmegaConf
+        for attr, attr_value in list(cls.__dict__.items()):  # need to copy as we mutate
+            if isinstance(attr_value, Field) and attr_value.metadata.get(
+                "omegaconf_ignore", False
+            ):
+                delattr(cls, attr)
+                del cls.__annotations__[attr]
+
 
 def _seq_name_to_seed(seq_name) -> int:
     """Generates numbers in [0, 2 ** 28)"""
     return int(hashlib.sha1(seq_name.encode("utf-8")).hexdigest()[:7], 16)
-
-
-def _safe_as_tensor(data, dtype):
-    return torch.tensor(data, dtype=dtype) if data is not None else None

pytorch3d/implicitron/dataset/sql_dataset_provider.py

@@ -4,6 +4,8 @@
 # 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 logging
 import os
@@ -43,7 +45,7 @@ logger = logging.getLogger(__name__)
 
 
 @registry.register
-class SqlIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class SqlIndexDatasetMapProvider(DatasetMapProviderBase):
     """
     Generates the training, validation, and testing dataset objects for
     a dataset laid out on disk like SQL-CO3D, with annotations in an SQLite data base.
@@ -193,9 +195,9 @@ class SqlIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
 
     # this is a mould that is never constructed, used to build self._dataset_map values
     dataset_class_type: str = "SqlIndexDataset"
-    dataset: SqlIndexDataset
+    dataset: SqlIndexDataset  # pyre-ignore [13]
 
-    path_manager_factory: PathManagerFactory
+    path_manager_factory: PathManagerFactory  # pyre-ignore [13]
     path_manager_factory_class_type: str = "PathManagerFactory"
 
     def __post_init__(self):
@@ -282,8 +284,14 @@ class SqlIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
                 logger.info(f"Val dataset: {str(val_dataset)}")
 
             logger.debug("Extracting test dataset.")
-            eval_batches_file = self._get_lists_file("eval_batches")
+            if self.eval_batches_path is None:
-            del common_dataset_kwargs["eval_batches_file"]
+                eval_batches_file = None
+            else:
+                eval_batches_file = self._get_lists_file("eval_batches")
+
+            if "eval_batches_file" in common_dataset_kwargs:
+                common_dataset_kwargs.pop("eval_batches_file", None)
+
             test_dataset = dataset_type(
                 **common_dataset_kwargs,
                 subsets=self._get_subsets(self.test_subsets, True),

pytorch3d/implicitron/dataset/utils.py

@@ -87,6 +87,15 @@ def is_train_frame(
 def get_bbox_from_mask(
     mask: np.ndarray, thr: float, decrease_quant: float = 0.05
 ) -> Tuple[int, int, int, int]:
+    # these corner cases need to be handled in order to avoid an infinite loop
+    if mask.size == 0:
+        warnings.warn("Empty mask is provided for bbox extraction.", stacklevel=1)
+        return 0, 0, 1, 1
+
+    if not mask.min() >= 0.0:
+        warnings.warn("Negative values in the mask for bbox extraction.", stacklevel=1)
+        mask = mask.clip(min=0.0)
+
     # bbox in xywh
     masks_for_box = np.zeros_like(mask)
     while masks_for_box.sum() <= 1.0:
@@ -134,7 +143,15 @@ T = TypeVar("T", bound=torch.Tensor)
 def bbox_xyxy_to_xywh(xyxy: T) -> T:
     wh = xyxy[2:] - xyxy[:2]
     xywh = torch.cat([xyxy[:2], wh])
-    return xywh  # pyre-ignore
+    return xywh  # pyre-ignore[7]
+
+
+def bbox_xywh_to_xyxy(xywh: T, clamp_size: float | int | None = None) -> T:
+    wh = xywh[2:]
+    if clamp_size is not None:
+        wh = wh.clamp(min=clamp_size)
+    xyxy = torch.cat([xywh[:2], xywh[:2] + wh])
+    return xyxy  # pyre-ignore[7]
 
 
 def get_clamp_bbox(
@@ -180,16 +197,6 @@ def rescale_bbox(
     return bbox * rel_size
 
 
-def bbox_xywh_to_xyxy(
-    xywh: torch.Tensor, clamp_size: Optional[int] = None
-) -> torch.Tensor:
-    xyxy = xywh.clone()
-    if clamp_size is not None:
-        xyxy[2:] = torch.clamp(xyxy[2:], clamp_size)
-    xyxy[2:] += xyxy[:2]
-    return xyxy
-
-
 def get_1d_bounds(arr: np.ndarray) -> Tuple[int, int]:
     nz = np.flatnonzero(arr)
     return nz[0], nz[-1] + 1
@@ -201,18 +208,24 @@ def resize_image(
     image_width: Optional[int],
     mode: str = "bilinear",
 ) -> Tuple[torch.Tensor, float, torch.Tensor]:
-
     if isinstance(image, np.ndarray):
         image = torch.from_numpy(image)
 
-    if image_height is None or image_width is None:
+    if (
+        image_height is None
+        or image_width is None
+        or image.shape[-2] == 0
+        or image.shape[-1] == 0
+    ):
         # skip the resizing
         return image, 1.0, torch.ones_like(image[:1])
+
     # takes numpy array or tensor, returns pytorch tensor
     minscale = min(
         image_height / image.shape[-2],
         image_width / image.shape[-1],
     )
+
     imre = torch.nn.functional.interpolate(
         image[None],
         scale_factor=minscale,
@@ -220,6 +233,7 @@ def resize_image(
         align_corners=False if mode == "bilinear" else None,
         recompute_scale_factor=True,
     )[0]
+
     imre_ = torch.zeros(image.shape[0], image_height, image_width)
     imre_[:, 0 : imre.shape[1], 0 : imre.shape[2]] = imre
     mask = torch.zeros(1, image_height, image_width)
@@ -232,9 +246,21 @@ def transpose_normalize_image(image: np.ndarray) -> np.ndarray:
     return im.astype(np.float32) / 255.0
 
 
-def load_image(path: str) -> np.ndarray:
+def load_image(
+    path: str, try_read_alpha: bool = False, pil_format: str = "RGB"
+) -> np.ndarray:
+    """
+    Load an image from a path and return it as a numpy array.
+    If try_read_alpha is True, the image is read as RGBA and the alpha channel is
+    returned as the fourth channel.
+    Otherwise, the image is read as RGB and a three-channel image is returned.
+    """
     with Image.open(path) as pil_im:
-        im = np.array(pil_im.convert("RGB"))
+        # Check if the image has an alpha channel
+        if try_read_alpha and pil_im.mode == "RGBA":
+            im = np.array(pil_im)
+        else:
+            im = np.array(pil_im.convert(pil_format))
 
     return transpose_normalize_image(im)
 
@@ -329,6 +355,7 @@ def adjust_camera_to_bbox_crop_(
 
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
+        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         camera.principal_point[0],
         image_size_wh,
     )
@@ -341,6 +368,7 @@ def adjust_camera_to_bbox_crop_(
     )
 
     camera.focal_length = focal_length[None]
+    # pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
     camera.principal_point = principal_point_cropped[None]
 
 
@@ -352,6 +380,7 @@ def adjust_camera_to_image_scale_(
 ) -> PerspectiveCameras:
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
+        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         camera.principal_point[0],
         original_size_wh,
     )
@@ -368,7 +397,8 @@ def adjust_camera_to_image_scale_(
         image_size_wh_output,
     )
     camera.focal_length = focal_length_scaled[None]
-    camera.principal_point = principal_point_scaled[None]
+    # pyre-fixme[16]: `PerspectiveCameras` has no attribute `principal_point`.
+    camera.principal_point = principal_point_scaled[None]  # pyre-ignore[16]
 
 
 # NOTE this cache is per-worker; they are implemented as processes.

pytorch3d/implicitron/evaluation/evaluate_new_view_synthesis.py

@@ -299,7 +299,6 @@ def eval_batch(
     )
 
     for loss_fg_mask, name_postfix in zip((mask_crop, mask_fg), ("_masked", "_fg")):
-
         loss_mask_now = mask_crop * loss_fg_mask
 
         for rgb_metric_name, rgb_metric_fun in zip(

pytorch3d/implicitron/models/feature_extractor/resnet_feature_extractor.py

@@ -106,7 +106,7 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
             self.layers = torch.nn.ModuleList()
             self.proj_layers = torch.nn.ModuleList()
             for stage in range(self.max_stage):
-                stage_name = f"layer{stage+1}"
+                stage_name = f"layer{stage + 1}"
                 feature_name = self._get_resnet_stage_feature_name(stage)
                 if (stage + 1) in self.stages:
                     if (
@@ -139,12 +139,18 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
         self.stages = set(self.stages)  # convert to set for faster "in"
 
     def _get_resnet_stage_feature_name(self, stage) -> str:
-        return f"res_layer_{stage+1}"
+        return f"res_layer_{stage + 1}"
 
     def _resnet_normalize_image(self, img: torch.Tensor) -> torch.Tensor:
+        # pyre-fixme[58]: `-` is not supported for operand types `Tensor` and
+        #  `Union[Tensor, Module]`.
+        # pyre-fixme[58]: `/` is not supported for operand types `Tensor` and
+        #  `Union[Tensor, Module]`.
         return (img - self._resnet_mean) / self._resnet_std
 
     def get_feat_dims(self) -> int:
+        # pyre-fixme[29]: `Union[(self: TensorBase) -> Tensor, Tensor, Module]` is
+        #  not a function.
         return sum(self._feat_dim.values())
 
     def forward(
@@ -183,7 +189,12 @@ class ResNetFeatureExtractor(FeatureExtractorBase):
             else:
                 imgs_normed = imgs_resized
             #  is not a function.
+            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             feats = self.stem(imgs_normed)
+            # pyre-fixme[6]: For 1st argument expected `Iterable[_T1]` but got
+            #  `Union[Tensor, Module]`.
+            # pyre-fixme[6]: For 2nd argument expected `Iterable[_T2]` but got
+            #  `Union[Tensor, Module]`.
             for stage, (layer, proj) in enumerate(zip(self.layers, self.proj_layers)):
                 feats = layer(feats)
                 # just a sanity check below

pytorch3d/implicitron/models/generic_model.py

@@ -478,6 +478,8 @@ class GenericModel(ImplicitronModelBase):
             )
         custom_args["global_code"] = global_code
 
+        # pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a
+        #  function.
         for func in self._implicit_functions:
             func.bind_args(**custom_args)
 
@@ -500,6 +502,8 @@ class GenericModel(ImplicitronModelBase):
         # Unbind the custom arguments to prevent pytorch from storing
         # large buffers of intermediate results due to points in the
         # bound arguments.
+        # pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a
+        #  function.
         for func in self._implicit_functions:
             func.unbind_args()
 

pytorch3d/implicitron/models/global_encoder/autodecoder.py

@@ -71,6 +71,7 @@ class Autodecoder(Configurable, torch.nn.Module):
         return key_map
 
     def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:
+        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute `weight`.
         return (self._autodecoder_codes.weight**2).mean()
 
     def get_encoding_dim(self) -> int:
@@ -95,6 +96,7 @@ class Autodecoder(Configurable, torch.nn.Module):
                 # pyre-fixme[9]: x has type `Union[List[str], LongTensor]`; used as
                 #  `Tensor`.
                 x = torch.tensor(
+                    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, ...
                     [self._key_map[elem] for elem in x],
                     dtype=torch.long,
                     device=next(self.parameters()).device,
@@ -102,6 +104,7 @@ class Autodecoder(Configurable, torch.nn.Module):
             except StopIteration:
                 raise ValueError("Not enough n_instances in the autodecoder") from None
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self._autodecoder_codes(x)
 
     def _load_key_map_hook(

pytorch3d/implicitron/models/global_encoder/global_encoder.py

@@ -122,6 +122,7 @@ class HarmonicTimeEncoder(GlobalEncoderBase, torch.nn.Module):
         if frame_timestamp.shape[-1] != 1:
             raise ValueError("Frame timestamp's last dimensions should be one.")
         time = frame_timestamp / self.time_divisor
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self._harmonic_embedding(time)
 
     def calculate_squared_encoding_norm(self) -> Optional[torch.Tensor]:

pytorch3d/implicitron/models/implicit_function/decoding_functions.py

@@ -232,9 +232,14 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):
             # if the skip tensor is None, we use `x` instead.
             z = x
         skipi = 0
+        # pyre-fixme[6]: For 1st argument expected `Iterable[_T]` but got
+        #  `Union[Tensor, Module]`.
         for li, layer in enumerate(self.mlp):
+            # pyre-fixme[58]: `in` is not supported for right operand type
+            #  `Union[Tensor, Module]`.
             if li in self._input_skips:
                 if self._skip_affine_trans:
+                    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, ...
                     y = self._apply_affine_layer(self.skip_affines[skipi], y, z)
                 else:
                     y = torch.cat((y, z), dim=-1)

pytorch3d/implicitron/models/implicit_function/idr_feature_field.py

@@ -141,11 +141,16 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
             self.embed_fn is None and fun_viewpool is None and global_code is None
         ):
             return torch.tensor(
-                [], device=rays_points_world.device, dtype=rays_points_world.dtype
+                [],
+                device=rays_points_world.device,
+                dtype=rays_points_world.dtype,
+                # pyre-fixme[6]: For 2nd argument expected `Union[int, SymInt]` but got
+                #  `Union[Module, Tensor]`.
             ).view(0, self.out_dim)
 
         embeddings = []
         if self.embed_fn is not None:
+            # pyre-fixme[29]: `Union[Module, Tensor]` is not a function.
             embeddings.append(self.embed_fn(rays_points_world))
 
         if fun_viewpool is not None:
@@ -164,13 +169,19 @@ class IdrFeatureField(ImplicitFunctionBase, torch.nn.Module):
 
         embedding = torch.cat(embeddings, dim=-1)
         x = embedding
+        # pyre-fixme[29]: `Union[(self: TensorBase, other: Union[bool, complex,
+        #  float, int, Tensor]) -> Tensor, Module, Tensor]` is not a function.
         for layer_idx in range(self.num_layers - 1):
             if layer_idx in self.skip_in:
                 x = torch.cat([x, embedding], dim=-1) / 2**0.5
 
+            # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[An...
             x = self.linear_layers[layer_idx](x)
 
+            # pyre-fixme[29]: `Union[(self: TensorBase, other: Union[bool, complex,
+            #  float, int, Tensor]) -> Tensor, Module, Tensor]` is not a function.
             if layer_idx < self.num_layers - 2:
+                # pyre-fixme[29]: `Union[Module, Tensor]` is not a function.
                 x = self.softplus(x)
 
         return x

pytorch3d/implicitron/models/implicit_function/neural_radiance_field.py

@@ -123,8 +123,10 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         # Normalize the ray_directions to unit l2 norm.
         rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
         # Obtain the harmonic embedding of the normalized ray directions.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         rays_embedding = self.harmonic_embedding_dir(rays_directions_normed)
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self.color_layer((self.intermediate_linear(features), rays_embedding))
 
     @staticmethod
@@ -195,6 +197,8 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         embeds = create_embeddings_for_implicit_function(
             xyz_world=rays_points_world,
             #  for 2nd param but got `Union[None, torch.Tensor, torch.nn.Module]`.
+            # pyre-fixme[6]: For 2nd argument expected `Optional[(...) -> Any]` but
+            #  got `Union[None, Tensor, Module]`.
             xyz_embedding_function=(
                 self.harmonic_embedding_xyz if self.input_xyz else None
             ),
@@ -206,12 +210,14 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
         )
 
         # embeds.shape = [minibatch x n_src x n_rays x n_pts x self.n_harmonic_functions*6+3]
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         features = self.xyz_encoder(embeds)
         # features.shape = [minibatch x ... x self.n_hidden_neurons_xyz]
         # NNs operate on the flattenned rays; reshaping to the correct spatial size
         # TODO: maybe make the transformer work on non-flattened tensors to avoid this reshape
         features = features.reshape(*rays_points_world.shape[:-1], -1)
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         raw_densities = self.density_layer(features)
         # raw_densities.shape = [minibatch x ... x 1] in [0-1]
 
@@ -219,6 +225,8 @@ class NeuralRadianceFieldBase(ImplicitFunctionBase, torch.nn.Module):
             if camera is None:
                 raise ValueError("Camera must be given if xyz_ray_dir_in_camera_coords")
 
+            # pyre-fixme[58]: `@` is not supported for operand types `Tensor` and
+            #  `Union[Tensor, Module]`.
             directions = ray_bundle.directions @ camera.R
         else:
             directions = ray_bundle.directions

pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py

@@ -103,6 +103,8 @@ class SRNRaymarchFunction(Configurable, torch.nn.Module):
 
         embeds = create_embeddings_for_implicit_function(
             xyz_world=rays_points_world,
+            # pyre-fixme[6]: For 2nd argument expected `Optional[(...) -> Any]` but
+            #  got `Union[Tensor, Module]`.
             xyz_embedding_function=self._harmonic_embedding,
             global_code=global_code,
             fun_viewpool=fun_viewpool,
@@ -112,6 +114,7 @@ class SRNRaymarchFunction(Configurable, torch.nn.Module):
 
         # Before running the network, we have to resize embeds to ndims=3,
         # otherwise the SRN layers consume huge amounts of memory.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         raymarch_features = self._net(
             embeds.view(embeds.shape[0], -1, embeds.shape[-1])
         )
@@ -166,7 +169,9 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
         # Normalize the ray_directions to unit l2 norm.
         rays_directions_normed = torch.nn.functional.normalize(rays_directions, dim=-1)
         # Obtain the harmonic embedding of the normalized ray directions.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         rays_embedding = self._harmonic_embedding(rays_directions_normed)
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self._color_layer((features, rays_embedding))
 
     def forward(
@@ -195,6 +200,7 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
                 denoting the color of each ray point.
         """
         # raymarch_features.shape = [minibatch x ... x pts_per_ray x 3]
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         features = self._net(raymarch_features)
         # features.shape = [minibatch x ... x self.n_hidden_units]
 
@@ -202,6 +208,8 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
             if camera is None:
                 raise ValueError("Camera must be given if xyz_ray_dir_in_camera_coords")
 
+            # pyre-fixme[58]: `@` is not supported for operand types `Tensor` and
+            #  `Union[Tensor, Module]`.
             directions = ray_bundle.directions @ camera.R
         else:
             directions = ray_bundle.directions
@@ -209,6 +217,7 @@ class SRNPixelGenerator(Configurable, torch.nn.Module):
         # NNs operate on the flattenned rays; reshaping to the correct spatial size
         features = features.reshape(*raymarch_features.shape[:-1], -1)
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         raw_densities = self._density_layer(features)
 
         rays_colors = self._get_colors(features, directions)
@@ -269,6 +278,7 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
         srn_raymarch_function.
         """
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         net = self._hypernet(global_code)
 
         # use the hyper-net generated network to instantiate the raymarch module
@@ -296,7 +306,6 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
         global_code=None,
         **kwargs,
     ):
-
         if global_code is None:
             raise ValueError("SRN Hypernetwork requires a non-trivial global code.")
 
@@ -304,6 +313,8 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):
         # across LSTM iterations for the same global_code.
         if self.cached_srn_raymarch_function is None:
             # generate the raymarching network from the hypernet
+            # pyre-fixme[16]: `SRNRaymarchHyperNet` has no attribute
+            #  `cached_srn_raymarch_function`.
             self.cached_srn_raymarch_function = self._run_hypernet(global_code)
         (srn_raymarch_function,) = cast(
             Tuple[SRNRaymarchFunction], self.cached_srn_raymarch_function
@@ -331,6 +342,7 @@ class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
     def create_raymarch_function(self) -> None:
         self.raymarch_function = SRNRaymarchFunction(
             latent_dim=self.latent_dim,
+            # pyre-fixme[32]: Keyword argument must be a mapping with string keys.
             **self.raymarch_function_args,
         )
 
@@ -389,6 +401,7 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         self.hypernet = SRNRaymarchHyperNet(
             latent_dim=self.latent_dim,
             latent_dim_hypernet=self.latent_dim_hypernet,
+            # pyre-fixme[32]: Keyword argument must be a mapping with string keys.
             **self.hypernet_args,
         )
 

pytorch3d/implicitron/models/implicit_function/utils.py

@@ -40,7 +40,6 @@ def create_embeddings_for_implicit_function(
     xyz_embedding_function: Optional[Callable],
     diag_cov: Optional[torch.Tensor] = None,
 ) -> torch.Tensor:
-
     bs, *spatial_size, pts_per_ray, _ = xyz_world.shape
 
     if xyz_in_camera_coords:
@@ -64,7 +63,6 @@ def create_embeddings_for_implicit_function(
             0,
         )
     else:
-
         embeds = xyz_embedding_function(ray_points_for_embed, diag_cov=diag_cov)
         embeds = embeds.reshape(
             bs,

pytorch3d/implicitron/models/implicit_function/voxel_grid.py

@@ -269,6 +269,7 @@ class VoxelGridBase(ReplaceableBase, torch.nn.Module):
                 for name, tensor in vars(grid_values_with_wanted_resolution).items()
             }
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         return self.values_type(**params), True
 
     def get_resolution_change_epochs(self) -> Tuple[int, ...]:
@@ -882,6 +883,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
             torch.Tensor of shape (..., n_features)
         """
         locator = self._get_volume_locator()
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         grid_values = self.voxel_grid.values_type(**self.params)
         # voxel grids operate with extra n_grids dimension, which we fix to one
         return self.voxel_grid.evaluate_world(points[None], grid_values, locator)[0]
@@ -895,6 +897,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
                 replace current parameters
         """
         if self.hold_voxel_grid_as_parameters:
+            # pyre-fixme[16]: `VoxelGridModule` has no attribute `params`.
             self.params = torch.nn.ParameterDict(
                 {
                     k: torch.nn.Parameter(val)
@@ -945,6 +948,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         Returns:
             True if parameter change has happened else False.
         """
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         grid_values = self.voxel_grid.values_type(**self.params)
         grid_values, change = self.voxel_grid.change_resolution(
             grid_values, epoch=epoch
@@ -992,16 +996,21 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         """
         '''
         new_params = {}
+        # pyre-fixme[29]: `Union[(self: Tensor) -> Any, Tensor, Module]` is not a
+        #  function.
         for name in self.params:
             key = prefix + "params." + name
             if key in state_dict:
                 new_params[name] = torch.zeros_like(state_dict[key])
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         self.set_voxel_grid_parameters(self.voxel_grid.values_type(**new_params))
 
     def get_device(self) -> torch.device:
         """
         Returns torch.device on which module parameters are located
         """
+        # pyre-fixme[29]: `Union[(self: TensorBase) -> Tensor, Tensor, Module]` is
+        #  not a function.
         return next(val for val in self.params.values() if val is not None).device
 
     def crop_self(self, min_point: torch.Tensor, max_point: torch.Tensor) -> None:
@@ -1018,6 +1027,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         """
         locator = self._get_volume_locator()
         #  torch.nn.modules.module.Module]` is not a function.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         old_grid_values = self.voxel_grid.values_type(**self.params)
         new_grid_values = self.voxel_grid.crop_world(
             min_point, max_point, old_grid_values, locator
@@ -1025,6 +1035,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
         grid_values, _ = self.voxel_grid.change_resolution(
             new_grid_values, grid_values_with_wanted_resolution=old_grid_values
         )
+        # pyre-fixme[16]: `VoxelGridModule` has no attribute `params`.
         self.params = torch.nn.ParameterDict(
             {
                 k: torch.nn.Parameter(val)

pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py

@@ -192,16 +192,26 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
     def __post_init__(self) -> None:
         run_auto_creation(self)
+        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
+        #  `voxel_grid_scaffold`.
         self.voxel_grid_scaffold = self._create_voxel_grid_scaffold()
+        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
+        #  `harmonic_embedder_xyz_density`.
         self.harmonic_embedder_xyz_density = HarmonicEmbedding(
             **self.harmonic_embedder_xyz_density_args
         )
+        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
+        #  `harmonic_embedder_xyz_color`.
         self.harmonic_embedder_xyz_color = HarmonicEmbedding(
             **self.harmonic_embedder_xyz_color_args
         )
+        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
+        #  `harmonic_embedder_dir_color`.
         self.harmonic_embedder_dir_color = HarmonicEmbedding(
             **self.harmonic_embedder_dir_color_args
         )
+        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
+        #  `_scaffold_ready`.
         self._scaffold_ready = False
 
     def forward(
@@ -252,6 +262,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         # ########## filter the points using the scaffold ########## #
         if self._scaffold_ready and self.scaffold_filter_points:
             with torch.no_grad():
+                # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
                 non_empty_points = self.voxel_grid_scaffold(points)[..., 0] > 0
             points = points[non_empty_points]
             if len(points) == 0:
@@ -363,6 +374,7 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
                 feature dimensionality which `decoder_density` returns
         """
         embeds_density = self.voxel_grid_density(points)
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         harmonic_embedding_density = self.harmonic_embedder_xyz_density(embeds_density)
         # shape = [..., density_dim]
         return self.decoder_density(harmonic_embedding_density)
@@ -397,6 +409,8 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         if self.xyz_ray_dir_in_camera_coords:
             if camera is None:
                 raise ValueError("Camera must be given if xyz_ray_dir_in_camera_coords")
+            # pyre-fixme[58]: `@` is not supported for operand types `Tensor` and
+            #  `Union[Tensor, Module]`.
             directions = directions @ camera.R
 
         # ########## get voxel grid output ########## #
@@ -405,11 +419,13 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
 
         # ########## embed with the harmonic function ########## #
         # Obtain the harmonic embedding of the voxel grid output.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         harmonic_embedding_color = self.harmonic_embedder_xyz_color(embeds_color)
 
         # Normalize the ray_directions to unit l2 norm.
         rays_directions_normed = torch.nn.functional.normalize(directions, dim=-1)
         # Obtain the harmonic embedding of the normalized ray directions.
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         harmonic_embedding_dir = self.harmonic_embedder_dir_color(
             rays_directions_normed
         )
@@ -478,8 +494,11 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
             an object inside, else False.
         """
         # find bounding box
+        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
+        #  `get_grid_points`.
         points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
         assert self._scaffold_ready, "Scaffold has to be calculated before cropping."
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         occupancy = self.voxel_grid_scaffold(points)[..., 0] > 0
         non_zero_idxs = torch.nonzero(occupancy)
         if len(non_zero_idxs) == 0:
@@ -511,6 +530,8 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         """
 
         planes = []
+        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute
+        #  `get_grid_points`.
         points = self.voxel_grid_scaffold.get_grid_points(epoch=epoch)
 
         chunk_size = (
@@ -530,7 +551,10 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
             stride=1,
         )
         occupancy_cube = density_cube > self.scaffold_empty_space_threshold
+        # pyre-fixme[16]: Item `Tensor` of `Tensor | Module` has no attribute `params`.
         self.voxel_grid_scaffold.params["voxel_grid"] = occupancy_cube.float()
+        # pyre-fixme[16]: `VoxelGridImplicitFunction` has no attribute
+        #  `_scaffold_ready`.
         self._scaffold_ready = True
 
         return False
@@ -547,6 +571,8 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         decoding function to this value.
         """
         grid_args = self.voxel_grid_density_args
+        # pyre-fixme[6]: For 1st argument expected `DictConfig` but got
+        #  `Union[Tensor, Module]`.
         grid_output_dim = VoxelGridModule.get_output_dim(grid_args)
 
         embedder_args = self.harmonic_embedder_xyz_density_args
@@ -575,6 +601,8 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
         decoding function to this value.
         """
         grid_args = self.voxel_grid_color_args
+        # pyre-fixme[6]: For 1st argument expected `DictConfig` but got
+        #  `Union[Tensor, Module]`.
         grid_output_dim = VoxelGridModule.get_output_dim(grid_args)
 
         embedder_args = self.harmonic_embedder_xyz_color_args
@@ -608,7 +636,9 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
                     `self.voxel_grid_density`
         """
         return VoxelGridModule(
+            # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[An...
             extents=self.voxel_grid_density_args["extents"],
+            # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[An...
             translation=self.voxel_grid_density_args["translation"],
             voxel_grid_class_type="FullResolutionVoxelGrid",
             hold_voxel_grid_as_parameters=False,

pytorch3d/implicitron/models/metrics.py

@@ -6,7 +6,6 @@
 
 # pyre-unsafe
 
-
 import warnings
 from typing import Any, Dict, Optional
 
@@ -298,9 +297,8 @@ class ViewMetrics(ViewMetricsBase):
                 _rgb_metrics(
                     image_rgb,
                     image_rgb_pred,
-                    fg_probability,
+                    masks=fg_probability,
-                    fg_probability_pred,
+                    masks_crop=mask_crop,
-                    mask_crop,
                 )
             )
 
@@ -310,9 +308,21 @@ class ViewMetrics(ViewMetricsBase):
             metrics["mask_neg_iou"] = utils.neg_iou_loss(
                 fg_probability_pred, fg_probability, mask=mask_crop
             )
-            metrics["mask_bce"] = utils.calc_bce(
+            if torch.is_autocast_enabled():
-                fg_probability_pred, fg_probability, mask=mask_crop
+                # To avoid issues with mixed precision
-            )
+                metrics["mask_bce"] = utils.calc_bce(
+                    fg_probability_pred.logit(),
+                    fg_probability,
+                    mask=mask_crop,
+                    pred_logits=True,
+                )
+            else:
+                metrics["mask_bce"] = utils.calc_bce(
+                    fg_probability_pred,
+                    fg_probability,
+                    mask=mask_crop,
+                    pred_logits=False,
+                )
 
         if depth_map is not None and depth_map_pred is not None:
             assert mask_crop is not None
@@ -324,7 +334,11 @@ class ViewMetrics(ViewMetricsBase):
             if fg_probability is not None:
                 mask = fg_probability * mask_crop
                 _, abs_ = utils.eval_depth(
-                    depth_map_pred, depth_map, get_best_scale=True, mask=mask, crop=0
+                    depth_map_pred,
+                    depth_map,
+                    get_best_scale=True,
+                    mask=mask,
+                    crop=0,
                 )
                 metrics["depth_abs_fg"] = abs_.mean()
 
@@ -346,18 +360,26 @@ class ViewMetrics(ViewMetricsBase):
         return metrics
 
 
-def _rgb_metrics(images, images_pred, masks, masks_pred, masks_crop):
+def _rgb_metrics(
+    images,
+    images_pred,
+    masks=None,
+    masks_crop=None,
+    huber_scaling: float = 0.03,
+):
     assert masks_crop is not None
     if images.shape[1] != images_pred.shape[1]:
         raise ValueError(
             f"Network output's RGB images had {images_pred.shape[1]} "
             f"channels. {images.shape[1]} expected."
         )
+    rgb_abs = ((images_pred - images).abs()).mean(dim=1, keepdim=True)
     rgb_squared = ((images_pred - images) ** 2).mean(dim=1, keepdim=True)
-    rgb_loss = utils.huber(rgb_squared, scaling=0.03)
+    rgb_loss = utils.huber(rgb_squared, scaling=huber_scaling)
     crop_mass = masks_crop.sum().clamp(1.0)
     results = {
         "rgb_huber": (rgb_loss * masks_crop).sum() / crop_mass,
+        "rgb_l1": (rgb_abs * masks_crop).sum() / crop_mass,
         "rgb_mse": (rgb_squared * masks_crop).sum() / crop_mass,
         "rgb_psnr": utils.calc_psnr(images_pred, images, mask=masks_crop),
     }

pytorch3d/implicitron/models/renderer/lstm_renderer.py

@@ -135,6 +135,7 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
                 break
 
             # run the lstm marcher
+            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             state_h, state_c = self._lstm(
                 raymarch_features.view(-1, raymarch_features.shape[-1]),
                 states[-1],
@@ -142,6 +143,7 @@ class LSTMRenderer(BaseRenderer, torch.nn.Module):
             if state_h.requires_grad:
                 state_h.register_hook(lambda x: x.clamp(min=-10, max=10))
             # predict the next step size
+            # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
             signed_distance = self._out_layer(state_h).view(ray_bundle_t.lengths.shape)
             # log the lstm states
             states.append((state_h, state_c))

pytorch3d/implicitron/models/renderer/ray_sampler.py

@@ -207,6 +207,7 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
         """
         sample_mask = None
         if (
+            # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[An...
             self._sampling_mode[evaluation_mode] == RenderSamplingMode.MASK_SAMPLE
             and mask is not None
         ):
@@ -223,6 +224,7 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
             EvaluationMode.EVALUATION: self._evaluation_raysampler,
         }[evaluation_mode]
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         ray_bundle = raysampler(
             cameras=cameras,
             mask=sample_mask,
@@ -240,6 +242,8 @@ class AbstractMaskRaySampler(RaySamplerBase, torch.nn.Module):
                 "Heterogeneous ray bundle is not supported for conical frustum computation yet"
             )
         elif self.cast_ray_bundle_as_cone:
+            # pyre-fixme[9]: pixel_hw has type `Tuple[float, float]`; used as
+            #  `Tuple[Union[Tensor, Module], Union[Tensor, Module]]`.
             pixel_hw: Tuple[float, float] = (self.pixel_height, self.pixel_width)
             pixel_radii_2d = compute_radii(cameras, ray_bundle.xys[..., :2], pixel_hw)
             return ImplicitronRayBundle(

pytorch3d/implicitron/models/renderer/raymarcher.py

@@ -179,8 +179,10 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
             rays_densities = torch.relu(rays_densities)
 
         weighted_densities = deltas * rays_densities
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         capped_densities = self._capping_function(weighted_densities)
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         rays_opacities = self._capping_function(
             torch.cumsum(weighted_densities, dim=-1)
         )
@@ -190,6 +192,7 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
         )
         absorption_shifted[..., : self.surface_thickness] = 1.0
 
+        # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
         weights = self._weight_function(capped_densities, absorption_shifted)
         features = (weights[..., None] * rays_features).sum(dim=-2)
         depth = (weights * ray_lengths)[..., None].sum(dim=-2)
@@ -197,6 +200,8 @@ class AccumulativeRaymarcherBase(RaymarcherBase, torch.nn.Module):
         alpha = opacities if self.blend_output else 1
         if self._bg_color.shape[-1] not in [1, features.shape[-1]]:
             raise ValueError("Wrong number of background color channels.")
+        # pyre-fixme[58]: `*` is not supported for operand types `int` and
+        #  `Union[Tensor, Module]`.
         features = alpha * features + (1 - opacities) * self._bg_color
 
         return RendererOutput(

pytorch3d/implicitron/models/renderer/sdf_renderer.py

@@ -61,6 +61,7 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
 
     def create_ray_tracer(self) -> None:
         self.ray_tracer = RayTracing(
+            # pyre-fixme[32]: Keyword argument must be a mapping with string keys.
             **self.ray_tracer_args,
             object_bounding_sphere=self.object_bounding_sphere,
         )
@@ -149,6 +150,8 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
                 n_eik_points,
                 3,
                 #  but got `Union[device, Tensor, Module]`.
+                # pyre-fixme[6]: For 3rd argument expected `Union[None, int, str,
+                #  device]` but got `Union[device, Tensor, Module]`.
                 device=self._bg_color.device,
             ).uniform_(-eik_bounding_box, eik_bounding_box)
             eikonal_pixel_points = points.clone()
@@ -205,6 +208,7 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
             ]
             normals_full.view(-1, 3)[surface_mask] = normals
             render_full.view(-1, self.render_features_dimensions)[surface_mask] = (
+                # pyre-fixme[29]: `Union[Tensor, Module]` is not a function.
                 self._rgb_network(
                     features,
                     differentiable_surface_points[None],
@@ -216,8 +220,7 @@ class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
             )
             mask_full.view(-1, 1)[~surface_mask] = torch.sigmoid(
                 # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
-                -self.soft_mask_alpha
+                -self.soft_mask_alpha * sdf_output[~surface_mask]
-                * sdf_output[~surface_mask]
             )
 
         # scatter points with surface_mask

pytorch3d/implicitron/models/view_pooler/feature_aggregator.py

@@ -532,6 +532,7 @@ def _get_ray_dir_dot_prods(camera: CamerasBase, pts: torch.Tensor):
 
     # does not produce nans randomly unlike get_camera_center() below
     cam_centers_rep = -torch.bmm(
+        # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, A...
         camera_rep.T[:, None],
         camera_rep.R.permute(0, 2, 1),
     ).reshape(-1, *([1] * (pts.ndim - 2)), 3)

pytorch3d/implicitron/models/view_pooler/view_sampler.py

@@ -209,6 +209,7 @@ def handle_seq_id(
         seq_id = torch.tensor(seq_id, dtype=torch.long, device=device)
     # pyre-fixme[16]: Item `List` of `Union[List[int], List[str], LongTensor]` has
     #  no attribute `to`.
+    # pyre-fixme[7]: Expected `LongTensor` but got `Tensor`.
     return seq_id.to(device)
 
 

pytorch3d/implicitron/tools/depth_cleanup.py

@@ -21,7 +21,6 @@ def cleanup_eval_depth(
     sigma: float = 0.01,
     image=None,
 ):
-
     ba, _, H, W = depth.shape
 
     pcl = point_cloud.points_padded()

pytorch3d/implicitron/tools/metric_utils.py

@@ -6,12 +6,15 @@
 
 # pyre-unsafe
 
+import logging
 import math
 from typing import Optional, Tuple
 
 import torch
 from torch.nn import functional as F
 
+logger = logging.getLogger(__name__)
+
 
 def eval_depth(
     pred: torch.Tensor,
@@ -21,6 +24,8 @@ def eval_depth(
     get_best_scale: bool = True,
     mask_thr: float = 0.5,
     best_scale_clamp_thr: float = 1e-4,
+    use_disparity: bool = False,
+    disparity_eps: float = 1e-4,
 ) -> Tuple[torch.Tensor, torch.Tensor]:
     """
     Evaluate the depth error between the prediction `pred` and the ground
@@ -64,6 +69,13 @@ def eval_depth(
         # 	s.t. we get best possible mse error
         scale_best = estimate_depth_scale_factor(pred, gt, dmask, best_scale_clamp_thr)
         pred = pred * scale_best[:, None, None, None]
+    if use_disparity:
+        gt = torch.div(1.0, (gt + disparity_eps))
+        pred = torch.div(1.0, (pred + disparity_eps))
+        scale_best = estimate_depth_scale_factor(
+            pred, gt, dmask, best_scale_clamp_thr
+        ).detach()
+        pred = pred * scale_best[:, None, None, None]
 
     df = gt - pred
 
@@ -117,6 +129,7 @@ def calc_bce(
     pred_eps: float = 0.01,
     mask: Optional[torch.Tensor] = None,
     lerp_bound: Optional[float] = None,
+    pred_logits: bool = False,
 ) -> torch.Tensor:
     """
     Calculates the binary cross entropy.
@@ -139,9 +152,23 @@ def calc_bce(
         weight = torch.ones_like(gt) * mask
 
     if lerp_bound is not None:
+        # binary_cross_entropy_lerp requires pred to be in [0, 1]
+        if pred_logits:
+            pred = F.sigmoid(pred)
+
         return binary_cross_entropy_lerp(pred, gt, weight, lerp_bound)
     else:
-        return F.binary_cross_entropy(pred, gt, reduction="mean", weight=weight)
+        if pred_logits:
+            loss = F.binary_cross_entropy_with_logits(
+                pred,
+                gt,
+                reduction="none",
+                weight=weight,
+            )
+        else:
+            loss = F.binary_cross_entropy(pred, gt, reduction="none", weight=weight)
+
+        return loss.mean()
 
 
 def binary_cross_entropy_lerp(

pytorch3d/implicitron/tools/model_io.py

@@ -111,10 +111,10 @@ def load_model(fl, map_location: Optional[dict]):
     flstats = get_stats_path(fl)
     flmodel = get_model_path(fl)
     flopt = get_optimizer_path(fl)
-    model_state_dict = torch.load(flmodel, map_location=map_location)
+    model_state_dict = torch.load(flmodel, map_location=map_location, weights_only=True)
     stats = load_stats(flstats)
     if os.path.isfile(flopt):
-        optimizer = torch.load(flopt, map_location=map_location)
+        optimizer = torch.load(flopt, map_location=map_location, weights_only=True)
     else:
         optimizer = None
 

pytorch3d/implicitron/tools/point_cloud_utils.py

@@ -100,7 +100,6 @@ def render_point_cloud_pytorch3d(
     bin_size: Optional[int] = None,
     **kwargs,
 ):
-
     # feature dimension
     featdim = point_cloud.features_packed().shape[-1]
 

pytorch3d/implicitron/tools/stats.py

@@ -37,7 +37,6 @@ class AverageMeter:
         self.count = 0
 
     def update(self, val, n=1, epoch=0):
-
         # make sure the history is of the same len as epoch
         while len(self.history) <= epoch:
             self.history.append([])
@@ -115,7 +114,6 @@ class Stats:
         visdom_server="http://localhost",
         visdom_port=8097,
     ):
-
         self.log_vars = log_vars
         self.visdom_env = visdom_env
         self.visdom_server = visdom_server
@@ -202,7 +200,6 @@ class Stats:
                 self.log_vars.append(add_log_var)
 
     def update(self, preds, time_start=None, freeze_iter=False, stat_set="train"):
-
         if self.epoch == -1:  # uninitialized
             logger.warning(
                 "epoch==-1 means uninitialized stats structure -> new_epoch() called"
@@ -219,7 +216,6 @@ class Stats:
         epoch = self.epoch
 
         for stat in self.log_vars:
-
             if stat not in self.stats[stat_set]:
                 self.stats[stat_set][stat] = AverageMeter()
 
@@ -248,7 +244,6 @@ class Stats:
                 self.stats[stat_set][stat].update(val, epoch=epoch, n=1)
 
     def get_epoch_averages(self, epoch=None):
-
         stat_sets = list(self.stats.keys())
 
         if epoch is None:
@@ -345,7 +340,6 @@ class Stats:
     def plot_stats(
         self, visdom_env=None, plot_file=None, visdom_server=None, visdom_port=None
     ):
-
         # use the cached visdom env if none supplied
         if visdom_env is None:
             visdom_env = self.visdom_env
@@ -449,7 +443,6 @@ class Stats:
                 warnings.warn("Cant dump stats due to insufficient permissions!")
 
     def synchronize_logged_vars(self, log_vars, default_val=float("NaN")):
-
         stat_sets = list(self.stats.keys())
 
         # remove the additional log_vars
@@ -490,11 +483,12 @@ class Stats:
                 for ep in range(lastep):
                     self.stats[stat_set][stat].update(default_val, n=1, epoch=ep)
                 epoch_generated = self.stats[stat_set][stat].get_epoch()
-                assert (
+                assert epoch_generated == self.epoch + 1, (
-                    epoch_generated == self.epoch + 1
+                    "bad epoch of synchronized log_var! %d vs %d"
-                ), "bad epoch of synchronized log_var! %d vs %d" % (
+                    % (
-                    self.epoch + 1,
+                        self.epoch + 1,
-                    epoch_generated,
+                        epoch_generated,
+                    )
                 )
 
 

pytorch3d/implicitron/tools/video_writer.py

@@ -16,8 +16,17 @@ from typing import Optional, Tuple, Union
 import matplotlib
 import matplotlib.pyplot as plt
 import numpy as np
+import torch
+
 from PIL import Image
 
+_NO_TORCHVISION = False
+try:
+    import torchvision
+except ImportError:
+    _NO_TORCHVISION = True
+
+
 _DEFAULT_FFMPEG = os.environ.get("FFMPEG", "ffmpeg")
 
 matplotlib.use("Agg")
@@ -36,6 +45,7 @@ class VideoWriter:
         fps: int = 20,
         output_format: str = "visdom",
         rmdir_allowed: bool = False,
+        use_torchvision_video_writer: bool = False,
         **kwargs,
     ) -> None:
         """
@@ -49,6 +59,8 @@ class VideoWriter:
                 is supported.
             rmdir_allowed: If `True` delete and create `cache_dir` in case
                 it is not empty.
+            use_torchvision_video_writer: If `True` use `torchvision.io.write_video`
+            to write the video
         """
         self.rmdir_allowed = rmdir_allowed
         self.output_format = output_format
@@ -56,10 +68,14 @@ class VideoWriter:
         self.out_path = out_path
         self.cache_dir = cache_dir
         self.ffmpeg_bin = ffmpeg_bin
+        self.use_torchvision_video_writer = use_torchvision_video_writer
         self.frames = []
         self.regexp = "frame_%08d.png"
         self.frame_num = 0
 
+        if self.use_torchvision_video_writer:
+            assert not _NO_TORCHVISION, "torchvision not available"
+
         if self.cache_dir is not None:
             self.tmp_dir = None
             if os.path.isdir(self.cache_dir):
@@ -114,7 +130,7 @@ class VideoWriter:
                 resize = im.size
             # make sure size is divisible by 2
             resize = tuple([resize[i] + resize[i] % 2 for i in (0, 1)])
-            # pyre-fixme[16]: Module `Image` has no attribute `ANTIALIAS`.
+
             im = im.resize(resize, Image.ANTIALIAS)
             im.save(outfile)
 
@@ -139,38 +155,56 @@ class VideoWriter:
         #  got `Optional[str]`.
         regexp = os.path.join(self.cache_dir, self.regexp)
 
-        if shutil.which(self.ffmpeg_bin) is None:
-            raise ValueError(
-                f"Cannot find ffmpeg as `{self.ffmpeg_bin}`. "
-                + "Please set FFMPEG in the environment or ffmpeg_bin on this class."
-            )
-
         if self.output_format == "visdom":  # works for ppt too
-            args = [
+            # Video codec parameters
-                self.ffmpeg_bin,
+            video_codec = "h264"
-                "-r",
+            crf = "18"
-                str(self.fps),
+            b = "2000k"
-                "-i",
+            pix_fmt = "yuv420p"
-                regexp,
+
-                "-vcodec",
+            if self.use_torchvision_video_writer:
-                "h264",
+                torchvision.io.write_video(
-                "-f",
+                    self.out_path,
-                "mp4",
+                    torch.stack(
-                "-y",
+                        [torch.from_numpy(np.array(Image.open(f))) for f in self.frames]
-                "-crf",
+                    ),
-                "18",
+                    fps=self.fps,
-                "-b",
+                    video_codec=video_codec,
-                "2000k",
+                    options={"crf": crf, "b": b, "pix_fmt": pix_fmt},
-                "-pix_fmt",
-                "yuv420p",
-                self.out_path,
-            ]
-            if quiet:
-                subprocess.check_call(
-                    args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
                 )
+
             else:
-                subprocess.check_call(args)
+                if shutil.which(self.ffmpeg_bin) is None:
+                    raise ValueError(
+                        f"Cannot find ffmpeg as `{self.ffmpeg_bin}`. "
+                        + "Please set FFMPEG in the environment or ffmpeg_bin on this class."
+                    )
+
+                args = [
+                    self.ffmpeg_bin,
+                    "-r",
+                    str(self.fps),
+                    "-i",
+                    regexp,
+                    "-vcodec",
+                    video_codec,
+                    "-f",
+                    "mp4",
+                    "-y",
+                    "-crf",
+                    crf,
+                    "-b",
+                    b,
+                    "-pix_fmt",
+                    pix_fmt,
+                    self.out_path,
+                ]
+                if quiet:
+                    subprocess.check_call(
+                        args, stdout=subprocess.DEVNULL, stderr=subprocess.DEVNULL
+                    )
+                else:
+                    subprocess.check_call(args)
         else:
             raise ValueError("no such output type %s" % str(self.output_format))
 

pytorch3d/io/experimental_gltf_io.py

@@ -163,6 +163,8 @@ def _read_chunks(
     if binary_data is not None:
         binary_data = np.frombuffer(binary_data, dtype=np.uint8)
 
+    assert binary_data is not None
+
     return json_data, binary_data
 
 

pytorch3d/io/mtl_io.py

@@ -7,6 +7,7 @@
 # pyre-unsafe
 
 """This module implements utility functions for loading .mtl files and textures."""
+
 import os
 import warnings
 from typing import Dict, List, Optional, Tuple

pytorch3d/io/obj_io.py

@@ -8,6 +8,7 @@
 
 
 """This module implements utility functions for loading and saving meshes."""
+
 import os
 import warnings
 from collections import namedtuple
@@ -813,7 +814,6 @@ def _save(
     save_texture: bool = False,
     save_normals: bool = False,
 ) -> None:
-
     if len(verts) and (verts.dim() != 2 or verts.size(1) != 3):
         message = "'verts' should either be empty or of shape (num_verts, 3)."
         raise ValueError(message)

pytorch3d/io/off_io.py

@@ -14,6 +14,7 @@ meshes as .off files.
 This format is introduced, for example, at
 http://www.geomview.org/docs/html/OFF.html .
 """
+
 import warnings
 from typing import cast, Optional, Tuple, Union
 
@@ -84,7 +85,7 @@ def _read_faces_lump(
             )
             data = np.loadtxt(file, dtype=np.float32, ndmin=2, max_rows=n_faces)
     except ValueError as e:
-        if n_faces > 1 and "Wrong number of columns" in e.args[0]:
+        if n_faces > 1 and "number of columns" in e.args[0]:
             file.seek(old_offset)
             return None
         raise ValueError("Not enough face data.") from None

pytorch3d/io/ply_io.py

@@ -11,6 +11,7 @@
 This module implements utility functions for loading and saving
 meshes and point clouds as PLY files.
 """
+
 import itertools
 import os
 import struct
@@ -1246,7 +1247,7 @@ def _save_ply(
         return
 
     color_np_type = np.ubyte if colors_as_uint8 else np.float32
-    verts_dtype = [("verts", np.float32, 3)]
+    verts_dtype: list = [("verts", np.float32, 3)]
     if verts_normals is not None:
         verts_dtype.append(("normals", np.float32, 3))
     if verts_colors is not None:

pytorch3d/ops/cameras_alignment.py

@@ -122,12 +122,17 @@ def corresponding_cameras_alignment(
 
     # create a new cameras object and set the R and T accordingly
     cameras_src_aligned = cameras_src.clone()
+    # pyre-fixme[6]: For 2nd argument expected `Tensor` but got `Union[Tensor, Module]`.
     cameras_src_aligned.R = torch.bmm(align_t_R.expand_as(cameras_src.R), cameras_src.R)
     cameras_src_aligned.T = (
         torch.bmm(
             align_t_T[:, None].repeat(cameras_src.R.shape[0], 1, 1),
+            # pyre-fixme[6]: For 2nd argument expected `Tensor` but got
+            #  `Union[Tensor, Module]`.
             cameras_src.R,
         )[:, 0]
+        # pyre-fixme[29]: `Union[(self: TensorBase, other: Union[bool, complex,
+        #  float, int, Tensor]) -> Tensor, Tensor, Module]` is not a function.
         + cameras_src.T * align_t_s
     )
 
@@ -175,6 +180,7 @@ def _align_camera_extrinsics(
         R_A = (U V^T)^T
         ```
     """
+    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor, Module]`.
     RRcov = torch.bmm(cameras_src.R, cameras_tgt.R.transpose(2, 1)).mean(0)
     U, _, V = torch.svd(RRcov)
     align_t_R = V @ U.t()
@@ -204,7 +210,11 @@ def _align_camera_extrinsics(
         T_A = mean(B) - mean(A) * s_A
         ```
     """
+    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor, Module]`.
+    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, Any, ...
     A = torch.bmm(cameras_src.R, cameras_src.T[:, :, None])[:, :, 0]
+    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `Union[Tensor, Module]`.
+    # pyre-fixme[29]: `Union[(self: TensorBase, indices: Union[None, slice[Any, Any, ...
     B = torch.bmm(cameras_src.R, cameras_tgt.T[:, :, None])[:, :, 0]
     Amu = A.mean(0, keepdim=True)
     Bmu = B.mean(0, keepdim=True)

pytorch3d/ops/cubify.py

@@ -62,7 +62,7 @@ def cubify(
     *,
     feats: Optional[torch.Tensor] = None,
     device=None,
-    align: str = "topleft"
+    align: str = "topleft",
 ) -> Meshes:
     r"""
     Converts a voxel to a mesh by replacing each occupied voxel with a cube