update version to 0.7.8

Summary: as title Reviewed By: das-intensity Differential Revision: D62588556 fbshipit-source-id: 55bae19dd1df796e83179cd29d805fcd871b6d23
remove pytorch2.0 builds
2025-12-21 23:00:34 +08:00 · 2024-09-13 02:31:49 -07:00 · 2024-09-13 02:07:25 -07:00 · 2024-09-13 02:00:49 -07:00 · 2024-09-11 15:09:43 -07:00 · 2024-08-15 16:18:22 -07:00
64 changed files with 439 additions and 341 deletions
--- a/.circleci/config.yml
+++ b/.circleci/config.yml
@@ -162,90 +162,6 @@ workflows:
    jobs:
      # - main:
      #     context: DOCKERHUB_TOKEN
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda113
-          context: DOCKERHUB_TOKEN
-          cu_version: cu113
-          name: linux_conda_py38_cu113_pyt1120
-          python_version: '3.8'
-          pytorch_version: 1.12.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py38_cu116_pyt1120
-          python_version: '3.8'
-          pytorch_version: 1.12.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda113
-          context: DOCKERHUB_TOKEN
-          cu_version: cu113
-          name: linux_conda_py38_cu113_pyt1121
-          python_version: '3.8'
-          pytorch_version: 1.12.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py38_cu116_pyt1121
-          python_version: '3.8'
-          pytorch_version: 1.12.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py38_cu116_pyt1130
-          python_version: '3.8'
-          pytorch_version: 1.13.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py38_cu117_pyt1130
-          python_version: '3.8'
-          pytorch_version: 1.13.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py38_cu116_pyt1131
-          python_version: '3.8'
-          pytorch_version: 1.13.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py38_cu117_pyt1131
-          python_version: '3.8'
-          pytorch_version: 1.13.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py38_cu117_pyt200
-          python_version: '3.8'
-          pytorch_version: 2.0.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda118
-          context: DOCKERHUB_TOKEN
-          cu_version: cu118
-          name: linux_conda_py38_cu118_pyt200
-          python_version: '3.8'
-          pytorch_version: 2.0.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py38_cu117_pyt201
-          python_version: '3.8'
-          pytorch_version: 2.0.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda118
-          context: DOCKERHUB_TOKEN
-          cu_version: cu118
-          name: linux_conda_py38_cu118_pyt201
-          python_version: '3.8'
-          pytorch_version: 2.0.1
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
@@ -331,89 +247,33 @@ workflows:
          python_version: '3.8'
          pytorch_version: 2.3.1
      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda113
+          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
-          cu_version: cu113
-          name: linux_conda_py39_cu113_pyt1120
-          python_version: '3.9'
-          pytorch_version: 1.12.0
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt240
+          python_version: '3.8'
+          pytorch_version: 2.4.0
      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
+          conda_docker_image: pytorch/conda-builder:cuda121
          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py39_cu116_pyt1120
-          python_version: '3.9'
-          pytorch_version: 1.12.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda113
-          context: DOCKERHUB_TOKEN
-          cu_version: cu113
-          name: linux_conda_py39_cu113_pyt1121
-          python_version: '3.9'
-          pytorch_version: 1.12.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py39_cu116_pyt1121
-          python_version: '3.9'
-          pytorch_version: 1.12.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py39_cu116_pyt1130
-          python_version: '3.9'
-          pytorch_version: 1.13.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py39_cu117_pyt1130
-          python_version: '3.9'
-          pytorch_version: 1.13.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py39_cu116_pyt1131
-          python_version: '3.9'
-          pytorch_version: 1.13.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py39_cu117_pyt1131
-          python_version: '3.9'
-          pytorch_version: 1.13.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py39_cu117_pyt200
-          python_version: '3.9'
-          pytorch_version: 2.0.0
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt240
+          python_version: '3.8'
+          pytorch_version: 2.4.0
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
          cu_version: cu118
-          name: linux_conda_py39_cu118_pyt200
-          python_version: '3.9'
-          pytorch_version: 2.0.0
+          name: linux_conda_py38_cu118_pyt241
+          python_version: '3.8'
+          pytorch_version: 2.4.1
      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
+          conda_docker_image: pytorch/conda-builder:cuda121
          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py39_cu117_pyt201
-          python_version: '3.9'
-          pytorch_version: 2.0.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda118
-          context: DOCKERHUB_TOKEN
-          cu_version: cu118
-          name: linux_conda_py39_cu118_pyt201
-          python_version: '3.9'
-          pytorch_version: 2.0.1
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt241
+          python_version: '3.8'
+          pytorch_version: 2.4.1
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
@@ -499,89 +359,33 @@ workflows:
          python_version: '3.9'
          pytorch_version: 2.3.1
      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda113
+          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
-          cu_version: cu113
-          name: linux_conda_py310_cu113_pyt1120
-          python_version: '3.10'
-          pytorch_version: 1.12.0
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt240
+          python_version: '3.9'
+          pytorch_version: 2.4.0
      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
+          conda_docker_image: pytorch/conda-builder:cuda121
          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py310_cu116_pyt1120
-          python_version: '3.10'
-          pytorch_version: 1.12.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda113
-          context: DOCKERHUB_TOKEN
-          cu_version: cu113
-          name: linux_conda_py310_cu113_pyt1121
-          python_version: '3.10'
-          pytorch_version: 1.12.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py310_cu116_pyt1121
-          python_version: '3.10'
-          pytorch_version: 1.12.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py310_cu116_pyt1130
-          python_version: '3.10'
-          pytorch_version: 1.13.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py310_cu117_pyt1130
-          python_version: '3.10'
-          pytorch_version: 1.13.0
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda116
-          context: DOCKERHUB_TOKEN
-          cu_version: cu116
-          name: linux_conda_py310_cu116_pyt1131
-          python_version: '3.10'
-          pytorch_version: 1.13.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py310_cu117_pyt1131
-          python_version: '3.10'
-          pytorch_version: 1.13.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
-          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py310_cu117_pyt200
-          python_version: '3.10'
-          pytorch_version: 2.0.0
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt240
+          python_version: '3.9'
+          pytorch_version: 2.4.0
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
          cu_version: cu118
-          name: linux_conda_py310_cu118_pyt200
-          python_version: '3.10'
-          pytorch_version: 2.0.0
+          name: linux_conda_py39_cu118_pyt241
+          python_version: '3.9'
+          pytorch_version: 2.4.1
      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda117
+          conda_docker_image: pytorch/conda-builder:cuda121
          context: DOCKERHUB_TOKEN
-          cu_version: cu117
-          name: linux_conda_py310_cu117_pyt201
-          python_version: '3.10'
-          pytorch_version: 2.0.1
-      - binary_linux_conda:
-          conda_docker_image: pytorch/conda-builder:cuda118
-          context: DOCKERHUB_TOKEN
-          cu_version: cu118
-          name: linux_conda_py310_cu118_pyt201
-          python_version: '3.10'
-          pytorch_version: 2.0.1
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt241
+          python_version: '3.9'
+          pytorch_version: 2.4.1
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
@@ -666,6 +470,34 @@ workflows:
          name: linux_conda_py310_cu121_pyt231
          python_version: '3.10'
          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt240
+          python_version: '3.10'
+          pytorch_version: 2.4.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt240
+          python_version: '3.10'
+          pytorch_version: 2.4.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt241
+          python_version: '3.10'
+          pytorch_version: 2.4.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt241
+          python_version: '3.10'
+          pytorch_version: 2.4.1
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
@@ -750,6 +582,34 @@ workflows:
          name: linux_conda_py311_cu121_pyt231
          python_version: '3.11'
          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt240
+          python_version: '3.11'
+          pytorch_version: 2.4.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt240
+          python_version: '3.11'
+          pytorch_version: 2.4.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt241
+          python_version: '3.11'
+          pytorch_version: 2.4.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt241
+          python_version: '3.11'
+          pytorch_version: 2.4.1
      - binary_linux_conda:
          conda_docker_image: pytorch/conda-builder:cuda118
          context: DOCKERHUB_TOKEN
@@ -792,6 +652,34 @@ workflows:
          name: linux_conda_py312_cu121_pyt231
          python_version: '3.12'
          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py312_cu118_pyt240
+          python_version: '3.12'
+          pytorch_version: 2.4.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py312_cu121_pyt240
+          python_version: '3.12'
+          pytorch_version: 2.4.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py312_cu118_pyt241
+          python_version: '3.12'
+          pytorch_version: 2.4.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py312_cu121_pyt241
+          python_version: '3.12'
+          pytorch_version: 2.4.1
      - binary_linux_conda_cuda:
          name: testrun_conda_cuda_py310_cu117_pyt201
          context: DOCKERHUB_TOKEN
--- a/.circleci/regenerate.py
+++ b/.circleci/regenerate.py
@@ -19,18 +19,14 @@ from packaging import version
 # The CUDA versions which have pytorch conda packages available for linux for each
 # version of pytorch.
 CONDA_CUDA_VERSIONS = {
-    "1.12.0": ["cu113", "cu116"],
-    "1.12.1": ["cu113", "cu116"],
-    "1.13.0": ["cu116", "cu117"],
-    "1.13.1": ["cu116", "cu117"],
-    "2.0.0": ["cu117", "cu118"],
-    "2.0.1": ["cu117", "cu118"],
    "2.1.0": ["cu118", "cu121"],
    "2.1.1": ["cu118", "cu121"],
    "2.1.2": ["cu118", "cu121"],
    "2.2.0": ["cu118", "cu121"],
    "2.2.2": ["cu118", "cu121"],
    "2.3.1": ["cu118", "cu121"],
+    "2.4.0": ["cu118", "cu121"],
+    "2.4.1": ["cu118", "cu121"],
 }


--- a/INSTALL.md
+++ b/INSTALL.md
@@ -8,11 +8,10 @@
 The core library is written in PyTorch. Several components have underlying implementation in CUDA for improved performance. A subset of these components have CPU implementations in C++/PyTorch. It is advised to use PyTorch3D with GPU support in order to use all the features.

 - Linux or macOS or Windows
- Python 3.8, 3.9 or 3.10
- PyTorch 1.12.0, 1.12.1, 1.13.0, 2.0.0, 2.0.1, 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.2.2, 2.3.0 or 2.3.1.
+- Python
+- PyTorch 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.4.0 or 2.4.1.
 - torchvision that matches the PyTorch installation. You can install them together as explained at pytorch.org to make sure of this.
 - gcc & g++ ≥ 4.9
- [fvcore](https://github.com/facebookresearch/fvcore)
 - [ioPath](https://github.com/facebookresearch/iopath)
 - If CUDA is to be used, use a version which is supported by the corresponding pytorch version and at least version 9.2.
 - If CUDA older than 11.7 is to be used and you are building from source, the CUB library must be available. We recommend version 1.10.0.
@@ -22,7 +21,7 @@ The runtime dependencies can be installed by running:
 conda create -n pytorch3d python=3.9
 conda activate pytorch3d
 conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
-conda install -c fvcore -c iopath -c conda-forge fvcore iopath
+conda install -c iopath iopath
 ```

 For the CUB build time dependency, which you only need if you have CUDA older than 11.7, if you are using conda, you can continue with
@@ -49,6 +48,7 @@ For developing on top of PyTorch3D or contributing, you will need to run the lin
 - tdqm
 - jupyter
 - imageio
+- fvcore
 - plotly
 - opencv-python

@@ -59,6 +59,7 @@ conda install jupyter
 pip install scikit-image matplotlib imageio plotly opencv-python

 # Tests/Linting
+conda install -c fvcore -c conda-forge fvcore
 pip install black usort flake8 flake8-bugbear flake8-comprehensions
 ```

@@ -97,7 +98,7 @@ version_str="".join([
    torch.version.cuda.replace(".",""),
    f"_pyt{pyt_version_str}"
 ])
-!pip install fvcore iopath
+!pip install iopath
 !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
 ```

--- a/dev/run_tutorials.sh
+++ b/dev/run_tutorials.sh
@@ -23,7 +23,7 @@ conda init bash
 source ~/.bashrc
 conda create -y -n myenv python=3.8 matplotlib ipython ipywidgets nbconvert
 conda activate myenv
-conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath
+conda install -y -c iopath iopath
 conda install -y -c pytorch pytorch=1.6.0 cudatoolkit=10.1 torchvision
 conda install -y -c pytorch3d-nightly pytorch3d
 pip install plotly scikit-image
--- a/docs/requirements.txt
+++ b/docs/requirements.txt
@@ -5,7 +5,6 @@ sphinx_rtd_theme
 sphinx_markdown_tables
 numpy
 iopath
-fvcore
 https://download.pytorch.org/whl/cpu/torchvision-0.15.2%2Bcpu-cp311-cp311-linux_x86_64.whl
 https://download.pytorch.org/whl/cpu/torch-2.0.1%2Bcpu-cp311-cp311-linux_x86_64.whl
 omegaconf
--- a/docs/tutorials/bundle_adjustment.ipynb
+++ b/docs/tutorials/bundle_adjustment.ipynb
@@ -96,7 +96,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb
+++ b/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb
@@ -83,7 +83,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/dataloaders_ShapeNetCore_R2N2.ipynb
+++ b/docs/tutorials/dataloaders_ShapeNetCore_R2N2.ipynb
@@ -58,7 +58,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb
+++ b/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb
@@ -97,7 +97,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/fit_simple_neural_radiance_field.ipynb
+++ b/docs/tutorials/fit_simple_neural_radiance_field.ipynb
@@ -63,7 +63,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/fit_textured_mesh.ipynb
+++ b/docs/tutorials/fit_textured_mesh.ipynb
@@ -75,7 +75,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/fit_textured_volume.ipynb
+++ b/docs/tutorials/fit_textured_volume.ipynb
@@ -54,7 +54,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/implicitron_config_system.ipynb
+++ b/docs/tutorials/implicitron_config_system.ipynb
@@ -85,7 +85,7 @@
        "        torch.version.cuda.replace(\".\",\"\"),\n",
        "        f\"_pyt{pyt_version_str}\"\n",
        "    ])\n",
-        "    !pip install fvcore iopath\n",
+        "    !pip install iopath\n",
        "    if sys.platform.startswith(\"linux\"):\n",
        "        print(\"Trying to install wheel for PyTorch3D\")\n",
        "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/implicitron_volumes.ipynb
+++ b/docs/tutorials/implicitron_volumes.ipynb
@@ -79,7 +79,7 @@
        "        torch.version.cuda.replace(\".\",\"\"),\n",
        "        f\"_pyt{pyt_version_str}\"\n",
        "    ])\n",
-        "    !pip install fvcore iopath\n",
+        "    !pip install iopath\n",
        "    if sys.platform.startswith(\"linux\"):\n",
        "        print(\"Trying to install wheel for PyTorch3D\")\n",
        "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/render_colored_points.ipynb
+++ b/docs/tutorials/render_colored_points.ipynb
@@ -57,7 +57,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/render_densepose.ipynb
+++ b/docs/tutorials/render_densepose.ipynb
@@ -64,7 +64,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/docs/tutorials/render_textured_meshes.ipynb
+++ b/docs/tutorials/render_textured_meshes.ipynb
@@ -80,7 +80,7 @@
    "        torch.version.cuda.replace(\".\",\"\"),\n",
    "        f\"_pyt{pyt_version_str}\"\n",
    "    ])\n",
-    "    !pip install fvcore iopath\n",
+    "    !pip install iopath\n",
    "    if sys.platform.startswith(\"linux\"):\n",
    "        print(\"Trying to install wheel for PyTorch3D\")\n",
    "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
--- a/packaging/build_conda.py
+++ b/packaging/build_conda.py
@@ -80,6 +80,12 @@ def setup_cuda():
 def setup_conda_pytorch_constraint() -> List[str]:
    pytorch_constraint = f"- pytorch=={PYTORCH_VERSION}"
    os.environ["CONDA_PYTORCH_CONSTRAINT"] = pytorch_constraint
+    if pytorch_major_minor < (2, 2):
+        os.environ["CONDA_PYTORCH_MKL_CONSTRAINT"] = "- mkl!=2024.1.0"
+        os.environ["SETUPTOOLS_CONSTRAINT"] = "- setuptools<70"
+    else:
+        os.environ["CONDA_PYTORCH_MKL_CONSTRAINT"] = ""
+        os.environ["SETUPTOOLS_CONSTRAINT"] = "- setuptools"
    os.environ["CONDA_PYTORCH_BUILD_CONSTRAINT"] = pytorch_constraint
    os.environ["PYTORCH_VERSION_NODOT"] = PYTORCH_VERSION.replace(".", "")

@@ -117,7 +123,7 @@ def do_build(start_args: List[str]):
    if test_flag is not None:
        args.append(test_flag)

-    args.extend(["-c", "bottler", "-c", "fvcore", "-c", "iopath", "-c", "conda-forge"])
+    args.extend(["-c", "bottler", "-c", "iopath", "-c", "conda-forge"])
    args.append("--no-anaconda-upload")
    args.extend(["--python", os.environ["PYTHON_VERSION"]])
    args.append("packaging/pytorch3d")
--- a/packaging/linux_wheels/README.md
+++ b/packaging/linux_wheels/README.md
@@ -26,6 +26,6 @@ version_str="".join([
    torch.version.cuda.replace(".",""),
    f"_pyt{pyt_version_str}"
 ])
-!pip install fvcore iopath
+!pip install iopath
 !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
 ```
--- a/packaging/linux_wheels/inside.sh
+++ b/packaging/linux_wheels/inside.sh
@@ -144,7 +144,7 @@ do
            conda activate "$tag"
            # shellcheck disable=SC2086
            conda install -y -c pytorch $extra_channel "pytorch=$pytorch_version" "$cudatools=$CUDA_TAG"
-            pip install fvcore iopath
+            pip install iopath
            echo "python version" "$python_version" "pytorch version" "$pytorch_version" "cuda version" "$cu_version" "tag" "$tag"

            rm -rf dist
--- a/packaging/pytorch3d/meta.yaml
+++ b/packaging/pytorch3d/meta.yaml
@@ -12,8 +12,9 @@ requirements:

  host:
    - python
-    - setuptools
+    {{ environ.get('SETUPTOOLS_CONSTRAINT') }}
    {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }}
+    {{ environ.get('CONDA_PYTORCH_MKL_CONSTRAINT') }}
    {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
    {{ environ.get('CONDA_CPUONLY_FEATURE') }}

@@ -21,7 +22,6 @@ requirements:
    - python
    - numpy >=1.11
    - torchvision >=0.5
-    - fvcore
    - iopath
    {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }}
    {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
--- a/projects/implicitron_trainer/experiment.py
+++ b/projects/implicitron_trainer/experiment.py
@@ -99,7 +99,7 @@ except ModuleNotFoundError:
 no_accelerate = os.environ.get("PYTORCH3D_NO_ACCELERATE") is not None


-class Experiment(Configurable):  # pyre-ignore: 13
+class Experiment(Configurable):
    """
    This class is at the top level of Implicitron's config hierarchy. Its
    members are high-level components necessary for training an implicit rende-
@@ -120,12 +120,16 @@ class Experiment(Configurable):  # pyre-ignore: 13
            will be saved here.
    """

+    # pyre-fixme[13]: Attribute `data_source` is never initialized.
    data_source: DataSourceBase
    data_source_class_type: str = "ImplicitronDataSource"
+    # pyre-fixme[13]: Attribute `model_factory` is never initialized.
    model_factory: ModelFactoryBase
    model_factory_class_type: str = "ImplicitronModelFactory"
+    # pyre-fixme[13]: Attribute `optimizer_factory` is never initialized.
    optimizer_factory: OptimizerFactoryBase
    optimizer_factory_class_type: str = "ImplicitronOptimizerFactory"
+    # pyre-fixme[13]: Attribute `training_loop` is never initialized.
    training_loop: TrainingLoopBase
    training_loop_class_type: str = "ImplicitronTrainingLoop"

--- a/projects/implicitron_trainer/impl/model_factory.py
+++ b/projects/implicitron_trainer/impl/model_factory.py
@@ -45,7 +45,7 @@ class ModelFactoryBase(ReplaceableBase):


@registry.register
-class ImplicitronModelFactory(ModelFactoryBase):  # pyre-ignore [13]
+class ImplicitronModelFactory(ModelFactoryBase):
    """
    A factory class that initializes an implicit rendering model.

@@ -61,6 +61,7 @@ class ImplicitronModelFactory(ModelFactoryBase):  # pyre-ignore [13]

    """

+    # pyre-fixme[13]: Attribute `model` is never initialized.
    model: ImplicitronModelBase
    model_class_type: str = "GenericModel"
    resume: bool = True
--- a/projects/implicitron_trainer/impl/training_loop.py
+++ b/projects/implicitron_trainer/impl/training_loop.py
@@ -30,13 +30,13 @@ from .utils import seed_all_random_engines
 logger = logging.getLogger(__name__)


-# pyre-fixme[13]: Attribute `evaluator` is never initialized.
 class TrainingLoopBase(ReplaceableBase):
    """
    Members:
        evaluator: An EvaluatorBase instance, used to evaluate training results.
    """

+    # pyre-fixme[13]: Attribute `evaluator` is never initialized.
    evaluator: Optional[EvaluatorBase]
    evaluator_class_type: Optional[str] = "ImplicitronEvaluator"

--- a/pytorch3d/init.py
+++ b/pytorch3d/init.py
@@ -6,4 +6,4 @@

 # pyre-unsafe

-__version__ = "0.7.6"
+__version__ = "0.7.8"
--- a/pytorch3d/csrc/ext.cpp
+++ b/pytorch3d/csrc/ext.cpp
@@ -7,11 +7,15 @@
 */

 // 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"
@@ -99,6 +103,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
  m.def("marching_cubes", &MarchingCubes);

  // Pulsar.
+  // Pulsar not enabled on AMD.
+#if !defined(USE_ROCM)
 #ifdef PULSAR_LOGGING_ENABLED
  c10::ShowLogInfoToStderr();
 #endif
@@ -183,4 +189,5 @@ 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
 }
--- a/pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu
+++ b/pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu
@@ -144,7 +144,7 @@ __device__ void CheckPixelInsideFace(
  const bool zero_face_area =
      (face_area <= kEpsilon && face_area >= -1.0f * kEpsilon);

-  if (zmax < 0 || cull_backfaces && back_face || outside_bbox ||
+  if (zmax < 0 || (cull_backfaces && back_face) || outside_bbox ||
      zero_face_area) {
    return;
  }
--- a/pytorch3d/csrc/utils/float_math.cuh
+++ b/pytorch3d/csrc/utils/float_math.cuh
@@ -18,6 +18,8 @@ const auto vEpsilon = 1e-8;

 // Common functions and operators for float2.

+// Complex arithmetic is already defined for AMD.
+#if !defined(USE_ROCM)
 __device__ inline float2 operator-(const float2& a, const float2& b) {
  return make_float2(a.x - b.x, a.y - b.y);
 }
@@ -41,6 +43,7 @@ __device__ inline float2 operator*(const float2& a, const float2& b) {
 __device__ inline float2 operator*(const float a, const float2& b) {
  return make_float2(a * b.x, a * b.y);
 }
+#endif

 __device__ inline float FloatMin3(const float a, const float b, const float c) {
  return fminf(a, fminf(b, c));
--- a/pytorch3d/csrc/utils/warp_reduce.cuh
+++ b/pytorch3d/csrc/utils/warp_reduce.cuh
@@ -23,37 +23,51 @@ WarpReduceMin(scalar_t* min_dists, int64_t* min_idxs, const size_t tid) {
    min_idxs[tid] = min_idxs[tid + 32];
    min_dists[tid] = min_dists[tid + 32];
  }
+// AMD does not use explicit syncwarp and instead automatically inserts memory
+// fences during compilation.
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  // s = 16
  if (min_dists[tid] > min_dists[tid + 16]) {
    min_idxs[tid] = min_idxs[tid + 16];
    min_dists[tid] = min_dists[tid + 16];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  // s = 8
  if (min_dists[tid] > min_dists[tid + 8]) {
    min_idxs[tid] = min_idxs[tid + 8];
    min_dists[tid] = min_dists[tid + 8];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  // s = 4
  if (min_dists[tid] > min_dists[tid + 4]) {
    min_idxs[tid] = min_idxs[tid + 4];
    min_dists[tid] = min_dists[tid + 4];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  // s = 2
  if (min_dists[tid] > min_dists[tid + 2]) {
    min_idxs[tid] = min_idxs[tid + 2];
    min_dists[tid] = min_dists[tid + 2];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  // s = 1
  if (min_dists[tid] > min_dists[tid + 1]) {
    min_idxs[tid] = min_idxs[tid + 1];
    min_dists[tid] = min_dists[tid + 1];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
 }

 template <typename scalar_t>
@@ -65,30 +79,42 @@ __device__ void WarpReduceMax(
    dists[tid] = dists[tid + 32];
    dists_idx[tid] = dists_idx[tid + 32];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  if (dists[tid] < dists[tid + 16]) {
    dists[tid] = dists[tid + 16];
    dists_idx[tid] = dists_idx[tid + 16];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  if (dists[tid] < dists[tid + 8]) {
    dists[tid] = dists[tid + 8];
    dists_idx[tid] = dists_idx[tid + 8];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  if (dists[tid] < dists[tid + 4]) {
    dists[tid] = dists[tid + 4];
    dists_idx[tid] = dists_idx[tid + 4];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  if (dists[tid] < dists[tid + 2]) {
    dists[tid] = dists[tid + 2];
    dists_idx[tid] = dists_idx[tid + 2];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
  if (dists[tid] < dists[tid + 1]) {
    dists[tid] = dists[tid + 1];
    dists_idx[tid] = dists_idx[tid + 1];
  }
+#if !defined(USE_ROCM)
  __syncwarp();
+#endif
 }
--- a/pytorch3d/implicitron/dataset/data_source.py
+++ b/pytorch3d/implicitron/dataset/data_source.py
@@ -41,7 +41,7 @@ class DataSourceBase(ReplaceableBase):


@registry.register
-class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
+class ImplicitronDataSource(DataSourceBase):
    """
    Represents the data used in Implicitron. This is the only implementation
    of DataSourceBase provided.
@@ -52,8 +52,11 @@ class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
        data_loader_map_provider_class_type: identifies type for data_loader_map_provider.
    """

+    # pyre-fixme[13]: Attribute `dataset_map_provider` is never initialized.
    dataset_map_provider: DatasetMapProviderBase
+    # pyre-fixme[13]: Attribute `dataset_map_provider_class_type` is never initialized.
    dataset_map_provider_class_type: str
+    # pyre-fixme[13]: Attribute `data_loader_map_provider` is never initialized.
    data_loader_map_provider: DataLoaderMapProviderBase
    data_loader_map_provider_class_type: str = "SequenceDataLoaderMapProvider"

--- a/pytorch3d/implicitron/dataset/frame_data.py
+++ b/pytorch3d/implicitron/dataset/frame_data.py
@@ -276,6 +276,7 @@ class FrameData(Mapping[str, Any]):
            image_size_hw=tuple(self.effective_image_size_hw),  # pyre-ignore
        )
        crop_bbox_xywh = bbox_xyxy_to_xywh(clamp_bbox_xyxy)
+        self.crop_bbox_xywh = crop_bbox_xywh

        if self.fg_probability is not None:
            self.fg_probability = crop_around_box(
@@ -434,7 +435,7 @@ class FrameData(Mapping[str, Any]):
            # TODO: don't store K; enforce working in NDC space
            return join_cameras_as_batch(batch)
        else:
-            return torch.utils.data._utils.collate.default_collate(batch)
+            return torch.utils.data.dataloader.default_collate(batch)


 FrameDataSubtype = TypeVar("FrameDataSubtype", bound=FrameData)
--- a/pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py
+++ b/pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py
@@ -66,7 +66,7 @@ _NEED_CONTROL: Tuple[str, ...] = (


@registry.register
-class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class JsonIndexDatasetMapProvider(DatasetMapProviderBase):
    """
    Generates the training / validation and testing dataset objects for
    a dataset laid out on disk like Co3D, with annotations in json files.
@@ -95,6 +95,7 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
        path_manager_factory_class_type: The class type of `path_manager_factory`.
    """

+    # pyre-fixme[13]: Attribute `category` is never initialized.
    category: str
    task_str: str = "singlesequence"
    dataset_root: str = _CO3D_DATASET_ROOT
@@ -104,8 +105,10 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
    test_restrict_sequence_id: int = -1
    assert_single_seq: bool = False
    only_test_set: bool = False
+    # pyre-fixme[13]: Attribute `dataset` is never initialized.
    dataset: JsonIndexDataset
    dataset_class_type: str = "JsonIndexDataset"
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
    path_manager_factory: PathManagerFactory
    path_manager_factory_class_type: str = "PathManagerFactory"

--- a/pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py
+++ b/pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py
@@ -56,7 +56,7 @@ logger = logging.getLogger(__name__)


@registry.register
-class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
+class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
    """
    Generates the training, validation, and testing dataset objects for
    a dataset laid out on disk like CO3Dv2, with annotations in gzipped json files.
@@ -171,7 +171,9 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
        path_manager_factory_class_type: The class type of `path_manager_factory`.
    """

+    # pyre-fixme[13]: Attribute `category` is never initialized.
    category: str
+    # pyre-fixme[13]: Attribute `subset_name` is never initialized.
    subset_name: str
    dataset_root: str = _CO3DV2_DATASET_ROOT

@@ -183,8 +185,10 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
    n_known_frames_for_test: int = 0

    dataset_class_type: str = "JsonIndexDataset"
+    # pyre-fixme[13]: Attribute `dataset` is never initialized.
    dataset: JsonIndexDataset

+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
    path_manager_factory: PathManagerFactory
    path_manager_factory_class_type: str = "PathManagerFactory"

--- a/pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py
+++ b/pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py
@@ -32,7 +32,7 @@ from .utils import DATASET_TYPE_KNOWN


@registry.register
-class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):
    """
    A simple single-scene dataset based on PyTorch3D renders of a mesh.
    Provides `num_views` renders of the mesh as train, with no val
@@ -76,6 +76,7 @@ class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13
    resolution: int = 128
    use_point_light: bool = True
    gpu_idx: Optional[int] = 0
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
    path_manager_factory: PathManagerFactory
    path_manager_factory_class_type: str = "PathManagerFactory"

--- a/pytorch3d/implicitron/dataset/single_sequence_dataset.py
+++ b/pytorch3d/implicitron/dataset/single_sequence_dataset.py
@@ -83,7 +83,6 @@ class SingleSceneDataset(DatasetBase, Configurable):
        return self.eval_batches


-# pyre-fixme[13]: Uninitialized attribute
 class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
    """
    Base for provider of data for one scene from LLFF or blender datasets.
@@ -100,8 +99,11 @@ class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
            testing frame.
    """

+    # pyre-fixme[13]: Attribute `base_dir` is never initialized.
    base_dir: str
+    # pyre-fixme[13]: Attribute `object_name` is never initialized.
    object_name: str
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
    path_manager_factory: PathManagerFactory
    path_manager_factory_class_type: str = "PathManagerFactory"
    n_known_frames_for_test: Optional[int] = None
--- a/pytorch3d/implicitron/dataset/utils.py
+++ b/pytorch3d/implicitron/dataset/utils.py
@@ -348,6 +348,7 @@ def adjust_camera_to_image_scale_(
    camera: PerspectiveCameras,
    original_size_wh: torch.Tensor,
    new_size_wh: torch.LongTensor,
+    # pyre-fixme[7]: Expected `PerspectiveCameras` but got implicit return value of `None`.
 ) -> PerspectiveCameras:
    focal_length_px, principal_point_px = _convert_ndc_to_pixels(
        camera.focal_length[0],
@@ -367,7 +368,7 @@ 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-ignore
+    camera.principal_point = principal_point_scaled[None]


 # NOTE this cache is per-worker; they are implemented as processes.
--- a/pytorch3d/implicitron/models/generic_model.py
+++ b/pytorch3d/implicitron/models/generic_model.py
@@ -65,7 +65,7 @@ logger = logging.getLogger(__name__)


@registry.register
-class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13
+class GenericModel(ImplicitronModelBase):
    """
    GenericModel is a wrapper for the neural implicit
    rendering and reconstruction pipeline which consists
@@ -226,34 +226,42 @@ class GenericModel(ImplicitronModelBase):  # pyre-ignore: 13

    # ---- global encoder settings
    global_encoder_class_type: Optional[str] = None
+    # pyre-fixme[13]: Attribute `global_encoder` is never initialized.
    global_encoder: Optional[GlobalEncoderBase]

    # ---- raysampler
    raysampler_class_type: str = "AdaptiveRaySampler"
+    # pyre-fixme[13]: Attribute `raysampler` is never initialized.
    raysampler: RaySamplerBase

    # ---- renderer configs
    renderer_class_type: str = "MultiPassEmissionAbsorptionRenderer"
+    # pyre-fixme[13]: Attribute `renderer` is never initialized.
    renderer: BaseRenderer

    # ---- image feature extractor settings
    # (This is only created if view_pooler is enabled)
+    # pyre-fixme[13]: Attribute `image_feature_extractor` is never initialized.
    image_feature_extractor: Optional[FeatureExtractorBase]
    image_feature_extractor_class_type: Optional[str] = None
    # ---- view pooler settings
    view_pooler_enabled: bool = False
+    # pyre-fixme[13]: Attribute `view_pooler` is never initialized.
    view_pooler: Optional[ViewPooler]

    # ---- implicit function settings
    implicit_function_class_type: str = "NeuralRadianceFieldImplicitFunction"
    # This is just a model, never constructed.
    # The actual implicit functions live in self._implicit_functions
+    # pyre-fixme[13]: Attribute `implicit_function` is never initialized.
    implicit_function: ImplicitFunctionBase

    # ----- metrics
+    # pyre-fixme[13]: Attribute `view_metrics` is never initialized.
    view_metrics: ViewMetricsBase
    view_metrics_class_type: str = "ViewMetrics"

+    # pyre-fixme[13]: Attribute `regularization_metrics` is never initialized.
    regularization_metrics: RegularizationMetricsBase
    regularization_metrics_class_type: str = "RegularizationMetrics"

--- a/pytorch3d/implicitron/models/global_encoder/global_encoder.py
+++ b/pytorch3d/implicitron/models/global_encoder/global_encoder.py
@@ -59,12 +59,13 @@ class GlobalEncoderBase(ReplaceableBase):

 # TODO: probabilistic embeddings?
@registry.register
-class SequenceAutodecoder(GlobalEncoderBase, torch.nn.Module):  # pyre-ignore: 13
+class SequenceAutodecoder(GlobalEncoderBase, torch.nn.Module):
    """
    A global encoder implementation which provides an autodecoder encoding
    of the frame's sequence identifier.
    """

+    # pyre-fixme[13]: Attribute `autodecoder` is never initialized.
    autodecoder: Autodecoder

    def __post_init__(self):
--- a/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
+++ b/pytorch3d/implicitron/models/implicit_function/decoding_functions.py
@@ -244,7 +244,6 @@ class MLPWithInputSkips(Configurable, torch.nn.Module):


@registry.register
-# pyre-fixme[13]: Attribute `network` is never initialized.
 class MLPDecoder(DecoderFunctionBase):
    """
    Decoding function which uses `MLPWithIputSkips` to convert the embedding to output.
@@ -272,6 +271,7 @@ class MLPDecoder(DecoderFunctionBase):

    input_dim: int = 3
    param_groups: Dict[str, str] = field(default_factory=lambda: {})
+    # pyre-fixme[13]: Attribute `network` is never initialized.
    network: MLPWithInputSkips

    def __post_init__(self):
--- a/pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py
+++ b/pytorch3d/implicitron/models/implicit_function/scene_representation_networks.py
@@ -318,10 +318,11 @@ class SRNRaymarchHyperNet(Configurable, torch.nn.Module):


@registry.register
-# pyre-fixme[13]: Uninitialized attribute
 class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
    latent_dim: int = 0
+    # pyre-fixme[13]: Attribute `raymarch_function` is never initialized.
    raymarch_function: SRNRaymarchFunction
+    # pyre-fixme[13]: Attribute `pixel_generator` is never initialized.
    pixel_generator: SRNPixelGenerator

    def __post_init__(self):
@@ -366,7 +367,6 @@ class SRNImplicitFunction(ImplicitFunctionBase, torch.nn.Module):


@registry.register
-# pyre-fixme[13]: Uninitialized attribute
 class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
    """
    This implicit function uses a hypernetwork to generate the
@@ -377,7 +377,9 @@ class SRNHyperNetImplicitFunction(ImplicitFunctionBase, torch.nn.Module):

    latent_dim_hypernet: int = 0
    latent_dim: int = 0
+    # pyre-fixme[13]: Attribute `hypernet` is never initialized.
    hypernet: SRNRaymarchHyperNet
+    # pyre-fixme[13]: Attribute `pixel_generator` is never initialized.
    pixel_generator: SRNPixelGenerator

    def __post_init__(self):
--- a/pytorch3d/implicitron/models/implicit_function/voxel_grid.py
+++ b/pytorch3d/implicitron/models/implicit_function/voxel_grid.py
@@ -805,7 +805,6 @@ class VMFactorizedVoxelGrid(VoxelGridBase):
        )


-# pyre-fixme[13]: Attribute `voxel_grid` is never initialized.
 class VoxelGridModule(Configurable, torch.nn.Module):
    """
    A wrapper torch.nn.Module for the VoxelGrid classes, which
@@ -845,6 +844,7 @@ class VoxelGridModule(Configurable, torch.nn.Module):
    """

    voxel_grid_class_type: str = "FullResolutionVoxelGrid"
+    # pyre-fixme[13]: Attribute `voxel_grid` is never initialized.
    voxel_grid: VoxelGridBase

    extents: Tuple[float, float, float] = (2.0, 2.0, 2.0)
--- a/pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py
+++ b/pytorch3d/implicitron/models/implicit_function/voxel_grid_implicit_function.py
@@ -39,7 +39,6 @@ enable_get_default_args(HarmonicEmbedding)


@registry.register
-# pyre-ignore[13]
 class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
    """
    This implicit function consists of two streams, one for the density calculation and one
@@ -145,9 +144,11 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):
    """

    # ---- voxel grid for density
+    # pyre-fixme[13]: Attribute `voxel_grid_density` is never initialized.
    voxel_grid_density: VoxelGridModule

    # ---- voxel grid for color
+    # pyre-fixme[13]: Attribute `voxel_grid_color` is never initialized.
    voxel_grid_color: VoxelGridModule

    # ---- harmonic embeddings density
@@ -163,10 +164,12 @@ class VoxelGridImplicitFunction(ImplicitFunctionBase, torch.nn.Module):

    # ---- decoder function for density
    decoder_density_class_type: str = "MLPDecoder"
+    # pyre-fixme[13]: Attribute `decoder_density` is never initialized.
    decoder_density: DecoderFunctionBase

    # ---- decoder function for color
    decoder_color_class_type: str = "MLPDecoder"
+    # pyre-fixme[13]: Attribute `decoder_color` is never initialized.
    decoder_color: DecoderFunctionBase

    # ---- cuda streams
--- a/pytorch3d/implicitron/models/overfit_model.py
+++ b/pytorch3d/implicitron/models/overfit_model.py
@@ -69,7 +69,7 @@ IMPLICIT_FUNCTION_ARGS_TO_REMOVE: List[str] = [


@registry.register
-class OverfitModel(ImplicitronModelBase):  # pyre-ignore: 13
+class OverfitModel(ImplicitronModelBase):
    """
    OverfitModel is a wrapper for the neural implicit
    rendering and reconstruction pipeline which consists
@@ -198,27 +198,34 @@ class OverfitModel(ImplicitronModelBase):  # pyre-ignore: 13

    # ---- global encoder settings
    global_encoder_class_type: Optional[str] = None
+    # pyre-fixme[13]: Attribute `global_encoder` is never initialized.
    global_encoder: Optional[GlobalEncoderBase]

    # ---- raysampler
    raysampler_class_type: str = "AdaptiveRaySampler"
+    # pyre-fixme[13]: Attribute `raysampler` is never initialized.
    raysampler: RaySamplerBase

    # ---- renderer configs
    renderer_class_type: str = "MultiPassEmissionAbsorptionRenderer"
+    # pyre-fixme[13]: Attribute `renderer` is never initialized.
    renderer: BaseRenderer

    # ---- implicit function settings
    share_implicit_function_across_passes: bool = False
    implicit_function_class_type: str = "NeuralRadianceFieldImplicitFunction"
+    # pyre-fixme[13]: Attribute `implicit_function` is never initialized.
    implicit_function: ImplicitFunctionBase
    coarse_implicit_function_class_type: Optional[str] = None
+    # pyre-fixme[13]: Attribute `coarse_implicit_function` is never initialized.
    coarse_implicit_function: Optional[ImplicitFunctionBase]

    # ----- metrics
+    # pyre-fixme[13]: Attribute `view_metrics` is never initialized.
    view_metrics: ViewMetricsBase
    view_metrics_class_type: str = "ViewMetrics"

+    # pyre-fixme[13]: Attribute `regularization_metrics` is never initialized.
    regularization_metrics: RegularizationMetricsBase
    regularization_metrics_class_type: str = "RegularizationMetrics"

--- a/pytorch3d/implicitron/models/renderer/multipass_ea.py
+++ b/pytorch3d/implicitron/models/renderer/multipass_ea.py
@@ -18,9 +18,7 @@ from .raymarcher import RaymarcherBase


@registry.register
-class MultiPassEmissionAbsorptionRenderer(  # pyre-ignore: 13
-    BaseRenderer, torch.nn.Module
-):
+class MultiPassEmissionAbsorptionRenderer(BaseRenderer, torch.nn.Module):
    """
    Implements the multi-pass rendering function, in particular,
    with emission-absorption ray marching used in NeRF [1]. First, it evaluates
@@ -86,6 +84,7 @@ class MultiPassEmissionAbsorptionRenderer(  # pyre-ignore: 13
    """

    raymarcher_class_type: str = "EmissionAbsorptionRaymarcher"
+    # pyre-fixme[13]: Attribute `raymarcher` is never initialized.
    raymarcher: RaymarcherBase

    n_pts_per_ray_fine_training: int = 64
--- a/pytorch3d/implicitron/models/renderer/ray_point_refiner.py
+++ b/pytorch3d/implicitron/models/renderer/ray_point_refiner.py
@@ -16,8 +16,6 @@ from pytorch3d.renderer.implicit.sample_pdf import sample_pdf


@expand_args_fields
-# pyre-fixme[13]: Attribute `n_pts_per_ray` is never initialized.
-# pyre-fixme[13]: Attribute `random_sampling` is never initialized.
 class RayPointRefiner(Configurable, torch.nn.Module):
    """
    Implements the importance sampling of points along rays.
@@ -45,7 +43,9 @@ class RayPointRefiner(Configurable, torch.nn.Module):
            for Anti-Aliasing Neural Radiance Fields." ICCV 2021.
    """

+    # pyre-fixme[13]: Attribute `n_pts_per_ray` is never initialized.
    n_pts_per_ray: int
+    # pyre-fixme[13]: Attribute `random_sampling` is never initialized.
    random_sampling: bool
    add_input_samples: bool = True
    blurpool_weights: bool = False
--- a/pytorch3d/implicitron/models/renderer/sdf_renderer.py
+++ b/pytorch3d/implicitron/models/renderer/sdf_renderer.py
@@ -24,9 +24,10 @@ from .rgb_net import RayNormalColoringNetwork


@registry.register
-class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):  # pyre-ignore[13]
+class SignedDistanceFunctionRenderer(BaseRenderer, torch.nn.Module):
    render_features_dimensions: int = 3
    object_bounding_sphere: float = 1.0
+    # pyre-fixme[13]: Attribute `ray_tracer` is never initialized.
    ray_tracer: RayTracing
    ray_normal_coloring_network_args: DictConfig = get_default_args_field(
        RayNormalColoringNetwork
--- a/pytorch3d/implicitron/models/utils.py
+++ b/pytorch3d/implicitron/models/utils.py
@@ -121,7 +121,6 @@ def weighted_sum_losses(
        return None
    loss = sum(losses_weighted)
    assert torch.is_tensor(loss)
-    # pyre-fixme[7]: Expected `Optional[Tensor]` but got `int`.
    return loss


--- a/pytorch3d/implicitron/models/view_pooler/view_pooler.py
+++ b/pytorch3d/implicitron/models/view_pooler/view_pooler.py
@@ -16,7 +16,6 @@ from .feature_aggregator import FeatureAggregatorBase
 from .view_sampler import ViewSampler


-# pyre-ignore: 13
 class ViewPooler(Configurable, torch.nn.Module):
    """
    Implements sampling of image-based features at the 2d projections of a set
@@ -35,8 +34,10 @@ class ViewPooler(Configurable, torch.nn.Module):
            from a set of source images. FeatureAggregator executes step (4) above.
    """

+    # pyre-fixme[13]: Attribute `view_sampler` is never initialized.
    view_sampler: ViewSampler
    feature_aggregator_class_type: str = "AngleWeightedReductionFeatureAggregator"
+    # pyre-fixme[13]: Attribute `feature_aggregator` is never initialized.
    feature_aggregator: FeatureAggregatorBase

    def __post_init__(self):
--- a/pytorch3d/implicitron/tools/point_cloud_utils.py
+++ b/pytorch3d/implicitron/tools/point_cloud_utils.py
@@ -156,7 +156,6 @@ def render_point_cloud_pytorch3d(
    cumprod = torch.cat((torch.ones_like(cumprod[..., :1]), cumprod[..., :-1]), dim=-1)
    depths = (weights * cumprod * fragments.zbuf).sum(dim=-1)
    # add the rendering mask
-    # pyre-fixme[6]: For 1st param expected `Tensor` but got `float`.
    render_mask = -torch.prod(1.0 - weights, dim=-1) + 1.0

    # cat depths and render mask
--- a/pytorch3d/io/mtl_io.py
+++ b/pytorch3d/io/mtl_io.py
@@ -409,6 +409,7 @@ def _parse_mtl(
    texture_files = {}
    material_name = ""

+    # pyre-fixme[9]: f has type `str`; used as `IO[typing.Any]`.
    with _open_file(f, path_manager, "r") as f:
        for line in f:
            tokens = line.strip().split()
--- a/pytorch3d/io/obj_io.py
+++ b/pytorch3d/io/obj_io.py
@@ -649,8 +649,7 @@ def _load_obj(
        # Create an array of strings of material names for each face.
        # If faces_materials_idx == -1 then that face doesn't have a material.
        idx = faces_materials_idx.cpu().numpy()
-        face_material_names = np.array(material_names)[idx]  # (F,)
-        face_material_names[idx == -1] = ""
+        face_material_names = np.array([""] + material_names)[idx + 1]  # (F,)

        # Construct the atlas.
        texture_atlas = make_mesh_texture_atlas(
@@ -756,10 +755,13 @@ def save_obj(
    output_path = Path(f)

    # Save the .obj file
+    # pyre-fixme[9]: f has type `Union[Path, str]`; used as `IO[typing.Any]`.
    with _open_file(f, path_manager, "w") as f:
        if save_texture:
            # Add the header required for the texture info to be loaded correctly
            obj_header = "\nmtllib {0}.mtl\nusemtl mesh\n\n".format(output_path.stem)
+            # pyre-fixme[16]: Item `Path` of `Union[Path, str]` has no attribute
+            #  `write`.
            f.write(obj_header)
        _save(
            f,
--- a/pytorch3d/loss/chamfer.py
+++ b/pytorch3d/loss/chamfer.py
@@ -27,8 +27,10 @@ def _validate_chamfer_reduction_inputs(
    """
    if batch_reduction is not None and batch_reduction not in ["mean", "sum"]:
        raise ValueError('batch_reduction must be one of ["mean", "sum"] or None')
-    if point_reduction is not None and point_reduction not in ["mean", "sum"]:
-        raise ValueError('point_reduction must be one of ["mean", "sum"] or None')
+    if point_reduction is not None and point_reduction not in ["mean", "sum", "max"]:
+        raise ValueError(
+            'point_reduction must be one of ["mean", "sum", "max"] or None'
+        )
    if point_reduction is None and batch_reduction is not None:
        raise ValueError("Batch reduction must be None if point_reduction is None")

@@ -80,7 +82,6 @@ def _chamfer_distance_single_direction(
    x_normals,
    y_normals,
    weights,
-    batch_reduction: Union[str, None],
    point_reduction: Union[str, None],
    norm: int,
    abs_cosine: bool,
@@ -103,11 +104,6 @@ def _chamfer_distance_single_direction(
            raise ValueError("weights cannot be negative.")
        if weights.sum() == 0.0:
            weights = weights.view(N, 1)
-            if batch_reduction in ["mean", "sum"]:
-                return (
-                    (x.sum((1, 2)) * weights).sum() * 0.0,
-                    (x.sum((1, 2)) * weights).sum() * 0.0,
-                )
            return ((x.sum((1, 2)) * weights) * 0.0, (x.sum((1, 2)) * weights) * 0.0)

    cham_norm_x = x.new_zeros(())
@@ -135,7 +131,10 @@ def _chamfer_distance_single_direction(
        if weights is not None:
            cham_norm_x *= weights.view(N, 1)

-    if point_reduction is not None:
+    if point_reduction == "max":
+        assert not return_normals
+        cham_x = cham_x.max(1).values  # (N,)
+    elif point_reduction is not None:
        # Apply point reduction
        cham_x = cham_x.sum(1)  # (N,)
        if return_normals:
@@ -146,22 +145,34 @@ def _chamfer_distance_single_direction(
            if return_normals:
                cham_norm_x /= x_lengths_clamped

-        if batch_reduction is not None:
-            # batch_reduction == "sum"
-            cham_x = cham_x.sum()
-            if return_normals:
-                cham_norm_x = cham_norm_x.sum()
-            if batch_reduction == "mean":
-                div = weights.sum() if weights is not None else max(N, 1)
-                cham_x /= div
-                if return_normals:
-                    cham_norm_x /= div
-
    cham_dist = cham_x
    cham_normals = cham_norm_x if return_normals else None
    return cham_dist, cham_normals


+def _apply_batch_reduction(
+    cham_x, cham_norm_x, weights, batch_reduction: Union[str, None]
+):
+    if batch_reduction is None:
+        return (cham_x, cham_norm_x)
+    # batch_reduction == "sum"
+    N = cham_x.shape[0]
+    cham_x = cham_x.sum()
+    if cham_norm_x is not None:
+        cham_norm_x = cham_norm_x.sum()
+    if batch_reduction == "mean":
+        if weights is None:
+            div = max(N, 1)
+        elif weights.sum() == 0.0:
+            div = 1
+        else:
+            div = weights.sum()
+        cham_x /= div
+        if cham_norm_x is not None:
+            cham_norm_x /= div
+    return (cham_x, cham_norm_x)
+
+
 def chamfer_distance(
    x,
    y,
@@ -197,7 +208,8 @@ def chamfer_distance(
        batch_reduction: Reduction operation to apply for the loss across the
            batch, can be one of ["mean", "sum"] or None.
        point_reduction: Reduction operation to apply for the loss across the
-            points, can be one of ["mean", "sum"] or None.
+            points, can be one of ["mean", "sum", "max"] or None. Using "max" leads to the
+            Hausdorff distance.
        norm: int indicates the norm used for the distance. Supports 1 for L1 and 2 for L2.
        single_directional: If False (default), loss comes from both the distance between
            each point in x and its nearest neighbor in y and each point in y and its nearest
@@ -227,6 +239,10 @@ def chamfer_distance(

    if not ((norm == 1) or (norm == 2)):
        raise ValueError("Support for 1 or 2 norm.")
+
+    if point_reduction == "max" and (x_normals is not None or y_normals is not None):
+        raise ValueError('Normals must be None if point_reduction is "max"')
+
    x, x_lengths, x_normals = _handle_pointcloud_input(x, x_lengths, x_normals)
    y, y_lengths, y_normals = _handle_pointcloud_input(y, y_lengths, y_normals)

@@ -238,13 +254,13 @@ def chamfer_distance(
        x_normals,
        y_normals,
        weights,
-        batch_reduction,
        point_reduction,
        norm,
        abs_cosine,
    )
    if single_directional:
-        return cham_x, cham_norm_x
+        loss = cham_x
+        loss_normals = cham_norm_x
    else:
        cham_y, cham_norm_y = _chamfer_distance_single_direction(
            y,
@@ -254,17 +270,23 @@ def chamfer_distance(
            y_normals,
            x_normals,
            weights,
-            batch_reduction,
            point_reduction,
            norm,
            abs_cosine,
        )
-        if point_reduction is not None:
-            return (
-                cham_x + cham_y,
-                (cham_norm_x + cham_norm_y) if cham_norm_x is not None else None,
-            )
-        return (
-            (cham_x, cham_y),
-            (cham_norm_x, cham_norm_y) if cham_norm_x is not None else None,
-        )
+        if point_reduction == "max":
+            loss = torch.maximum(cham_x, cham_y)
+            loss_normals = None
+        elif point_reduction is not None:
+            loss = cham_x + cham_y
+            if cham_norm_x is not None:
+                loss_normals = cham_norm_x + cham_norm_y
+            else:
+                loss_normals = None
+        else:
+            loss = (cham_x, cham_y)
+            if cham_norm_x is not None:
+                loss_normals = (cham_norm_x, cham_norm_y)
+            else:
+                loss_normals = None
+    return _apply_batch_reduction(loss, loss_normals, weights, batch_reduction)
--- a/pytorch3d/ops/points_to_volumes.py
+++ b/pytorch3d/ops/points_to_volumes.py
@@ -617,6 +617,7 @@ def _splat_points_to_volumes(
                w = wX * wY * wZ

                # valid - binary indicators of votes that fall into the volume
+                # pyre-fixme[16]: `int` has no attribute `long`.
                valid = (
                    (0 <= X_)
                    * (X_ < grid_sizes_xyz[:, None, 0:1])
@@ -635,14 +636,19 @@ def _splat_points_to_volumes(
                idx_valid = idx * valid + rand_idx * (1 - valid)
                w_valid = w * valid.type_as(w)
                if mask is not None:
+                    # pyre-fixme[6]: For 1st argument expected `Tensor` but got `int`.
                    w_valid = w_valid * mask.type_as(w)[:, :, None]

                # scatter add casts the votes into the weight accumulator
                # and the feature accumulator
+                # pyre-fixme[6]: For 3rd argument expected `Tensor` but got
+                #  `Union[int, Tensor]`.
                volume_densities.scatter_add_(1, idx_valid, w_valid)

                # reshape idx_valid -> (minibatch, feature_dim, n_points)
                idx_valid = idx_valid.view(ba, 1, n_points).expand_as(points_features)
+                # pyre-fixme[16]: Item `int` of `Union[int, Tensor]` has no
+                #  attribute `view`.
                w_valid = w_valid.view(ba, 1, n_points)

                # volume_features of shape (minibatch, feature_dim, n_voxels)
@@ -724,6 +730,7 @@ def _round_points_to_volumes(
    # valid - binary indicators of votes that fall into the volume
    # pyre-fixme[9]: grid_sizes has type `LongTensor`; used as `Tensor`.
    grid_sizes = grid_sizes.type_as(XYZ)
+    # pyre-fixme[16]: `int` has no attribute `long`.
    valid = (
        (0 <= X)
        * (X < grid_sizes_xyz[:, None, 0:1])
--- a/pytorch3d/ops/utils.py
+++ b/pytorch3d/ops/utils.py
@@ -143,8 +143,6 @@ def convert_pointclouds_to_tensor(pcl: Union[torch.Tensor, "Pointclouds"]):
    elif torch.is_tensor(pcl):
        X = pcl
        num_points = X.shape[1] * torch.ones(  # type: ignore
-            # pyre-fixme[16]: Item `Pointclouds` of `Union[Pointclouds, Tensor]` has
-            #  no attribute `shape`.
            X.shape[0],
            device=X.device,
            dtype=torch.int64,
--- a/pytorch3d/renderer/init.py
+++ b/pytorch3d/renderer/init.py
@@ -6,6 +6,8 @@

 # pyre-unsafe

+import torch
+
 from .blending import (
    BlendParams,
    hard_rgb_blend,
@@ -74,9 +76,13 @@ from .points import (
    PointsRasterizationSettings,
    PointsRasterizer,
    PointsRenderer,
-    PulsarPointsRenderer,
    rasterize_points,
 )
+
+# Pulsar is not enabled on amd.
+if not torch.version.hip:
+    from .points import PulsarPointsRenderer
+
 from .splatter_blend import SplatterBlender
 from .utils import (
    convert_to_tensors_and_broadcast,
--- a/pytorch3d/renderer/blending.py
+++ b/pytorch3d/renderer/blending.py
@@ -212,15 +212,15 @@ def softmax_rgb_blend(

    # Reshape to be compatible with (N, H, W, K) values in fragments
    if torch.is_tensor(zfar):
-        # pyre-fixme[16]
        zfar = zfar[:, None, None, None]
    if torch.is_tensor(znear):
-        # pyre-fixme[16]: Item `float` of `Union[float, Tensor]` has no attribute
-        #  `__getitem__`.
        znear = znear[:, None, None, None]

+    # pyre-fixme[6]: Expected `float` but got `Union[float, Tensor]`
    z_inv = (zfar - fragments.zbuf) / (zfar - znear) * mask
+    # pyre-fixme[6]: Expected `Tensor` but got `float`
    z_inv_max = torch.max(z_inv, dim=-1).values[..., None].clamp(min=eps)
+    # pyre-fixme[6]: Expected `Tensor` but got `float`
    weights_num = prob_map * torch.exp((z_inv - z_inv_max) / blend_params.gamma)

    # Also apply exp normalize trick for the background color weight.
--- a/pytorch3d/renderer/cameras.py
+++ b/pytorch3d/renderer/cameras.py
@@ -1782,8 +1782,6 @@ def get_ndc_to_screen_transform(
    K = torch.zeros((cameras._N, 4, 4), device=cameras.device, dtype=torch.float32)
    if not torch.is_tensor(image_size):
        image_size = torch.tensor(image_size, device=cameras.device)
-    # pyre-fixme[16]: Item `List` of `Union[List[typing.Any], Tensor, Tuple[Any,
-    #  ...]]` has no attribute `view`.
    image_size = image_size.view(-1, 2)  # of shape (1 or B)x2
    height, width = image_size.unbind(1)

--- a/pytorch3d/renderer/mesh/clip.py
+++ b/pytorch3d/renderer/mesh/clip.py
@@ -497,6 +497,7 @@ def clip_faces(
    faces_case3 = face_verts_unclipped[case3_unclipped_idx]

    # index (0, 1, or 2) of the vertex in front of the clipping plane
+    # pyre-fixme[61]: `faces_clipped_verts` is undefined, or not always defined.
    p1_face_ind = torch.where(~faces_clipped_verts[case3_unclipped_idx])[1]

    # Solve for the points p4, p5 that intersect the clipping plane
@@ -540,6 +541,7 @@ def clip_faces(
    faces_case4 = face_verts_unclipped[case4_unclipped_idx]

    # index (0, 1, or 2) of the vertex behind the clipping plane
+    # pyre-fixme[61]: `faces_clipped_verts` is undefined, or not always defined.
    p1_face_ind = torch.where(faces_clipped_verts[case4_unclipped_idx])[1]

    # Solve for the points p4, p5 that intersect the clipping plane
--- a/pytorch3d/renderer/points/init.py
+++ b/pytorch3d/renderer/points/init.py
@@ -6,8 +6,13 @@

 # pyre-unsafe

+import torch
+
 from .compositor import AlphaCompositor, NormWeightedCompositor
-from .pulsar.unified import PulsarPointsRenderer
+
+# Pulsar not enabled on amd.
+if not torch.version.hip:
+    from .pulsar.unified import PulsarPointsRenderer

 from .rasterize_points import rasterize_points
 from .rasterizer import PointsRasterizationSettings, PointsRasterizer
--- a/pytorch3d/renderer/utils.py
+++ b/pytorch3d/renderer/utils.py
@@ -270,6 +270,8 @@ class TensorProperties(nn.Module):
                        # to have the same shape as the input tensor.
                        new_dims = len(tensor_dims) - len(idx_dims)
                        new_shape = idx_dims + (1,) * new_dims
+                        # pyre-fixme[58]: `+` is not supported for operand types
+                        # `Tuple[int]` and `torch._C.Size`
                        expand_dims = (-1,) + tensor_dims[1:]
                        _batch_idx = _batch_idx.view(*new_shape)
                        _batch_idx = _batch_idx.expand(*expand_dims)
--- a/pytorch3d/transforms/rotation_conversions.py
+++ b/pytorch3d/transforms/rotation_conversions.py
@@ -97,7 +97,10 @@ def _sqrt_positive_part(x: torch.Tensor) -> torch.Tensor:
    """
    ret = torch.zeros_like(x)
    positive_mask = x > 0
-    ret[positive_mask] = torch.sqrt(x[positive_mask])
+    if torch.is_grad_enabled():
+        ret[positive_mask] = torch.sqrt(x[positive_mask])
+    else:
+        ret = torch.where(positive_mask, torch.sqrt(x), ret)
    return ret


--- a/pytorch3d/vis/plotly_vis.py
+++ b/pytorch3d/vis/plotly_vis.py
@@ -369,6 +369,7 @@ def plot_scene(
        # update camera viewpoint if provided
        if viewpoints_eye_at_up_world is not None:
            # Use camera params for batch index or the first camera if only one provided.
+            # pyre-fixme[61]: `n_viewpoint_cameras` is undefined, or not always defined.
            viewpoint_idx = min(n_viewpoint_cameras - 1, subplot_idx)

            eye, at, up = (i[viewpoint_idx] for i in viewpoints_eye_at_up_world)
@@ -627,7 +628,7 @@ def _add_struct_from_batch(


 def _add_mesh_trace(
-    fig: go.Figure,  # pyre-ignore[11]
+    fig: go.Figure,
    meshes: Meshes,
    trace_name: str,
    subplot_idx: int,
@@ -673,6 +674,7 @@ def _add_mesh_trace(
    verts[~verts_used] = verts_center

    row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
    fig.add_trace(
        go.Mesh3d(
            x=verts[:, 0],
@@ -739,6 +741,7 @@ def _add_pointcloud_trace(

    row = subplot_idx // ncols + 1
    col = subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
    fig.add_trace(
        go.Scatter3d(
            x=verts[:, 0],
@@ -800,6 +803,7 @@ def _add_camera_trace(
    x, y, z = all_cam_wires.detach().cpu().numpy().T.astype(float)

    row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
    fig.add_trace(
        go.Scatter3d(x=x, y=y, z=z, marker={"size": 1}, name=trace_name),
        row=row,
@@ -894,6 +898,7 @@ def _add_ray_bundle_trace(
        ray_lines = torch.cat((ray_lines, nan_tensor, ray_line))
    x, y, z = ray_lines.detach().cpu().numpy().T.astype(float)
    row, col = subplot_idx // ncols + 1, subplot_idx % ncols + 1
+    # pyre-fixme[16]: `Figure` has no attribute `add_trace`.
    fig.add_trace(
        go.Scatter3d(
            x=x,
@@ -988,7 +993,7 @@ def _gen_fig_with_subplots(
 def _update_axes_bounds(
    verts_center: torch.Tensor,
    max_expand: float,
-    current_layout: go.Scene,  # pyre-ignore[11]
+    current_layout: go.Scene,
 ) -> None:  # pragma: no cover
    """
    Takes in the vertices' center point and max spread, and the current plotly figure
@@ -1005,6 +1010,7 @@ def _update_axes_bounds(

    # Ensure that within a subplot, the bounds capture all traces
    old_xrange, old_yrange, old_zrange = (
+        # pyre-fixme[16]: `Scene` has no attribute `__getitem__`.
        current_layout["xaxis"]["range"],
        current_layout["yaxis"]["range"],
        current_layout["zaxis"]["range"],
@@ -1023,6 +1029,7 @@ def _update_axes_bounds(
    xaxis = {"range": x_range}
    yaxis = {"range": y_range}
    zaxis = {"range": z_range}
+    # pyre-fixme[16]: `Scene` has no attribute `update`.
    current_layout.update({"xaxis": xaxis, "yaxis": yaxis, "zaxis": zaxis})


--- a/setup.py
+++ b/setup.py
@@ -153,7 +153,7 @@ setup(
    )
    + [trainer],
    package_dir={trainer: "projects/implicitron_trainer"},
-    install_requires=["fvcore", "iopath"],
+    install_requires=["iopath"],
    extras_require={
        "all": ["matplotlib", "tqdm>4.29.0", "imageio", "ipywidgets"],
        "dev": ["flake8", "usort"],
--- a/tests/test_chamfer.py
+++ b/tests/test_chamfer.py
@@ -847,6 +847,85 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
            loss, loss_norm, pred_loss[0], pred_loss_norm[0], p1, p11, p2, p22
        )

+    def test_chamfer_point_reduction_max(self):
+        """
+        Compare output of vectorized chamfer loss with naive implementation
+        for point_reduction = "max" and batch_reduction = None.
+        """
+        N, P1, P2 = 7, 10, 18
+        device = get_random_cuda_device()
+        points_normals = TestChamfer.init_pointclouds(N, P1, P2, device)
+        p1 = points_normals.p1
+        p2 = points_normals.p2
+        weights = points_normals.weights
+        p11 = p1.detach().clone()
+        p22 = p2.detach().clone()
+        p11.requires_grad = True
+        p22.requires_grad = True
+
+        pred_loss, unused_pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, x_normals=None, y_normals=None
+        )
+
+        loss, loss_norm = chamfer_distance(
+            p11,
+            p22,
+            x_normals=None,
+            y_normals=None,
+            weights=weights,
+            batch_reduction=None,
+            point_reduction="max",
+        )
+        pred_loss_max = torch.maximum(
+            pred_loss[0].max(1).values, pred_loss[1].max(1).values
+        )
+        pred_loss_max *= weights
+        self.assertClose(loss, pred_loss_max)
+
+        self.assertIsNone(loss_norm)
+
+        # Check gradients
+        self._check_gradients(loss, loss_norm, pred_loss_max, None, p1, p11, p2, p22)
+
+    def test_single_directional_chamfer_point_reduction_max(self):
+        """
+        Compare output of vectorized single directional chamfer loss with naive implementation
+        for point_reduction = "max" and batch_reduction = None.
+        """
+        N, P1, P2 = 7, 10, 18
+        device = get_random_cuda_device()
+        points_normals = TestChamfer.init_pointclouds(N, P1, P2, device)
+        p1 = points_normals.p1
+        p2 = points_normals.p2
+        weights = points_normals.weights
+        p11 = p1.detach().clone()
+        p22 = p2.detach().clone()
+        p11.requires_grad = True
+        p22.requires_grad = True
+
+        pred_loss, unused_pred_loss_norm = TestChamfer.chamfer_distance_naive(
+            p1, p2, x_normals=None, y_normals=None
+        )
+
+        loss, loss_norm = chamfer_distance(
+            p11,
+            p22,
+            x_normals=None,
+            y_normals=None,
+            weights=weights,
+            batch_reduction=None,
+            point_reduction="max",
+            single_directional=True,
+        )
+        pred_loss_max = pred_loss[0].max(1).values
+        pred_loss_max *= weights
+        self.assertClose(loss, pred_loss_max)
+
+        self.assertIsNone(loss_norm)
+
+        # Check gradients
+        self._check_gradients(loss, loss_norm, pred_loss_max, None, p1, p11, p2, p22)
+
    def _check_gradients(
        self,
        loss,
@@ -1020,9 +1099,9 @@ class TestChamfer(TestCaseMixin, unittest.TestCase):
        with self.assertRaisesRegex(ValueError, "batch_reduction must be one of"):
            chamfer_distance(p1, p2, weights=weights, batch_reduction="max")

-        # Error when point_reduction is not in ["mean", "sum"] or None.
+        # Error when point_reduction is not in ["mean", "sum", "max"] or None.
        with self.assertRaisesRegex(ValueError, "point_reduction must be one of"):
-            chamfer_distance(p1, p2, weights=weights, point_reduction="max")
+            chamfer_distance(p1, p2, weights=weights, point_reduction="min")

    def test_incorrect_weights(self):
        N, P1, P2 = 16, 64, 128
Author	SHA1	Message	Date
Jeremy Reizenstein	75ebeeaea0	update version to 0.7.8 Summary: as title Reviewed By: das-intensity Differential Revision: D62588556 fbshipit-source-id: 55bae19dd1df796e83179cd29d805fcd871b6d23	2024-09-13 02:31:49 -07:00
Jeremy Reizenstein	ab793177c6	remove pytorch2.0 builds Summary: these are failing in ci Reviewed By: das-intensity Differential Revision: D62594666 fbshipit-source-id: 5e3a7441be2978803dc2d3e361365e0fffa7ad3b	2024-09-13 02:07:25 -07:00
Jeremy Reizenstein	9acdd67b83	fix obj material indexing bug #1368 Summary: Make the negative index actually not an error fixes https://github.com/facebookresearch/pytorch3d/issues/1368 Reviewed By: das-intensity Differential Revision: D62177991 fbshipit-source-id: e5ed433bde1f54251c4d4b6db073c029cbe87343	2024-09-13 02:00:49 -07:00
Nicholas Dahm	3f428d9981	pytorch 2.4.0 + 2.4.1 Summary: Apparently pytorch 2.4 is now supported as per [this closed issue](https://github.com/facebookresearch/pytorch3d/issues/1863). Added the `2.4.0` & `2.4.1` versions to `regenerate.py` then ran that as per the `README_fb.md` which generated `config.yml` changes. Reviewed By: bottler Differential Revision: D62517831 fbshipit-source-id: 002e276dfe2fa078136ff2f6c747d937abbadd1a	2024-09-11 15:09:43 -07:00
Josh Fromm	05cbea115a	Hipify Pytorch3D (#1851 ) Summary: X-link: https://github.com/pytorch/pytorch/pull/133343 X-link: https://github.com/fairinternal/pytorch3d/pull/45 Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1851 Enables pytorch3d to build on AMD. An important part of enabling this was not compiling the Pulsar backend when the target is AMD. There are simply too many kernel incompatibilites to make it work (I tried haha). Fortunately, it doesnt seem like most modern applications of pytorch3d rely on Pulsar. We should be able to unlock most of pytorch3d's goodness on AMD without it. Reviewed By: bottler, houseroad Differential Revision: D61171993 fbshipit-source-id: fd4aee378a3568b22676c5bf2b727c135ff710af	2024-08-15 16:18:22 -07:00
generatedunixname89002005307016	38afdcfc68	upgrade pyre version in `fbcode/vision` - batch 2 Reviewed By: bottler Differential Revision: D60992234 fbshipit-source-id: 899db6ed590ef966ff651c11027819e59b8401a3	2024-08-09 02:07:45 -07:00
Christine Sun	1e0b1d9c72	Remove Python versions from Install.md Summary: To avoid the installation instructions for PyTorch3D becoming out-of-date, instead of specifying certain Python versions, update to just `Python`. Reader will understand it has to be a Python version compatible with GitHub. Reviewed By: bottler Differential Revision: D60919848 fbshipit-source-id: 5e974970a0db3d3d32fae44e5dd30cbc1ce237a9	2024-08-07 13:46:31 -07:00
Rebecca Chen (Python)	44702fdb4b	Add "max" point reduction for chamfer distance Summary: * Adds a "max" option for the point_reduction input to the chamfer_distance function. * When combining the x and y directions, maxes the losses instead of summing them when point_reduction="max". * Moves batch reduction to happen after the directions are combined. * Adds test_chamfer_point_reduction_max and test_single_directional_chamfer_point_reduction_max tests. Fixes https://github.com/facebookresearch/pytorch3d/issues/1838 Reviewed By: bottler Differential Revision: D60614661 fbshipit-source-id: 7879816acfda03e945bada951b931d2c522756eb	2024-08-02 10:46:07 -07:00
Jeremy Reizenstein	7edaee71a9	allow matrix_to_quaternion onnx export Summary: Attempt to allow torch.onnx.dynamo_export(matrix_to_quaternion) to work. Differential Revision: D59812279 fbshipit-source-id: 4497e5b543bec9d5c2bdccfb779d154750a075ad	2024-07-16 11:30:20 -07:00
Roman Shapovalov	d0d0e02007	Fix: setting FrameData.crop_bbox_xywh for backwards compatibility Summary: This diff is fixing a backwards compatibility issue in PyTorch3D's dataset API. The code ensures that the `crop_bbox_xywh` attribute is set when box_crop flag is on. This is an implementation detail that people should not really use, however some people depend on this behaviour. Reviewed By: bottler Differential Revision: D59777449 fbshipit-source-id: b875e9eb909038b8629ccdade87661bb2c39d529	2024-07-16 02:21:13 -07:00
Jeremy Reizenstein	4df110b0a9	remove fvcore dependency Summary: This is not actually needed and is causing a conda-forge confusion to do with python_abi - which needs users to have `-c conda-forge` when they install pytorch3d. Reviewed By: patricklabatut Differential Revision: D59587930 fbshipit-source-id: 961ae13a62e1b2b2ce6d8781db38bd97eca69e65	2024-07-11 04:35:38 -07:00
Huy Do	51fd114d8b	Forward fix internal pyre failure from D58983461 Summary: X-link: https://github.com/pytorch/pytorch/pull/129525 Somehow, using underscore alias of some builtin types breaks pyre Reviewed By: malfet, clee2000 Differential Revision: D59029768 fbshipit-source-id: cfa2171b66475727b9545355e57a8297c1dc0bc6	2024-06-27 07:35:18 -07:00
Jeremy Reizenstein	89653419d0	version 0.7.7 Summary: New version Reviewed By: MichaelRamamonjisoa Differential Revision: D58668979 fbshipit-source-id: 195eaf83e4da51a106ef72e38dbb98c51c51724c	2024-06-25 06:59:24 -07:00
Jeremy Reizenstein	7980854d44	require pytorch 2.0+ Summary: Problems with timeouts on old builds. Reviewed By: MichaelRamamonjisoa Differential Revision: D58819435 fbshipit-source-id: e1976534a102ad3841f3b297c772e916aeea12cb	2024-06-21 08:15:17 -07:00
Jeremy Reizenstein	51d7c06ddd	MKL version fix in CI (#1820 ) Summary: Fix for "undefined symbol: iJIT_NotifyEvent" build issue, Pull Request resolved: https://github.com/facebookresearch/pytorch3d/pull/1820 Reviewed By: MichaelRamamonjisoa Differential Revision: D58685326 fbshipit-source-id: 48b54367c00851cc6fbb111ca98d69a2ace8361b	2024-06-21 08:15:17 -07:00
Sergii Dymchenko	00c36ec01c	Update deprecated PyTorch functions in fbcode/vision Reviewed By: bottler Differential Revision: D58762015 fbshipit-source-id: a0d05fe63a88d33e3f7783b5a7b2a476dd3a7449	2024-06-20 14:06:28 -07:00