apply Black 25.11.0 style in fbcode (70/92)

Summary:
Formats the covered files with pyfmt.

paintitblack

Reviewed By: itamaro

Differential Revision: D90476295

fbshipit-source-id: 5101d4aae980a9f8955a4cb10bae23997c48837f

.circleci/build_count.py

@@ -0,0 +1,33 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+Print the number of nightly builds
+"""
+
+from collections import Counter
+
+import yaml
+
+
+conf = yaml.safe_load(open("config.yml"))
+jobs = conf["workflows"]["build_and_test"]["jobs"]
+
+
+def jobtype(job):
+    if isinstance(job, str):
+        return job
+    if len(job) == 1:
+        [name] = job.keys()
+        return name
+    return "MULTIPLE PARTS"
+
+
+for i, j in Counter(map(jobtype, jobs)).items():
+    print(i, j)
+print()
+print(len(jobs))

.circleci/check.sh

@@ -1,6 +1,13 @@
 #!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
 
 # Run this script before committing config.yml to verify it is valid yaml.
 
-python -c 'import yaml; yaml.safe_load(open("config.yml"))' && echo OK
+python -c 'import yaml; yaml.safe_load(open("config.yml"))' && echo OK - valid yaml
+
+msg="circleci not installed so can't check schema"
+command -v circleci > /dev/null && (cd ..; circleci config validate) || echo "$msg"

.circleci/config.in.yml

@@ -18,21 +18,12 @@ setupcuda: &setupcuda
     working_directory: ~/
     command: |
       # download and install nvidia drivers, cuda, etc
-      wget --no-verbose --no-clobber -P ~/nvidia-downloads 'https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-430.40.run'
-      wget --no-verbose --no-clobber -P ~/nvidia-downloads http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
-      sudo /bin/bash ~/nvidia-downloads/NVIDIA-Linux-x86_64-430.40.run --no-drm -q --ui=none
-      sudo sh ~/nvidia-downloads/cuda_10.2.89_440.33.01_linux.run --silent
+      wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run
+      sudo sh ~/nvidia-downloads/cuda_11.3.1_465.19.01_linux.run --silent
       echo "Done installing CUDA."
       pyenv versions
       nvidia-smi
-      pyenv global 3.7.0
-
-gpu: &gpu
-  environment:
-    CUDA_VERSION: "10.2"
-  machine:
-    image: default
-  resource_class: gpu.medium # tesla m60
+      pyenv global 3.9.1
 
 binary_common: &binary_common
   parameters:
@@ -56,39 +47,54 @@ binary_common: &binary_common
       description: "Wheel only: what docker image to use"
       type: string
       default: "pytorch/manylinux-cuda101"
+    conda_docker_image:
+      description: "what docker image to use for docker"
+      type: string
+      default: "pytorch/conda-cuda"
   environment:
     PYTHON_VERSION: << parameters.python_version >>
     BUILD_VERSION: << parameters.build_version >>
     PYTORCH_VERSION: << parameters.pytorch_version >>
     CU_VERSION: << parameters.cu_version >>
+    TESTRUN_DOCKER_IMAGE: << parameters.conda_docker_image >>
 
 jobs:
   main:
-    <<: *gpu
+    environment:
+      CUDA_VERSION: "11.3"
+    resource_class: gpu.nvidia.small.multi
     machine:
-      image: ubuntu-1604:201903-01
+      image: linux-cuda-11:default
     steps:
       - checkout
       - <<: *setupcuda
-      - run: pip3 install --progress-bar off wheel matplotlib 'pillow<7'
-      - run: pip3 install --progress-bar off torch torchvision
+      - run: pip3 install --progress-bar off imageio wheel matplotlib 'pillow<7'
+      - run: pip3 install --progress-bar off torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
       # - run: conda create -p ~/conda_env python=3.7 numpy
       # - run: conda activate ~/conda_env
       # - run: conda install -c pytorch pytorch torchvision
 
       - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore'
-      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python3 setup.py build_ext --inplace
-      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python -m unittest discover -v -s tests
+      - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/iopath'
+      - run:
+          name: build
+          command: |
+            export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64
+            python3 setup.py build_ext --inplace
+      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64 python -m unittest discover -v -s tests -t .
       - run: python3 setup.py bdist_wheel
 
   binary_linux_wheel:
     <<: *binary_common
     docker:
       - image: << parameters.wheel_docker_image >>
+        auth:
+          username: $DOCKERHUB_USERNAME
+          password: $DOCKERHUB_TOKEN
     resource_class: 2xlarge+
     steps:
       - checkout
-      - run: packaging/build_wheel.sh
+      - run: MAX_JOBS=15 packaging/build_wheel.sh
       - store_artifacts:
           path: dist
       - persist_to_workspace:
@@ -99,13 +105,19 @@ jobs:
   binary_linux_conda:
     <<: *binary_common
     docker:
-      - image: "pytorch/conda-cuda"
+      - image: "<< parameters.conda_docker_image >>"
+        auth:
+          username: $DOCKERHUB_USERNAME
+          password: $DOCKERHUB_TOKEN
     resource_class: 2xlarge+
     steps:
       - checkout
       # This is building with cuda but no gpu present,
       # so we aren't running the tests.
-      - run: TEST_FLAG=--no-test packaging/build_conda.sh
+      - run:
+          name: build
+          no_output_timeout: 40m
+          command: MAX_JOBS=15 TEST_FLAG=--no-test python3 packaging/build_conda.py
       - store_artifacts:
           path: /opt/conda/conda-bld/linux-64
       - persist_to_workspace:
@@ -116,84 +128,44 @@ jobs:
   binary_linux_conda_cuda:
     <<: *binary_common
     machine:
-      image: ubuntu-1604:201903-01
-    resource_class: gpu.medium
+      image: linux-cuda-11:default
+    resource_class: gpu.nvidia.small.multi
     steps:
     - checkout
-    - run:
-        name: Setup environment
-        command: |
-          set -e
-
-          curl -L https://packagecloud.io/circleci/trusty/gpgkey | sudo apt-key add -
-          curl -L https://dl.google.com/linux/linux_signing_key.pub | sudo apt-key add -
-
-          sudo apt-get update
-
-          sudo apt-get install \
-              apt-transport-https \
-              ca-certificates \
-              curl \
-              gnupg-agent \
-              software-properties-common
-
-          curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
-
-          sudo add-apt-repository \
-             "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
-             $(lsb_release -cs) \
-             stable"
-
-          sudo apt-get update
-          export DOCKER_VERSION="5:19.03.2~3-0~ubuntu-xenial"
-          sudo apt-get install docker-ce=${DOCKER_VERSION} docker-ce-cli=${DOCKER_VERSION} containerd.io=1.2.6-3
-
-          # Add the package repositories
-          distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
-          curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
-          curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
-
-          export NVIDIA_CONTAINER_VERSION="1.0.3-1"
-          sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit=${NVIDIA_CONTAINER_VERSION}
-          sudo systemctl restart docker
-
-          DRIVER_FN="NVIDIA-Linux-x86_64-410.104.run"
-          wget "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"
-          sudo /bin/bash "$DRIVER_FN" -s --no-drm || (sudo cat /var/log/nvidia-installer.log && false)
-          nvidia-smi
 
     - run:
         name: Pull docker image
         command: |
+          nvidia-smi
           set -e
-          export DOCKER_IMAGE=pytorch/conda-cuda
-          echo Pulling docker image $DOCKER_IMAGE
-          docker pull $DOCKER_IMAGE >/dev/null
 
+          { docker login -u="$DOCKERHUB_USERNAME" -p="$DOCKERHUB_TOKEN" ; } 2> /dev/null
+
+          echo Pulling docker image $TESTRUN_DOCKER_IMAGE
+          docker pull $TESTRUN_DOCKER_IMAGE
     - run:
         name: Build and run tests
+        no_output_timeout: 40m
         command: |
           set -e
 
           cd ${HOME}/project/
 
-          export DOCKER_IMAGE=pytorch/conda-cuda
-          export VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e UNICODE_ABI -e CU_VERSION"
+          export JUST_TESTRUN=1
+          VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION -e JUST_TESTRUN"
 
-          docker run --gpus all  --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${DOCKER_IMAGE} ./packaging/build_conda.sh
+          docker run --gpus all  --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${TESTRUN_DOCKER_IMAGE} python3 ./packaging/build_conda.py
 
 workflows:
   version: 2
   build_and_test:
     jobs:
-      - main
+      # - main:
+      #     context: DOCKERHUB_TOKEN
       {{workflows()}}
-      - binary_linux_conda:
-          cu_version: cu101
-          name: binary_linux_conda_py3.7_cu101
-          python_version: '3.7'
       - binary_linux_conda_cuda:
-          name: testrun_conda_cuda_py3.7_cu100
-          python_version: "3.7"
-          pytorch_version: "1.4"
-          cu_version: "cu100"
+          name: testrun_conda_cuda_py310_cu117_pyt201
+          context: DOCKERHUB_TOKEN
+          python_version: "3.10"
+          pytorch_version: '2.0.1'
+          cu_version: "cu117"

.circleci/config.yml

@@ -18,21 +18,12 @@ setupcuda: &setupcuda
     working_directory: ~/
     command: |
       # download and install nvidia drivers, cuda, etc
-      wget --no-verbose --no-clobber -P ~/nvidia-downloads 'https://s3.amazonaws.com/ossci-linux/nvidia_driver/NVIDIA-Linux-x86_64-430.40.run'
-      wget --no-verbose --no-clobber -P ~/nvidia-downloads http://developer.download.nvidia.com/compute/cuda/10.2/Prod/local_installers/cuda_10.2.89_440.33.01_linux.run
-      sudo /bin/bash ~/nvidia-downloads/NVIDIA-Linux-x86_64-430.40.run --no-drm -q --ui=none
-      sudo sh ~/nvidia-downloads/cuda_10.2.89_440.33.01_linux.run --silent
+      wget --no-verbose --no-clobber -P ~/nvidia-downloads https://developer.download.nvidia.com/compute/cuda/11.3.1/local_installers/cuda_11.3.1_465.19.01_linux.run
+      sudo sh ~/nvidia-downloads/cuda_11.3.1_465.19.01_linux.run --silent
       echo "Done installing CUDA."
       pyenv versions
       nvidia-smi
-      pyenv global 3.7.0
-
-gpu: &gpu
-  environment:
-    CUDA_VERSION: "10.2"
-  machine:
-    image: default
-  resource_class: gpu.medium # tesla m60
+      pyenv global 3.9.1
 
 binary_common: &binary_common
   parameters:
@@ -56,39 +47,54 @@ binary_common: &binary_common
       description: "Wheel only: what docker image to use"
       type: string
       default: "pytorch/manylinux-cuda101"
+    conda_docker_image:
+      description: "what docker image to use for docker"
+      type: string
+      default: "pytorch/conda-cuda"
   environment:
     PYTHON_VERSION: << parameters.python_version >>
     BUILD_VERSION: << parameters.build_version >>
     PYTORCH_VERSION: << parameters.pytorch_version >>
     CU_VERSION: << parameters.cu_version >>
+    TESTRUN_DOCKER_IMAGE: << parameters.conda_docker_image >>
 
 jobs:
   main:
-    <<: *gpu
+    environment:
+      CUDA_VERSION: "11.3"
+    resource_class: gpu.nvidia.small.multi
     machine:
-      image: ubuntu-1604:201903-01
+      image: linux-cuda-11:default
     steps:
       - checkout
       - <<: *setupcuda
-      - run: pip3 install --progress-bar off wheel matplotlib 'pillow<7'
-      - run: pip3 install --progress-bar off torch torchvision
+      - run: pip3 install --progress-bar off imageio wheel matplotlib 'pillow<7'
+      - run: pip3 install --progress-bar off torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/cu113/torch_stable.html
       # - run: conda create -p ~/conda_env python=3.7 numpy
       # - run: conda activate ~/conda_env
       # - run: conda install -c pytorch pytorch torchvision
 
       - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/fvcore'
-      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python3 setup.py build_ext --inplace
-      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-10.2/lib64 python -m unittest discover -v -s tests
+      - run: pip3 install --progress-bar off 'git+https://github.com/facebookresearch/iopath'
+      - run:
+          name: build
+          command: |
+            export LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64
+            python3 setup.py build_ext --inplace
+      - run: LD_LIBRARY_PATH=$LD_LIBARY_PATH:/usr/local/cuda-11.3/lib64 python -m unittest discover -v -s tests -t .
       - run: python3 setup.py bdist_wheel
 
   binary_linux_wheel:
     <<: *binary_common
     docker:
       - image: << parameters.wheel_docker_image >>
+        auth:
+          username: $DOCKERHUB_USERNAME
+          password: $DOCKERHUB_TOKEN
     resource_class: 2xlarge+
     steps:
       - checkout
-      - run: packaging/build_wheel.sh
+      - run: MAX_JOBS=15 packaging/build_wheel.sh
       - store_artifacts:
           path: dist
       - persist_to_workspace:
@@ -99,13 +105,19 @@ jobs:
   binary_linux_conda:
     <<: *binary_common
     docker:
-      - image: "pytorch/conda-cuda"
+      - image: "<< parameters.conda_docker_image >>"
+        auth:
+          username: $DOCKERHUB_USERNAME
+          password: $DOCKERHUB_TOKEN
     resource_class: 2xlarge+
     steps:
       - checkout
       # This is building with cuda but no gpu present,
       # so we aren't running the tests.
-      - run: TEST_FLAG=--no-test packaging/build_conda.sh
+      - run:
+          name: build
+          no_output_timeout: 40m
+          command: MAX_JOBS=15 TEST_FLAG=--no-test python3 packaging/build_conda.py
       - store_artifacts:
           path: /opt/conda/conda-bld/linux-64
       - persist_to_workspace:
@@ -116,143 +128,561 @@ jobs:
   binary_linux_conda_cuda:
     <<: *binary_common
     machine:
-      image: ubuntu-1604:201903-01
-    resource_class: gpu.medium
+      image: linux-cuda-11:default
+    resource_class: gpu.nvidia.small.multi
     steps:
     - checkout
-    - run:
-        name: Setup environment
-        command: |
-          set -e
-
-          curl -L https://packagecloud.io/circleci/trusty/gpgkey | sudo apt-key add -
-          curl -L https://dl.google.com/linux/linux_signing_key.pub | sudo apt-key add -
-
-          sudo apt-get update
-
-          sudo apt-get install \
-              apt-transport-https \
-              ca-certificates \
-              curl \
-              gnupg-agent \
-              software-properties-common
-
-          curl -fsSL https://download.docker.com/linux/ubuntu/gpg | sudo apt-key add -
-
-          sudo add-apt-repository \
-             "deb [arch=amd64] https://download.docker.com/linux/ubuntu \
-             $(lsb_release -cs) \
-             stable"
-
-          sudo apt-get update
-          export DOCKER_VERSION="5:19.03.2~3-0~ubuntu-xenial"
-          sudo apt-get install docker-ce=${DOCKER_VERSION} docker-ce-cli=${DOCKER_VERSION} containerd.io=1.2.6-3
-
-          # Add the package repositories
-          distribution=$(. /etc/os-release;echo $ID$VERSION_ID)
-          curl -s -L https://nvidia.github.io/nvidia-docker/gpgkey | sudo apt-key add -
-          curl -s -L https://nvidia.github.io/nvidia-docker/$distribution/nvidia-docker.list | sudo tee /etc/apt/sources.list.d/nvidia-docker.list
-
-          export NVIDIA_CONTAINER_VERSION="1.0.3-1"
-          sudo apt-get update && sudo apt-get install -y nvidia-container-toolkit=${NVIDIA_CONTAINER_VERSION}
-          sudo systemctl restart docker
-
-          DRIVER_FN="NVIDIA-Linux-x86_64-410.104.run"
-          wget "https://s3.amazonaws.com/ossci-linux/nvidia_driver/$DRIVER_FN"
-          sudo /bin/bash "$DRIVER_FN" -s --no-drm || (sudo cat /var/log/nvidia-installer.log && false)
-          nvidia-smi
 
     - run:
         name: Pull docker image
         command: |
+          nvidia-smi
           set -e
-          export DOCKER_IMAGE=pytorch/conda-cuda
-          echo Pulling docker image $DOCKER_IMAGE
-          docker pull $DOCKER_IMAGE >/dev/null
 
+          { docker login -u="$DOCKERHUB_USERNAME" -p="$DOCKERHUB_TOKEN" ; } 2> /dev/null
+
+          echo Pulling docker image $TESTRUN_DOCKER_IMAGE
+          docker pull $TESTRUN_DOCKER_IMAGE
     - run:
         name: Build and run tests
+        no_output_timeout: 40m
         command: |
           set -e
 
           cd ${HOME}/project/
 
-          export DOCKER_IMAGE=pytorch/conda-cuda
-          export VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e UNICODE_ABI -e CU_VERSION"
+          export JUST_TESTRUN=1
+          VARS_TO_PASS="-e PYTHON_VERSION -e BUILD_VERSION -e PYTORCH_VERSION -e CU_VERSION -e JUST_TESTRUN"
 
-          docker run --gpus all  --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${DOCKER_IMAGE} ./packaging/build_conda.sh
+          docker run --gpus all  --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${TESTRUN_DOCKER_IMAGE} python3 ./packaging/build_conda.py
 
 workflows:
   version: 2
   build_and_test:
     jobs:
-      - main
+      # - main:
+      #     context: DOCKERHUB_TOKEN
       - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu92
-          name: binary_linux_conda_py3.6_cu92
-          python_version: '3.6'
-          pytorch_version: '1.4'
-          wheel_docker_image: pytorch/manylinux-cuda92
-      - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu100
-          name: binary_linux_conda_py3.6_cu100
-          python_version: '3.6'
-          pytorch_version: '1.4'
-          wheel_docker_image: pytorch/manylinux-cuda100
-      - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu101
-          name: binary_linux_conda_py3.6_cu101
-          python_version: '3.6'
-          pytorch_version: '1.4'
-      - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu92
-          name: binary_linux_conda_py3.7_cu92
-          python_version: '3.7'
-          pytorch_version: '1.4'
-          wheel_docker_image: pytorch/manylinux-cuda92
-      - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu100
-          name: binary_linux_conda_py3.7_cu100
-          python_version: '3.7'
-          pytorch_version: '1.4'
-          wheel_docker_image: pytorch/manylinux-cuda100
-      - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu101
-          name: binary_linux_conda_py3.7_cu101
-          python_version: '3.7'
-          pytorch_version: '1.4'
-      - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu92
-          name: binary_linux_conda_py3.8_cu92
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt210
           python_version: '3.8'
-          pytorch_version: '1.4'
-          wheel_docker_image: pytorch/manylinux-cuda92
+          pytorch_version: 2.1.0
       - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu100
-          name: binary_linux_conda_py3.8_cu100
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt210
           python_version: '3.8'
-          pytorch_version: '1.4'
-          wheel_docker_image: pytorch/manylinux-cuda100
+          pytorch_version: 2.1.0
       - binary_linux_conda:
-          build_version: 0.1.0
-          cu_version: cu101
-          name: binary_linux_conda_py3.8_cu101
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt211
           python_version: '3.8'
-          pytorch_version: '1.4'
+          pytorch_version: 2.1.1
       - binary_linux_conda:
-          cu_version: cu101
-          name: binary_linux_conda_py3.7_cu101
-          python_version: '3.7'
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt211
+          python_version: '3.8'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt212
+          python_version: '3.8'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt212
+          python_version: '3.8'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt220
+          python_version: '3.8'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt220
+          python_version: '3.8'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt222
+          python_version: '3.8'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt222
+          python_version: '3.8'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py38_cu118_pyt231
+          python_version: '3.8'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py38_cu121_pyt231
+          python_version: '3.8'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          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:cuda121
+          context: DOCKERHUB_TOKEN
+          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_py38_cu118_pyt241
+          python_version: '3.8'
+          pytorch_version: 2.4.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          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
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt210
+          python_version: '3.9'
+          pytorch_version: 2.1.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt210
+          python_version: '3.9'
+          pytorch_version: 2.1.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt211
+          python_version: '3.9'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt211
+          python_version: '3.9'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt212
+          python_version: '3.9'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt212
+          python_version: '3.9'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt220
+          python_version: '3.9'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt220
+          python_version: '3.9'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt222
+          python_version: '3.9'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt222
+          python_version: '3.9'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py39_cu118_pyt231
+          python_version: '3.9'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py39_cu121_pyt231
+          python_version: '3.9'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          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:cuda121
+          context: DOCKERHUB_TOKEN
+          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_py39_cu118_pyt241
+          python_version: '3.9'
+          pytorch_version: 2.4.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          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
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt210
+          python_version: '3.10'
+          pytorch_version: 2.1.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt210
+          python_version: '3.10'
+          pytorch_version: 2.1.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt211
+          python_version: '3.10'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt211
+          python_version: '3.10'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt212
+          python_version: '3.10'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt212
+          python_version: '3.10'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt220
+          python_version: '3.10'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt220
+          python_version: '3.10'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt222
+          python_version: '3.10'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py310_cu121_pyt222
+          python_version: '3.10'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py310_cu118_pyt231
+          python_version: '3.10'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          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
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt210
+          python_version: '3.11'
+          pytorch_version: 2.1.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt210
+          python_version: '3.11'
+          pytorch_version: 2.1.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt211
+          python_version: '3.11'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt211
+          python_version: '3.11'
+          pytorch_version: 2.1.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt212
+          python_version: '3.11'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt212
+          python_version: '3.11'
+          pytorch_version: 2.1.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt220
+          python_version: '3.11'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt220
+          python_version: '3.11'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt222
+          python_version: '3.11'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py311_cu121_pyt222
+          python_version: '3.11'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py311_cu118_pyt231
+          python_version: '3.11'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          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
+          cu_version: cu118
+          name: linux_conda_py312_cu118_pyt220
+          python_version: '3.12'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py312_cu121_pyt220
+          python_version: '3.12'
+          pytorch_version: 2.2.0
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py312_cu118_pyt222
+          python_version: '3.12'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          name: linux_conda_py312_cu121_pyt222
+          python_version: '3.12'
+          pytorch_version: 2.2.2
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda118
+          context: DOCKERHUB_TOKEN
+          cu_version: cu118
+          name: linux_conda_py312_cu118_pyt231
+          python_version: '3.12'
+          pytorch_version: 2.3.1
+      - binary_linux_conda:
+          conda_docker_image: pytorch/conda-builder:cuda121
+          context: DOCKERHUB_TOKEN
+          cu_version: cu121
+          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_py3.7_cu100
-          python_version: "3.7"
-          pytorch_version: "1.4"
-          cu_version: "cu100"
+          name: testrun_conda_cuda_py310_cu117_pyt201
+          context: DOCKERHUB_TOKEN
+          python_version: "3.10"
+          pytorch_version: '2.0.1'
+          cu_version: "cu117"

.circleci/regenerate.py

@@ -1,48 +1,103 @@
 #!/usr/bin/env python3
-# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
 
 """
 This script is adapted from the torchvision one.
-There is no python2.7 nor macos.
-TODO: python 3.8 when pytorch 1.4.
 """
 
 import os.path
+
 import jinja2
 import yaml
+from packaging import version
+
+
+# The CUDA versions which have pytorch conda packages available for linux for each
+# version of pytorch.
+CONDA_CUDA_VERSIONS = {
+    "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"],
+}
+
+
+def conda_docker_image_for_cuda(cuda_version):
+    if len(cuda_version) != 5:
+        raise ValueError("Unknown cuda version")
+    return "pytorch/conda-builder:cuda" + cuda_version[2:]
+
+
+def pytorch_versions_for_python(python_version):
+    if python_version in ["3.8", "3.9"]:
+        return list(CONDA_CUDA_VERSIONS)
+    if python_version == "3.10":
+        return [
+            i
+            for i in CONDA_CUDA_VERSIONS
+            if version.Version(i) >= version.Version("1.11.0")
+        ]
+    if python_version == "3.11":
+        return [
+            i
+            for i in CONDA_CUDA_VERSIONS
+            if version.Version(i) >= version.Version("2.1.0")
+        ]
+    if python_version == "3.12":
+        return [
+            i
+            for i in CONDA_CUDA_VERSIONS
+            if version.Version(i) >= version.Version("2.2.0")
+        ]
 
 
 def workflows(prefix="", filter_branch=None, upload=False, indentation=6):
     w = []
-    # add "wheel" here for pypi
     for btype in ["conda"]:
-        for python_version in ["3.6", "3.7", "3.8"]:
-            for cu_version in ["cu92", "cu100", "cu101"]:
-                w += workflow_pair(
-                    btype=btype,
-                    python_version=python_version,
-                    cu_version=cu_version,
-                    prefix=prefix,
-                    upload=upload,
-                    filter_branch=filter_branch,
-                )
+        for python_version in ["3.8", "3.9", "3.10", "3.11", "3.12"]:
+            for pytorch_version in pytorch_versions_for_python(python_version):
+                for cu_version in CONDA_CUDA_VERSIONS[pytorch_version]:
+                    w += workflow_pair(
+                        btype=btype,
+                        python_version=python_version,
+                        pytorch_version=pytorch_version,
+                        cu_version=cu_version,
+                        prefix=prefix,
+                        upload=upload,
+                        filter_branch=filter_branch,
+                    )
 
     return indent(indentation, w)
 
 
 def workflow_pair(
-    *, btype, python_version, cu_version, prefix="", upload=False, filter_branch
+    *,
+    btype,
+    python_version,
+    pytorch_version,
+    cu_version,
+    prefix="",
+    upload=False,
+    filter_branch,
 ):
-
     w = []
-    base_workflow_name = (
-        f"{prefix}binary_linux_{btype}_py{python_version}_{cu_version}"
-    )
+    py = python_version.replace(".", "")
+    pyt = pytorch_version.replace(".", "")
+    base_workflow_name = f"{prefix}linux_{btype}_py{py}_{cu_version}_pyt{pyt}"
 
     w.append(
         generate_base_workflow(
             base_workflow_name=base_workflow_name,
             python_version=python_version,
+            pytorch_version=pytorch_version,
             cu_version=cu_version,
             btype=btype,
             filter_branch=filter_branch,
@@ -63,21 +118,25 @@ def workflow_pair(
 
 
 def generate_base_workflow(
-    *, base_workflow_name, python_version, cu_version, btype, filter_branch=None
+    *,
+    base_workflow_name,
+    python_version,
+    cu_version,
+    pytorch_version,
+    btype,
+    filter_branch=None,
 ):
-
     d = {
         "name": base_workflow_name,
         "python_version": python_version,
         "cu_version": cu_version,
-        "build_version": "0.1.0",
-        "pytorch_version": "1.4",
+        "pytorch_version": pytorch_version,
+        "context": "DOCKERHUB_TOKEN",
     }
 
-    if cu_version == "cu92":
-        d["wheel_docker_image"] = "pytorch/manylinux-cuda92"
-    elif cu_version == "cu100":
-        d["wheel_docker_image"] = "pytorch/manylinux-cuda100"
+    conda_docker_image = conda_docker_image_for_cuda(cu_version)
+    if conda_docker_image is not None:
+        d["conda_docker_image"] = conda_docker_image
 
     if filter_branch is not None:
         d["filters"] = {"branches": {"only": filter_branch}}
@@ -85,9 +144,7 @@ def generate_base_workflow(
     return {f"binary_linux_{btype}": d}
 
 
-def generate_upload_workflow(
-    *, base_workflow_name, btype, cu_version, filter_branch
-):
+def generate_upload_workflow(*, base_workflow_name, btype, cu_version, filter_branch):
     d = {
         "name": f"{base_workflow_name}_upload",
         "context": "org-member",
@@ -104,6 +161,8 @@ def generate_upload_workflow(
 
 
 def indent(indentation, data_list):
+    if len(data_list) == 0:
+        return ""
     return ("\n" + " " * indentation).join(
         yaml.dump(data_list, default_flow_style=False).splitlines()
     )
@@ -112,7 +171,10 @@ def indent(indentation, data_list):
 if __name__ == "__main__":
     d = os.path.dirname(__file__)
     env = jinja2.Environment(
-        loader=jinja2.FileSystemLoader(d), lstrip_blocks=True, autoescape=False
+        loader=jinja2.FileSystemLoader(d),
+        lstrip_blocks=True,
+        autoescape=False,
+        keep_trailing_newline=True,
     )
 
     with open(os.path.join(d, "config.yml"), "w") as f:

.clang-format

@@ -42,7 +42,7 @@ ContinuationIndentWidth: 4
 Cpp11BracedListStyle: true
 DerivePointerAlignment: false
 DisableFormat:   false
-ForEachMacros:   [ FOR_EACH, FOR_EACH_ENUMERATE, FOR_EACH_KV, FOR_EACH_R, FOR_EACH_RANGE, ]
+ForEachMacros:   [ FOR_EACH, FOR_EACH_R, FOR_EACH_RANGE, ]
 IncludeCategories:
   - Regex:           '^<.*\.h(pp)?>'
     Priority:        1

.flake8

@@ -1,6 +1,9 @@
 [flake8]
-ignore = E203, E266, E501, W503, E221
-max-line-length = 80
+# B028 No explicit stacklevel argument found.
+# B907 'foo' is manually surrounded by quotes, consider using the `!r` conversion flag.
+# B905 `zip()` without an explicit `strict=` parameter.
+ignore = E203, E266, E501, W503, E221, B028, B905, B907
+max-line-length = 88
 max-complexity = 18
 select = B,C,E,F,W,T4,B9
 exclude = build,__init__.py

.github/CONTRIBUTING.md

@@ -8,7 +8,7 @@ We actively welcome your pull requests.
 However, if you're adding any significant features, please make sure to have a corresponding issue to outline your proposal and motivation and allow time for us to give feedback, *before* you send a PR.
 We do not always accept new features, and we take the following factors into consideration:
 
-- Whether the same feature can be achieved without modifying PyTorch3d directly. If any aspect of the API is not extensible, please highlight this in an issue so we can work on making this more extensible.
+- Whether the same feature can be achieved without modifying PyTorch3D directly. If any aspect of the API is not extensible, please highlight this in an issue so we can work on making this more extensible.
 - Whether the feature is potentially useful to a large audience, or only to a small portion of users.
 - Whether the proposed solution has a good design and interface.
 - Whether the proposed solution adds extra mental/practical overhead to users who don't need such feature.
@@ -16,14 +16,13 @@ We do not always accept new features, and we take the following factors into con
 
 When sending a PR, please ensure you complete the following steps:
 
-1. Fork the repo and create your branch from `master`. Follow the instructions
+1. Fork the repo and create your branch from `main`. Follow the instructions
    in [INSTALL.md](../INSTALL.md) to build the repo.
 2. If you've added code that should be tested, add tests.
 3. If you've changed any APIs, please update the documentation.
-4. Ensure the test suite passes:
+4. Ensure the test suite passes, by running this from the project root:
     ```
-    cd pytorch3d/tests
-    python -m unittest -v
+    python -m unittest discover -v -s tests -t .
     ```
 5. Make sure your code lints by running `dev/linter.sh` from  the project root.
 6. If a PR contains multiple orthogonal changes, split it into multiple separate PRs.
@@ -43,10 +42,10 @@ Facebook has a [bounty program](https://www.facebook.com/whitehat/) for the safe
 disclosure of security bugs. In those cases, please go through the process
 outlined on that page and do not file a public issue.
 
-## Coding Style  
+## Coding Style
 We follow these [python](http://google.github.io/styleguide/pyguide.html) and [C++](https://google.github.io/styleguide/cppguide.html) style guides.
 
-For the linter to work, you will need to install `black`, `flake`, `isort` and `clang-format`, and
+For the linter to work, you will need to install `black`, `flake`, `usort` and `clang-format`, and
 they need to be fairly up to date.
 
 ## License

.github/ISSUE_TEMPLATE/bugs.md

@@ -1,6 +1,6 @@
 ---
 name: "🐛 Bugs / Unexpected behaviors"
-about: Please report unexpected behaviors or bugs in PyTorch3d.
+about: Please report unexpected behaviors or bugs in PyTorch3D.
 
 ---
 
@@ -10,6 +10,8 @@ post according to this template:
 ## 🐛 Bugs / Unexpected behaviors
 <!-- A clear and concise description of the issue -->
 
+NOTE: Please look at the existing list of Issues tagged with the label ['bug`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3Abug). **Only open a new issue if this bug has not already been reported. If an issue already exists, please comment there instead.**.
+
 ## Instructions To Reproduce the Issue:
 
 Please include the following (depending on what the issue is):
@@ -25,4 +27,4 @@ Please include the following (depending on what the issue is):
 ```
 
 Please also simplify the steps as much as possible so they do not require additional resources to
-	 run, such as a private dataset.
+   run, such as a private dataset.

.github/ISSUE_TEMPLATE/feature_request.md

@@ -1,12 +1,14 @@
 ---
 name: "\U0001F680 Feature Request"
-about: Submit a proposal/request for a new PyTorch3d feature
+about: Submit a proposal/request for a new PyTorch3D feature
 
 ---
 
 ## 🚀 Feature
 <!-- A clear and concise description of the feature proposal -->
 
+NOTE: Please look at the existing list of Issues tagged with the label ['enhancement`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3Aenhancement). **Only open a new issue if you do not see your feature request there**.
+
 ## Motivation
 
 <!-- Please outline the motivation for the proposal.

.github/ISSUE_TEMPLATE/questions-help.md

@@ -1,18 +1,21 @@
 ---
 name: "❓ Questions"
-about: How do I do X with PyTorch3d? How does PyTorch3d do X?
+about: How do I do X with PyTorch3D? How does PyTorch3D do X?
 
 ---
 
-## ❓ Questions on how to use PyTorch3d
+## ❓ Questions on how to use PyTorch3D
 
 <!-- A clear and concise description of the question you need help with. -->
 
-NOTE:
 
-1. If you encountered any errors or unexpected issues while using PyTorch3d and need help resolving them,
+NOTE: Please look at the existing list of Issues tagged with the label ['question`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3Aquestion) or ['how-to`](https://github.com/facebookresearch/pytorch3d/issues?q=label%3A%22how+to%22). **Only open a new issue if you cannot find an answer there**.
+
+Also note the following:
+
+1. If you encountered any errors or unexpected issues while using PyTorch3D and need help resolving them,
    please use the "Bugs / Unexpected behaviors" issue template.
 
 2. We do not answer general machine learning / computer vision questions that are not specific to
-	 PyTorch3d, such as how a model works or what algorithm/methods can be
-	 used to achieve X.
+   PyTorch3D, such as how a model works or what algorithm/methods can be
+   used to achieve X.

.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

.gitignore

@@ -2,3 +2,20 @@ build/
 dist/
 *.egg-info/
 **/__pycache__/
+*-checkpoint.ipynb
+**/.ipynb_checkpoints
+**/.ipynb_checkpoints/**
+
+
+# Docusaurus site
+website/yarn.lock
+website/build/
+website/i18n/
+website/node_modules/*
+website/npm-debug.log
+
+## Generated for tutorials
+website/_tutorials/
+website/static/files/
+website/pages/tutorials/*
+!website/pages/tutorials/index.js

INSTALL.md

@@ -5,70 +5,153 @@
 
 ### Core library
 
-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.
+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
-- Python ≥ 3.6
-- PyTorch 1.4
-- torchvision that matches the PyTorch installation. You can install them together at pytorch.org to make sure of this.
+- Linux or macOS or Windows
+- 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
-- CUDA 9.2 or 10.0 or 10.1
-- [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.
 
-These can be installed by running:
+The runtime dependencies can be installed by running:
 ```
-conda create -n pytorch3d python=3.6
+conda create -n pytorch3d python=3.9
 conda activate pytorch3d
-conda install -c pytorch pytorch torchvision cudatoolkit=10.0
-conda install -c conda-forge -c takatosp1 fvcore
+conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
+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
+```
+conda install -c bottler nvidiacub
+```
+Otherwise download the CUB library from https://github.com/NVIDIA/cub/releases and unpack it to a folder of your choice.
+Define the environment variable CUB_HOME before building and point it to the directory that contains `CMakeLists.txt` for CUB.
+For example on Linux/Mac,
+```
+curl -LO https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
+tar xzf 1.10.0.tar.gz
+export CUB_HOME=$PWD/cub-1.10.0
 ```
 
 ### Tests/Linting and Demos
 
-For developing on top of PyTorch3d or contributing, you will need to run the linter and tests. If you want to run any of the notebook tutorials as `docs/tutorials` you will also need matplotlib.
+For developing on top of PyTorch3D or contributing, you will need to run the linter and tests. If you want to run any of the notebook tutorials as `docs/tutorials` or the examples in `docs/examples` you will also need matplotlib and OpenCV.
 - scikit-image
 - black
-- isort
+- usort
 - flake8
 - matplotlib
 - tdqm
 - jupyter
 - imageio
+- fvcore
+- plotly
+- opencv-python
 
 These can be installed by running:
 ```
-# Demos
+# Demos and examples
 conda install jupyter
-pip install scikit-image matplotlib imageio
+pip install scikit-image matplotlib imageio plotly opencv-python
 
 # Tests/Linting
-pip install black isort flake8
+conda install -c fvcore -c conda-forge fvcore
+pip install black usort flake8 flake8-bugbear flake8-comprehensions
 ```
 
-## Build/Install Pytorch3d
+## Installing prebuilt binaries for PyTorch3D
 After installing the above dependencies, run one of the following commands:
 
-### 1. Install from Anaconda Cloud
+### 1. Install with CUDA support from Anaconda Cloud, on Linux only
 
 ```
 # Anaconda Cloud
-conda install pytorch3d
+conda install pytorch3d -c pytorch3d
 ```
 
-### 2. Install from GitHub
+Or, to install a nightly (non-official, alpha) build:
 ```
-pip install 'git+https://github.com/facebookresearch/pytorch3d.git'
-# (add --user if you don't have permission)
+# Anaconda Cloud
+conda install pytorch3d -c pytorch3d-nightly
 ```
 
-### 3. Install from a local clone
+### 2. Install wheels for Linux
+We have prebuilt wheels with CUDA for Linux for PyTorch 1.11.0, for each of the supported CUDA versions,
+for Python 3.8 and 3.9. This is for ease of use on Google Colab.
+These are installed in a special way.
+For example, to install for Python 3.8, PyTorch 1.11.0 and CUDA 11.3
+```
+pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/py38_cu113_pyt1110/download.html
+```
+
+In general, from inside IPython, or in Google Colab or a jupyter notebook, you can install with
+```
+import sys
+import torch
+pyt_version_str=torch.__version__.split("+")[0].replace(".", "")
+version_str="".join([
+    f"py3{sys.version_info.minor}_cu",
+    torch.version.cuda.replace(".",""),
+    f"_pyt{pyt_version_str}"
+])
+!pip install iopath
+!pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
+```
+
+## Building / installing from source.
+CUDA support will be included if CUDA is available in pytorch or if the environment variable
+`FORCE_CUDA` is set to `1`.
+
+### 1. Install from GitHub
+```
+pip install "git+https://github.com/facebookresearch/pytorch3d.git"
+```
+To install using the code of the released version instead of from the main branch, use the following instead.
+```
+pip install "git+https://github.com/facebookresearch/pytorch3d.git@stable"
+```
+
+For CUDA builds with versions earlier than CUDA 11, set `CUB_HOME` before building as described above.
+
+**Install from Github on macOS:**
+Some environment variables should be provided, like this.
+```
+MACOSX_DEPLOYMENT_TARGET=10.14 CC=clang CXX=clang++ pip install "git+https://github.com/facebookresearch/pytorch3d.git"
+```
+
+### 2. Install from a local clone
 ```
 git clone https://github.com/facebookresearch/pytorch3d.git
 cd pytorch3d && pip install -e .
 ```
-To rebuild after installing from a local clone run, `rm -rf build/ **/*.so` then `pip install -e` .. You often need to rebuild pytorch3d after reinstalling PyTorch.
+To rebuild after installing from a local clone run, `rm -rf build/ **/*.so` then `pip install -e .`. You often need to rebuild pytorch3d after reinstalling PyTorch. For CUDA builds with versions earlier than CUDA 11, set `CUB_HOME` before building as described above.
 
 **Install from local clone on macOS:**
 ```
-MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ pip install -e .
+MACOSX_DEPLOYMENT_TARGET=10.14 CC=clang CXX=clang++ pip install -e .
 ```
+
+**Install from local clone on Windows:**
+
+Depending on the version of PyTorch, changes to some PyTorch headers may be needed before compilation. These are often discussed in issues in this repository.
+
+After any necessary patching, you can go to "x64 Native Tools Command Prompt for VS 2019" to compile and install
+```
+cd pytorch3d
+python3 setup.py install
+```
+
+After installing, you can run **unit tests**
+```
+python3 -m unittest discover -v -s tests -t .
+```
+
+# FAQ
+
+### Can I use Docker?
+
+We don't provide a docker file but see [#113](https://github.com/facebookresearch/pytorch3d/issues/113) for a docker file shared by a user (NOTE: this has not been tested by the PyTorch3D team).

LICENSE

@@ -1,8 +1,8 @@
 BSD License
 
-For PyTorch3d software
+For PyTorch3D software
 
-Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+Copyright (c) Meta Platforms, Inc. and affiliates. All rights reserved.
 
 Redistribution and use in source and binary forms, with or without modification,
 are permitted provided that the following conditions are met:
@@ -14,7 +14,7 @@ are permitted provided that the following conditions are met:
    this list of conditions and the following disclaimer in the documentation
    and/or other materials provided with the distribution.
 
- * Neither the name Facebook nor the names of its contributors may be used to
+ * Neither the name Meta nor the names of its contributors may be used to
    endorse or promote products derived from this software without specific
    prior written permission.
 

LICENSE-3RD-PARTY

@@ -0,0 +1,71 @@
+SRN license ( https://github.com/vsitzmann/scene-representation-networks/ ):
+
+MIT License
+
+Copyright (c) 2019 Vincent Sitzmann
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+IDR license ( github.com/lioryariv/idr ):
+
+MIT License
+
+Copyright (c) 2020 Lior Yariv
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.
+
+
+NeRF https://github.com/bmild/nerf/
+
+Copyright (c) 2020 bmild
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,27 +1,30 @@
-<img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/pytorch3dlogo.png" width="900"/>
+<img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/pytorch3dlogo.png" width="900"/>
 
 [![CircleCI](https://circleci.com/gh/facebookresearch/pytorch3d.svg?style=svg)](https://circleci.com/gh/facebookresearch/pytorch3d)
 [![Anaconda-Server Badge](https://anaconda.org/pytorch3d/pytorch3d/badges/version.svg)](https://anaconda.org/pytorch3d/pytorch3d)
 
 # Introduction
 
-PyTorch3d provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org).
+PyTorch3D provides efficient, reusable components for 3D Computer Vision research with [PyTorch](https://pytorch.org).
 
 Key features include:
 
 - Data structure for storing and manipulating triangle meshes
 - Efficient operations on triangle meshes (projective transformations, graph convolution, sampling, loss functions)
 - A differentiable mesh renderer
+- Implicitron, see [its README](projects/implicitron_trainer), a framework for new-view synthesis via implicit representations. ([blog post](https://ai.facebook.com/blog/implicitron-a-new-modular-extensible-framework-for-neural-implicit-representations-in-pytorch3d/))
 
-PyTorch3d is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
-For this reason, all operators in PyTorch3d:
+PyTorch3D is designed to integrate smoothly with deep learning methods for predicting and manipulating 3D data.
+For this reason, all operators in PyTorch3D:
 
 - Are implemented using PyTorch tensors
 - Can handle minibatches of hetereogenous data
 - Can be differentiated
 - Can utilize GPUs for acceleration
 
-Within FAIR, PyTorch3d has been used to power research projects such as [Mesh R-CNN](https://arxiv.org/abs/1906.02739).
+Within FAIR, PyTorch3D has been used to power research projects such as [Mesh R-CNN](https://arxiv.org/abs/1906.02739).
+
+See our [blog post](https://ai.facebook.com/blog/-introducing-pytorch3d-an-open-source-library-for-3d-deep-learning/) to see more demos and learn about PyTorch3D.
 
 ## Installation
 
@@ -29,49 +32,152 @@ For detailed instructions refer to [INSTALL.md](INSTALL.md).
 
 ## License
 
-PyTorch3d is released under the [BSD-3-Clause License](LICENSE).
+PyTorch3D is released under the [BSD License](LICENSE).
 
 ## Tutorials
 
-Get started with PyTorch3d by trying one of the tutorial notebooks.
+Get started with PyTorch3D by trying one of the tutorial notebooks.
 
-|<img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/dolphin_deform.gif" width="310"/>|<img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/bundle_adjust.gif" width="310"/>|
+|<img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/dolphin_deform.gif" width="310"/>|<img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/bundle_adjust.gif" width="310"/>|
 |:-----------------------------------------------------------------------------------------------------------:|:--------------------------------------------------:|
-| [Deform a sphere mesh to dolphin](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb)| [Bundle adjustment](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/bundle_adjustment.ipynb) |
+| [Deform a sphere mesh to dolphin](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/deform_source_mesh_to_target_mesh.ipynb)| [Bundle adjustment](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/bundle_adjustment.ipynb) |
 
-| <img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/render_textured_mesh.gif" width="310"/> | <img src="https://github.com/facebookresearch/pytorch3d/blob/master/.github/camera_position_teapot.gif" width="310" height="310"/>
+| <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/render_textured_mesh.gif" width="310"/> | <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/camera_position_teapot.gif" width="310" height="310"/>
 |:------------------------------------------------------------:|:--------------------------------------------------:|
-| [Render textured meshes](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/render_textured_meshes.ipynb)| [Camera position optimization](https://github.com/fairinternal/pytorch3d/blob/master/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb)|
+| [Render textured meshes](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_textured_meshes.ipynb)| [Camera position optimization](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb)|
+
+| <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/pointcloud_render.png" width="310"/> | <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/cow_deform.gif" width="310" height="310"/>
+|:------------------------------------------------------------:|:--------------------------------------------------:|
+| [Render textured pointclouds](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_colored_points.ipynb)| [Fit a mesh with texture](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_mesh.ipynb)|
+
+| <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/densepose_render.png" width="310"/> | <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/shapenet_render.png" width="310" height="310"/>
+|:------------------------------------------------------------:|:--------------------------------------------------:|
+| [Render DensePose data](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/render_densepose.ipynb)| [Load & Render ShapeNet data](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/dataloaders_ShapeNetCore_R2N2.ipynb)|
+
+| <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/fit_textured_volume.gif" width="310"/> | <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/fit_nerf.gif" width="310" height="310"/>
+|:------------------------------------------------------------:|:--------------------------------------------------:|
+| [Fit Textured Volume](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_textured_volume.ipynb)| [Fit A Simple Neural Radiance Field](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/fit_simple_neural_radiance_field.ipynb)|
+
+| <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/fit_textured_volume.gif" width="310"/> | <img src="https://raw.githubusercontent.com/facebookresearch/pytorch3d/main/.github/implicitron_config.gif" width="310" height="310"/>
+|:------------------------------------------------------------:|:--------------------------------------------------:|
+| [Fit Textured Volume in Implicitron](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_volumes.ipynb)| [Implicitron Config System](https://github.com/facebookresearch/pytorch3d/blob/main/docs/tutorials/implicitron_config_system.ipynb)|
+
+
+
+
 
 ## Documentation
 
-Learn more about the API by reading the PyTorch3d [documentation](https://pytorch3d.readthedocs.org/).
+Learn more about the API by reading the PyTorch3D [documentation](https://pytorch3d.readthedocs.org/).
 
 We also have deep dive notes on several API components:
 
-- [Heterogeneous Batching](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/batching.md)
-- [Mesh IO](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/meshes_io.md)
-- [Differentiable Rendering](https://github.com/facebookresearch/pytorch3d/tree/master/docs/notes/renderer_getting_started.md)
+- [Heterogeneous Batching](https://github.com/facebookresearch/pytorch3d/tree/main/docs/notes/batching.md)
+- [Mesh IO](https://github.com/facebookresearch/pytorch3d/tree/main/docs/notes/meshes_io.md)
+- [Differentiable Rendering](https://github.com/facebookresearch/pytorch3d/tree/main/docs/notes/renderer_getting_started.md)
+
+### Overview Video
+
+We have created a short (~14 min) video tutorial providing an overview of the PyTorch3D codebase including several code examples. Click on the image below to watch the video on YouTube:
+
+<a href="http://www.youtube.com/watch?v=Pph1r-x9nyY"><img src="http://img.youtube.com/vi/Pph1r-x9nyY/0.jpg" height="225" ></a>
 
 ## Development
 
-We welcome new contributions to Pytorch3d and we will be actively maintaining this library! Please refer to [CONTRIBUTING.md](./.github/CONTRIBUTING.md) for full instructions on how to run the code, tests and linter, and submit your pull requests.
+We welcome new contributions to PyTorch3D and we will be actively maintaining this library! Please refer to [CONTRIBUTING.md](./.github/CONTRIBUTING.md) for full instructions on how to run the code, tests and linter, and submit your pull requests.
 
+## Development and Compatibility
+
+- `main` branch: actively developed, without any guarantee, Anything can be broken at any time
+  - REMARK: this includes nightly builds which are built from `main`
+  - HINT: the commit history can help locate regressions or changes
+- backward-compatibility between releases: no guarantee. Best efforts to communicate breaking changes and facilitate migration of code or data (incl. models).
 
 ## Contributors
 
-PyTorch3d is written and maintained by the Facebook AI Research Computer Vision Team.
+PyTorch3D is written and maintained by the Facebook AI Research Computer Vision Team.
+
+In alphabetical order:
+
+* Amitav Baruah
+* Steve Branson
+* Krzysztof Chalupka
+* Jiali Duan
+* Luya Gao
+* Georgia Gkioxari
+* Taylor Gordon
+* Justin Johnson
+* Patrick Labatut
+* Christoph Lassner
+* Wan-Yen Lo
+* David Novotny
+* Nikhila Ravi
+* Jeremy Reizenstein
+* Dave Schnizlein
+* Roman Shapovalov
+* Olivia Wiles
 
 ## Citation
 
-If you find PyTorch3d useful in your research, please cite:
+If you find PyTorch3D useful in your research, please cite our tech report:
 
 ```bibtex
-@misc{ravi2020pytorch3d,
-  author =       {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
+@article{ravi2020pytorch3d,
+    author = {Nikhila Ravi and Jeremy Reizenstein and David Novotny and Taylor Gordon
                   and Wan-Yen Lo and Justin Johnson and Georgia Gkioxari},
-  title =        {PyTorch3D},
-  howpublished = {\url{https://github.com/facebookresearch/pytorch3d}},
-  year =         {2020}
+    title = {Accelerating 3D Deep Learning with PyTorch3D},
+    journal = {arXiv:2007.08501},
+    year = {2020},
 }
 ```
+
+If you are using the pulsar backend for sphere-rendering (the `PulsarPointRenderer` or `pytorch3d.renderer.points.pulsar.Renderer`), please cite the tech report:
+
+```bibtex
+@article{lassner2020pulsar,
+    author = {Christoph Lassner and Michael Zollh\"ofer},
+    title = {Pulsar: Efficient Sphere-based Neural Rendering},
+    journal = {arXiv:2004.07484},
+    year = {2020},
+}
+```
+
+## News
+
+Please see below for a timeline of the codebase updates in reverse chronological order. We are sharing updates on the releases as well as research projects which are built with PyTorch3D. The changelogs for the releases are available under [`Releases`](https://github.com/facebookresearch/pytorch3d/releases),  and the builds can be installed using `conda` as per the instructions in [INSTALL.md](INSTALL.md).
+
+**[Oct 31st 2023]:**   PyTorch3D [v0.7.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.5) released.
+
+**[May 10th 2023]:**   PyTorch3D [v0.7.4](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.4) released.
+
+**[Apr 5th 2023]:**   PyTorch3D [v0.7.3](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.3) released.
+
+**[Dec 19th 2022]:**   PyTorch3D [v0.7.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.2) released.
+
+**[Oct 23rd 2022]:**   PyTorch3D [v0.7.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.1) released.
+
+**[Aug 10th 2022]:**   PyTorch3D [v0.7.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.7.0) released with Implicitron and MeshRasterizerOpenGL.
+
+**[Apr 28th 2022]:**   PyTorch3D [v0.6.2](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.2) released
+
+**[Dec 16th 2021]:**   PyTorch3D [v0.6.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.1) released
+
+**[Oct 6th 2021]:**   PyTorch3D [v0.6.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.6.0) released
+
+**[Aug 5th 2021]:**   PyTorch3D [v0.5.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.5.0) released
+
+**[Feb 9th 2021]:** PyTorch3D [v0.4.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.4.0) released with support for implicit functions, volume rendering and a [reimplementation of NeRF](https://github.com/facebookresearch/pytorch3d/tree/main/projects/nerf).
+
+**[November 2nd 2020]:** PyTorch3D [v0.3.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.3.0) released, integrating the pulsar backend.
+
+**[Aug 28th 2020]:**   PyTorch3D [v0.2.5](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.5) released
+
+**[July 17th 2020]:**   PyTorch3D tech report published on ArXiv: https://arxiv.org/abs/2007.08501
+
+**[April 24th 2020]:**   PyTorch3D [v0.2.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.2.0) released
+
+**[March 25th 2020]:**   [SynSin](https://arxiv.org/abs/1912.08804) codebase released using PyTorch3D: https://github.com/facebookresearch/synsin
+
+**[March 8th 2020]:**   PyTorch3D [v0.1.1](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.1.1) bug fix release
+
+**[Jan 23rd 2020]:**   PyTorch3D [v0.1.0](https://github.com/facebookresearch/pytorch3d/releases/tag/v0.1.0) released. [Mesh R-CNN](https://arxiv.org/abs/1906.02739) codebase released: https://github.com/facebookresearch/meshrcnn

dev/linter.sh

@@ -1,30 +1,40 @@
 #!/bin/bash -e
-# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
 
 # Run this script at project root by "./dev/linter.sh" before you commit
 
-{
-  V=$(black --version|cut '-d ' -f3)
-  code='import distutils.version; assert "19.3" < distutils.version.LooseVersion("'$V'")'
-  python -c "${code}" 2> /dev/null
-} || {
-  echo "Linter requires black 19.3b0 or higher!"
-  exit 1
-}
-
 DIR="$( cd "$( dirname "${BASH_SOURCE[0]}" )" >/dev/null 2>&1 && pwd )"
-DIR="${DIR}/.."
+DIR=$(dirname "${DIR}")
 
-echo "Running isort..."
-isort -y -sp "${DIR}"
+if [[ -f "${DIR}/BUCK" ]]
+then
+  pyfmt "${DIR}"
+else
+# run usort externally only
+  echo "Running usort..."
+  usort  "${DIR}"
+fi
 
 echo "Running black..."
-black -l 80 "${DIR}"
+black "${DIR}"
 
 echo "Running flake..."
-flake8 "${DIR}"
+flake8 "${DIR}" || true
 
 echo "Running clang-format ..."
-find "${DIR}" -regex ".*\.\(cpp\|c\|cc\|cu\|cuh\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 clang-format -i
+clangformat=$(command -v clang-format-8 || echo clang-format)
+find "${DIR}" -regex ".*\.\(cpp\|c\|cc\|cu\|cuh\|cxx\|h\|hh\|hpp\|hxx\|tcc\|mm\|m\)" -print0 | xargs -0 "${clangformat}" -i
 
-(cd "${DIR}"; command -v arc > /dev/null && arc lint) || true
+# Run arc and pyre internally only.
+if [[ -f "${DIR}/TARGETS" ]]
+then
+  (cd "${DIR}"; command -v arc > /dev/null && arc lint) || true
+
+  echo "Running pyre..."
+  echo "To restart/kill pyre server, run 'pyre restart' or 'pyre kill' in fbcode/"
+  ( cd ~/fbsource/fbcode; arc pyre check //vision/fair/pytorch3d/... )
+fi

dev/run_tutorials.sh

@@ -0,0 +1,56 @@
+#!/usr/bin/bash
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# This script is for running some of the tutorials using the nightly build in
+# an isolated environment. It is designed to be run in docker.
+
+# If you run this script in this directory with
+#   sudo docker run --runtime=nvidia -it --rm -v $PWD/../docs/tutorials:/notebooks -v $PWD:/loc pytorch/conda-cuda bash /loc/run_tutorials.sh | tee log.txt
+# it should execute some tutorials with the nightly build and resave them, and
+# save a log in the current directory.
+
+# We use nbconvert. runipy would be an alternative but it currently doesn't
+# work well with plotly.
+
+set -e
+
+conda init bash
+# shellcheck source=/dev/null
+source ~/.bashrc
+conda create -y -n myenv python=3.8 matplotlib ipython ipywidgets nbconvert
+conda activate myenv
+conda install -y -c iopath iopath
+conda install -y -c pytorch pytorch=1.6.0 cudatoolkit=10.1 torchvision
+conda install -y -c pytorch3d-nightly pytorch3d
+pip install plotly scikit-image
+
+for notebook in /notebooks/*.ipynb
+do
+    name=$(basename "$notebook")
+
+    if [[ "$name" == "dataloaders_ShapeNetCore_R2N2.ipynb" ]]
+    then
+        #skip as data not easily available
+        continue
+    fi
+    if [[ "$name" == "render_densepose.ipynb" ]]
+    then
+        #skip as data not easily available
+        continue
+    fi
+
+    #comment the lines which install torch, torchvision and pytorch3d
+    sed -Ei '/(torchvision)|(pytorch3d)/ s/!pip/!#pip/' "$notebook"
+    #Don't let tqdm use widgets
+    sed -i 's/from tqdm.notebook import tqdm/from tqdm import tqdm/' "$notebook"
+
+    echo
+    echo "###   ###   ###"
+    echo "starting $name"
+    time jupyter nbconvert --to notebook --inplace --ExecutePreprocessor.kernel_name=python3 --execute "$notebook" || true
+    echo "ending $name"
+done

dev/test_list.py

@@ -0,0 +1,65 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+import ast
+from pathlib import Path
+from typing import List
+
+
+"""
+This module outputs a list of tests for completion.
+It has no dependencies.
+"""
+
+
+def get_test_files() -> List[Path]:
+    root = Path(__file__).parent.parent
+    dirs = ["tests", "projects/implicitron_trainer"]
+    return [i for dir in dirs for i in (root / dir).glob("**/test*.py")]
+
+
+def tests_from_file(path: Path, base: str) -> List[str]:
+    """
+    Returns all the tests in the given file, in format
+    expected as arguments when running the tests.
+    e.g.
+        file_stem
+        file_stem.TestFunctionality
+        file_stem.TestFunctionality.test_f
+        file_stem.TestFunctionality.test_g
+    """
+    with open(path) as f:
+        node = ast.parse(f.read())
+    out = [base]
+    for cls in node.body:
+        if not isinstance(cls, ast.ClassDef):
+            continue
+        if not cls.name.startswith("Test"):
+            continue
+        class_base = base + "." + cls.name
+        out.append(class_base)
+        for method in cls.body:
+            if not isinstance(method, ast.FunctionDef):
+                continue
+            if not method.name.startswith("test"):
+                continue
+            out.append(class_base + "." + method.name)
+    return out
+
+
+def main() -> None:
+    files = get_test_files()
+    test_root = Path(__file__).parent.parent
+    all_tests = []
+    for f in files:
+        file_base = str(f.relative_to(test_root))[:-3].replace("/", ".")
+        all_tests.extend(tests_from_file(f, file_base))
+    for test in sorted(all_tests):
+        print(test)
+
+
+if __name__ == "__main__":
+    main()

docs/.readthedocs.yaml

@@ -0,0 +1,27 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+# Read the Docs configuration file
+# See https://docs.readthedocs.io/en/stable/config-file/v2.html for details
+
+# Required
+version: 2
+
+# Set the version of Python and other tools you might need
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.11"
+
+# Build documentation in the docs/ directory with Sphinx
+sphinx:
+  configuration: docs/conf.py
+
+# We recommend specifying your dependencies to enable reproducible builds:
+# https://docs.readthedocs.io/en/stable/guides/reproducible-builds.html
+python:
+  install:
+  - requirements: docs/requirements.txt

docs/Makefile

@@ -1,4 +1,8 @@
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
 
 # Minimal makefile for Sphinx documentation
 

docs/README.md

@@ -1,10 +1,9 @@
-
 ## Setup
 
 ### Install dependencies
 
 ```
-pip install -U recommonmark mock sphinx sphinx_rtd_theme sphinx_markdown_tables
+pip install -U recommonmark sphinx sphinx_rtd_theme sphinx_markdown_tables
 ```
 
 ### Add symlink to the root README.md
@@ -12,7 +11,7 @@ pip install -U recommonmark mock sphinx sphinx_rtd_theme sphinx_markdown_tables
 We want to include the root readme as an overview. Before generating the docs create a symlink to the root readme.
 
 ```
-cd docs        
+cd docs
 ln -s ../README.md  overview.md
 ```
 

docs/conf.py

@@ -1,5 +1,9 @@
 # -*- coding: utf-8 -*-
-# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
 
 # flake8: noqa
 
@@ -15,15 +19,14 @@
 #
 import os
 import sys
+import unittest.mock as mock
 
-import pytorch3d  # isort: skip
-
-import mock
 from recommonmark.parser import CommonMarkParser
 from recommonmark.states import DummyStateMachine
 from sphinx.builders.html import StandaloneHTMLBuilder
 from sphinx.ext.autodoc import between
 
+
 # Monkey patch to fix recommonmark 0.4 doc reference issues.
 orig_run_role = DummyStateMachine.run_role
 
@@ -55,11 +58,6 @@ DEPLOY = os.environ.get("READTHEDOCS") == "True"
 needs_sphinx = "1.7"
 
 
-# The short X.Y version
-version = pytorch3d.__version__
-# The full version, including alpha/beta/rc tags
-release = version
-
 try:
     import torch  # noqa
 except ImportError:
@@ -84,11 +82,14 @@ for m in ["cv2", "scipy", "numpy", "pytorch3d._C", "np.eye", "np.zeros"]:
 # -- Project information -----------------------------------------------------
 
 project = "PyTorch3D"
-copyright = "2019, facebookresearch"
+copyright = "Meta Platforms, Inc"
 author = "facebookresearch"
 
+# The short X.Y version
+version = ""
+
 # The full version, including alpha/beta/rc tags
-release = "v0.1"
+release = version
 
 # -- General configuration ---------------------------------------------------
 
@@ -158,9 +159,7 @@ html_theme_options = {"collapse_navigation": True}
 def url_resolver(url):
     if ".html" not in url:
         url = url.replace("../", "")
-        return (
-            "https://github.com/facebookresearch/pytorch3d/blob/master/" + url
-        )
+        return "https://github.com/facebookresearch/pytorch3d/blob/main/" + url
     else:
         if DEPLOY:
             return "http://pytorch3d.readthedocs.io/" + url
@@ -192,9 +191,7 @@ def setup(app):
 
     # Register a sphinx.ext.autodoc.between listener to ignore everything
     # between lines that contain the word IGNORE
-    app.connect(
-        "autodoc-process-docstring", between("^.*IGNORE.*$", exclude=True)
-    )
+    app.connect("autodoc-process-docstring", between("^.*IGNORE.*$", exclude=True))
     app.add_transform(AutoStructify)
 
     return app

docs/examples/pulsar_basic.py

@@ -0,0 +1,77 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+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
+
+import imageio
+import torch
+from pytorch3d.renderer.points.pulsar import Renderer
+
+
+LOGGER = logging.getLogger(__name__)
+
+
+def cli():
+    """
+    Basic example for the pulsar sphere renderer.
+
+    Writes to `basic.png`.
+    """
+    LOGGER.info("Rendering on GPU...")
+    torch.manual_seed(1)
+    n_points = 10
+    width = 1_000
+    height = 1_000
+    device = torch.device("cuda")
+    # The PyTorch3D system is right handed; in pulsar you can choose the handedness.
+    # For easy reproducibility we use a right handed coordinate system here.
+    renderer = Renderer(width, height, n_points, right_handed_system=True).to(device)
+    # Generate sample data.
+    vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0
+    vert_pos[:, 2] += 25.0
+    vert_pos[:, :2] -= 5.0
+    vert_col = torch.rand(n_points, 3, dtype=torch.float32, device=device)
+    vert_rad = torch.rand(n_points, dtype=torch.float32, device=device)
+    cam_params = torch.tensor(
+        [
+            0.0,
+            0.0,
+            0.0,  # Position 0, 0, 0 (x, y, z).
+            0.0,
+            math.pi,  # Because of the right handed system, the camera must look 'back'.
+            0.0,  # Rotation 0, 0, 0 (in axis-angle format).
+            5.0,  # Focal length in world size.
+            2.0,  # Sensor size in world size.
+        ],
+        dtype=torch.float32,
+        device=device,
+    )
+    # Render.
+    image = renderer(
+        vert_pos,
+        vert_col,
+        vert_rad,
+        cam_params,
+        1.0e-1,  # Renderer blending parameter gamma, in [1., 1e-5].
+        45.0,  # Maximum depth.
+    )
+    LOGGER.info("Writing image to `%s`.", path.abspath("basic.png"))
+    imageio.imsave("basic.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy())
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/examples/pulsar_basic_unified.py

@@ -0,0 +1,90 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This example demonstrates the most trivial use of the pulsar PyTorch3D
+interface for sphere renderering. It renders and saves an image with
+10 random spheres.
+Output: basic-pt3d.png.
+"""
+
+import logging
+from os import path
+
+import imageio
+import torch
+from pytorch3d.renderer import (
+    PerspectiveCameras,
+    PointsRasterizationSettings,
+    PointsRasterizer,
+    PulsarPointsRenderer,
+)
+from pytorch3d.structures import Pointclouds
+
+
+LOGGER = logging.getLogger(__name__)
+
+
+def cli():
+    """
+    Basic example for the pulsar sphere renderer using the PyTorch3D interface.
+
+    Writes to `basic-pt3d.png`.
+    """
+    LOGGER.info("Rendering on GPU...")
+    torch.manual_seed(1)
+    n_points = 10
+    width = 1_000
+    height = 1_000
+    device = torch.device("cuda")
+    # Generate sample data.
+    vert_pos = torch.rand(n_points, 3, dtype=torch.float32, device=device) * 10.0
+    vert_pos[:, 2] += 25.0
+    vert_pos[:, :2] -= 5.0
+    vert_col = torch.rand(n_points, 3, dtype=torch.float32, device=device)
+    pcl = Pointclouds(points=vert_pos[None, ...], features=vert_col[None, ...])
+    # Alternatively, you can also use the look_at_view_transform to get R and T:
+    # R, T = look_at_view_transform(
+    #     dist=30.0, elev=0.0, azim=180.0, at=((0.0, 0.0, 30.0),), up=((0, 1, 0),),
+    # )
+    cameras = PerspectiveCameras(
+        # The focal length must be double the size for PyTorch3D because of the NDC
+        # coordinates spanning a range of two - and they must be normalized by the
+        # sensor width (see the pulsar example). This means we need here
+        # 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar.
+        focal_length=(5.0 * 2.0 / 2.0,),
+        R=torch.eye(3, dtype=torch.float32, device=device)[None, ...],
+        T=torch.zeros((1, 3), dtype=torch.float32, device=device),
+        image_size=((height, width),),
+        device=device,
+    )
+    vert_rad = torch.rand(n_points, dtype=torch.float32, device=device)
+    raster_settings = PointsRasterizationSettings(
+        image_size=(height, width),
+        radius=vert_rad,
+    )
+    rasterizer = PointsRasterizer(cameras=cameras, raster_settings=raster_settings)
+    renderer = PulsarPointsRenderer(rasterizer=rasterizer).to(device)
+    # Render.
+    image = renderer(
+        pcl,
+        gamma=(1.0e-1,),  # Renderer blending parameter gamma, in [1., 1e-5].
+        znear=(1.0,),
+        zfar=(45.0,),
+        radius_world=True,
+        bg_col=torch.ones((3,), dtype=torch.float32, device=device),
+    )[0]
+    LOGGER.info("Writing image to `%s`.", path.abspath("basic-pt3d.png"))
+    imageio.imsave(
+        "basic-pt3d.png", (image.cpu().detach() * 255.0).to(torch.uint8).numpy()
+    )
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/examples/pulsar_cam.py

@@ -0,0 +1,181 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This example demonstrates camera parameter optimization with the plain
+pulsar interface. For this, a reference image has been pre-generated
+(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_cam.png`).
+The same scene parameterization is loaded and the camera parameters
+distorted. Gradient-based optimization is used to converge towards the
+original camera parameters.
+Output: cam.gif.
+"""
+
+import logging
+import math
+from os import path
+
+import cv2
+import imageio
+import numpy as np
+import torch
+from pytorch3d.renderer.points.pulsar import Renderer
+from pytorch3d.transforms import axis_angle_to_matrix, matrix_to_rotation_6d
+from torch import nn, optim
+
+
+LOGGER = logging.getLogger(__name__)
+N_POINTS = 20
+WIDTH = 1_000
+HEIGHT = 1_000
+DEVICE = torch.device("cuda")
+
+
+class SceneModel(nn.Module):
+    """
+    A simple scene model to demonstrate use of pulsar in PyTorch modules.
+
+    The scene model is parameterized with sphere locations (vert_pos),
+    channel content (vert_col), radiuses (vert_rad), camera position (cam_pos),
+    camera rotation (cam_rot) and sensor focal length and width (cam_sensor).
+
+    The forward method of the model renders this scene description. Any
+    of these parameters could instead be passed as inputs to the forward
+    method and come from a different model.
+    """
+
+    def __init__(self):
+        super(SceneModel, self).__init__()
+        self.gamma = 0.1
+        # Points.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False))
+        self.register_parameter(
+            "vert_col",
+            nn.Parameter(
+                torch.rand(N_POINTS, 3, dtype=torch.float32), requires_grad=False
+            ),
+        )
+        self.register_parameter(
+            "vert_rad",
+            nn.Parameter(
+                torch.rand(N_POINTS, dtype=torch.float32), requires_grad=False
+            ),
+        )
+        self.register_parameter(
+            "cam_pos",
+            nn.Parameter(
+                torch.tensor([0.1, 0.1, 0.0], dtype=torch.float32), requires_grad=True
+            ),
+        )
+        self.register_parameter(
+            "cam_rot",
+            # We're using the 6D rot. representation for better gradients.
+            nn.Parameter(
+                matrix_to_rotation_6d(
+                    axis_angle_to_matrix(
+                        torch.tensor(
+                            [
+                                [0.02, math.pi + 0.02, 0.01],
+                            ],
+                            dtype=torch.float32,
+                        )
+                    )
+                )[0],
+                requires_grad=True,
+            ),
+        )
+        self.register_parameter(
+            "cam_sensor",
+            nn.Parameter(
+                torch.tensor([4.8, 1.8], dtype=torch.float32), requires_grad=True
+            ),
+        )
+        self.renderer = Renderer(WIDTH, HEIGHT, N_POINTS, right_handed_system=True)
+
+    def forward(self):
+        return self.renderer.forward(
+            self.vert_pos,
+            self.vert_col,
+            self.vert_rad,
+            torch.cat([self.cam_pos, self.cam_rot, self.cam_sensor]),
+            self.gamma,
+            45.0,
+        )
+
+
+def cli():
+    """
+    Camera optimization example using pulsar.
+
+    Writes to `cam.gif`.
+    """
+    LOGGER.info("Loading reference...")
+    # Load reference.
+    ref = (
+        torch.from_numpy(
+            imageio.imread(
+                "../../tests/pulsar/reference/examples_TestRenderer_test_cam.png"
+            )[:, ::-1, :].copy()
+        ).to(torch.float32)
+        / 255.0
+    ).to(DEVICE)
+    # Set up model.
+    model = SceneModel().to(DEVICE)
+    # Optimizer.
+    optimizer = optim.SGD(
+        [
+            {"params": [model.cam_pos], "lr": 1e-4},  # 1e-3
+            {"params": [model.cam_rot], "lr": 5e-6},
+            {"params": [model.cam_sensor], "lr": 1e-4},
+        ]
+    )
+
+    LOGGER.info("Writing video to `%s`.", path.abspath("cam.gif"))
+    writer = imageio.get_writer("cam.gif", format="gif", fps=25)
+
+    # Optimize.
+    for i in range(300):
+        optimizer.zero_grad()
+        result = model()
+        # Visualize.
+        result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+        cv2.imshow("opt", result_im[:, :, ::-1])
+        writer.append_data(result_im)
+        overlay_img = np.ascontiguousarray(
+            ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[
+                :, :, ::-1
+            ]
+        )
+        overlay_img = cv2.putText(
+            overlay_img,
+            "Step %d" % (i),
+            (10, 40),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+            False,
+        )
+        cv2.imshow("overlay", overlay_img)
+        cv2.waitKey(1)
+        # Update.
+        loss = ((result - ref) ** 2).sum()
+        LOGGER.info("loss %d: %f", i, loss.item())
+        loss.backward()
+        optimizer.step()
+    writer.close()
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/examples/pulsar_cam_unified.py

@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This example demonstrates camera parameter optimization with the pulsar
+PyTorch3D interface. For this, a reference image has been pre-generated
+(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_cam.png`).
+The same scene parameterization is loaded and the camera parameters
+distorted. Gradient-based optimization is used to converge towards the
+original camera parameters.
+Output: cam-pt3d.gif
+"""
+
+import logging
+from os import path
+
+import cv2
+import imageio
+import numpy as np
+import torch
+from pytorch3d.renderer.cameras import PerspectiveCameras
+from pytorch3d.renderer.points import (
+    PointsRasterizationSettings,
+    PointsRasterizer,
+    PulsarPointsRenderer,
+)
+from pytorch3d.structures.pointclouds import Pointclouds
+from pytorch3d.transforms import axis_angle_to_matrix
+from torch import nn, optim
+
+
+LOGGER = logging.getLogger(__name__)
+N_POINTS = 20
+WIDTH = 1_000
+HEIGHT = 1_000
+DEVICE = torch.device("cuda")
+
+
+class SceneModel(nn.Module):
+    """
+    A simple scene model to demonstrate use of pulsar in PyTorch modules.
+
+    The scene model is parameterized with sphere locations (vert_pos),
+    channel content (vert_col), radiuses (vert_rad), camera position (cam_pos),
+    camera rotation (cam_rot) and sensor focal length and width (cam_sensor).
+
+    The forward method of the model renders this scene description. Any
+    of these parameters could instead be passed as inputs to the forward
+    method and come from a different model.
+    """
+
+    def __init__(self):
+        super(SceneModel, self).__init__()
+        self.gamma = 0.1
+        # Points.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=False))
+        self.register_parameter(
+            "vert_col",
+            nn.Parameter(
+                torch.rand(N_POINTS, 3, dtype=torch.float32),
+                requires_grad=False,
+            ),
+        )
+        self.register_parameter(
+            "vert_rad",
+            nn.Parameter(
+                torch.rand(N_POINTS, dtype=torch.float32),
+                requires_grad=False,
+            ),
+        )
+        self.register_parameter(
+            "cam_pos",
+            nn.Parameter(
+                torch.tensor([0.1, 0.1, 0.0], dtype=torch.float32),
+                requires_grad=True,
+            ),
+        )
+        self.register_parameter(
+            "cam_rot",
+            # We're using the 6D rot. representation for better gradients.
+            nn.Parameter(
+                axis_angle_to_matrix(
+                    torch.tensor(
+                        [
+                            [0.02, 0.02, 0.01],
+                        ],
+                        dtype=torch.float32,
+                    )
+                )[0],
+                requires_grad=True,
+            ),
+        )
+        self.register_parameter(
+            "focal_length",
+            nn.Parameter(
+                torch.tensor(
+                    [
+                        4.8 * 2.0 / 2.0,
+                    ],
+                    dtype=torch.float32,
+                ),
+                requires_grad=True,
+            ),
+        )
+        self.cameras = PerspectiveCameras(
+            # The focal length must be double the size for PyTorch3D because of the NDC
+            # coordinates spanning a range of two - and they must be normalized by the
+            # sensor width (see the pulsar example). This means we need here
+            # 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar.
+            #
+            # R, T and f are provided here, but will be provided again
+            # at every call to the forward method. The reason are problems
+            # with PyTorch which makes device placement for gradients problematic
+            # for tensors which are themselves on a 'gradient path' but not
+            # leafs in the calculation tree. This will be addressed by an architectural
+            # change in PyTorch3D in the future. Until then, this workaround is
+            # recommended.
+            focal_length=self.focal_length,
+            R=self.cam_rot[None, ...],
+            T=self.cam_pos[None, ...],
+            image_size=((HEIGHT, WIDTH),),
+            device=DEVICE,
+        )
+        raster_settings = PointsRasterizationSettings(
+            image_size=(HEIGHT, WIDTH),
+            radius=self.vert_rad,
+        )
+        rasterizer = PointsRasterizer(
+            cameras=self.cameras, raster_settings=raster_settings
+        )
+        self.renderer = PulsarPointsRenderer(rasterizer=rasterizer)
+
+    def forward(self):
+        # The Pointclouds object creates copies of it's arguments - that's why
+        # we have to create a new object in every forward step.
+        pcl = Pointclouds(
+            points=self.vert_pos[None, ...], features=self.vert_col[None, ...]
+        )
+        return self.renderer(
+            pcl,
+            gamma=(self.gamma,),
+            zfar=(45.0,),
+            znear=(1.0,),
+            radius_world=True,
+            bg_col=torch.ones((3,), dtype=torch.float32, device=DEVICE),
+            # As mentioned above: workaround for device placement of gradients for
+            # camera parameters.
+            focal_length=self.focal_length,
+            R=self.cam_rot[None, ...],
+            T=self.cam_pos[None, ...],
+        )[0]
+
+
+def cli():
+    """
+    Camera optimization example using pulsar.
+
+    Writes to `cam.gif`.
+    """
+    LOGGER.info("Loading reference...")
+    # Load reference.
+    ref = (
+        torch.from_numpy(
+            imageio.imread(
+                "../../tests/pulsar/reference/examples_TestRenderer_test_cam.png"
+            )[:, ::-1, :].copy()
+        ).to(torch.float32)
+        / 255.0
+    ).to(DEVICE)
+    # Set up model.
+    model = SceneModel().to(DEVICE)
+    # Optimizer.
+    optimizer = optim.SGD(
+        [
+            {"params": [model.cam_pos], "lr": 1e-4},
+            {"params": [model.cam_rot], "lr": 5e-6},
+            # Using a higher lr for the focal length here, because
+            # the sensor width can not be optimized directly.
+            {"params": [model.focal_length], "lr": 1e-3},
+        ]
+    )
+
+    LOGGER.info("Writing video to `%s`.", path.abspath("cam-pt3d.gif"))
+    writer = imageio.get_writer("cam-pt3d.gif", format="gif", fps=25)
+
+    # Optimize.
+    for i in range(300):
+        optimizer.zero_grad()
+        result = model()
+        # Visualize.
+        result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+        cv2.imshow("opt", result_im[:, :, ::-1])
+        writer.append_data(result_im)
+        overlay_img = np.ascontiguousarray(
+            ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[
+                :, :, ::-1
+            ]
+        )
+        overlay_img = cv2.putText(
+            overlay_img,
+            "Step %d" % (i),
+            (10, 40),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+            False,
+        )
+        cv2.imshow("overlay", overlay_img)
+        cv2.waitKey(1)
+        # Update.
+        loss = ((result - ref) ** 2).sum()
+        LOGGER.info("loss %d: %f", i, loss.item())
+        loss.backward()
+        optimizer.step()
+    writer.close()
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/examples/pulsar_multiview.py

@@ -0,0 +1,231 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This example demonstrates multiview 3D reconstruction using the plain
+pulsar interface. For this, reference images have been pre-generated
+(you can find them at
+`../../tests/pulsar/reference/examples_TestRenderer_test_multiview_%d.png`).
+The camera parameters are assumed given. The scene is initialized with
+random spheres. Gradient-based optimization is used to optimize sphere
+parameters and prune spheres to converge to a 3D representation.
+
+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
+
+import cv2
+import imageio
+import numpy as np
+import torch
+from pytorch3d.renderer.points.pulsar import Renderer
+from torch import nn, optim
+
+
+LOGGER = logging.getLogger(__name__)
+N_POINTS = 400_000
+WIDTH = 1_000
+HEIGHT = 1_000
+VISUALIZE_IDS = [0, 1]
+DEVICE = torch.device("cuda")
+
+
+class SceneModel(nn.Module):
+    """
+    A simple scene model to demonstrate use of pulsar in PyTorch modules.
+
+    The scene model is parameterized with sphere locations (vert_pos),
+    channel content (vert_col), radiuses (vert_rad), camera position (cam_pos),
+    camera rotation (cam_rot) and sensor focal length and width (cam_sensor).
+
+    The forward method of the model renders this scene description. Any
+    of these parameters could instead be passed as inputs to the forward
+    method and come from a different model. Optionally, camera parameters can
+    be provided to the forward method in which case the scene is rendered
+    using those parameters.
+    """
+
+    def __init__(self):
+        super(SceneModel, self).__init__()
+        self.gamma = 1.0
+        # Points.
+        torch.manual_seed(1)
+        vert_pos = torch.rand((1, N_POINTS, 3), dtype=torch.float32) * 10.0
+        vert_pos[:, :, 2] += 25.0
+        vert_pos[:, :, :2] -= 5.0
+        self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True))
+        self.register_parameter(
+            "vert_col",
+            nn.Parameter(
+                torch.ones(1, N_POINTS, 3, dtype=torch.float32) * 0.5,
+                requires_grad=True,
+            ),
+        )
+        self.register_parameter(
+            "vert_rad",
+            nn.Parameter(
+                torch.ones(1, N_POINTS, dtype=torch.float32) * 0.05, requires_grad=True
+            ),
+        )
+        self.register_parameter(
+            "vert_opy",
+            nn.Parameter(
+                torch.ones(1, N_POINTS, dtype=torch.float32), requires_grad=True
+            ),
+        )
+        self.register_buffer(
+            "cam_params",
+            torch.tensor(
+                [
+                    [
+                        np.sin(angle) * 35.0,
+                        0.0,
+                        30.0 - np.cos(angle) * 35.0,
+                        0.0,
+                        -angle + math.pi,
+                        0.0,
+                        5.0,
+                        2.0,
+                    ]
+                    for angle in [-1.5, -0.8, -0.4, -0.1, 0.1, 0.4, 0.8, 1.5]
+                ],
+                dtype=torch.float32,
+            ),
+        )
+        self.renderer = Renderer(WIDTH, HEIGHT, N_POINTS, right_handed_system=True)
+
+    def forward(self, cam=None):
+        if cam is None:
+            cam = self.cam_params
+            n_views = 8
+        else:
+            n_views = 1
+        return self.renderer.forward(
+            self.vert_pos.expand(n_views, -1, -1),
+            self.vert_col.expand(n_views, -1, -1),
+            self.vert_rad.expand(n_views, -1),
+            cam,
+            self.gamma,
+            45.0,
+        )
+
+
+def cli():
+    """
+    Simple demonstration for a multi-view 3D reconstruction using pulsar.
+
+    This example makes use of opacity, which is not yet supported through
+    the unified PyTorch3D interface.
+
+    Writes to `multiview.gif`.
+    """
+    LOGGER.info("Loading reference...")
+    # Load reference.
+    ref = torch.stack(
+        [
+            torch.from_numpy(
+                imageio.imread(
+                    "../../tests/pulsar/reference/examples_TestRenderer_test_multiview_%d.png"
+                    % idx
+                )
+            ).to(torch.float32)
+            / 255.0
+            for idx in range(8)
+        ]
+    ).to(DEVICE)
+    # Set up model.
+    model = SceneModel().to(DEVICE)
+    # Optimizer.
+    optimizer = optim.SGD(
+        [
+            {"params": [model.vert_col], "lr": 1e-1},
+            {"params": [model.vert_rad], "lr": 1e-3},
+            {"params": [model.vert_pos], "lr": 1e-3},
+        ]
+    )
+
+    # For visualization.
+    angle = 0.0
+    LOGGER.info("Writing video to `%s`.", path.abspath("multiview.avi"))
+    writer = imageio.get_writer("multiview.gif", format="gif", fps=25)
+
+    # Optimize.
+    for i in range(300):
+        optimizer.zero_grad()
+        result = model()
+        # Visualize.
+        result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+        cv2.imshow("opt", result_im[0, :, :, ::-1])
+        overlay_img = np.ascontiguousarray(
+            ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[
+                0, :, :, ::-1
+            ]
+        )
+        overlay_img = cv2.putText(
+            overlay_img,
+            "Step %d" % (i),
+            (10, 40),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+            False,
+        )
+        cv2.imshow("overlay", overlay_img)
+        cv2.waitKey(1)
+        # Update.
+        loss = ((result - ref) ** 2).sum()
+        LOGGER.info("loss %d: %f", i, loss.item())
+        loss.backward()
+        optimizer.step()
+        # Cleanup.
+        with torch.no_grad():
+            model.vert_col.data = torch.clamp(model.vert_col.data, 0.0, 1.0)
+            # Remove points.
+            model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0
+            model.vert_rad.data[model.vert_rad < 0.001] = 0.0001
+            vd = (
+                (model.vert_col - torch.ones(1, 1, 3, dtype=torch.float32).to(DEVICE))
+                .abs()
+                .sum(dim=2)
+            )
+            model.vert_pos.data[vd <= 0.2] = -1000.0
+        # Rotating visualization.
+        cam_control = torch.tensor(
+            [
+                [
+                    np.sin(angle) * 35.0,
+                    0.0,
+                    30.0 - np.cos(angle) * 35.0,
+                    0.0,
+                    -angle + math.pi,
+                    0.0,
+                    5.0,
+                    2.0,
+                ]
+            ],
+            dtype=torch.float32,
+        ).to(DEVICE)
+        with torch.no_grad():
+            result = model.forward(cam=cam_control)[0]
+            result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+            cv2.imshow("vis", result_im[:, :, ::-1])
+            writer.append_data(result_im)
+            angle += 0.05
+    writer.close()
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/examples/pulsar_optimization.py

@@ -0,0 +1,165 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This example demonstrates scene optimization with the plain
+pulsar interface. For this, a reference image has been pre-generated
+(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png`).
+The scene is initialized with random spheres. Gradient-based
+optimization is used to converge towards a faithful
+scene representation.
+"""
+
+import logging
+import math
+
+import cv2
+import imageio
+import numpy as np
+import torch
+from pytorch3d.renderer.points.pulsar import Renderer
+from torch import nn, optim
+
+
+LOGGER = logging.getLogger(__name__)
+N_POINTS = 10_000
+WIDTH = 1_000
+HEIGHT = 1_000
+DEVICE = torch.device("cuda")
+
+
+class SceneModel(nn.Module):
+    """
+    A simple scene model to demonstrate use of pulsar in PyTorch modules.
+
+    The scene model is parameterized with sphere locations (vert_pos),
+    channel content (vert_col), radiuses (vert_rad), camera position (cam_pos),
+    camera rotation (cam_rot) and sensor focal length and width (cam_sensor).
+
+    The forward method of the model renders this scene description. Any
+    of these parameters could instead be passed as inputs to the forward
+    method and come from a different model.
+    """
+
+    def __init__(self):
+        super(SceneModel, self).__init__()
+        self.gamma = 1.0
+        # Points.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True))
+        self.register_parameter(
+            "vert_col",
+            nn.Parameter(
+                torch.ones(N_POINTS, 3, dtype=torch.float32) * 0.5, requires_grad=True
+            ),
+        )
+        self.register_parameter(
+            "vert_rad",
+            nn.Parameter(
+                torch.ones(N_POINTS, dtype=torch.float32) * 0.3, requires_grad=True
+            ),
+        )
+        self.register_buffer(
+            "cam_params",
+            torch.tensor(
+                [0.0, 0.0, 0.0, 0.0, math.pi, 0.0, 5.0, 2.0], dtype=torch.float32
+            ),
+        )
+        # The volumetric optimization works better with a higher number of tracked
+        # intersections per ray.
+        self.renderer = Renderer(
+            WIDTH, HEIGHT, N_POINTS, n_track=32, right_handed_system=True
+        )
+
+    def forward(self):
+        return self.renderer.forward(
+            self.vert_pos,
+            self.vert_col,
+            self.vert_rad,
+            self.cam_params,
+            self.gamma,
+            45.0,
+            return_forward_info=True,
+        )
+
+
+def cli():
+    """
+    Scene optimization example using pulsar.
+    """
+    LOGGER.info("Loading reference...")
+    # Load reference.
+    ref = (
+        torch.from_numpy(
+            imageio.imread(
+                "../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png"
+            )[:, ::-1, :].copy()
+        ).to(torch.float32)
+        / 255.0
+    ).to(DEVICE)
+    # Set up model.
+    model = SceneModel().to(DEVICE)
+    # Optimizer.
+    optimizer = optim.SGD(
+        [
+            {"params": [model.vert_col], "lr": 1e0},
+            {"params": [model.vert_rad], "lr": 5e-3},
+            {"params": [model.vert_pos], "lr": 1e-2},
+        ]
+    )
+    LOGGER.info("Optimizing...")
+    # Optimize.
+    for i in range(500):
+        optimizer.zero_grad()
+        result, result_info = model()
+        # Visualize.
+        result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+        cv2.imshow("opt", result_im[:, :, ::-1])
+        overlay_img = np.ascontiguousarray(
+            ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[
+                :, :, ::-1
+            ]
+        )
+        overlay_img = cv2.putText(
+            overlay_img,
+            "Step %d" % (i),
+            (10, 40),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+            False,
+        )
+        cv2.imshow("overlay", overlay_img)
+        cv2.waitKey(1)
+        # Update.
+        loss = ((result - ref) ** 2).sum()
+        LOGGER.info("loss %d: %f", i, loss.item())
+        loss.backward()
+        optimizer.step()
+        # Cleanup.
+        with torch.no_grad():
+            model.vert_col.data = torch.clamp(model.vert_col.data, 0.0, 1.0)
+            # Remove points.
+            model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0
+            model.vert_rad.data[model.vert_rad < 0.001] = 0.0001
+            vd = (
+                (model.vert_col - torch.ones(3, dtype=torch.float32).to(DEVICE))
+                .abs()
+                .sum(dim=1)
+            )
+            model.vert_pos.data[vd <= 0.2] = -1000.0
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/examples/pulsar_optimization_unified.py

@@ -0,0 +1,190 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This example demonstrates scene optimization with the PyTorch3D
+pulsar interface. For this, a reference image has been pre-generated
+(you can find it at `../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png`).
+The scene is initialized with random spheres. Gradient-based
+optimization is used to converge towards a faithful
+scene representation.
+"""
+
+import logging
+import math
+
+import cv2
+import imageio
+import numpy as np
+import torch
+from pytorch3d.renderer.cameras import PerspectiveCameras
+from pytorch3d.renderer.points import (
+    PointsRasterizationSettings,
+    PointsRasterizer,
+    PulsarPointsRenderer,
+)
+from pytorch3d.structures.pointclouds import Pointclouds
+from torch import nn, optim
+
+
+LOGGER = logging.getLogger(__name__)
+N_POINTS = 10_000
+WIDTH = 1_000
+HEIGHT = 1_000
+DEVICE = torch.device("cuda")
+
+
+class SceneModel(nn.Module):
+    """
+    A simple scene model to demonstrate use of pulsar in PyTorch modules.
+
+    The scene model is parameterized with sphere locations (vert_pos),
+    channel content (vert_col), radiuses (vert_rad), camera position (cam_pos),
+    camera rotation (cam_rot) and sensor focal length and width (cam_sensor).
+
+    The forward method of the model renders this scene description. Any
+    of these parameters could instead be passed as inputs to the forward
+    method and come from a different model.
+    """
+
+    def __init__(self):
+        super(SceneModel, self).__init__()
+        self.gamma = 1.0
+        # Points.
+        torch.manual_seed(1)
+        vert_pos = torch.rand(N_POINTS, 3, dtype=torch.float32, device=DEVICE) * 10.0
+        vert_pos[:, 2] += 25.0
+        vert_pos[:, :2] -= 5.0
+        self.register_parameter("vert_pos", nn.Parameter(vert_pos, requires_grad=True))
+        self.register_parameter(
+            "vert_col",
+            nn.Parameter(
+                torch.ones(N_POINTS, 3, dtype=torch.float32, device=DEVICE) * 0.5,
+                requires_grad=True,
+            ),
+        )
+        self.register_parameter(
+            "vert_rad",
+            nn.Parameter(
+                torch.ones(N_POINTS, dtype=torch.float32) * 0.3, requires_grad=True
+            ),
+        )
+        self.register_buffer(
+            "cam_params",
+            torch.tensor(
+                [0.0, 0.0, 0.0, 0.0, math.pi, 0.0, 5.0, 2.0], dtype=torch.float32
+            ),
+        )
+        self.cameras = PerspectiveCameras(
+            # The focal length must be double the size for PyTorch3D because of the NDC
+            # coordinates spanning a range of two - and they must be normalized by the
+            # sensor width (see the pulsar example). This means we need here
+            # 5.0 * 2.0 / 2.0 to get the equivalent results as in pulsar.
+            focal_length=5.0,
+            R=torch.eye(3, dtype=torch.float32, device=DEVICE)[None, ...],
+            T=torch.zeros((1, 3), dtype=torch.float32, device=DEVICE),
+            image_size=((HEIGHT, WIDTH),),
+            device=DEVICE,
+        )
+        raster_settings = PointsRasterizationSettings(
+            image_size=(HEIGHT, WIDTH),
+            radius=self.vert_rad,
+        )
+        rasterizer = PointsRasterizer(
+            cameras=self.cameras, raster_settings=raster_settings
+        )
+        self.renderer = PulsarPointsRenderer(rasterizer=rasterizer, n_track=32)
+
+    def forward(self):
+        # The Pointclouds object creates copies of it's arguments - that's why
+        # we have to create a new object in every forward step.
+        pcl = Pointclouds(
+            points=self.vert_pos[None, ...], features=self.vert_col[None, ...]
+        )
+        return self.renderer(
+            pcl,
+            gamma=(self.gamma,),
+            zfar=(45.0,),
+            znear=(1.0,),
+            radius_world=True,
+            bg_col=torch.ones((3,), dtype=torch.float32, device=DEVICE),
+        )[0]
+
+
+def cli():
+    """
+    Scene optimization example using pulsar and the unified PyTorch3D interface.
+    """
+    LOGGER.info("Loading reference...")
+    # Load reference.
+    ref = (
+        torch.from_numpy(
+            imageio.imread(
+                "../../tests/pulsar/reference/examples_TestRenderer_test_smallopt.png"
+            )[:, ::-1, :].copy()
+        ).to(torch.float32)
+        / 255.0
+    ).to(DEVICE)
+    # Set up model.
+    model = SceneModel().to(DEVICE)
+    # Optimizer.
+    optimizer = optim.SGD(
+        [
+            {"params": [model.vert_col], "lr": 1e0},
+            {"params": [model.vert_rad], "lr": 5e-3},
+            {"params": [model.vert_pos], "lr": 1e-2},
+        ]
+    )
+    LOGGER.info("Optimizing...")
+    # Optimize.
+    for i in range(500):
+        optimizer.zero_grad()
+        result = model()
+        # Visualize.
+        result_im = (result.cpu().detach().numpy() * 255).astype(np.uint8)
+        cv2.imshow("opt", result_im[:, :, ::-1])
+        overlay_img = np.ascontiguousarray(
+            ((result * 0.5 + ref * 0.5).cpu().detach().numpy() * 255).astype(np.uint8)[
+                :, :, ::-1
+            ]
+        )
+        overlay_img = cv2.putText(
+            overlay_img,
+            "Step %d" % (i),
+            (10, 40),
+            cv2.FONT_HERSHEY_SIMPLEX,
+            1,
+            (0, 0, 0),
+            2,
+            cv2.LINE_AA,
+            False,
+        )
+        cv2.imshow("overlay", overlay_img)
+        cv2.waitKey(1)
+        # Update.
+        loss = ((result - ref) ** 2).sum()
+        LOGGER.info("loss %d: %f", i, loss.item())
+        loss.backward()
+        optimizer.step()
+        # Cleanup.
+        with torch.no_grad():
+            model.vert_col.data = torch.clamp(model.vert_col.data, 0.0, 1.0)
+            # Remove points.
+            model.vert_pos.data[model.vert_rad < 0.001, :] = -1000.0
+            model.vert_rad.data[model.vert_rad < 0.001] = 0.0001
+            vd = (
+                (model.vert_col - torch.ones(3, dtype=torch.float32).to(DEVICE))
+                .abs()
+                .sum(dim=1)
+            )
+            model.vert_pos.data[vd <= 0.2] = -1000.0
+    LOGGER.info("Done.")
+
+
+if __name__ == "__main__":
+    logging.basicConfig(level=logging.INFO)
+    cli()

docs/generate_stubs.py

@@ -0,0 +1,162 @@
+#!/usr/bin/env python3
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the BSD-style license found in the
+# LICENSE file in the root directory of this source tree.
+
+"""
+This script makes the stubs for implicitron in docs/modules.
+"""
+
+from pathlib import Path
+
+ROOT_DIR = Path(__file__).resolve().parent.parent
+
+
+def paths_to_modules(paths):
+    """
+    Given an iterable of paths, return equivalent list of modules.
+    """
+    return [
+        str(i.relative_to(ROOT_DIR))[:-3].replace("/", ".")
+        for i in paths
+        if "__pycache__" not in str(i)
+    ]
+
+
+def create_one_file(title, description, sources, dest_file):
+    with open(dest_file, "w") as f:
+        print(title, file=f)
+        print("=" * len(title), file=f)
+        print(file=f)
+        print(description, file=f)
+        for source in sources:
+            if source.find("._") != -1:
+                # ignore internal modules including __init__.py
+                continue
+            print(f"\n.. automodule:: {source}", file=f)
+            print("    :members:", file=f)
+            print("    :undoc-members:", file=f)
+            print("    :show-inheritance:", file=f)
+
+
+def iterate_directory(directory_path, dest):
+    """
+    Create a file for each module in the given path
+    """
+    toc = []
+    if not dest.exists():
+        dest.mkdir()
+    for file in sorted(directory_path.glob("*.py")):
+        if file.stem.startswith("_"):
+            continue
+        module = paths_to_modules([file])
+        create_one_file(module[0], file.stem, module, dest / f"{file.stem}.rst")
+        toc.append(file.stem)
+
+    for subdir in directory_path.iterdir():
+        if not subdir.is_dir():
+            continue
+        if subdir.name == "fb":
+            continue
+        if subdir.name.startswith("_"):
+            continue
+        iterate_directory(subdir, dest / (subdir.name))
+        toc.append(f"{subdir.name}/index")
+
+    paths_to_modules_ = paths_to_modules([directory_path.with_suffix(".XX")])
+    if len(paths_to_modules_) == 0:
+        return
+    title = paths_to_modules_[0]
+
+    with open(dest / "index.rst", "w") as f:
+        print(title, file=f)
+        print("=" * len(title), file=f)
+        print("\n.. toctree::\n", file=f)
+        for item in toc:
+            print(f"    {item}", file=f)
+
+
+def make_directory_index(title: str, directory_path: Path):
+    index_file = directory_path / "index.rst"
+    directory_rsts = sorted(directory_path.glob("*.rst"))
+    subdirs = sorted([f for f in directory_path.iterdir() if f.is_dir()])
+    with open(index_file, "w") as f:
+        print(title, file=f)
+        print("=" * len(title), file=f)
+        print("\n.. toctree::\n", file=f)
+        for subdir in subdirs:
+            print(f"    {subdir.stem}/index.rst", file=f)
+        for rst in directory_rsts:
+            if rst.stem == "index":
+                continue
+            print(f"    {rst.stem}", file=f)
+
+
+def do_implicitron():
+    DEST_DIR = Path(__file__).resolve().parent / "modules/implicitron"
+
+    iterate_directory(ROOT_DIR / "pytorch3d/implicitron/models", DEST_DIR / "models")
+
+    unwanted_tools = ["configurable", "depth_cleanup", "utils"]
+    tools_sources = sorted(ROOT_DIR.glob("pytorch3d/implicitron/tools/*.py"))
+    tools_modules = [
+        str(i.relative_to(ROOT_DIR))[:-3].replace("/", ".")
+        for i in tools_sources
+        if i.stem not in unwanted_tools
+    ]
+    create_one_file(
+        "pytorch3d.implicitron.tools",
+        "Tools for implicitron",
+        tools_modules,
+        DEST_DIR / "tools.rst",
+    )
+
+    dataset_files = sorted(ROOT_DIR.glob("pytorch3d/implicitron/dataset/*.py"))
+    basic_dataset = [
+        "dataset_base",
+        "dataset_map_provider",
+        "data_loader_map_provider",
+        "data_source",
+        "scene_batch_sampler",
+    ]
+    basic_dataset_modules = [
+        f"pytorch3d.implicitron.dataset.{i}" for i in basic_dataset
+    ]
+    create_one_file(
+        "pytorch3d.implicitron.dataset in general",
+        "Basics of data for implicitron",
+        basic_dataset_modules,
+        DEST_DIR / "data_basics.rst",
+    )
+
+    specific_dataset_files = [
+        i for i in dataset_files if i.stem.find("_dataset_map_provider") != -1
+    ]
+    create_one_file(
+        "pytorch3d.implicitron.dataset specific datasets",
+        "specific datasets",
+        paths_to_modules(specific_dataset_files),
+        DEST_DIR / "datasets.rst",
+    )
+
+    evaluation_files = sorted(ROOT_DIR.glob("pytorch3d/implicitron/evaluation/*.py"))
+    create_one_file(
+        "pytorch3d.implicitron.evaluation",
+        "evaluation",
+        paths_to_modules(evaluation_files),
+        DEST_DIR / "evaluation.rst",
+    )
+
+    make_directory_index("pytorch3d.implicitron", DEST_DIR)
+
+
+def iterate_toplevel_module(name: str) -> None:
+    dest_dir = Path(__file__).resolve().parent / "modules" / name
+    iterate_directory(ROOT_DIR / "pytorch3d" / name, dest_dir)
+
+
+do_implicitron()
+iterate_toplevel_module("renderer")
+iterate_toplevel_module("vis")

docs/modules/common.rst

@@ -1,7 +1,6 @@
-shader
+pytorch3d.common
 ===========================
 
-.. automodule:: pytorch3d.renderer.mesh.shader
+.. automodule:: pytorch3d.common
     :members:
     :undoc-members:
-    

docs/modules/datasets.rst

@@ -0,0 +1,9 @@
+pytorch3d.datasets
+===========================
+
+Dataset loaders for datasets including ShapeNetCore.
+
+.. automodule:: pytorch3d.datasets
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/data_basics.rst

@@ -0,0 +1,29 @@
+pytorch3d.implicitron.dataset in general
+========================================
+
+Basics of data for implicitron
+
+.. automodule:: pytorch3d.implicitron.dataset.dataset_base
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.data_loader_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.data_source
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.scene_batch_sampler
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/datasets.rst

@@ -0,0 +1,29 @@
+pytorch3d.implicitron.dataset specific datasets
+===============================================
+
+specific datasets
+
+.. automodule:: pytorch3d.implicitron.dataset.blender_dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.json_index_dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.json_index_dataset_map_provider_v2
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.llff_dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.dataset.rendered_mesh_dataset_map_provider
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/evaluation.rst

@@ -0,0 +1,14 @@
+pytorch3d.implicitron.evaluation
+================================
+
+evaluation
+
+.. automodule:: pytorch3d.implicitron.evaluation.evaluate_new_view_synthesis
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.evaluation.evaluator
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/index.rst

@@ -0,0 +1,10 @@
+pytorch3d.implicitron
+=====================
+
+.. toctree::
+
+    models/index.rst
+    data_basics
+    datasets
+    evaluation
+    tools

docs/modules/implicitron/models/base_model.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.base_model
+=======================================
+
+base_model
+
+.. automodule:: pytorch3d.implicitron.models.base_model
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/feature_extractor/feature_extractor.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.feature_extractor.feature_extractor
+================================================================
+
+feature_extractor
+
+.. automodule:: pytorch3d.implicitron.models.feature_extractor.feature_extractor
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/feature_extractor/index.rst

@@ -0,0 +1,7 @@
+pytorch3d.implicitron.models.feature_extractor
+==============================================
+
+.. toctree::
+
+    feature_extractor
+    resnet_feature_extractor

docs/modules/implicitron/models/feature_extractor/resnet_feature_extractor.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.feature_extractor.resnet_feature_extractor
+=======================================================================
+
+resnet_feature_extractor
+
+.. automodule:: pytorch3d.implicitron.models.feature_extractor.resnet_feature_extractor
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/generic_model.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.generic_model
+==========================================
+
+generic_model
+
+.. automodule:: pytorch3d.implicitron.models.generic_model
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/global_encoder/autodecoder.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.global_encoder.autodecoder
+=======================================================
+
+autodecoder
+
+.. automodule:: pytorch3d.implicitron.models.global_encoder.autodecoder
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/global_encoder/global_encoder.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.global_encoder.global_encoder
+==========================================================
+
+global_encoder
+
+.. automodule:: pytorch3d.implicitron.models.global_encoder.global_encoder
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/global_encoder/index.rst

@@ -0,0 +1,7 @@
+pytorch3d.implicitron.models.global_encoder
+===========================================
+
+.. toctree::
+
+    autodecoder
+    global_encoder

docs/modules/implicitron/models/implicit_function/base.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.base
+===================================================
+
+base
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.base
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/decoding_functions.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.decoding_functions
+=================================================================
+
+decoding_functions
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.decoding_functions
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/idr_feature_field.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.idr_feature_field
+================================================================
+
+idr_feature_field
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.idr_feature_field
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/index.rst

@@ -0,0 +1,13 @@
+pytorch3d.implicitron.models.implicit_function
+==============================================
+
+.. toctree::
+
+    base
+    decoding_functions
+    idr_feature_field
+    neural_radiance_field
+    scene_representation_networks
+    utils
+    voxel_grid
+    voxel_grid_implicit_function

docs/modules/implicitron/models/implicit_function/neural_radiance_field.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.neural_radiance_field
+====================================================================
+
+neural_radiance_field
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.neural_radiance_field
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/scene_representation_networks.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.scene_representation_networks
+============================================================================
+
+scene_representation_networks
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.scene_representation_networks
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/utils.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.utils
+====================================================
+
+utils
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.utils
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/voxel_grid.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.voxel_grid
+=========================================================
+
+voxel_grid
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.voxel_grid
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/implicit_function/voxel_grid_implicit_function.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.implicit_function.voxel_grid_implicit_function
+===========================================================================
+
+voxel_grid_implicit_function
+
+.. automodule:: pytorch3d.implicitron.models.implicit_function.voxel_grid_implicit_function
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/index.rst

@@ -0,0 +1,15 @@
+pytorch3d.implicitron.models
+============================
+
+.. toctree::
+
+    base_model
+    generic_model
+    metrics
+    model_dbir
+    feature_extractor/index
+    global_encoder/index
+    implicit_function/index
+    renderer/index
+    view_pooler/index
+    visualization/index

docs/modules/implicitron/models/metrics.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.metrics
+====================================
+
+metrics
+
+.. automodule:: pytorch3d.implicitron.models.metrics
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/model_dbir.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.model_dbir
+=======================================
+
+model_dbir
+
+.. automodule:: pytorch3d.implicitron.models.model_dbir
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/base.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.base
+==========================================
+
+base
+
+.. automodule:: pytorch3d.implicitron.models.renderer.base
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/index.rst

@@ -0,0 +1,14 @@
+pytorch3d.implicitron.models.renderer
+=====================================
+
+.. toctree::
+
+    base
+    lstm_renderer
+    multipass_ea
+    ray_point_refiner
+    ray_sampler
+    ray_tracing
+    raymarcher
+    rgb_net
+    sdf_renderer

docs/modules/implicitron/models/renderer/lstm_renderer.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.lstm_renderer
+===================================================
+
+lstm_renderer
+
+.. automodule:: pytorch3d.implicitron.models.renderer.lstm_renderer
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/multipass_ea.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.multipass_ea
+==================================================
+
+multipass_ea
+
+.. automodule:: pytorch3d.implicitron.models.renderer.multipass_ea
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/ray_point_refiner.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.ray_point_refiner
+=======================================================
+
+ray_point_refiner
+
+.. automodule:: pytorch3d.implicitron.models.renderer.ray_point_refiner
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/ray_sampler.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.ray_sampler
+=================================================
+
+ray_sampler
+
+.. automodule:: pytorch3d.implicitron.models.renderer.ray_sampler
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/ray_tracing.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.ray_tracing
+=================================================
+
+ray_tracing
+
+.. automodule:: pytorch3d.implicitron.models.renderer.ray_tracing
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/raymarcher.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.raymarcher
+================================================
+
+raymarcher
+
+.. automodule:: pytorch3d.implicitron.models.renderer.raymarcher
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/rgb_net.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.rgb_net
+=============================================
+
+rgb_net
+
+.. automodule:: pytorch3d.implicitron.models.renderer.rgb_net
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/renderer/sdf_renderer.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.renderer.sdf_renderer
+==================================================
+
+sdf_renderer
+
+.. automodule:: pytorch3d.implicitron.models.renderer.sdf_renderer
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/view_pooler/feature_aggregator.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.view_pooler.feature_aggregator
+===========================================================
+
+feature_aggregator
+
+.. automodule:: pytorch3d.implicitron.models.view_pooler.feature_aggregator
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/view_pooler/index.rst

@@ -0,0 +1,8 @@
+pytorch3d.implicitron.models.view_pooler
+========================================
+
+.. toctree::
+
+    feature_aggregator
+    view_pooler
+    view_sampler

docs/modules/implicitron/models/view_pooler/view_pooler.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.view_pooler.view_pooler
+====================================================
+
+view_pooler
+
+.. automodule:: pytorch3d.implicitron.models.view_pooler.view_pooler
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/view_pooler/view_sampler.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.view_pooler.view_sampler
+=====================================================
+
+view_sampler
+
+.. automodule:: pytorch3d.implicitron.models.view_pooler.view_sampler
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/models/visualization/index.rst

@@ -0,0 +1,6 @@
+pytorch3d.implicitron.models.visualization
+==========================================
+
+.. toctree::
+
+    render_flyaround

docs/modules/implicitron/models/visualization/render_flyaround.rst

@@ -0,0 +1,9 @@
+pytorch3d.implicitron.models.visualization.render_flyaround
+===========================================================
+
+render_flyaround
+
+.. automodule:: pytorch3d.implicitron.models.visualization.render_flyaround
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/implicitron/tools.rst

@@ -0,0 +1,64 @@
+pytorch3d.implicitron.tools
+===========================
+
+Tools for implicitron
+
+.. automodule:: pytorch3d.implicitron.tools.camera_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.circle_fitting
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.config
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.eval_video_trajectory
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.image_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.metric_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.model_io
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.point_cloud_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.rasterize_mc
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.stats
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.video_writer
+    :members:
+    :undoc-members:
+    :show-inheritance:
+
+.. automodule:: pytorch3d.implicitron.tools.vis_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/index.rst

@@ -9,5 +9,8 @@ API Documentation
     ops
     renderer/index
     transforms
-    utils 
-    
+    utils
+    datasets
+    common
+    vis/index
+    implicitron/index

docs/modules/renderer/blending.rst

@@ -1,7 +1,9 @@
-blending
+pytorch3d.renderer.blending
 ===========================
 
+blending
+
 .. automodule:: pytorch3d.renderer.blending
     :members:
     :undoc-members:
-    :show-inheritance:
+    :show-inheritance:

docs/modules/renderer/camera_conversions.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.camera_conversions
+=====================================
+
+camera_conversions
+
+.. automodule:: pytorch3d.renderer.camera_conversions
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/camera_utils.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.camera_utils
+===============================
+
+camera_utils
+
+.. automodule:: pytorch3d.renderer.camera_utils
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/cameras.rst

@@ -1,7 +1,9 @@
+pytorch3d.renderer.cameras
+==========================
+
 cameras
-===========================
 
 .. automodule:: pytorch3d.renderer.cameras
     :members:
     :undoc-members:
-    :show-inheritance:
+    :show-inheritance:

docs/modules/renderer/compositing.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.compositing
+==============================
+
+compositing
+
+.. automodule:: pytorch3d.renderer.compositing
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/fisheyecameras.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.fisheyecameras
+=================================
+
+fisheyecameras
+
+.. automodule:: pytorch3d.renderer.fisheyecameras
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/implicit/harmonic_embedding.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.implicit.harmonic_embedding
+==============================================
+
+harmonic_embedding
+
+.. automodule:: pytorch3d.renderer.implicit.harmonic_embedding
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/implicit/index.rst

@@ -0,0 +1,11 @@
+pytorch3d.renderer.implicit
+===========================
+
+.. toctree::
+
+    harmonic_embedding
+    raymarching
+    raysampling
+    renderer
+    sample_pdf
+    utils

docs/modules/renderer/implicit/raymarching.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.implicit.raymarching
+=======================================
+
+raymarching
+
+.. automodule:: pytorch3d.renderer.implicit.raymarching
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/implicit/raysampling.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.implicit.raysampling
+=======================================
+
+raysampling
+
+.. automodule:: pytorch3d.renderer.implicit.raysampling
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/implicit/renderer.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.implicit.renderer
+====================================
+
+renderer
+
+.. automodule:: pytorch3d.renderer.implicit.renderer
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/implicit/sample_pdf.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.implicit.sample_pdf
+======================================
+
+sample_pdf
+
+.. automodule:: pytorch3d.renderer.implicit.sample_pdf
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/implicit/utils.rst

@@ -0,0 +1,9 @@
+pytorch3d.renderer.implicit.utils
+=================================
+
+utils
+
+.. automodule:: pytorch3d.renderer.implicit.utils
+    :members:
+    :undoc-members:
+    :show-inheritance:

docs/modules/renderer/index.rst

@@ -1,15 +1,19 @@
-pytorch3d.renderer 
-===========================
+pytorch3d.renderer
+==================
 
 .. toctree::
 
-    rasterizer
+    blending
+    camera_conversions
+    camera_utils
     cameras
+    compositing
+    fisheyecameras
     lighting
     materials
-    texturing
-    blending
-    shading
-    shader
-    renderer
-    utils
+    splatter_blend
+    utils
+    implicit/index
+    mesh/index
+    opengl/index
+    points/index