update version to 0.7.8

Summary: as title

Reviewed By: das-intensity

Differential Revision: D62588556

fbshipit-source-id: 55bae19dd1df796e83179cd29d805fcd871b6d23

.circleci/config.in.yml

@@ -64,7 +64,7 @@ jobs:
       CUDA_VERSION: "11.3"
     resource_class: gpu.nvidia.small.multi
     machine:
-      image: ubuntu-2004:202101-01
+      image: linux-cuda-11:default
     steps:
       - checkout
       - <<: *setupcuda
@@ -116,7 +116,7 @@ jobs:
       # so we aren't running the tests.
       - run:
           name: build
-          no_output_timeout: 20m
+          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
@@ -128,7 +128,7 @@ jobs:
   binary_linux_conda_cuda:
     <<: *binary_common
     machine:
-      image: ubuntu-1604-cuda-10.2:202012-01
+      image: linux-cuda-11:default
     resource_class: gpu.nvidia.small.multi
     steps:
     - checkout
@@ -145,7 +145,7 @@ jobs:
           docker pull $TESTRUN_DOCKER_IMAGE
     - run:
         name: Build and run tests
-        no_output_timeout: 20m
+        no_output_timeout: 40m
         command: |
           set -e
 
@@ -156,24 +156,6 @@ jobs:
 
           docker run --gpus all  --ipc=host -v $(pwd):/remote -w /remote ${VARS_TO_PASS} ${TESTRUN_DOCKER_IMAGE} python3 ./packaging/build_conda.py
 
-  binary_macos_wheel:
-    <<: *binary_common
-    macos:
-      xcode: "13.4.1"
-    steps:
-      - checkout
-      - run:
-          # Cannot easily deduplicate this as source'ing activate
-          # will set environment variables which we need to propagate
-          # to build_wheel.sh
-          command: |
-            curl -o conda.sh https://repo.anaconda.com/miniconda/Miniconda3-latest-MacOSX-x86_64.sh
-            sh conda.sh -b
-            source $HOME/miniconda3/bin/activate
-            packaging/build_wheel.sh
-      - store_artifacts:
-          path: dist
-
 workflows:
   version: 2
   build_and_test:
@@ -182,23 +164,8 @@ workflows:
       #     context: DOCKERHUB_TOKEN
       {{workflows()}}
       - binary_linux_conda_cuda:
-          name: testrun_conda_cuda_py38_cu102_pyt190
+          name: testrun_conda_cuda_py310_cu117_pyt201
           context: DOCKERHUB_TOKEN
-          python_version: "3.8"
-          pytorch_version: '1.9.0'
-          cu_version: "cu102"
-      - binary_macos_wheel:
-          cu_version: cpu
-          name: macos_wheel_py3.8_cpu
-          python_version: '3.8'
-          pytorch_version: '1.13.0'
-      - binary_macos_wheel:
-          cu_version: cpu
-          name: macos_wheel_py3.9_cpu
-          python_version: '3.9'
-          pytorch_version: '1.13.0'
-      - binary_macos_wheel:
-          cu_version: cpu
-          name: macos_wheel_py3.10_cpu
-          python_version: '3.10'
-          pytorch_version: '1.13.0'
+          python_version: "3.10"
+          pytorch_version: '2.0.1'
+          cu_version: "cu117"

.circleci/regenerate.py

@@ -18,24 +18,19 @@ from packaging import version
 
 # The CUDA versions which have pytorch conda packages available for linux for each
 # version of pytorch.
-# Pytorch 1.4 also supports cuda 10.0 but we no longer build for cuda 10.0 at all.
 CONDA_CUDA_VERSIONS = {
-    "1.10.0": ["cu102", "cu111", "cu113"],
-    "1.10.1": ["cu102", "cu111", "cu113"],
-    "1.10.2": ["cu102", "cu111", "cu113"],
-    "1.11.0": ["cu102", "cu111", "cu113", "cu115"],
-    "1.12.0": ["cu102", "cu113", "cu116"],
-    "1.12.1": ["cu102", "cu113", "cu116"],
-    "1.13.0": ["cu116", "cu117"],
-    "1.13.1": ["cu116", "cu117"],
-    "2.0.0": ["cu117", "cu118"],
-    "2.0.1": ["cu117", "cu118"],
+    "2.1.0": ["cu118", "cu121"],
+    "2.1.1": ["cu118", "cu121"],
+    "2.1.2": ["cu118", "cu121"],
+    "2.2.0": ["cu118", "cu121"],
+    "2.2.2": ["cu118", "cu121"],
+    "2.3.1": ["cu118", "cu121"],
+    "2.4.0": ["cu118", "cu121"],
+    "2.4.1": ["cu118", "cu121"],
 }
 
 
 def conda_docker_image_for_cuda(cuda_version):
-    if cuda_version in ("cu101", "cu102", "cu111"):
-        return None
     if len(cuda_version) != 5:
         raise ValueError("Unknown cuda version")
     return "pytorch/conda-builder:cuda" + cuda_version[2:]
@@ -50,12 +45,24 @@ def pytorch_versions_for_python(python_version):
             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 = []
     for btype in ["conda"]:
-        for python_version in ["3.8", "3.9", "3.10"]:
+        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(

.flake8

@@ -1,5 +1,8 @@
 [flake8]
-ignore = E203, E266, E501, W503, E221
+# 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

INSTALL.md

@@ -8,11 +8,10 @@
 The core library is written in PyTorch. Several components have underlying implementation in CUDA for improved performance. A subset of these components have CPU implementations in C++/PyTorch. It is advised to use PyTorch3D with GPU support in order to use all the features.
 
 - Linux or macOS or Windows
-- Python 3.8, 3.9 or 3.10
-- PyTorch 1.10.0, 1.10.1, 1.10.2, 1.11.0, 1.12.0, 1.12.1, 1.13.0, 2.0.0 or 2.0.1.
+- Python
+- PyTorch 2.1.0, 2.1.1, 2.1.2, 2.2.0, 2.2.1, 2.2.2, 2.3.0, 2.3.1, 2.4.0 or 2.4.1.
 - torchvision that matches the PyTorch installation. You can install them together as explained at pytorch.org to make sure of this.
 - gcc & g++ ≥ 4.9
-- [fvcore](https://github.com/facebookresearch/fvcore)
 - [ioPath](https://github.com/facebookresearch/iopath)
 - If CUDA is to be used, use a version which is supported by the corresponding pytorch version and at least version 9.2.
 - If CUDA older than 11.7 is to be used and you are building from source, the CUB library must be available. We recommend version 1.10.0.
@@ -22,7 +21,7 @@ The runtime dependencies can be installed by running:
 conda create -n pytorch3d python=3.9
 conda activate pytorch3d
 conda install pytorch=1.13.0 torchvision pytorch-cuda=11.6 -c pytorch -c nvidia
-conda install -c fvcore -c iopath -c conda-forge fvcore iopath
+conda install -c iopath iopath
 ```
 
 For the CUB build time dependency, which you only need if you have CUDA older than 11.7, if you are using conda, you can continue with
@@ -49,6 +48,7 @@ For developing on top of PyTorch3D or contributing, you will need to run the lin
 - tdqm
 - jupyter
 - imageio
+- fvcore
 - plotly
 - opencv-python
 
@@ -59,6 +59,7 @@ conda install jupyter
 pip install scikit-image matplotlib imageio plotly opencv-python
 
 # Tests/Linting
+conda install -c fvcore -c conda-forge fvcore
 pip install black usort flake8 flake8-bugbear flake8-comprehensions
 ```
 
@@ -77,13 +78,8 @@ Or, to install a nightly (non-official, alpha) build:
 # Anaconda Cloud
 conda install pytorch3d -c pytorch3d-nightly
 ```
-### 2. Install from PyPI, on Mac only.
-This works with pytorch 1.13.0 only. The build is CPU only.
-```
-pip install pytorch3d
-```
 
-### 3. Install wheels for Linux
+### 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.
@@ -102,7 +98,7 @@ version_str="".join([
     torch.version.cuda.replace(".",""),
     f"_pyt{pyt_version_str}"
 ])
-!pip install fvcore iopath
+!pip install iopath
 !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
 ```
 

README.md

@@ -146,6 +146,12 @@ If you are using the pulsar backend for sphere-rendering (the `PulsarPointRender
 
 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.

dev/run_tutorials.sh

@@ -23,7 +23,7 @@ conda init bash
 source ~/.bashrc
 conda create -y -n myenv python=3.8 matplotlib ipython ipywidgets nbconvert
 conda activate myenv
-conda install -y -c fvcore -c iopath -c conda-forge fvcore iopath
+conda install -y -c iopath iopath
 conda install -y -c pytorch pytorch=1.6.0 cudatoolkit=10.1 torchvision
 conda install -y -c pytorch3d-nightly pytorch3d
 pip install plotly scikit-image

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/README.md

@@ -3,7 +3,7 @@
 ### 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

docs/requirements.txt

@@ -1,12 +1,10 @@
 docutils>=0.14
 Sphinx>=1.7
-recommonmark==0.4.0
+recommonmark
 sphinx_rtd_theme
 sphinx_markdown_tables
-mock
 numpy
 iopath
-fvcore
-https://download.pytorch.org/whl/cpu/torchvision-0.8.2%2Bcpu-cp37-cp37m-linux_x86_64.whl
-https://download.pytorch.org/whl/cpu/torch-1.7.1%2Bcpu-cp37-cp37m-linux_x86_64.whl
+https://download.pytorch.org/whl/cpu/torchvision-0.15.2%2Bcpu-cp311-cp311-linux_x86_64.whl
+https://download.pytorch.org/whl/cpu/torch-2.0.1%2Bcpu-cp311-cp311-linux_x86_64.whl
 omegaconf

docs/tutorials/bundle_adjustment.ipynb

@@ -83,25 +83,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/camera_position_optimization_with_differentiable_rendering.ipynb

@@ -70,25 +70,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/dataloaders_ShapeNetCore_R2N2.ipynb

@@ -45,25 +45,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {
@@ -405,7 +411,7 @@
    "outputs": [],
    "source": [
     "random_model_images = shapenet_dataset.render(\n",
-    "    sample_nums=[3],\n",
+    "    sample_nums=[5],\n",
     "    device=device,\n",
     "    cameras=cameras,\n",
     "    raster_settings=raster_settings,\n",

docs/tutorials/deform_source_mesh_to_target_mesh.ipynb

@@ -84,25 +84,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {
@@ -262,7 +268,7 @@
     "    points = sample_points_from_meshes(mesh, 5000)\n",
     "    x, y, z = points.clone().detach().cpu().squeeze().unbind(1)    \n",
     "    fig = plt.figure(figsize=(5, 5))\n",
-    "    ax = Axes3D(fig)\n",
+    "    ax = fig.add_subplot(111, projection='3d')\n",
     "    ax.scatter3D(x, z, -y)\n",
     "    ax.set_xlabel('x')\n",
     "    ax.set_ylabel('z')\n",

docs/tutorials/fit_simple_neural_radiance_field.ipynb

@@ -50,25 +50,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/fit_textured_mesh.ipynb

@@ -62,25 +62,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/fit_textured_volume.ipynb

@@ -41,25 +41,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/implicitron_config_system.ipynb

@@ -72,25 +72,31 @@
         "import os\n",
         "import sys\n",
         "import torch\n",
+        "import subprocess\n",
         "need_pytorch3d=False\n",
         "try:\n",
         "    import pytorch3d\n",
         "except ModuleNotFoundError:\n",
         "    need_pytorch3d=True\n",
         "if need_pytorch3d:\n",
-        "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-        "        # We try to install PyTorch3D via a released wheel.\n",
-        "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-        "        version_str=\"\".join([\n",
-        "            f\"py3{sys.version_info.minor}_cu\",\n",
-        "            torch.version.cuda.replace(\".\",\"\"),\n",
-        "            f\"_pyt{pyt_version_str}\"\n",
-        "        ])\n",
-        "        !pip install fvcore iopath\n",
+        "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+        "    version_str=\"\".join([\n",
+        "        f\"py3{sys.version_info.minor}_cu\",\n",
+        "        torch.version.cuda.replace(\".\",\"\"),\n",
+        "        f\"_pyt{pyt_version_str}\"\n",
+        "    ])\n",
+        "    !pip install iopath\n",
+        "    if sys.platform.startswith(\"linux\"):\n",
+        "        print(\"Trying to install wheel for PyTorch3D\")\n",
         "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-        "    else:\n",
-        "        # We try to install PyTorch3D from source.\n",
-        "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+        "        pip_list = !pip freeze\n",
+        "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+        "    if need_pytorch3d:\n",
+        "        print(f\"failed to find/install wheel for {version_str}\")\n",
+        "if need_pytorch3d:\n",
+        "    print(\"Installing PyTorch3D from source\")\n",
+        "    !pip install ninja\n",
+        "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
       ]
     },
     {

docs/tutorials/implicitron_volumes.ipynb

@@ -66,25 +66,31 @@
         "import os\n",
         "import sys\n",
         "import torch\n",
+        "import subprocess\n",
         "need_pytorch3d=False\n",
         "try:\n",
         "    import pytorch3d\n",
         "except ModuleNotFoundError:\n",
         "    need_pytorch3d=True\n",
         "if need_pytorch3d:\n",
-        "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-        "        # We try to install PyTorch3D via a released wheel.\n",
-        "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-        "        version_str=\"\".join([\n",
-        "            f\"py3{sys.version_info.minor}_cu\",\n",
-        "            torch.version.cuda.replace(\".\",\"\"),\n",
-        "            f\"_pyt{pyt_version_str}\"\n",
-        "        ])\n",
-        "        !pip install fvcore iopath\n",
+        "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+        "    version_str=\"\".join([\n",
+        "        f\"py3{sys.version_info.minor}_cu\",\n",
+        "        torch.version.cuda.replace(\".\",\"\"),\n",
+        "        f\"_pyt{pyt_version_str}\"\n",
+        "    ])\n",
+        "    !pip install iopath\n",
+        "    if sys.platform.startswith(\"linux\"):\n",
+        "        print(\"Trying to install wheel for PyTorch3D\")\n",
         "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-        "    else:\n",
-        "        # We try to install PyTorch3D from source.\n",
-        "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+        "        pip_list = !pip freeze\n",
+        "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+        "    if need_pytorch3d:\n",
+        "        print(f\"failed to find/install wheel for {version_str}\")\n",
+        "if need_pytorch3d:\n",
+        "    print(\"Installing PyTorch3D from source\")\n",
+        "    !pip install ninja\n",
+        "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
       ]
     },
     {

docs/tutorials/render_colored_points.ipynb

@@ -44,25 +44,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/render_densepose.ipynb

@@ -51,25 +51,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/render_textured_meshes.ipynb

@@ -67,25 +67,31 @@
     "import os\n",
     "import sys\n",
     "import torch\n",
+    "import subprocess\n",
     "need_pytorch3d=False\n",
     "try:\n",
     "    import pytorch3d\n",
     "except ModuleNotFoundError:\n",
     "    need_pytorch3d=True\n",
     "if need_pytorch3d:\n",
-    "    if torch.__version__.startswith((\"1.13.\", \"2.0.\")) and sys.platform.startswith(\"linux\"):\n",
-    "        # We try to install PyTorch3D via a released wheel.\n",
-    "        pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
-    "        version_str=\"\".join([\n",
-    "            f\"py3{sys.version_info.minor}_cu\",\n",
-    "            torch.version.cuda.replace(\".\",\"\"),\n",
-    "            f\"_pyt{pyt_version_str}\"\n",
-    "        ])\n",
-    "        !pip install fvcore iopath\n",
+    "    pyt_version_str=torch.__version__.split(\"+\")[0].replace(\".\", \"\")\n",
+    "    version_str=\"\".join([\n",
+    "        f\"py3{sys.version_info.minor}_cu\",\n",
+    "        torch.version.cuda.replace(\".\",\"\"),\n",
+    "        f\"_pyt{pyt_version_str}\"\n",
+    "    ])\n",
+    "    !pip install iopath\n",
+    "    if sys.platform.startswith(\"linux\"):\n",
+    "        print(\"Trying to install wheel for PyTorch3D\")\n",
     "        !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html\n",
-    "    else:\n",
-    "        # We try to install PyTorch3D from source.\n",
-    "        !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
+    "        pip_list = !pip freeze\n",
+    "        need_pytorch3d = not any(i.startswith(\"pytorch3d==\") for  i in pip_list)\n",
+    "    if need_pytorch3d:\n",
+    "        print(f\"failed to find/install wheel for {version_str}\")\n",
+    "if need_pytorch3d:\n",
+    "    print(\"Installing PyTorch3D from source\")\n",
+    "    !pip install ninja\n",
+    "    !pip install 'git+https://github.com/facebookresearch/pytorch3d.git@stable'"
    ]
   },
   {

docs/tutorials/utils/camera_visualization.py

@@ -33,7 +33,7 @@ def plot_camera_scene(cameras, cameras_gt, status: str):
     a string passed inside the `status` argument.
     """
     fig = plt.figure()
-    ax = fig.gca(projection="3d")
+    ax = fig.add_subplot(projection="3d")
     ax.clear()
     ax.set_title(status)
     handle_cam = plot_cameras(ax, cameras, color="#FF7D1E")

packaging/build_conda.py

@@ -50,7 +50,6 @@ def setup_cuda():
     os.environ["FORCE_CUDA"] = "1"
 
     basic_nvcc_flags = (
-        "-gencode=arch=compute_35,code=sm_35 "
         "-gencode=arch=compute_50,code=sm_50 "
         "-gencode=arch=compute_60,code=sm_60 "
         "-gencode=arch=compute_70,code=sm_70 "
@@ -58,13 +57,19 @@ def setup_cuda():
         "-gencode=arch=compute_50,code=compute_50"
     )
     if CU_VERSION == "cu102":
-        nvcc_flags = basic_nvcc_flags
-    elif CU_VERSION == "cu110":
-        nvcc_flags = "-gencode=arch=compute_80,code=sm_80 " + basic_nvcc_flags
+        nvcc_flags = "-gencode=arch=compute_35,code=sm_35 " + basic_nvcc_flags
+    elif CU_VERSION < ("cu118"):
+        nvcc_flags = (
+            "-gencode=arch=compute_35,code=sm_35 "
+            + "-gencode=arch=compute_80,code=sm_80 "
+            + "-gencode=arch=compute_86,code=sm_86 "
+            + basic_nvcc_flags
+        )
     else:
         nvcc_flags = (
             "-gencode=arch=compute_80,code=sm_80 "
             + "-gencode=arch=compute_86,code=sm_86 "
+            + "-gencode=arch=compute_90,code=sm_90 "
             + basic_nvcc_flags
         )
 
@@ -75,6 +80,12 @@ def setup_cuda():
 def setup_conda_pytorch_constraint() -> List[str]:
     pytorch_constraint = f"- pytorch=={PYTORCH_VERSION}"
     os.environ["CONDA_PYTORCH_CONSTRAINT"] = pytorch_constraint
+    if pytorch_major_minor < (2, 2):
+        os.environ["CONDA_PYTORCH_MKL_CONSTRAINT"] = "- mkl!=2024.1.0"
+        os.environ["SETUPTOOLS_CONSTRAINT"] = "- setuptools<70"
+    else:
+        os.environ["CONDA_PYTORCH_MKL_CONSTRAINT"] = ""
+        os.environ["SETUPTOOLS_CONSTRAINT"] = "- setuptools"
     os.environ["CONDA_PYTORCH_BUILD_CONSTRAINT"] = pytorch_constraint
     os.environ["PYTORCH_VERSION_NODOT"] = PYTORCH_VERSION.replace(".", "")
 
@@ -112,7 +123,7 @@ def do_build(start_args: List[str]):
     if test_flag is not None:
         args.append(test_flag)
 
-    args.extend(["-c", "bottler", "-c", "fvcore", "-c", "iopath", "-c", "conda-forge"])
+    args.extend(["-c", "bottler", "-c", "iopath", "-c", "conda-forge"])
     args.append("--no-anaconda-upload")
     args.extend(["--python", os.environ["PYTHON_VERSION"]])
     args.append("packaging/pytorch3d")

packaging/linux_wheels/README.md

@@ -26,6 +26,6 @@ version_str="".join([
     torch.version.cuda.replace(".",""),
     f"_pyt{pyt_version_str}"
 ])
-!pip install fvcore iopath
+!pip install iopath
 !pip install --no-index --no-cache-dir pytorch3d -f https://dl.fbaipublicfiles.com/pytorch3d/packaging/wheels/{version_str}/download.html
 ```

packaging/linux_wheels/go.sh

@@ -5,7 +5,13 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-sudo docker run --rm  -v "$PWD/../../:/inside" pytorch/conda-cuda bash inside/packaging/linux_wheels/inside.sh
-sudo docker run --rm  -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu113 pytorch/conda-builder:cuda113 bash inside/packaging/linux_wheels/inside.sh
-sudo docker run --rm  -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu115 pytorch/conda-builder:cuda115 bash inside/packaging/linux_wheels/inside.sh
-sudo docker run --rm  -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu116 pytorch/conda-builder:cuda116 bash inside/packaging/linux_wheels/inside.sh
+# Some directory to persist downloaded conda packages
+conda_cache=/raid/$USER/building_conda_cache
+
+mkdir -p "$conda_cache"
+
+sudo docker run --rm -v "$conda_cache:/conda_cache" -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu113 pytorch/conda-builder:cuda113 bash inside/packaging/linux_wheels/inside.sh
+sudo docker run --rm -v "$conda_cache:/conda_cache" -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu115 pytorch/conda-builder:cuda115 bash inside/packaging/linux_wheels/inside.sh
+sudo docker run --rm -v "$conda_cache:/conda_cache" -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu116 pytorch/conda-builder:cuda116 bash inside/packaging/linux_wheels/inside.sh
+sudo docker run --rm -v "$conda_cache:/conda_cache" -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu117 pytorch/conda-builder:cuda117 bash inside/packaging/linux_wheels/inside.sh
+sudo docker run --rm -v "$conda_cache:/conda_cache" -v "$PWD/../../:/inside" -e SELECTED_CUDA=cu118 pytorch/conda-builder:cuda118 bash inside/packaging/linux_wheels/inside.sh

packaging/linux_wheels/inside.sh

@@ -16,23 +16,32 @@ VERSION=$(python -c "exec(open('pytorch3d/__init__.py').read()); print(__version
 
 export BUILD_VERSION=$VERSION
 export FORCE_CUDA=1
+export MAX_JOBS=8
+export CONDA_PKGS_DIRS=/conda_cache
 
-wget --no-verbose https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
-tar xzf 1.10.0.tar.gz
-CUB_HOME=$(realpath ./cub-1.10.0)
-export CUB_HOME
-echo "CUB_HOME is now $CUB_HOME"
+if false
+then
+    # We used to have to do this for old versions of CUDA
+    wget --no-verbose https://github.com/NVIDIA/cub/archive/1.10.0.tar.gz
+    tar xzf 1.10.0.tar.gz
+    CUB_HOME=$(realpath ./cub-1.10.0)
+    export CUB_HOME
+    echo "CUB_HOME is now $CUB_HOME"
+fi
 
 # As a rule, we want to build for any combination of dependencies which is supported by
 # PyTorch3D and not older than the current Google Colab set up.
 
-PYTHON_VERSIONS="3.7 3.8 3.9 3.10"
+PYTHON_VERSIONS="3.8 3.9 3.10"
 # the keys are pytorch versions
 declare -A CONDA_CUDA_VERSIONS=(
-    ["1.10.1"]="cu111 cu113"
-    ["1.10.2"]="cu111 cu113"
-    ["1.10.0"]="cu111 cu113"
-    ["1.11.0"]="cu111 cu113 cu115"
+#    ["1.11.0"]="cu113"
+#    ["1.12.0"]="cu113"
+#    ["1.12.1"]="cu113"
+#    ["1.13.0"]="cu116"
+#    ["1.13.1"]="cu116 cu117"
+#    ["2.0.0"]="cu117 cu118"
+    ["2.0.1"]="cu117 cu118"
 )
 
 
@@ -41,39 +50,43 @@ for python_version in $PYTHON_VERSIONS
 do
     for pytorch_version in "${!CONDA_CUDA_VERSIONS[@]}"
     do
-        if [[ "3.7 3.8" != *$python_version* ]] && [[ "1.7.0" == *$pytorch_version* ]]
-        then
-            #python 3.9 and later not supported by pytorch 1.7.0 and before
-            continue
-        fi
         if [[ "3.7 3.8 3.9" != *$python_version* ]] && [[ "1.7.0 1.7.1 1.8.0 1.8.1 1.9.0 1.9.1 1.10.0 1.10.1 1.10.2" == *$pytorch_version* ]]
         then
             #python 3.10 and later not supported by pytorch 1.10.2 and before
             continue
         fi
 
-        extra_channel="-c conda-forge"
+        extra_channel="-c nvidia"
+        cudatools="pytorch-cuda"
         if [[ "1.11.0" == "$pytorch_version" ]]
         then
             extra_channel=""
+            cudatools="cudatoolkit"
+        fi
+        if [[ "1.12.0" == "$pytorch_version" ]] || [[ "1.12.1" == "$pytorch_version" ]]
+        then
+            extra_channel="-c conda-forge"
+            cudatools="cudatoolkit"
         fi
 
         for cu_version in ${CONDA_CUDA_VERSIONS[$pytorch_version]}
         do
-            if [[ "cu113 cu115 cu116" == *$cu_version* ]]
-            #       ^^^ CUDA versions listed here have to be built
-            # in their own containers.
-            then
             if [[ $SELECTED_CUDA != "$cu_version" ]]
-                then
-                    continue
-                fi
-            elif [[ $SELECTED_CUDA != "" ]]
             then
                 continue
             fi
 
             case "$cu_version" in
+                cu118)
+                    export CUDA_HOME=/usr/local/cuda-11.8/
+                    export CUDA_TAG=11.8
+                    export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
+                ;;
+                cu117)
+                    export CUDA_HOME=/usr/local/cuda-11.7/
+                    export CUDA_TAG=11.7
+                    export NVCC_FLAGS="-gencode=arch=compute_35,code=sm_35 -gencode=arch=compute_50,code=sm_50 -gencode=arch=compute_60,code=sm_60 -gencode=arch=compute_70,code=sm_70 -gencode=arch=compute_75,code=sm_75 -gencode=arch=compute_80,code=sm_80 -gencode=arch=compute_86,code=sm_86 -gencode=arch=compute_50,code=compute_50"
+                ;;
                 cu116)
                     export CUDA_HOME=/usr/local/cuda-11.6/
                     export CUDA_TAG=11.6
@@ -130,8 +143,8 @@ do
             conda create -y -n "$tag" "python=$python_version"
             conda activate "$tag"
             # shellcheck disable=SC2086
-            conda install -y -c pytorch $extra_channel "pytorch=$pytorch_version" "cudatoolkit=$CUDA_TAG" torchvision
-            pip install fvcore iopath
+            conda install -y -c pytorch $extra_channel "pytorch=$pytorch_version" "$cudatools=$CUDA_TAG"
+            pip install iopath
             echo "python version" "$python_version" "pytorch version" "$pytorch_version" "cuda version" "$cu_version" "tag" "$tag"
 
             rm -rf dist

packaging/pytorch3d/meta.yaml

@@ -12,8 +12,9 @@ requirements:
 
   host:
     - python
-    - setuptools
+    {{ environ.get('SETUPTOOLS_CONSTRAINT') }}
     {{ environ.get('CONDA_PYTORCH_BUILD_CONSTRAINT') }}
+    {{ environ.get('CONDA_PYTORCH_MKL_CONSTRAINT') }}
     {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}
     {{ environ.get('CONDA_CPUONLY_FEATURE') }}
 
@@ -21,7 +22,6 @@ requirements:
     - python
     - numpy >=1.11
     - torchvision >=0.5
-    - fvcore
     - iopath
     {{ environ.get('CONDA_PYTORCH_CONSTRAINT') }}
     {{ environ.get('CONDA_CUDATOOLKIT_CONSTRAINT') }}

projects/implicitron_trainer/__init__.py

@@ -3,3 +3,5 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe

projects/implicitron_trainer/experiment.py

@@ -5,6 +5,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 """"
 This file is the entry point for launching experiments with Implicitron.
 
@@ -97,7 +99,7 @@ except ModuleNotFoundError:
 no_accelerate = os.environ.get("PYTORCH3D_NO_ACCELERATE") is not None
 
 
-class Experiment(Configurable):  # pyre-ignore: 13
+class Experiment(Configurable):
     """
     This class is at the top level of Implicitron's config hierarchy. Its
     members are high-level components necessary for training an implicit rende-
@@ -118,12 +120,16 @@ class Experiment(Configurable):  # pyre-ignore: 13
             will be saved here.
     """
 
+    # pyre-fixme[13]: Attribute `data_source` is never initialized.
     data_source: DataSourceBase
     data_source_class_type: str = "ImplicitronDataSource"
+    # pyre-fixme[13]: Attribute `model_factory` is never initialized.
     model_factory: ModelFactoryBase
     model_factory_class_type: str = "ImplicitronModelFactory"
+    # pyre-fixme[13]: Attribute `optimizer_factory` is never initialized.
     optimizer_factory: OptimizerFactoryBase
     optimizer_factory_class_type: str = "ImplicitronOptimizerFactory"
+    # pyre-fixme[13]: Attribute `training_loop` is never initialized.
     training_loop: TrainingLoopBase
     training_loop_class_type: str = "ImplicitronTrainingLoop"
 

projects/implicitron_trainer/impl/__init__.py

@@ -3,3 +3,5 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe

projects/implicitron_trainer/impl/model_factory.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import logging
 import os
 from typing import Optional
@@ -43,7 +45,7 @@ class ModelFactoryBase(ReplaceableBase):
 
 
 @registry.register
-class ImplicitronModelFactory(ModelFactoryBase):  # pyre-ignore [13]
+class ImplicitronModelFactory(ModelFactoryBase):
     """
     A factory class that initializes an implicit rendering model.
 
@@ -59,6 +61,7 @@ class ImplicitronModelFactory(ModelFactoryBase):  # pyre-ignore [13]
 
     """
 
+    # pyre-fixme[13]: Attribute `model` is never initialized.
     model: ImplicitronModelBase
     model_class_type: str = "GenericModel"
     resume: bool = True

projects/implicitron_trainer/impl/optimizer_factory.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import inspect
 import logging
 import os
@@ -121,7 +123,6 @@ class ImplicitronOptimizerFactory(OptimizerFactoryBase):
         """
         # Get the parameters to optimize
         if hasattr(model, "_get_param_groups"):  # use the model function
-            # pyre-ignore[29]
             p_groups = model._get_param_groups(self.lr, wd=self.weight_decay)
         else:
             p_groups = [

projects/implicitron_trainer/impl/training_loop.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import logging
 import os
 import time
@@ -21,7 +23,6 @@ from pytorch3d.implicitron.tools.config import (
     run_auto_creation,
 )
 from pytorch3d.implicitron.tools.stats import Stats
-from pytorch3d.renderer.cameras import CamerasBase
 from torch.utils.data import DataLoader, Dataset
 
 from .utils import seed_all_random_engines
@@ -29,13 +30,13 @@ from .utils import seed_all_random_engines
 logger = logging.getLogger(__name__)
 
 
-# pyre-fixme[13]: Attribute `evaluator` is never initialized.
 class TrainingLoopBase(ReplaceableBase):
     """
     Members:
         evaluator: An EvaluatorBase instance, used to evaluate training results.
     """
 
+    # pyre-fixme[13]: Attribute `evaluator` is never initialized.
     evaluator: Optional[EvaluatorBase]
     evaluator_class_type: Optional[str] = "ImplicitronEvaluator"
 
@@ -111,6 +112,8 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
     def __post_init__(self):
         run_auto_creation(self)
 
+    # pyre-fixme[14]: `run` overrides method defined in `TrainingLoopBase`
+    #  inconsistently.
     def run(
         self,
         *,
@@ -256,7 +259,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
             list(log_vars),
             plot_file=os.path.join(exp_dir, "train_stats.pdf"),
             visdom_env=visdom_env_charts,
-            verbose=False,
             visdom_server=self.visdom_server,
             visdom_port=self.visdom_port,
         )
@@ -382,7 +384,8 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
 
             # print textual status update
             if it % self.metric_print_interval == 0 or last_iter:
-                stats.print(stat_set=trainmode, max_it=n_batches)
+                std_out = stats.get_status_string(stat_set=trainmode, max_it=n_batches)
+                logger.info(std_out)
 
             # visualize results
             if (
@@ -392,7 +395,6 @@ class ImplicitronTrainingLoop(TrainingLoopBase):
             ):
                 prefix = f"e{stats.epoch}_it{stats.it[trainmode]}"
                 if hasattr(model, "visualize"):
-                    # pyre-ignore [29]
                     model.visualize(
                         viz,
                         visdom_env_imgs,

projects/implicitron_trainer/impl/utils.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import random
 

projects/implicitron_trainer/tests/__init__.py

@@ -3,3 +3,5 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe

projects/implicitron_trainer/tests/experiment.yaml

@@ -129,6 +129,19 @@ data_source_ImplicitronDataSource_args:
     dataset_length_train: 0
     dataset_length_val: 0
     dataset_length_test: 0
+  data_loader_map_provider_TrainEvalDataLoaderMapProvider_args:
+    batch_size: 1
+    num_workers: 0
+    dataset_length_train: 0
+    dataset_length_val: 0
+    dataset_length_test: 0
+    train_conditioning_type: SAME
+    val_conditioning_type: SAME
+    test_conditioning_type: KNOWN
+    images_per_seq_options: []
+    sample_consecutive_frames: false
+    consecutive_frames_max_gap: 0
+    consecutive_frames_max_gap_seconds: 0.1
 model_factory_ImplicitronModelFactory_args:
   resume: true
   model_class_type: GenericModel
@@ -203,6 +216,7 @@ model_factory_ImplicitronModelFactory_args:
       n_rays_total_training: null
       stratified_point_sampling_training: true
       stratified_point_sampling_evaluation: false
+      cast_ray_bundle_as_cone: false
       scene_extent: 8.0
       scene_center:
       - 0.0
@@ -215,6 +229,7 @@ model_factory_ImplicitronModelFactory_args:
       n_rays_total_training: null
       stratified_point_sampling_training: true
       stratified_point_sampling_evaluation: false
+      cast_ray_bundle_as_cone: false
       min_depth: 0.1
       max_depth: 8.0
     renderer_LSTMRenderer_args:
@@ -234,6 +249,8 @@ model_factory_ImplicitronModelFactory_args:
       append_coarse_samples_to_fine: true
       density_noise_std_train: 0.0
       return_weights: false
+      blurpool_weights: false
+      sample_pdf_eps: 1.0e-05
       raymarcher_CumsumRaymarcher_args:
         surface_thickness: 1
         bg_color:
@@ -346,6 +363,7 @@ model_factory_ImplicitronModelFactory_args:
       n_hidden_neurons_dir: 128
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
+      use_integrated_positional_encoding: false
       transformer_dim_down_factor: 2.0
       n_hidden_neurons_xyz: 80
       n_layers_xyz: 2
@@ -357,6 +375,7 @@ model_factory_ImplicitronModelFactory_args:
       n_hidden_neurons_dir: 128
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
+      use_integrated_positional_encoding: false
       transformer_dim_down_factor: 1.0
       n_hidden_neurons_xyz: 256
       n_layers_xyz: 8
@@ -629,6 +648,7 @@ model_factory_ImplicitronModelFactory_args:
       n_rays_total_training: null
       stratified_point_sampling_training: true
       stratified_point_sampling_evaluation: false
+      cast_ray_bundle_as_cone: false
       scene_extent: 8.0
       scene_center:
       - 0.0
@@ -641,6 +661,7 @@ model_factory_ImplicitronModelFactory_args:
       n_rays_total_training: null
       stratified_point_sampling_training: true
       stratified_point_sampling_evaluation: false
+      cast_ray_bundle_as_cone: false
       min_depth: 0.1
       max_depth: 8.0
     renderer_LSTMRenderer_args:
@@ -660,6 +681,8 @@ model_factory_ImplicitronModelFactory_args:
       append_coarse_samples_to_fine: true
       density_noise_std_train: 0.0
       return_weights: false
+      blurpool_weights: false
+      sample_pdf_eps: 1.0e-05
       raymarcher_CumsumRaymarcher_args:
         surface_thickness: 1
         bg_color:
@@ -724,6 +747,7 @@ model_factory_ImplicitronModelFactory_args:
       n_hidden_neurons_dir: 128
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
+      use_integrated_positional_encoding: false
       transformer_dim_down_factor: 2.0
       n_hidden_neurons_xyz: 80
       n_layers_xyz: 2
@@ -735,6 +759,7 @@ model_factory_ImplicitronModelFactory_args:
       n_hidden_neurons_dir: 128
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
+      use_integrated_positional_encoding: false
       transformer_dim_down_factor: 1.0
       n_hidden_neurons_xyz: 256
       n_layers_xyz: 8
@@ -962,6 +987,7 @@ model_factory_ImplicitronModelFactory_args:
       n_hidden_neurons_dir: 128
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
+      use_integrated_positional_encoding: false
       transformer_dim_down_factor: 2.0
       n_hidden_neurons_xyz: 80
       n_layers_xyz: 2
@@ -973,6 +999,7 @@ model_factory_ImplicitronModelFactory_args:
       n_hidden_neurons_dir: 128
       input_xyz: true
       xyz_ray_dir_in_camera_coords: false
+      use_integrated_positional_encoding: false
       transformer_dim_down_factor: 1.0
       n_hidden_neurons_xyz: 256
       n_layers_xyz: 8

projects/implicitron_trainer/tests/test_experiment.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import os
 import tempfile
 import unittest
@@ -132,6 +134,13 @@ class TestExperiment(unittest.TestCase):
         # Check that the default config values, defined by Experiment and its
         # members, is what we expect it to be.
         cfg = OmegaConf.structured(experiment.Experiment)
+        # the following removes the possible effect of env variables
+        ds_arg = cfg.data_source_ImplicitronDataSource_args
+        ds_arg.dataset_map_provider_JsonIndexDatasetMapProvider_args.dataset_root = ""
+        ds_arg.dataset_map_provider_JsonIndexDatasetMapProviderV2_args.dataset_root = ""
+        if "dataset_map_provider_SqlIndexDatasetMapProvider_args" in ds_arg:
+            del ds_arg.dataset_map_provider_SqlIndexDatasetMapProvider_args
+        cfg.training_loop_ImplicitronTrainingLoop_args.visdom_port = 8097
         yaml = OmegaConf.to_yaml(cfg, sort_keys=False)
         if DEBUG:
             (DATA_DIR / "experiment.yaml").write_text(yaml)

projects/implicitron_trainer/tests/test_optimizer_factory.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import logging
 import os
 import unittest

projects/implicitron_trainer/tests/test_visualize.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import os
 import unittest
 

projects/implicitron_trainer/tests/utils.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import contextlib
 import logging
 import os

projects/implicitron_trainer/visualize_reconstruction.py

@@ -5,6 +5,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 """
 Script to visualize a previously trained model. Example call:
 

projects/nerf/nerf/nerf_renderer.py

@@ -343,12 +343,14 @@ class RadianceFieldRenderer(torch.nn.Module):
             # For a full render pass concatenate the output chunks,
             # and reshape to image size.
             out = {
-                k: torch.cat(
-                    [ch_o[k] for ch_o in chunk_outputs],
-                    dim=1,
-                ).view(-1, *self._image_size, 3)
-                if chunk_outputs[0][k] is not None
-                else None
+                k: (
+                    torch.cat(
+                        [ch_o[k] for ch_o in chunk_outputs],
+                        dim=1,
+                    ).view(-1, *self._image_size, 3)
+                    if chunk_outputs[0][k] is not None
+                    else None
+                )
                 for k in ("rgb_fine", "rgb_coarse", "rgb_gt")
             }
         else:

projects/nerf/nerf/raysampler.py

@@ -330,9 +330,9 @@ class NeRFRaysampler(torch.nn.Module):
 
             if self.training:
                 # During training we randomly subsample rays.
-                sel_rays = torch.randperm(n_pixels, device=device)[
-                    : self._mc_raysampler._n_rays_per_image
-                ]
+                sel_rays = torch.randperm(
+                    n_pixels, device=full_ray_bundle.lengths.device
+                )[: self._mc_raysampler._n_rays_per_image]
             else:
                 # In case we test, we take only the requested chunk.
                 if chunksize is None:

pytorch3d/__init__.py

@@ -4,4 +4,6 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-__version__ = "0.7.4"
+# pyre-unsafe
+
+__version__ = "0.7.8"

pytorch3d/common/__init__.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from .datatypes import Device, get_device, make_device
 
 

pytorch3d/common/compat.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from typing import Sequence, Tuple, Union
 
 import torch

pytorch3d/common/datatypes.py

@@ -4,7 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
-import sys
+# pyre-unsafe
+
 from typing import Optional, Union
 
 import torch

pytorch3d/common/linear_with_repeat.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import math
 from typing import Tuple
 

pytorch3d/common/workaround/__init__.py

@@ -4,5 +4,7 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from .symeig3x3 import symeig3x3
 from .utils import _safe_det_3x3

pytorch3d/common/workaround/symeig3x3.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import math
 from typing import Optional, Tuple
 

pytorch3d/common/workaround/utils.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import torch
 

pytorch3d/csrc/ext.cpp

@@ -7,11 +7,15 @@
  */
 
 // clang-format off
+#if !defined(USE_ROCM)
 #include "./pulsar/global.h" // Include before <torch/extension.h>.
+#endif
 #include <torch/extension.h>
 // clang-format on
+#if !defined(USE_ROCM)
 #include "./pulsar/pytorch/renderer.h"
 #include "./pulsar/pytorch/tensor_util.h"
+#endif
 #include "ball_query/ball_query.h"
 #include "blending/sigmoid_alpha_blend.h"
 #include "compositing/alpha_composite.h"
@@ -99,6 +103,8 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("marching_cubes", &MarchingCubes);
 
   // Pulsar.
+  // Pulsar not enabled on AMD.
+#if !defined(USE_ROCM)
 #ifdef PULSAR_LOGGING_ENABLED
   c10::ShowLogInfoToStderr();
 #endif
@@ -183,4 +189,5 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.attr("MAX_UINT") = py::int_(MAX_UINT);
   m.attr("MAX_USHORT") = py::int_(MAX_USHORT);
   m.attr("PULSAR_MAX_GRAD_SPHERES") = py::int_(MAX_GRAD_SPHERES);
+#endif
 }

pytorch3d/csrc/iou_box3d/iou_box3d.cu

@@ -12,8 +12,6 @@
 #include <math.h>
 #include <stdio.h>
 #include <stdlib.h>
-#include <thrust/device_vector.h>
-#include <thrust/tuple.h>
 #include "iou_box3d/iou_utils.cuh"
 
 // Parallelize over N*M computations which can each be done

pytorch3d/csrc/iou_box3d/iou_utils.cuh

@@ -8,7 +8,6 @@
 
 #include <float.h>
 #include <math.h>
-#include <thrust/device_vector.h>
 #include <cstdio>
 #include "utils/float_math.cuh"
 

pytorch3d/csrc/knn/knn.cu

@@ -338,7 +338,7 @@ std::tuple<at::Tensor, at::Tensor> KNearestNeighborIdxCuda(
 
   TORCH_CHECK((norm == 1) || (norm == 2), "Norm must be 1 or 2.");
 
-  TORCH_CHECK(p2.size(2) == D, "Point sets must have the same last dimension");
+  TORCH_CHECK(p1.size(2) == D, "Point sets must have the same last dimension");
   auto long_dtype = lengths1.options().dtype(at::kLong);
   auto idxs = at::zeros({N, P1, K}, long_dtype);
   auto dists = at::zeros({N, P1, K}, p1.options());
@@ -495,7 +495,7 @@ __global__ void KNearestNeighborBackwardKernel(
     if ((p1_idx < num1) && (k < num2)) {
       const float grad_dist = grad_dists[n * P1 * K + p1_idx * K + k];
       // index of point in p2 corresponding to the k-th nearest neighbor
-      const size_t p2_idx = idxs[n * P1 * K + p1_idx * K + k];
+      const int64_t p2_idx = idxs[n * P1 * K + p1_idx * K + k];
       // If the index is the pad value of -1 then ignore it
       if (p2_idx == -1) {
         continue;

pytorch3d/csrc/marching_cubes/marching_cubes.cu

@@ -9,8 +9,6 @@
 #include <ATen/ATen.h>
 #include <ATen/cuda/CUDAContext.h>
 #include <c10/cuda/CUDAGuard.h>
-#include <thrust/device_vector.h>
-#include <thrust/scan.h>
 #include <cstdio>
 #include "marching_cubes/tables.h"
 
@@ -40,20 +38,6 @@ through" each cube in the grid.
 // EPS: Used to indicate if two float values are close
 __constant__ const float EPSILON = 1e-5;
 
-// Thrust wrapper for exclusive scan
-//
-// Args:
-//    output: pointer to on-device output array
-//    input: pointer to on-device input array, where scan is performed
-//    numElements: number of elements for the input array
-//
-void ThrustScanWrapper(int* output, int* input, int numElements) {
-  thrust::exclusive_scan(
-      thrust::device_ptr<int>(input),
-      thrust::device_ptr<int>(input + numElements),
-      thrust::device_ptr<int>(output));
-}
-
 // Linearly interpolate the position where an isosurface cuts an edge
 // between two vertices, based on their scalar values
 //
@@ -239,7 +223,7 @@ __global__ void CompactVoxelsKernel(
         compactedVoxelArray,
     const at::PackedTensorAccessor32<int, 1, at::RestrictPtrTraits>
         voxelOccupied,
-    const at::PackedTensorAccessor32<int, 1, at::RestrictPtrTraits>
+    const at::PackedTensorAccessor32<int64_t, 1, at::RestrictPtrTraits>
         voxelOccupiedScan,
     uint numVoxels) {
   uint id = blockIdx.x * blockDim.x + threadIdx.x;
@@ -271,7 +255,8 @@ __global__ void GenerateFacesKernel(
     at::PackedTensorAccessor<int64_t, 1, at::RestrictPtrTraits> ids,
     at::PackedTensorAccessor32<int, 1, at::RestrictPtrTraits>
         compactedVoxelArray,
-    at::PackedTensorAccessor32<int, 1, at::RestrictPtrTraits> numVertsScanned,
+    at::PackedTensorAccessor32<int64_t, 1, at::RestrictPtrTraits>
+        numVertsScanned,
     const uint activeVoxels,
     const at::PackedTensorAccessor32<float, 3, at::RestrictPtrTraits> vol,
     const at::PackedTensorAccessor32<int, 2, at::RestrictPtrTraits> faceTable,
@@ -397,6 +382,44 @@ __global__ void GenerateFacesKernel(
   } // end for grid-strided kernel
 }
 
+// ATen/Torch does not have an exclusive-scan operator. Additionally, in the
+// code below we need to get the "total number of items to work on" after
+// a scan, which with an inclusive-scan would simply be the value of the last
+// element in the tensor.
+//
+// This utility function hits two birds with one stone, by running
+// an inclusive-scan into a right-shifted view of a tensor that's
+// allocated to be one element bigger than the input tensor.
+//
+// Note; return tensor is `int64_t` per element, even if the input
+// tensor is only 32-bit. Also, the return tensor is one element bigger
+// than the input one.
+//
+// Secondary optional argument is an output argument that gets the
+// value of the last element of the return tensor (because you almost
+// always need this CPU-side right after this function anyway).
+static at::Tensor ExclusiveScanAndTotal(
+    const at::Tensor& inTensor,
+    int64_t* optTotal = nullptr) {
+  const auto inSize = inTensor.sizes()[0];
+  auto retTensor = at::zeros({inSize + 1}, at::kLong).to(inTensor.device());
+
+  using at::indexing::None;
+  using at::indexing::Slice;
+  auto rightShiftedView = retTensor.index({Slice(1, None)});
+
+  // Do an (inclusive-scan) cumulative sum in to the view that's
+  // shifted one element to the right...
+  at::cumsum_out(rightShiftedView, inTensor, 0, at::kLong);
+
+  if (optTotal) {
+    *optTotal = retTensor[inSize].cpu().item<int64_t>();
+  }
+
+  // ...so that the not-shifted tensor holds the exclusive-scan
+  return retTensor;
+}
+
 // Entrance for marching cubes cuda extension. Marching Cubes is an algorithm to
 // create triangle meshes from an implicit function (one of the form f(x, y, z)
 // = 0). It works by iteratively checking a grid of cubes superimposed over a
@@ -455,6 +478,9 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
     grid.x = 65535;
   }
 
+  using at::indexing::None;
+  using at::indexing::Slice;
+
   auto d_voxelVerts =
       at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
           .to(vol.device());
@@ -477,18 +503,9 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
   // count for voxels in the grid and compute the number of active voxels.
   // If the number of active voxels is 0, return zero tensor for verts and
   // faces.
+  int64_t activeVoxels = 0;
   auto d_voxelOccupiedScan =
-      at::zeros({numVoxels}, at::TensorOptions().dtype(at::kInt))
-          .to(vol.device());
-  ThrustScanWrapper(
-      d_voxelOccupiedScan.data_ptr<int>(),
-      d_voxelOccupied.data_ptr<int>(),
-      numVoxels);
-
-  // number of active voxels
-  int lastElement = d_voxelVerts[numVoxels - 1].cpu().item<int>();
-  int lastScan = d_voxelOccupiedScan[numVoxels - 1].cpu().item<int>();
-  int activeVoxels = lastElement + lastScan;
+      ExclusiveScanAndTotal(d_voxelOccupied, &activeVoxels);
 
   const int device_id = vol.device().index();
   auto opt = at::TensorOptions().dtype(at::kInt).device(at::kCUDA, device_id);
@@ -503,28 +520,21 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
     return std::make_tuple(verts, faces, ids);
   }
 
-  // Execute "CompactVoxelsKernel" kernel to compress voxels for accleration.
+  // Execute "CompactVoxelsKernel" kernel to compress voxels for acceleration.
   // This allows us to run triangle generation on only the occupied voxels.
   auto d_compVoxelArray = at::zeros({activeVoxels}, opt);
   CompactVoxelsKernel<<<grid, threads, 0, stream>>>(
       d_compVoxelArray.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
       d_voxelOccupied.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
-      d_voxelOccupiedScan.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
+      d_voxelOccupiedScan
+          .packed_accessor32<int64_t, 1, at::RestrictPtrTraits>(),
       numVoxels);
   AT_CUDA_CHECK(cudaGetLastError());
   cudaDeviceSynchronize();
 
   // Scan d_voxelVerts array to generate offsets of vertices for each voxel
-  auto d_voxelVertsScan = at::zeros({numVoxels}, opt);
-  ThrustScanWrapper(
-      d_voxelVertsScan.data_ptr<int>(),
-      d_voxelVerts.data_ptr<int>(),
-      numVoxels);
-
-  // total number of vertices
-  lastElement = d_voxelVerts[numVoxels - 1].cpu().item<int>();
-  lastScan = d_voxelVertsScan[numVoxels - 1].cpu().item<int>();
-  int totalVerts = lastElement + lastScan;
+  int64_t totalVerts = 0;
+  auto d_voxelVertsScan = ExclusiveScanAndTotal(d_voxelVerts, &totalVerts);
 
   // Execute "GenerateFacesKernel" kernel
   // This runs only on the occupied voxels.
@@ -544,7 +554,7 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCuda(
       faces.packed_accessor<int64_t, 2, at::RestrictPtrTraits>(),
       ids.packed_accessor<int64_t, 1, at::RestrictPtrTraits>(),
       d_compVoxelArray.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
-      d_voxelVertsScan.packed_accessor32<int, 1, at::RestrictPtrTraits>(),
+      d_voxelVertsScan.packed_accessor32<int64_t, 1, at::RestrictPtrTraits>(),
       activeVoxels,
       vol.packed_accessor32<float, 3, at::RestrictPtrTraits>(),
       faceTable.packed_accessor32<int, 2, at::RestrictPtrTraits>(),

pytorch3d/csrc/marching_cubes/marching_cubes_cpu.cpp

@@ -71,8 +71,8 @@ std::tuple<at::Tensor, at::Tensor, at::Tensor> MarchingCubesCpu(
           if ((j + 1) % 3 == 0 && ps[0] != ps[1] && ps[1] != ps[2] &&
               ps[2] != ps[0]) {
             for (int k = 0; k < 3; k++) {
-              int v = tri[k];
-              edge_id_to_v[tri.at(k)] = ps.at(k);
+              int64_t v = tri.at(k);
+              edge_id_to_v[v] = ps.at(k);
               if (!uniq_edge_id.count(v)) {
                 uniq_edge_id[v] = verts.size();
                 verts.push_back(edge_id_to_v[v]);

pytorch3d/csrc/pulsar/global.h

@@ -30,11 +30,18 @@
 #define GLOBAL __global__
 #define RESTRICT __restrict__
 #define DEBUGBREAK()
+#ifdef __NVCC_DIAG_PRAGMA_SUPPORT__
+#pragma nv_diag_suppress 1866
+#pragma nv_diag_suppress 2941
+#pragma nv_diag_suppress 2951
+#pragma nv_diag_suppress 2967
+#else
 #pragma diag_suppress = attribute_not_allowed
 #pragma diag_suppress = 1866
 #pragma diag_suppress = 2941
 #pragma diag_suppress = 2951
 #pragma diag_suppress = 2967
+#endif
 #else // __CUDACC__
 #define INLINE inline
 #define HOST
@@ -49,6 +56,7 @@
 #pragma clang diagnostic pop
 #ifdef WITH_CUDA
 #include <ATen/cuda/CUDAContext.h>
+#include <vector_functions.h>
 #else
 #ifndef cudaStream_t
 typedef void* cudaStream_t;
@@ -65,8 +73,6 @@ struct float2 {
 struct float3 {
   float x, y, z;
 };
-#endif
-namespace py = pybind11;
 inline float3 make_float3(const float& x, const float& y, const float& z) {
   float3 res;
   res.x = x;
@@ -74,6 +80,8 @@ inline float3 make_float3(const float& x, const float& y, const float& z) {
   res.z = z;
   return res;
 }
+#endif
+namespace py = pybind11;
 
 inline bool operator==(const float3& a, const float3& b) {
   return a.x == b.x && a.y == b.y && a.z == b.z;

pytorch3d/csrc/pulsar/host/commands.h

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

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

@@ -93,7 +93,7 @@ HOST void construct(
   MALLOC(self->di_sorted_d, DrawInfo, max_num_balls);
   MALLOC(self->region_flags_d, char, max_num_balls);
   MALLOC(self->num_selected_d, size_t, 1);
-  MALLOC(self->forw_info_d, float, width* height*(3 + 2 * n_track));
+  MALLOC(self->forw_info_d, float, width* height * (3 + 2 * n_track));
   MALLOC(self->min_max_pixels_d, IntersectInfo, 1);
   MALLOC(self->grad_pos_d, float3, max_num_balls);
   MALLOC(self->grad_col_d, float, max_num_balls* n_channels);

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

@@ -102,6 +102,7 @@ void forward(
       self->workspace_d,
       self->workspace_size,
       stream);
+  CHECKLAUNCH();
   SORT_ASCENDING_WS(
       self->min_depth_d,
       self->min_depth_sorted_d,
@@ -111,6 +112,7 @@ void forward(
       self->workspace_d,
       self->workspace_size,
       stream);
+  CHECKLAUNCH();
   SORT_ASCENDING_WS(
       self->min_depth_d,
       self->min_depth_sorted_d,

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

@@ -99,7 +99,7 @@ GLOBAL void render(
   /** Whether loading of balls is completed. */
   SHARED bool loading_done;
   /** The number of balls loaded overall (just for statistics). */
-  SHARED int n_balls_loaded;
+  [[maybe_unused]] SHARED int n_balls_loaded;
   /** The area this thread block covers. */
   SHARED IntersectInfo block_area;
   if (thread_block.thread_rank() == 0) {

pytorch3d/csrc/pulsar/pytorch/camera.h

@@ -37,7 +37,7 @@ inline void fill_cam_vecs(
   res->pixel_dir_y.x = pixel_dir_y.data_ptr<float>()[0];
   res->pixel_dir_y.y = pixel_dir_y.data_ptr<float>()[1];
   res->pixel_dir_y.z = pixel_dir_y.data_ptr<float>()[2];
-  auto sensor_dir_z = pixel_dir_y.cross(pixel_dir_x);
+  auto sensor_dir_z = pixel_dir_y.cross(pixel_dir_x, -1);
   sensor_dir_z /= sensor_dir_z.norm();
   if (right_handed) {
     sensor_dir_z *= -1.f;

pytorch3d/csrc/rasterize_coarse/rasterize_coarse.cu

@@ -244,8 +244,7 @@ at::Tensor RasterizeCoarseCuda(
   if (num_bins_y >= kMaxItemsPerBin || num_bins_x >= kMaxItemsPerBin) {
     std::stringstream ss;
     ss << "In RasterizeCoarseCuda got num_bins_y: " << num_bins_y
-       << ", num_bins_x: " << num_bins_x << ", "
-       << "; that's too many!";
+       << ", num_bins_x: " << num_bins_x << ", " << "; that's too many!";
     AT_ERROR(ss.str());
   }
   auto opts = elems_per_batch.options().dtype(at::kInt);

pytorch3d/csrc/rasterize_meshes/rasterize_meshes.cu

@@ -144,7 +144,7 @@ __device__ void CheckPixelInsideFace(
   const bool zero_face_area =
       (face_area <= kEpsilon && face_area >= -1.0f * kEpsilon);
 
-  if (zmax < 0 || cull_backfaces && back_face || outside_bbox ||
+  if (zmax < 0 || (cull_backfaces && back_face) || outside_bbox ||
       zero_face_area) {
     return;
   }

pytorch3d/csrc/utils/float_math.cuh

@@ -18,6 +18,8 @@ const auto vEpsilon = 1e-8;
 
 // Common functions and operators for float2.
 
+// Complex arithmetic is already defined for AMD.
+#if !defined(USE_ROCM)
 __device__ inline float2 operator-(const float2& a, const float2& b) {
   return make_float2(a.x - b.x, a.y - b.y);
 }
@@ -41,6 +43,7 @@ __device__ inline float2 operator*(const float2& a, const float2& b) {
 __device__ inline float2 operator*(const float a, const float2& b) {
   return make_float2(a * b.x, a * b.y);
 }
+#endif
 
 __device__ inline float FloatMin3(const float a, const float b, const float c) {
   return fminf(a, fminf(b, c));

pytorch3d/csrc/utils/warp_reduce.cuh

@@ -23,37 +23,51 @@ WarpReduceMin(scalar_t* min_dists, int64_t* min_idxs, const size_t tid) {
     min_idxs[tid] = min_idxs[tid + 32];
     min_dists[tid] = min_dists[tid + 32];
   }
+// AMD does not use explicit syncwarp and instead automatically inserts memory
+// fences during compilation.
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   // s = 16
   if (min_dists[tid] > min_dists[tid + 16]) {
     min_idxs[tid] = min_idxs[tid + 16];
     min_dists[tid] = min_dists[tid + 16];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   // s = 8
   if (min_dists[tid] > min_dists[tid + 8]) {
     min_idxs[tid] = min_idxs[tid + 8];
     min_dists[tid] = min_dists[tid + 8];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   // s = 4
   if (min_dists[tid] > min_dists[tid + 4]) {
     min_idxs[tid] = min_idxs[tid + 4];
     min_dists[tid] = min_dists[tid + 4];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   // s = 2
   if (min_dists[tid] > min_dists[tid + 2]) {
     min_idxs[tid] = min_idxs[tid + 2];
     min_dists[tid] = min_dists[tid + 2];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   // s = 1
   if (min_dists[tid] > min_dists[tid + 1]) {
     min_idxs[tid] = min_idxs[tid + 1];
     min_dists[tid] = min_dists[tid + 1];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
 }
 
 template <typename scalar_t>
@@ -65,30 +79,42 @@ __device__ void WarpReduceMax(
     dists[tid] = dists[tid + 32];
     dists_idx[tid] = dists_idx[tid + 32];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   if (dists[tid] < dists[tid + 16]) {
     dists[tid] = dists[tid + 16];
     dists_idx[tid] = dists_idx[tid + 16];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   if (dists[tid] < dists[tid + 8]) {
     dists[tid] = dists[tid + 8];
     dists_idx[tid] = dists_idx[tid + 8];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   if (dists[tid] < dists[tid + 4]) {
     dists[tid] = dists[tid + 4];
     dists_idx[tid] = dists_idx[tid + 4];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   if (dists[tid] < dists[tid + 2]) {
     dists[tid] = dists[tid + 2];
     dists_idx[tid] = dists_idx[tid + 2];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
   if (dists[tid] < dists[tid + 1]) {
     dists[tid] = dists[tid + 1];
     dists_idx[tid] = dists_idx[tid + 1];
   }
+#if !defined(USE_ROCM)
   __syncwarp();
+#endif
 }

pytorch3d/datasets/__init__.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from .r2n2 import BlenderCamera, collate_batched_R2N2, R2N2, render_cubified_voxels
 from .shapenet import ShapeNetCore
 from .utils import collate_batched_meshes

pytorch3d/datasets/r2n2/__init__.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from .r2n2 import R2N2
 from .utils import BlenderCamera, collate_batched_R2N2, render_cubified_voxels
 

pytorch3d/datasets/r2n2/r2n2.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import json
 import warnings
 from os import path

pytorch3d/datasets/r2n2/utils.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import math
 from typing import Dict, List
 

pytorch3d/datasets/shapenet/__init__.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from .shapenet_core import ShapeNetCore
 
 

pytorch3d/datasets/shapenet/shapenet_core.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import json
 import os
 import warnings

pytorch3d/datasets/shapenet_base.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import warnings
 from typing import Dict, List, Optional, Tuple
 

pytorch3d/datasets/utils.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from typing import Dict, List
 
 from pytorch3d.renderer.mesh import TexturesAtlas

pytorch3d/implicitron/__init__.py

@@ -3,3 +3,5 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe

pytorch3d/implicitron/dataset/__init__.py

@@ -3,3 +3,5 @@
 #
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
+
+# pyre-unsafe

pytorch3d/implicitron/dataset/blender_dataset_map_provider.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import torch
 from pytorch3d.implicitron.tools.config import registry

pytorch3d/implicitron/dataset/data_loader_map_provider.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from dataclasses import dataclass
 from enum import Enum
 from typing import Iterator, List, Optional, Tuple

pytorch3d/implicitron/dataset/data_source.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from typing import Optional, Tuple
 
 from pytorch3d.implicitron.tools.config import (
@@ -39,7 +41,7 @@ class DataSourceBase(ReplaceableBase):
 
 
 @registry.register
-class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
+class ImplicitronDataSource(DataSourceBase):
     """
     Represents the data used in Implicitron. This is the only implementation
     of DataSourceBase provided.
@@ -50,8 +52,11 @@ class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
         data_loader_map_provider_class_type: identifies type for data_loader_map_provider.
     """
 
+    # pyre-fixme[13]: Attribute `dataset_map_provider` is never initialized.
     dataset_map_provider: DatasetMapProviderBase
+    # pyre-fixme[13]: Attribute `dataset_map_provider_class_type` is never initialized.
     dataset_map_provider_class_type: str
+    # pyre-fixme[13]: Attribute `data_loader_map_provider` is never initialized.
     data_loader_map_provider: DataLoaderMapProviderBase
     data_loader_map_provider_class_type: str = "SequenceDataLoaderMapProvider"
 
@@ -72,6 +77,16 @@ class ImplicitronDataSource(DataSourceBase):  # pyre-ignore[13]
             from .rendered_mesh_dataset_map_provider import (  # noqa: F401
                 RenderedMeshDatasetMapProvider,
             )
+            from .train_eval_data_loader_provider import (  # noqa: F401
+                TrainEvalDataLoaderMapProvider,
+            )
+
+            try:
+                from .sql_dataset_provider import (  # noqa: F401  # pyre-ignore
+                    SqlIndexDatasetMapProvider,
+                )
+            except ModuleNotFoundError:
+                pass  # environment without SQL dataset
         finally:
             pass
 

pytorch3d/implicitron/dataset/dataset_base.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from collections import defaultdict
 from dataclasses import dataclass
 from typing import (

pytorch3d/implicitron/dataset/dataset_map_provider.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import logging
 import os
 from dataclasses import dataclass

pytorch3d/implicitron/dataset/frame_data.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import os
 from abc import ABC, abstractmethod
 from collections import defaultdict
@@ -203,7 +205,10 @@ class FrameData(Mapping[str, Any]):
                 when no image has been loaded)
         """
         if self.bbox_xywh is None:
-            raise ValueError("Attempted cropping by metadata with empty bounding box")
+            raise ValueError(
+                "Attempted cropping by metadata with empty bounding box. Consider either"
+                " to remove_empty_masks or turn off box_crop in the dataset config."
+            )
 
         if not self._uncropped:
             raise ValueError(
@@ -271,6 +276,7 @@ class FrameData(Mapping[str, Any]):
             image_size_hw=tuple(self.effective_image_size_hw),  # pyre-ignore
         )
         crop_bbox_xywh = bbox_xyxy_to_xywh(clamp_bbox_xyxy)
+        self.crop_bbox_xywh = crop_bbox_xywh
 
         if self.fg_probability is not None:
             self.fg_probability = crop_around_box(
@@ -429,7 +435,7 @@ class FrameData(Mapping[str, Any]):
             # TODO: don't store K; enforce working in NDC space
             return join_cameras_as_batch(batch)
         else:
-            return torch.utils.data._utils.collate.default_collate(batch)
+            return torch.utils.data.dataloader.default_collate(batch)
 
 
 FrameDataSubtype = TypeVar("FrameDataSubtype", bound=FrameData)
@@ -528,12 +534,7 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
                 "Make sure it is set in either FrameDataBuilder or Dataset params."
             )
 
-        if self.path_manager is None:
-            dataset_root_exists = os.path.isdir(self.dataset_root)  # pyre-ignore
-        else:
-            dataset_root_exists = self.path_manager.isdir(self.dataset_root)
-
-        if load_any_blob and not dataset_root_exists:
+        if load_any_blob and not self._exists_in_dataset_root(""):
             raise ValueError(
                 f"dataset_root is passed but {self.dataset_root} does not exist."
             )
@@ -578,16 +579,16 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
             camera_quality_score=safe_as_tensor(
                 sequence_annotation.viewpoint_quality_score, torch.float
             ),
-            point_cloud_quality_score=safe_as_tensor(
-                point_cloud.quality_score, torch.float
-            )
-            if point_cloud is not None
-            else None,
+            point_cloud_quality_score=(
+                safe_as_tensor(point_cloud.quality_score, torch.float)
+                if point_cloud is not None
+                else None
+            ),
         )
 
+        fg_mask_np: Optional[np.ndarray] = None
         mask_annotation = frame_annotation.mask
         if mask_annotation is not None:
-            fg_mask_np: Optional[np.ndarray] = None
             if load_blobs and self.load_masks:
                 fg_mask_np, mask_path = self._load_fg_probability(frame_annotation)
                 frame_data.mask_path = mask_path
@@ -604,19 +605,32 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
             frame_data.image_size_hw = image_size_hw  # original image size
             # image size after crop/resize
             frame_data.effective_image_size_hw = image_size_hw
+            image_path = None
+            dataset_root = self.dataset_root
+            if frame_annotation.image.path is not None and dataset_root is not None:
+                image_path = os.path.join(dataset_root, frame_annotation.image.path)
+                frame_data.image_path = image_path
 
             if load_blobs and self.load_images:
-                (
-                    frame_data.image_rgb,
-                    frame_data.image_path,
-                ) = self._load_images(frame_annotation, frame_data.fg_probability)
+                if image_path is None:
+                    raise ValueError("Image path is required to load images.")
 
-        if load_blobs and self.load_depths and frame_annotation.depth is not None:
+                image_np = load_image(self._local_path(image_path))
+                frame_data.image_rgb = self._postprocess_image(
+                    image_np, frame_annotation.image.size, frame_data.fg_probability
+                )
+
+        if (
+            load_blobs
+            and self.load_depths
+            and frame_annotation.depth is not None
+            and frame_annotation.depth.path is not None
+        ):
             (
                 frame_data.depth_map,
                 frame_data.depth_path,
                 frame_data.depth_mask,
-            ) = self._load_mask_depth(frame_annotation, frame_data.fg_probability)
+            ) = self._load_mask_depth(frame_annotation, fg_mask_np)
 
         if load_blobs and self.load_point_clouds and point_cloud is not None:
             pcl_path = self._fix_point_cloud_path(point_cloud.path)
@@ -652,47 +666,45 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
 
         return fg_probability, full_path
 
-    def _load_images(
+    def _postprocess_image(
         self,
-        entry: types.FrameAnnotation,
+        image_np: np.ndarray,
+        image_size: Tuple[int, int],
         fg_probability: Optional[torch.Tensor],
-    ) -> Tuple[torch.Tensor, str]:
-        assert self.dataset_root is not None and entry.image is not None
-        path = os.path.join(self.dataset_root, entry.image.path)
-        image_rgb = load_image(self._local_path(path))
+    ) -> torch.Tensor:
+        image_rgb = safe_as_tensor(image_np, torch.float)
 
-        if image_rgb.shape[-2:] != entry.image.size:
-            raise ValueError(
-                f"bad image size: {image_rgb.shape[-2:]} vs {entry.image.size}!"
-            )
+        if image_rgb.shape[-2:] != image_size:
+            raise ValueError(f"bad image size: {image_rgb.shape[-2:]} vs {image_size}!")
 
         if self.mask_images:
             assert fg_probability is not None
             image_rgb *= fg_probability
 
-        return image_rgb, path
+        return image_rgb
 
     def _load_mask_depth(
         self,
         entry: types.FrameAnnotation,
-        fg_probability: Optional[torch.Tensor],
+        fg_mask: Optional[np.ndarray],
     ) -> Tuple[torch.Tensor, str, torch.Tensor]:
         entry_depth = entry.depth
-        assert self.dataset_root is not None and entry_depth is not None
-        path = os.path.join(self.dataset_root, entry_depth.path)
+        dataset_root = self.dataset_root
+        assert dataset_root is not None
+        assert entry_depth is not None and entry_depth.path is not None
+        path = os.path.join(dataset_root, entry_depth.path)
         depth_map = load_depth(self._local_path(path), entry_depth.scale_adjustment)
 
         if self.mask_depths:
-            assert fg_probability is not None
-            depth_map *= fg_probability
+            assert fg_mask is not None
+            depth_map *= fg_mask
 
-        if self.load_depth_masks:
-            assert entry_depth.mask_path is not None
-            # pyre-ignore
-            mask_path = os.path.join(self.dataset_root, entry_depth.mask_path)
+        mask_path = entry_depth.mask_path
+        if self.load_depth_masks and mask_path is not None:
+            mask_path = os.path.join(dataset_root, mask_path)
             depth_mask = load_depth_mask(self._local_path(mask_path))
         else:
-            depth_mask = torch.ones_like(depth_map)
+            depth_mask = (depth_map > 0.0).astype(np.float32)
 
         return torch.tensor(depth_map), path, torch.tensor(depth_mask)
 
@@ -745,6 +757,16 @@ class GenericFrameDataBuilder(FrameDataBuilderBase[FrameDataSubtype], ABC):
             return path
         return self.path_manager.get_local_path(path)
 
+    def _exists_in_dataset_root(self, relpath) -> bool:
+        if not self.dataset_root:
+            return False
+
+        full_path = os.path.join(self.dataset_root, relpath)
+        if self.path_manager is None:
+            return os.path.exists(full_path)
+        else:
+            return self.path_manager.exists(full_path)
+
 
 @registry.register
 class FrameDataBuilder(GenericWorkaround, GenericFrameDataBuilder[FrameData]):

pytorch3d/implicitron/dataset/json_index_dataset.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import copy
 import functools
 import gzip
@@ -124,9 +126,9 @@ class JsonIndexDataset(DatasetBase, ReplaceableBase):
             dimension of the cropping bounding box, relative to box size.
     """
 
-    frame_annotations_type: ClassVar[
-        Type[types.FrameAnnotation]
-    ] = types.FrameAnnotation
+    frame_annotations_type: ClassVar[Type[types.FrameAnnotation]] = (
+        types.FrameAnnotation
+    )
 
     path_manager: Any = None
     frame_annotations_file: str = ""

pytorch3d/implicitron/dataset/json_index_dataset_map_provider.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import json
 import os
@@ -64,7 +66,7 @@ _NEED_CONTROL: Tuple[str, ...] = (
 
 
 @registry.register
-class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class JsonIndexDatasetMapProvider(DatasetMapProviderBase):
     """
     Generates the training / validation and testing dataset objects for
     a dataset laid out on disk like Co3D, with annotations in json files.
@@ -93,6 +95,7 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
         path_manager_factory_class_type: The class type of `path_manager_factory`.
     """
 
+    # pyre-fixme[13]: Attribute `category` is never initialized.
     category: str
     task_str: str = "singlesequence"
     dataset_root: str = _CO3D_DATASET_ROOT
@@ -102,8 +105,10 @@ class JsonIndexDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
     test_restrict_sequence_id: int = -1
     assert_single_seq: bool = False
     only_test_set: bool = False
+    # pyre-fixme[13]: Attribute `dataset` is never initialized.
     dataset: JsonIndexDataset
     dataset_class_type: str = "JsonIndexDataset"
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 

pytorch3d/implicitron/dataset/json_index_dataset_map_provider_v2.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import copy
 import json
@@ -54,7 +56,7 @@ logger = logging.getLogger(__name__)
 
 
 @registry.register
-class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
+class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):
     """
     Generates the training, validation, and testing dataset objects for
     a dataset laid out on disk like CO3Dv2, with annotations in gzipped json files.
@@ -169,7 +171,9 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
         path_manager_factory_class_type: The class type of `path_manager_factory`.
     """
 
+    # pyre-fixme[13]: Attribute `category` is never initialized.
     category: str
+    # pyre-fixme[13]: Attribute `subset_name` is never initialized.
     subset_name: str
     dataset_root: str = _CO3DV2_DATASET_ROOT
 
@@ -181,8 +185,10 @@ class JsonIndexDatasetMapProviderV2(DatasetMapProviderBase):  # pyre-ignore [13]
     n_known_frames_for_test: int = 0
 
     dataset_class_type: str = "JsonIndexDataset"
+    # pyre-fixme[13]: Attribute `dataset` is never initialized.
     dataset: JsonIndexDataset
 
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 

pytorch3d/implicitron/dataset/llff_dataset_map_provider.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import numpy as np
 import torch

pytorch3d/implicitron/dataset/load_blender.py

@@ -1,6 +1,8 @@
 # @lint-ignore-every LICENSELINT
 # Adapted from https://github.com/bmild/nerf/blob/master/load_blender.py
 # Copyright (c) 2020 bmild
+
+# pyre-unsafe
 import json
 import os
 

pytorch3d/implicitron/dataset/load_llff.py

@@ -1,6 +1,8 @@
 # @lint-ignore-every LICENSELINT
 # Adapted from https://github.com/bmild/nerf/blob/master/load_llff.py
 # Copyright (c) 2020 bmild
+
+# pyre-unsafe
 import logging
 import os
 import warnings
@@ -34,11 +36,7 @@ def _minify(basedir, path_manager, factors=(), resolutions=()):
 
     imgdir = os.path.join(basedir, "images")
     imgs = [os.path.join(imgdir, f) for f in sorted(_ls(path_manager, imgdir))]
-    imgs = [
-        f
-        for f in imgs
-        if any([f.endswith(ex) for ex in ["JPG", "jpg", "png", "jpeg", "PNG"]])
-    ]
+    imgs = [f for f in imgs if f.endswith("JPG", "jpg", "png", "jpeg", "PNG")]
     imgdir_orig = imgdir
 
     wd = os.getcwd()

pytorch3d/implicitron/dataset/orm_types.py

@@ -33,7 +33,35 @@ from sqlalchemy.types import TypeDecorator
 
 
 # these produce policies to serialize structured types to blobs
-def ArrayTypeFactory(shape):
+def ArrayTypeFactory(shape=None):
+    if shape is None:
+
+        class VariableShapeNumpyArrayType(TypeDecorator):
+            impl = LargeBinary
+
+            def process_bind_param(self, value, dialect):
+                if value is None:
+                    return None
+
+                ndim_bytes = np.int32(value.ndim).tobytes()
+                shape_bytes = np.array(value.shape, dtype=np.int64).tobytes()
+                value_bytes = value.astype(np.float32).tobytes()
+                return ndim_bytes + shape_bytes + value_bytes
+
+            def process_result_value(self, value, dialect):
+                if value is None:
+                    return None
+
+                ndim = np.frombuffer(value[:4], dtype=np.int32)[0]
+                value_start = 4 + 8 * ndim
+                shape = np.frombuffer(value[4:value_start], dtype=np.int64)
+                assert shape.shape == (ndim,)
+                return np.frombuffer(value[value_start:], dtype=np.float32).reshape(
+                    shape
+                )
+
+        return VariableShapeNumpyArrayType
+
     class NumpyArrayType(TypeDecorator):
         impl = LargeBinary
 
@@ -158,4 +186,4 @@ class SqlSequenceAnnotation(Base):
         mapped_column("_point_cloud_n_points", nullable=True),
     )
     # the bigger the better
-    viewpoint_quality_score: Mapped[Optional[float]] = mapped_column(default=None)
+    viewpoint_quality_score: Mapped[Optional[float]] = mapped_column()

pytorch3d/implicitron/dataset/rendered_mesh_dataset_map_provider.py

@@ -4,15 +4,13 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from os.path import dirname, join, realpath
 from typing import Optional, Tuple
 
 import torch
-from pytorch3d.implicitron.tools.config import (
-    expand_args_fields,
-    registry,
-    run_auto_creation,
-)
+from pytorch3d.implicitron.tools.config import registry, run_auto_creation
 from pytorch3d.io import IO
 from pytorch3d.renderer import (
     AmbientLights,
@@ -34,7 +32,7 @@ from .utils import DATASET_TYPE_KNOWN
 
 
 @registry.register
-class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13]
+class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):
     """
     A simple single-scene dataset based on PyTorch3D renders of a mesh.
     Provides `num_views` renders of the mesh as train, with no val
@@ -78,6 +76,7 @@ class RenderedMeshDatasetMapProvider(DatasetMapProviderBase):  # pyre-ignore [13
     resolution: int = 128
     use_point_light: bool = True
     gpu_idx: Optional[int] = 0
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
 

pytorch3d/implicitron/dataset/scene_batch_sampler.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import warnings
 from collections import Counter

pytorch3d/implicitron/dataset/single_sequence_dataset.py

@@ -4,12 +4,14 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 # This file defines a base class for dataset map providers which
 # provide data for a single scene.
 
 from dataclasses import field
-from typing import Iterable, Iterator, List, Optional, Tuple
+from typing import Iterable, Iterator, List, Optional, Sequence, Tuple
 
 import numpy as np
 import torch
@@ -47,13 +49,12 @@ class SingleSceneDataset(DatasetBase, Configurable):
     def __len__(self) -> int:
         return len(self.poses)
 
-    # pyre-fixme[14]: `sequence_frames_in_order` overrides method defined in
-    #  `DatasetBase` inconsistently.
     def sequence_frames_in_order(
-        self, seq_name: str
+        self, seq_name: str, subset_filter: Optional[Sequence[str]] = None
     ) -> Iterator[Tuple[float, int, int]]:
         for i in range(len(self)):
-            yield (0.0, i, i)
+            if subset_filter is None or self.frame_types[i] in subset_filter:
+                yield 0.0, i, i
 
     def __getitem__(self, index) -> FrameData:
         if index >= len(self):
@@ -82,7 +83,6 @@ class SingleSceneDataset(DatasetBase, Configurable):
         return self.eval_batches
 
 
-# pyre-fixme[13]: Uninitialized attribute
 class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
     """
     Base for provider of data for one scene from LLFF or blender datasets.
@@ -99,8 +99,11 @@ class SingleSceneDatasetMapProviderBase(DatasetMapProviderBase):
             testing frame.
     """
 
+    # pyre-fixme[13]: Attribute `base_dir` is never initialized.
     base_dir: str
+    # pyre-fixme[13]: Attribute `object_name` is never initialized.
     object_name: str
+    # pyre-fixme[13]: Attribute `path_manager_factory` is never initialized.
     path_manager_factory: PathManagerFactory
     path_manager_factory_class_type: str = "PathManagerFactory"
     n_known_frames_for_test: Optional[int] = None

pytorch3d/implicitron/dataset/sql_dataset.py

@@ -89,6 +89,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         pick_categories: Restrict the dataset to the given list of categories.
         pick_sequences: A Sequence of sequence names to restrict the dataset to.
         exclude_sequences: A Sequence of the names of the sequences to exclude.
+        limit_sequences_per_category_to: Limit the dataset to the first up to N
+            sequences within each category (applies after all other sequence filters
+            but before `limit_sequences_to`).
         limit_sequences_to: Limit the dataset to the first `limit_sequences_to`
             sequences (after other sequence filters have been applied but before
             frame-based filters).
@@ -115,6 +118,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
 
     pick_sequences: Tuple[str, ...] = ()
     exclude_sequences: Tuple[str, ...] = ()
+    limit_sequences_per_category_to: int = 0
     limit_sequences_to: int = 0
     limit_to: int = 0
     n_frames_per_sequence: int = -1
@@ -142,8 +146,10 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         run_auto_creation(self)
         self.frame_data_builder.path_manager = self.path_manager
 
-        # pyre-ignore
-        self._sql_engine = sa.create_engine(f"sqlite:///{self.sqlite_metadata_file}")
+        # pyre-ignore  # NOTE: sqlite-specific args (read-only mode).
+        self._sql_engine = sa.create_engine(
+            f"sqlite:///file:{self.sqlite_metadata_file}?mode=ro&uri=true"
+        )
 
         sequences = self._get_filtered_sequences_if_any()
 
@@ -210,6 +216,9 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             seq, frame = self._index.index[frame_idx]
         else:
             seq, frame, *rest = frame_idx
+            if isinstance(frame, torch.LongTensor):
+                frame = frame.item()
+
             if (seq, frame) not in self._index.index:
                 raise IndexError(
                     f"Sequence-frame index {frame_idx} not found; was it filtered out?"
@@ -368,6 +377,7 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             self.remove_empty_masks
             or self.limit_to > 0
             or self.limit_sequences_to > 0
+            or self.limit_sequences_per_category_to > 0
             or len(self.pick_sequences) > 0
             or len(self.exclude_sequences) > 0
             or len(self.pick_categories) > 0
@@ -375,20 +385,38 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
         )
 
     def _get_filtered_sequences_if_any(self) -> Optional[pd.Series]:
-        # maximum possible query: WHERE category IN 'self.pick_categories'
+        # maximum possible filter (if limit_sequences_per_category_to == 0):
+        # WHERE category IN 'self.pick_categories'
         # AND sequence_name IN 'self.pick_sequences'
         # AND sequence_name NOT IN 'self.exclude_sequences'
         # LIMIT 'self.limit_sequence_to'
 
-        stmt = sa.select(SqlSequenceAnnotation.sequence_name)
-
         where_conditions = [
             *self._get_category_filters(),
             *self._get_pick_filters(),
             *self._get_exclude_filters(),
         ]
-        if where_conditions:
-            stmt = stmt.where(*where_conditions)
+
+        def add_where(stmt):
+            return stmt.where(*where_conditions) if where_conditions else stmt
+
+        if self.limit_sequences_per_category_to <= 0:
+            stmt = add_where(sa.select(SqlSequenceAnnotation.sequence_name))
+        else:
+            subquery = sa.select(
+                SqlSequenceAnnotation.sequence_name,
+                sa.func.row_number()
+                .over(
+                    order_by=sa.text("ROWID"),  # NOTE: ROWID is SQLite-specific
+                    partition_by=SqlSequenceAnnotation.category,
+                )
+                .label("row_number"),
+            )
+
+            subquery = add_where(subquery).subquery()
+            stmt = sa.select(subquery.c.sequence_name).where(
+                subquery.c.row_number <= self.limit_sequences_per_category_to
+            )
 
         if self.limit_sequences_to > 0:
             logger.info(
@@ -397,7 +425,11 @@ class SqlIndexDataset(DatasetBase, ReplaceableBase):  # pyre-ignore
             # NOTE: ROWID is SQLite-specific
             stmt = stmt.order_by(sa.text("ROWID")).limit(self.limit_sequences_to)
 
-        if not where_conditions and self.limit_sequences_to <= 0:
+        if (
+            not where_conditions
+            and self.limit_sequences_to <= 0
+            and self.limit_sequences_per_category_to <= 0
+        ):
             # we will not need to filter by sequences
             return None
 

pytorch3d/implicitron/dataset/train_eval_data_loader_provider.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 import logging
 from typing import Any, Dict, Optional, Tuple
 

pytorch3d/implicitron/dataset/types.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import dataclasses
 import gzip

pytorch3d/implicitron/dataset/utils.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import functools
 import warnings
@@ -200,7 +202,7 @@ def resize_image(
     mode: str = "bilinear",
 ) -> Tuple[torch.Tensor, float, torch.Tensor]:
 
-    if type(image) == np.ndarray:
+    if isinstance(image, np.ndarray):
         image = torch.from_numpy(image)
 
     if image_height is None or image_width is None:
@@ -225,26 +227,41 @@ def resize_image(
     return imre_, minscale, mask
 
 
+def transpose_normalize_image(image: np.ndarray) -> np.ndarray:
+    im = np.atleast_3d(image).transpose((2, 0, 1))
+    return im.astype(np.float32) / 255.0
+
+
 def load_image(path: str) -> np.ndarray:
     with Image.open(path) as pil_im:
         im = np.array(pil_im.convert("RGB"))
-    im = im.transpose((2, 0, 1))
-    im = im.astype(np.float32) / 255.0
-    return im
+
+    return transpose_normalize_image(im)
 
 
 def load_mask(path: str) -> np.ndarray:
     with Image.open(path) as pil_im:
         mask = np.array(pil_im)
-    mask = mask.astype(np.float32) / 255.0
-    return mask[None]  # fake feature channel
+
+    return transpose_normalize_image(mask)
 
 
 def load_depth(path: str, scale_adjustment: float) -> np.ndarray:
-    if not path.lower().endswith(".png"):
+    if path.lower().endswith(".exr"):
+        # NOTE: environment variable OPENCV_IO_ENABLE_OPENEXR must be set to 1
+        # You will have to accept these vulnerabilities by using OpenEXR:
+        # https://github.com/opencv/opencv/issues/21326
+        import cv2
+
+        d = cv2.imread(path, cv2.IMREAD_ANYCOLOR | cv2.IMREAD_ANYDEPTH)[..., 0]
+        d[d > 1e9] = 0.0
+    elif path.lower().endswith(".png"):
+        d = load_16big_png_depth(path)
+    else:
         raise ValueError('unsupported depth file name "%s"' % path)
 
-    d = load_16big_png_depth(path) * scale_adjustment
+    d = d * scale_adjustment
+
     d[~np.isfinite(d)] = 0.0
     return d[None]  # fake feature channel
 
@@ -312,7 +329,7 @@ def adjust_camera_to_bbox_crop_(
 
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
-        camera.principal_point[0],  # pyre-ignore
+        camera.principal_point[0],
         image_size_wh,
     )
     principal_point_px_cropped = principal_point_px - clamp_bbox_xywh[:2]
@@ -324,17 +341,18 @@ def adjust_camera_to_bbox_crop_(
     )
 
     camera.focal_length = focal_length[None]
-    camera.principal_point = principal_point_cropped[None]  # pyre-ignore
+    camera.principal_point = principal_point_cropped[None]
 
 
 def adjust_camera_to_image_scale_(
     camera: PerspectiveCameras,
     original_size_wh: torch.Tensor,
     new_size_wh: torch.LongTensor,
+    # pyre-fixme[7]: Expected `PerspectiveCameras` but got implicit return value of `None`.
 ) -> PerspectiveCameras:
     focal_length_px, principal_point_px = _convert_ndc_to_pixels(
         camera.focal_length[0],
-        camera.principal_point[0],  # pyre-ignore
+        camera.principal_point[0],
         original_size_wh,
     )
 
@@ -350,7 +368,7 @@ def adjust_camera_to_image_scale_(
         image_size_wh_output,
     )
     camera.focal_length = focal_length_scaled[None]
-    camera.principal_point = principal_point_scaled[None]  # pyre-ignore
+    camera.principal_point = principal_point_scaled[None]
 
 
 # NOTE this cache is per-worker; they are implemented as processes.

pytorch3d/implicitron/dataset/visualize.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 from typing import cast, Optional, Tuple
 
 import torch
@@ -88,10 +90,11 @@ def get_implicitron_sequence_pointcloud(
             frame_data.camera,
             frame_data.image_rgb,
             frame_data.depth_map,
-            (cast(torch.Tensor, frame_data.fg_probability) > 0.5).float()
-            if frame_data.fg_probability is not None
-            else None,
-            mask_points=mask_points,
+            (
+                (cast(torch.Tensor, frame_data.fg_probability) > 0.5).float()
+                if mask_points and frame_data.fg_probability is not None
+                else None
+            ),
         )
 
     return point_cloud, frame_data

pytorch3d/implicitron/eval_demo.py

@@ -4,6 +4,8 @@
 # This source code is licensed under the BSD-style license found in the
 # LICENSE file in the root directory of this source tree.
 
+# pyre-unsafe
+
 
 import dataclasses
 import os