Bump version to v1.1.0rc2

Bump to v1.1.0rc2

.circleci/config.yml

@@ -26,6 +26,7 @@ workflows:
             tools/.* lint_only false
             configs/.* lint_only false
             .circleci/.* lint_only false
+            projects/.* lint_only false
           base-revision: dev-1.x
           # this is the path of the configuration we should trigger once
           # path filtering and pipeline parameter value updates are

.dev_scripts/test_benchmark.sh

@@ -73,11 +73,11 @@ GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION dv_mvx-f
 $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/latest.pth --eval map \
 2>&1|tee $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
-echo 'configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
-mkdir -p $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
-GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
-$CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/latest.pth --eval map \
-2>&1|tee $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
+echo 'configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
 echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
 mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

.dev_scripts/train_benchmark.sh

@@ -73,11 +73,11 @@ GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION dv_mvx-
 $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
 2>&1|tee $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
-echo 'configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
-mkdir -p $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
-GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
-$CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
-2>&1|tee $CHECKPOINT_DIR/configs/parta2/PartA2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
+echo 'configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_PartA2_secfpn_2x8_cyclic_80e_kitti-3d-3class configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py/FULL_LOG.txt &
 
 echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
 mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py

.github/workflows/lint.yml

+name: lint
+
+on: [push, pull_request]
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  lint:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python 3.7
+        uses: actions/setup-python@v2
+        with:
+          python-version: 3.7
+      - name: Install pre-commit hook
+        run: |
+          pip install pre-commit
+          pre-commit install
+      - name: Linting
+        run: pre-commit run --all-files
+      - name: Check docstring coverage
+        run: |
+          pip install interrogate
+          interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 90 mmdet3d

.github/workflows/merge_stage_test.yml

+name: merge_stage_test
+
+on:
+  push:
+    paths-ignore:
+      - 'README.md'
+      - 'README_zh-CN.md'
+      - 'docs/**'
+      - 'demo/**'
+      - '.dev_scripts/**'
+      - '.circleci/**'
+    branches:
+      - dev-1.x
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build_cpu_py:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7, 3.8, 3.9]
+        torch: [1.8.1]
+        include:
+          - torch: 1.8.1
+            torchvision: 0.9.1
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMEngine
+        run: pip install git+https://github.com/open-mmlab/mmengine.git@main
+      - name: Install MMCV
+        run: |
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+      - name: Install MMDet
+        run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+      - name: Install other dependencies
+        run: pip install -r requirements/tests.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmdet3d -m pytest tests/
+          coverage xml
+          coverage report -m
+  build_cpu_pt:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.6.0, 1.7.1, 1.8.1, 1.9.1, 1.10.1, 1.11.0, 1.12.1]
+        include:
+          - torch: 1.6.0
+            torchvision: 0.7.0
+          - torch: 1.7.1
+            torchvision: 0.8.2
+          - torch: 1.8.1
+            torchvision: 0.9.1
+          - torch: 1.9.1
+            torchvision: 0.10.1
+          - torch: 1.10.1
+            torchvision: 0.11.2
+          - torch: 1.11.0
+            torchvision: 0.12.0
+          - torch: 1.12.1
+            torchvision: 0.13.1
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMEngine
+        run: pip install git+https://github.com/open-mmlab/mmengine.git@main
+      - name: Install MMCV
+        run: |
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+      - name: Install MMDet
+        run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+      - name: Install other dependencies
+        run: pip install -r requirements/tests.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmdet3d -m pytest tests/
+          coverage xml
+          coverage report -m
+      # Only upload coverage report for python3.7 && pytorch1.8.1 cpu
+      - name: Upload coverage to Codecov
+        if: ${{matrix.torch == '1.8.1' && matrix.python-version == '3.7'}}
+        uses: codecov/codecov-action@v1.0.14
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
+  build_cu102:
+    runs-on: ubuntu-18.04
+    container:
+      image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel
+    strategy:
+      matrix:
+        python-version: [3.7]
+        include:
+          - torch: 1.8.1
+            cuda: 10.2
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Fetch GPG keys
+        run: |
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+      - name: Install Python-dev
+        run: apt-get update && apt-get install -y python${{matrix.python-version}}-dev
+        if: ${{matrix.python-version != 3.9}}
+      - name: Install system dependencies
+        run: |
+          apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
+      - name: Install PyTorch
+        run: python -m pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install mmdet3d dependencies
+        run: |
+          pip install git+https://github.com/open-mmlab/mmengine.git@main
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+          pip install -r requirements/tests.txt
+      - name: Build and install
+        run: |
+          python setup.py check -m -s
+          TORCH_CUDA_ARCH_LIST=7.0 pip install -e .
+  build_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.7]
+        platform: [cpu, cu111]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install lmdb
+        run: pip install lmdb
+      - name: Install PyTorch
+        run: pip install torch==1.8.1+${{matrix.platform}} torchvision==0.9.1+${{matrix.platform}} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install mmdet3d dependencies
+        run: |
+          pip install git+https://github.com/open-mmlab/mmengine.git@main
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+          pip install -r requirements/tests.txt
+      - name: Build and install
+        run: |
+          pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          pytest tests/

.github/workflows/pr_stage_test.yml

+name: pr_stage_test
+
+on:
+  pull_request:
+    paths-ignore:
+      - 'README.md'
+      - 'README_zh-CN.md'
+      - 'docs/**'
+      - 'demo/**'
+      - '.dev_scripts/**'
+      - '.circleci/**'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build_cpu:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        include:
+          - torch: 1.8.1
+            torchvision: 0.9.1
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install MMEngine
+        run: pip install git+https://github.com/open-mmlab/mmengine.git@main
+      - name: Install MMCV
+        run: |
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+      - name: Install MMDet
+        run: pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+      - name: Install other dependencies
+        run: pip install -r requirements/tests.txt
+      - name: Build and install
+        run: rm -rf .eggs && pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          coverage run --branch --source mmdet3d -m pytest tests/
+          coverage xml
+          coverage report -m
+      # Upload coverage report for python3.7 && pytorch1.8.1 cpu
+      - name: Upload coverage to Codecov
+        uses: codecov/codecov-action@v1.0.14
+        with:
+          file: ./coverage.xml
+          flags: unittests
+          env_vars: OS,PYTHON
+          name: codecov-umbrella
+          fail_ci_if_error: false
+
+  build_cu102:
+    runs-on: ubuntu-18.04
+    container:
+      image: pytorch/pytorch:1.8.1-cuda10.2-cudnn7-devel
+    strategy:
+      matrix:
+        python-version: [3.8]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Fetch GPG keys
+        run: |
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+          apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+      - name: Install Python-dev
+        run: apt-get update && apt-get install -y python${{matrix.python-version}}-dev
+        if: ${{matrix.python-version != 3.9}}
+      - name: Install system dependencies
+        run: |
+          apt-get update
+          apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libxrender-dev
+      - name: Install PyTorch
+        run: python -m pip install torch==1.8.1+cu102 torchvision==0.9.1+cu102 -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install mmdet3d dependencies
+        run: |
+          pip install git+https://github.com/open-mmlab/mmengine.git@main
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+          pip install -r requirements/tests.txt
+      - name: Build and install
+        run: |
+          python setup.py check -m -s
+          TORCH_CUDA_ARCH_LIST=7.0 pip install -e .
+  build_windows:
+    runs-on: ${{ matrix.os }}
+    strategy:
+      matrix:
+        os: [windows-2022]
+        python: [3.7]
+        platform: [cpu, cu111]
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python }}
+      - name: Upgrade pip
+        run: python -m pip install pip --upgrade
+      - name: Install lmdb
+        run: pip install lmdb
+      - name: Install PyTorch
+        run: pip install torch==1.8.1+${{matrix.platform}} torchvision==0.9.1+${{matrix.platform}} -f https://download.pytorch.org/whl/lts/1.8/torch_lts.html
+      - name: Install mmdet3d dependencies
+        run: |
+          pip install git+https://github.com/open-mmlab/mmengine.git@main
+          pip install -U openmim
+          mim install 'mmcv >= 2.0.0rc1'
+          pip install git+https://github.com/open-mmlab/mmdetection.git@dev-3.x
+          pip install -r requirements/tests.txt
+      - name: Build and install
+        run: |
+          pip install -e .
+      - name: Run unittests and generate coverage report
+        run: |
+          pytest tests/

.github/workflows/test_mim.yml

+name: test-mim
+
+on:
+  push:
+    paths:
+      - 'model-index.yml'
+      - 'configs/**'
+
+  pull_request:
+    paths:
+      - 'model-index.yml'
+      - 'configs/**'
+
+concurrency:
+  group: ${{ github.workflow }}-${{ github.ref }}
+  cancel-in-progress: true
+
+jobs:
+  build_cpu:
+    runs-on: ubuntu-18.04
+    strategy:
+      matrix:
+        python-version: [3.7]
+        torch: [1.8.0]
+        include:
+          - torch: 1.8.0
+            torch_version: torch1.8
+            torchvision: 0.9.0
+    steps:
+      - uses: actions/checkout@v2
+      - name: Set up Python ${{ matrix.python-version }}
+        uses: actions/setup-python@v2
+        with:
+          python-version: ${{ matrix.python-version }}
+      - name: Upgrade pip
+        run: pip install pip --upgrade
+      - name: Install PyTorch
+        run: pip install torch==${{matrix.torch}}+cpu torchvision==${{matrix.torchvision}}+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - name: Install openmim
+        run: pip install openmim
+      - name: Build and install
+        run: rm -rf .eggs && mim install -e .
+      - name: test commands of mim
+        run: mim search mmdet3d

README.md

@@ -26,7 +26,7 @@
 
 **News**:
 
-**v1.1.0rc1** was released in 11/10/2022
+**v1.1.0rc2** was released in 2/12/2022
 
 The compatibilities of models are broken due to the unification and simplification of coordinate systems after v1.0.0rc0. For now, most models are benchmarked with similar performance, though few models are still being benchmarked. In the following release, we will update all the model checkpoints and benchmarks. See more details in the [Changelog](docs/en/notes/changelog.md) and [Changelog-v1.0.x](docs/en/notes/changelog_v1.0.x.md).
 
@@ -60,7 +60,7 @@ a part of the OpenMMLab project developed by [MMLab](http://mmlab.ie.cuhk.edu.hk
 
 - **High efficiency**
 
-  It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
+  It trains faster than other codebases. The main results are as below. Details can be found in [benchmark.md](./docs/en/notes/benchmarks.md). We compare the number of samples trained per second (the higher, the better). The models that are not supported by other codebases are marked by `×`.
 
   |       Methods       | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
   | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
@@ -78,7 +78,7 @@ This project is released under the [Apache 2.0 license](LICENSE).
 
 ## Changelog
 
-**1.1.0rc1** was released in 11/10/2022.
+**1.1.0rc2** was released in 2/12/2022.
 
 Please refer to [changelog.md](docs/en/notes/changelog.md) for details and release history.
 
@@ -159,12 +159,14 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
             <li><a href="configs/point_rcnn">PointRCNN (CVPR'2019)</a></li>
             <li><a href="configs/parta2">Part-A2 (TPAMI'2020)</a></li>
             <li><a href="configs/centerpoint">CenterPoint (CVPR'2021)</a></li>
+            <li><a href="configs/pv_rcnn">PV-RCNN (CVPR'2020)</a></li>
         </ul>
         <li><b>Indoor</b></li>
         <ul>
             <li><a href="configs/votenet">VoteNet (ICCV'2019)</a></li>
             <li><a href="configs/h3dnet">H3DNet (ECCV'2020)</a></li>
             <li><a href="configs/groupfree3d">Group-Free-3D (ICCV'2021)</a></li>
+            <li><a href="configs/fcaf3d">FCAF3D (ECCV'2022)</a></li>
       </ul>
       </td>
       <td>
@@ -202,31 +204,33 @@ Results and models are available in the [model zoo](docs/en/model_zoo.md).
   </tbody>
 </table>
 
-|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA |
-| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: |
-| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |
-| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
-| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
-| SA-SSD        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-
-**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/en/model_zoo.md) can be trained or used in this codebase.
+|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA | MinkResNet |
+| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: | :--------: |
+| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
+| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
+| SA-SSD        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| FCAF3D        |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✓      |
+| PV-RCNN       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+
+**Note:** All the about **300+ models, methods of 40+ papers** in 2D detection supported by [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/en/model_zoo.md) can be trained or used in this codebase.
 
 ## Installation
 
@@ -234,7 +238,7 @@ Please refer to [getting_started.md](docs/en/getting_started.md) for installatio
 
 ## Get Started
 
-Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/1_exist_data_model.md) and [with customized dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [adding new dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
+Please see [getting_started.md](docs/en/getting_started.md) for the basic usage of MMDetection3D. We provide guidance for quick run [with existing dataset](docs/en/user_guides/train_test.md) and [with new dataset](docs/en/user_guides/2_new_data_model.md) for beginners. There are also tutorials for [learning configuration systems](docs/en/user_guides/config.md), [customizing dataset](docs/en/advanced_guides/customize_dataset.md), [designing data pipeline](docs/en/user_guides/data_pipeline.md), [customizing models](docs/en/advanced_guides/customize_models.md), [customizing runtime settings](docs/en/advanced_guides/customize_runtime.md) and [Waymo dataset](docs/en/advanced_guides/datasets/waymo_det.md).
 
 Please refer to [FAQ](docs/en/notes/faq.md) for frequently asked questions. When updating the version of MMDetection3D, please also check the [compatibility doc](docs/en/notes/compatibility.md) to be aware of the BC-breaking updates introduced in each version.
 
@@ -264,11 +268,13 @@ We wish that the toolbox and benchmark could serve the growing research communit
 
 - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab foundational library for training deep learning models.
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab foundational library for computer vision.
+- [MMEval](https://github.com/open-mmlab/mmeval): A unified evaluation library for multiple machine learning libraries.
 - [MIM](https://github.com/open-mmlab/mim): MIM installs OpenMMLab packages.
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab image classification toolbox and benchmark.
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab detection toolbox and benchmark.
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab's next-generation platform for general 3D object detection.
 - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab rotated object detection toolbox and benchmark.
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO series toolbox and benchmark.
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab semantic segmentation toolbox and benchmark.
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab text detection, recognition, and understanding toolbox.
 - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab pose estimation toolbox and benchmark.

README_zh-CN.md

@@ -19,18 +19,18 @@
   <div>&nbsp;</div>
 </div>
 
-[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/en/1.1/)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/zh_CN/1.1/)
 [![badge](https://github.com/open-mmlab/mmdetection3d/workflows/build/badge.svg)](https://github.com/open-mmlab/mmdetection3d/actions)
 [![codecov](https://codecov.io/gh/open-mmlab/mmdetection3d/branch/master/graph/badge.svg)](https://codecov.io/gh/open-mmlab/mmdetection3d)
 [![license](https://img.shields.io/github/license/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/blob/master/LICENSE)
 
-**新闻**:
+**新闻**：
 
-**v1.1.0rc1** 版本已经在 2022.10.11 发布。
+**v1.1.0rc2** 版本已经在 2022.12.2 发布。
 
-由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在接下来的版本中，我们将更新所有的模型权重文件和基准。您可以在 [变更日志](docs/en/changelog.md) 和 [v1.0.x版本变更日志](docs/en/notes/changelog_v1.0.x.md) 中查看更多详细信息。
+由于坐标系的统一和简化，模型的兼容性会受到影响。目前，大多数模型都以类似的性能对齐了精度，但仍有少数模型在进行基准测试。在接下来的版本中，我们将更新所有的模型权重文件和基准。您可以在[变更日志](docs/zh_cn/notes/changelog.md)和 [v1.0.x 版本变更日志](docs/zh_cn/notes/changelog_v1.0.x.md)中查看更多详细信息。
 
-文档: https://mmdetection3d.readthedocs.io/
+文档：https://mmdetection3d.readthedocs.io/
 
 ## 简介
 
@@ -38,7 +38,7 @@
 
 主分支代码目前支持 PyTorch 1.6 以上的版本。
 
-MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代面向3D检测的平台. 它是 OpenMMlab 项目的一部分，这个项目由香港中文大学多媒体实验室和商汤科技联合发起.
+MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱，下一代面向 3D 检测的平台。它是 OpenMMlab 项目的一部分，这个项目由香港中文大学多媒体实验室和商汤科技联合发起。
 
 ![demo image](resources/mmdet3d_outdoor_demo.gif)
 
@@ -50,17 +50,16 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 - **支持户内/户外的数据集**
 
-  支持室内/室外的3D检测数据集，包括 ScanNet, SUNRGB-D, Waymo, nuScenes, Lyft, KITTI.
-
-  对于 nuScenes 数据集, 我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/1.1/configs/nuimages).
+  支持室内/室外的 3D 检测数据集，包括 ScanNet，SUNRGB-D，Waymo，nuScenes，Lyft，KITTI。
+  对于 nuScenes 数据集，我们也支持 [nuImages 数据集](https://github.com/open-mmlab/mmdetection3d/tree/1.1/configs/nuimages)。
 
 - **与 2D 检测器的自然整合**
 
-  [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的**300+个模型 , 40+的论文算法**, 和相关模块都可以在此代码库中训练或使用。
+  [MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的 **300+ 个模型，40+ 的论文算法**，和相关模块都可以在此代码库中训练或使用。
 
 - **性能高**
 
-  训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
+  训练速度比其他代码库更快。下表可见主要的对比结果。更多的细节可见[基准测评文档](./docs/zh_cn/notes/benchmarks.md)。我们对比了每秒训练的样本数（值越高越好）。其他代码库不支持的模型被标记为 `×`。
 
   |       Methods       | MMDetection3D | [OpenPCDet](https://github.com/open-mmlab/OpenPCDet) | [votenet](https://github.com/facebookresearch/votenet) | [Det3D](https://github.com/poodarchu/Det3D) |
   | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
@@ -70,7 +69,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
   |       SECOND        |      40       |                          30                          |                           ×                            |                      ×                      |
   |       Part-A2       |      17       |                          14                          |                           ×                            |                      ×                      |
 
-和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样, MMDetection3D 也可以作为一个库去支持各式各样的项目.
+和 [MMDetection](https://github.com/open-mmlab/mmdetection)，[MMCV](https://github.com/open-mmlab/mmcv) 一样，MMDetection3D 也可以作为一个库去支持各式各样的项目。
 
 ## 开源许可证
 
@@ -78,9 +77,9 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 ## 更新日志
 
-我们在 2022.10.11 发布了 **1.1.0rc1** 版本.
+我们在 2022.12.2 发布了 **1.1.0rc2** 版本。
 
-更多细节和版本发布历史可以参考[changelog.md](docs/en/notes/changelog.md).
+更多细节和版本发布历史可以参考 [changelog.md](docs/zh_cn/notes/changelog.md)。
 
 ## 基准测试和模型库
 
@@ -165,6 +164,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
             <li><a href="configs/votenet">VoteNet (ICCV'2019)</a></li>
             <li><a href="configs/h3dnet">H3DNet (ECCV'2020)</a></li>
             <li><a href="configs/groupfree3d">Group-Free-3D (ICCV'2021)</a></li>
+            <li><a href="configs/fcaf3d">FCAF3D (ECCV'2022)</a></li>
       </ul>
       </td>
       <td>
@@ -202,31 +202,32 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
   </tbody>
 </table>
 
-|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA |
-| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: |
-| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |
-| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |
-| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |
-| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
-| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |
-| SA-SSD        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |
-
-**注意：** [MMDetection](https://github.com/open-mmlab/mmdetection/blob/master/docs/zh_cn/model_zoo.md) 支持的基于2D检测的**300+个模型 , 40+的论文算法**在 MMDetection3D 中都可以被训练或使用。
+|               | ResNet | ResNeXt | SENet | PointNet++ | DGCNN | HRNet | RegNetX | Res2Net | DLA | MinkResNet |
+| ------------- | :----: | :-----: | :---: | :--------: | :---: | :---: | :-----: | :-----: | :-: | :--------: |
+| SECOND        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| PointPillars  |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| FreeAnchor    |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| VoteNet       |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| H3DNet        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| 3DSSD         |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| Part-A2       |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| MVXNet        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| CenterPoint   |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| SSN           |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ✓    |    ☐    |  ✗  |     ✗      |
+| ImVoteNet     |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| FCOS3D        |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| PointNet++    |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| Group-Free-3D |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| ImVoxelNet    |   ✓    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| PAConv        |   ✗    |    ✗    |   ✗   |     ✓      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| DGCNN         |   ✗    |    ✗    |   ✗   |     ✗      |   ✓   |   ✗   |    ✗    |    ✗    |  ✗  |     ✗      |
+| SMOKE         |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
+| PGD           |   ✓    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| MonoFlex      |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✓  |     ✗      |
+| SA-SSD        |   ☐    |    ☐    |   ☐   |     ✗      |   ✗   |   ☐   |    ☐    |    ☐    |  ✗  |     ✗      |
+| FCAF3D        |   ✗    |    ✗    |   ✗   |     ✗      |   ✗   |   ✗   |    ✗    |    ✗    |  ✗  |     ✓      |
+
+**注意：**[MMDetection](https://github.com/open-mmlab/mmdetection/blob/3.x/docs/zh_cn/model_zoo.md) 支持的基于 2D 检测的 **300+ 个模型，40+ 的论文算法**在 MMDetection3D 中都可以被训练或使用。
 
 ## 安装
 
@@ -234,7 +235,7 @@ MMDetection3D 是一个基于 PyTorch 的目标检测开源工具箱, 下一代
 
 ## 快速入门
 
-请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。 我们为新手提供了分别针对[已有数据集](docs/zh_cn/user_guides/1_exist_data_model.md)和[新数据集](docs/zh_cn/user_guides/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/user_guides/config.md), [增加数据集支持](docs/zh_cn/advanced_guides/customize_dataset.md), [设计新的数据预处理流程](docs/zh_cn/user_guides/data_pipeline.md), [增加自定义模型](docs/zh_cn/advanced_guides/customize_models.md), [增加自定义的运行时配置](docs/zh_cn/advanced_guides/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/advanced_guides/datasets/waymo.md).
+请参考[快速入门文档](docs/zh_cn/getting_started.md)学习 MMDetection3D 的基本使用。我们为新手提供了分别针对[已有数据集](docs/zh_cn/user_guides/train_test.md)和[新数据集](docs/zh_cn/user_guides/2_new_data_model.md)的使用指南。我们也提供了一些进阶教程，内容覆盖了[学习配置文件](docs/zh_cn/user_guides/config.md)，[增加自定义数据集](docs/zh_cn/advanced_guides/customize_dataset.md)，[设计新的数据预处理流程](docs/zh_cn/user_guides/data_pipeline.md)，[增加自定义模型](docs/zh_cn/advanced_guides/customize_models.md)，[增加自定义的运行时配置](docs/zh_cn/advanced_guides/customize_runtime.md)和 [Waymo 数据集](docs/zh_cn/advanced_guides/datasets/waymo_det.md)。
 
 请参考 [FAQ](docs/zh_cn/notes/faq.md) 查看一些常见的问题与解答。在升级 MMDetection3D 的版本时，请查看[兼容性文档](docs/zh_cn/notes/compatibility.md)以知晓每个版本引入的不与之前版本兼容的更新。
 
@@ -263,11 +264,13 @@ MMDetection3D 是一款由来自不同高校和企业的研发人员共同参与
 
 - [MMEngine](https://github.com/open-mmlab/mmengine): OpenMMLab 深度学习模型训练基础库
 - [MMCV](https://github.com/open-mmlab/mmcv): OpenMMLab 计算机视觉基础库
+- [MMEval](https://github.com/open-mmlab/mmeval): 统一开放的跨框架算法评测库
 - [MIM](https://github.com/open-mmlab/mim): MIM 是 OpenMMlab 项目、算法、模型的统一入口
 - [MMClassification](https://github.com/open-mmlab/mmclassification): OpenMMLab 图像分类工具箱
 - [MMDetection](https://github.com/open-mmlab/mmdetection): OpenMMLab 目标检测工具箱
 - [MMDetection3D](https://github.com/open-mmlab/mmdetection3d): OpenMMLab 新一代通用 3D 目标检测平台
 - [MMRotate](https://github.com/open-mmlab/mmrotate): OpenMMLab 旋转框检测工具箱与测试基准
+- [MMYOLO](https://github.com/open-mmlab/mmyolo): OpenMMLab YOLO 系列工具箱与测试基准
 - [MMSegmentation](https://github.com/open-mmlab/mmsegmentation): OpenMMLab 语义分割工具箱
 - [MMOCR](https://github.com/open-mmlab/mmocr): OpenMMLab 全流程文字检测识别理解工具包
 - [MMPose](https://github.com/open-mmlab/mmpose): OpenMMLab 姿态估计工具箱

configs/_base_/datasets/kitti-3d-3class.py

@@ -4,7 +4,7 @@ data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 db_sampler = dict(
     data_root=data_root,

configs/_base_/datasets/kitti-3d-car.py

@@ -4,7 +4,7 @@ data_root = 'data/kitti/'
 class_names = ['Car']
 point_cloud_range = [0, -40, -3, 70.4, 40, 1]
 input_modality = dict(use_lidar=True, use_camera=False)
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 db_sampler = dict(
     data_root=data_root,

configs/_base_/datasets/kitti-mono3d.py

@@ -2,7 +2,7 @@ dataset_type = 'KittiDataset'
 data_root = 'data/kitti/'
 class_names = ['Pedestrian', 'Cyclist', 'Car']
 input_modality = dict(use_lidar=False, use_camera=True)
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 
 file_client_args = dict(backend='disk')
 # Uncomment the following if use ceph or other file clients.
@@ -52,6 +52,7 @@ train_dataloader = dict(
         data_prefix=dict(img='training/image_2'),
         pipeline=train_pipeline,
         modality=input_modality,
+        load_type='fov_image_based',
         test_mode=False,
         metainfo=metainfo,
         # we use box_type_3d='Camera' in monocular 3d
@@ -70,6 +71,7 @@ val_dataloader = dict(
         ann_file='kitti_infos_val.pkl',
         pipeline=test_pipeline,
         modality=input_modality,
+        load_type='fov_image_based',
         metainfo=metainfo,
         test_mode=True,
         box_type_3d='Camera'))

configs/_base_/datasets/lyft-3d-range100.py

@@ -39,8 +39,9 @@ train_pipeline = [
     dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
     dict(type='PointShuffle'),
-    dict(type='DefaultFormatBundle3D', class_names=class_names),
-    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
 ]
 test_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
@@ -59,23 +60,15 @@ test_pipeline = [
             dict(type='RandomFlip3D'),
             dict(
                 type='PointsRangeFilter', point_cloud_range=point_cloud_range),
-            dict(
-                type='DefaultFormatBundle3D',
-                class_names=class_names,
-                with_label=False),
-            dict(type='Collect3D', keys=['points'])
-        ])
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 # construct a pipeline for data and gt loading in show function
 # please keep its loading function consistent with test_pipeline (e.g. client)
 eval_pipeline = [
     dict(type='LoadPointsFromFile', coord_type='LIDAR', load_dim=5, use_dim=5),
     dict(type='LoadPointsFromMultiSweeps', sweeps_num=10),
-    dict(
-        type='DefaultFormatBundle3D',
-        class_names=class_names,
-        with_label=False),
-    dict(type='Collect3D', keys=['points'])
+    dict(type='Pack3DDetInputs', keys=['points'])
 ]
 
 data = dict(

configs/_base_/datasets/lyft-3d.py

@@ -78,7 +78,7 @@ train_dataloader = dict(
         data_root=data_root,
         ann_file='lyft_infos_train.pkl',
         pipeline=train_pipeline,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         modality=input_modality,
         data_prefix=data_prefix,
         test_mode=False,
@@ -94,7 +94,7 @@ test_dataloader = dict(
         data_root=data_root,
         ann_file='lyft_infos_val.pkl',
         pipeline=test_pipeline,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         modality=input_modality,
         data_prefix=data_prefix,
         test_mode=True,
@@ -110,7 +110,7 @@ val_dataloader = dict(
         data_root=data_root,
         ann_file='lyft_infos_val.pkl',
         pipeline=test_pipeline,
-        metainfo=dict(CLASSES=class_names),
+        metainfo=dict(classes=class_names),
         modality=input_modality,
         test_mode=True,
         data_prefix=data_prefix,

configs/_base_/datasets/nuim-instance.py

@@ -4,8 +4,6 @@ class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
-img_norm_cfg = dict(
-    mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
 
 file_client_args = dict(backend='disk')
 # Uncomment the following if use ceph or other file clients.
@@ -23,10 +21,7 @@ train_pipeline = [
         multiscale_mode='range',
         keep_ratio=True),
     dict(type='RandomFlip', flip_ratio=0.5),
-    dict(type='Normalize', **img_norm_cfg),
-    dict(type='Pad', size_divisor=32),
-    dict(type='DefaultFormatBundle'),
-    dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels', 'gt_masks']),
+    dict(type='PackDetInputs'),
 ]
 test_pipeline = [
     dict(type='LoadImageFromFile'),
@@ -37,11 +32,11 @@ test_pipeline = [
         transforms=[
             dict(type='Resize', keep_ratio=True),
             dict(type='RandomFlip'),
-            dict(type='Normalize', **img_norm_cfg),
-            dict(type='Pad', size_divisor=32),
-            dict(type='ImageToTensor', keys=['img']),
-            dict(type='Collect', keys=['img']),
-        ])
+        ]),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor')),
 ]
 data = dict(
     samples_per_gpu=2,

configs/_base_/datasets/nus-3d.py

 # If point cloud range is changed, the models should also change their point
 # cloud range accordingly
 point_cloud_range = [-50, -50, -5, 50, 50, 3]
+# Using calibration info convert the Lidar-coordinate point cloud range to the
+# ego-coordinate point cloud range could bring a little promotion in nuScenes.
+# point_cloud_range = [-50, -50.8, -5, 50, 49.2, 3]
 # For nuScenes we usually do 10-class detection
 class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 dataset_type = 'NuScenesDataset'
 data_root = 'data/nuscenes/'
 # Input modality for nuScenes dataset, this is consistent with the submission

configs/_base_/datasets/nus-mono3d.py

@@ -4,7 +4,7 @@ class_names = [
     'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
     'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
 ]
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 # Input modality for nuScenes dataset, this is consistent with the submission
 # format which requires the information in input_modality.
 input_modality = dict(use_lidar=False, use_camera=True)
@@ -65,7 +65,7 @@ train_dataloader = dict(
             CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
             CAM_BACK_LEFT='samples/CAM_BACK_LEFT'),
         ann_file='nuscenes_infos_train.pkl',
-        task='mono_det',
+        load_type='mv_image_based',
         pipeline=train_pipeline,
         metainfo=metainfo,
         modality=input_modality,
@@ -92,7 +92,7 @@ val_dataloader = dict(
             CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
             CAM_BACK_LEFT='samples/CAM_BACK_LEFT'),
         ann_file='nuscenes_infos_val.pkl',
-        task='mono_det',
+        load_type='mv_image_based',
         pipeline=test_pipeline,
         modality=input_modality,
         metainfo=metainfo,

configs/_base_/datasets/s3dis-3d.py

+# dataset settings
+dataset_type = 'S3DISDataset'
+data_root = 'data/s3dis/'
+
+metainfo = dict(classes=('table', 'chair', 'sofa', 'bookcase', 'board'))
+train_area = [1, 2, 3, 4, 6]
+test_area = 5
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='PointSample', num_points=100000),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.087266, 0.087266],
+        scale_ratio_range=[0.9, 1.1],
+        translation_std=[.1, .1, .1],
+        shift_height=False),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5]),
+    dict(
+        type='MultiScaleFlipAug3D',
+        img_scale=(1333, 800),
+        pts_scale_ratio=1,
+        flip=False,
+        transforms=[
+            dict(
+                type='GlobalRotScaleTrans',
+                rot_range=[0, 0],
+                scale_ratio_range=[1., 1.],
+                translation_std=[0, 0, 0]),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.5,
+                flip_ratio_bev_vertical=0.5),
+            dict(type='PointSample', num_points=100000),
+            dict(type='NormalizePointsColor', color_mean=None),
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=13,
+        dataset=dict(
+            type='ConcatDataset',
+            datasets=[
+                dict(
+                    type=dataset_type,
+                    data_root=data_root,
+                    ann_file=f's3dis_infos_Area_{i}.pkl',
+                    pipeline=train_pipeline,
+                    filter_empty_gt=True,
+                    metainfo=metainfo,
+                    box_type_3d='Depth') for i in train_area
+            ])))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=f's3dis_infos_Area_{test_area}.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Depth'))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file=f's3dis_infos_Area_{test_area}.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Depth'))
+val_evaluator = dict(type='IndoorMetric')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/s3dis-seg.py

 # For S3DIS seg we usually do 13-class segmentation
 class_names = ('ceiling', 'floor', 'wall', 'beam', 'column', 'window', 'door',
                'table', 'chair', 'sofa', 'bookcase', 'board', 'clutter')
-metainfo = dict(CLASSES=class_names)
+metainfo = dict(classes=class_names)
 dataset_type = 'S3DISSegDataset'
 data_root = 'data/s3dis/'
 input_modality = dict(use_lidar=True, use_camera=False)