mm2

.circleci/config.yml

@@ -0,0 +1,35 @@
+version: 2.1
+
+# this allows you to use CircleCI's dynamic configuration feature
+setup: true
+
+# the path-filtering orb is required to continue a pipeline based on
+# the path of an updated fileset
+orbs:
+  path-filtering: circleci/[email protected]
+
+workflows:
+  # the always-run workflow is always triggered, regardless of the pipeline parameters.
+  always-run:
+    jobs:
+      # the path-filtering/filter job determines which pipeline
+      # parameters to update.
+      - path-filtering/filter:
+          name: check-updated-files
+          # 3-column, whitespace-delimited mapping. One mapping per
+          # line:
+          # <regex path-to-test> <parameter-to-set> <value-of-pipeline-parameter>
+          mapping: |
+            mmdet3d/.* lint_only false
+            requirements/.* lint_only false
+            tests/.* lint_only false
+            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
+          # complete. In this case, we are using the parent dynamic
+          # configuration itself.
+          config-path: .circleci/test.yml

.circleci/docker/Dockerfile

@@ -0,0 +1,13 @@
+ARG PYTORCH="1.8.1"
+ARG CUDA="10.2"
+ARG CUDNN="7"
+
+ARG DEBIAN_FRONTEND=noninteractive
+
+FROM pytorch/pytorch:${PYTORCH}-cuda${CUDA}-cudnn${CUDNN}-devel
+
+# To fix GPG key error when running apt-get update
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/cuda/repos/ubuntu1804/x86_64/3bf863cc.pub
+RUN apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
+
+RUN apt-get update && apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx

.circleci/test.yml

@@ -0,0 +1,199 @@
+version: 2.1
+
+# the default pipeline parameters, which will be updated according to
+# the results of the path-filtering orb
+parameters:
+  lint_only:
+    type: boolean
+    default: true
+
+jobs:
+  lint:
+    docker:
+      - image: cimg/python:3.7.4
+    steps:
+      - checkout
+      - run:
+          name: Install pre-commit hook
+          command: |
+            pip install pre-commit
+            pre-commit install
+      - run:
+          name: Linting
+          command: pre-commit run --all-files
+      - run:
+          name: Check docstring coverage
+          command: |
+            pip install interrogate
+            interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 90 mmdet3d
+
+  build_cpu:
+    parameters:
+      # The python version must match available image tags in
+      # https://circleci.com/developer/images/image/cimg/python
+      python:
+        type: string
+      torch:
+        type: string
+      torchvision:
+        type: string
+    docker:
+      - image: cimg/python:<< parameters.python >>
+    resource_class: large
+    steps:
+      - checkout
+      - run:
+          name: Install Libraries
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6 libgl1-mesa-glx libjpeg-dev zlib1g-dev libtinfo-dev libncurses5
+      - run:
+          name: Configure Python & pip
+          command: |
+            pip install --upgrade pip
+            pip install wheel
+      - run:
+          name: Install PyTorch
+          command: pip install torch==<< parameters.torch >>+cpu torchvision==<< parameters.torchvision >>+cpu -f https://download.pytorch.org/whl/torch_stable.html
+      - when:
+          condition:
+            equal: ["3.9.0", << parameters.python >>]
+          steps:
+            - run: pip install "protobuf <= 3.20.1" && sudo apt-get update && sudo apt-get -y install libprotobuf-dev protobuf-compiler cmake
+      - run:
+          name: Install mmdet3d dependencies
+          command: |
+            pip install git+ssh://[email protected]/open-mmlab/mmengine.git@main
+            pip install -U openmim
+            mim install 'mmcv >= 2.0.0rc4'
+            pip install git+ssh://[email protected]/open-mmlab/[email protected]
+            pip install -r requirements/tests.txt
+      - run:
+          name: Build and install
+          command: |
+            pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            coverage run --branch --source mmdet3d -m pytest tests/
+            coverage xml
+            coverage report -m
+
+  build_cuda:
+    parameters:
+      torch:
+        type: string
+      cuda:
+        type: enum
+        enum: ["10.2", "11.7"]
+      cudnn:
+        type: integer
+        default: 8
+    machine:
+      image: linux-cuda-11:default
+      # docker_layer_caching: true
+    resource_class: gpu.nvidia.small.multi
+    steps:
+      - checkout
+      - run:
+          name: Install nvidia-container-toolkit and Restart Docker
+          command: |
+            sudo apt-get update
+            sudo apt-get install -y nvidia-container-toolkit
+            sudo systemctl restart docker
+      - run:
+          # Cloning repos in VM since Docker doesn't have access to the private key
+          name: Clone Repos
+          command: |
+            git clone -b main --depth 1 ssh://[email protected]/open-mmlab/mmengine.git /home/circleci/mmengine
+            git clone -b dev-3.x --depth 1 ssh://[email protected]/open-mmlab/mmdetection.git /home/circleci/mmdetection
+      - run:
+          name: Build Docker image
+          command: |
+            docker build .circleci/docker -t mmdet3d:gpu --build-arg PYTORCH=<< parameters.torch >> --build-arg CUDA=<< parameters.cuda >> --build-arg CUDNN=<< parameters.cudnn >>
+            docker run --gpus all -t -d -v /home/circleci/project:/mmdetection3d -v /home/circleci/mmengine:/mmengine -v /home/circleci/mmdetection:/mmdetection -w /mmdetection3d --name mmdet3d mmdet3d:gpu
+            docker exec mmdet3d apt-get install -y git
+      - run:
+          name: Install mmdet3d dependencies
+          command: |
+            docker exec mmdet3d pip install -e /mmengine
+            docker exec mmdet3d pip install -U openmim
+            docker exec mmdet3d mim install 'mmcv >= 2.0.0rc4'
+            docker exec mmdet3d pip install -e /mmdetection
+            docker exec mmdet3d pip install -r requirements/tests.txt
+      - run:
+          name: Build and install
+          command: |
+            docker exec mmdet3d pip install -e .
+      - run:
+          name: Run unittests
+          command: |
+            docker exec mmdet3d pytest tests/
+
+workflows:
+  pr_stage_lint:
+    when: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - dev-1.x
+  pr_stage_test:
+    when:
+      not: << pipeline.parameters.lint_only >>
+    jobs:
+      - lint:
+          name: lint
+          filters:
+            branches:
+              ignore:
+                - dev-1.x
+      - build_cpu:
+          name: minimum_version_cpu
+          torch: 1.8.1
+          torchvision: 0.9.1
+          python: 3.7.4 # The lowest python 3.7.x version available on CircleCI images
+          requires:
+            - lint
+      - build_cpu:
+          name: maximum_version_cpu
+          torch: 2.0.0
+          torchvision: 0.15.1
+          python: 3.9.0
+          requires:
+            - minimum_version_cpu
+      - hold:
+          type: approval
+          requires:
+            - maximum_version_cpu
+      - build_cuda:
+          name: mainstream_version_gpu
+          torch: 1.8.1
+          # Use double quotation mark to explicitly specify its type
+          # as string instead of number
+          cuda: "10.2"
+          cudnn: 7
+          requires:
+            - hold
+      - build_cuda:
+          name: maximum_version_gpu
+          torch: 2.0.0
+          cuda: "11.7"
+          cudnn: 8
+          requires:
+            - hold
+  merge_stage_test:
+    when:
+      not: << pipeline.parameters.lint_only >>
+    jobs:
+      - build_cuda:
+          name: minimum_version_gpu
+          torch: 1.8.1
+          cuda: "10.2"
+          cudnn: 7
+          filters:
+            branches:
+              only:
+                - dev-1.x

.dev_scripts/benchmark_full_models.txt

@@ -0,0 +1,26 @@
+configs/3dssd/3dssd_4xb4_kitti-3d-car.py
+configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py
+configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py
+configs/fcaf3d/fcaf3d_2xb8_s3dis-3d-5class.py
+configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py
+configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
+configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py
+configs/h3dnet/h3dnet_8xb3_scannet-seg.py
+configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py
+configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py
+configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py
+configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py
+configs/paconv/paconv_ssg_8xb8-cosine-150e_s3dis-seg.py
+configs/parta2/parta2_hv_secfpn_8xb2-cyclic-80e_kitti-3d-3class.py
+configs/pgd/pgd_r101-caffe_fpn_head-gn_4xb3-4x_kitti-mono3d.py
+configs/point_rcnn/point-rcnn_8xb2_kitti-3d-3class.py
+configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
+configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
+configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py
+configs/pv_rcnn/pv_rcnn_8xb2-80e_kitti-3d-3class.py
+configs/regnet/pointpillars_hv_regnet-1.6gf_fpn_sbn-all_8xb4-2x_nus-3d.py
+configs/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class.py
+configs/second/second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class.py
+configs/smoke/smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
+configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_nus-3d.py
+configs/votenet/votenet_8xb8_scannet-3d.py

.dev_scripts/benchmark_options.py

@@ -0,0 +1,11 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+
+third_part_libs = [
+    'conda install openblas-devel -c anaconda',
+    "pip install -U git+https://github.com/NVIDIA/MinkowskiEngine -v --no-deps --install-option='--blas_include_dirs=/opt/conda/include' --install-option='--blas=openblas'"  # noqa
+]
+default_floating_range = 0.5
+model_floating_ranges = {
+    'configs/pointpillars/pointpillars_hv_secfpn_sbn-all_16xb2-2x_waymoD5-3d-3class.py': # noqa
+    0.7
+}

.dev_scripts/benchmark_train_models.txt

@@ -0,0 +1,13 @@
+configs/3dssd/3dssd_4xb4_kitti-3d-car.py
+configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn_8xb4-cyclic-20e_nus-3d.py
+configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py
+configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py
+configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py
+configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
+configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
+configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py
+configs/pv_rcnn/pv_rcnn_8xb2-80e_kitti-3d-3class.py
+configs/second/second_hv_secfpn_8xb6-80e_kitti-3d-3class.py
+configs/second/second_hv_secfpn_8xb6-amp-80e_kitti-3d-3class.py
+configs/smoke/smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
+configs/votenet/votenet_8xb8_scannet-3d.py

.dev_scripts/covignore.cfg

@@ -0,0 +1,6 @@
+# Each line should be the relative path to the root directory
+# of this repo. Support regular expression as well.
+# For example:
+# .*/utils.py
+
+.*/__init__.py

.dev_scripts/diff_coverage_test.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+
+readarray -t IGNORED_FILES < $( dirname "$0" )/covignore.cfg
+
+
+REUSE_COVERAGE_REPORT=${REUSE_COVERAGE_REPORT:-0}
+REPO=${1:-"origin"}
+BRANCH=${2:-"refactor_dev"}
+
+git fetch $REPO $BRANCH
+
+PY_FILES=""
+for FILE_NAME in $(git diff --name-only ${REPO}/${BRANCH}); do
+    # Only test python files in mmdet3d/ existing in current branch, and not ignored in covignore.cfg
+    if [ ${FILE_NAME: -3} == ".py" ] && [ ${FILE_NAME:0:8} == "mmdet3d/" ] && [ -f "$FILE_NAME" ]; then
+        IGNORED=false
+        for IGNORED_FILE_NAME in "${IGNORED_FILES[@]}"; do
+            # Skip blank lines
+            if [ -z "$IGNORED_FILE_NAME" ]; then
+                continue
+            fi
+            if [ "${IGNORED_FILE_NAME::1}" != "#" ] && [[ "$FILE_NAME" =~ $IGNORED_FILE_NAME ]]; then
+                echo "Ignoring $FILE_NAME"
+                IGNORED=true
+                break
+            fi
+        done
+        if [ "$IGNORED" = false ]; then
+            PY_FILES="$PY_FILES $FILE_NAME"
+        fi
+    fi
+done
+
+# Only test the coverage when PY_FILES are not empty, otherwise they will test the entire project
+if [ ! -z "${PY_FILES}" ]
+then
+    if [ "$REUSE_COVERAGE_REPORT" == "0" ]; then
+        coverage run --branch --source mmdet3d -m pytest tests/
+    fi
+    coverage report --fail-under 80 -m $PY_FILES
+    interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 95 $PY_FILES
+fi

.dev_scripts/gather_models.py

@@ -0,0 +1,229 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+"""Script to gather benchmarked models and prepare them for upload.
+
+Usage:
+python gather_models.py ${root_path} ${out_dir}
+
+Example:
+python gather_models.py \
+work_dirs/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d \
+work_dirs/pgd_r101_caffe_fpn_gn-head_3x4_4x_kitti-mono3d
+
+Note that before running the above command, rename the directory with the
+config name if you did not use the default directory name, create
+a corresponding directory 'pgd' under the above path and put the used config
+into it.
+"""
+
+import argparse
+import glob
+import json
+import shutil
+import subprocess
+from os import path as osp
+
+import mmengine
+import torch
+
+# build schedule look-up table to automatically find the final model
+SCHEDULES_LUT = {
+    '_1x_': 12,
+    '_2x_': 24,
+    '_20e_': 20,
+    '_3x_': 36,
+    '_4x_': 48,
+    '_24e_': 24,
+    '_6x_': 73,
+    '_50e_': 50,
+    '_80e_': 80,
+    '_100e_': 100,
+    '_150e_': 150,
+    '_200e_': 200,
+    '_250e_': 250,
+    '_400e_': 400
+}
+
+# TODO: add support for lyft dataset
+RESULTS_LUT = {
+    'coco': ['bbox_mAP', 'segm_mAP'],
+    'nus': ['pts_bbox_NuScenes/NDS', 'NDS'],
+    'kitti-3d-3class': ['KITTI/Overall_3D_moderate', 'Overall_3D_moderate'],
+    'kitti-3d-car': ['KITTI/Car_3D_moderate_strict', 'Car_3D_moderate_strict'],
+    'lyft': ['score'],
+    'scannet_seg': ['miou'],
+    's3dis_seg': ['miou'],
+    'scannet': ['mAP_0.50'],
+    'sunrgbd': ['mAP_0.50'],
+    'kitti-mono3d': [
+        'img_bbox/KITTI/Car_3D_AP40_moderate_strict',
+        'Car_3D_AP40_moderate_strict'
+    ],
+    'nus-mono3d': ['img_bbox_NuScenes/NDS', 'NDS']
+}
+
+
+def get_model_dataset(log_json_path):
+    for key in RESULTS_LUT:
+        if log_json_path.find(key) != -1:
+            return key
+
+
+def process_checkpoint(in_file, out_file):
+    checkpoint = torch.load(in_file, map_location='cpu')
+    # remove optimizer for smaller file size
+    if 'optimizer' in checkpoint:
+        del checkpoint['optimizer']
+    # if it is necessary to remove some sensitive data in checkpoint['meta'],
+    # add the code here.
+    torch.save(checkpoint, out_file)
+    sha = subprocess.check_output(['sha256sum', out_file]).decode()
+    final_file = out_file.rstrip('.pth') + '-{}.pth'.format(sha[:8])
+    subprocess.Popen(['mv', out_file, final_file])
+    return final_file
+
+
+def get_final_epoch(config):
+    if config.find('grid_rcnn') != -1 and config.find('2x') != -1:
+        # grid_rcnn 2x trains 25 epochs
+        return 25
+
+    for schedule_name, epoch_num in SCHEDULES_LUT.items():
+        if config.find(schedule_name) != -1:
+            return epoch_num
+
+
+def get_best_results(log_json_path):
+    dataset = get_model_dataset(log_json_path)
+    max_dict = dict()
+    max_memory = 0
+    with open(log_json_path, 'r') as f:
+        for line in f.readlines():
+            log_line = json.loads(line)
+            if 'mode' not in log_line.keys():
+                continue
+
+            # record memory and find best results & epochs
+            if log_line['mode'] == 'train' \
+                    and max_memory <= log_line['memory']:
+                max_memory = log_line['memory']
+
+            elif log_line['mode'] == 'val':
+                result_dict = {
+                    key: log_line[key]
+                    for key in RESULTS_LUT[dataset] if key in log_line
+                }
+                if len(max_dict) == 0:
+                    max_dict = result_dict
+                    max_dict['epoch'] = log_line['epoch']
+                elif all(
+                    [max_dict[key] <= result_dict[key]
+                     for key in result_dict]):
+                    max_dict.update(result_dict)
+                    max_dict['epoch'] = log_line['epoch']
+
+        max_dict['memory'] = max_memory
+        return max_dict
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Gather benchmarked models')
+    parser.add_argument(
+        'root',
+        type=str,
+        help='root path of benchmarked models to be gathered')
+    parser.add_argument(
+        'out', type=str, help='output path of gathered models to be stored')
+
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+    models_root = args.root
+    models_out = args.out
+    mmengine.mkdir_or_exist(models_out)
+
+    # find all models in the root directory to be gathered
+    raw_configs = list(mmengine.scandir('./configs', '.py', recursive=True))
+
+    # filter configs that is not trained in the experiments dir
+    used_configs = []
+    for raw_config in raw_configs:
+        if osp.exists(osp.join(models_root, raw_config)):
+            used_configs.append(raw_config)
+    print(f'Find {len(used_configs)} models to be gathered')
+
+    # find final_ckpt and log file for trained each config
+    # and parse the best performance
+    model_infos = []
+    for used_config in used_configs:
+        # get logs
+        log_json_path = glob.glob(osp.join(models_root, '*.log.json'))[0]
+        log_txt_path = glob.glob(osp.join(models_root, '*.log'))[0]
+        model_performance = get_best_results(log_json_path)
+        final_epoch = model_performance['epoch']
+        final_model = 'epoch_{}.pth'.format(final_epoch)
+        model_path = osp.join(models_root, final_model)
+
+        # skip if the model is still training
+        if not osp.exists(model_path):
+            print(f'Expected {model_path} does not exist!')
+            continue
+
+        if model_performance is None:
+            print(f'Obtained no performance for model {used_config}')
+            continue
+
+        model_time = osp.split(log_txt_path)[-1].split('.')[0]
+        model_infos.append(
+            dict(
+                config=used_config,
+                results=model_performance,
+                epochs=final_epoch,
+                model_time=model_time,
+                log_json_path=osp.split(log_json_path)[-1]))
+
+    # publish model for each checkpoint
+    publish_model_infos = []
+    for model in model_infos:
+        model_publish_dir = osp.join(models_out, model['config'].rstrip('.py'))
+        mmengine.mkdir_or_exist(model_publish_dir)
+
+        model_name = model['config'].split('/')[-1].rstrip(
+            '.py') + '_' + model['model_time']
+        publish_model_path = osp.join(model_publish_dir, model_name)
+        trained_model_path = osp.join(models_root,
+                                      'epoch_{}.pth'.format(model['epochs']))
+
+        # convert model
+        final_model_path = process_checkpoint(trained_model_path,
+                                              publish_model_path)
+
+        # copy log
+        shutil.copy(
+            osp.join(models_root, model['log_json_path']),
+            osp.join(model_publish_dir, f'{model_name}.log.json'))
+        shutil.copy(
+            osp.join(models_root, model['log_json_path'].rstrip('.json')),
+            osp.join(model_publish_dir, f'{model_name}.log'))
+
+        # copy config to guarantee reproducibility
+        config_path = model['config']
+        config_path = osp.join(
+            'configs',
+            config_path) if 'configs' not in config_path else config_path
+        target_cconfig_path = osp.split(config_path)[-1]
+        shutil.copy(config_path,
+                    osp.join(model_publish_dir, target_cconfig_path))
+
+        model['model_path'] = final_model_path
+        publish_model_infos.append(model)
+
+    models = dict(models=publish_model_infos)
+    print(f'Totally gathered {len(publish_model_infos)} models')
+    mmengine.dump(models, osp.join(models_out, 'model_info.json'))
+
+
+if __name__ == '__main__':
+    main()

.dev_scripts/gen_benchmark_script.py

@@ -0,0 +1,193 @@
+import argparse
+import re
+from os import path as osp
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Generate benchmark training/testing scripts')
+    parser.add_argument(
+        '--input_file',
+        required=False,
+        type=str,
+        help='Input file containing the paths '
+        'of configs to be trained/tested.')
+    parser.add_argument(
+        '--output_file',
+        required=True,
+        type=str,
+        help='Output file containing the '
+        'commands to train/test selected models.')
+    parser.add_argument(
+        '--gpus_per_node',
+        type=int,
+        default=8,
+        help='GPUs per node config for slurm, '
+        'should be set according to your slurm environment')
+    parser.add_argument(
+        '--cpus_per_task',
+        type=int,
+        default=5,
+        help='CPUs per task config for slurm, '
+        'should be set according to your slurm environment')
+    parser.add_argument(
+        '--gpus',
+        type=int,
+        default=8,
+        help='Totally used num of GPUs config for slurm (in testing), '
+        'should be set according to your slurm environment')
+    parser.add_argument(
+        '--mode', type=str, default='train', help='Train or test')
+    parser.add_argument(
+        '--long_work_dir',
+        action='store_true',
+        help='Whether use full relative path of config as work dir')
+    parser.add_argument(
+        '--max_keep_ckpts',
+        type=int,
+        default=1,
+        help='The max number of checkpoints saved in training')
+    parser.add_argument(
+        '--full_log',
+        action='store_true',
+        help='Whether save full log in a file')
+
+    args = parser.parse_args()
+    return args
+
+
+args = parse_args()
+assert args.mode in ['train', 'test'], 'Currently we only support ' \
+    'automatically generating training or testing scripts.'
+
+config_paths = []
+
+if args.input_file is not None:
+    with open(args.input_file, 'r') as fi:
+        config_paths = fi.read().strip().split('\n')
+else:
+    while True:
+        print('Please type a config path and '
+              'press enter (press enter directly to exit):')
+        config_path = input()
+        if config_path != '':
+            config_paths.append(config_path)
+        else:
+            break
+
+script = '''PARTITION=$1
+CHECKPOINT_DIR=$2
+
+'''
+
+if args.mode == 'train':
+    for i, config_path in enumerate(config_paths):
+        root_dir = osp.dirname(osp.dirname(osp.abspath(__file__)))
+        if not osp.exists(osp.join(root_dir, config_path)):
+            print(f'Invalid config path (does not exist):\n{config_path}')
+            continue
+
+        config_name = config_path.split('/')[-1][:-3]
+        match_obj = re.match(r'^.*_[0-9]+x([0-9]+)_.*$', config_name)
+        if match_obj is None:
+            print(f'Invalid config path (no GPU num in '
+                  f'config name):\n{config_path}')
+            continue
+
+        gpu_num = int(match_obj.group(1))
+        work_dir_name = config_path if args.long_work_dir else config_name
+
+        script += f"echo '{config_path}' &\n"
+        if args.full_log:
+            script += f'mkdir -p $CHECKPOINT_DIR/{work_dir_name}\n'
+
+        # training commands
+        script += f'GPUS={gpu_num} GPUS_PER_NODE={args.gpus_per_node} ' \
+                  f'CPUS_PER_TASK={args.cpus_per_task} ' \
+                  f'./tools/slurm_train.sh $PARTITION {config_name} ' \
+                  f'{config_path} \\\n'
+        script += f'$CHECKPOINT_DIR/{work_dir_name} --cfg-options ' \
+                  f'checkpoint_config.max_keep_ckpts=' \
+                  f'{args.max_keep_ckpts} \\\n' \
+
+        # if output full log, redirect stdout and stderr to
+        # another log file in work dir
+        if args.full_log:
+            script += f'2>&1|tee $CHECKPOINT_DIR/{work_dir_name}' \
+                      f'/FULL_LOG.txt &\n'
+        else:
+            script += '>/dev/null &\n'
+
+        if i != len(config_paths) - 1:
+            script += '\n'
+
+        print(f'Successfully generated script for {config_name}')
+
+    with open(args.output_file, 'w') as fo:
+        fo.write(script)
+
+elif args.mode == 'test':
+    for i, config_path in enumerate(config_paths):
+        root_dir = osp.dirname(osp.dirname(osp.abspath(__file__)))
+        if not osp.exists(osp.join(root_dir, config_path)):
+            print(f'Invalid config path (does not exist):\n{config_path}')
+            continue
+
+        config_name = config_path.split('/')[-1][:-3]
+
+        tasks = {
+            'scannet_seg', 'scannet', 's3dis_seg', 'sunrgbd', 'kitti', 'nus',
+            'lyft', 'waymo'
+        }
+        eval_option = None
+        for task in tasks:
+            if task in config_name:
+                eval_option = task
+                break
+        if eval_option is None:
+            print(f'Invalid config path (invalid task):\n{config_path}')
+            continue
+
+        work_dir_name = config_path if args.long_work_dir else config_name
+
+        script += f"echo '{config_path}' &\n"
+        if args.full_log:
+            script += f'mkdir -p $CHECKPOINT_DIR/{work_dir_name}\n'
+
+        # training commands
+        script += f'GPUS={args.gpus} GPUS_PER_NODE={args.gpus_per_node} ' \
+                  f'CPUS_PER_TASK={args.cpus_per_task} ' \
+                  f'./tools/slurm_test.sh $PARTITION {config_name} ' \
+                  f'{config_path} \\\n'
+        script += f'$CHECKPOINT_DIR/{work_dir_name}/latest.pth ' \
+
+        if eval_option in ['scannet_seg', 's3dis_seg']:
+            script += '--eval mIoU \\\n'
+        elif eval_option in ['scannet', 'sunrgbd', 'kitti', 'nus']:
+            script += '--eval map \\\n'
+        elif eval_option in ['lyft']:
+            script += f'--format-only --eval-options jsonfile_prefix=' \
+                      f'$CHECKPOINT_DIR/{work_dir_name}/results_challenge ' \
+                      f'csv_savepath=$CHECKPOINT_DIR/{work_dir_name}/' \
+                      f'results_challenge.csv \\\n'
+        elif eval_option in ['waymo']:
+            script += f'--eval waymo --eval-options pklfile_prefix=' \
+                      f'$CHECKPOINT_DIR/{work_dir_name}/kitti_results ' \
+                      f'submission_prefix=$CHECKPOINT_DIR/{work_dir_name}/' \
+                      f'kitti_results \\\n'
+
+        # if output full log, redirect stdout and stderr to
+        # another log file in work dir
+        if args.full_log:
+            script += f'2>&1|tee $CHECKPOINT_DIR/{work_dir_name}' \
+                      f'/FULL_LOG.txt &\n'
+        else:
+            script += '>/dev/null &\n'
+
+        if i != len(config_paths) - 1:
+            script += '\n'
+
+        print(f'Successfully generated script for {config_name}')
+
+    with open(args.output_file, 'w') as fo:
+        fo.write(script)

.dev_scripts/linter.sh

@@ -0,0 +1,3 @@
+yapf -r -i mmdet3d/ configs/ tests/ tools/
+isort mmdet3d/ configs/ tests/ tools/
+flake8 .

.dev_scripts/test_benchmark.sh

@@ -0,0 +1,128 @@
+PARTITION=$1
+CHECKPOINT_DIR=$2
+
+echo 'configs/3dssd/3dssd_4xb4_kitti-3d-car.py' &
+mkdir -p $CHECKPOINT_DIR/configs/3dssd/3dssd_4xb4_kitti-3d-car.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION 3dssd_4x4_kitti-3d-car configs/3dssd/3dssd_4xb4_kitti-3d-car.py \
+$CHECKPOINT_DIR/configs/3dssd/3dssd_4xb4_kitti-3d-car.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/3dssd/3dssd_4xb4_kitti-3d-car.py/FULL_LOG.txt &
+
+echo 'configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION centerpoint_02pillar_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py \
+$CHECKPOINT_DIR/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py/FULL_LOG.txt &
+
+echo 'configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py \
+$CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py/FULL_LOG.txt &
+
+echo 'configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py \
+$CHECKPOINT_DIR/configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py/FULL_LOG.txt &
+
+echo 'configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION groupfree3d_8x4_scannet-3d-18class-L6-O256 configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py \
+$CHECKPOINT_DIR/configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py/FULL_LOG.txt &
+
+echo 'configs/h3dnet/h3dnet_8xb3_scannet-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/h3dnet/h3dnet_8xb3_scannet-seg.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION h3dnet_3x8_scannet-3d-18class configs/h3dnet/h3dnet_8xb3_scannet-seg.py \
+$CHECKPOINT_DIR/configs/h3dnet/h3dnet_8xb3_scannet-seg.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/h3dnet/h3dnet_8xb3_scannet-seg.py/FULL_LOG.txt &
+
+echo 'configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py \
+$CHECKPOINT_DIR/configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py/FULL_LOG.txt &
+
+echo 'configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION imvotenet_stage2_16x8_sunrgbd-3d-10class configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py \
+$CHECKPOINT_DIR/configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py/FULL_LOG.txt &
+
+echo 'configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py' &
+mkdir -p $CHECKPOINT_DIR/configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION imvoxelnet_4x8_kitti-3d-car configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py \
+$CHECKPOINT_DIR/configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py/FULL_LOG.txt &
+
+echo 'configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py \
+$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/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py \
+$CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py/latest.pth --eval mIoU \
+2>&1|tee $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py/FULL_LOG.txt &
+
+echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py \
+$CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py/FULL_LOG.txt &
+
+echo 'configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py \
+$CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py/latest.pth --format-only --eval-options jsonfile_prefix=$CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py/results_challenge csv_savepath=$CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py/results_challenge.csv \
+2>&1|tee $CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py/FULL_LOG.txt &
+
+echo 'configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py \
+$CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py/latest.pth --eval waymo --eval-options pklfile_prefix=$CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py/kitti_results submission_prefix=$CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py/kitti_results \
+2>&1|tee $CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py \
+$CHECKPOINT_DIR/configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py/FULL_LOG.txt &
+
+echo 'configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_second_secfpn_6x8_80e_kitti-3d-3class configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py \
+$CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py/latest.pth --format-only --eval-options jsonfile_prefix=$CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py/results_challenge csv_savepath=$CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py/results_challenge.csv \
+2>&1|tee $CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py/FULL_LOG.txt &
+
+echo 'configs/votenet/votenet_8xb8_scannet-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/votenet/votenet_8xb8_scannet-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_test.sh $PARTITION votenet_8x8_scannet-3d-18class configs/votenet/votenet_8xb8_scannet-3d.py \
+$CHECKPOINT_DIR/configs/votenet/votenet_8xb8_scannet-3d.py/latest.pth --eval map \
+2>&1|tee $CHECKPOINT_DIR/configs/votenet/votenet_8xb8_scannet-3d.py/FULL_LOG.txt &

.dev_scripts/train_benchmark.sh

@@ -0,0 +1,128 @@
+PARTITION=$1
+CHECKPOINT_DIR=$2
+
+echo 'configs/3dssd/3dssd_4xb4_kitti-3d-car.py' &
+mkdir -p $CHECKPOINT_DIR/configs/3dssd/3dssd_4xb4_kitti-3d-car.py
+GPUS=4 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION 3dssd_4x4_kitti-3d-car configs/3dssd/3dssd_4xb4_kitti-3d-car.py \
+$CHECKPOINT_DIR/configs/3dssd/3dssd_4xb4_kitti-3d-car.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/3dssd/3dssd_4xb4_kitti-3d-car.py/FULL_LOG.txt &
+
+echo 'configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION centerpoint_02pillar_second_secfpn_dcn_circlenms_4x8_cyclic_20e_nus configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py \
+$CHECKPOINT_DIR/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/centerpoint/centerpoint_pillar02_second_secfpn_head-dcn-circlenms_8xb4-cyclic-20e_nus-3d.py/FULL_LOG.txt &
+
+echo 'configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION dv_second_secfpn_2x8_cosine_80e_kitti-3d-3class configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/dynamic_voxelization/second_dv_secfpn_8xb2-cosine-80e_kitti-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/fcos3d/fcos3d_r101-caffe-dcn_fpn_head-gn_8xb2-1x_nus-mono3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION fcos3d_r101_caffe_fpn_gn-head_dcn_2x8_1x_nus-mono3d configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py \
+$CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/fcos3d/fcos3d_r101-caffe-fpn-head-gn-dcn_8xb2-1x_nus-mono3d.py/FULL_LOG.txt &
+
+echo 'configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/second/hv_second_secfpn_fp16_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_pointpillars_regnet-1.6gf_fpn_sbn-all_free-anchor_strong-aug_4x8_3x_nus-3d configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py \
+$CHECKPOINT_DIR/configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/free_anchor/pointpillars_hv_regnet-1.6gf_fpn_head-free-anchor_sbn-all_8xb4-strong-aug-3x_nus-3d.py/FULL_LOG.txt &
+
+echo 'configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py
+GPUS=4 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION groupfree3d_8x4_scannet-3d-18class-L6-O256 configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py \
+$CHECKPOINT_DIR/configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/groupfree3d/groupfree3d_head-L6-O256_4xb8_scannet-seg.py/FULL_LOG.txt &
+
+echo 'configs/h3dnet/h3dnet_8xb3_scannet-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/h3dnet/h3dnet_8xb3_scannet-seg.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION h3dnet_3x8_scannet-3d-18class configs/h3dnet/h3dnet_8xb3_scannet-seg.py \
+$CHECKPOINT_DIR/configs/h3dnet/h3dnet_8xb3_scannet-seg.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/h3dnet/h3dnet_8xb3_scannet-seg.py/FULL_LOG.txt &
+
+echo 'configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py
+GPUS=4 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION imvotenet_faster_rcnn_r50_fpn_2x4_sunrgbd-3d-10class configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py \
+$CHECKPOINT_DIR/configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/imvotenet/imvotenet_faster-rcnn-r50_fpn_4xb2_sunrgbd-3d.py/FULL_LOG.txt &
+
+echo 'configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION imvotenet_stage2_16x8_sunrgbd-3d-10class configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py \
+$CHECKPOINT_DIR/configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/imvotenet/imvotenet_stage2_8xb16_sunrgbd-3d.py/FULL_LOG.txt &
+
+echo 'configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py' &
+mkdir -p $CHECKPOINT_DIR/configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION imvoxelnet_4x8_kitti-3d-car configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py \
+$CHECKPOINT_DIR/configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/imvoxelnet/imvoxelnet_8xb4_kitti-3d-car.py/FULL_LOG.txt &
+
+echo 'configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION dv_mvx-fpn_second_secfpn_adamw_2x8_80e_kitti-3d-3class configs/mvxnet/mvxnet_fpn_dv_second_secfpn_8xb2-80e_kitti-3d-3class.py \
+$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/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py
+GPUS=2 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION pointnet2_msg_16x2_cosine_80e_s3dis_seg-3d-13class configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py \
+$CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-80e_s3dis-seg.py/FULL_LOG.txt &
+
+echo 'configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py
+GPUS=2 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION pointnet2_msg_16x2_cosine_250e_scannet_seg-3d-20class configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py \
+$CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/pointnet2/pointnet2_msg_2xb16-cosine-250e_scannet-seg.py/FULL_LOG.txt &
+
+echo 'configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_pointpillars_fpn_sbn-all_2x8_2x_lyft-3d configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py \
+$CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/pointpillars/pointpillars_hv_fpn_sbn-all_8xb2-2x_lyft-3d.py/FULL_LOG.txt &
+
+echo 'configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py
+GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py \
+$CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/pointpillars/hv_pointpillars_secfpn_sbn_2x16_2x_waymoD5-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py \
+$CHECKPOINT_DIR/configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/regnet/hv_pointpillars_regnet-1.6gf_fpn_sbn-all_4x8_2x_nus-3d.py/FULL_LOG.txt &
+
+echo 'configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py' &
+mkdir -p $CHECKPOINT_DIR/configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_second_secfpn_6x8_80e_kitti-3d-3class configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py \
+$CHECKPOINT_DIR/configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/second/hv_second_secfpn_6x8_80e_kitti-3d-3class.py/FULL_LOG.txt &
+
+echo 'configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py
+GPUS=16 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION hv_ssn_secfpn_sbn-all_2x16_2x_lyft-3d configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py \
+$CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/ssn/ssn_hv_secfpn_sbn-all_16xb2-2x_lyft-3d.py/FULL_LOG.txt &
+
+echo 'configs/votenet/votenet_8xb8_scannet-3d.py' &
+mkdir -p $CHECKPOINT_DIR/configs/votenet/votenet_8xb8_scannet-3d.py
+GPUS=8 GPUS_PER_NODE=8 CPUS_PER_TASK=5 ./tools/slurm_train.sh $PARTITION votenet_8x8_scannet-3d-18class configs/votenet/votenet_8xb8_scannet-3d.py \
+$CHECKPOINT_DIR/configs/votenet/votenet_8xb8_scannet-3d.py --cfg-options checkpoint_config.max_keep_ckpts=1 \
+2>&1|tee $CHECKPOINT_DIR/configs/votenet/votenet_8xb8_scannet-3d.py/FULL_LOG.txt &

.gitignore

@@ -0,0 +1,137 @@
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+*.ipynb
+
+# C extensions
+*.so
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+.hypothesis/
+.pytest_cache/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+
+# Flask stuff:
+instance/
+.webassets-cache
+
+# Scrapy stuff:
+.scrapy
+
+# Sphinx documentation
+docs/en/_build/
+docs/zh_cn/_build/
+
+# PyBuilder
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# pyenv
+.python-version
+
+# celery beat schedule file
+celerybeat-schedule
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
+.spyproject
+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+
+# cython generated cpp
+data
+.vscode
+.idea
+
+# custom
+*.pkl
+*.pkl.json
+*.log.json
+work_dirs/
+exps/
+*~
+mmdet3d/.mim
+
+# Pytorch
+*.pth
+
+# demo
+*.jpg
+*.png
+data/s3dis/Stanford3dDataset_v1.2_Aligned_Version/
+data/scannet/scans/
+data/sunrgbd/OFFICIAL_SUNRGBD/
+*.obj
+*.ply
+
+# Waymo evaluation
+mmdet3d/evaluation/functional/waymo_utils/compute_detection_metrics_main
+mmdet3d/evaluation/functional/waymo_utils/compute_detection_let_metrics_main
+mmdet3d/evaluation/functional/waymo_utils/compute_segmentation_metrics_main

.pre-commit-config-zh-cn.yaml

@@ -0,0 +1,50 @@
+repos:
+  - repo: https://gitee.com/openmmlab/mirrors-flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://gitee.com/openmmlab/mirrors-isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://gitee.com/openmmlab/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://gitee.com/openmmlab/mirrors-pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://gitee.com/openmmlab/mirrors-codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://gitee.com/openmmlab/mirrors-mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: ["--number"]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://gitee.com/openmmlab/mirrors-docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://gitee.com/openmmlab/pre-commit-hooks
+    rev: v0.2.0
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet3d"]

.pre-commit-config.yaml

@@ -0,0 +1,50 @@
+repos:
+  - repo: https://github.com/PyCQA/flake8
+    rev: 5.0.4
+    hooks:
+      - id: flake8
+  - repo: https://github.com/PyCQA/isort
+    rev: 5.11.5
+    hooks:
+      - id: isort
+  - repo: https://github.com/pre-commit/mirrors-yapf
+    rev: v0.32.0
+    hooks:
+      - id: yapf
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.3.0
+    hooks:
+      - id: trailing-whitespace
+      - id: check-yaml
+      - id: end-of-file-fixer
+      - id: requirements-txt-fixer
+      - id: double-quote-string-fixer
+      - id: check-merge-conflict
+      - id: fix-encoding-pragma
+        args: ["--remove"]
+      - id: mixed-line-ending
+        args: ["--fix=lf"]
+  - repo: https://github.com/codespell-project/codespell
+    rev: v2.2.1
+    hooks:
+      - id: codespell
+  - repo: https://github.com/executablebooks/mdformat
+    rev: 0.7.9
+    hooks:
+      - id: mdformat
+        args: [ "--number" ]
+        additional_dependencies:
+          - mdformat-openmmlab
+          - mdformat_frontmatter
+          - linkify-it-py
+  - repo: https://github.com/myint/docformatter
+    rev: v1.3.1
+    hooks:
+      - id: docformatter
+        args: ["--in-place", "--wrap-descriptions", "79"]
+  - repo: https://github.com/open-mmlab/pre-commit-hooks
+    rev: v0.2.0  # Use the ref you want to point at
+    hooks:
+      - id: check-algo-readme
+      - id: check-copyright
+        args: ["mmdet3d"]  # replace the dir_to_check with your expected directory to check

.readthedocs.yml

@@ -0,0 +1,14 @@
+version: 2
+
+build:
+  os: ubuntu-22.04
+  tools:
+    python: "3.8"
+
+formats:
+  - epub
+
+python:
+  install:
+    - requirements: requirements/docs.txt
+    - requirements: requirements/readthedocs.txt

CITATION.cff

@@ -0,0 +1,8 @@
+cff-version: 1.2.0
+message: "If you use this software, please cite it as below."
+authors:
+  - name: "MMDetection3D Contributors"
+title: "OpenMMLab's Next-generation Platform for General 3D Object Detection"
+date-released: 2020-07-23
+url: "https://github.com/open-mmlab/mmdetection3d"
+license: Apache-2.0

LICENSE

@@ -0,0 +1,203 @@
+Copyright 2018-2019 Open-MMLab. All rights reserved.
+
+                                 Apache License
+                           Version 2.0, January 2004
+                        http://www.apache.org/licenses/
+
+   TERMS AND CONDITIONS FOR USE, REPRODUCTION, AND DISTRIBUTION
+
+   1. Definitions.
+
+      "License" shall mean the terms and conditions for use, reproduction,
+      and distribution as defined by Sections 1 through 9 of this document.
+
+      "Licensor" shall mean the copyright owner or entity authorized by
+      the copyright owner that is granting the License.
+
+      "Legal Entity" shall mean the union of the acting entity and all
+      other entities that control, are controlled by, or are under common
+      control with that entity. For the purposes of this definition,
+      "control" means (i) the power, direct or indirect, to cause the
+      direction or management of such entity, whether by contract or
+      otherwise, or (ii) ownership of fifty percent (50%) or more of the
+      outstanding shares, or (iii) beneficial ownership of such entity.
+
+      "You" (or "Your") shall mean an individual or Legal Entity
+      exercising permissions granted by this License.
+
+      "Source" form shall mean the preferred form for making modifications,
+      including but not limited to software source code, documentation
+      source, and configuration files.
+
+      "Object" form shall mean any form resulting from mechanical
+      transformation or translation of a Source form, including but
+      not limited to compiled object code, generated documentation,
+      and conversions to other media types.
+
+      "Work" shall mean the work of authorship, whether in Source or
+      Object form, made available under the License, as indicated by a
+      copyright notice that is included in or attached to the work
+      (an example is provided in the Appendix below).
+
+      "Derivative Works" shall mean any work, whether in Source or Object
+      form, that is based on (or derived from) the Work and for which the
+      editorial revisions, annotations, elaborations, or other modifications
+      represent, as a whole, an original work of authorship. For the purposes
+      of this License, Derivative Works shall not include works that remain
+      separable from, or merely link (or bind by name) to the interfaces of,
+      the Work and Derivative Works thereof.
+
+      "Contribution" shall mean any work of authorship, including
+      the original version of the Work and any modifications or additions
+      to that Work or Derivative Works thereof, that is intentionally
+      submitted to Licensor for inclusion in the Work by the copyright owner
+      or by an individual or Legal Entity authorized to submit on behalf of
+      the copyright owner. For the purposes of this definition, "submitted"
+      means any form of electronic, verbal, or written communication sent
+      to the Licensor or its representatives, including but not limited to
+      communication on electronic mailing lists, source code control systems,
+      and issue tracking systems that are managed by, or on behalf of, the
+      Licensor for the purpose of discussing and improving the Work, but
+      excluding communication that is conspicuously marked or otherwise
+      designated in writing by the copyright owner as "Not a Contribution."
+
+      "Contributor" shall mean Licensor and any individual or Legal Entity
+      on behalf of whom a Contribution has been received by Licensor and
+      subsequently incorporated within the Work.
+
+   2. Grant of Copyright License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      copyright license to reproduce, prepare Derivative Works of,
+      publicly display, publicly perform, sublicense, and distribute the
+      Work and such Derivative Works in Source or Object form.
+
+   3. Grant of Patent License. Subject to the terms and conditions of
+      this License, each Contributor hereby grants to You a perpetual,
+      worldwide, non-exclusive, no-charge, royalty-free, irrevocable
+      (except as stated in this section) patent license to make, have made,
+      use, offer to sell, sell, import, and otherwise transfer the Work,
+      where such license applies only to those patent claims licensable
+      by such Contributor that are necessarily infringed by their
+      Contribution(s) alone or by combination of their Contribution(s)
+      with the Work to which such Contribution(s) was submitted. If You
+      institute patent litigation against any entity (including a
+      cross-claim or counterclaim in a lawsuit) alleging that the Work
+      or a Contribution incorporated within the Work constitutes direct
+      or contributory patent infringement, then any patent licenses
+      granted to You under this License for that Work shall terminate
+      as of the date such litigation is filed.
+
+   4. Redistribution. You may reproduce and distribute copies of the
+      Work or Derivative Works thereof in any medium, with or without
+      modifications, and in Source or Object form, provided that You
+      meet the following conditions:
+
+      (a) You must give any other recipients of the Work or
+          Derivative Works a copy of this License; and
+
+      (b) You must cause any modified files to carry prominent notices
+          stating that You changed the files; and
+
+      (c) You must retain, in the Source form of any Derivative Works
+          that You distribute, all copyright, patent, trademark, and
+          attribution notices from the Source form of the Work,
+          excluding those notices that do not pertain to any part of
+          the Derivative Works; and
+
+      (d) If the Work includes a "NOTICE" text file as part of its
+          distribution, then any Derivative Works that You distribute must
+          include a readable copy of the attribution notices contained
+          within such NOTICE file, excluding those notices that do not
+          pertain to any part of the Derivative Works, in at least one
+          of the following places: within a NOTICE text file distributed
+          as part of the Derivative Works; within the Source form or
+          documentation, if provided along with the Derivative Works; or,
+          within a display generated by the Derivative Works, if and
+          wherever such third-party notices normally appear. The contents
+          of the NOTICE file are for informational purposes only and
+          do not modify the License. You may add Your own attribution
+          notices within Derivative Works that You distribute, alongside
+          or as an addendum to the NOTICE text from the Work, provided
+          that such additional attribution notices cannot be construed
+          as modifying the License.
+
+      You may add Your own copyright statement to Your modifications and
+      may provide additional or different license terms and conditions
+      for use, reproduction, or distribution of Your modifications, or
+      for any such Derivative Works as a whole, provided Your use,
+      reproduction, and distribution of the Work otherwise complies with
+      the conditions stated in this License.
+
+   5. Submission of Contributions. Unless You explicitly state otherwise,
+      any Contribution intentionally submitted for inclusion in the Work
+      by You to the Licensor shall be under the terms and conditions of
+      this License, without any additional terms or conditions.
+      Notwithstanding the above, nothing herein shall supersede or modify
+      the terms of any separate license agreement you may have executed
+      with Licensor regarding such Contributions.
+
+   6. Trademarks. This License does not grant permission to use the trade
+      names, trademarks, service marks, or product names of the Licensor,
+      except as required for reasonable and customary use in describing the
+      origin of the Work and reproducing the content of the NOTICE file.
+
+   7. Disclaimer of Warranty. Unless required by applicable law or
+      agreed to in writing, Licensor provides the Work (and each
+      Contributor provides its Contributions) on an "AS IS" BASIS,
+      WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or
+      implied, including, without limitation, any warranties or conditions
+      of TITLE, NON-INFRINGEMENT, MERCHANTABILITY, or FITNESS FOR A
+      PARTICULAR PURPOSE. You are solely responsible for determining the
+      appropriateness of using or redistributing the Work and assume any
+      risks associated with Your exercise of permissions under this License.
+
+   8. Limitation of Liability. In no event and under no legal theory,
+      whether in tort (including negligence), contract, or otherwise,
+      unless required by applicable law (such as deliberate and grossly
+      negligent acts) or agreed to in writing, shall any Contributor be
+      liable to You for damages, including any direct, indirect, special,
+      incidental, or consequential damages of any character arising as a
+      result of this License or out of the use or inability to use the
+      Work (including but not limited to damages for loss of goodwill,
+      work stoppage, computer failure or malfunction, or any and all
+      other commercial damages or losses), even if such Contributor
+      has been advised of the possibility of such damages.
+
+   9. Accepting Warranty or Additional Liability. While redistributing
+      the Work or Derivative Works thereof, You may choose to offer,
+      and charge a fee for, acceptance of support, warranty, indemnity,
+      or other liability obligations and/or rights consistent with this
+      License. However, in accepting such obligations, You may act only
+      on Your own behalf and on Your sole responsibility, not on behalf
+      of any other Contributor, and only if You agree to indemnify,
+      defend, and hold each Contributor harmless for any liability
+      incurred by, or claims asserted against, such Contributor by reason
+      of your accepting any such warranty or additional liability.
+
+   END OF TERMS AND CONDITIONS
+
+   APPENDIX: How to apply the Apache License to your work.
+
+      To apply the Apache License to your work, attach the following
+      boilerplate notice, with the fields enclosed by brackets "[]"
+      replaced with your own identifying information. (Don't include
+      the brackets!)  The text should be enclosed in the appropriate
+      comment syntax for the file format. We also recommend that a
+      file or class name and description of purpose be included on the
+      same "printed page" as the copyright notice for easier
+      identification within third-party archives.
+
+   Copyright 2018-2019 Open-MMLab.
+
+   Licensed under the Apache License, Version 2.0 (the "License");
+   you may not use this file except in compliance with the License.
+   You may obtain a copy of the License at
+
+       http://www.apache.org/licenses/LICENSE-2.0
+
+   Unless required by applicable law or agreed to in writing, software
+   distributed under the License is distributed on an "AS IS" BASIS,
+   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+   See the License for the specific language governing permissions and
+   limitations under the License.

MANIFEST.in

@@ -0,0 +1,6 @@
+include mmdet3d/.mim/model-index.yml
+include mmdet3d/.mim/dataset-index.yml
+include requirements/*.txt
+recursive-include mmdet3d/.mim/ops *.cpp *.cu *.h *.cc
+recursive-include mmdet3d/.mim/configs *.py *.yml
+recursive-include mmdet3d/.mim/tools *.sh *.py

README_zh-CN.md

@@ -0,0 +1,427 @@
+<div align="center">
+  <img src="resources/mmdet3d-logo.png" width="600"/>
+  <div>&nbsp;</div>
+  <div align="center">
+    <b><font size="5">OpenMMLab 官网</font></b>
+    <sup>
+      <a href="https://openmmlab.com">
+        <i><font size="4">HOT</font></i>
+      </a>
+    </sup>
+    &nbsp;&nbsp;&nbsp;&nbsp;
+    <b><font size="5">OpenMMLab 开放平台</font></b>
+    <sup>
+      <a href="https://platform.openmmlab.com">
+        <i><font size="4">TRY IT OUT</font></i>
+      </a>
+    </sup>
+  </div>
+  <div>&nbsp;</div>
+
+[![PyPI](https://img.shields.io/pypi/v/mmdet3d)](https://pypi.org/project/mmdet3d)
+[![docs](https://img.shields.io/badge/docs-latest-blue)](https://mmdetection3d.readthedocs.io/zh_CN/latest/)
+[![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/main/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/main/LICENSE)
+[![open issues](https://isitmaintained.com/badge/open/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/issues)
+[![issue resolution](https://isitmaintained.com/badge/resolution/open-mmlab/mmdetection3d.svg)](https://github.com/open-mmlab/mmdetection3d/issues)
+
+[📘使用文档](https://mmdetection3d.readthedocs.io/zh_CN/latest/) |
+[🛠️安装教程](https://mmdetection3d.readthedocs.io/zh_CN/latest/get_started.html) |
+[👀模型库](https://mmdetection3d.readthedocs.io/zh_CN/latest/model_zoo.html) |
+[🆕更新日志](https://mmdetection3d.readthedocs.io/en/latest/notes/changelog.html) |
+[🚀进行中的项目](https://github.com/open-mmlab/mmdetection3d/projects) |
+[🤔报告问题](https://github.com/open-mmlab/mmdetection3d/issues/new/choose)
+
+</div>
+
+<div align="center">
+
+[English](README.md) | 简体中文
+
+</div>
+
+<div align="center">
+  <a href="https://openmmlab.medium.com/" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219255827-67c1a27f-f8c5-46a9-811d-5e57448c61d1.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://discord.com/channels/1037617289144569886/1046608014234370059" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218347213-c080267f-cbb6-443e-8532-8e1ed9a58ea9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://twitter.com/OpenMMLab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346637-d30c8a0f-3eba-4699-8131-512fb06d46db.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.youtube.com/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/218346691-ceb2116a-465a-40af-8424-9f30d2348ca9.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://space.bilibili.com/1293512903" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026751-d7d14cce-a7c9-4e82-9942-8375fca65b99.png" width="3%" alt="" /></a>
+  <img src="https://user-images.githubusercontent.com/25839884/218346358-56cc8e2f-a2b8-487f-9088-32480cceabcf.png" width="3%" alt="" />
+  <a href="https://www.zhihu.com/people/openmmlab" style="text-decoration:none;">
+    <img src="https://user-images.githubusercontent.com/25839884/219026120-ba71e48b-6e94-4bd4-b4e9-b7d175b5e362.png" width="3%" alt="" /></a>
+</div>
+
+## 简介
+
+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](https://openmmlab.com/) 项目的一部分。
+
+主分支代码目前支持 PyTorch 1.8 以上的版本。
+
+![demo image](resources/mmdet3d_outdoor_demo.gif)
+
+<details open>
+<summary>主要特性</summary>
+
+- **支持多模态/单模态的检测器**
+
+  支持多模态/单模态检测器，包括 MVXNet，VoteNet，PointPillars 等。
+
+- **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/main/configs/nuimages)。
+
+- **与 2D 检测器的自然整合**
+
+  [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���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/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) |
+  | :-----------------: | :-----------: | :--------------------------------------------------: | :----------------------------------------------------: | :-----------------------------------------: |
+  |       VoteNet       |      358      |                          ✗                           |                           77                           |                      ✗                      |
+  |  PointPillars-car   |      141      |                          ✗                           |                           ✗                            |                     140                     |
+  | PointPillars-3class |      107      |                          44                          |                           ✗                            |                      ✗                      |
+  |       SECOND        |      40       |                          30                          |                           ✗                            |                      ✗                      |
+  |       Part-A2       |      17       |                          14                          |                           ✗                            |                      ✗                      |
+
+</details>
+
+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。
+
+## 最新进展
+
+### 亮点
+
+在1.4版本中，MMDetecion3D 重构了 Waymo 数据集, 加速了 Waymo 数据集的预处理、训练/测试启动、验证的速度。并且在 Waymo 上拓展了对 单目/BEV 等基于相机的三维目标检测模型的支持。在[这里](https://mmdetection3d.readthedocs.io/en/latest/advanced_guides/datasets/waymo.html)提供了对 Waymo 数据信息的详细解读。
+
+此外，在1.4版本中，MMDetection3D 提供了 [Waymo-mini](https://download.openmmlab.com/mmdetection3d/data/waymo_mmdet3d_after_1x4/waymo_mini.tar.gz) 来帮助社区用户上手 Waymo 并用于快速迭代开发。
+
+**v1.4.0** 版本已经在 2024.1.8 发布：
+
+- 在 `projects` 中支持了 [DSVT](<(https://arxiv.org/abs/2301.06051)>) 的训练
+- 在 `projects` 中支持了 [Nerf-Det](https://arxiv.org/abs/2307.14620)
+- 重构了 Waymo 数据集
+
+**v1.3.0** 版本已经在 2023.10.18 发布：
+
+- 在 `projects` 中支持 [CENet](https://arxiv.org/abs/2207.12691)
+- 使用新的 3D inferencers 增强演示代码效果
+
+**v1.2.0** 版本已经在 2023.7.4 发布：
+
+- 在 `mmdet3d/configs`中支持 [新Config样式](https://mmengine.readthedocs.io/en/latest/advanced_tutorials/config.html#a-pure-python-style-configuration-file-beta)
+- 在 `projects` 中支持 [DSVT](<(https://arxiv.org/abs/2301.06051)>) 的推理
+- 支持通过 `mim` 从 [OpenDataLab](https://opendatalab.com/) 下载数据集
+
+**v1.1.1** 版本已经在 2023.5.30 发布：
+
+- 在 `projects` 中支持 [TPVFormer](https://arxiv.org/pdf/2302.07817.pdf)
+- 在 `projects` 中支持 BEVFusion 的训练
+- 支持基于激光雷达的 3D 语义分割基准
+
+## 安装
+
+请参考[快速入门文档](https://mmdetection3d.readthedocs.io/zh_CN/latest/get_started.html)进行安装。
+
+## 教程
+
+<details>
+<summary>用户指南</summary>
+
+- [训练 & 测试](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/index.html#train-test)
+  - [学习配置文件](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/config.html)
+  - [坐标系](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/coord_sys_tutorial.html)
+  - [数据预处理](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/dataset_prepare.html)
+  - [自定义数据预处理流程](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/data_pipeline.html)
+  - [在标注数据集上测试和训练](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/train_test.html)
+  - [推理](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/inference.html)
+  - [在自定义数据集上进行训练](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/new_data_model.html)
+- [实用工具](https://mmdetection3d.readthedocs.io/zh_CN/latest/user_guides/index.html#useful-tools)
+
+</details>
+
+<details>
+<summary>进阶教程</summary>
+
+- [数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/index.html#datasets)
+  - [KITTI 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/kitti.html)
+  - [NuScenes 数���集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/nuscenes.html)
+  - [Lyft 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/lyft.html)
+  - [Waymo 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/waymo.html)
+  - [SUN RGB-D 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/sunrgbd.html)
+  - [ScanNet 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/scannet.html)
+  - [S3DIS 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/s3dis.html)
+  - [SemanticKITTI 数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/datasets/semantickitti.html)
+- [支持的任务](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/index.html#supported-tasks)
+  - [基于激光雷达的 3D 检测](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/supported_tasks/lidar_det3d.html)
+  - [基于视觉的 3D 检测](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/supported_tasks/vision_det3d.html)
+  - [基于激光雷达的 3D 语义分割](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/supported_tasks/lidar_sem_seg3d.html)
+- [自定义项目](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/index.html#customization)
+  - [自定义数据集](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/customize_dataset.html)
+  - [自定义模型](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/customize_models.html)
+  - [自定义运行时配置](https://mmdetection3d.readthedocs.io/zh_CN/latest/advanced_guides/customize_runtime.html)
+
+</details>
+
+## 基准测试和模型库
+
+测试结果和模型可以在[模型库](docs/zh_cn/model_zoo.md)中找到。
+
+<div align="center">
+  <b>模块组件</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="bottom">
+      <td>
+        <b>主干网络</b>
+      </td>
+      <td>
+        <b>检测头</b>
+      </td>
+      <td>
+        <b>特性</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+      <ul>
+        <li><a href="configs/pointnet2">PointNet (CVPR'2017)</a></li>
+        <li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
+        <li><a href="configs/regnet">RegNet (CVPR'2020)</a></li>
+        <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
+        <li>DLA (CVPR'2018)</li>
+        <li>MinkResNet (CVPR'2019)</li>
+        <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
+        <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
+      </ul>
+      </td>
+      <td>
+      <ul>
+        <li><a href="configs/free_anchor">FreeAnchor (NeurIPS'2019)</a></li>
+      </ul>
+      </td>
+      <td>
+      <ul>
+        <li><a href="configs/dynamic_voxelization">Dynamic Voxelization (CoRL'2019)</a></li>
+      </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+<div align="center">
+  <b>算法模型</b>
+</div>
+<table align="center">
+  <tbody>
+    <tr align="center" valign="middle">
+      <td>
+        <b>激光雷达 3D 目标检测</b>
+      </td>
+      <td>
+        <b>相机 3D 目标检测</b>
+      </td>
+      <td>
+        <b>多模态 3D 目标检测</b>
+      </td>
+      <td>
+        <b>3D 语义分割</b>
+      </td>
+    </tr>
+    <tr valign="top">
+      <td>
+        <li><b>室外</b></li>
+        <ul>
+            <li><a href="configs/second">SECOND (Sensor'2018)</a></li>
+            <li><a href="configs/pointpillars">PointPillars (CVPR'2019)</a></li>
+            <li><a href="configs/ssn">SSN (ECCV'2020)</a></li>
+            <li><a href="configs/3dssd">3DSSD (CVPR'2020)</a></li>
+            <li><a href="configs/sassd">SA-SSD (CVPR'2020)</a></li>
+            <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>
+            <li><a href="projects/CenterFormer">CenterFormer (ECCV'2022)</a></li>
+        </ul>
+        <li><b>室内</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>
+            <li><a href="projects/TR3D">TR3D (ArXiv'2023)</a></li>
+      </ul>
+      </td>
+      <td>
+        <li><b>室外</b></li>
+        <ul>
+          <li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
+          <li><a href="configs/smoke">SMOKE (CVPRW'2020)</a></li>
+          <li><a href="configs/fcos3d">FCOS3D (ICCVW'2021)</a></li>
+          <li><a href="configs/pgd">PGD (CoRL'2021)</a></li>
+          <li><a href="configs/monoflex">MonoFlex (CVPR'2021)</a></li>
+          <li><a href="projects/DETR3D">DETR3D (CoRL'2021)</a></li>
+          <li><a href="projects/PETR">PETR (ECCV'2022)</a></li>
+        </ul>
+        <li><b>Indoor</b></li>
+        <ul>
+          <li><a href="configs/imvoxelnet">ImVoxelNet (WACV'2022)</a></li>
+        </ul>
+      </td>
+      <td>
+        <li><b>室外</b></li>
+        <ul>
+          <li><a href="configs/mvxnet">MVXNet (ICRA'2019)</a></li>
+          <li><a href="projects/BEVFusion">BEVFusion (ICRA'2023)</a></li>
+        </ul>
+        <li><b>室内</b></li>
+        <ul>
+          <li><a href="configs/imvotenet">ImVoteNet (CVPR'2020)</a></li>
+        </ul>
+      </td>
+      <td>
+        <li><b>室外</b></li>
+        <ul>
+          <li><a href="configs/minkunet">MinkUNet (CVPR'2019)</a></li>
+          <li><a href="configs/spvcnn">SPVCNN (ECCV'2020)</a></li>
+          <li><a href="configs/cylinder3d">Cylinder3D (CVPR'2021)</a></li>
+          <li><a href="projects/TPVFormer">TPVFormer (CVPR'2023)</a></li>
+        </ul>
+        <li><b>室内</b></li>
+        <ul>
+          <li><a href="configs/pointnet2">PointNet++ (NeurIPS'2017)</a></li>
+          <li><a href="configs/paconv">PAConv (CVPR'2021)</a></li>
+          <li><a href="configs/dgcnn">DGCNN (TOG'2019)</a></li>
+        </ul>
+      </ul>
+      </td>
+    </tr>
+</td>
+    </tr>
+  </tbody>
+</table>
+
+|               | ResNet | VoVNet | Swin-T | PointNet++ | SECOND | DGCNN | RegNetX | DLA | MinkResNet | Cylinder3D | MinkUNet |
+| :-----------: | :----: | :----: | :----: | :--------: | :----: | :---: | :-----: | :-: | :--------: | :--------: | :------: |
+|    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    |   ✗    |   ✗    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
+|  Cylinder3D   |   ✗    |   ✗    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✓      |    ✗     |
+|   MinkUNet    |   ✗    |   ✗    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✓     |
+|    SPVCNN     |   ✗    |   ✗    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✓     |
+|   BEVFusion   |   ✗    |   ✗    |   ✓    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
+| CenterFormer  |   ✗    |   ✗    |   ✗    |     ✗      |   ✓    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
+|     TR3D      |   ✗    |   ✗    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✓      |     ✗      |    ✗     |
+|    DETR3D     |   ✓    |   ✓    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
+|     PETR      |   ✗    |   ✓    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
+|   TPVFormer   |   ✓    |   ✗    |   ✗    |     ✗      |   ✗    |   ✗   |    ✗    |  ✗  |     ✗      |     ✗      |    ✗     |
+
+**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。
+
+## 常见问题
+
+请参考 [FAQ](docs/zh_cn/notes/faq.md) 了解其他用户的常见问题。
+
+## 贡献指南
+
+我们感谢所有的贡献者为改进和提升 MMDetection3D 所作出的努力。请参考[贡献指南](docs/en/notes/contribution_guides.md)来了解参与项目贡献的相关指引。
+
+## 致谢
+
+MMDetection3D 是一款由来自不同高校和企业的研发人员共同参与贡献的开源项目。我们感谢所有为项目提供算法复现和新功能支持的贡献者，以及提供宝贵反馈的用户。我们希望这个工具箱和基准测试可以为社区提供灵活的代码工具，供用户复现已有算法并开发自己的新的 3D 检测模型。
+
+## 引用
+
+如果你觉得本项目对你的研究工作有所帮助，请参考如下 bibtex 引用 MMdetection3D：
+
+```latex
+@misc{mmdet3d2020,
+    title={{MMDetection3D: OpenMMLab} next-generation platform for general {3D} object detection},
+    author={MMDetection3D Contributors},
+    howpublished = {\url{https://github.com/open-mmlab/mmdetection3d}},
+    year={2020}
+}
+```
+
+## 开源许可证
+
+该项目采用 [Apache 2.0 开源许可证](LICENSE)。
+
+## OpenMMLab 的其他项目
+
+- [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
+- [MMPreTrain](https://github.com/open-mmlab/mmpretrain): OpenMMLab 深度学习预训练工具箱
+- [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
+- [MMHuman3D](https://github.com/open-mmlab/mmhuman3d): OpenMMLab 人体参数化模型工具箱与测试基准
+- [MMSelfSup](https://github.com/open-mmlab/mmselfsup): OpenMMLab 自监督学习工具箱与测试基准
+- [MMRazor](https://github.com/open-mmlab/mmrazor): OpenMMLab 模型压缩工具箱与测试基准
+- [MMFewShot](https://github.com/open-mmlab/mmfewshot): OpenMMLab 少样本学习工具箱与测试基准
+- [MMAction2](https://github.com/open-mmlab/mmaction2): OpenMMLab 新一代视频理解工具箱
+- [MMTracking](https://github.com/open-mmlab/mmtracking): OpenMMLab 一体化视频目标感知平台
+- [MMFlow](https://github.com/open-mmlab/mmflow): OpenMMLab 光流估计工具箱与测试基准
+- [MMagic](https://github.com/open-mmlab/mmagic): OpenMMLab 新一代人工智能内容生成（AIGC）工具箱
+- [MMGeneration](https://github.com/open-mmlab/mmgeneration): OpenMMLab 图片视频生成模型工具箱
+- [MMDeploy](https://github.com/open-mmlab/mmdeploy): OpenMMLab 模型部署框架
+
+## 欢迎加入 OpenMMLab 社区
+
+扫描下方的二维码可关注 OpenMMLab ���队的 [知乎官方账号](https://www.zhihu.com/people/openmmlab)，扫描下方微信二维码添加喵喵好友，进入 MMDetection3D 微信交流社群。【加好友申请格式：研究方向+地区+学校/公司+姓名】
+
+<div align="center">
+<img src="https://user-images.githubusercontent.com/58739961/187154320-f3312cdf-31f2-4316-9dbb-8d7b0e1b7e08.jpg" height="400" />  <img src="https://github.com/open-mmlab/mmdetection3d/assets/62195058/dfb3f6a9-25c6-47a5-936b-3f1d7347a42b" height="400" />
+</div>
+
+我们会在 OpenMMLab 社区为大家
+
+- 📢 分享 AI 框架的前沿核心技术
+- 💻 解读 PyTorch 常用模块源码
+- 📰 发布 OpenMMLab 的相关新闻
+- 🚀 介绍 OpenMMLab 开发的前沿算法
+- 🏃 获取更高效的问题答疑和意见反馈
+- 🔥 提供与各行各业开发者充分交流的平台
+
+干货满满 📘，等你来撩 💗，OpenMMLab 社区期待您的加入 👬

configs/3dssd/3dssd_4xb4_kitti-3d-car.py

@@ -0,0 +1,119 @@
+_base_ = [
+    '../_base_/models/3dssd.py', '../_base_/datasets/kitti-3d-car.py',
+    '../_base_/default_runtime.py'
+]
+
+# dataset settings
+dataset_type = 'KittiDataset'
+data_root = 'data/kitti/'
+class_names = ['Car']
+point_cloud_range = [0, -40, -5, 70, 40, 3]
+input_modality = dict(use_lidar=True, use_camera=False)
+backend_args = None
+
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
+    classes=class_names,
+    sample_groups=dict(Car=15),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    backend_args=backend_args)
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        translation_std=[1.0, 1.0, 0],
+        global_rot_range=[0.0, 0.0],
+        rot_range=[-1.0471975511965976, 1.0471975511965976]),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.9, 1.1]),
+    # 3DSSD can get a higher performance without this transform
+    # dict(type='BackgroundPointsFilter', bbox_enlarge_range=(0.5, 2.0, 0.5)),
+    dict(type='PointSample', num_points=16384),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+            dict(type='PointSample', num_points=16384),
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+
+train_dataloader = dict(
+    batch_size=4, dataset=dict(dataset=dict(pipeline=train_pipeline, )))
+test_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+val_dataloader = dict(dataset=dict(pipeline=test_pipeline))
+
+# model settings
+model = dict(
+    bbox_head=dict(
+        num_classes=1,
+        bbox_coder=dict(
+            type='AnchorFreeBBoxCoder', num_dir_bins=12, with_rot=True)))
+
+# optimizer
+lr = 0.002  # max learning rate
+optim_wrapper = dict(
+    type='OptimWrapper',
+    optimizer=dict(type='AdamW', lr=lr, weight_decay=0.),
+    clip_grad=dict(max_norm=35, norm_type=2),
+)
+
+# training schedule for 1x
+train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=80, val_interval=2)
+val_cfg = dict(type='ValLoop')
+test_cfg = dict(type='TestLoop')
+
+# learning rate
+param_scheduler = [
+    dict(
+        type='MultiStepLR',
+        begin=0,
+        end=80,
+        by_epoch=True,
+        milestones=[45, 60],
+        gamma=0.1)
+]

configs/3dssd/README.md

@@ -0,0 +1,45 @@
+# 3DSSD: Point-based 3D Single Stage Object Detector
+
+> [3DSSD: Point-based 3D Single Stage Object Detector](https://arxiv.org/abs/2002.10187)
+
+<!-- [ALGORITHM] -->
+
+## Abstract
+
+Currently, there have been many kinds of voxel-based 3D single stage detectors, while point-based single stage methods are still underexplored. In this paper, we first present a lightweight and effective point-based 3D single stage object detector, named 3DSSD, achieving a good balance between accuracy and efficiency. In this paradigm, all upsampling layers and refinement stage, which are indispensable in all existing point-based methods, are abandoned to reduce the large computation cost. We novelly propose a fusion sampling strategy in downsampling process to make detection on less representative points feasible. A delicate box prediction network including a candidate generation layer, an anchor-free regression head with a 3D center-ness assignment strategy is designed to meet with our demand of accuracy and speed. Our paradigm is an elegant single stage anchor-free framework, showing great superiority to other existing methods. We evaluate 3DSSD on widely used KITTI dataset and more challenging nuScenes dataset. Our method outperforms all state-of-the-art voxel-based single stage methods by a large margin, and has comparable performance to two stage point-based methods as well, with inference speed more than 25 FPS, 2x faster than former state-of-the-art point-based methods.
+
+<div align=center>
+<img src="https://user-images.githubusercontent.com/30491025/143854187-54ed1257-a046-4764-81cd-d2c8404137d3.png" width="800"/>
+</div>
+
+## Introduction
+
+We implement 3DSSD and provide the results and checkpoints on KITTI datasets.
+
+Some settings in our implementation are different from the [official implementation](https://github.com/Jia-Research-Lab/3DSSD), which bring marginal differences to the performance on KITTI datasets in our experiments. To simplify and unify the models of our implementation, we skip them in our models. These differences are listed as below:
+
+1. We keep the scenes without any object while the official code skips these scenes in training. In the official implementation, only 3229 and 3394 samples are used as training and validation sets, respectively. In our implementation, we keep using 3712 and 3769 samples as training and validation sets, respectively, as those used for all the other models in our implementation on KITTI datasets.
+2. We do not modify the decay of `batch normalization` during training.
+3. While using [`DataBaseSampler`](https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/datasets/pipelines/dbsampler.py#L80) for data augmentation, the official code uses road planes as reference to place the sampled objects while we do not.
+4. We perform detection using LIDAR coordinates while the official code uses camera coordinates.
+
+## Results and models
+
+### KITTI
+
+|                    Backbone                    | Class | Lr schd | Mem (GB) | Inf time (fps) |           mAP            |                                                                                                                                                Download                                                                                                                                                |
+| :--------------------------------------------: | :---: | :-----: | :------: | :------------: | :----------------------: | :----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------: |
+| [PointNet2SAMSG](./3dssd_4xb4_kitti-3d-car.py) |  Car  |   72e   |   4.7    |                | 78.58(81.27)<sup>1</sup> | [model](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828-b89c8fc4.pth) \| [log](https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828.log.json) |
+
+\[1\]: We report two different 3D object detection performance here. 78.58mAP is evaluated by our evaluation code and 81.27mAP is evaluated by the official development kit （so as that used in the paper and official code of 3DSSD ）. We found that the commonly used Python implementation of [`rotate_iou`](https://github.com/traveller59/second.pytorch/blob/e42e4a0e17262ab7d180ee96a0a36427f2c20a44/second/core/non_max_suppression/nms_gpu.py#L605) which is used in our KITTI dataset evaluation, is different from the official implementation in [KITTI benchmark](http://www.cvlibs.net/datasets/kitti/eval_object.php?obj_benchmark=3d).
+
+## Citation
+
+```latex
+@inproceedings{yang20203dssd,
+    author = {Zetong Yang and Yanan Sun and Shu Liu and Jiaya Jia},
+    title = {3DSSD: Point-based 3D Single Stage Object Detector},
+    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
+    year = {2020}
+}
+```

configs/3dssd/metafile.yml

@@ -0,0 +1,29 @@
+Collections:
+  - Name: 3DSSD
+    Metadata:
+      Training Data: KITTI
+      Training Techniques:
+        - AdamW
+      Training Resources: 4x TITAN X
+      Architecture:
+        - PointNet++
+    Paper:
+      URL: https://arxiv.org/abs/2002.10187
+      Title: '3DSSD: Point-based 3D Single Stage Object Detector'
+    README: configs/3dssd/README.md
+    Code:
+      URL: https://github.com/open-mmlab/mmdetection3d/blob/master/mmdet3d/models/detectors/ssd3dnet.py#L7
+      Version: v0.6.0
+
+Models:
+  - Name: 3dssd_4x4_kitti-3d-car
+    In Collection: 3DSSD
+    Config: configs/3dssd/3dssd_4xb4_kitti-3d-car.py
+    Metadata:
+      Training Memory (GB): 4.7
+    Results:
+      - Task: 3D Object Detection
+        Dataset: KITTI
+        Metrics:
+          mAP: 78.58
+    Weights: https://download.openmmlab.com/mmdetection3d/v1.0.0_models/3dssd/3dssd_4x4_kitti-3d-car/3dssd_4x4_kitti-3d-car_20210818_203828-b89c8fc4.pth

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

@@ -0,0 +1,167 @@
+# dataset settings
+dataset_type = 'KittiDataset'
+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)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(
+        filter_by_difficulty=[-1],
+        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+    classes=class_names,
+    sample_groups=dict(Car=12, Pedestrian=6, Cyclist=6),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    backend_args=backend_args)
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,  # x, y, z, intensity
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        translation_std=[1.0, 1.0, 0.5],
+        global_rot_range=[0.0, 0.0],
+        rot_range=[-0.78539816, 0.78539816]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    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=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+train_dataloader = dict(
+    batch_size=6,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='kitti_infos_train.pkl',
+            data_prefix=dict(pts='training/velodyne_reduced'),
+            pipeline=train_pipeline,
+            modality=input_modality,
+            test_mode=False,
+            metainfo=metainfo,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR',
+            backend_args=backend_args)))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne_reduced'),
+        ann_file='kitti_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne_reduced'),
+        ann_file='kitti_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+val_evaluator = dict(
+    type='KittiMetric',
+    ann_file=data_root + 'kitti_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

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

@@ -0,0 +1,165 @@
+# dataset settings
+dataset_type = 'KittiDataset'
+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)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'kitti_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
+    classes=class_names,
+    sample_groups=dict(Car=15),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    backend_args=backend_args)
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,  # x, y, z, intensity
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='ObjectNoise',
+        num_try=100,
+        translation_std=[1.0, 1.0, 0.5],
+        global_rot_range=[0.0, 0.0],
+        rot_range=[-0.78539816, 0.78539816]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    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=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+train_dataloader = dict(
+    batch_size=6,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='kitti_infos_train.pkl',
+            data_prefix=dict(pts='training/velodyne_reduced'),
+            pipeline=train_pipeline,
+            modality=input_modality,
+            test_mode=False,
+            metainfo=metainfo,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR',
+            backend_args=backend_args)))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne_reduced'),
+        ann_file='kitti_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne_reduced'),
+        ann_file='kitti_infos_val.pkl',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+val_evaluator = dict(
+    type='KittiMetric',
+    ann_file=data_root + 'kitti_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/kitti-mono3d.py

@@ -0,0 +1,100 @@
+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)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/kitti/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(type='Resize', scale=(1242, 375), keep_ratio=True),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers_2d', 'depths'
+        ]),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(type='Resize', scale=(1242, 375), keep_ratio=True),
+    dict(type='Pack3DDetInputs', keys=['img'])
+]
+eval_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['img'])
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='kitti_infos_train.pkl',
+        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
+        # detection task
+        box_type_3d='Camera',
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(img='training/image_2'),
+        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',
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='KittiMetric',
+    ann_file=data_root + 'kitti_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

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

@@ -0,0 +1,150 @@
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-100, -100, -5, 100, 100, 3]
+# For Lyft we usually do 9-class detection
+class_names = [
+    'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
+    'bicycle', 'pedestrian', 'animal'
+]
+dataset_type = 'LyftDataset'
+data_root = 'data/lyft/'
+data_prefix = dict(pts='v1.01-train/lidar', img='', sweeps='v1.01-train/lidar')
+# Input modality for Lyft dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(
+    use_lidar=True,
+    use_camera=False,
+    use_radar=False,
+    use_map=False,
+    use_external=False)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/lyft/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    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,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+        ]),
+    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,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='lyft_infos_train.pkl',
+        pipeline=train_pipeline,
+        metainfo=dict(classes=class_names),
+        modality=input_modality,
+        data_prefix=data_prefix,
+        test_mode=False,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='lyft_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=dict(classes=class_names),
+        modality=input_modality,
+        test_mode=True,
+        data_prefix=data_prefix,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='LyftMetric',
+    data_root=data_root,
+    ann_file='lyft_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

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

@@ -0,0 +1,160 @@
+# If point cloud range is changed, the models should also change their point
+# cloud range accordingly
+point_cloud_range = [-80, -80, -5, 80, 80, 3]
+# For Lyft we usually do 9-class detection
+class_names = [
+    'car', 'truck', 'bus', 'emergency_vehicle', 'other_vehicle', 'motorcycle',
+    'bicycle', 'pedestrian', 'animal'
+]
+dataset_type = 'LyftDataset'
+data_root = 'data/lyft/'
+# Input modality for Lyft dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(use_lidar=True, use_camera=False)
+data_prefix = dict(pts='v1.01-train/lidar', img='', sweeps='v1.01-train/lidar')
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/lyft/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    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,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    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,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='lyft_infos_train.pkl',
+        pipeline=train_pipeline,
+        metainfo=dict(classes=class_names),
+        modality=input_modality,
+        data_prefix=data_prefix,
+        test_mode=False,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='lyft_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=dict(classes=class_names),
+        modality=input_modality,
+        data_prefix=data_prefix,
+        test_mode=True,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='lyft_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=dict(classes=class_names),
+        modality=input_modality,
+        test_mode=True,
+        data_prefix=data_prefix,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='LyftMetric',
+    data_root=data_root,
+    ann_file='lyft_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/nuim-instance.py

@@ -0,0 +1,70 @@
+dataset_type = 'CocoDataset'
+data_root = 'data/nuimages/'
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/nuimages/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='Resize',
+        img_scale=[(1280, 720), (1920, 1080)],
+        multiscale_mode='range',
+        keep_ratio=True),
+    dict(type='RandomFlip', flip_ratio=0.5),
+    dict(type='PackDetInputs'),
+]
+test_pipeline = [
+    dict(type='LoadImageFromFile', backend_args=backend_args),
+    dict(
+        type='MultiScaleFlipAug',
+        img_scale=(1600, 900),
+        flip=False,
+        transforms=[
+            dict(type='Resize', keep_ratio=True),
+            dict(type='RandomFlip'),
+        ]),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
+                   'scale_factor')),
+]
+data = dict(
+    samples_per_gpu=2,
+    workers_per_gpu=2,
+    train=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/nuimages_v1.0-train.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=train_pipeline),
+    val=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline),
+    test=dict(
+        type=dataset_type,
+        ann_file=data_root + 'annotations/nuimages_v1.0-val.json',
+        img_prefix=data_root,
+        classes=class_names,
+        pipeline=test_pipeline))
+evaluation = dict(metric=['bbox', 'segm'])

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

@@ -0,0 +1,169 @@
+# 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)
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+# Input modality for nuScenes dataset, this is consistent with the submission
+# format which requires the information in input_modality.
+input_modality = dict(use_lidar=True, use_camera=False)
+data_prefix = dict(pts='samples/LIDAR_TOP', img='', sweeps='sweeps/LIDAR_TOP')
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/nuscenes/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=5,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.3925, 0.3925],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0, 0, 0]),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectNameFilter', classes=class_names),
+    dict(type='PointShuffle'),
+    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,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        test_mode=True,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    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,
+        backend_args=backend_args),
+    dict(
+        type='LoadPointsFromMultiSweeps',
+        sweeps_num=10,
+        test_mode=True,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+train_dataloader = dict(
+    batch_size=4,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='nuscenes_infos_train.pkl',
+        pipeline=train_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        test_mode=False,
+        data_prefix=data_prefix,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        data_prefix=data_prefix,
+        test_mode=True,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='nuscenes_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        test_mode=True,
+        data_prefix=data_prefix,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='NuScenesMetric',
+    data_root=data_root,
+    ann_file=data_root + 'nuscenes_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/nus-mono3d.py

@@ -0,0 +1,119 @@
+dataset_type = 'NuScenesDataset'
+data_root = 'data/nuscenes/'
+class_names = [
+    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
+    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
+]
+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)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/nuscenes/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=True,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(type='Resize', scale=(1600, 900), keep_ratio=True),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'img', 'gt_bboxes', 'gt_bboxes_labels', 'attr_labels',
+            'gt_bboxes_3d', 'gt_labels_3d', 'centers_2d', 'depths'
+        ]),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(type='mmdet.Resize', scale=(1600, 900), keep_ratio=True),
+    dict(type='Pack3DDetInputs', keys=['img'])
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='',
+            CAM_FRONT='samples/CAM_FRONT',
+            CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
+            CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
+            CAM_BACK='samples/CAM_BACK',
+            CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
+            CAM_BACK_LEFT='samples/CAM_BACK_LEFT'),
+        ann_file='nuscenes_infos_train.pkl',
+        load_type='mv_image_based',
+        pipeline=train_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        test_mode=False,
+        # we use box_type_3d='Camera' in monocular 3d
+        # detection task
+        box_type_3d='Camera',
+        use_valid_flag=True,
+        backend_args=backend_args))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=2,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='',
+            CAM_FRONT='samples/CAM_FRONT',
+            CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
+            CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
+            CAM_BACK='samples/CAM_BACK',
+            CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
+            CAM_BACK_LEFT='samples/CAM_BACK_LEFT'),
+        ann_file='nuscenes_infos_val.pkl',
+        load_type='mv_image_based',
+        pipeline=test_pipeline,
+        modality=input_modality,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Camera',
+        use_valid_flag=True,
+        backend_args=backend_args))
+test_dataloader = val_dataloader
+
+val_evaluator = dict(
+    type='NuScenesMetric',
+    data_root=data_root,
+    ann_file=data_root + 'nuscenes_infos_val.pkl',
+    metric='bbox',
+    backend_args=backend_args)
+
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

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

@@ -0,0 +1,134 @@
+# dataset settings
+dataset_type = 'S3DISDataset'
+data_root = 'data/s3dis/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/s3dis/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+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],
+        backend_args=backend_args),
+    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],
+        backend_args=backend_args),
+    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',
+                    backend_args=backend_args) 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',
+        backend_args=backend_args))
+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',
+        backend_args=backend_args))
+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

@@ -0,0 +1,169 @@
+# 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)
+dataset_type = 'S3DISSegDataset'
+data_root = 'data/s3dis/'
+input_modality = dict(use_lidar=True, use_camera=False)
+data_prefix = dict(
+    pts='points',
+    pts_instance_mask='instance_mask',
+    pts_semantic_mask='semantic_mask')
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/s3dis/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+num_points = 4096
+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],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='IndoorPatchPointSample',
+        num_points=num_points,
+        block_size=1.0,
+        ignore_index=len(class_names),
+        use_normalized_coord=True,
+        enlarge_size=0.2,
+        min_unique_num=None),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
+]
+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],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='NormalizePointsColor', color_mean=None),
+    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)
+# we need to load gt seg_mask!
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5],
+        backend_args=backend_args),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+tta_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(
+        type='TestTimeAug',
+        transforms=[[
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.,
+                flip_ratio_bev_vertical=0.)
+        ], [dict(type='Pack3DDetInputs', keys=['points'])]])
+]
+
+# train on area 1, 2, 3, 4, 6
+# test on area 5
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_files=[f's3dis_infos_Area_{i}.pkl' for i in train_area],
+        metainfo=metainfo,
+        data_prefix=data_prefix,
+        pipeline=train_pipeline,
+        modality=input_modality,
+        ignore_index=len(class_names),
+        scene_idxs=[
+            f'seg_info/Area_{i}_resampled_scene_idxs.npy' for i in train_area
+        ],
+        test_mode=False,
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_files=f's3dis_infos_Area_{test_area}.pkl',
+        metainfo=metainfo,
+        data_prefix=data_prefix,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        ignore_index=len(class_names),
+        scene_idxs=f'seg_info/Area_{test_area}_resampled_scene_idxs.npy',
+        test_mode=True,
+        backend_args=backend_args))
+val_dataloader = test_dataloader
+
+val_evaluator = dict(type='SegMetric')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+tta_model = dict(type='Seg3DTTAModel')

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

@@ -0,0 +1,141 @@
+# dataset settings
+dataset_type = 'ScanNetDataset'
+data_root = 'data/scannet/'
+
+metainfo = dict(
+    classes=('cabinet', 'bed', 'chair', 'sofa', 'table', 'door', 'window',
+             'bookshelf', 'picture', 'counter', 'desk', 'curtain',
+             'refrigerator', 'showercurtrain', 'toilet', 'sink', 'bathtub',
+             'garbagebin'))
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/scannet/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_mask_3d=True,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='GlobalAlignment', rotation_axis=2),
+    dict(type='PointSegClassMapping'),
+    dict(type='PointSample', num_points=40000),
+    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=[1.0, 1.0],
+        shift_height=True),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'points', 'gt_bboxes_3d', 'gt_labels_3d', 'pts_semantic_mask',
+            'pts_instance_mask'
+        ])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2],
+        backend_args=backend_args),
+    dict(type='GlobalAlignment', rotation_axis=2),
+    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=40000),
+        ]),
+    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=5,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='scannet_infos_train.pkl',
+            pipeline=train_pipeline,
+            filter_empty_gt=False,
+            metainfo=metainfo,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='Depth',
+            backend_args=backend_args)))
+
+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='scannet_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Depth',
+        backend_args=backend_args))
+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='scannet_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Depth',
+        backend_args=backend_args))
+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/scannet-seg.py

@@ -0,0 +1,164 @@
+# For ScanNet seg we usually do 20-class segmentation
+class_names = ('wall', 'floor', 'cabinet', 'bed', 'chair', 'sofa', 'table',
+               'door', 'window', 'bookshelf', 'picture', 'counter', 'desk',
+               'curtain', 'refrigerator', 'showercurtrain', 'toilet', 'sink',
+               'bathtub', 'otherfurniture')
+metainfo = dict(classes=class_names)
+dataset_type = 'ScanNetSegDataset'
+data_root = 'data/scannet/'
+input_modality = dict(use_lidar=True, use_camera=False)
+data_prefix = dict(
+    pts='points',
+    pts_instance_mask='instance_mask',
+    pts_semantic_mask='semantic_mask')
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/scannet/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+num_points = 8192
+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],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='IndoorPatchPointSample',
+        num_points=num_points,
+        block_size=1.5,
+        ignore_index=len(class_names),
+        use_normalized_coord=False,
+        enlarge_size=0.2,
+        min_unique_num=None),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
+]
+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],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='NormalizePointsColor', color_mean=None),
+    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)
+# we need to load gt seg_mask!
+eval_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5],
+        backend_args=backend_args),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+tta_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=False,
+        use_color=True,
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4, 5],
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_mask_3d=False,
+        with_seg_3d=True,
+        backend_args=backend_args),
+    dict(type='NormalizePointsColor', color_mean=None),
+    dict(
+        type='TestTimeAug',
+        transforms=[[
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.,
+                flip_ratio_bev_vertical=0.)
+        ], [dict(type='Pack3DDetInputs', keys=['points'])]])
+]
+
+train_dataloader = dict(
+    batch_size=8,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='scannet_infos_train.pkl',
+        metainfo=metainfo,
+        data_prefix=data_prefix,
+        pipeline=train_pipeline,
+        modality=input_modality,
+        ignore_index=len(class_names),
+        scene_idxs=data_root + 'seg_info/train_resampled_scene_idxs.npy',
+        test_mode=False,
+        backend_args=backend_args))
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='scannet_infos_val.pkl',
+        metainfo=metainfo,
+        data_prefix=data_prefix,
+        pipeline=test_pipeline,
+        modality=input_modality,
+        ignore_index=len(class_names),
+        test_mode=True,
+        backend_args=backend_args))
+val_dataloader = test_dataloader
+
+val_evaluator = dict(type='SegMetric')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+tta_model = dict(type='Seg3DTTAModel')

configs/_base_/datasets/semantickitti.py

@@ -0,0 +1,224 @@
+# For SemanticKitti we usually do 19-class segmentation.
+# For labels_map we follow the uniform format of MMDetection & MMSegmentation
+# i.e. we consider the unlabeled class as the last one, which is different
+# from the original implementation of some methods e.g. Cylinder3D.
+dataset_type = 'SemanticKittiDataset'
+data_root = 'data/semantickitti/'
+class_names = [
+    'car', 'bicycle', 'motorcycle', 'truck', 'bus', 'person', 'bicyclist',
+    'motorcyclist', 'road', 'parking', 'sidewalk', 'other-ground', 'building',
+    'fence', 'vegetation', 'trunck', 'terrian', 'pole', 'traffic-sign'
+]
+labels_map = {
+    0: 19,  # "unlabeled"
+    1: 19,  # "outlier" mapped to "unlabeled" --------------mapped
+    10: 0,  # "car"
+    11: 1,  # "bicycle"
+    13: 4,  # "bus" mapped to "other-vehicle" --------------mapped
+    15: 2,  # "motorcycle"
+    16: 4,  # "on-rails" mapped to "other-vehicle" ---------mapped
+    18: 3,  # "truck"
+    20: 4,  # "other-vehicle"
+    30: 5,  # "person"
+    31: 6,  # "bicyclist"
+    32: 7,  # "motorcyclist"
+    40: 8,  # "road"
+    44: 9,  # "parking"
+    48: 10,  # "sidewalk"
+    49: 11,  # "other-ground"
+    50: 12,  # "building"
+    51: 13,  # "fence"
+    52: 19,  # "other-structure" mapped to "unlabeled" ------mapped
+    60: 8,  # "lane-marking" to "road" ---------------------mapped
+    70: 14,  # "vegetation"
+    71: 15,  # "trunk"
+    72: 16,  # "terrain"
+    80: 17,  # "pole"
+    81: 18,  # "traffic-sign"
+    99: 19,  # "other-object" to "unlabeled" ----------------mapped
+    252: 0,  # "moving-car" to "car" ------------------------mapped
+    253: 6,  # "moving-bicyclist" to "bicyclist" ------------mapped
+    254: 5,  # "moving-person" to "person" ------------------mapped
+    255: 7,  # "moving-motorcyclist" to "motorcyclist" ------mapped
+    256: 4,  # "moving-on-rails" mapped to "other-vehic------mapped
+    257: 4,  # "moving-bus" mapped to "other-vehicle" -------mapped
+    258: 3,  # "moving-truck" to "truck" --------------------mapped
+    259: 4  # "moving-other"-vehicle to "other-vehicle"-----mapped
+}
+
+metainfo = dict(
+    classes=class_names, seg_label_mapping=labels_map, max_label=259)
+
+input_modality = dict(use_lidar=True, use_camera=False)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/semantickitti/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05],
+        translation_std=[0.1, 0.1, 0.1],
+    ),
+    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(type='Pack3DDetInputs', keys=['points', 'pts_semantic_mask'])
+]
+# 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=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+tta_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=4,
+        use_dim=4,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox_3d=False,
+        with_label_3d=False,
+        with_seg_3d=True,
+        seg_3d_dtype='np.int32',
+        seg_offset=2**16,
+        dataset_type='semantickitti',
+        backend_args=backend_args),
+    dict(type='PointSegClassMapping'),
+    dict(
+        type='TestTimeAug',
+        transforms=[[
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.,
+                flip_ratio_bev_vertical=0.),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=0.,
+                flip_ratio_bev_vertical=1.),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=1.,
+                flip_ratio_bev_vertical=0.),
+            dict(
+                type='RandomFlip3D',
+                sync_2d=False,
+                flip_ratio_bev_horizontal=1.,
+                flip_ratio_bev_vertical=1.)
+        ],
+                    [
+                        dict(
+                            type='GlobalRotScaleTrans',
+                            rot_range=[pcd_rotate_range, pcd_rotate_range],
+                            scale_ratio_range=[
+                                pcd_scale_factor, pcd_scale_factor
+                            ],
+                            translation_std=[0, 0, 0])
+                        for pcd_rotate_range in [-0.78539816, 0.0, 0.78539816]
+                        for pcd_scale_factor in [0.95, 1.0, 1.05]
+                    ], [dict(type='Pack3DDetInputs', keys=['points'])]])
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=4,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='semantickitti_infos_train.pkl',
+        pipeline=train_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        ignore_index=19,
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='semantickitti_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        modality=input_modality,
+        ignore_index=19,
+        test_mode=True,
+        backend_args=backend_args))
+
+val_dataloader = test_dataloader
+
+val_evaluator = dict(type='SegMetric')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')
+
+tta_model = dict(type='Seg3DTTAModel')

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

@@ -0,0 +1,126 @@
+dataset_type = 'SUNRGBDDataset'
+data_root = 'data/sunrgbd/'
+class_names = ('bed', 'table', 'sofa', 'chair', 'toilet', 'desk', 'dresser',
+               'night_stand', 'bookshelf', 'bathtub')
+
+metainfo = dict(classes=class_names)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/sunrgbd/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2],
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D'),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+    ),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.523599, 0.523599],
+        scale_ratio_range=[0.85, 1.15],
+        shift_height=True),
+    dict(type='PointSample', num_points=20000),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='DEPTH',
+        shift_height=True,
+        load_dim=6,
+        use_dim=[0, 1, 2],
+        backend_args=backend_args),
+    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,
+            ),
+            dict(type='PointSample', num_points=20000)
+        ]),
+    dict(type='Pack3DDetInputs', keys=['points'])
+]
+
+train_dataloader = dict(
+    batch_size=16,
+    num_workers=4,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=5,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='sunrgbd_infos_train.pkl',
+            pipeline=train_pipeline,
+            filter_empty_gt=False,
+            metainfo=metainfo,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='Depth',
+            backend_args=backend_args)))
+
+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='sunrgbd_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Depth',
+        backend_args=backend_args))
+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='sunrgbd_infos_val.pkl',
+        pipeline=test_pipeline,
+        metainfo=metainfo,
+        test_mode=True,
+        box_type_3d='Depth',
+        backend_args=backend_args))
+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/waymoD3-fov-mono3d-3class.py

@@ -0,0 +1,184 @@
+# dataset settings
+# D3 in the config name means the whole dataset is divided into 3 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+metainfo = dict(classes=class_names)
+input_modality = dict(use_lidar=False, use_camera=True)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    # base shape (1248, 832), scale (0.95, 1.05)
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(0.95, 1.05),
+        # ratio_range=(1., 1.),
+        interpolation='nearest',
+        keep_ratio=True,
+    ),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers_2d', 'depths'
+        ]),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        interpolation='nearest',
+        keep_ratio=True),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=[
+            'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
+            'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
+        ]),
+]
+# 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='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        interpolation='nearest',
+        keep_ratio=True),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=[
+            'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
+            'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
+        ]),
+]
+
+train_dataloader = dict(
+    batch_size=3,
+    num_workers=3,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_train.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        metainfo=metainfo,
+        cam_sync_instances=True,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='fov_image_based',
+        # load one frame every three frames
+        load_interval=3,
+        backend_args=backend_args))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        cam_sync_instances=True,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='fov_image_based',
+        load_eval_anns=False,
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        cam_sync_instances=True,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='fov_image_based',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='WaymoMetric',
+    waymo_bin_file='./data/waymo/waymo_format/fov_gt.bin',
+    metric='LET_mAP',
+    load_type='fov_image_based',
+    result_prefix='./pgd_fov_pred')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/waymoD3-mv-mono3d-3class.py

@@ -0,0 +1,191 @@
+# dataset settings
+# D3 in the config name means the whole dataset is divided into 3 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+metainfo = dict(classes=class_names)
+input_modality = dict(use_lidar=False, use_camera=True)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    # base shape (1248, 832), scale (0.95, 1.05)
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        # ratio_range=(1., 1.),
+        ratio_range=(0.95, 1.05),
+        interpolation='nearest',
+        keep_ratio=True,
+    ),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers_2d', 'depths'
+        ]),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='Resize3D',
+        scale_factor=0.65,
+        interpolation='nearest',
+        keep_ratio=True),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=[
+            'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
+            'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
+        ]),
+]
+# 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='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='Resize3D',
+        scale_factor=0.65,
+        interpolation='nearest',
+        keep_ratio=True),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=[
+            'box_type_3d', 'img_shape', 'cam2img', 'scale_factor',
+            'sample_idx', 'context_name', 'timestamp', 'lidar2cam'
+        ]),
+]
+
+train_dataloader = dict(
+    batch_size=3,
+    num_workers=3,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_train.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        metainfo=metainfo,
+        cam_sync_instances=True,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='mv_image_based',
+        # load one frame every three frames
+        load_interval=3,
+        backend_args=backend_args))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=0,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        cam_sync_instances=True,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='mv_image_based',
+        # load_eval_anns=False,
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=0,
+    persistent_workers=False,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        cam_sync_instances=True,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='mv_image_based',
+        load_eval_anns=False,
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='WaymoMetric',
+    waymo_bin_file='./data/waymo/waymo_format/cam_gt.bin',
+    metric='LET_mAP',
+    load_type='mv_image_based',
+    result_prefix='./pgd_mv_pred',
+    nms_cfg=dict(
+        use_rotate_nms=True,
+        nms_across_levels=False,
+        nms_pre=500,
+        nms_thr=0.05,
+        score_thr=0.001,
+        min_bbox_size=0,
+        max_per_frame=100))
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/waymoD5-3d-3class.py

@@ -0,0 +1,178 @@
+# dataset settings
+# D5 in the config name means the whole dataset is divided into 5 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+data_root = 'data/waymo/kitti_format/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+class_names = ['Car', 'Pedestrian', 'Cyclist']
+metainfo = dict(classes=class_names)
+
+point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'waymo_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(
+        filter_by_difficulty=[-1],
+        filter_by_min_points=dict(Car=5, Pedestrian=10, Cyclist=10)),
+    classes=class_names,
+    sample_groups=dict(Car=15, Pedestrian=10, Cyclist=10),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4],
+        backend_args=backend_args),
+    backend_args=backend_args)
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    # dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points'],
+        meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
+]
+# 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=6,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points'],
+        meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='waymo_infos_train.pkl',
+            data_prefix=dict(
+                pts='training/velodyne', sweeps='training/velodyne'),
+            pipeline=train_pipeline,
+            modality=input_modality,
+            test_mode=False,
+            metainfo=metainfo,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR',
+            # load one frame every five frames
+            load_interval=5,
+            backend_args=backend_args)))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='WaymoMetric', waymo_bin_file='./data/waymo/waymo_format/gt.bin')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/waymoD5-3d-car.py

@@ -0,0 +1,173 @@
+# dataset settings
+# D5 in the config name means the whole dataset is divided into 5 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+class_names = ['Car']
+metainfo = dict(classes=class_names)
+
+point_cloud_range = [-74.88, -74.88, -2, 74.88, 74.88, 4]
+input_modality = dict(use_lidar=True, use_camera=False)
+db_sampler = dict(
+    data_root=data_root,
+    info_path=data_root + 'waymo_dbinfos_train.pkl',
+    rate=1.0,
+    prepare=dict(filter_by_difficulty=[-1], filter_by_min_points=dict(Car=5)),
+    classes=class_names,
+    sample_groups=dict(Car=15),
+    points_loader=dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=[0, 1, 2, 3, 4],
+        backend_args=backend_args),
+    backend_args=backend_args)
+
+train_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
+    dict(type='ObjectSample', db_sampler=db_sampler),
+    dict(
+        type='RandomFlip3D',
+        sync_2d=False,
+        flip_ratio_bev_horizontal=0.5,
+        flip_ratio_bev_vertical=0.5),
+    dict(
+        type='GlobalRotScaleTrans',
+        rot_range=[-0.78539816, 0.78539816],
+        scale_ratio_range=[0.95, 1.05]),
+    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='PointShuffle'),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points'],
+        meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
+]
+test_pipeline = [
+    dict(
+        type='LoadPointsFromFile',
+        coord_type='LIDAR',
+        load_dim=6,
+        use_dim=5,
+        backend_args=backend_args),
+    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'),
+            dict(
+                type='PointsRangeFilter', point_cloud_range=point_cloud_range)
+        ]),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['points'],
+        meta_keys=['box_type_3d', 'sample_idx', 'context_name', 'timestamp'])
+]
+# 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=6,
+        use_dim=5,
+        backend_args=backend_args),
+    dict(type='Pack3DDetInputs', keys=['points']),
+]
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type='RepeatDataset',
+        times=2,
+        dataset=dict(
+            type=dataset_type,
+            data_root=data_root,
+            ann_file='waymo_infos_train.pkl',
+            data_prefix=dict(
+                pts='training/velodyne', sweeps='training/velodyne'),
+            pipeline=train_pipeline,
+            modality=input_modality,
+            test_mode=False,
+            metainfo=metainfo,
+            # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+            # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+            box_type_3d='LiDAR',
+            # load one frame every five frames
+            load_interval=5,
+            backend_args=backend_args)))
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(pts='training/velodyne', sweeps='training/velodyne'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='LiDAR',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='WaymoMetric', waymo_bin_file='./data/waymo/waymo_format/gt.bin')
+test_evaluator = val_evaluator
+
+vis_backends = [dict(type='LocalVisBackend')]
+visualizer = dict(
+    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')

configs/_base_/datasets/waymoD5-fov-mono3d-3class.py

@@ -0,0 +1,163 @@
+# dataset settings
+# D3 in the config name means the whole dataset is divided into 3 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+class_names = ['Car', 'Pedestrian', 'Cyclist']
+input_modality = dict(use_lidar=False, use_camera=True)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    # base shape (1248, 832), scale (0.95, 1.05)
+    dict(
+        type='RandomResize3D',
+        scale=(1284, 832),
+        ratio_range=(0.95, 1.05),
+        keep_ratio=True,
+    ),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers_2d', 'depths'
+        ]),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        keep_ratio=True),
+    dict(type='Pack3DDetInputs', keys=['img']),
+]
+# 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='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        keep_ratio=True),
+    dict(type='Pack3DDetInputs', keys=['img']),
+]
+
+metainfo = dict(CLASSES=class_names)
+
+train_dataloader = dict(
+    batch_size=3,
+    num_workers=3,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_train.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        metainfo=metainfo,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='fov_image_based',
+        # load one frame every three frames
+        load_interval=5,
+        backend_args=backend_args))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='fov_image_based',
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='fov_image_based',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='WaymoMetric',
+    ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl',
+    waymo_bin_file='./data/waymo/waymo_format/fov_gt.bin',
+    data_root='./data/waymo/waymo_format',
+    metric='LET_mAP',
+    load_type='fov_image_based',
+    backend_args=backend_args)
+test_evaluator = val_evaluator

configs/_base_/datasets/waymoD5-mv-mono3d-3class.py

@@ -0,0 +1,163 @@
+# dataset settings
+# D3 in the config name means the whole dataset is divided into 3 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+class_names = ['Car', 'Pedestrian', 'Cyclist']
+input_modality = dict(use_lidar=False, use_camera=True)
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+train_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    # base shape (1248, 832), scale (0.95, 1.05)
+    dict(
+        type='RandomResize3D',
+        scale=(1284, 832),
+        ratio_range=(0.95, 1.05),
+        keep_ratio=True,
+    ),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5),
+    dict(
+        type='Pack3DDetInputs',
+        keys=[
+            'img', 'gt_bboxes', 'gt_bboxes_labels', 'gt_bboxes_3d',
+            'gt_labels_3d', 'centers_2d', 'depths'
+        ]),
+]
+
+test_pipeline = [
+    dict(type='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        keep_ratio=True),
+    dict(type='Pack3DDetInputs', keys=['img']),
+]
+# 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='LoadImageFromFileMono3D', backend_args=backend_args),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        keep_ratio=True),
+    dict(type='Pack3DDetInputs', keys=['img']),
+]
+
+metainfo = dict(classes=class_names)
+
+train_dataloader = dict(
+    batch_size=3,
+    num_workers=3,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_train.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        metainfo=metainfo,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='mv_image_based',
+        # load one frame every three frames
+        load_interval=5,
+        backend_args=backend_args))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='mv_image_based',
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        ann_file='waymo_infos_val.pkl',
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
+        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
+        box_type_3d='Camera',
+        load_type='mv_image_based',
+        backend_args=backend_args))
+
+val_evaluator = dict(
+    type='WaymoMetric',
+    ann_file='./data/waymo/kitti_format/waymo_infos_val.pkl',
+    waymo_bin_file='./data/waymo/waymo_format/cam_gt.bin',
+    data_root='./data/waymo/waymo_format',
+    metric='LET_mAP',
+    load_type='mv_image_based',
+    backend_args=backend_args)
+test_evaluator = val_evaluator

configs/_base_/datasets/waymoD5-mv3d-3class.py

@@ -0,0 +1,178 @@
+# dataset settings
+# D5 in the config name means the whole dataset is divided into 5 folds
+# We only use one fold for efficient experiments
+dataset_type = 'WaymoDataset'
+data_root = 'data/waymo/kitti_format/'
+
+# Example to use different file client
+# Method 1: simply set the data root and let the file I/O module
+# automatically infer from prefix (not support LMDB and Memcache yet)
+
+# data_root = 's3://openmmlab/datasets/detection3d/waymo/kitti_format/'
+
+# Method 2: Use backend_args, file_client_args in versions before 1.1.0
+# backend_args = dict(
+#     backend='petrel',
+#     path_mapping=dict({
+#         './data/': 's3://openmmlab/datasets/detection3d/',
+#          'data/': 's3://openmmlab/datasets/detection3d/'
+#      }))
+backend_args = None
+
+class_names = ['Pedestrian', 'Cyclist', 'Car']
+input_modality = dict(use_lidar=False, use_camera=True)
+point_cloud_range = [-35.0, -75.0, -2, 75.0, 75.0, 4]
+
+train_transforms = [
+    dict(type='PhotoMetricDistortion3D'),
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(0.95, 1.05),
+        keep_ratio=True),
+    dict(type='RandomCrop3D', crop_size=(1080, 720)),
+    dict(type='RandomFlip3D', flip_ratio_bev_horizontal=0.5, flip_box3d=False),
+]
+
+train_pipeline = [
+    dict(
+        type='LoadMultiViewImageFromFiles',
+        to_float32=True,
+        backend_args=backend_args),
+    dict(
+        type='LoadAnnotations3D',
+        with_bbox=True,
+        with_label=True,
+        with_attr_label=False,
+        with_bbox_3d=True,
+        with_label_3d=True,
+        with_bbox_depth=True),
+    dict(type='MultiViewWrapper', transforms=train_transforms),
+    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
+    dict(type='ObjectNameFilter', classes=class_names),
+    dict(
+        type='Pack3DDetInputs', keys=[
+            'img',
+            'gt_bboxes_3d',
+            'gt_labels_3d',
+        ]),
+]
+test_transforms = [
+    dict(
+        type='RandomResize3D',
+        scale=(1248, 832),
+        ratio_range=(1., 1.),
+        keep_ratio=True)
+]
+test_pipeline = [
+    dict(
+        type='LoadMultiViewImageFromFiles',
+        to_float32=True,
+        backend_args=backend_args),
+    dict(type='MultiViewWrapper', transforms=test_transforms),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=[
+            'box_type_3d', 'img_shape', 'ori_cam2img', 'scale_factor',
+            'sample_idx', 'context_name', 'timestamp', 'lidar2cam',
+            'num_ref_frames', 'num_views'
+        ])
+]
+# 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='LoadMultiViewImageFromFiles',
+        to_float32=True,
+        backend_args=backend_args),
+    dict(type='MultiViewWrapper', transforms=test_transforms),
+    dict(
+        type='Pack3DDetInputs',
+        keys=['img'],
+        meta_keys=[
+            'box_type_3d', 'img_shape', 'ori_cam2img', 'scale_factor',
+            'sample_idx', 'context_name', 'timestamp', 'lidar2cam',
+            'num_ref_frames', 'num_views'
+        ])
+]
+metainfo = dict(classes=class_names)
+
+train_dataloader = dict(
+    batch_size=2,
+    num_workers=2,
+    persistent_workers=True,
+    sampler=dict(type='DefaultSampler', shuffle=True),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_train.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=train_pipeline,
+        modality=input_modality,
+        test_mode=False,
+        cam_sync_instances=True,
+        metainfo=metainfo,
+        box_type_3d='Lidar',
+        load_interval=5,
+        backend_args=backend_args))
+
+val_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_val.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=eval_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='Lidar',
+        backend_args=backend_args))
+
+test_dataloader = dict(
+    batch_size=1,
+    num_workers=1,
+    persistent_workers=True,
+    drop_last=False,
+    sampler=dict(type='DefaultSampler', shuffle=False),
+    dataset=dict(
+        type=dataset_type,
+        data_root=data_root,
+        ann_file='waymo_infos_val.pkl',
+        data_prefix=dict(
+            pts='training/velodyne',
+            CAM_FRONT='training/image_0',
+            CAM_FRONT_LEFT='training/image_1',
+            CAM_FRONT_RIGHT='training/image_2',
+            CAM_SIDE_LEFT='training/image_3',
+            CAM_SIDE_RIGHT='training/image_4'),
+        pipeline=test_pipeline,
+        modality=input_modality,
+        test_mode=True,
+        metainfo=metainfo,
+        box_type_3d='Lidar',
+        backend_args=backend_args))
+val_evaluator = dict(
+    type='WaymoMetric',
+    waymo_bin_file='./data/waymo/waymo_format/cam_gt.bin',
+    metric='LET_mAP')
+
+test_evaluator = val_evaluator

configs/_base_/default_runtime.py

@@ -0,0 +1,23 @@
+default_scope = 'mmdet3d'
+
+default_hooks = dict(
+    timer=dict(type='IterTimerHook'),
+    logger=dict(type='LoggerHook', interval=50),
+    param_scheduler=dict(type='ParamSchedulerHook'),
+    checkpoint=dict(type='CheckpointHook', interval=-1),
+    sampler_seed=dict(type='DistSamplerSeedHook'),
+    visualization=dict(type='Det3DVisualizationHook'))
+
+env_cfg = dict(
+    cudnn_benchmark=False,
+    mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
+    dist_cfg=dict(backend='nccl'),
+)
+
+log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
+
+log_level = 'INFO'
+load_from = None
+resume = False
+
+# TODO: support auto scaling lr

configs/_base_/models/3dssd.py

@@ -0,0 +1,76 @@
+model = dict(
+    type='SSD3DNet',
+    data_preprocessor=dict(type='Det3DDataPreprocessor'),
+    backbone=dict(
+        type='PointNet2SAMSG',
+        in_channels=4,
+        num_points=(4096, 512, (256, 256)),
+        radii=((0.2, 0.4, 0.8), (0.4, 0.8, 1.6), (1.6, 3.2, 4.8)),
+        num_samples=((32, 32, 64), (32, 32, 64), (32, 32, 32)),
+        sa_channels=(((16, 16, 32), (16, 16, 32), (32, 32, 64)),
+                     ((64, 64, 128), (64, 64, 128), (64, 96, 128)),
+                     ((128, 128, 256), (128, 192, 256), (128, 256, 256))),
+        aggregation_channels=(64, 128, 256),
+        fps_mods=(('D-FPS'), ('FS'), ('F-FPS', 'D-FPS')),
+        fps_sample_range_lists=((-1), (-1), (512, -1)),
+        norm_cfg=dict(type='BN2d', eps=1e-3, momentum=0.1),
+        sa_cfg=dict(
+            type='PointSAModuleMSG',
+            pool_mod='max',
+            use_xyz=True,
+            normalize_xyz=False)),
+    bbox_head=dict(
+        type='SSD3DHead',
+        vote_module_cfg=dict(
+            in_channels=256,
+            num_points=256,
+            gt_per_seed=1,
+            conv_channels=(128, ),
+            conv_cfg=dict(type='Conv1d'),
+            norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.1),
+            with_res_feat=False,
+            vote_xyz_range=(3.0, 3.0, 2.0)),
+        vote_aggregation_cfg=dict(
+            type='PointSAModuleMSG',
+            num_point=256,
+            radii=(4.8, 6.4),
+            sample_nums=(16, 32),
+            mlp_channels=((256, 256, 256, 512), (256, 256, 512, 1024)),
+            norm_cfg=dict(type='BN2d', eps=1e-3, momentum=0.1),
+            use_xyz=True,
+            normalize_xyz=False,
+            bias=True),
+        pred_layer_cfg=dict(
+            in_channels=1536,
+            shared_conv_channels=(512, 128),
+            cls_conv_channels=(128, ),
+            reg_conv_channels=(128, ),
+            conv_cfg=dict(type='Conv1d'),
+            norm_cfg=dict(type='BN1d', eps=1e-3, momentum=0.1),
+            bias=True),
+        objectness_loss=dict(
+            type='mmdet.CrossEntropyLoss',
+            use_sigmoid=True,
+            reduction='sum',
+            loss_weight=1.0),
+        center_loss=dict(
+            type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0),
+        dir_class_loss=dict(
+            type='mmdet.CrossEntropyLoss', reduction='sum', loss_weight=1.0),
+        dir_res_loss=dict(
+            type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0),
+        size_res_loss=dict(
+            type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0),
+        corner_loss=dict(
+            type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0),
+        vote_loss=dict(
+            type='mmdet.SmoothL1Loss', reduction='sum', loss_weight=1.0)),
+    # model training and testing settings
+    train_cfg=dict(
+        sample_mode='spec', pos_distance_thr=10.0, expand_dims_length=0.05),
+    test_cfg=dict(
+        nms_cfg=dict(type='nms', iou_thr=0.1),
+        sample_mode='spec',
+        score_thr=0.0,
+        per_class_proposal=True,
+        max_output_num=100))

configs/_base_/models/cascade-mask-rcnn_r50_fpn.py

@@ -0,0 +1,199 @@
+# model settings
+model = dict(
+    type='CascadeRCNN',
+    pretrained='torchvision://resnet50',
+    _scope_='mmdet',
+    backbone=dict(
+        type='ResNet',
+        depth=50,
+        num_stages=4,
+        out_indices=(0, 1, 2, 3),
+        frozen_stages=1,
+        norm_cfg=dict(type='BN', requires_grad=True),
+        norm_eval=True,
+        style='pytorch'),
+    neck=dict(
+        type='FPN',
+        in_channels=[256, 512, 1024, 2048],
+        out_channels=256,
+        num_outs=5),
+    rpn_head=dict(
+        type='RPNHead',
+        in_channels=256,
+        feat_channels=256,
+        anchor_generator=dict(
+            type='AnchorGenerator',
+            scales=[8],
+            ratios=[0.5, 1.0, 2.0],
+            strides=[4, 8, 16, 32, 64]),
+        bbox_coder=dict(
+            type='DeltaXYWHBBoxCoder',
+            target_means=[.0, .0, .0, .0],
+            target_stds=[1.0, 1.0, 1.0, 1.0]),
+        loss_cls=dict(
+            type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0),
+        loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
+    roi_head=dict(
+        type='CascadeRoIHead',
+        num_stages=3,
+        stage_loss_weights=[1, 0.5, 0.25],
+        bbox_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        bbox_head=[
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.1, 0.1, 0.2, 0.2]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.05, 0.05, 0.1, 0.1]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0,
+                               loss_weight=1.0)),
+            dict(
+                type='Shared2FCBBoxHead',
+                in_channels=256,
+                fc_out_channels=1024,
+                roi_feat_size=7,
+                num_classes=80,
+                bbox_coder=dict(
+                    type='DeltaXYWHBBoxCoder',
+                    target_means=[0., 0., 0., 0.],
+                    target_stds=[0.033, 0.033, 0.067, 0.067]),
+                reg_class_agnostic=True,
+                loss_cls=dict(
+                    type='CrossEntropyLoss',
+                    use_sigmoid=False,
+                    loss_weight=1.0),
+                loss_bbox=dict(type='SmoothL1Loss', beta=1.0, loss_weight=1.0))
+        ],
+        mask_roi_extractor=dict(
+            type='SingleRoIExtractor',
+            roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
+            out_channels=256,
+            featmap_strides=[4, 8, 16, 32]),
+        mask_head=dict(
+            type='FCNMaskHead',
+            num_convs=4,
+            in_channels=256,
+            conv_out_channels=256,
+            num_classes=80,
+            loss_mask=dict(
+                type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
+    # model training and testing settings
+    train_cfg=dict(
+        rpn=dict(
+            assigner=dict(
+                type='MaxIoUAssigner',
+                pos_iou_thr=0.7,
+                neg_iou_thr=0.3,
+                min_pos_iou=0.3,
+                match_low_quality=True,
+                ignore_iof_thr=-1),
+            sampler=dict(
+                type='RandomSampler',
+                num=256,
+                pos_fraction=0.5,
+                neg_pos_ub=-1,
+                add_gt_as_proposals=False),
+            allowed_border=0,
+            pos_weight=-1,
+            debug=False),
+        rpn_proposal=dict(
+            nms_pre=2000,
+            nms_post=2000,
+            max_per_img=2000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=[
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.5,
+                    neg_iou_thr=0.5,
+                    min_pos_iou=0.5,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.6,
+                    neg_iou_thr=0.6,
+                    min_pos_iou=0.6,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False),
+            dict(
+                assigner=dict(
+                    type='MaxIoUAssigner',
+                    pos_iou_thr=0.7,
+                    neg_iou_thr=0.7,
+                    min_pos_iou=0.7,
+                    match_low_quality=False,
+                    ignore_iof_thr=-1),
+                sampler=dict(
+                    type='RandomSampler',
+                    num=512,
+                    pos_fraction=0.25,
+                    neg_pos_ub=-1,
+                    add_gt_as_proposals=True),
+                mask_size=28,
+                pos_weight=-1,
+                debug=False)
+        ]),
+    test_cfg=dict(
+        rpn=dict(
+            nms_pre=1000,
+            nms_post=1000,
+            max_per_img=1000,
+            nms=dict(type='nms', iou_threshold=0.7),
+            min_bbox_size=0),
+        rcnn=dict(
+            score_thr=0.05,
+            nms=dict(type='nms', iou_threshold=0.5),
+            max_per_img=100,
+            mask_thr_binary=0.5)))