fix_mmdetection

BEVfusion_fix/fix_weight.py

+import torch
+
+# 指向你刚刚发给我的纯 LiDAR 官方权重
+ckpt_path = 'pth/bevfusion_lidar_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d-2628f933.pth'
+ckpt = torch.load(ckpt_path, map_location='cpu')
+state_dict = ckpt['state_dict']
+
+fixed_count = 0
+for key in list(state_dict.keys()):
+    # 修复 3D 稀疏卷积维度 (16,3,3,3,16) -> (3,3,3,16,16)
+    if 'pts_middle_encoder' in key and state_dict[key].dim() == 5:
+        state_dict[key] = state_dict[key].permute(1, 2, 3, 4, 0).contiguous()
+        fixed_count += 1
+
+ckpt['state_dict'] = state_dict
+fixed_path = ckpt_path.replace('.pth', '_fixed.pth')
+torch.save(ckpt, fixed_path)
+print(f'✅ 纯 LiDAR 权重修复完成！已保存至 {fixed_path}，共处理 {fixed_count} 个层。')
\ No newline at end of file

BEVfusion_fix/pip_过程.txt

+pip  uninstall  megatron-core
+pip uninstall  vllm
+pip uninstall  mmcv-full
+pip install OpenMPI 
+pip install mpi4py   Pillow  mmengine
+pip install mmdet
+pip install nuscenes-devkit
+
+pip install opencv-python==4.9.0.80
+pip install mmcv-2.1.0+das.opt1.dtk24043-cp310-cp310-manylinux_2_28_x86_64.whl
+
+pip install -v -e . --no-build-isolation
+pip install -e . --no-build-isolation
+
+  Created wheel for mmdet3d: filename=mmdet3d-0.0.0-0.editable-cp310-cp310-linux_x86_64.whl size=6984 sha256=97be2853385856a1d76c007755805580a7380dbf324a1d24acb5b81e453c8732
+
+  python3 setup.py build_ext -v --inplace
\ No newline at end of file

BEVfusion_fix/setup.cfg

+[yapf]
+BASED_ON_STYLE = pep8
+BLANK_LINE_BEFORE_NESTED_CLASS_OR_DEF = true
+SPLIT_BEFORE_EXPRESSION_AFTER_OPENING_PAREN = true
+
+[isort]
+line_length = 79
+multi_line_output = 0
+extra_standard_library = setuptools
+known_first_party = mmdet3d
+known_third_party = cv2,imageio,indoor3d_util,load_scannet_data,lyft_dataset_sdk,m2r,matplotlib,mmcv,mmdet,mmengine,nuimages,numba,numpy,nuscenes,pandas,plyfile,pycocotools,pyquaternion,pytest,pytorch_sphinx_theme,recommonmark,requests,scannet_utils,scipy,seaborn,shapely,skimage,sphinx,tensorflow,terminaltables,torch,trimesh,ts,waymo_open_dataset
+no_lines_before = STDLIB,LOCALFOLDER
+default_section = THIRDPARTY
+
+[codespell]
+ignore-words-list = ans,refridgerator,crate,hist,formating,dout,wan,nd,fo,avod,AVOD,warmup
+
+[flake8]
+per-file-ignores = mmdet3d/configs/*:F401,F403,F405

BEVfusion_fix/setup.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os
+import platform
+import shutil
+import sys
+import warnings
+from os import path as osp
+from setuptools import find_packages, setup
+
+import torch
+from torch.utils.cpp_extension import (BuildExtension, CppExtension,
+                                       CUDAExtension)
+
+
+def readme():
+    with open('README.md', encoding='utf-8') as f:
+        content = f.read()
+    return content
+
+
+version_file = 'mmdet3d/version.py'
+
+
+def get_version():
+    with open(version_file, 'r') as f:
+        exec(compile(f.read(), version_file, 'exec'))
+    return locals()['__version__']
+
+
+def make_cuda_ext(name,
+                  module,
+                  sources,
+                  sources_cuda=[],
+                  extra_args=[],
+                  extra_include_path=[]):
+
+    define_macros = []
+    extra_compile_args = {'cxx': [] + extra_args}
+
+    if torch.cuda.is_available() or os.getenv('FORCE_CUDA', '0') == '1':
+        define_macros += [('WITH_CUDA', None)]
+        extension = CUDAExtension
+        extra_compile_args['nvcc'] = extra_args + [
+            '-D__CUDA_NO_HALF_OPERATORS__',
+            '-D__CUDA_NO_HALF_CONVERSIONS__',
+            '-D__CUDA_NO_HALF2_OPERATORS__',
+        ]
+        sources += sources_cuda
+    else:
+        print('Compiling {} without CUDA'.format(name))
+        extension = CppExtension
+        # raise EnvironmentError('CUDA is required to compile MMDetection!')
+
+    return extension(
+        name='{}.{}'.format(module, name),
+        sources=[os.path.join(*module.split('.'), p) for p in sources],
+        include_dirs=extra_include_path,
+        define_macros=define_macros,
+        extra_compile_args=extra_compile_args)
+
+
+def parse_requirements(fname='requirements.txt', with_version=True):
+    """Parse the package dependencies listed in a requirements file but strips
+    specific versioning information.
+
+    Args:
+        fname (str): path to requirements file
+        with_version (bool, default=False): if True include version specs
+
+    Returns:
+        list[str]: list of requirements items
+
+    CommandLine:
+        python -c "import setup; print(setup.parse_requirements())"
+    """
+    import re
+    import sys
+    from os.path import exists
+    require_fpath = fname
+
+    def parse_line(line):
+        """Parse information from a line in a requirements text file."""
+        if line.startswith('-r '):
+            # Allow specifying requirements in other files
+            target = line.split(' ')[1]
+            for info in parse_require_file(target):
+                yield info
+        else:
+            info = {'line': line}
+            if line.startswith('-e '):
+                info['package'] = line.split('#egg=')[1]
+            else:
+                # Remove versioning from the package
+                pat = '(' + '|'.join(['>=', '==', '>']) + ')'
+                parts = re.split(pat, line, maxsplit=1)
+                parts = [p.strip() for p in parts]
+
+                info['package'] = parts[0]
+                if len(parts) > 1:
+                    op, rest = parts[1:]
+                    if ';' in rest:
+                        # Handle platform specific dependencies
+                        # http://setuptools.readthedocs.io/en/latest/setuptools.html#declaring-platform-specific-dependencies
+                        version, platform_deps = map(str.strip,
+                                                     rest.split(';'))
+                        info['platform_deps'] = platform_deps
+                    else:
+                        version = rest  # NOQA
+                    info['version'] = (op, version)
+            yield info
+
+    def parse_require_file(fpath):
+        with open(fpath, 'r') as f:
+            for line in f.readlines():
+                line = line.strip()
+                if line and not line.startswith('#'):
+                    for info in parse_line(line):
+                        yield info
+
+    def gen_packages_items():
+        if exists(require_fpath):
+            for info in parse_require_file(require_fpath):
+                parts = [info['package']]
+                if with_version and 'version' in info:
+                    parts.extend(info['version'])
+                if not sys.version.startswith('3.4'):
+                    # apparently package_deps are broken in 3.4
+                    platform_deps = info.get('platform_deps')
+                    if platform_deps is not None:
+                        parts.append(';' + platform_deps)
+                item = ''.join(parts)
+                yield item
+
+    packages = list(gen_packages_items())
+    return packages
+
+
+def add_mim_extention():
+    """Add extra files that are required to support MIM into the package.
+
+    These files will be added by creating a symlink to the originals if the
+    package is installed in `editable` mode (e.g. pip install -e .), or by
+    copying from the originals otherwise.
+    """
+
+    # parse installment mode
+    if 'develop' in sys.argv:
+        # installed by `pip install -e .`
+        if platform.system() == 'Windows':
+            # set `copy` mode here since symlink fails on Windows.
+            mode = 'copy'
+        else:
+            mode = 'symlink'
+    elif 'sdist' in sys.argv or 'bdist_wheel' in sys.argv:
+        # installed by `pip install .`
+        # or create source distribution by `python setup.py sdist`
+        mode = 'copy'
+    else:
+        return
+
+    filenames = [
+        'tools', 'configs', 'demo', 'model-index.yml', 'dataset-index.yml'
+    ]
+    repo_path = osp.dirname(__file__)
+    mim_path = osp.join(repo_path, 'mmdet3d', '.mim')
+    os.makedirs(mim_path, exist_ok=True)
+
+    for filename in filenames:
+        if osp.exists(filename):
+            src_path = osp.join(repo_path, filename)
+            tar_path = osp.join(mim_path, filename)
+
+            if osp.isfile(tar_path) or osp.islink(tar_path):
+                os.remove(tar_path)
+            elif osp.isdir(tar_path):
+                shutil.rmtree(tar_path)
+
+            if mode == 'symlink':
+                src_relpath = osp.relpath(src_path, osp.dirname(tar_path))
+                os.symlink(src_relpath, tar_path)
+            elif mode == 'copy':
+                if osp.isfile(src_path):
+                    shutil.copyfile(src_path, tar_path)
+                elif osp.isdir(src_path):
+                    shutil.copytree(src_path, tar_path)
+                else:
+                    warnings.warn(f'Cannot copy file {src_path}.')
+            else:
+                raise ValueError(f'Invalid mode {mode}')
+
+
+if __name__ == '__main__':
+    add_mim_extention()
+    setup(
+        name='mmdet3d',
+        version=get_version(),
+        description=("OpenMMLab's next-generation platform"
+                     'for general 3D object detection.'),
+        long_description=readme(),
+        long_description_content_type='text/markdown',
+        author='MMDetection3D Contributors',
+        author_email='zwwdev@gmail.com',
+        keywords='computer vision, 3D object detection',
+        url='https://github.com/open-mmlab/mmdetection3d',
+        packages=find_packages(exclude=('configs', 'tools', 'demo')),
+        include_package_data=True,
+        classifiers=[
+            'Development Status :: 5 - Production/Stable',
+            'License :: OSI Approved :: Apache Software License',
+            'Operating System :: OS Independent',
+            'Programming Language :: Python :: 3',
+            'Programming Language :: Python :: 3.7',
+            'Programming Language :: Python :: 3.8',
+            'Programming Language :: Python :: 3.9',
+        ],
+        license='Apache License 2.0',
+        install_requires=parse_requirements('requirements/runtime.txt'),
+        extras_require={
+            'all': parse_requirements('requirements.txt'),
+            'tests': parse_requirements('requirements/tests.txt'),
+            'build': parse_requirements('requirements/build.txt'),
+            'optional': parse_requirements('requirements/optional.txt'),
+            'mim': parse_requirements('requirements/mminstall.txt'),
+        },
+        ext_modules=[],
+        cmdclass={'build_ext': BuildExtension},
+        zip_safe=False)

BEVfusion_fix/testmodel.py

+import torch
+from mmengine.config import Config
+from mmdet3d.registry import MODELS
+
+# 1. 唤醒 MMDetection3D 的全家桶注册表 (修复报错的这行极其关键！)
+from mmdet3d.utils import register_all_modules
+register_all_modules(init_default_scope=True)
+
+# 2. 显式导入 BEVFusion 项目，触发自定义算子和模块的注册！
+import projects.BEVFusion.bevfusion
+
+print("🔍 正在解析 BEVFusion 配置文件...")
+# 使用官方提供的默认配置文件
+config_file = 'projects/BEVFusion/configs/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py'
+cfg = Config.fromfile(config_file)
+
+print("🧱 正在海光 DCU 上构建 BEVFusion 模型架构...")
+try:
+    # 实例化模型
+    model = MODELS.build(cfg.model)
+    
+    # 推入海光 GPU (DCU) 显存
+    model.cuda()
+    
+    # 打印一下网络参数量，确认实体存在
+    num_params = sum(p.numel() for p in model.parameters() if p.requires_grad)
+    print(f"\n✅ 帅！模型在显存中构建成功！")
+    print(f"📊 模型总可训练参数量: {num_params / 1e6:.2f} M (百万)")
+    print("🚀 恭喜！高层 API 与配置文件解析完美通关！")
+    
+except Exception as e:
+    print(f"\n❌ 模型构建失败，报错信息如下:\n{e}")
\ No newline at end of file

BEVfusion_fix/命令

+python3 tools/test.py \
+    projects/BEVFusion/configs/bevfusion_lidar_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py \
+    pth/bevfusion_lidar-cam_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d-5239b1af_fixed.pth \
+    --cfg-options \
+    test_dataloader.dataset.ann_file=nuscenes_infos_mini_val.pkl test_dataloader.batch_size=4
+
+python3 tools/test.py \
+    projects/BEVFusion/configs/bevfusion_lidar_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d.py \
+    pth/bevfusion_lidar_voxel0075_second_secfpn_8xb4-cyclic-20e_nus-3d-2628f933_fixed.pth \
+    --cfg-options \
+    test_dataloader.dataset.ann_file=nuscenes_infos_mini_val.pkl
\ No newline at end of file

mmde/.circleci/config.yml

+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/path-filtering@0.1.2
+
+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

mmde/.circleci/docker/Dockerfile

+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

mmde/.circleci/test.yml

+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://git@github.com/open-mmlab/mmengine.git@main
+            pip install -U openmim
+            mim install 'mmcv >= 2.0.0rc4'
+            pip install git+ssh://git@github.com/open-mmlab/mmdetection.git@dev-3.x
+            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://git@github.com/open-mmlab/mmengine.git /home/circleci/mmengine
+            git clone -b dev-3.x --depth 1 ssh://git@github.com/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

mmde/.dev_scripts/benchmark_full_models.txt

+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

mmde/.dev_scripts/benchmark_options.py

+# 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
+}

mmde/.dev_scripts/benchmark_train_models.txt

+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

mmde/.dev_scripts/covignore.cfg

+# 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

mmde/.dev_scripts/diff_coverage_test.sh

+#!/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

mmde/.dev_scripts/gather_models.py

+# 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()

mmde/.dev_scripts/gen_benchmark_script.py

+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)

mmde/.dev_scripts/linter.sh

+yapf -r -i mmdet3d/ configs/ tests/ tools/
+isort mmdet3d/ configs/ tests/ tools/
+flake8 .

mmde/.dev_scripts/test_benchmark.sh

+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 &