安装旧版本mmcv-full==1.4.8;mmdet==4.5.5.64

前言

因为要复现一个代码,要用到上古时代的mmcv-full。找了挺多资料,装了半天才搞定。记录一下,下次若还有需要可以更快地解决。

原配置:

神舟s8d6
RTX4060
i7-12650H
cuda11.8
mmcv 3.x

实现步骤

  1. 安装低版本的cuda、cudnn、对应版本的pytorch。 难度不大,主要是我之前已经有cuda11.8了,这次装了11.5,想多版本共存并实现切换。(不过在安装torch的时候,发现没有cuda11.5对应的版本,网上参考了下,发现11.3,11.1都可以,我安装了11.3(用了11.3的版本),成功)Windows10下多版本CUDA的安装与切换 超详细教程
  2. 安装mmcv-full 官方教程里面要下载vs2019,然后还有一些环境变量的设置。我电脑上已经有了vs2019,我就直接安装mmcv-full了,想着失败再按教程走,最后成功。下面是各个版本的mmcv-full,上面也有安装代码,github上各个mmcv-full版本,但是安装后发现mmcv-full安装了1.7.1的,不是我想要的1.4.8,故在安装时强制了一下版本,成功。
    pip install mmcv-full==1.4.8 -f https://download.openmmlab.com/mmcv/dist/cu115/torch1.11/index.html
  3. 安装mmdet 这个简单,直接 pip install mmdet==v2.25.0 完事。

题外话

之前最开始安装anaconda的时候,说是他可以解决一堆依赖问题什么的,但目前的体验来看,pip还是比conda好用,具体体现在1.anaconda安装torch似乎会安装成cpu版本的。2.目前尝试的好多个程序,配置环境时,在pycharm里直接根据requirement.txt安装(conda方式的),总是会有失败,最后还是pip手敲解决。不知道是为什么,如果有大佬知道欢迎留言。

参考

Windows10下多版本CUDA的安装与切换 超详细教程
笔记–Win11安装mmcv-full
windows下mmcv-full1.5.0安装
github上各个mmcv-full版本
https://blog.youkuaiyun.com/qq_24815615/article/details/125566755

我看yaml里有下列包,给我生成终端conda执行的语句:dependencies: - blas=1.0=mkl - bzip2=1.0.8=h2466b09_7 - ca-certificates=2025.1.31=h56e8100_0 - cuda-cccl=12.8.55=0 - cuda-cccl_win-64=12.8.55=0 - cuda-cudart=11.8.89=0 - cuda-cudart-dev=11.8.89=0 - cuda-cupti=11.8.87=0 - cuda-libraries=11.8.0=0 - cuda-libraries-dev=11.8.0=0 - cuda-nvrtc=11.8.89=0 - cuda-nvrtc-dev=11.8.89=0 - cuda-nvtx=11.8.86=0 - cuda-profiler-api=12.8.55=0 - cuda-runtime=11.8.0=0 - cuda-version=12.8=3 - filelock=3.17.0=pyhd8ed1ab_0 - intel-openmp=2025.0.0=h57928b3_1168 - libcublas=11.11.3.6=0 - libcublas-dev=11.11.3.6=0 - libcufft=10.9.0.58=0 - libcufft-dev=10.9.0.58=0 - libcurand=10.3.9.55=0 - libcurand-dev=10.3.9.55=0 - libcusolver=11.4.1.48=0 - libcusolver-dev=11.4.1.48=0 - libcusparse=11.7.5.86=0 - libcusparse-dev=11.7.5.86=0 - libexpat=2.6.4=he0c23c2_0 - libffi=3.4.6=h537db12_0 - libhwloc=2.11.2=default_ha69328c_1001 - libiconv=1.18=h135ad9c_1 - liblzma=5.6.4=h2466b09_0 - libnpp=11.8.0.86=0 - libnpp-dev=11.8.0.86=0 - libnvjpeg=11.9.0.86=0 - libnvjpeg-dev=11.9.0.86=0 - libsqlite=3.49.1=h67fdade_1 - libuv=1.50.0=h2466b09_0 - libwinpthread=12.0.0.r4.gg4f2fc60ca=h57928b3_9 - libxml2=2.13.6=he286e8c_0 - libzlib=1.3.1=h2466b09_2 - markupsafe=3.0.2=py312h31fea79_1 - mkl=2023.1.0=h6a75c08_48682 - mpmath=1.3.0=pyhd8ed1ab_1 - networkx=3.4.2=pyh267e887_2 - openssl=3.4.1=ha4e3fda_0 - pip=25.0.1=pyh8b19718_0 - python=3.12.9=h3f84c4b_1_cpython - python_abi=3.12=5_cp312 - pytorch=2.5.1=py3.12_cuda11.8_cudnn9_0 - pytorch-cuda=11.8=h24eeafa_6 - pytorch-mutex=1.0=cuda - pyyaml=6.0.2=py312h31fea79_2 - setuptools=75.8.2=pyhff2d567_0 - tbb=2021.13.0=h62715c5_1 - tk=8.6.13=h5226925_1 - typing_extensions=4.12.2=pyha770c72_1 - ucrt=10.0.22621.0=h57928b3_1 - vc=14.3=hbf610ac_24 - vc14_runtime=14.42.34438=hfd919c2_24 - vs2015_runtime=14.42.34438=h7142326_24 - wheel=0.45.1=pyhd8ed1ab_1 - yaml=0.2.5=h8ffe710_2 - pip: - colorama==0.4.6 - contourpy==1.3.1 - cycler==0.12.1 - fonttools==4.56.0 - fsspec==2025.2.0 - jinja2==3.1.6 - kiwisolver==1.4.8 - matplotlib==3.10.1 - numpy==2.1.1 - opencv-python==4.11.0.86 - packaging==24.2 - pandas==2.2.3 - psutil==7.0.0 - py-cpuinfo==9.0.0 - pyparsing==3.2.1 - python-dateutil==2.9.0.post0 - pytz==2025.1 - scipy==1.15.2 - seaborn==0.13.2 - six==1.17.0 - sympy==1.13.1 - tqdm==4.67.1 - tzdata==2025.1 - ultralytics==8.3.84 - ultralytics-thop==2.0.14
03-08
(base) nvidia@nvidia-desktop:~/pi$ cat openpi.yaml name: openpi channels: - https://repo.anaconda.com/pkgs/main - defaults dependencies: - _libgcc_mutex=0.1=main - _openmp_mutex=5.1=1_gnu - bzip2=1.0.8=h5eee18b_6 - ca-certificates=2025.2.25=h06a4308_0 - ld_impl_linux-64=2.40=h12ee557_0 - libffi=3.3=he6710b0_2 - libgcc-ng=11.2.0=h1234567_1 - libgomp=11.2.0=h1234567_1 - libstdcxx-ng=11.2.0=h1234567_1 - libuuid=1.41.5=h5eee18b_0 - ncurses=6.4=h6a678d5_0 - openssl=1.1.1w=h7f8727e_0 - pip=25.0=py310h06a4308_0 - python=3.10.0=h12debd9_5 - readline=8.2=h5eee18b_0 - setuptools=75.8.0=py310h06a4308_0 - sqlite=3.45.3=h5eee18b_0 - tk=8.6.14=h39e8969_0 - wheel=0.45.1=py310h06a4308_0 - xz=5.6.4=h5eee18b_1 - zlib=1.2.13=h5eee18b_1 - pip: - absl-py==2.2.2 - accelerate==1.2.0 - aiohappyeyeballs==2.6.1 - aiohttp==3.12.13 - aiosignal==1.4.0 - annotated-types==0.7.0 - antlr4-python3-runtime==4.9.3 - arm-pytorch-utilities==0.4.3 - asciitree==0.3.3 - asttokens==3.0.0 - async-timeout==5.0.1 - attrs==25.3.0 - av==15.0.0 - beautifulsoup4==4.13.4 - blinker==1.9.0 - catkin-pkg==1.0.0 - certifi==2022.12.7 - cffi==1.17.1 - charset-normalizer==2.1.1 - click==8.1.8 - cloudpickle==3.1.1 - cmake==4.0.3 - contourpy==1.3.2 - cycler==0.12.1 - dacite==1.9.2 - datasets==3.6.0 - decorator==5.2.1 - deepdiff==8.5.0 - deepspeed==0.14.2 - diffusers==0.27.2 - dill==0.3.8 - distro==1.9.0 - dm-tree==0.1.9 - docker-pycreds==0.4.0 - docstring-parser==0.16 - docutils==0.21.2 - draccus==0.10.0 - einops==0.8.1 - evdev==1.9.2 - exceptiongroup==1.2.2 - executing==2.2.0 - farama-notifications==0.0.4 - fast-kinematics==0.2.2 - fasteners==0.19 - filelock==3.13.1 - flask==3.1.1 - fonttools==4.57.0 - frozenlist==1.7.0 - fsspec==2024.6.1 - future==1.0.0 - gdown==5.2.0 - gitdb==4.0.12 - gitpython==3.1.44 - grpcio==1.71.0 - gymnasium==0.29.1 - h5py==3.11.0 - hf-transfer==0.1.9 - hf-xet==1.1.5 - hjson==3.1.0 - huggingface-hub==0.33.2 - idna==3.4 - imageio==2.37.0 - imageio-ffmpeg==0.6.0 - imgaug==0.4.0 - importlib-metadata==8.6.1 - iniconfig==2.1.0 - inquirerpy==0.3.4 - ipython==8.35.0 - itsdangerous==2.2.0 - jedi==0.19.2 - jinja2==3.1.4 - joblib==1.5.2 - jsonlines==4.0.0 - kiwisolver==1.4.8 - lazy-loader==0.4 - lerobot==0.1.0 - lightning==2.5.2 - lightning-utilities==0.14.3 - llvmlite==0.44.0 - lxml==5.3.2 - mani-skill==3.0.0b20 - markdown==3.8 - markdown-it-py==3.0.0 - markupsafe==2.1.5 - matplotlib==3.10.1 - matplotlib-inline==0.1.7 - mdurl==0.1.2 - mergedeep==1.3.4 - mplib==0.1.1 - mpmath==1.3.0 - msgpack==1.1.1 - multidict==6.6.3 - multiprocess==0.70.16 - mypy-extensions==1.1.0 - natsort==8.4.0 - networkx==3.3 - ninja==1.11.1.4 - numba==0.61.2 - numcodecs==0.13.1 - numpy==1.26.4 - nvidia-cublas-cu12==12.1.3.1 - nvidia-cuda-cupti-cu12==12.1.105 - nvidia-cuda-nvrtc-cu12==12.1.105 - nvidia-cuda-runtime-cu12==12.1.105 - nvidia-cudnn-cu12==8.9.2.26 - nvidia-cufft-cu12==11.0.2.54 - nvidia-cufile-cu12==1.11.1.6 - nvidia-curand-cu12==10.3.2.106 - nvidia-cusolver-cu12==11.4.5.107 - nvidia-cusparse-cu12==12.1.0.106 - nvidia-cusparselt-cu12==0.6.3 - nvidia-ml-py==12.570.86 - nvidia-nccl-cu12==2.20.5 - nvidia-nvjitlink-cu12==12.6.85 - nvidia-nvtx-cu12==12.1.105 - omegaconf==2.3.0 - opencv-python==4.9.0.80 - opencv-python-headless==4.11.0.86 - openpi-client==0.1.0 - orderly-set==5.4.1 - packaging==25.0 - pandas==2.3.0 - parso==0.8.4 - peft==0.16.0 - pexpect==4.9.0 - pfzy==0.3.4 - pillow==11.0.0 - platformdirs==4.3.7 - pluggy==1.6.0 - polars==1.30.0 - prompt-toolkit==3.0.51 - propcache==0.3.2 - protobuf==4.25.6 - psutil==7.0.0 - ptyprocess==0.7.0 - pure-eval==0.2.3 - py-cpuinfo==9.0.0 - pyarrow==20.0.0 - pycparser==2.22 - pydantic==2.11.3 - pydantic-core==2.33.1 - pygments==2.19.1 - pymunk==6.11.1 - pynput==1.8.1 - pynvml==12.0.0 - pyparsing==3.2.3 - pyperclip==1.9.0 - pyserial==3.5 - pysocks==1.7.1 - pytest==8.4.1 - python-dateutil==2.9.0.post0 - python-xlib==0.33 - pytorch-kinematics==0.7.5 - pytorch-lightning==1.1.2 - pytorch-seed==0.2.0 - pytz==2025.2 - pyyaml==6.0.2 - pyyaml-include==1.4.1 - pyzmq==27.0.0 - regex==2024.11.6 - requests==2.32.4 - rerun-sdk==0.23.4 - rich==14.0.0 - rospkg==1.6.0 - safetensors==0.5.3 - sapien==3.0.0b1 - scikit-image==0.25.2 - scikit-learn==1.7.2 - scipy==1.15.2 - sentencepiece==0.2.0 - sentry-sdk==2.26.1 - setproctitle==1.3.5 - shapely==2.1.0 - shtab==1.7.2 - six==1.17.0 - smmap==5.0.2 - soupsieve==2.7 - stack-data==0.6.3 - svgwrite==1.4.3 - sympy==1.14.0 - tabulate==0.9.0 - tensorboard==2.19.0 - tensorboard-data-server==0.7.2 - termcolor==3.1.0 - threadpoolctl==3.6.0 - tifffile==2025.3.30 - timm==1.0.3 - tokenizers==0.21.2 - toml==0.10.2 - tomli==2.2.1 - toppra==0.6.3 - torch==2.3.0 - torchcodec==0.4.0 - torchmetrics==1.7.3 - torchvision==0.18.0 - tqdm==4.67.1 - traitlets==5.14.3 - transformers==4.53.1 - transforms3d==0.4.2 - tree==0.2.4 - trimesh==4.6.6 - triton==2.3.0 - typeguard==4.4.2 - typing-extensions==4.13.2 - typing-inspect==0.9.0 - typing-inspection==0.4.0 - tyro==0.9.22 - tzdata==2025.2 - urllib3==1.26.13 - wandb==0.17.0 - wcwidth==0.2.13 - websockets==15.0.1 - werkzeug==3.1.3 - wrapt==1.17.2 - xxhash==3.5.0 - yarl==1.20.1 - zarr==2.18.3 - zipp==3.21.0 修改为更适合在ARM架构中安装版本
最新发布
11-06
评论 1
添加红包

请填写红包祝福语或标题

红包个数最小为10个

红包金额最低5元

当前余额3.43前往充值 >
需支付:10.00
成就一亿技术人!
领取后你会自动成为博主和红包主的粉丝 规则
hope_wisdom
发出的红包
实付
使用余额支付
点击重新获取
扫码支付
钱包余额 0

抵扣说明:

1.余额是钱包充值的虚拟货币,按照1:1的比例进行支付金额的抵扣。
2.余额无法直接购买下载,可以购买VIP、付费专栏及课程。

余额充值