dinov2 mmcv installation issue

ModuleNotFoundError Traceback (most recent call last) Cell In[2], line 7 5 import torch 6 import torch.nn.functional as F ----> 7 from mmseg.apis import init_segmentor, inference_segmentor 9 import dinov2.eval.segmentation.models 12 class CenterPadding(torch.nn.Module):

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/apis/init.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from .inference import inference_segmentor, init_segmentor, show_result_pyplot 3 from .test import multi_gpu_test, single_gpu_test 4 from .train import (get_root_logger, init_random_seed, set_random_seed, 5 train_segmentor)

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/apis/inference.py:9 6 from mmcv.runner import load_checkpoint 8 from mmseg.datasets.pipelines import Compose ----> 9 from mmseg.models import build_segmentor 12 def init_segmentor(config, checkpoint=None, device='cuda:0'): 13 """Initialize a segmentor from config file. 14 15 Args: (...) 23 nn.Module: The constructed segmentor. 24 """

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/init.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from .backbones import * # noqa: F401,F403 3 from .builder import (BACKBONES, HEADS, LOSSES, SEGMENTORS, build_backbone, 4 build_head, build_loss, build_segmentor) 5 from .decode_heads import * # noqa: F401,F403

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/backbones/init.py:7 5 from .cgnet import CGNet 6 from .erfnet import ERFNet ----> 7 from .fast_scnn import FastSCNN 8 from .hrnet import HRNet 9 from .icnet import ICNet

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/backbones/fast_scnn.py:7 4 from mmcv.cnn import ConvModule, DepthwiseSeparableConvModule 5 from mmcv.runner import BaseModule ----> 7 from mmseg.models.decode_heads.psp_head import PPM 8 from mmseg.ops import resize 9 from ..builder import BACKBONES

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/decode_heads/init.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from .ann_head import ANNHead 3 from .apc_head import APCHead 4 from .aspp_head import ASPPHead

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/decode_heads/ann_head.py:8 6 from ..builder import HEADS 7 from ..utils import SelfAttentionBlock as _SelfAttentionBlock ----> 8 from .decode_head import BaseDecodeHead 11 class PPMConcat(nn.ModuleList): 12 """Pyramid Pooling Module that only concat the features of each layer. 13 14 Args: 15 pool_scales (tuple[int]): Pooling scales used in Pooling Pyramid 16 Module. 17 """

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/decode_heads/decode_head.py:11 9 from mmseg.ops import resize 10 from ..builder import build_loss ---> 11 from ..losses import accuracy 14 class BaseDecodeHead(BaseModule, metaclass=ABCMeta): 15 """Base class for BaseDecodeHead. 16 17 Args: (...) 51 init_cfg (dict or list[dict], optional): Initialization config dict. 52 """

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/losses/init.py:6 3 from .cross_entropy_loss import (CrossEntropyLoss, binary_cross_entropy, 4 cross_entropy, mask_cross_entropy) 5 from .dice_loss import DiceLoss ----> 6 from .focal_loss import FocalLoss 7 from .lovasz_loss import LovaszLoss 8 from .utils import reduce_loss, weight_reduce_loss, weighted_loss

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmseg/models/losses/focal_loss.py:6 4 import torch.nn as nn 5 import torch.nn.functional as F ----> 6 from mmcv.ops import sigmoid_focal_loss as _sigmoid_focal_loss 8 from ..builder import LOSSES 9 from .utils import weight_reduce_loss

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmcv/ops/init.py:2 1 # Copyright (c) OpenMMLab. All rights reserved. ----> 2 from .bbox import bbox_overlaps 3 from .border_align import BorderAlign, border_align 4 from .box_iou_rotated import box_iou_rotated

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmcv/ops/bbox.py:4 1 # Copyright (c) OpenMMLab. All rights reserved. 2 from ..utils import ext_loader ----> 4 ext_module = ext_loader.load_ext('_ext', ['bbox_overlaps']) 7 def bbox_overlaps(bboxes1, bboxes2, mode='iou', aligned=False, offset=0): 8 """Calculate overlap between two set of bboxes. 9 10 If aligned is False, then calculate the ious between each bbox (...) 47 >>> assert tuple(bbox_overlaps(empty, empty).shape) == (0, 0) 48 """

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/site-packages/mmcv/utils/ext_loader.py:13, in load_ext(name, funcs) 12 def load_ext(name, funcs): ---> 13 ext = importlib.import_module('mmcv.' + name) 14 for fun in funcs: 15 assert hasattr(ext, fun), f'{fun} miss in module {name}'

File ~/anaconda3/envs/dinov2-extras/lib/python3.9/importlib/init.py:127, in import_module(name, package) 125 break 126 level += 1 --> 127 return _bootstrap._gcd_import(name[level:], package, level)

ModuleNotFoundError: No module named 'mmcv._ext'

Sep 01 '23 17:09 shravankumar-concat

You seem to have installed the dependencies via conda, which specific platform are you using and could you perhaps share the output of conda env export?

Sep 01 '23 20:09 patricklabatut

Hi @patricklabatut ,

when I first installed with conda env create -f conda-extras.yaml

This issue raised:

raise RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda))
      RuntimeError:
      The detected CUDA version (9.1) mismatches the version that was used to compile
      PyTorch (11.7). Please make sure to use the same CUDA versions.
      
      [end of output]
  
  note: This error originates from a subprocess, and is likely not a problem with pip.
  ERROR: Failed building wheel for mmcv-full
ERROR: Could not build wheels for mmcv-full, which is required to install pyproject.toml-based projects

failed

CondaEnvException: Pip failed

However, the rest are installed so here is the conda env export:

channels:
  - xformers
  - pytorch
  - nvidia
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - antlr-python-runtime=4.9.3=pyhd8ed1ab_1
  - blas=1.0=mkl
  - brotlipy=0.7.0=py39h27cfd23_1003
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2023.05.30=h06a4308_0
  - certifi=2023.7.22=py39h06a4308_0
  - cffi=1.15.1=py39h5eee18b_3
  - charset-normalizer=2.0.4=pyhd3eb1b0_0
  - cryptography=41.0.2=py39h22a60cf_0
  - cuda-cudart=11.7.99=0
  - cuda-cupti=11.7.101=0
  - cuda-libraries=11.7.1=0
  - cuda-nvrtc=11.7.99=0
  - cuda-nvtx=11.7.91=0
  - cuda-runtime=11.7.1=0
  - dataclasses=0.8=pyh6d0b6a4_7
  - ffmpeg=4.2.2=h20bf706_0
  - filelock=3.9.0=py39h06a4308_0
  - freetype=2.12.1=h4a9f257_0
  - fvcore=0.1.5.post20221221=pyhd8ed1ab_0
  - giflib=5.2.1=h5eee18b_3
  - gmp=6.2.1=h295c915_3
  - gnutls=3.6.15=he1e5248_0
  - idna=3.4=py39h06a4308_0
  - intel-openmp=2023.1.0=hdb19cb5_46305
  - iopath=0.1.9=pyhd8ed1ab_0
  - jinja2=3.1.2=py39h06a4308_0
  - jpeg=9e=h5eee18b_1
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.38=h1181459_1
  - lerc=3.0=h295c915_0
  - libcublas=11.10.3.66=0
  - libcufft=10.7.2.124=h4fbf590_0
  - libcufile=1.7.2.10=0
  - libcurand=10.3.3.141=0
  - libcusolver=11.4.0.1=0
  - libcusparse=11.7.4.91=0
  - libdeflate=1.17=h5eee18b_0
  - libffi=3.4.4=h6a678d5_0
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libidn2=2.3.4=h5eee18b_0
  - libnpp=11.7.4.75=0
  - libnvjpeg=11.8.0.2=0
  - libopus=1.3.1=h7b6447c_0
  - libpng=1.6.39=h5eee18b_0
  - libstdcxx-ng=11.2.0=h1234567_1
  - libtasn1=4.19.0=h5eee18b_0
  - libtiff=4.5.1=h6a678d5_0
  - libunistring=0.9.10=h27cfd23_0
  - libvpx=1.7.0=h439df22_0
  - libwebp=1.2.4=h11a3e52_1
  - libwebp-base=1.2.4=h5eee18b_1
  - lz4-c=1.9.4=h6a678d5_0
  - markupsafe=2.1.1=py39h7f8727e_0
  - mkl=2023.1.0=h213fc3f_46343
  - mkl-service=2.4.0=py39h5eee18b_1
  - mkl_fft=1.3.6=py39h417a72b_1
  - mkl_random=1.2.2=py39h417a72b_1
  - mpmath=1.3.0=py39h06a4308_0
  - ncurses=6.4=h6a678d5_0
  - nettle=3.7.3=hbbd107a_1
  - networkx=3.1=py39h06a4308_0
  - numpy=1.25.2=py39h5f9d8c6_0
  - numpy-base=1.25.2=py39hb5e798b_0
  - omegaconf=2.3.0=pyhd8ed1ab_0
  - openh264=2.1.1=h4ff587b_0
  - openssl=3.0.10=h7f8727e_2
  - packaging=23.1=py39h06a4308_0
  - pillow=9.4.0=py39h6a678d5_0
  - pip=23.2.1=py39h06a4308_0
  - portalocker=2.3.0=py39h06a4308_1
  - pycparser=2.21=pyhd3eb1b0_0
  - pyopenssl=23.2.0=py39h06a4308_0
  - pysocks=1.7.1=py39h06a4308_0
  - python=3.9.17=h955ad1f_0
  - pytorch=2.0.0=py3.9_cuda11.7_cudnn8.5.0_0
  - pytorch-cuda=11.7=h778d358_5
  - pytorch-mutex=1.0=cuda
  - pyyaml=6.0=py39h5eee18b_1
  - readline=8.2=h5eee18b_0
  - requests=2.31.0=py39h06a4308_0
  - setuptools=68.0.0=py39h06a4308_0
  - sqlite=3.41.2=h5eee18b_0
  - sympy=1.12=pyh04b8f61_3
  - tabulate=0.8.10=py39h06a4308_0
  - tbb=2021.8.0=hdb19cb5_0
  - termcolor=2.1.0=py39h06a4308_0
  - tk=8.6.12=h1ccaba5_0
  - torchmetrics=0.10.3=pyhd8ed1ab_0
  - torchtriton=2.0.0=py39
  - torchvision=0.15.0=py39_cu117
  - tqdm=4.65.0=py39hb070fc8_0
  - typing_extensions=4.7.1=py39h06a4308_0
  - tzdata=2023c=h04d1e81_0
  - urllib3=1.26.16=py39h06a4308_0
  - wheel=0.38.4=py39h06a4308_0
  - x264=1!157.20191217=h7b6447c_0
  - xformers=0.0.18=py39_cu11.8.0_pyt2.0.0
  - xz=5.4.2=h5eee18b_0
  - yacs=0.1.6=pyhd3eb1b0_1
  - yaml=0.2.5=h7b6447c_0
  - zlib=1.2.13=h5eee18b_0
  - zstd=1.5.5=hc292b87_0
prefix: /home/shravan/anaconda3/envs/dinov2-extras```

Sep 02 '23 05:09 shravankumar-concat

Since the root cause of this error is :


 RuntimeError(CUDA_MISMATCH_MESSAGE.format(cuda_str_version, torch.version.cuda))
      RuntimeError:
      The detected CUDA version (9.1) mismatches the version that was used to compile
      PyTorch (11.7). Please make sure to use the same CUDA versions.

So I have used the instructions to set CUDA using below resource: setting-up-cuda-117-toolkit-


nvcc --version
nvcc: NVIDIA (R) Cuda compiler driver
Copyright (c) 2005-2022 NVIDIA Corporation
Built on Tue_May__3_18:49:52_PDT_2022
Cuda compilation tools, release 11.7, V11.7.64
Build cuda_11.7.r11.7/compiler.31294372_0

My torch versions:

list | grep "torch"
pytorch                   2.0.0           py3.9_cuda11.7_cudnn8.5.0_0    pytorch
pytorch-cuda              11.7                 h778d358_5    pytorch
pytorch-mutex             1.0                        cuda    pytorch
torchmetrics              0.10.3             pyhd8ed1ab_0    conda-forge
torchtriton               2.0.0                      py39    pytorch
torchvision               0.15.0               py39_cu117    pytorch

That's how I matched both CUDA and pytorch CUDA version

Sep 02 '23 14:09 shravankumar-concat

I have tested Segmentaion notebook, now it's working, Here is my final conda env:

name: dinov2-extras
channels:
  - xformers
  - pytorch
  - nvidia
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - antlr-python-runtime=4.9.3=pyhd8ed1ab_1
  - blas=1.0=mkl
  - brotlipy=0.7.0=py39h27cfd23_1003
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2023.05.30=h06a4308_0
  - certifi=2023.7.22=py39h06a4308_0
  - cffi=1.15.1=py39h5eee18b_3
  - charset-normalizer=2.0.4=pyhd3eb1b0_0
  - cryptography=41.0.2=py39h22a60cf_0
  - cuda-cudart=11.7.99=0
  - cuda-cupti=11.7.101=0
  - cuda-libraries=11.7.1=0
  - cuda-nvrtc=11.7.99=0
  - cuda-nvtx=11.7.91=0
  - cuda-runtime=11.7.1=0
  - dataclasses=0.8=pyh6d0b6a4_7
  - ffmpeg=4.2.2=h20bf706_0
  - filelock=3.9.0=py39h06a4308_0
  - freetype=2.12.1=h4a9f257_0
  - fvcore=0.1.5.post20221221=pyhd8ed1ab_0
  - giflib=5.2.1=h5eee18b_3
  - gmp=6.2.1=h295c915_3
  - gnutls=3.6.15=he1e5248_0
  - idna=3.4=py39h06a4308_0
  - intel-openmp=2023.1.0=hdb19cb5_46305
  - iopath=0.1.9=pyhd8ed1ab_0
  - jinja2=3.1.2=py39h06a4308_0
  - jpeg=9e=h5eee18b_1
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.38=h1181459_1
  - lerc=3.0=h295c915_0
  - libcublas=11.10.3.66=0
  - libcufft=10.7.2.124=h4fbf590_0
  - libcufile=1.7.2.10=0
  - libcurand=10.3.3.141=0
  - libcusolver=11.4.0.1=0
  - libcusparse=11.7.4.91=0
  - libdeflate=1.17=h5eee18b_0
  - libffi=3.4.4=h6a678d5_0
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libidn2=2.3.4=h5eee18b_0
  - libnpp=11.7.4.75=0
  - libnvjpeg=11.8.0.2=0
  - libopus=1.3.1=h7b6447c_0
  - libpng=1.6.39=h5eee18b_0
  - libstdcxx-ng=11.2.0=h1234567_1
  - libtasn1=4.19.0=h5eee18b_0
  - libtiff=4.5.1=h6a678d5_0
  - libunistring=0.9.10=h27cfd23_0
  - libvpx=1.7.0=h439df22_0
  - libwebp=1.2.4=h11a3e52_1
  - libwebp-base=1.2.4=h5eee18b_1
  - lz4-c=1.9.4=h6a678d5_0
  - markupsafe=2.1.1=py39h7f8727e_0
  - mkl=2023.1.0=h213fc3f_46343
  - mkl-service=2.4.0=py39h5eee18b_1
  - mkl_fft=1.3.6=py39h417a72b_1
  - mkl_random=1.2.2=py39h417a72b_1
  - mpmath=1.3.0=py39h06a4308_0
  - ncurses=6.4=h6a678d5_0
  - nettle=3.7.3=hbbd107a_1
  - networkx=3.1=py39h06a4308_0
  - omegaconf=2.3.0=pyhd8ed1ab_0
  - openh264=2.1.1=h4ff587b_0
  - openssl=3.0.10=h7f8727e_2
  - packaging=23.1=py39h06a4308_0
  - pillow=9.4.0=py39h6a678d5_0
  - pip=23.2.1=py39h06a4308_0
  - portalocker=2.3.0=py39h06a4308_1
  - pycparser=2.21=pyhd3eb1b0_0
  - pyopenssl=23.2.0=py39h06a4308_0
  - pysocks=1.7.1=py39h06a4308_0
  - python=3.9.17=h955ad1f_0
  - pytorch=2.0.0=py3.9_cuda11.7_cudnn8.5.0_0
  - pytorch-cuda=11.7=h778d358_5
  - pytorch-mutex=1.0=cuda
  - pyyaml=6.0=py39h5eee18b_1
  - readline=8.2=h5eee18b_0
  - requests=2.31.0=py39h06a4308_0
  - setuptools=68.0.0=py39h06a4308_0
  - sqlite=3.41.2=h5eee18b_0
  - sympy=1.12=pyh04b8f61_3
  - tabulate=0.8.10=py39h06a4308_0
  - tbb=2021.8.0=hdb19cb5_0
  - termcolor=2.1.0=py39h06a4308_0
  - tk=8.6.12=h1ccaba5_0
  - torchmetrics=0.10.3=pyhd8ed1ab_0
  - torchtriton=2.0.0=py39
  - torchvision=0.15.0=py39_cu117
  - tqdm=4.65.0=py39hb070fc8_0
  - typing_extensions=4.7.1=py39h06a4308_0
  - tzdata=2023c=h04d1e81_0
  - urllib3=1.26.16=py39h06a4308_0
  - wheel=0.38.4=py39h06a4308_0
  - x264=1!157.20191217=h7b6447c_0
  - xformers=0.0.18=py39_cu11.8.0_pyt2.0.0
  - xz=5.4.2=h5eee18b_0
  - yacs=0.1.6=pyhd3eb1b0_1
  - yaml=0.2.5=h7b6447c_0
  - zlib=1.2.13=h5eee18b_0
  - zstd=1.5.5=hc292b87_0
  - pip:
      - addict==2.4.0
      - asttokens==2.2.1
      - backcall==0.2.0
      - cachetools==5.3.1
      - click==8.1.7
      - cloudpickle==2.2.1
      - comm==0.1.4
      - contourpy==1.1.0
      - cubinlinker-cu11==0.3.0.post1
      - cuda-python==11.8.2
      - cudf-cu11==23.8.0
      - cuml-cu11==23.8.0
      - cupy-cuda11x==12.2.0
      - cycler==0.11.0
      - cython==3.0.2
      - dask==2023.7.1
      - dask-cuda==23.8.0
      - dask-cudf-cu11==23.8.0
      - debugpy==1.6.7.post1
      - decorator==5.1.1
      - distributed==2023.7.1
      - exceptiongroup==1.1.3
      - executing==1.2.0
      - fastrlock==0.8.2
      - fonttools==4.42.1
      - fsspec==2023.6.0
      - importlib-metadata==6.8.0
      - importlib-resources==6.0.1
      - ipykernel==6.25.1
      - ipython==8.15.0
      - jedi==0.19.0
      - joblib==1.3.2
      - jupyter-client==8.3.1
      - jupyter-core==5.3.1
      - kiwisolver==1.4.5
      - llvmlite==0.40.1
      - locket==1.0.0
      - matplotlib==3.7.2
      - matplotlib-inline==0.1.6
      - mmcls==0.25.0
      - mmcv-full==1.5.0
      - mmsegmentation==0.27.0
      - msgpack==1.0.5
      - nest-asyncio==1.5.7
      - numba==0.57.1
      - numpy==1.24.4
      - nvtx==0.2.7
      - opencv-python==4.8.0.76
      - pandas==1.5.3
      - parso==0.8.3
      - partd==1.4.0
      - pexpect==4.8.0
      - pickleshare==0.7.5
      - platformdirs==3.10.0
      - prettytable==3.8.0
      - prompt-toolkit==3.0.39
      - protobuf==4.24.2
      - psutil==5.9.5
      - ptxcompiler-cu11==0.7.0.post1
      - ptyprocess==0.7.0
      - pure-eval==0.2.2
      - pyarrow==11.0.0
      - pygments==2.16.1
      - pylibraft-cu11==23.8.0
      - pynvml==11.4.1
      - pyparsing==3.0.9
      - python-dateutil==2.8.2
      - pytz==2023.3
      - pyzmq==25.1.1
      - raft-dask-cu11==23.8.0
      - rmm-cu11==23.8.0
      - scipy==1.11.2
      - six==1.16.0
      - sortedcontainers==2.4.0
      - stack-data==0.6.2
      - tblib==2.0.0
      - tomli==2.0.1
      - toolz==0.12.0
      - tornado==6.3.3
      - traitlets==5.9.0
      - treelite==3.2.0
      - treelite-runtime==3.2.0
      - ucx-py-cu11==0.33.0
      - wcwidth==0.2.6
      - yapf==0.40.1
      - zict==3.0.0
      - zipp==3.16.2
prefix: /home/shravan/anaconda3/envs/dinov2-extras

Sep 02 '23 14:09 shravankumar-concat

Thanks for the updates and great to know it's working for you now. Hopefully once #188 and #190 are resolved (should make some progress this week), it should be much easier than having the exact environment we are specifying (at least for inference).

Sep 04 '23 11:09 patricklabatut

Hello, I am playing around with this notebook too. I had to do some lenghty manual installation of some of the library in the conda-extras.yaml. That's fine. The issue I have is more on the segmentation job. It seems to be that it is running on cpu rather than gpu, which is available. Am I missing something or this is normal ? (on windows 10 / WSL)

EDIT added the location to load_checkpoint and I am now using the gpu load_checkpoint(model, head_checkpoint_url, map_location='cuda:0')

Previously tried and failed with:

load_checkpoint(model, head_checkpoint_url, map_location=0)
load_checkpoint(model, head_checkpoint_url, map_location='0')
load_checkpoint(model, head_checkpoint_url, map_location='gpu')
DEVICE = torch.device("cuda") if torch.cuda.is_available() else torch.device("cpu") load_checkpoint(model, head_checkpoint_url, map_location=DEVICE)

Sep 09 '23 14:09 bozothegrey

我已经测试了 Segmentaion 笔记本，现在它可以工作了，这是我的最终 conda 环境：

name: dinov2-extras
channels:
  - xformers
  - pytorch
  - nvidia
  - conda-forge
  - defaults
dependencies:
  - _libgcc_mutex=0.1=main
  - _openmp_mutex=5.1=1_gnu
  - antlr-python-runtime=4.9.3=pyhd8ed1ab_1
  - blas=1.0=mkl
  - brotlipy=0.7.0=py39h27cfd23_1003
  - bzip2=1.0.8=h7b6447c_0
  - ca-certificates=2023.05.30=h06a4308_0
  - certifi=2023.7.22=py39h06a4308_0
  - cffi=1.15.1=py39h5eee18b_3
  - charset-normalizer=2.0.4=pyhd3eb1b0_0
  - cryptography=41.0.2=py39h22a60cf_0
  - cuda-cudart=11.7.99=0
  - cuda-cupti=11.7.101=0
  - cuda-libraries=11.7.1=0
  - cuda-nvrtc=11.7.99=0
  - cuda-nvtx=11.7.91=0
  - cuda-runtime=11.7.1=0
  - dataclasses=0.8=pyh6d0b6a4_7
  - ffmpeg=4.2.2=h20bf706_0
  - filelock=3.9.0=py39h06a4308_0
  - freetype=2.12.1=h4a9f257_0
  - fvcore=0.1.5.post20221221=pyhd8ed1ab_0
  - giflib=5.2.1=h5eee18b_3
  - gmp=6.2.1=h295c915_3
  - gnutls=3.6.15=he1e5248_0
  - idna=3.4=py39h06a4308_0
  - intel-openmp=2023.1.0=hdb19cb5_46305
  - iopath=0.1.9=pyhd8ed1ab_0
  - jinja2=3.1.2=py39h06a4308_0
  - jpeg=9e=h5eee18b_1
  - lame=3.100=h7b6447c_0
  - lcms2=2.12=h3be6417_0
  - ld_impl_linux-64=2.38=h1181459_1
  - lerc=3.0=h295c915_0
  - libcublas=11.10.3.66=0
  - libcufft=10.7.2.124=h4fbf590_0
  - libcufile=1.7.2.10=0
  - libcurand=10.3.3.141=0
  - libcusolver=11.4.0.1=0
  - libcusparse=11.7.4.91=0
  - libdeflate=1.17=h5eee18b_0
  - libffi=3.4.4=h6a678d5_0
  - libgcc-ng=11.2.0=h1234567_1
  - libgomp=11.2.0=h1234567_1
  - libidn2=2.3.4=h5eee18b_0
  - libnpp=11.7.4.75=0
  - libnvjpeg=11.8.0.2=0
  - libopus=1.3.1=h7b6447c_0
  - libpng=1.6.39=h5eee18b_0
  - libstdcxx-ng=11.2.0=h1234567_1
  - libtasn1=4.19.0=h5eee18b_0
  - libtiff=4.5.1=h6a678d5_0
  - libunistring=0.9.10=h27cfd23_0
  - libvpx=1.7.0=h439df22_0
  - libwebp=1.2.4=h11a3e52_1
  - libwebp-base=1.2.4=h5eee18b_1
  - lz4-c=1.9.4=h6a678d5_0
  - markupsafe=2.1.1=py39h7f8727e_0
  - mkl=2023.1.0=h213fc3f_46343
  - mkl-service=2.4.0=py39h5eee18b_1
  - mkl_fft=1.3.6=py39h417a72b_1
  - mkl_random=1.2.2=py39h417a72b_1
  - mpmath=1.3.0=py39h06a4308_0
  - ncurses=6.4=h6a678d5_0
  - nettle=3.7.3=hbbd107a_1
  - networkx=3.1=py39h06a4308_0
  - omegaconf=2.3.0=pyhd8ed1ab_0
  - openh264=2.1.1=h4ff587b_0
  - openssl=3.0.10=h7f8727e_2
  - packaging=23.1=py39h06a4308_0
  - pillow=9.4.0=py39h6a678d5_0
  - pip=23.2.1=py39h06a4308_0
  - portalocker=2.3.0=py39h06a4308_1
  - pycparser=2.21=pyhd3eb1b0_0
  - pyopenssl=23.2.0=py39h06a4308_0
  - pysocks=1.7.1=py39h06a4308_0
  - python=3.9.17=h955ad1f_0
  - pytorch=2.0.0=py3.9_cuda11.7_cudnn8.5.0_0
  - pytorch-cuda=11.7=h778d358_5
  - pytorch-mutex=1.0=cuda
  - pyyaml=6.0=py39h5eee18b_1
  - readline=8.2=h5eee18b_0
  - requests=2.31.0=py39h06a4308_0
  - setuptools=68.0.0=py39h06a4308_0
  - sqlite=3.41.2=h5eee18b_0
  - sympy=1.12=pyh04b8f61_3
  - tabulate=0.8.10=py39h06a4308_0
  - tbb=2021.8.0=hdb19cb5_0
  - termcolor=2.1.0=py39h06a4308_0
  - tk=8.6.12=h1ccaba5_0
  - torchmetrics=0.10.3=pyhd8ed1ab_0
  - torchtriton=2.0.0=py39
  - torchvision=0.15.0=py39_cu117
  - tqdm=4.65.0=py39hb070fc8_0
  - typing_extensions=4.7.1=py39h06a4308_0
  - tzdata=2023c=h04d1e81_0
  - urllib3=1.26.16=py39h06a4308_0
  - wheel=0.38.4=py39h06a4308_0
  - x264=1!157.20191217=h7b6447c_0
  - xformers=0.0.18=py39_cu11.8.0_pyt2.0.0
  - xz=5.4.2=h5eee18b_0
  - yacs=0.1.6=pyhd3eb1b0_1
  - yaml=0.2.5=h7b6447c_0
  - zlib=1.2.13=h5eee18b_0
  - zstd=1.5.5=hc292b87_0
  - pip:
      - addict==2.4.0
      - asttokens==2.2.1
      - backcall==0.2.0
      - cachetools==5.3.1
      - click==8.1.7
      - cloudpickle==2.2.1
      - comm==0.1.4
      - contourpy==1.1.0
      - cubinlinker-cu11==0.3.0.post1
      - cuda-python==11.8.2
      - cudf-cu11==23.8.0
      - cuml-cu11==23.8.0
      - cupy-cuda11x==12.2.0
      - cycler==0.11.0
      - cython==3.0.2
      - dask==2023.7.1
      - dask-cuda==23.8.0
      - dask-cudf-cu11==23.8.0
      - debugpy==1.6.7.post1
      - decorator==5.1.1
      - distributed==2023.7.1
      - exceptiongroup==1.1.3
      - executing==1.2.0
      - fastrlock==0.8.2
      - fonttools==4.42.1
      - fsspec==2023.6.0
      - importlib-metadata==6.8.0
      - importlib-resources==6.0.1
      - ipykernel==6.25.1
      - ipython==8.15.0
      - jedi==0.19.0
      - joblib==1.3.2
      - jupyter-client==8.3.1
      - jupyter-core==5.3.1
      - kiwisolver==1.4.5
      - llvmlite==0.40.1
      - locket==1.0.0
      - matplotlib==3.7.2
      - matplotlib-inline==0.1.6
      - mmcls==0.25.0
      - mmcv-full==1.5.0
      - mmsegmentation==0.27.0
      - msgpack==1.0.5
      - nest-asyncio==1.5.7
      - numba==0.57.1
      - numpy==1.24.4
      - nvtx==0.2.7
      - opencv-python==4.8.0.76
      - pandas==1.5.3
      - parso==0.8.3
      - partd==1.4.0
      - pexpect==4.8.0
      - pickleshare==0.7.5
      - platformdirs==3.10.0
      - prettytable==3.8.0
      - prompt-toolkit==3.0.39
      - protobuf==4.24.2
      - psutil==5.9.5
      - ptxcompiler-cu11==0.7.0.post1
      - ptyprocess==0.7.0
      - pure-eval==0.2.2
      - pyarrow==11.0.0
      - pygments==2.16.1
      - pylibraft-cu11==23.8.0
      - pynvml==11.4.1
      - pyparsing==3.0.9
      - python-dateutil==2.8.2
      - pytz==2023.3
      - pyzmq==25.1.1
      - raft-dask-cu11==23.8.0
      - rmm-cu11==23.8.0
      - scipy==1.11.2
      - six==1.16.0
      - sortedcontainers==2.4.0
      - stack-data==0.6.2
      - tblib==2.0.0
      - tomli==2.0.1
      - toolz==0.12.0
      - tornado==6.3.3
      - traitlets==5.9.0
      - treelite==3.2.0
      - treelite-runtime==3.2.0
      - ucx-py-cu11==0.33.0
      - wcwidth==0.2.6
      - yapf==0.40.1
      - zict==3.0.0
      - zipp==3.16.2
prefix: /home/shravan/anaconda3/envs/dinov2-extras

请问您是如何安装的mmcv-full==1.5.0与mmsegmentation==0.27.0,能否提供一些指导信息

Oct 24 '23 10:10 GZ-YourZY

If you cannot use the environment provided above for setup, try using pip

pip install mmcv-full==1.5.0
pip install mmsegmentation==0.27.0

Oct 25 '23 09:10 shravankumar-concat

pip install mmcv==1.7.1

works

Jan 14 '24 21:01 Sycamorers

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mmcv installation issue

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