mmcv installation issue

shravankumar-concat commented 1 year ago

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'

patricklabatut commented 1 year ago

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?

shravankumar-concat commented 1 year ago

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```

shravankumar-concat commented 1 year ago

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

shravankumar-concat commented 1 year ago

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

patricklabatut commented 1 year ago

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).

bozothegrey commented 1 year ago

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)

GZ-YourZY commented 11 months ago

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

shravankumar-concat commented 11 months ago

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

Sycamorers commented 8 months ago

pip install mmcv==1.7.1

works

facebookresearch / dinov2

mmcv installation issue #196