Amandaynzhou / MMT-PSM

[MICCAI2020] Code for paper : Deep Semi-supervised Knowledge Distillation for Overlapping Cervical Cell Instance Segmentation
MIT License
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preprocess.colors #5

Open Jackson-zhao97 opened 3 years ago

Jackson-zhao97 commented 3 years ago

File "/userdata/zhj/MMT-PSM-master/maskrcnn_benchmark/utils/visual.py", line 7, in from preprocess.colors import get_colors ModuleNotFoundError: No module named 'preprocess.colors';

Chester171 commented 1 year ago

File "/userdata/zhj/MMT-PSM-master/maskrcnn_benchmark/utils/visual.py", line 7, in from preprocess.colors import get_colors ModuleNotFoundError: No module named 'preprocess.colors';

first, make sure that your conda is setup properly with the right environment

for that, check that which conda, which pip and which python points to the

right path. From a clean conda env, this is what you need to do

conda create --name maskrcnn_benchmark source activate maskrcnn_benchmark

this installs the right pip and dependencies for the fresh python

conda install ipython

maskrcnn_benchmark and coco api dependencies

pip install ninja yacs cython matplotlib

follow PyTorch installation in https://pytorch.org/get-started/locally/

we give the instructions for CUDA 9.0

conda install pytorch-nightly -c pytorch

install torchvision

cd ~/github git clone https://github.com/pytorch/vision.git cd vision python setup.py install

install pycocotools

cd ~/github git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI python setup.py build_ext install

install PyTorch Detection

cd ~/github git clone https://github.com/facebookresearch/maskrcnn-benchmark.git cd maskrcnn-benchmark

the following will install the lib with

symbolic links, so that you can modify

the files if you want and won't need to

re-build it

python setup.py build develop

or if you are on macOS

MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develop

https://pytorch.org/get-started/locally/# first, make sure that your conda is setup properly with the right environment

for that, check that which conda, which pip and which python points to the

right path. From a clean conda env, this is what you need to do

conda create --name maskrcnn_benchmark source activate maskrcnn_benchmark

this installs the right pip and dependencies for the fresh python

conda install ipython

maskrcnn_benchmark and coco api dependencies

pip install ninja yacs cython matplotlib

follow PyTorch installation in https://pytorch.org/get-started/locally/

we give the instructions for CUDA 9.0

conda install pytorch-nightly -c pytorch

install torchvision

cd ~/github git clone https://github.com/pytorch/vision.git cd vision python setup.py install

install pycocotools

cd ~/github git clone https://github.com/cocodataset/cocoapi.git cd cocoapi/PythonAPI python setup.py build_ext install

install PyTorch Detection

cd ~/github git clone https://github.com/facebookresearch/maskrcnn-benchmark.git cd maskrcnn-benchmark

the following will install the lib with

symbolic links, so that you can modify

the files if you want and won't need to

re-build it

python setup.py build develop

or if you are on macOS

MACOSX_DEPLOYMENT_TARGET=10.9 CC=clang CXX=clang++ python setup.py build develophttps://github.com/pytorch/vision.gitsetup.pyhttps://github.com/cocodataset/cocoapi.githttps://github.com/facebookresearch/maskrcnn-benchmark.git