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| # Install required packages: | |
| # !pip -q install simplecrf nibabel wget | |
| # !BUILD_MONAI=1 pip -q install git+https://github.com/Project-MONAI/MONAI#egg=monai | |
| from monai.networks.blocks import CRF | |
| import wget | |
| import nibabel | |
| import denseCRF3D | |
| import numpy as np | |
| import torch | |
| from functools import wraps | |
| from time import time | |
| # define a simple timing decorator, use for both simplecrf's densecrf3d and monai's CRF | |
| def timing(f): | |
| @wraps(f) | |
| def wrap(*args, **kw): | |
| ts = time() | |
| result = f(*args, **kw) | |
| te = time() | |
| print('func:%r took: %2.4f sec' % (f.__name__, te-ts)) | |
| return result | |
| return wrap | |
| # run functions to get timings on the two functions | |
| # densecrf3d run with timing | |
| @timing | |
| def densecrf3d(I, P, param): | |
| return denseCRF3D.densecrf3d(I, P, param) | |
| # MONAI's CRF run with timing | |
| @timing | |
| def runmonaicrf(I, P, crf_func): | |
| return crf_func(P, I) | |
| def load_sample_data(): | |
| # download sample data used in this example from github simplecrf repository | |
| wget.download('https://github.com/HiLab-git/SimpleCRF/raw/master/data/2013_12_1_img.nii.gz') | |
| wget.download('https://github.com/HiLab-git/SimpleCRF/raw/master/data/2013_12_1_init.nii.gz') | |
| # Load some sample data | |
| I1Nii = nibabel.load('2013_12_1_img.nii.gz') | |
| PNii = nibabel.load('2013_12_1_init.nii.gz') | |
| I1 = I1Nii.get_data() | |
| P = PNii.get_data() | |
| # convert input to intenstiy range of [0, 255] | |
| I = np.asarray([I1], np.float32) | |
| I = np.transpose(I, [1, 2, 3, 0]) | |
| I = I / I.max()* 255 | |
| I = np.asarray(I, np.uint8) | |
| # probability map for each class | |
| P = 0.5 + (P - 0.5) * 0.8 | |
| P = np.asarray([1.0 - P, P], np.float32) | |
| P = np.transpose(P, [1, 2, 3, 0]) | |
| return I, P | |
| if __name__ == '__main__': | |
| I, P = load_sample_data() | |
| print(I.shape) | |
| print(P.shape) | |
| # define same number of iterations to run for each method | |
| ITERATIONS = 20 | |
| # run both methods on cpu | |
| # 1. SimpleCRF's CRF with ITERATIONS | |
| dense_crf_param = {} | |
| dense_crf_param['MaxIterations'] = ITERATIONS | |
| dense_crf_param['PosW'] = 3 | |
| dense_crf_param['PosRStd'] = 1 | |
| dense_crf_param['PosCStd'] = 1 | |
| dense_crf_param['PosZStd'] = 1 | |
| dense_crf_param['BilateralW'] = 5 | |
| dense_crf_param['BilateralRStd'] = 5 | |
| dense_crf_param['BilateralCStd'] = 5 | |
| dense_crf_param['BilateralZStd'] = 5 | |
| dense_crf_param['ModalityNum'] = 1 | |
| dense_crf_param['BilateralModsStds'] = (5,) | |
| lab = densecrf3d(I, P, dense_crf_param) | |
| # 2. MONAI's CRF with ITERATIONS - CPU | |
| device='cpu' | |
| I_pt = np.moveaxis(I, source=-1, destination=0) | |
| P_pt = np.moveaxis(P, source=-1, destination=0) | |
| I_pt = np.expand_dims(I_pt, axis=0) | |
| P_pt = np.expand_dims(P_pt, axis=0) | |
| I_pt = torch.from_numpy(I_pt).to(dtype=torch.float, device=torch.device(device)) | |
| P_pt = torch.from_numpy(P_pt).to(dtype=torch.float, device=torch.device(device)) | |
| crf_func = CRF( | |
| bilateral_weight=5.0, | |
| gaussian_weight=3.0, | |
| bilateral_spatial_sigma=1.0, | |
| bilateral_color_sigma=5.0, | |
| gaussian_spatial_sigma=0.5, | |
| update_factor=5.0, | |
| compatibility_kernel_range=1, | |
| iterations=ITERATIONS, | |
| ) | |
| runmonaicrf(I_pt, P_pt, crf_func) | |
| ## output of the script on my local machine: | |
| # func:'densecrf3d' took: 5.8184 sec | |
| # func:'runmonaicrf' took: 177.5296 sec |
Hi @ikatechis
You will need the output of your network after softmax (i.e. probabilites of 33 labels) as input of CRF. Regarding whether it will help, you need to try it out and look into literature for best configuration to use for such CT scans. These configurations are in the form of hyper-paramters for CRF layer itself. I recommend you read the CRF paper linked above in comments to understand this.
CRF is usually used as a post processing layer. This means that it post-processes the output of a network/model. With that said, there is nothing stopping you from using it within the network or separate from a CNN. Its just a matter of figuring out where it would help the most.
Hope that helps clear things for you!
If you have further questions, I recommend sending me a message on MONAI slack channel: https://github.com/Project-MONAI/MONAI#community - I will be happy to help you more with this!
how can i install this version please
BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=monai
Please see https://docs.monai.io/en/stable/installation.html#option-1-as-a-part-of-your-system-wide-module for details about installation.
You need to type the BUILD_MONAI=1 pip install git+https://github.com/Project-MONAI/MONAI#egg=monai in your terminal to build the CRF features in monai. More details about this are in the link I mentioned in this commnet.
when i try that, i get an error :
C:\Users\Demo 1A\Documents>BUILD_MONAI=1 pip install --no-build-isolation git+https://github.com/Project-MONAI/MONAI#egg=monai
'BUILD_MONAI' n’est pas reconnu en tant que commande interne
ou externe, un programme exécutable ou un fichier de commandes.
Do you know why i get this error when using BUILD_MONAI=1 before pip ?
when i try that, i get an error :
C:\Users\Demo 1A\Documents>BUILD_MONAI=1 pip install --no-build-isolation git+https://github.com/Project-MONAI/MONAI#egg=monai 'BUILD_MONAI' n’est pas reconnu en tant que commande interne ou externe, un programme exécutable ou un fichier de commandes.Do you know why i get this error when using BUILD_MONAI=1 before pip ?
Are you using windows?
Then try the following from cm/powersheel:
set BUILD_MONAI=1
pip install --no-build-isolation git+https://github.com/Project-MONAI/MONAI#egg=monai
Note: you will need relevant compilation tools on your Windows machine as this is compiling the monai c++ code. If the above fails then try to use python setup and see what error it gives:
set BUILD_MONAI=1
git clone https://github.com/Project-MONAI/MONAI
cd MONAI
python setup.py install
hello , i did run this on my virtuel env
set BUILD_MONAI=1
git clone https://github.com/Project-MONAI/MONAI
cd MONAI
python setup.py install
it looks like it managed to finish the processing of the dependencies
Finished processing dependencies for monai==1.1.0+105.g354c9c2b
however when i run my script to apply the CRF , i still get the same error :
monai.utils.module.OptionalImportError: import monai._C (No module named 'monai._C').
Do you have an idea why is that ?
Hi @anisaaaaaaaaaaaa ,
Thats good progress. I can see that you are able to run setup and from your previous comment it appears you are using powershell and that BUILD_MONAI was not detected by setup.py script.
I think I know the issue here. For compiling MONAI you need to set environment variable BUILD_MONAI=1. However, on windows there are two methods of setting this depending on whether you are using CMD or Powershell.
On CMD it should be as I told in my previous comment:
set BUILD_MONAI=1
git clone https://github.com/Project-MONAI/MONAI
cd MONAI
python setup.py install
On Powershell it should be the following (notice env variable is set differently here):
$env:BUILD_MONAI=1
git clone https://github.com/Project-MONAI/MONAI
cd MONAI
python setup.py install
Once you are able to run this command and no longer see the message:
"Please set environment variable BUILD_MONAI=1 to enable Cpp/CUDA extension build."
Then we can debug whether your machine has any issues compiling the CPP code. Until now, it has not attempted compiling CPP because BUILD_MONAI was not properly set.
Hope this helps.
hello @masadcv , i managed to run this lines with powershell on windows:
set BUILD_MONAI=1
git clone https://github.com/Project-MONAI/MONAI
cd MONAI
python setup.py install
it looks like it managed to finish the processing of the dependencies
Finished processing dependencies for monai==1.1.0+105.g354c9c2b
however when i run my script to apply the CRF , i still get the same error :
monai.utils.module.OptionalImportError: import monai._C (No module named 'monai._C').
Do you have an idea why is that ?
For Powershell, you need to change set BUILD_MONAI=1 to $env:BUILD_MONAI=1 as I indicated in my last comment
Successful install does not mean it has compiled CPP code. Check install logs and you will see that it skipped compilation because BUILD_MONAI was not set using proper method for Powershell
okey i see,
I stick with the cmd then ,
I just run the comand lines
set BUILD_MONAI=1
git clone https://github.com/Project-MONAI/MONAI
cd MONAI
python setup.py install
Now i get this error :
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include\crt/host_config.h(160): fatal error C1189: #error: -- unsupported Microsoft Visual Studio version! Only the versions between 2017 and 2019 (inclusive) are supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk.
error: command 'C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\bin\\nvcc.exe' failed with exit code 2
Do you know why i get this one ?
Now i get this error :
C:\Program Files\NVIDIA GPU Computing Toolkit\CUDA\v11.2\include\crt/host_config.h(160): fatal error C1189: #error: -- unsupported Microsoft Visual Studio version! Only the versions between 2017 and 2019 (inclusive) are supported! The nvcc flag '-allow-unsupported-compiler' can be used to override this version check; however, using an unsupported host compiler may cause compilation failure or incorrect run time execution. Use at your own risk. error: command 'C:\\Program Files\\NVIDIA GPU Computing Toolkit\\CUDA\\v11.2\\bin\\nvcc.exe' failed with exit code 2Do you know why i get this one ?
Hi @anisaaaaaaaaaaaa ,
Thanks for getting back with the error message you get.
This error means you have unsupported Microsoft Visual Studio version. As the error states, you need a version between 2017 and 2019 to compile this on your machine.
If you need more help on this, I recommend taking this conversation offline. As mentioned above, please try to reach me on Slack and I can help guide you.
If you want to fix this on your own, look at installing Microsoft Visual Studio 2017 or 2019 with C++ build tools.
Hi @AnisBoubekeur
If you join MONAI slack using instructions from here: https://github.com/Project-MONAI/MONAI#community
You can send me message at: Muhammad Asad with the same profile picture.
I would be happy to assist you further with this setup.
Hello and thank you for the script!
I am currently running a 33-label semantic segmentation with a UNet on CT scans and I was wondering how a CRF layer could improve me segmentation.
It is not clear to me however where I should put this layer. Should it be placed before or after the last convolution layer? Also I don't understand what the reference tensor should be for the MONAI implementation.
Thank you again,
Iasonas