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🚀 Feature
parallel version torchio.transforms.preprocessing.intensity.histogram_standardization.train
Motivation
Currently, this method is driven using single thread. The environment in which the deep learning model is learned using torchio is likely to be a server environment with multiple cpu cores, so it can be processed in parallel to increase efficiency.
Code
I wrote the code as below using multiprocessing.pool. I think this code is useful.
🚀 Feature
parallel version torchio.transforms.preprocessing.intensity.histogram_standardization.train
Motivation
Currently, this method is driven using single thread. The environment in which the deep learning model is learned using torchio is likely to be a server environment with multiple cpu cores, so it can be processed in parallel to increase efficiency.
Code
I wrote the code as below using multiprocessing.pool. I think this code is useful.
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