Label-free prediction of three-dimensional fluorescence images from transmitted light microscopy by Allen Institute
Link to paper: http://dx.doi.org/10.1038/s41592-018-0111-2
The Nextjournal pytorch enviroment
Nextjournal had K80 as the GPU and CUDA 9.2 as runtime.
Python and PyTorch versions are listed below.
Cloning the repository
release_1 branch to reporduce the results in the Nature paper.
Not required in the Nextjournal notebook.
Changes I made
The newer GPU models (e.g. Titan V) do not work on older pytorch versions as well as CUDA9 runtime. CUDA compile error would occur. Thus I had to remove version constraints on both
torchvision packages in
environment.yml. As a result, the latest PyTorch and CUDA 10 will be installed.
The environment file by me:
To create and activate fnet enviroment in conda
Not required in the Nextjournal notebook as the environment is already set up.
Installing fnet packages in the repo
Note: Install fnet package before download data or you'll overflow the temp directory from pip caching and the content in the repository.
Fix 'cannot import name imsave'
imsave() is deprecated and removed since scipy 1.2.0. Installing the older one instead.
Downloads all data (do not do this in the Nextjournal Notebook).
They are over 500GB in total so a large enough storage is required. I put them in the NAS and mount it via SMB.
Train a model
For example, to train the DNA image model with the first GPU:
Run predictions with the trained model
Consistent with this benchmark
Visualizing .czi files
Download and extract ImageJ
Dwonload BioFormats .jar package and put it into the folder of ImageJ