Installing on Linux is recommended (Our machine runs Ubuntu 18.04).

An nVIDIA graphics card with >10GB of GPU memory (Our machine is equipped with an nVIDIA Titan RTX with 24GB of memory).

• Install Miniconda or Anaconda if necessary.
• Create a Conda environment for the platform and install all the necessary dependencies:

conda env create -f environment.yml

• Activate the environment:

conda activate fnet

• Try executing the test script:

./scripts/test_run.sh

The installation was successful if the script executes without errors.

If installation continues to fail, try downloading the original pytorch-fnet-release_1 repository and replacing bufferedpatchdataset.py and fnet_nn_2d.py in that repo with the same files from this repo.

To try training an indian pines prediction model with the provided data:

• Make sure the fnet environment is active
• Change the indian_pines.csv file in /data/csvs/ so the "PATH" is the actual path in your directory
• try executing the script:

./scripts/test_indian_pines.sh indian_pines 0

If successful, it should run the training for 500 iterations and then stop

Once you have a quickly trained model try adding the line:

• PATH/data/Indian_Pines/indian_pines_200chan.tif,PATH/data/Indian_Pines/IP_gt_multichan_invert.tif to the bottom of test.csv in /data/csvs/indian_pines/

Then try executing:

./scripts/predict_model_UwU.sh indian_pines 0

This should predict all the test and training images into the /results/ folder which you can then view. The prediction should not be good after only 500 iterations. If you want to train a more robust model change test_indian_pines.sh so that 50000 iterations occur rather than 500 then run the same command to execute training. This will take a few hours depending on your hardware.

The Indian Pines dataset used can be found at: https://engineering.purdue.edu/~biehl/MultiSpec/hyperspectral.html

The MSI dataset used can be found at: https://www.ebi.ac.uk/pride/archive/projects/PXD016146

The Hypsepctral SRS/Fluorescence dataset can be found at: DOI: 10.6084/m9.figshare.13497138

See the AllenCellModeling/pytorch-fnet-release_1 github and readme for other basic functions and training of traditional U-Nets.

Create a 2 column csv file where the first row contains "path_signal,path_target". Then in each column place the corresponding path-to-files for the images to be used for model training. Save the file with the intended model name my_model.csv to the /data/csvs/ folder

Edit the /scripts/train_model_UwU.sh file to the desired training parameters. Edit the /fnet/data/bufferedpatchdataset.py file to match the patch size used in train_model_UwU.sh. Edit the fnet/nn_modules/fnet_nn_UwU.py file to match the desired initial, intermediate, and final channel size for the images and select the correct return line in forward() to match the chosen output channel dimensions.

Once all parameters have been set execute:

./scripts/train_model_UwU.sh my_model 0

Once you have trained a model you can execute:

./scripts/predict_UwU.sh my_model 0

to predict the withheld test images and the training images.

To use the trained model on new data, edit the test.csv or train.csv file to the desired path to files then reexecute the above script.

Note: reexecuting the prediction will require no existing folder for the model results in the /results/ folder. Rename precious executions to have the model continue predicting.

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Copyright © 2018. Allen Institute. All rights reserved.

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