UnMICST-P (PSPNet)

Pyramid Scene Parsing Network (PSPNet) Semantic Segmentation for CyCIF Images collected at Harvard Medical School.

The project structure and baseline PSPNet model is adapted from: https://github.com/meetshah1995/pytorch-semseg. Please refer to this repository for additional details.

Installation Instructions

Create a virtual environment.

Install all of the required packages using:

pip install -r requirements.txt

Edit the Configuration file (or add your own) in the configs/ directory. Let cfg be the Configuration file. Depending on your desired experiment, change the cfg['data']['dataset'] value to one of

DNA_Aug
DNA_GaussianAug
DNA_NES_Aug
DNA_NES_NoAug
DNA_NoAug

Operation Instructions

Determine whether or not you would like to load a pretrained model. To load a pretrained model, place the model file in the pretrained/ folder in https://www.dropbox.com/sh/3aqp83f5w1pxk0y/AABFgNRMJD2EvfSLFgCrXrBba?dl=0 and set the cfg["training"]["pretrained"] = True. Otherwise, set cfg["training"]["pretrained"] = False.

1) To train the model, on a SLURM compute node, execute:

sbatch train.py

The slurm/ folder will contain a standard output and standard error file associated with your experiment.

2) To train the model in a conventional Python environment, execute:

python -u train.py --config configs/psp_segmenter.yml

You may generate and use your own Configuration file, if desired.

The runs/ directory will automatically populate with an experiment folder that has the same name as the Configuration file and contain a dedicated folder with a uniquely generated run_id.

To test a desired model after training is complete, the model path and the dataset will need to be specified. Models can be downloaded in the /models/UnMicst-P folder of https://www.dropbox.com/sh/3aqp83f5w1pxk0y/AABFgNRMJD2EvfSLFgCrXrBba?dl=0
Note, the test dataset must match the dataset type used for training.

That is, training on

DNA_Aug, DNA_GaussianAug, DNA_NoAug requires testing using DNA_test

and training on

DNA_NES_Aug, DNA_NES_NoAug requires testing using DNA_NES_test

For example, to test the model associated with experiment run 9999 on dataset without augmentations (e.g. DNA_test), execute:

python -u test_pspnet.py --model_path "runs/psp_segmenter/9999/pspnet_segmenter_best_model.pkl" --dataset "DNA_test"

A SLURM sbatch script called test.sh has also been included for convenience.