# ------- Classification workflow -------
# Model training used run_project.py [tile_px=299, tile_um=302, lr in [0.001, 0.0001, 0.0005, 0.00001], epochs=[1,3,5], model='xception', val_strategy='k-fold-preserved-site']
python3 run_project.py -g 1 -p /media/ss4tbSSD/PROJECTS/HNSC/TCGA_HNSC_598px_604um

# ------- GAN workflow -------
# Train GAN, monitor output sample images for mode collapse
python3 train.py --outdir=/mnt/data/gan/CoGAPS12_training --slideflow=/mnt/data/PROJECTS/LATENT/gan_config.json --mirror=1 --cond=1 --augpipe=bgcfnc --metrics=none --gpus=2 --allow-tf32=1
# Generate images corresponding to the classes of interest
python3 generate.py --network=/mnt/data/gan/CoGAPS12_training/00000-LATENT-cond-mirror-auto2-bgcfnc/network-snapshot-007400.pkl --seeds=0-100 --class=5 --outdir=/mnt/data/gan/CoGAPS12_LUSC/1 --format=jpg --resize=True --gan-um=400 --gan-px=512 --target-um=302 --target-px=299
python3 generate.py --network=/mnt/data/gan/CoGAPS12_training/00000-LATENT-cond-mirror-auto2-bgcfnc/network-snapshot-007400.pkl --seeds=0-100 --class=2 --outdir=/mnt/data/gan/CoGAPS12_LUSC/0 --format=jpg --resize=True --gan-um=400 --gan-px=512 --target-um=302 --target-px=299
# Predict classes of interest
python3 predict.py --model=/mnt/data/gan/CoGAPS12_training/LUSC_00027-CoGAPS_logsc_12-HP0-kfold3_epoch5 --path=/mnt/data/gan/CoGAPS12_LUSC
# Generate interpolation between classes
python3 interpolate.py --network=/mnt/data/gan/CoGAPS12_training/00000-LATENT-cond-mirror-auto2-bgcfnc/network-snapshot-007400.pkl --seeds=/mnt/data/gan/CoGAPS12_LUSC/class_swap_seeds.txt --start=5 --end=2 --outdir=/mnt/data/gan/CoGAPS12_interp/LUSC/