#!/bin/bash

for pretrained in True False

do

    for model in r2plus1d_18 r3d_18 mc3_18

    do

        for frames in 96 64 32 16 8 4 1

        do

            batch=$((256 / frames))

            batch=$(( batch > 16 ? 16 : batch ))

            cmd="import echonet; echonet.utils.video.run(modelname=\"${model}\", frames=${frames}, period=1, pretrained=${pretrained}, batch_size=${batch})"

            python3 -c "${cmd}"

        done

        for period in 2 4 6 8

        do

            batch=$((256 / 64 * period))

            batch=$(( batch > 16 ? 16 : batch ))

            cmd="import echonet; echonet.utils.video.run(modelname=\"${model}\", frames=(64 // ${period}), period=${period}, pretrained=${pretrained}, batch_size=${batch})"

            python3 -c "${cmd}"

        done

    done

done

period=2

pretrained=True

for model in r2plus1d_18 r3d_18 mc3_18

do

    cmd="import echonet; echonet.utils.video.run(modelname=\"${model}\", frames=(64 // ${period}), period=${period}, pretrained=${pretrained}, run_test=True)"

    python3 -c "${cmd}"

done

python3 -c "import echonet; echonet.utils.segmentation.run(modelname=\"deeplabv3_resnet50\",  save_segmentation=True, pretrained=False)"

pretrained=True

model=r2plus1d_18

period=2

batch=$((256 / 64 * period))

batch=$(( batch > 16 ? 16 : batch ))

for patients in 16 32 64 128 256 512 1024 2048 4096 7460

do

    cmd="import echonet; echonet.utils.video.run(modelname=\"${model}\", frames=(64 // ${period}), period=${period}, pretrained=${pretrained}, batch_size=${batch}, num_epochs=min(50 * (8192 // ${patients}), 200), output=\"output/training_size/video/${patients}\", n_train_patients=${patients})"

    python3 -c "${cmd}"

    cmd="import echonet; echonet.utils.segmentation.run(modelname=\"deeplabv3_resnet50\", pretrained=False, num_epochs=min(50 * (8192 // ${patients}), 200), output=\"output/training_size/segmentation/${patients}\", n_train_patients=${patients})"

    python3 -c "${cmd}"

done