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#!/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
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