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train.sh
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#!/usr/bin/env bash
python3 preprocess_clahe.py --wdata_dir /wdata --dirs_to_process "$@"
python3 train.py \
--gpu="0" \
--data_dirs "$@" \
--network=inception-swish \
--models_dir trained_models \
--loss_function=bce_dice\
--preprocessing_function=tf \
--freeze_till_layer=input_1 \
--learning_rate=0.001 \
--schedule="0.0005:2,0.0001:15,0.00005:20,0.00003:25,0.00001:30" \
--optimizer=rmsprop \
--fold="0" \
--clahe \
--ohe_city \
--wdata_dir /wdata \
--crop_size=384 \
--crops_per_image=2 \
--batch_size=4 \
--epochs=30 \
--steps_per_epoch=2000 &
# linknet inception with CLAHE
python3 train.py \
--gpu="1" \
--data_dirs "$@" \
--network=linknet_inception \
--models_dir trained_models \
--loss_function=bce_dice\
--preprocessing_function=tf \
--freeze_till_layer=input_1 \
--learning_rate=0.001 \
--schedule="0.0005:2,0.0001:15,0.00005:20,0.00003:25,0.00001:30" \
--optimizer=rmsprop \
--fold="0" \
--clahe \
--ohe_city \
--wdata_dir /wdata \
--crop_size=384 \
--crops_per_image=2 \
--batch_size=4 \
--epochs=30 \
--steps_per_epoch=2000 &
# linknet inception without CLAHE
python3 train.py \
--gpu="2" \
--data_dirs "$@" \
--network=linknet_inception \
--models_dir trained_models \
--loss_function=bce_dice\
--preprocessing_function=tf \
--freeze_till_layer=input_1 \
--learning_rate=0.001 \
--schedule="0.0005:2,0.0001:15,0.00005:20,0.00003:25,0.00001:30" \
--optimizer=rmsprop \
--fold="0" \
--ohe_city \
--wdata_dir /wdata \
--crop_size=384 \
--crops_per_image=2 \
--batch_size=4 \
--epochs=30 \
--steps_per_epoch=2000 &
# unet inception without clahe with stretch and mean preprocessing
python3 train.py \
--gpu="3" \
--data_dirs "$@" \
--network=inception-unet \
--models_dir trained_models \
--loss_function=bce_dice\
--preprocessing_function=tf \
--freeze_till_layer=input_1 \
--learning_rate=0.001 \
--schedule="0.0005:2,0.0001:15,0.00005:20,0.00003:25,0.00001:30" \
--optimizer=rmsprop \
--fold="0" \
--stretch_and_mean \
--wdata_dir /wdata \
--crop_size=384 \
--crops_per_image=2 \
--batch_size=4 \
--epochs=30 \
--steps_per_epoch=2000 &
wait
# linknet resnet
python3 train.py \
--gpu="0" \
--data_dirs "$@" \
--network=linknet_resnet50 \
--models_dir trained_models \
--loss_function=bce_dice\
--preprocessing_function=caffe \
--freeze_till_layer=input_1 \
--learning_rate=0.001 \
--schedule="0.0005:2,0.0001:15,0.00005:20,0.00003:25,0.00001:30" \
--optimizer=rmsprop \
--fold="0" \
--clahe \
--ohe_city \
--wdata_dir /wdata \
--crop_size=384 \
--crops_per_image=2 \
--batch_size=4 \
--epochs=30 \
--steps_per_epoch=2000 &
# linknet inception with transposed convolutions
python3 train.py \
--gpu="1" \
--data_dirs "$@" \
--network=linknet_inception_lite \
--models_dir trained_models \
--loss_function=bce_dice\
--preprocessing_function=tf \
--freeze_till_layer=input_1 \
--learning_rate=0.001 \
--schedule="0.0005:2,0.0001:15,0.00005:20,0.00003:25,0.00001:30" \
--optimizer=rmsprop \
--fold="0" \
--clahe \
--ohe_city \
--wdata_dir /wdata \
--crop_size=384 \
--crops_per_image=2 \
--batch_size=4 \
--epochs=30 \
--steps_per_epoch=2000 &
wait
cp trained_models/000_all_linknet_inception.h5 trained_models/000_paris_linknet_inception.h5
cp trained_models/000_all_linknet_inception.h5 trained_models/000_vegas_linknet_inception.h5
cp trained_models/000_all_linknet_inception.h5 trained_models/000_shanghai_linknet_inception.h5
cp trained_models/000_all_linknet_inception.h5 trained_models/000_khartoum_linknet_inception.h5
cp trained_models/100_all_linknet_inception.h5 trained_models/100_paris_linknet_inception.h5
cp trained_models/100_all_linknet_inception.h5 trained_models/100_vegas_linknet_inception.h5
cp trained_models/100_all_linknet_inception.h5 trained_models/100_shanghai_linknet_inception.h5
cp trained_models/100_all_linknet_inception.h5 trained_models/100_khartoum_linknet_inception.h5
cp trained_models/010_all_inception-unet.h5 trained_models/010_paris_inception-unet.h5
cp trained_models/010_all_inception-unet.h5 trained_models/010_vegas_inception-unet.h5
cp trained_models/010_all_inception-unet.h5 trained_models/010_shanghai_inception-unet.h5
cp trained_models/010_all_inception-unet.h5 trained_models/010_khartoum_inception-unet.h5
cp trained_models/100_all_inception-swish.h5 trained_models/100_paris_inception-swish.h5
cp trained_models/100_all_inception-swish.h5 trained_models/100_vegas_inception-swish.h5
cp trained_models/100_all_inception-swish.h5 trained_models/100_shanghai_inception-swish.h5
cp trained_models/100_all_inception-swish.h5 trained_models/100_khartoum_inception-swish.h5
cp trained_models/100_all_linknet_inception_lite.h5 trained_models/100_paris_linknet_inception_lite.h5
cp trained_models/100_all_linknet_inception_lite.h5 trained_models/100_vegas_linknet_inception_lite.h5
cp trained_models/100_all_linknet_inception_lite.h5 trained_models/100_shanghai_linknet_inception_lite.h5
cp trained_models/100_all_linknet_inception_lite.h5 trained_models/100_khartoum_linknet_inception_lite.h5
cp trained_models/101_all_linknet_resnet50.h5 trained_models/101_paris_linknet_resnet50.h5
cp trained_models/101_all_linknet_resnet50.h5 trained_models/101_vegas_linknet_resnet50.h5
cp trained_models/101_all_linknet_resnet50.h5 trained_models/101_shanghai_linknet_resnet50.h5
cp trained_models/101_all_linknet_resnet50.h5 trained_models/101_khartoum_linknet_resnet50.h5