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eval_label.sh
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eval_label.sh
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#!/bin/bash
# ESPNet-CamVid
path_list_esp_cv=(\
model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200630-111157/espdnetue_2.0_480_best.pth \
model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200714-204420/espdnetue_2.0_480_best.pth \
model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_2.0_1.5_rgb/20200715-111451/espdnetue_2.0_480_best.pth \
model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_1.0_1.0_rgb/20200715-164256/espdnetue_2.0_480_best.pth \
model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200715-230509/espdnetue_2.0_480_best.pth \
)
# ESPNet-Cityscapes
path_list_esp_cs=(\
model_espdnetue_city/s_2.0_sch_hybrid_loss_ce_res_512_sc_0.25_0.5_rgb/20200715-170836/espdnetue_2.0_512_best.pth \
model_espdnetue_city/s_2.0_sch_hybrid_loss_ce_res_512_sc_0.25_0.5_rgb/20200717-161219/espdnetue_2.0_512_best.pth \
)
# ESPNet-Freiburg-Forest
path_list_esp_ff=(\
model_espdnetue_forest/s_2.0_sch_hybrid_loss_ce_res_480_sc_1.0_1.0_rgb/20200729-181614/espdnetue_2.0_480_best.pth \
)
# Deeplab-CamVid
path_list_dl_cv=(\
model_deeplabv3_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200710-185848/deeplabv3_2.0_480_best.pth \
model_deeplabv3_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200710-191117/deeplabv3_2.0_480_best.pth \
model_deeplabv3_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200711-141634/deeplabv3_2.0_480_best.pth \
model_deeplabv3_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200713-150748/deeplabv3_2.0_480_best.pth \
)
# Deeplab-Cityscapes
path_list_dl_cs=(\
model_deeplabv3_city/s_2.0_sch_hybrid_loss_ce_res_512_sc_0.25_0.5_rgb/20200714-172451/deeplabv3_2.0_512_best.pth \
)
outsource1=camvid
outsource2=cityscapes
outsource3=forest
os_model1=espdnetue
os_model2=espdnetue
os_model3=espdnetue
os_weights1="/tmp/runs/model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_1.0_1.0_rgb/20200715-164256/espdnetue_2.0_480_best.pth"
os_weights2="/tmp/runs/model_espdnetue_city/s_2.0_sch_hybrid_loss_ce_res_512_sc_0.25_0.5_rgb/20200717-161219/espdnetue_2.0_512_best.pth"
os_weights3="/tmp/runs/model_espdnetue_forest/s_2.0_sch_hybrid_loss_ce_res_480_sc_1.0_1.0_rgb/20200729-181614/espdnetue_2.0_480_best.pth"
val_list_a="val_greenhouse_more.lst"
val_list_b="val_greenhouse2.lst"
val_list_c="val_cucumber_r.lst"
val_lists=(
$val_list_a
$val_list_b
$val_list_c
)
CUDA_VISIBLE_DEVICES=0 python eval_label.py \
--data-path ./vision_datasets/camvid/ \
--savedir /tmp/runs/uest/for-paper/eval_label \
--val-list val_greenhouse.lst \
--batch-size 24 \
--crop-size 480 256 \
--dataset greenhouse \
--model espdnetue \
--output-image true \
--os-model1 espdnetue \
--outsource1 greenhouse \
--os-weights1 /tmp/runs/model_espdnetue_greenhouse/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200915-220819/espdnetue_2.0_480_best.pth
# CamVid
#for val in "${val_lists[@]}";
#do
#CUDA_VISIBLE_DEVICES=0 python eval_label.py \
# --data-path ./vision_datasets/camvid/ \
# --savedir /tmp/runs/uest/for-paper/eval_label \
# --val-list $val \
# --batch-size 24 \
# --crop-size 480 256 \
# --dataset $outsource1 \
# --model espdnetue \
# --output-image true \
# --os-model1 $os_model1 \
# --outsource1 $outsource1\
# --os-weights1 $os_weights1
#done
#
## Cityscapes
#for val in "${val_lists[@]}";
#do
#CUDA_VISIBLE_DEVICES=0 python eval_label.py \
# --data-path ./vision_datasets/camvid/ \
# --savedir /tmp/runs/uest/for-paper/eval_label \
# --val-list $val \
# --batch-size 24 \
# --crop-size 480 256 \
# --dataset city \
# --model espdnetue \
# --output-image true \
# --os-model1 $os_model2 \
# --outsource1 $outsource2\
# --os-weights1 $os_weights2
#done
#
## Forest
#for val in "${val_lists[@]}";
#do
#CUDA_VISIBLE_DEVICES=0 python eval_label.py \
# --data-path ./vision_datasets/camvid/ \
# --savedir /tmp/runs/uest/for-paper/eval_label \
# --val-list $val \
# --batch-size 24 \
# --crop-size 480 256 \
# --dataset $outsource3 \
# --model espdnetue \
# --output-image true \
# --os-model1 $os_model3 \
# --outsource1 $outsource3\
# --os-weights1 $os_weights3
#done
#--model espdnetue \
#--finetune /tmp/runs/model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_2.0_1.5_rgb/20200715-111451/espdnetue_2.0_480_best.pth \
#--model espdnetue \
#--finetune /tmp/runs/model_espdnetue_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200715-230509/espdnetue_2.0_480_best.pth \
#--model deeplabv3 \
#--finetune /tmp/runs/model_deeplabv3_camvid/s_2.0_sch_hybrid_loss_ce_res_480_sc_0.5_2.0_rgb/20200713-150748/deeplabv3_2.0_480_best.pth \
# CUDA_VISIBLE_DEVICES=0 python train_segmentation.py \
# --dataset greenhouse \
# --data-path ./vision_datasets/greenhouse/ \
# --batch-size 30 \
# --crop-size 480 256 \
# --ignore-idx 4 \
# --model espnetv2 \
# --s 2.0 \
# --lr 0.009 \
# --scheduler hybrid \
# --clr-max 61 \
# --epochs 500