Minor fixes to pretrain.sh and finetune.sh

sungnyun · Aug 7, 2022 · 346540a · 346540a
1 parent 625ac56
commit 346540a
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Showing 2 changed files with 11 additions and 21 deletions.
diff --git a/finetune.sh b/finetune.sh
@@ -2,7 +2,7 @@ export CUDA_VISIBLE_DEVICES=0
 
 
 SOURCES=("miniImageNet" "tieredImageNet" "ImageNet")
-SOURCE=${SOURCES[0]}
+SOURCE=${SOURCES[2]}
 
 TARGETS=("CropDisease" "ISIC" "EuroSAT" "ChestX" "places" "cub" "plantae" "cars")
 TARGET=${TARGETS[0]}
@@ -14,30 +14,20 @@ N_SHOT=5
 
 
 # Source SL
-for TARGET in "CropDisease" "ISIC" "EuroSAT" "ChestX" "places" "cub" "plantae" "cars"; do
-  python finetune.py --ls --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "base" --tag "default" --n_shot $N_SHOT
-done
+python finetune.py --ls --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "base" --tag "default" --n_shot $N_SHOT
 
 # Target SSL
-for TARGET in "CropDisease" "ISIC" "EuroSAT" "ChestX" "places" "cub" "plantae" "cars"; do
-  python finetune.py --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "default" --n_shot $N_SHOT
-  python finetune.py --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "default" --n_shot $N_SHOT
-done
+python finetune.py --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "default" --n_shot $N_SHOT
+python finetune.py --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "default" --n_shot $N_SHOT
 
 # MSL (Source SL + Target SSL)
-for TARGET in "CropDisease" "ISIC" "EuroSAT" "ChestX" "places" "cub" "plantae" "cars"; do
-  python finetune.py --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "gamma78" --n_shot $N_SHOT
-  python finetune.py --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "gamma78" --n_shot $N_SHOT
-done
+python finetune.py --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "gamma78" --n_shot $N_SHOT
+python finetune.py --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "gamma78" --n_shot $N_SHOT
 
 # Two-Stage SSL (Source SL -> Target SSL)
-for TARGET in "CropDisease" "ISIC" "EuroSAT" "ChestX" "places" "cub" "plantae" "cars"; do
-  python finetune.py --pls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "default" --n_shot $N_SHOT
-  python finetune.py --pls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "default" --n_shot $N_SHOT
-done
+python finetune.py --pls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "default" --n_shot $N_SHOT
+python finetune.py --pls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "default" --n_shot $N_SHOT
 
 # Two-Stage MSL (Source SL -> Source SL + Target SSL)
-for TARGET in "CropDisease" "ISIC" "EuroSAT" "ChestX" "places" "cub" "plantae" "cars"; do
-  python finetune.py --pls --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "gamma78" --n_shot $N_SHOT
-  python finetune.py --pls --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "gamma78" --n_shot $N_SHOT
-done
+python finetune.py --pls --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "simclr" --tag "gamma78" --n_shot $N_SHOT
+python finetune.py --pls --ls --ut --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "byol" --tag "gamma78" --n_shot $N_SHOT
diff --git a/pretrain.sh b/pretrain.sh
@@ -11,7 +11,7 @@ TARGET=${TARGETS[0]}
 BACKBONE=resnet18  # for tiered and full imagenet
 
 
-# Source SL (note, we adapt the torchvision pre-trained model for ResNet50 + ImageNet. Do not use this command as-is.)
+# Source SL (note, we adapt the torchvision pre-trained model for ResNet18 + ImageNet. Do not use this command as-is.)
 python pretrain.py --ls --source_dataset $SOURCE --target_dataset $TARGET --backbone $BACKBONE --model "base" --tag "default"
 
 # Target SSL