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train_cifar100_10t.sh
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#!/bin/sh
t=0
# Representation train
CUDA_VISIBLE_DEVICES=0 \
python train.py \
--dataset cifar100_10t \
--model resnet18 \
--mode sup_simclr_CSI \
--batch_size 128 \
--epoch 700 \
--t $t \
--amp \
--lamb0 1.5 \
--lamb1 1.0
# Linear layer train
CUDA_VISIBLE_DEVICES=0 \
python train.py \
--mode sup_CSI_linear \
--dataset cifar100_10t \
--model resnet18 \
--batch_size 128 \
--epoch 100 \
--t $t
# ACCURACY & ECE
CUDA_VISIBLE_DEVICES=0 \
python eval.py \
--mode test_marginalized_acc \
--dataset cifar100_10t \
--model resnet18 \
--t $t \
--all_dataset \
--printfn 'til.txt'
CUDA_VISIBLE_DEVICES=0 \
python eval.py \
--mode cil \
--dataset cifar100_10t \
--model resnet18 \
--batch_size 128 \
--cil_task $t \
--all_dataset \
--printfn "cil results.txt"
CUDA_VISIBLE_DEVICES=0 \
python eval.py \
--mode cil_pre \
--dataset cifar100_10t \
--model resnet18 \
--batch_size 32 \
--cil_task $t \
--printfn "calibration.txt" \
--adaptation_lr 0.01 \
--weight_decay=0
for t in 1 2 3 4 5 6 7 8 9
do
# Representation train
CUDA_VISIBLE_DEVICES=0 \
python train.py \
--dataset cifar100_10t \
--model resnet18 \
--mode sup_simclr_CSI \
--batch_size 128 \
--epoch 700 \
--t $t \
--amp \
--lamb0 1.5 \
--lamb1 1.0
# linear layer train
CUDA_VISIBLE_DEVICES=0 \
python train.py \
--mode sup_CSI_linear \
--dataset cifar100_10t \
--model resnet18 \
--batch_size 128 \
--epoch 100 \
--t $t
# ACCURACY & ECE
CUDA_VISIBLE_DEVICES=0 \
python eval.py \
--mode test_marginalized_acc \
--dataset cifar100_10t \
--model resnet18 \
--t $t \
--all_dataset \
--printfn 'til.txt'
CUDA_VISIBLE_DEVICES=0 \
python eval.py \
--mode cil \
--dataset cifar100_10t \
--model resnet18 \
--batch_size 128 \
--cil_task $t \
--all_dataset \
--printfn "cil results.txt"
CUDA_VISIBLE_DEVICES=0 \
python eval.py \
--mode cil_pre \
--dataset cifar100_10t \
--model resnet18 \
--batch_size 32 \
--cil_task $t \
--printfn "calibration.txt" \
--adaptation_lr 0.01 \
--weight_decay=0
done