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example_train_script.sh
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example_train_script.sh
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# example scripts for running various KD methods
# use cifar10, resnet110 (teacher) and resnet20 (student) as examples
# Baseline
CUDA_VISIBLE_DEVICES=0 python -u train_base.py \
--save_root "./results/base/" \
--data_name cifar10 \
--num_class 10 \
--net_name resnet20 \
--note base-c10-r20
# Logits
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/logits/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode logits \
--lambda_kd 0.1 \
--note logits-c10-r110-r20
# SoftTarget
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/st/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode st \
--lambda_kd 0.1 \
--T 4.0 \
--note st-c10-r110-r20
# AT
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/at/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode at \
--lambda_kd 1000.0 \
--p 2.0 \
--note at-c10-r110-r20
# Fitnet
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/fitnet/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode fitnet \
--lambda_kd 0.1 \
--note fitnet-c10-r110-r20
# NST
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/nst/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode nst \
--lambda_kd 10.0 \
--note nst-c10-r110-r20
# PKT
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/pkt/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode pkt \
--lambda_kd 10000.0 \
--note pkt-c10-r110-r20
# FSP
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/fsp/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode fsp \
--lambda_kd 1.0 \
--note fsp-c10-r110-r20
# FT
CUDA_VISIBLE_DEVICES=0 python -u train_ft.py \
--save_root "./results/ft/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--lambda_kd 200.0 \
--k 0.5 \
--note ft-c10-r110-r20
# RKD
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/rkd/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode rkd \
--lambda_kd 1.0 \
--w_dist 25.0 \
--w_angle 50.0 \
--note rkd-c10-r110-r20
# AB
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/ab/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode ab \
--lambda_kd 10.0 \
--m 2.0 \
--note ab-c10-r110-r20
# SP
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/sp/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode sp \
--lambda_kd 3000.0 \
--note sp-c10-r110-r20
# Sobolev
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/sobolev/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode sobolev \
--lambda_kd 20.0 \
--note sobolev-c10-r110-r20
# BSS
CUDA_VISIBLE_DEVICES=0 python -u train_bss.py \
--save_root "./results/bss/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--lambda_kd 2.0 \
--T 3.0 \
--attack_size 32 \
--note bss-c10-r110-r20
# CC
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/cc/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode cc \
--lambda_kd 100.0 \
--gamma 0.4 \
--P_order 2 \
--note cc-c10-r110-r20
# LwM
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/lwm/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode lwm \
--lambda_kd 0.4 \
--note lwm-c10-r110-r20
# IRG
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/irg/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode irg \
--lambda_kd 1.0 \
--w_irg_vert 0.1 \
--w_irg_edge 5.0 \
--w_irg_tran 5.0 \
--note irg-c10-r110-r20
# VID
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/vid/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode vid \
--lambda_kd 1.0 \
--sf 1.0 \
--init_var 5.0 \
--note vid-c10-r110-r20
# OFD
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/ofd/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode ofd \
--lambda_kd 0.5 \
--note ofd-c10-r110-r20
# AFD
CUDA_VISIBLE_DEVICES=0 python -u train_kd.py \
--save_root "./results/afd/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--kd_mode afd \
--lambda_kd 500.0 \
--att_f 1.0 \
--note afd-c10-r110-r20
# CRD
# lambda_kd=0.2 for CIFAR10, lambda_kd=0.8 for CIFAR100.
CUDA_VISIBLE_DEVICES=0 python -u train_crd.py \
--save_root "./results/crd/" \
--t_model "./results/base/base-c10-r110/model_best.pth.tar" \
--s_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--t_name resnet110 \
--s_name resnet20 \
--lambda_kd 0.2 \
--feat_dim 128 \
--nce_n 16384 \
--nce_t 0.1 \
--nce_mom 0.5 \
--mode 'exact' \
--note crd-c10-r110-r20
# DML
CUDA_VISIBLE_DEVICES=0 python -u train_dml.py \
--save_root "./results/dml/" \
--net1_init "./results/base/base-c10-r110/initial_r110.pth.tar" \
--net2_init "./results/base/base-c10-r20/initial_r20.pth.tar" \
--data_name cifar10 \
--num_class 10 \
--net1_name resnet110 \
--net2_name resnet20 \
--lambda_kd 1.0 \
--note dml-c10-r110-r20