-
Notifications
You must be signed in to change notification settings - Fork 0
/
run_train.sh
54 lines (40 loc) · 3.22 KB
/
run_train.sh
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
#!/bin/sh
#export CUDA_VISIBLE_DEVICES=1
#CUDA_VISIBLE_DEVICES=1 python main_match.py --method ENT --dataset multi --source real --target sketch --num 3 --net resnet34 --save_check
#CUDA_VISIBLE_DEVICES=0,1 python main_match.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --augmentation_policy rand_augment --save_check
#CUDA_VISIBLE_DEVICES=0,1 python main_max_acc.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --augmentation_policy rand_augment --save_check
#CUDA_VISIBLE_DEVICES=0,1 python main_match_majvot.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --augmentation_policy rand_augment --save_check
####CUDA_LAUNCH_BLOCKING=1
CUDA_VISIBLE_DEVICES=1 python main_classwise.py \
--method MME \
--dataset multi \
--source real \
--target sketch \
--num 3 \
--use_new_features 0 \
--net resnet34 \
--which_method SEW \
--patience 7 \
--data_parallel 0 \
--weigh_using target_acc \
--num_to_weigh 3 \
--save_interval 500 \
--log_interval 100 \
--label_target_iteration 8000 \
--SEW_iteration 2000 \
--SEW_interval 1000 \
--thresh 0.5 \
--phi 0.5 \
#CUDA_VISIBLE_DEVICES=0,1 python main_classwise.py --method MME --dataset multi --source real --target painting --num 3 --net resnet34 --augmentation_policy rand_augment --which_method SEW --uda 1 --use_bank 1 --use_cb 0 --use_new_features 0 --patience 5 --data_parallel 1 --weigh_using target_acc --num_to_weigh 5 --save_check
#CUDA_VISIBLE_DEVICES=0,1 python main_match_knn_analysis.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --augmentation_policy rand_augment --save_check
#CUDA_VISIBLE_DEVICES=0,1 python main_match_confident_source.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --augmentation_policy rand_augment --save_check
# Useful command to kill all nvidia processes
#fuser -v /dev/nvidia*
#CUDA_VISIBLE_DEVICES=0,1 python main_match_bank_analysis.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --augmentation_policy rand_augment --save_check
#CUDA_VISIBLE_DEVICES=1 python main_rot.py --dataset multi --target sketch --num 3 --net resnet34 --save_check
#CUDA_VISIBLE_DEVICES=1 python main_rot.py --dataset multi --target clipart --num 3 --net resnet34 --save_check
#CUDA_VISIBLE_DEVICES=0 python main_rot.py --dataset multi --target painting --num 3 --net resnet34 --save_check
#CUDA_VISIBLE_DEVICES=1 python main.py --method MME --dataset multi --source real --target sketch --num 3 --net resnet34 --pretrained_ckpt ./save_model_ssda/model_sketch_step_22500.pth.tar --save_check
#CUDA_VISIBLE_DEVICES=1 python main.py --method MME --dataset multi --source real --target clipart --num 3 --net resnet34 --pretrained_ckpt ./save_model_ssda/model_clipart_step_28000.pth.tar --save_check
#CUDA_VISIBLE_DEVICES=0 python main.py --method MME --dataset multi --source painting --target real --num 3 --net resnet34 --pretrained_ckpt ./save_model_ssda/model_real_step_21000.pth.tar --save_check
#CUDA_VISIBLE_DEVICES=0 python main.py --method MME --dataset multi --source real --target painting --num 3 --net resnet34 --pretrained_ckpt ./save_model_ssda/model_painting_step_22500.pth.tar --save_check