forked from HobbitLong/SupContrast
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathscript.py
63 lines (49 loc) · 4.72 KB
/
script.py
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
55
56
57
58
59
60
61
62
63
from random import choice
from cchelper import JobSubmiter
epoch = 1000
test_epoch=100
batch_size = 750
account = ["def-mpederso", "rrg-mpederso"]
# jobs = ["python main_supcon.py --batch_size 768 --learning_rate 0.5 --temp 0.1 --cosine ",
# "python main_ce.py --batch_size 768 --learning_rate 0.8 --cosine ",
# "python main_supcon.py --batch_size 768 --learning_rate 0.5 --temp 0.5 --cosine --method SimCLR"
# ]
baseline = [
# supcontrast
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.1 --epochs={epoch} --cosine --save_dir=save/supcontrast/baseline && "
f"python main_linear.py --batch_size {batch_size} --learning_rate 5 --epochs={test_epoch} --ckpt save/supcontrast/baseline/last.pth > save/supcontrast/baseline/result.txt",
# simclr
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --method SimCLR --save_dir=save/simclr/baseline &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/simclr/baseline/last.pth > save/simclr/baseline/result.txt",
# sup_ce
f"python main_ce.py --batch_size {batch_size} --learning_rate 0.8 --epochs={epoch} --cosine --save_dir=save/sup_ce > save/sup_ce.txt"
]
proposed1 = [
# simclr
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --method SimCLR --cluster_regweigt=0.0001 --train_cluster --save_dir=save/simclr/cluster_0.0001 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/simclr/cluster_0.0001/last.pth > save/simclr/cluster_0.0001/result.txt",
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --method SimCLR --cluster_regweigt=0.001 --train_cluster --save_dir=save/simclr/cluster_0.001 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/simclr/cluster_0.001/last.pth > save/simclr/cluster_0.001/result.txt",
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --method SimCLR --cluster_regweigt=0.01 --train_cluster --save_dir=save/simclr/cluster_0.01 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/simclr/cluster_0.01/last.pth > save/simclr/cluster_0.01/result.txt",
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --method SimCLR --cluster_regweigt=0.1 --train_cluster --save_dir=save/simclr/cluster_0.1 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/simclr/cluster_0.1/last.pth > save/simclr/cluster_0.1/result.txt",
]
proposed2 = [
# supcontrast
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --cluster_regweigt=0.0001 --train_cluster --save_dir=save/supcontrast/cluster_0.0001 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/supcontrast/cluster_0.0001/last.pth > save/supcontrast/cluster_0.0001/result.txt",
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --cluster_regweigt=0.001 --train_cluster --save_dir=save/supcontrast/cluster_0.001 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/supcontrast/cluster_0.001/last.pth > save/supcontrast/cluster_0.001/result.txt",
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --cluster_regweigt=0.01 --train_cluster --save_dir=save/supcontrast/cluster_0.01 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/supcontrast/cluster_0.01/last.pth > save/supcontrast/cluster_0.01/result.txt",
f"python main_supcon.py --batch_size {batch_size} --learning_rate 0.5 --temp 0.5 --epochs={epoch} --cosine --cluster_regweigt=0.1 --train_cluster --save_dir=save/supcontrast/cluster_0.1 &&"
f"python main_linear.py --batch_size {batch_size} --learning_rate 1 --epochs={test_epoch} --ckpt save/supcontrast/cluster_0.1/last.pth > save/supcontrast/cluster_0.1/result.txt",
]
submitter = JobSubmiter(project_path="./", time=20, mem=32, gres="gpu:4", on_local=False,cpus_per_task=24)
submitter.prepare_env(
["source ./venv/bin/activate", "export OMP_NUM_THREADS=1 ", ]
)
for cmd in [*baseline, *proposed1]:
submitter.account = choice(account)
submitter.run(cmd)