-
Notifications
You must be signed in to change notification settings - Fork 0
/
transfer.py
87 lines (67 loc) · 3.19 KB
/
transfer.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
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
import os, argparse
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from torchvision import datasets
from src.model import Classifier, resnet18_encoder, ProjectionHead
from src.procedures import train_classifier, test
from src.utils import load_checkpoint, save_checkpoint_classifier, load_checkpoint_classifier
def main(args):
in_dim = 512
projection_hidden_dim = 2048
classifier_hidden_dim = 1024
device = 'cuda' if torch.cuda.is_available() else 'cpu'
data_transform = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.5, 0.5, 0.5],
std=[0.5, 0.5, 0.5])])
if args.dataset == 'CIFAR-10':
classifier_path = 'checkpoints/classifier_cifar10.pt'
num_classes = 10
train_dataset = datasets.CIFAR10(
'./data', train=True, download=True, transform=data_transform)
test_dataset = datasets.CIFAR10(
'./data', train=False, download=True, transform=data_transform)
elif args.dataset == 'CIFAR-100':
classifier_path = 'checkpoints/classifier_cifar100.pt'
num_classes = 100
train_dataset = datasets.CIFAR100(
'./data', train=True, download=True, transform=data_transform)
test_dataset = datasets.CIFAR100(
'./data', train=False, download=True, transform=data_transform)
f, g = resnet18_encoder().to(device), ProjectionHead(in_dim, projection_hidden_dim).to(device)
load_checkpoint(f, g, args.checkpoint)
classifier = Classifier(in_dim, num_classes, classifier_hidden_dim).to(device)
if not os.path.exists(classifier_path):
### Train Classifier ###
train_loader = DataLoader(
train_dataset, batch_size=args.batch_size, num_workers=args.num_worker)
criterion = nn.CrossEntropyLoss()
if args.fine_tuning:
params = list(f.parameters()) + list(classifier.parameters())
else:
params = classifier.parameters()
optimizer = torch.optim.Adam(params, lr=1e-4)
train_classifier(args.epochs, train_loader,
f, classifier, criterion, optimizer)
save_checkpoint_classifier(classifier, classifier_path)
else:
load_checkpoint_classifier(classifier, classifier_path)
### Test ###
test_loader = DataLoader(
test_dataset, batch_size=args.batch_size, num_workers=args.num_worker)
accuracy = test(test_loader, f, classifier)
print("Test Accuracy : %.4f" % (accuracy))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description="SimCLR implementation")
parser.add_argument('--epochs', type=int, default=100)
parser.add_argument('--batch_size', type=int, default=128)
parser.add_argument('--num_worker', type=int, default=8)
parser.add_argument('--checkpoint', type=str,
default='checkpoints/best.pt')
parser.add_argument('--fine_tuning', action='store_true', default=False)
parser.add_argument('--dataset', type=str, default='CIFAR-10',
choices=['CIFAR-10', 'CIFAR-100'])
args = parser.parse_args()
main(args)