-
Notifications
You must be signed in to change notification settings - Fork 6
/
imagenet_train.py
48 lines (40 loc) · 1.46 KB
/
imagenet_train.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
import argparse
import torch.nn as nn
import torch.optim as optim
from torch.optim.lr_scheduler import StepLR
from back import Bone, utils
from datasets import imagenet
from models.resnet_with_block import resnet50, se_resnet50, srm_resnet50
data_dir = 'imagenet'
model_names = ['resnet', 'senet', 'srmnet']
num_classes = 1000
batch_size = 64
epochs_count = 100
num_workers = 16
parser = argparse.ArgumentParser()
parser.add_argument('--model_name', required=True, choices=model_names)
args = parser.parse_args()
datasets = imagenet.get_datasets(data_dir)
if args.model_name == 'resnet':
model = resnet50(num_classes=num_classes)
elif args.model_name == 'senet':
model = se_resnet50(num_classes=num_classes)
elif args.model_name == 'srmnet':
model = srm_resnet50(num_classes=num_classes)
optimizer = optim.SGD(model.parameters(), lr=0.1, momentum=0.9,
weight_decay=1e-4)
scheduler = StepLR(optimizer, 30, 0.1)
criterion = nn.CrossEntropyLoss()
backbone = Bone(model,
datasets,
criterion,
optimizer,
scheduler=scheduler,
scheduler_after_ep=False,
metric_fn=utils.accuracy_metric,
metric_increase=True,
batch_size=batch_size,
num_workers=num_workers,
weights_path=f'weights/imagenet_best_{args.model_name}.pth',
log_dir=f'logs/imagenet/{args.model_name}')
backbone.fit(epochs_count)