-
Notifications
You must be signed in to change notification settings - Fork 0
/
rotation.py
42 lines (36 loc) · 1.3 KB
/
rotation.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
import torch
import torch.optim as optim
import torch.nn as nn
from dataloader import RotationLoader
from model import Network
from train import train_model
from utils import save_state_dict
data_file = 'images.txt'
num_angles = 4
lr = 0.001
batch_size = 64
epochs = 200
model_path = None
filename = 'checkpoint_rotation_resnet50.pth.tar'
log_filename = 'log_rotation_resnet50.log'
state_dict_file_name = 'sd_rotation_resnet50.pth'
use_gpu = torch.cuda.is_available()
num_workers = 3
shuffle = True
device = torch.device("cuda" if use_gpu else "cpu")
dataset = RotationLoader(data_file, num_angles)
train_loader = torch.utils.data.DataLoader(dataset, shuffle = shuffle, batch_size = batch_size, num_workers = num_workers)
if model_path is not None:
checkpoint = torch.load(model_path)
start_epoch = checkpoint['epoch'] + 1
print('\nLoaded checkpoint from epoch %d.\n' % start_epoch)
net = checkpoint['model']
optimizer = checkpoint['optimizer']
else:
net = Network(num_outputs = num_angles)
optimizer = optim.SGD(net.parameters(), lr = lr, momentum = 0.9)
start_epoch = 0
net = net.to(device)
criterion = nn.CrossEntropyLoss()
train_model(net, criterion, optimizer, start_epoch, epochs, dataset, train_loader, device, filename, log_filename)
save_state_dict(filename, state_dict_file_name)