-
Notifications
You must be signed in to change notification settings - Fork 29
/
train.py
173 lines (140 loc) · 5.41 KB
/
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
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
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
import argparse
import time
from models import *
from utils.datasets import *
from utils.utils import *
parser = argparse.ArgumentParser()
parser.add_argument('-epochs', type=int, default=100, help='number of epochs')
parser.add_argument('-batch_size', type=int, default=4, help='size of each image batch')
parser.add_argument('-data_config_path', type=str, default='cfg/ICDAR2015.data', help='data config file path')
parser.add_argument('-cfg', type=str, default='cfg/yolov3.cfg', help='cfg file path')
parser.add_argument('-img_size', type=int, default=32 * 19, help='size of each image dimension')
parser.add_argument('-resume', default=False, help='resume training flag')
opt = parser.parse_args()
print(opt)
cuda = torch.cuda.is_available()
device = torch.device('cuda:0' if cuda else 'cpu')
random.seed(0)
np.random.seed(0)
torch.manual_seed(0)
if cuda:
torch.cuda.manual_seed(0)
torch.cuda.manual_seed_all(0)
torch.backends.cudnn.benchmark = True
def main(opt):
os.makedirs('weights', exist_ok=True)
# Configure run
data_config = parse_data_config(opt.data_config_path)
num_classes = int(data_config['classes'])
train_path = data_config['train']
# Initialize model
model = Darknet(opt.cfg, opt.img_size)
# Get dataloader
dataloader = load_images_and_labels(train_path, batch_size=opt.batch_size, img_size=opt.img_size, augment=True)
# Reload saved optimizer state
start_epoch = 0
best_loss = float('inf')
if opt.resume:
checkpoint = torch.load('weights/latest.pt', map_location='cpu')
model.load_state_dict(checkpoint['model'])
#if torch.cuda.device_count() > 1:
# print('Using ', torch.cuda.device_count(), ' GPUs')
# model = nn.DataParallel(model)
model.to(device).train()
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()),
lr=1e-3, momentum=.9, weight_decay=5e-4)
start_epoch = checkpoint['epoch'] + 1
if checkpoint['optimizer'] is not None:
optimizer.load_state_dict(checkpoint['optimizer'])
best_loss = checkpoint['best_loss']
del checkpoint # current, saved
else:
# Initialize model with darknet53 weights (optional)
if not os.path.isfile('weights/darknet53.conv.74'):
os.system('wget https://pjreddie.com/media/files/darknet53.conv.74 -P weights')
load_weights(model, 'weights/darknet53.conv.74')
model.to(device).train()
optimizer = torch.optim.SGD(model.parameters(), lr=1e-3, momentum=.9, weight_decay=5e-4)
#model_info(model)
t0, t1 = time.time(), time.time()
mean_recall, mean_precision = 0, 0
print('%11s' * 12 % (
'Epoch', 'Batch', 'conf', 'cls', 'loss', 'P', 'R', 'nTargets', 'TP', 'FP', 'FN', 'time'))
for epoch in range(opt.epochs):
epoch += start_epoch
# Update scheduler (manual) at 0, 54, 61 epochs to 1e-3, 1e-4, 1e-5
if epoch < 30:
lr = 1e-4
elif epoch < 60:
lr = 1e-5
else:
lr = 1e-6
for g in optimizer.param_groups:
g['lr'] = lr
ui = -1
rloss = defaultdict(float) # running loss
metrics = torch.zeros(3, num_classes)
optimizer.zero_grad()
for i, (imgs, targets) in enumerate(dataloader):
if sum([len(x) for x in targets]) < 1: # if no targets continue
continue
# SGD burn-in
if (epoch == 0) & (i <= 1000):
lr = 1e-4 * (i / 1000) ** 4
for g in optimizer.param_groups:
g['lr'] = lr
# Compute loss, compute gradient, update parameters
loss = model(imgs.to(device), targets, requestPrecision=True)
loss.backward()
# accumulated_batches = 1 # accumulate gradient for 4 batches before stepping optimizer
# if ((i+1) % accumulated_batches == 0) or (i == len(dataloader) - 1):
optimizer.step()
optimizer.zero_grad()
# Compute running epoch-means of tracked metrics
ui += 1
metrics += model.losses['metrics']
TP, FP, FN = metrics
for key, val in model.losses.items():
rloss[key] = (rloss[key] * ui + val) / (ui + 1)
# Precision
precision = TP / (TP + FP)
k = (TP + FP) > 0
if k.sum() > 0:
mean_precision = precision[k].mean()
# Recall
recall = TP / (TP + FN)
k = (TP + FN) > 0
if k.sum() > 0:
mean_recall = recall[k].mean()
s = ('%11s%11s' + '%11.3g' * 10) % (
'%g/%g' % (epoch, opt.epochs - 1), '%g/%g' % (i, len(dataloader) - 1), rloss['conf'], rloss['cls'],
rloss['loss'], mean_precision, mean_recall, model.losses['nT'], model.losses['TP'],
model.losses['FP'], model.losses['FN'], time.time() - t1)
t1 = time.time()
print(s)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '\n')
# Update best loss
loss_per_target = rloss['loss'] / rloss['nT']
if loss_per_target < best_loss:
best_loss = loss_per_target
# Save latest checkpoint
checkpoint = {'epoch': epoch,
'best_loss': best_loss,
'model': model.state_dict(),
'optimizer': optimizer.state_dict()}
torch.save(checkpoint, 'weights/latest.pt')
# Save best checkpoint
if best_loss == loss_per_target:
os.system('cp weights/latest.pt weights/best.pt')
# Save backup weights every 5 epochs
if (epoch > 0) & (epoch % 20 == 0):
os.system('cp weights/latest.pt weights/backup' + str(epoch) + '.pt')
# Save final model
dt = time.time() - t0
print('Finished %g epochs in %.2fs (%.2fs/epoch)' % (epoch, dt, dt / (epoch + 1)))
if __name__ == '__main__':
torch.cuda.empty_cache()
main(opt)
torch.cuda.empty_cache()