forked from clcarwin/sphereface_pytorch
-
Notifications
You must be signed in to change notification settings - Fork 0
/
train.py
146 lines (113 loc) · 4.25 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
from __future__ import print_function
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
from torch.autograd import Variable
torch.backends.cudnn.bencmark = True
import os,sys,cv2,random,datetime
import argparse
import numpy as np
from dataset import ImageDataset
from matlab_cp2tform import get_similarity_transform_for_cv2
import net_sphere
parser = argparse.ArgumentParser(description='PyTorch sphereface')
parser.add_argument('--net','-n', default='sphere20a', type=str)
parser.add_argument('--dataset', default='../../dataset/face/casia/casia.zip', type=str)
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--bs', default=256, type=int, help='')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
def alignment(src_img,src_pts):
of = 2
ref_pts = [ [30.2946+of, 51.6963+of],[65.5318+of, 51.5014+of],
[48.0252+of, 71.7366+of],[33.5493+of, 92.3655+of],[62.7299+of, 92.2041+of] ]
crop_size = (96+of*2, 112+of*2)
s = np.array(src_pts).astype(np.float32)
r = np.array(ref_pts).astype(np.float32)
tfm = get_similarity_transform_for_cv2(s, r)
face_img = cv2.warpAffine(src_img, tfm, crop_size)
return face_img
def dataset_load(name,filename,pindex,cacheobj,zfile):
position = filename.rfind('.zip:')
zipfilename = filename[0:position+4]
nameinzip = filename[position+5:]
split = nameinzip.split('\t')
nameinzip = split[0]
classid = int(split[1])
src_pts = []
for i in range(5):
src_pts.append([int(split[2*i+2]),int(split[2*i+3])])
data = np.frombuffer(zfile.read(nameinzip),np.uint8)
img = cv2.imdecode(data,1)
img = alignment(img,src_pts)
if ':train' in name:
if random.random()>0.5: img = cv2.flip(img,1)
if random.random()>0.5:
rx = random.randint(0,2*2)
ry = random.randint(0,2*2)
img = img[ry:ry+112,rx:rx+96,:]
else:
img = img[2:2+112,2:2+96,:]
else:
img = img[2:2+112,2:2+96,:]
img = img.transpose(2, 0, 1).reshape((1,3,112,96))
img = ( img - 127.5 ) / 128.0
label = np.zeros((1,1),np.float32)
label[0,0] = classid
return (img,label)
def printoneline(*argv):
s = ''
for arg in argv: s += str(arg) + ' '
s = s[:-1]
sys.stdout.write('\r'+s)
sys.stdout.flush()
def save_model(model,filename):
state = model.state_dict()
for key in state: state[key] = state[key].clone().cpu()
torch.save(state, filename)
def dt():
return datetime.datetime.now().strftime('%H:%M:%S')
def train(epoch,args):
net.train()
train_loss = 0
correct = 0
total = 0
batch_idx = 0
ds = ImageDataset(args.dataset,dataset_load,'data/casia_landmark.txt',name=args.net+':train',
bs=args.bs,shuffle=True,nthread=6,imagesize=128)
while True:
img,label = ds.get()
if img is None: break
inputs = torch.from_numpy(img).float()
targets = torch.from_numpy(label[:,0]).long()
if use_cuda: inputs, targets = inputs.cuda(), targets.cuda()
optimizer.zero_grad()
inputs, targets = Variable(inputs), Variable(targets)
outputs = net(inputs)
loss = criterion(outputs, targets)
lossd = loss.data[0]
loss.backward()
optimizer.step()
train_loss += loss.data[0]
outputs = outputs[0] # 0=cos_theta 1=phi_theta
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum()
printoneline(dt(),'Te=%d Loss=%.4f | AccT=%.4f%% (%d/%d) %.4f %.2f %d'
% (epoch,train_loss/(batch_idx+1), 100.0*correct/total, correct, total,
lossd, criterion.lamb, criterion.it))
batch_idx += 1
print('')
net = getattr(net_sphere,args.net)()
# net.load_state_dict(torch.load('sphere20a_0.pth'))
net.cuda()
criterion = net_sphere.AngleLoss()
print('start: time={}'.format(dt()))
for epoch in range(0, 20):
if epoch in [0,10,15,18]:
if epoch!=0: args.lr *= 0.1
optimizer = optim.SGD(net.parameters(), lr=args.lr, momentum=0.9, weight_decay=5e-4)
train(epoch,args)
save_model(net, '{}_{}.pth'.format(args.net,epoch))
print('finish: time={}\n'.format(dt()))