/
model.py
182 lines (159 loc) · 7.23 KB
/
model.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
174
175
176
177
178
179
180
181
182
import torch
import torch.optim as optim
from utils.io import load_ckpt
from utils.io import save_ckpt
from torchvision.utils import make_grid
from torchvision.utils import save_image
from modules.RFRNet import RFRNet, VGG16FeatureExtractor
import os
import time
class RFRNetModel():
def __init__(self):
self.G = None
self.lossNet = None
self.iter = None
self.optm_G = None
self.device = None
self.real_A = None
self.real_B = None
self.fake_B = None
self.comp_B = None
self.l1_loss_val = 0.0
def initialize_model(self, path=None, train=True):
self.G = RFRNet()
self.optm_G = optim.Adam(self.G.parameters(), lr = 2e-4)
if train:
self.lossNet = VGG16FeatureExtractor()
try:
start_iter = load_ckpt(path, [('generator', self.G)], [('optimizer_G', self.optm_G)])
if train:
self.optm_G = optim.Adam(self.G.parameters(), lr = 2e-4)
print('Model Initialized, iter: ', start_iter)
self.iter = start_iter
except:
print('No trained model, from start')
self.iter = 0
def cuda(self):
if torch.cuda.is_available():
self.device = torch.device("cuda")
print("Model moved to cuda")
self.G.cuda()
if self.lossNet is not None:
self.lossNet.cuda()
else:
self.device = torch.device("cpu")
def train(self, train_loader, save_path, finetune = False, iters=450000):
# writer = SummaryWriter(log_dir="log_info")
self.G.train(finetune = finetune)
if finetune:
self.optm_G = optim.Adam(filter(lambda p:p.requires_grad, self.G.parameters()), lr = 5e-5)
print("Starting training from iteration:{:d}".format(self.iter))
s_time = time.time()
while self.iter<iters:
for items in train_loader:
gt_images, masks = self.__cuda__(*items)
masked_images = gt_images * masks
self.forward(masked_images, masks, gt_images)
self.update_parameters()
self.iter += 1
if self.iter % 50 == 0:
e_time = time.time()
int_time = e_time - s_time
print("Iteration:%d, l1_loss:%.4f, time_taken:%.2f" %(self.iter, self.l1_loss_val/50, int_time))
s_time = time.time()
self.l1_loss_val = 0.0
if self.iter % 40000 == 0:
if not os.path.exists('{:s}'.format(save_path)):
os.makedirs('{:s}'.format(save_path))
save_ckpt('{:s}/g_{:d}.pth'.format(save_path, self.iter ), [('generator', self.G)], [('optimizer_G', self.optm_G)], self.iter)
if not os.path.exists('{:s}'.format(save_path)):
os.makedirs('{:s}'.format(save_path))
save_ckpt('{:s}/g_{:s}.pth'.format(save_path, "final"), [('generator', self.G)], [('optimizer_G', self.optm_G)], self.iter)
def test(self, test_loader, result_save_path):
self.G.eval()
for para in self.G.parameters():
para.requires_grad = False
count = 0
for items in test_loader:
gt_images, masks = self.__cuda__(*items)
masked_images = gt_images * masks
masks = torch.cat([masks]*3, dim = 1)
fake_B, mask = self.G(masked_images, masks)
comp_B = fake_B * (1 - masks) + gt_images * masks
if not os.path.exists('{:s}/results'.format(result_save_path)):
os.makedirs('{:s}/results'.format(result_save_path))
for k in range(comp_B.size(0)):
count += 1
grid = make_grid(comp_B[k:k+1])
file_path = '{:s}/results/img_{:d}.png'.format(result_save_path, count)
save_image(grid, file_path)
grid = make_grid(masked_images[k:k+1] +1 - masks[k:k+1] )
file_path = '{:s}/results/masked_img_{:d}.png'.format(result_save_path, count)
save_image(grid, file_path)
def forward(self, masked_image, mask, gt_image):
self.real_A = masked_image
self.real_B = gt_image
self.mask = mask
fake_B, _ = self.G(masked_image, mask)
self.fake_B = fake_B
self.comp_B = self.fake_B * (1 - mask) + self.real_B * mask
def update_parameters(self):
self.update_G()
self.update_D()
def update_G(self):
self.optm_G.zero_grad()
loss_G = self.get_g_loss()
loss_G.backward()
self.optm_G.step()
def update_D(self):
return
def get_g_loss(self):
real_B = self.real_B
fake_B = self.fake_B
comp_B = self.comp_B
real_B_feats = self.lossNet(real_B)
fake_B_feats = self.lossNet(fake_B)
comp_B_feats = self.lossNet(comp_B)
tv_loss = self.TV_loss(comp_B * (1 - self.mask))
style_loss = self.style_loss(real_B_feats, fake_B_feats) + self.style_loss(real_B_feats, comp_B_feats)
preceptual_loss = self.preceptual_loss(real_B_feats, fake_B_feats) + self.preceptual_loss(real_B_feats, comp_B_feats)
valid_loss = self.l1_loss(real_B, fake_B, self.mask)
hole_loss = self.l1_loss(real_B, fake_B, (1 - self.mask))
loss_G = ( tv_loss * 0.1
+ style_loss * 120
+ preceptual_loss * 0.05
+ valid_loss * 1
+ hole_loss * 6)
self.l1_loss_val += valid_loss.detach() + hole_loss.detach()
return loss_G
def l1_loss(self, f1, f2, mask = 1):
return torch.mean(torch.abs(f1 - f2)*mask)
def style_loss(self, A_feats, B_feats):
assert len(A_feats) == len(B_feats), "the length of two input feature maps lists should be the same"
loss_value = 0.0
for i in range(len(A_feats)):
A_feat = A_feats[i]
B_feat = B_feats[i]
_, c, w, h = A_feat.size()
A_feat = A_feat.view(A_feat.size(0), A_feat.size(1), A_feat.size(2) * A_feat.size(3))
B_feat = B_feat.view(B_feat.size(0), B_feat.size(1), B_feat.size(2) * B_feat.size(3))
A_style = torch.matmul(A_feat, A_feat.transpose(2, 1))
B_style = torch.matmul(B_feat, B_feat.transpose(2, 1))
loss_value += torch.mean(torch.abs(A_style - B_style)/(c * w * h))
return loss_value
def TV_loss(self, x):
h_x = x.size(2)
w_x = x.size(3)
h_tv = torch.mean(torch.abs(x[:,:,1:,:]-x[:,:,:h_x-1,:]))
w_tv = torch.mean(torch.abs(x[:,:,:,1:]-x[:,:,:,:w_x-1]))
return h_tv + w_tv
def preceptual_loss(self, A_feats, B_feats):
assert len(A_feats) == len(B_feats), "the length of two input feature maps lists should be the same"
loss_value = 0.0
for i in range(len(A_feats)):
A_feat = A_feats[i]
B_feat = B_feats[i]
loss_value += torch.mean(torch.abs(A_feat - B_feat))
return loss_value
def __cuda__(self, *args):
return (item.to(self.device) for item in args)