Spaces:
Running
on
Zero
Running
on
Zero
Commit
•
703c10a
1
Parent(s):
242e411
Update third_party/insightface_backbone_conv.py
Browse files- third_party/insightface_backbone_conv.py +236 -236
third_party/insightface_backbone_conv.py
CHANGED
@@ -1,237 +1,237 @@
|
|
1 |
-
import os
|
2 |
-
import torch
|
3 |
-
from torch import nn
|
4 |
-
|
5 |
-
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200', 'getarcface']
|
6 |
-
|
7 |
-
|
8 |
-
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
|
9 |
-
"""3x3 convolution with padding"""
|
10 |
-
return nn.Conv2d(in_planes,
|
11 |
-
out_planes,
|
12 |
-
kernel_size=3,
|
13 |
-
stride=stride,
|
14 |
-
padding=dilation,
|
15 |
-
groups=groups,
|
16 |
-
bias=False,
|
17 |
-
dilation=dilation)
|
18 |
-
|
19 |
-
|
20 |
-
def conv1x1(in_planes, out_planes, stride=1):
|
21 |
-
"""1x1 convolution"""
|
22 |
-
return nn.Conv2d(in_planes,
|
23 |
-
out_planes,
|
24 |
-
kernel_size=1,
|
25 |
-
stride=stride,
|
26 |
-
bias=False)
|
27 |
-
|
28 |
-
|
29 |
-
class IBasicBlock(nn.Module):
|
30 |
-
expansion = 1
|
31 |
-
def __init__(self, inplanes, planes, stride=1, downsample=None,
|
32 |
-
groups=1, base_width=64, dilation=1):
|
33 |
-
super(IBasicBlock, self).__init__()
|
34 |
-
if groups != 1 or base_width != 64:
|
35 |
-
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
|
36 |
-
if dilation > 1:
|
37 |
-
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
|
38 |
-
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
|
39 |
-
self.conv1 = conv3x3(inplanes, planes)
|
40 |
-
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
|
41 |
-
self.prelu = nn.PReLU(planes)
|
42 |
-
self.conv2 = conv3x3(planes, planes, stride)
|
43 |
-
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
|
44 |
-
self.downsample = downsample
|
45 |
-
self.stride = stride
|
46 |
-
|
47 |
-
def forward(self, x):
|
48 |
-
identity = x
|
49 |
-
out = self.bn1(x)
|
50 |
-
out = self.conv1(out)
|
51 |
-
out = self.bn2(out)
|
52 |
-
out = self.prelu(out)
|
53 |
-
out = self.conv2(out)
|
54 |
-
out = self.bn3(out)
|
55 |
-
if self.downsample is not None:
|
56 |
-
identity = self.downsample(x)
|
57 |
-
out += identity
|
58 |
-
return out
|
59 |
-
|
60 |
-
|
61 |
-
class IResNet(nn.Module):
|
62 |
-
fc_scale = 7 * 7
|
63 |
-
def __init__(self,
|
64 |
-
block, layers, dropout=0, num_features=512, zero_init_residual=False,
|
65 |
-
groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
|
66 |
-
super(IResNet, self).__init__()
|
67 |
-
self.fp16 = fp16
|
68 |
-
self.inplanes = 64
|
69 |
-
self.dilation = 1
|
70 |
-
if replace_stride_with_dilation is None:
|
71 |
-
replace_stride_with_dilation = [False, False, False]
|
72 |
-
if len(replace_stride_with_dilation) != 3:
|
73 |
-
raise ValueError("replace_stride_with_dilation should be None "
|
74 |
-
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
|
75 |
-
self.groups = groups
|
76 |
-
self.base_width = width_per_group
|
77 |
-
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
|
78 |
-
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
|
79 |
-
self.prelu = nn.PReLU(self.inplanes)
|
80 |
-
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
|
81 |
-
self.layer2 = self._make_layer(block,
|
82 |
-
128,
|
83 |
-
layers[1],
|
84 |
-
stride=2,
|
85 |
-
dilate=replace_stride_with_dilation[0])
|
86 |
-
self.layer3 = self._make_layer(block,
|
87 |
-
256,
|
88 |
-
layers[2],
|
89 |
-
stride=2,
|
90 |
-
dilate=replace_stride_with_dilation[1])
|
91 |
-
self.layer4 = self._make_layer(block,
|
92 |
-
512,
|
93 |
-
layers[3],
|
94 |
-
stride=2,
|
95 |
-
dilate=replace_stride_with_dilation[2])
|
96 |
-
self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
|
97 |
-
self.dropout = nn.Dropout(p=dropout, inplace=True)
|
98 |
-
self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
|
99 |
-
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
|
100 |
-
nn.init.constant_(self.features.weight, 1.0)
|
101 |
-
self.features.weight.requires_grad = False
|
102 |
-
|
103 |
-
for m in self.modules():
|
104 |
-
if isinstance(m, nn.Conv2d):
|
105 |
-
nn.init.normal_(m.weight, 0, 0.1)
|
106 |
-
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
|
107 |
-
nn.init.constant_(m.weight, 1)
|
108 |
-
nn.init.constant_(m.bias, 0)
|
109 |
-
|
110 |
-
if zero_init_residual:
|
111 |
-
for m in self.modules():
|
112 |
-
if isinstance(m, IBasicBlock):
|
113 |
-
nn.init.constant_(m.bn2.weight, 0)
|
114 |
-
|
115 |
-
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
|
116 |
-
downsample = None
|
117 |
-
previous_dilation = self.dilation
|
118 |
-
if dilate:
|
119 |
-
self.dilation *= stride
|
120 |
-
stride = 1
|
121 |
-
if stride != 1 or self.inplanes != planes * block.expansion:
|
122 |
-
downsample = nn.Sequential(
|
123 |
-
conv1x1(self.inplanes, planes * block.expansion, stride),
|
124 |
-
nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
|
125 |
-
)
|
126 |
-
layers = []
|
127 |
-
layers.append(
|
128 |
-
block(self.inplanes, planes, stride, downsample, self.groups,
|
129 |
-
self.base_width, previous_dilation))
|
130 |
-
self.inplanes = planes * block.expansion
|
131 |
-
for _ in range(1, blocks):
|
132 |
-
layers.append(
|
133 |
-
block(self.inplanes,
|
134 |
-
planes,
|
135 |
-
groups=self.groups,
|
136 |
-
base_width=self.base_width,
|
137 |
-
dilation=self.dilation))
|
138 |
-
|
139 |
-
return nn.Sequential(*layers)
|
140 |
-
|
141 |
-
def forward(self, x, return_id512=False):
|
142 |
-
|
143 |
-
bz = x.shape[0]
|
144 |
-
# with torch.cuda.amp.autocast(self.fp16):
|
145 |
-
x = self.conv1(x)
|
146 |
-
x = self.bn1(x)
|
147 |
-
x = self.prelu(x)
|
148 |
-
x = self.layer1(x)
|
149 |
-
x = self.layer2(x)
|
150 |
-
x = self.layer3(x)
|
151 |
-
x = self.layer4(x)
|
152 |
-
if not return_id512:
|
153 |
-
return x.view(bz,512,-1).permute(0,2,1).contiguous()
|
154 |
-
else:
|
155 |
-
x = self.bn2(x)
|
156 |
-
x = torch.flatten(x, 1)
|
157 |
-
# x = self.dropout(x)
|
158 |
-
# x = self.fc(x.float() if self.fp16 else x)
|
159 |
-
x = self.fc(x)
|
160 |
-
x = self.features(x)
|
161 |
-
return x
|
162 |
-
|
163 |
-
|
164 |
-
|
165 |
-
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
|
166 |
-
model = IResNet(block, layers, **kwargs)
|
167 |
-
if pretrained:
|
168 |
-
raise ValueError()
|
169 |
-
return model
|
170 |
-
|
171 |
-
|
172 |
-
def iresnet18(pretrained=False, progress=True, **kwargs):
|
173 |
-
return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
|
174 |
-
progress, **kwargs)
|
175 |
-
|
176 |
-
|
177 |
-
def iresnet34(pretrained=False, progress=True, **kwargs):
|
178 |
-
return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
|
179 |
-
progress, **kwargs)
|
180 |
-
|
181 |
-
|
182 |
-
def iresnet50(pretrained=False, progress=True, **kwargs):
|
183 |
-
return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
|
184 |
-
progress, **kwargs)
|
185 |
-
|
186 |
-
|
187 |
-
def iresnet100(pretrained=False, progress=True, **kwargs):
|
188 |
-
return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
|
189 |
-
progress, **kwargs)
|
190 |
-
|
191 |
-
|
192 |
-
def iresnet200(pretrained=False, progress=True, **kwargs):
|
193 |
-
return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
|
194 |
-
progress, **kwargs)
|
195 |
-
|
196 |
-
|
197 |
-
def getarcface(pretrained=None):
|
198 |
-
model = iresnet100().eval()
|
199 |
-
for param in model.parameters():
|
200 |
-
param.requires_grad=False
|
201 |
-
|
202 |
-
if pretrained is not None and os.path.exists(pretrained):
|
203 |
-
info = model.load_state_dict(torch.load(pretrained))
|
204 |
-
print(info)
|
205 |
-
return model
|
206 |
-
|
207 |
-
|
208 |
-
if __name__=='__main__':
|
209 |
-
ckpt = 'pretrained/insightface_glint360k.pth'
|
210 |
-
arcface = iresnet100().eval()
|
211 |
-
info = arcface.load_state_dict(torch.load(ckpt))
|
212 |
-
print(info)
|
213 |
-
|
214 |
-
id = arcface(torch.randn(1,3,128,128))
|
215 |
-
print(id.shape)
|
216 |
-
|
217 |
-
# import cv2
|
218 |
-
# import numpy as np
|
219 |
-
# im1_crop256 = cv2.imread('happy.jpg')
|
220 |
-
# im2_crop256 = cv2.imread('angry.jpg')
|
221 |
-
|
222 |
-
# im1_crop112 = cv2.resize(im1_crop256, (128,128))[0:112,8:120,:]
|
223 |
-
# im2_crop112 = cv2.resize(im2_crop256, (128,128))[0:112,8:120,:]
|
224 |
-
|
225 |
-
# cv2.imwrite('1_112.jpg', im1_crop112)
|
226 |
-
# cv2.imwrite('2_112.jpg', im2_crop112)
|
227 |
-
|
228 |
-
# # [-1,1] rgb
|
229 |
-
# im1_crop112_tensor = torch.from_numpy(im1_crop112[:,:,[2,1,0]].transpose(2, 0, 1).astype(np.float32)).unsqueeze(0)/127.5-1
|
230 |
-
# im2_crop112_tensor = torch.from_numpy(im2_crop112[:,:,[2,1,0]].transpose(2, 0, 1).astype(np.float32)).unsqueeze(0)/127.5-1
|
231 |
-
|
232 |
-
# im1_id = arcface(im1_crop112_tensor)
|
233 |
-
# im2_id = arcface(im2_crop112_tensor)
|
234 |
-
|
235 |
-
# loss_cos = torch.mean(1-torch.cosine_similarity(im1_id, im2_id, dim=1))
|
236 |
-
|
237 |
# print(loss_cos)
|
|
|
1 |
+
import os
|
2 |
+
import torch
|
3 |
+
from torch import nn
|
4 |
+
|
5 |
+
__all__ = ['iresnet18', 'iresnet34', 'iresnet50', 'iresnet100', 'iresnet200', 'getarcface']
|
6 |
+
|
7 |
+
|
8 |
+
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
|
9 |
+
"""3x3 convolution with padding"""
|
10 |
+
return nn.Conv2d(in_planes,
|
11 |
+
out_planes,
|
12 |
+
kernel_size=3,
|
13 |
+
stride=stride,
|
14 |
+
padding=dilation,
|
15 |
+
groups=groups,
|
16 |
+
bias=False,
|
17 |
+
dilation=dilation)
|
18 |
+
|
19 |
+
|
20 |
+
def conv1x1(in_planes, out_planes, stride=1):
|
21 |
+
"""1x1 convolution"""
|
22 |
+
return nn.Conv2d(in_planes,
|
23 |
+
out_planes,
|
24 |
+
kernel_size=1,
|
25 |
+
stride=stride,
|
26 |
+
bias=False)
|
27 |
+
|
28 |
+
|
29 |
+
class IBasicBlock(nn.Module):
|
30 |
+
expansion = 1
|
31 |
+
def __init__(self, inplanes, planes, stride=1, downsample=None,
|
32 |
+
groups=1, base_width=64, dilation=1):
|
33 |
+
super(IBasicBlock, self).__init__()
|
34 |
+
if groups != 1 or base_width != 64:
|
35 |
+
raise ValueError('BasicBlock only supports groups=1 and base_width=64')
|
36 |
+
if dilation > 1:
|
37 |
+
raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
|
38 |
+
self.bn1 = nn.BatchNorm2d(inplanes, eps=1e-05,)
|
39 |
+
self.conv1 = conv3x3(inplanes, planes)
|
40 |
+
self.bn2 = nn.BatchNorm2d(planes, eps=1e-05,)
|
41 |
+
self.prelu = nn.PReLU(planes)
|
42 |
+
self.conv2 = conv3x3(planes, planes, stride)
|
43 |
+
self.bn3 = nn.BatchNorm2d(planes, eps=1e-05,)
|
44 |
+
self.downsample = downsample
|
45 |
+
self.stride = stride
|
46 |
+
|
47 |
+
def forward(self, x):
|
48 |
+
identity = x
|
49 |
+
out = self.bn1(x)
|
50 |
+
out = self.conv1(out)
|
51 |
+
out = self.bn2(out)
|
52 |
+
out = self.prelu(out)
|
53 |
+
out = self.conv2(out)
|
54 |
+
out = self.bn3(out)
|
55 |
+
if self.downsample is not None:
|
56 |
+
identity = self.downsample(x)
|
57 |
+
out += identity
|
58 |
+
return out
|
59 |
+
|
60 |
+
|
61 |
+
class IResNet(nn.Module):
|
62 |
+
fc_scale = 7 * 7
|
63 |
+
def __init__(self,
|
64 |
+
block, layers, dropout=0, num_features=512, zero_init_residual=False,
|
65 |
+
groups=1, width_per_group=64, replace_stride_with_dilation=None, fp16=False):
|
66 |
+
super(IResNet, self).__init__()
|
67 |
+
self.fp16 = fp16
|
68 |
+
self.inplanes = 64
|
69 |
+
self.dilation = 1
|
70 |
+
if replace_stride_with_dilation is None:
|
71 |
+
replace_stride_with_dilation = [False, False, False]
|
72 |
+
if len(replace_stride_with_dilation) != 3:
|
73 |
+
raise ValueError("replace_stride_with_dilation should be None "
|
74 |
+
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
|
75 |
+
self.groups = groups
|
76 |
+
self.base_width = width_per_group
|
77 |
+
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=3, stride=1, padding=1, bias=False)
|
78 |
+
self.bn1 = nn.BatchNorm2d(self.inplanes, eps=1e-05)
|
79 |
+
self.prelu = nn.PReLU(self.inplanes)
|
80 |
+
self.layer1 = self._make_layer(block, 64, layers[0], stride=2)
|
81 |
+
self.layer2 = self._make_layer(block,
|
82 |
+
128,
|
83 |
+
layers[1],
|
84 |
+
stride=2,
|
85 |
+
dilate=replace_stride_with_dilation[0])
|
86 |
+
self.layer3 = self._make_layer(block,
|
87 |
+
256,
|
88 |
+
layers[2],
|
89 |
+
stride=2,
|
90 |
+
dilate=replace_stride_with_dilation[1])
|
91 |
+
self.layer4 = self._make_layer(block,
|
92 |
+
512,
|
93 |
+
layers[3],
|
94 |
+
stride=2,
|
95 |
+
dilate=replace_stride_with_dilation[2])
|
96 |
+
self.bn2 = nn.BatchNorm2d(512 * block.expansion, eps=1e-05,)
|
97 |
+
self.dropout = nn.Dropout(p=dropout, inplace=True)
|
98 |
+
self.fc = nn.Linear(512 * block.expansion * self.fc_scale, num_features)
|
99 |
+
self.features = nn.BatchNorm1d(num_features, eps=1e-05)
|
100 |
+
nn.init.constant_(self.features.weight, 1.0)
|
101 |
+
self.features.weight.requires_grad = False
|
102 |
+
|
103 |
+
for m in self.modules():
|
104 |
+
if isinstance(m, nn.Conv2d):
|
105 |
+
nn.init.normal_(m.weight, 0, 0.1)
|
106 |
+
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
|
107 |
+
nn.init.constant_(m.weight, 1)
|
108 |
+
nn.init.constant_(m.bias, 0)
|
109 |
+
|
110 |
+
if zero_init_residual:
|
111 |
+
for m in self.modules():
|
112 |
+
if isinstance(m, IBasicBlock):
|
113 |
+
nn.init.constant_(m.bn2.weight, 0)
|
114 |
+
|
115 |
+
def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
|
116 |
+
downsample = None
|
117 |
+
previous_dilation = self.dilation
|
118 |
+
if dilate:
|
119 |
+
self.dilation *= stride
|
120 |
+
stride = 1
|
121 |
+
if stride != 1 or self.inplanes != planes * block.expansion:
|
122 |
+
downsample = nn.Sequential(
|
123 |
+
conv1x1(self.inplanes, planes * block.expansion, stride),
|
124 |
+
nn.BatchNorm2d(planes * block.expansion, eps=1e-05, ),
|
125 |
+
)
|
126 |
+
layers = []
|
127 |
+
layers.append(
|
128 |
+
block(self.inplanes, planes, stride, downsample, self.groups,
|
129 |
+
self.base_width, previous_dilation))
|
130 |
+
self.inplanes = planes * block.expansion
|
131 |
+
for _ in range(1, blocks):
|
132 |
+
layers.append(
|
133 |
+
block(self.inplanes,
|
134 |
+
planes,
|
135 |
+
groups=self.groups,
|
136 |
+
base_width=self.base_width,
|
137 |
+
dilation=self.dilation))
|
138 |
+
|
139 |
+
return nn.Sequential(*layers)
|
140 |
+
|
141 |
+
def forward(self, x, return_id512=False):
|
142 |
+
|
143 |
+
bz = x.shape[0]
|
144 |
+
# with torch.cuda.amp.autocast(self.fp16):
|
145 |
+
x = self.conv1(x)
|
146 |
+
x = self.bn1(x)
|
147 |
+
x = self.prelu(x)
|
148 |
+
x = self.layer1(x)
|
149 |
+
x = self.layer2(x)
|
150 |
+
x = self.layer3(x)
|
151 |
+
x = self.layer4(x)
|
152 |
+
if not return_id512:
|
153 |
+
return x.view(bz,512,-1).permute(0,2,1).contiguous()
|
154 |
+
else:
|
155 |
+
x = self.bn2(x)
|
156 |
+
x = torch.flatten(x, 1)
|
157 |
+
# x = self.dropout(x)
|
158 |
+
# x = self.fc(x.float() if self.fp16 else x)
|
159 |
+
x = self.fc(x)
|
160 |
+
x = self.features(x)
|
161 |
+
return x
|
162 |
+
|
163 |
+
|
164 |
+
|
165 |
+
def _iresnet(arch, block, layers, pretrained, progress, **kwargs):
|
166 |
+
model = IResNet(block, layers, **kwargs)
|
167 |
+
if pretrained:
|
168 |
+
raise ValueError()
|
169 |
+
return model
|
170 |
+
|
171 |
+
|
172 |
+
def iresnet18(pretrained=False, progress=True, **kwargs):
|
173 |
+
return _iresnet('iresnet18', IBasicBlock, [2, 2, 2, 2], pretrained,
|
174 |
+
progress, **kwargs)
|
175 |
+
|
176 |
+
|
177 |
+
def iresnet34(pretrained=False, progress=True, **kwargs):
|
178 |
+
return _iresnet('iresnet34', IBasicBlock, [3, 4, 6, 3], pretrained,
|
179 |
+
progress, **kwargs)
|
180 |
+
|
181 |
+
|
182 |
+
def iresnet50(pretrained=False, progress=True, **kwargs):
|
183 |
+
return _iresnet('iresnet50', IBasicBlock, [3, 4, 14, 3], pretrained,
|
184 |
+
progress, **kwargs)
|
185 |
+
|
186 |
+
|
187 |
+
def iresnet100(pretrained=False, progress=True, **kwargs):
|
188 |
+
return _iresnet('iresnet100', IBasicBlock, [3, 13, 30, 3], pretrained,
|
189 |
+
progress, **kwargs)
|
190 |
+
|
191 |
+
|
192 |
+
def iresnet200(pretrained=False, progress=True, **kwargs):
|
193 |
+
return _iresnet('iresnet200', IBasicBlock, [6, 26, 60, 6], pretrained,
|
194 |
+
progress, **kwargs)
|
195 |
+
|
196 |
+
|
197 |
+
def getarcface(pretrained=None):
|
198 |
+
model = iresnet100().eval()
|
199 |
+
for param in model.parameters():
|
200 |
+
param.requires_grad=False
|
201 |
+
|
202 |
+
if pretrained is not None and os.path.exists(pretrained):
|
203 |
+
info = model.load_state_dict(torch.load(pretrained, map_location=lambda storage, loc: storage))
|
204 |
+
print(info)
|
205 |
+
return model
|
206 |
+
|
207 |
+
|
208 |
+
if __name__=='__main__':
|
209 |
+
ckpt = 'pretrained/insightface_glint360k.pth'
|
210 |
+
arcface = iresnet100().eval()
|
211 |
+
info = arcface.load_state_dict(torch.load(ckpt))
|
212 |
+
print(info)
|
213 |
+
|
214 |
+
id = arcface(torch.randn(1,3,128,128))
|
215 |
+
print(id.shape)
|
216 |
+
|
217 |
+
# import cv2
|
218 |
+
# import numpy as np
|
219 |
+
# im1_crop256 = cv2.imread('happy.jpg')
|
220 |
+
# im2_crop256 = cv2.imread('angry.jpg')
|
221 |
+
|
222 |
+
# im1_crop112 = cv2.resize(im1_crop256, (128,128))[0:112,8:120,:]
|
223 |
+
# im2_crop112 = cv2.resize(im2_crop256, (128,128))[0:112,8:120,:]
|
224 |
+
|
225 |
+
# cv2.imwrite('1_112.jpg', im1_crop112)
|
226 |
+
# cv2.imwrite('2_112.jpg', im2_crop112)
|
227 |
+
|
228 |
+
# # [-1,1] rgb
|
229 |
+
# im1_crop112_tensor = torch.from_numpy(im1_crop112[:,:,[2,1,0]].transpose(2, 0, 1).astype(np.float32)).unsqueeze(0)/127.5-1
|
230 |
+
# im2_crop112_tensor = torch.from_numpy(im2_crop112[:,:,[2,1,0]].transpose(2, 0, 1).astype(np.float32)).unsqueeze(0)/127.5-1
|
231 |
+
|
232 |
+
# im1_id = arcface(im1_crop112_tensor)
|
233 |
+
# im2_id = arcface(im2_crop112_tensor)
|
234 |
+
|
235 |
+
# loss_cos = torch.mean(1-torch.cosine_similarity(im1_id, im2_id, dim=1))
|
236 |
+
|
237 |
# print(loss_cos)
|