forked from chenxuluo/GST-video
-
Notifications
You must be signed in to change notification settings - Fork 0
/
GST.py
171 lines (135 loc) · 5.12 KB
/
GST.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
import torch
import torch.nn as nn
import math
import torch.utils.model_zoo as model_zoo
import torchvision.models as models
__all__ = ['ResNet', 'resnet50', 'resnet101','resnet152']
model_urls = {
'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
}
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, alpha, beta, stride = 1, downsample = None):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv3d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm3d(planes)
self.conv2 = nn.Conv3d(planes// beta, planes//alpha*(alpha-1), kernel_size=(1,3,3), stride=(1,stride,stride),
padding=(0,1,1), bias=False)
self.Tconv = nn.Conv3d(planes//beta, planes//alpha, kernel_size = 3, bias = False,stride=(1,stride,stride),
padding = (1,1,1))
self.bn2 = nn.BatchNorm3d(planes)
self.conv3 = nn.Conv3d(planes, planes * self.expansion, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm3d(planes * self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
self.alpha = alpha
self.beta = beta
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
if self.beta == 2:
nchannels = out.size()[1] // self.beta
left = out[:,:nchannels]
right = out[:,nchannels:]
out1 = self.conv2(left)
out2 = self.Tconv(right)
else:
out1 = self.conv2(out)
out2 = self.Tconv(out)
out = torch.cat((out1,out2),dim=1)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
residual = self.downsample(residual)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers, alpha, beta, num_classes=1000):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv3d(3, 64, kernel_size=(1,7,7), stride=(1,2,2), padding=(0,3,3),
bias=False)
self.bn1 = nn.BatchNorm3d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool3d(kernel_size=(1,3,3), stride=(1,2,2), padding=(0,1,1))
self.layer1 = self._make_layer(block, 64, layers[0], alpha, beta)
self.layer2 = self._make_layer(block, 128, layers[1], alpha, beta, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], alpha, beta, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], alpha, beta, stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv3d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm3d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, alpha, beta, stride=1):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv3d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=(1,stride,stride), bias=False),
nn.BatchNorm3d(planes * block.expansion),
)
layers = []
layers.append(block(self.inplanes, planes, alpha, beta, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, alpha, beta))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = x.transpose(1,2).contiguous()
x = x.view((-1,)+x.size()[2:])
x = self.avgpool(x)
x = x.view(x.size(0), -1)
x = self.fc(x)
return x
def resnet50(alpha, beta,**kwargs):
"""Constructs a ResNet-50 based model.
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], alpha, beta, **kwargs)
checkpoint = model_zoo.load_url(model_urls['resnet50'])
layer_name = list(checkpoint.keys())
for ln in layer_name:
if 'conv' in ln or 'downsample.0.weight' in ln:
checkpoint[ln] = checkpoint[ln].unsqueeze(2)
if 'conv2' in ln:
n_out, n_in, _, _, _ = checkpoint[ln].size()
checkpoint[ln] = checkpoint[ln][:n_out // alpha * (alpha - 1), :n_in//beta,:,:,:]
model.load_state_dict(checkpoint,strict = False)
return model
def resnet101(alpha, beta ,**kwargs):
"""Constructs a ResNet-101 model.
Args:
groups
"""
model = ResNet(Bottleneck, [3, 4, 23, 3], **kwargs)
checkpoint = model_zoo.load_url(model_urls['resnet101'])
layer_name = list(checkpoint.keys())
for ln in layer_name:
if 'conv' in ln or 'downsample.0.weight' in ln:
checkpoint[ln] = checkpoint[ln].unsqueeze(2)
if 'conv2' in ln:
n_out, n_in, _, _, _ = checkpoint[ln].size()
checkpoint[ln] = checkpoint[ln][:n_out // alpha * (alpha - 1), :n_in//beta,:,:,:]
model.load_state_dict(checkpoint,strict = False)
return model