forked from DL-Practise/ShufflenetV2_PLUS
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathshufflenet_v2_se_attention.py
201 lines (161 loc) · 6.72 KB
/
shufflenet_v2_se_attention.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
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
import torch
import torch.nn as nn
__all__ = [
'ShuffleNetV2', 'shufflenet_v2_x0_5', 'shufflenet_v2_x1_0',
'shufflenet_v2_x1_5', 'shufflenet_v2_x2_0'
]
model_urls = {
'shufflenetv2_x0.5': 'https://download.pytorch.org/models/shufflenetv2_x0.5-f707e7126e.pth',
'shufflenetv2_x1.0': 'https://download.pytorch.org/models/shufflenetv2_x1-5666bf0f80.pth',
'shufflenetv2_x1.5': None,
'shufflenetv2_x2.0': None,
}
def channel_shuffle(x, groups):
# type: (torch.Tensor, int) -> torch.Tensor
batchsize, num_channels, height, width = x.data.size()
channels_per_group = num_channels // groups
# reshape
x = x.view(batchsize, groups,
channels_per_group, height, width)
x = torch.transpose(x, 1, 2).contiguous()
# flatten
x = x.view(batchsize, -1, height, width)
return x
class SeAttention(nn.Module):
def __init__(self, channel_num, r=4):
super(SeAttention, self).__init__()
self.channel_num = channel_num
self.r = r
self.inter_channel = int( float(self.channel_num) / self.r)
self.fc_e1 = torch.nn.Linear(channel_num, self.inter_channel)
#self.bn_e1 = torch.nn.BatchNorm2d(self.inter_channel)
self.relu_e1 = nn.ReLU(inplace=True)
self.fc_e2 = torch.nn.Linear(self.inter_channel, channel_num)
#self.bn_e2 = torch.nn.BatchNorm2d(channel_num)
def forward(self, x):
y = torch.nn.functional.adaptive_avg_pool2d(x, (1, 1)).squeeze()
y = self.fc_e1(y)
#y = self.bn_e1(y)
y = self.relu_e1(y)
y = self.fc_e2(y)
#y = self.bn_e2(y)
y = torch.sigmoid(y).unsqueeze(-1).unsqueeze(-1)
#y = y.unsqueeze(-1)
return x*y
class InvertedResidual(nn.Module):
def __init__(self, inp, oup, stride):
super(InvertedResidual, self).__init__()
if not (1 <= stride <= 3):
raise ValueError('illegal stride value')
self.stride = stride
branch_features = oup // 2
assert (self.stride != 1) or (inp == branch_features << 1)
if self.stride > 1:
self.branch1 = nn.Sequential(
self.depthwise_conv(inp, inp, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(inp),
nn.Conv2d(inp, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
else:
self.branch1 = nn.Sequential()
self.branch2 = nn.Sequential(
nn.Conv2d(inp if (self.stride > 1) else branch_features,
branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
self.depthwise_conv(branch_features, branch_features, kernel_size=3, stride=self.stride, padding=1),
nn.BatchNorm2d(branch_features),
nn.Conv2d(branch_features, branch_features, kernel_size=1, stride=1, padding=0, bias=False),
nn.BatchNorm2d(branch_features),
nn.ReLU(inplace=True),
)
@staticmethod
def depthwise_conv(i, o, kernel_size, stride=1, padding=0, bias=False):
return nn.Conv2d(i, o, kernel_size, stride, padding, bias=bias, groups=i)
def forward(self, x):
if self.stride == 1:
x1, x2 = x.chunk(2, dim=1)
out = torch.cat((x1, self.branch2(x2)), dim=1)
else:
out = torch.cat((self.branch1(x), self.branch2(x)), dim=1)
out = channel_shuffle(out, 2)
return out
class ShuffleNetV2SE(nn.Module):
def __init__(self, arg_dict):
super(ShuffleNetV2SE, self).__init__()
num_classes = arg_dict['class_num']
width_mult = arg_dict['channel_ratio']
inverted_residual=InvertedResidual
if width_mult == 0.5:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 48, 96, 192, 1024]
elif width_mult == 1.0:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 116, 232, 464, 1024]
elif width_mult == 1.5:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 176, 352, 704, 1024]
elif width_mult == 2.0:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 244, 488, 976, 2048]
elif width_mult == 0.25:
stages_repeats = [4, 8, 4]
stages_out_channels = [24, 28, 48, 96, 512]
else:
assert(False)
if len(stages_repeats) != 3:
raise ValueError('expected stages_repeats as list of 3 positive ints')
if len(stages_out_channels) != 5:
raise ValueError('expected stages_out_channels as list of 5 positive ints')
self._stage_out_channels = stages_out_channels
input_channels = 3
output_channels = self._stage_out_channels[0]
self.conv1 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 3, 2, 1, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
input_channels = output_channels
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
stage_names = ['stage{}'.format(i) for i in [2, 3, 4]]
for name, repeats, output_channels in zip(
stage_names, stages_repeats, self._stage_out_channels[1:]):
seq = [inverted_residual(input_channels, output_channels, 2)]
for i in range(repeats - 1):
seq.append(inverted_residual(output_channels, output_channels, 1))
seq.append(SeAttention(output_channels))
setattr(self, name, nn.Sequential(*seq))
input_channels = output_channels
output_channels = self._stage_out_channels[-1]
self.conv5 = nn.Sequential(
nn.Conv2d(input_channels, output_channels, 1, 1, 0, bias=False),
nn.BatchNorm2d(output_channels),
nn.ReLU(inplace=True),
)
self.fc = nn.Linear(output_channels, num_classes)
def _forward_impl(self, x):
# See note [TorchScript super()]
x = self.conv1(x)
x = self.maxpool(x)
x = self.stage2(x)
x = self.stage3(x)
x = self.stage4(x)
x = self.conv5(x)
x = x.mean([2, 3]) # globalpool
x = self.fc(x)
return x
def forward(self, x):
x = self._forward_impl(x)
if self.training:
return x
else:
return torch.softmax(x, dim=1)
def train_step(self, images, labels):
out = self.forward(images)
loss = nn.functional.cross_entropy(out, labels)
return loss
def eval_step(self, images):
out = self.forward(images)
return out