-
Notifications
You must be signed in to change notification settings - Fork 2
/
Copy pathblocks.py
77 lines (59 loc) · 3.22 KB
/
blocks.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
import torch
import math
import torch.nn as nn
import torch.nn.functional as F
class Flatten(nn.Module):
def forward(self, x):
return x.view(x.size(0), -1)
class ChannelGate(nn.Module):
def __init__(self, gate_channel, reduction_ratio=16, num_layers=1):
super(ChannelGate, self).__init__()
# self.gate_activation = gate_activation
self.gate_c = nn.Sequential()
self.gate_c.add_module( 'flatten', Flatten() )
gate_channels = [gate_channel]
gate_channels += [gate_channel // reduction_ratio] * num_layers
gate_channels += [gate_channel]
for i in range( len(gate_channels) - 2 ):
self.gate_c.add_module( 'gate_c_fc_%d'%i, nn.Linear(gate_channels[i], gate_channels[i+1]) )
self.gate_c.add_module( 'gate_c_bn_%d'%(i+1), nn.BatchNorm1d(gate_channels[i+1]) )
self.gate_c.add_module( 'gate_c_relu_%d'%(i+1), nn.ReLU() )
self.gate_c.add_module( 'gate_c_fc_final', nn.Linear(gate_channels[-2], gate_channels[-1]) )
def forward(self, in_tensor):
avg_pool = F.avg_pool2d( in_tensor, in_tensor.size(2), stride=in_tensor.size(2) )
return self.gate_c( avg_pool ).unsqueeze(2).unsqueeze(3).expand_as(in_tensor)
class SpatialGate(nn.Module):
def __init__(self, gate_channel, reduction_ratio=16, dilation_conv_num=2, dilation_val=4):
super(SpatialGate, self).__init__()
self.gate_s = nn.Sequential()
self.gate_s.add_module( 'gate_s_conv_reduce0', nn.Conv2d(gate_channel, gate_channel//reduction_ratio, kernel_size=1))
self.gate_s.add_module( 'gate_s_bn_reduce0', nn.BatchNorm2d(gate_channel//reduction_ratio) )
self.gate_s.add_module( 'gate_s_relu_reduce0',nn.ReLU() )
for i in range( dilation_conv_num ):
self.gate_s.add_module( 'gate_s_conv_di_%d'%i, nn.Conv2d(gate_channel//reduction_ratio, gate_channel//reduction_ratio, kernel_size=3, \
padding=dilation_val, dilation=dilation_val) )
self.gate_s.add_module( 'gate_s_bn_di_%d'%i, nn.BatchNorm2d(gate_channel//reduction_ratio) )
self.gate_s.add_module( 'gate_s_relu_di_%d'%i, nn.ReLU() )
self.gate_s.add_module( 'gate_s_conv_final', nn.Conv2d(gate_channel//reduction_ratio, 1, kernel_size=1) )
def forward(self, in_tensor):
return self.gate_s( in_tensor ).expand_as(in_tensor)
class BAM(nn.Module):
def __init__(self, gate_channel):
super(BAM, self).__init__()
self.channel_att = ChannelGate(gate_channel)
self.spatial_att = SpatialGate(gate_channel)
def forward(self,in_tensor):
att = 1 + F.sigmoid( self.channel_att(in_tensor) * self.spatial_att(in_tensor) )
return att * in_tensor
class SE(nn.Module):
def __init__(self, in_chnls, ratio=8):
super(SE, self).__init__()
self.squeeze = nn.AdaptiveAvgPool2d((1, 1))
self.compress = nn.Conv2d(in_chnls, in_chnls // ratio, 1, 1, 0)
self.excitation = nn.Conv2d(in_chnls // ratio, in_chnls, 1, 1, 0)
def forward(self, x):
out = self.squeeze(x)
out = self.compress(out)
out = F.relu(out)
out = self.excitation(out)
return x*F.sigmoid(out)