-
Notifications
You must be signed in to change notification settings - Fork 23
/
Copy pathfeature_extractor_fast.py
207 lines (175 loc) · 7.25 KB
/
feature_extractor_fast.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
201
202
203
204
205
206
# -*- coding: UTF-8 -*-
# ---------------------------------------------------------------------------
# Official code of our paper:Bilateral Grid Learning for Stereo Matching Network
# Written by Bin Xu
# ---------------------------------------------------------------------------
from __future__ import print_function
import torch
import torch.nn as nn
import torch.utils.data
import torch.nn.functional as F
def convbn_relu(in_planes, out_planes, kernel_size, stride, pad, dilation):
return nn.Sequential(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=dilation if dilation > 1 else pad, dilation=dilation, bias=False),
nn.BatchNorm2d(out_planes),
nn.ReLU(inplace=True))
def convbn(in_planes, out_planes, kernel_size, stride, pad, dilation):
return nn.Sequential(nn.Conv2d(in_planes, out_planes, kernel_size=kernel_size, stride=stride,
padding=dilation if dilation > 1 else pad, dilation=dilation, bias=False),
nn.BatchNorm2d(out_planes))
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride, downsample, pad, dilation):
super(BasicBlock, self).__init__()
self.conv1 = convbn_relu(inplanes, planes, 3, stride, pad, dilation)
self.conv2 = convbn(planes, planes, 3, 1, pad, dilation)
self.downsample = downsample
self.stride = stride
def forward(self, x):
out = self.conv1(x)
out = self.conv2(out)
if self.downsample is not None:
x = self.downsample(x)
out += x
return out
class BasicConv(nn.Module):
def __init__(self, in_channels, out_channels, deconv=False, is_3d=False, bn=True, relu=True, **kwargs):
super(BasicConv, self).__init__()
self.relu = relu
self.use_bn = bn
if is_3d:
if deconv:
self.conv = nn.ConvTranspose3d(in_channels, out_channels, bias=False, **kwargs)
else:
self.conv = nn.Conv3d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm3d(out_channels)
else:
if deconv:
self.conv = nn.ConvTranspose2d(in_channels, out_channels, bias=False, **kwargs)
else:
self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
self.bn = nn.BatchNorm2d(out_channels)
def forward(self, x):
x = self.conv(x)
# if self.use_bn:
# x = self.bn(x)
if self.relu:
x = F.relu(x, inplace=True)
return x
class Conv2x(nn.Module):
def __init__(self, in_channels, out_channels, deconv=False, is_3d=False, concat=True, bn=True, relu=True):
super(Conv2x, self).__init__()
self.concat = concat
if deconv and is_3d:
kernel = (3, 4, 4)
elif deconv:
kernel = 4
else:
kernel = 3
self.conv1 = BasicConv(in_channels, out_channels, deconv, is_3d, bn=False, relu=True, kernel_size=kernel, stride=2, padding=1)
if self.concat:
self.conv2 = BasicConv(out_channels*2, out_channels, False, is_3d, bn, relu, kernel_size=3, stride=1, padding=1)
else:
self.conv2 = BasicConv(out_channels, out_channels, False, is_3d, bn, relu, kernel_size=3, stride=1, padding=1)
def forward(self, x, rem):
x = self.conv1(x)
assert(x.size() == rem.size())
if self.concat:
x = torch.cat((x, rem), 1)
else:
x = x + rem
x = self.conv2(x)
return x
class feature_extraction(nn.Module):
def __init__(self):
super(feature_extraction, self).__init__()
self.inplanes = 32
# self.firstconv = convbn_relu(1, 32, 7, 2, 3, 1)
self.firstconv = nn.Sequential(convbn_relu(1, 32, 3, 2, 1, 1),
convbn_relu(32, 32, 3, 1, 1, 1),
convbn_relu(32, 32, 3, 1, 1, 1))
self.layer1 = self._make_layer(BasicBlock, 32, 1, 1, 1, 1)
self.layer2 = self._make_layer(BasicBlock, 64, 1, 2, 1, 1)
self.layer3 = self._make_layer(BasicBlock, 128, 1, 2, 1, 1)
self.layer4 = self._make_layer(BasicBlock, 128, 1, 1, 1, 1)
self.reduce = convbn_relu(128, 32, 3, 1, 1, 1)
self.conv1a = BasicConv(32, 48, kernel_size=3, stride=2, padding=1)
self.conv2a = BasicConv(48, 64, kernel_size=3, stride=2, padding=1)
self.conv3a = BasicConv(64, 96, kernel_size=3, stride=2, padding=1)
# self.conv4a = BasicConv(96, 128, kernel_size=3, stride=2, padding=1)
# self.deconv4a = Conv2x(128, 96, deconv=True)
self.deconv3a = Conv2x(96, 64, deconv=True)
self.deconv2a = Conv2x(64, 48, deconv=True)
self.deconv1a = Conv2x(48, 32, deconv=True)
self.conv1b = Conv2x(32, 48)
self.conv2b = Conv2x(48, 64)
self.conv3b = Conv2x(64, 96)
# self.conv4b = Conv2x(96, 128)
# self.deconv4b = Conv2x(128, 96, deconv=True)
self.deconv3b = Conv2x(96, 64, deconv=True)
self.deconv2b = Conv2x(64, 48, deconv=True)
self.deconv1b = Conv2x(48, 32, deconv=True)
def _make_layer(self, block, planes, blocks, stride, pad, dilation):
downsample = None
if stride != 1 or self.inplanes != planes * block.expansion:
downsample = nn.Sequential(
nn.Conv2d(self.inplanes, planes * block.expansion,
kernel_size=1, stride=stride, bias=False),
nn.BatchNorm2d(planes * block.expansion),)
layers = []
layers.append(block(self.inplanes, planes, stride, downsample, pad, dilation))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, 1, None, pad, dilation))
return nn.Sequential(*layers)
def forward(self, x):
#1/2
x = self.firstconv(x)
x = self.layer1(x)
conv0a = x
x = self.layer2(x) #1/4
conv1a = x
x = self.layer3(x) #1/8 * 128
feat0 = x
x = self.layer4(x) #1/8 * 128
feat1 = x
x = self.reduce(x) #1/8 * 32
feat2 = x
rem0 = x
#1/2 * 1/2 * 48
x = self.conv1a(x)
rem1 = x
#1/4 * 1/4 * 64
x = self.conv2a(x)
rem2 = x
#1/8 * 1/8 * 96
x = self.conv3a(x)
rem3 = x
#1/16 * 1/16 * 128
# x = self.conv4a(x)
# rem4 = x
# x = self.deconv4a(x, rem3)
# rem3 = x
x = self.deconv3a(x, rem2)
rem2 = x
x = self.deconv2a(x, rem1)
rem1 = x
x = self.deconv1a(x, rem0)
feat3 = x
rem0 = x
#1/2
x = self.conv1b(x, rem1)
rem1 = x
x = self.conv2b(x, rem2)
rem2 = x
x = self.conv3b(x, rem3)
rem3 = x
#1/16
# x = self.conv4b(x, rem4)
# x = self.deconv4b(x, rem3)
x = self.deconv3b(x, rem2)
x = self.deconv2b(x, rem1)
x = self.deconv1b(x, rem0)
feat4 = x
gwc_feature = torch.cat((feat0,feat1,feat2,feat3,feat4),dim = 1)
return conv0a,gwc_feature