-
Notifications
You must be signed in to change notification settings - Fork 0
/
extractors.py
executable file
·253 lines (198 loc) · 8.69 KB
/
extractors.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
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
from collections import OrderedDict
import math
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils import model_zoo
from models.sync_batchnorm import SynchronizedBatchNorm2d
def load_weights_sequential(target, source_state):
new_dict = OrderedDict()
# for (k1, v1), (k2, v2) in zip(target.state_dict().items(), source_state.items()):
# print(k1, v1.shape, k2, v2.shape)
# new_dict[k1] = v2
for k1, v1 in target.state_dict().items():
if not 'num_batches_tracked' in k1:
tar_v = source_state[k1]
if v1.shape != tar_v.shape:
# Init the new segmentation channel with zeros
# print(v1.shape, tar_v.shape)
c, _, w, h = v1.shape
tar_v = torch.cat([
tar_v,
torch.zeros((c,3,w,h)),
], 1)
new_dict[k1] = tar_v
target.load_state_dict(new_dict)
def load_weight_wo_fist(target, source_state):
new_dict = OrderedDict()
for k1, v1 in target.state_dict().items():
if k1 == 'conv1.weight': # Skip the first conv layer weights
continue # Skip copying this layer
if k1 not in source_state:
print(f"Warning: '{k1}' not in source_state, using target's original weight.")
new_dict[k1] = v1
continue
# Existing code logic for handling different shapes, excluding 'conv1'
if not 'num_batches_tracked' in k1:
tar_v = source_state[k1]
if v1.shape != tar_v.shape:
print(f"Shape mismatch at {k1}: target {v1.shape}, source {tar_v.shape}. Skipping.")
continue # Skip layers with mismatched shapes
new_dict[k1] = tar_v
# Load the state dict with the adjusted weights, strict=False allows skipping unmatched keys
target.load_state_dict(new_dict, strict=False)
model_urls = {
'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
}
def conv3x3(in_planes, out_planes, stride=1, dilation=1):
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
padding=dilation, dilation=dilation, bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(BasicBlock, self).__init__()
self.conv1 = conv3x3(inplanes, planes, stride=stride, dilation=dilation)
self.bn1 = SynchronizedBatchNorm2d(planes)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(planes, planes, stride=1, dilation=dilation)
self.bn2 = SynchronizedBatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample is not None:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, dilation=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
self.bn1 = SynchronizedBatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, dilation=dilation,
padding=dilation, bias=False)
self.bn2 = SynchronizedBatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
self.bn3 = SynchronizedBatchNorm2d(planes * 4)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
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(x)
out += residual
out = self.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, layers=(3, 4, 23, 3)):
self.inplanes = 64
super(ResNet, self).__init__()
self.conv1 = nn.Conv2d(6, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = SynchronizedBatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, SynchronizedBatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
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),
SynchronizedBatchNorm2d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation))
return nn.Sequential(*layers)
def forward(self, x):
x_1 = self.conv1(x) # /2
x = self.bn1(x_1)
x = self.relu(x)
x = self.maxpool(x) # /2
x_2 = self.layer1(x)
x = self.layer2(x_2) # /2
x = self.layer3(x)
x = self.layer4(x)
return x, x_1, x_2
class ResNet_UOAIS(nn.Module):
def __init__(self, block, layers=(3, 4, 23, 3)):
self.inplanes = 64
super(ResNet_UOAIS, self).__init__()
self.conv1 = nn.Conv2d(7, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = SynchronizedBatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0])
self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], stride=1, dilation=2)
self.layer4 = self._make_layer(block, 512, layers[3], stride=1, dilation=4)
for m in self.modules():
if isinstance(m, nn.Conv2d):
n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
m.weight.data.normal_(0, math.sqrt(2. / n))
elif isinstance(m, SynchronizedBatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
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),
SynchronizedBatchNorm2d(planes * block.expansion),
)
layers = [block(self.inplanes, planes, stride, downsample)]
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, dilation=dilation))
return nn.Sequential(*layers)
def forward(self, x):
x_1 = self.conv1(x) # /2
x = self.bn1(x_1)
x = self.relu(x)
x = self.maxpool(x) # /2
x_2 = self.layer1(x)
x = self.layer2(x_2) # /2
x = self.layer3(x)
x = self.layer4(x)
return x, x_1, x_2
def resnet50(pretrained=True):
model = ResNet(Bottleneck, [3, 4, 6, 3])
if pretrained:
load_weights_sequential(model, model_zoo.load_url(model_urls['resnet50']))
return model
def resnet50_uoais(pretrained =False):
model = ResNet_UOAIS(Bottleneck, [3, 4, 6, 3])
print("The base model pretrained is {}".format(pretrained))
if pretrained:
load_weight_wo_fist(model, model_zoo.load_url(model_urls['resnet50']))
return model