-
Notifications
You must be signed in to change notification settings - Fork 517
/
preact_resnet.py
executable file
·124 lines (91 loc) · 4.11 KB
/
preact_resnet.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
"""Pre-activation ResNet in PyTorch.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Identity Mappings in Deep Residual Networks. arXiv:1603.05027
Based on https://github.com/kuangliu/pytorch-cifar/blob/master/models/preact_resnet.py
"""
import torch
import torch.nn as nn
import torch.nn.functional as F
from super_gradients.training.models import BaseClassifier
class PreActBlock(nn.Module):
"""Pre-activation version of the BasicBlock."""
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(PreActBlock, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False))
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out += shortcut
return out
class PreActBottleneck(nn.Module):
"""Pre-activation version of the original Bottleneck module."""
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(PreActBottleneck, self).__init__()
self.bn1 = nn.BatchNorm2d(in_planes)
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn3 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
if stride != 1 or in_planes != self.expansion * planes:
self.shortcut = nn.Sequential(nn.Conv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False))
def forward(self, x):
out = F.relu(self.bn1(x))
shortcut = self.shortcut(out) if hasattr(self, "shortcut") else x
out = self.conv1(out)
out = self.conv2(F.relu(self.bn2(out)))
out = self.conv3(F.relu(self.bn3(out)))
out += shortcut
return out
class PreActResNet(BaseClassifier):
def __init__(self, block, num_blocks, num_classes=10):
super(PreActResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.linear = nn.Linear(512 * block.expansion, num_classes)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1] * (num_blocks - 1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = self.conv1(x)
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
def PreActResNet18():
return PreActResNet(PreActBlock, [2, 2, 2, 2])
def PreActResNet34():
return PreActResNet(PreActBlock, [3, 4, 6, 3])
def PreActResNet50():
return PreActResNet(PreActBottleneck, [3, 4, 6, 3])
def PreActResNet101():
return PreActResNet(PreActBottleneck, [3, 4, 23, 3])
def PreActResNet152():
return PreActResNet(PreActBottleneck, [3, 8, 36, 3])
def test():
net = PreActResNet18()
y = net((torch.randn(1, 3, 32, 32)))
print(y.size())
# test()