forked from DequanWang/tent
-
Notifications
You must be signed in to change notification settings - Fork 0
/
Copy pathnets.py
147 lines (121 loc) · 8.46 KB
/
nets.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
"""
Network architectures and functionality for adding stats layers to networks // originated from BUFR
"""
from __future__ import division, print_function, absolute_import
import torch
import torch.nn as nn
import numpy as np
import torch.nn.utils.weight_norm as weightNorm
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.shortcut = nn.Sequential()
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),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(planes)
self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(planes)
self.conv3 = nn.Conv2d(planes, self.expansion * planes, kernel_size=1, bias=False)
self.bn3 = nn.BatchNorm2d(self.expansion * planes)
self.shortcut = nn.Sequential()
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),
nn.BatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = nn.BatchNorm2d(64)
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 = F.relu(self.bn1(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
"""
Cifar sizes, printing x.shape and every out.shape in the forward pass
torch.Size([128, 3, 32, 32]) - Input
torch.Size([128, 64, 32, 32]) - First layers (conv+bn) output
torch.Size([128, 64, 32, 32]) - self.layer1 output
torch.Size([128, 128, 16, 16]) - self.layer2 output
torch.Size([128, 256, 8, 8]) - self.layer3 output
torch.Size([128, 512, 4, 4]) - self.layer4 output
torch.Size([128, 512, 1, 1]) - avg_pool output
torch.Size([128, 512]) - reshape output
torch.Size([128, 10]) - linear output
Camelyon using this resnet:
torch.Size([128, 3, 96, 96])
torch.Size([128, 64, 96, 96])
torch.Size([128, 64, 96, 96])
torch.Size([128, 128, 48, 48])
torch.Size([128, 256, 24, 24])
torch.Size([128, 512, 12, 12])
torch.Size([128, 512, 3, 3])
torch.Size([128, 4608]) <- breaks here as this is should be 128, 512 after reshape then go to
If we use the resnet in nets_wilds:
torch.Size([128, 3, 96, 96]) - Input
torch.Size([128, 64, 48, 48]) - First layers (conv+bn) output
torch.Size([128, 64, 24, 24]) - Max pool output
torch.Size([128, 64, 24, 24]) - self.layer1 output
torch.Size([128, 128, 12, 12]) - self.layer2 output
torch.Size([128, 256, 6, 6]) - self.layer3 output
torch.Size([128, 512, 3, 3]) - self.layer4 output
torch.Size([128, 512, 1, 1]) - AdaptiveAvgPool2d output
torch.Size([128, 512]) - reshape output
torch.Size([128, 1]) - linear output
"""
def ResNet18(n_classes=10):
return ResNet(BasicBlock, [2, 2, 2, 2], num_classes=n_classes)
def ResNet34(n_classes=10):
return ResNet(BasicBlock, [3, 4, 6, 3], num_classes=n_classes)
#50layer가 아닌뎅? Bottleneck 이라 그런듯. depth가 깊어지면 basicblock 대신 bottleneck 사용
def ResNet50(n_classes=10):
return ResNet(Bottleneck, [3, 4, 6, 3], num_classes=n_classes)
def ResNet101(n_classes=10):
return ResNet(Bottleneck, [3, 4, 23, 3], num_classes=n_classes)