1.文章原文地址
Deep Residual Learning for Image Recognition
2.文章摘要
神经网络的层次越深越难训练。我们提出了一个残差学习框架来简化网络的训练，这些网络比之前使用的网络都要深的多。我们明确地将层变为学习关于层输入的残差函数，而不是学习未参考的函数。我们提供了综合的实验证据来表明这个残差网络更容易优化，以及通过极大提升网络深度可以获得更好的准确率。在ImageNet数据集上，我们评估了残差网络，该网络有152层，层数是VGG网络的8倍，但是有更低的复杂度。几个残差网络的集成在ImageNet数据集上取得了3.57%错误率。这个结果在ILSVRC2015分类任务上取得第一名的成绩。我们也使用了100和1000层网络用在了数据集CIFAR-10上加以分析。
在许多视觉识别任务中，表征的深度是至关重要的。仅仅通过极端深的表征，我们在COCO目标检测数据集上得到了28%的相对提高。深度残差网络是我们提交到ILSVRC & COCO2015竞赛的网络基础，在这里我们获得了ImageNet检测任务、ImageNet定位任务，COCO检测任务和COCO分割任务的第一名。
3.网络结构
4.Pytorch实现
1 import torch.nn as nn
2 from torch.utils.model_zoo import load_url as load_state_dict_from_url
3
4
5 __all__ = ['ResNet', 'resnet18', 'resnet34', 'resnet50', 'resnet101',
6 'resnet152', 'resnext50_32x4d', 'resnext101_32x8d']
7
8
9 model_urls = {
10 'resnet18': 'https://download.pytorch.org/models/resnet18-5c106cde.pth',
11 'resnet34': 'https://download.pytorch.org/models/resnet34-333f7ec4.pth',
12 'resnet50': 'https://download.pytorch.org/models/resnet50-19c8e357.pth',
13 'resnet101': 'https://download.pytorch.org/models/resnet101-5d3b4d8f.pth',
14 'resnet152': 'https://download.pytorch.org/models/resnet152-b121ed2d.pth',
15 }
16
17
18 def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
19 """3x3 convolution with padding"""
20 return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
21 padding=dilation, groups=groups, bias=False, dilation=dilation)
22
23
24 def conv1x1(in_planes, out_planes, stride=1):
25 """1x1 convolution"""
26 return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
27
28
29 class BasicBlock(nn.Module):
30 expansion = 1
31
32 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
33 base_width=64, dilation=1, norm_layer=None):
34 super(BasicBlock, self).__init__()
35 if norm_layer is None:
36 norm_layer = nn.BatchNorm2d
37 if groups != 1 or base_width != 64:
38 raise ValueError('BasicBlock only supports groups=1 and base_width=64')
39 if dilation > 1:
40 raise NotImplementedError("Dilation > 1 not supported in BasicBlock")
41 # Both self.conv1 and self.downsample layers downsample the input when stride != 1
42 self.conv1 = conv3x3(inplanes, planes, stride)
43 self.bn1 = norm_layer(planes)
44 self.relu = nn.ReLU(inplace=True)
45 self.conv2 = conv3x3(planes, planes)
46 self.bn2 = norm_layer(planes)
47 self.downsample = downsample
48 self.stride = stride
49
50 def forward(self, x):
51 identity = x
52
53 out = self.conv1(x)
54 out = self.bn1(out)
55 out = self.relu(out)
56
57 out = self.conv2(out)
58 out = self.bn2(out)
59
60 if self.downsample is not None:
61 identity = self.downsample(x)
62
63 out += identity
64 out = self.relu(out)
65
66 return out
67
68
69 class Bottleneck(nn.Module):
70 expansion = 4
71
72 def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
73 base_width=64, dilation=1, norm_layer=None):
74 super(Bottleneck, self).__init__()
75 if norm_layer is None:
76 norm_layer = nn.BatchNorm2d
77 width = int(planes * (base_width / 64.)) * groups
78 # Both self.conv2 and self.downsample layers downsample the input when stride != 1
79 self.conv1 = conv1x1(inplanes, width)
80 self.bn1 = norm_layer(width)
81 self.conv2 = conv3x3(width, width, stride, groups, dilation)
82 self.bn2 = norm_layer(width)
83 self.conv3 = conv1x1(width, planes * self.expansion)
84 self.bn3 = norm_layer(planes * self.expansion)
85 self.relu = nn.ReLU(inplace=True)
86 self.downsample = downsample
87 self.stride = stride
88
89 def forward(self, x):
90 identity = x
91
92 out = self.conv1(x)
93 out = self.bn1(out)
94 out = self.relu(out)
95
96 out = self.conv2(out)
97 out = self.bn2(out)
98 out = self.relu(out)
99
100 out = self.conv3(out)
101 out = self.bn3(out)
102
103 if self.downsample is not None:
104 identity = self.downsample(x)
105
106 out += identity
107 out = self.relu(out)
108
109 return out
110
111
112 class ResNet(nn.Module):
113
114 def __init__(self, block, layers, num_classes=1000, zero_init_residual=False,
115 groups=1, width_per_group=64, replace_stride_with_dilation=None,
116 norm_layer=None):
117 super(ResNet, self).__init__()
118 if norm_layer is None:
119 norm_layer = nn.BatchNorm2d
120 self._norm_layer = norm_layer
121
122 self.inplanes = 64
123 self.dilation = 1
124 if replace_stride_with_dilation is None:
125 # each element in the tuple indicates if we should replace
126 # the 2x2 stride with a dilated convolution instead
127 replace_stride_with_dilation = [False, False, False]
128 if len(replace_stride_with_dilation) != 3:
129 raise ValueError("replace_stride_with_dilation should be None "
130 "or a 3-element tuple, got {}".format(replace_stride_with_dilation))
131 self.groups = groups
132 self.base_width = width_per_group
133 self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
134 bias=False)
135 self.bn1 = norm_layer(self.inplanes)
136 self.relu = nn.ReLU(inplace=True)
137 self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
138 self.layer1 = self._make_layer(block, 64, layers[0])
139 self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
140 dilate=replace_stride_with_dilation[0])
141 self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
142 dilate=replace_stride_with_dilation[1])
143 self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
144 dilate=replace_stride_with_dilation[2])
145 self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
146 self.fc = nn.Linear(512 * block.expansion, num_classes)
147
148 for m in self.modules():
149 if isinstance(m, nn.Conv2d):
150 nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
151 elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
152 nn.init.constant_(m.weight, 1)
153 nn.init.constant_(m.bias, 0)
154
155 # Zero-initialize the last BN in each residual branch,
156 # so that the residual branch starts with zeros, and each residual block behaves like an identity.
157 # This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
158 if zero_init_residual:
159 for m in self.modules():
160 if isinstance(m, Bottleneck):
161 nn.init.constant_(m.bn3.weight, 0)
162 elif isinstance(m, BasicBlock):
163 nn.init.constant_(m.bn2.weight, 0)
164
165 def _make_layer(self, block, planes, blocks, stride=1, dilate=False):
166 norm_layer = self._norm_layer
167 downsample = None
168 previous_dilation = self.dilation
169 if dilate:
170 self.dilation *= stride
171 stride = 1
172 if stride != 1 or self.inplanes != planes * block.expansion:
173 downsample = nn.Sequential(
174 conv1x1(self.inplanes, planes * block.expansion, stride),
175 norm_layer(planes * block.expansion),
176 )
177
178 layers = []
179 layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
180 self.base_width, previous_dilation, norm_layer))
181 self.inplanes = planes * block.expansion
182 for _ in range(1, blocks):
183 layers.append(block(self.inplanes, planes, groups=self.groups,
184 base_width=self.base_width, dilation=self.dilation,
185 norm_layer=norm_layer))
186
187 return nn.Sequential(*layers)
188
189 def forward(self, x):
190 x = self.conv1(x)
191 x = self.bn1(x)
192 x = self.relu(x)
193 x = self.maxpool(x)
194
195 x = self.layer1(x)
196 x = self.layer2(x)
197 x = self.layer3(x)
198 x = self.layer4(x)
199
200 x = self.avgpool(x)
201 x = x.reshape(x.size(0), -1)
202 x = self.fc(x)
203
204 return x
205
206
207 def _resnet(arch, inplanes, planes, pretrained, progress, **kwargs):
208 model = ResNet(inplanes, planes, **kwargs)
209 if pretrained:
210 state_dict = load_state_dict_from_url(model_urls[arch],
211 progress=progress)
212 model.load_state_dict(state_dict)
213 return model
214
215
216 def resnet18(pretrained=False, progress=True, **kwargs):
217 """Constructs a ResNet-18 model.
218 Args:
219 pretrained (bool): If True, returns a model pre-trained on ImageNet
220 progress (bool): If True, displays a progress bar of the download to stderr
221 """
222 return _resnet('resnet18', BasicBlock, [2, 2, 2, 2], pretrained, progress,
223 **kwargs)
224
225
226 def resnet34(pretrained=False, progress=True, **kwargs):
227 """Constructs a ResNet-34 model.
228 Args:
229 pretrained (bool): If True, returns a model pre-trained on ImageNet
230 progress (bool): If True, displays a progress bar of the download to stderr
231 """
232 return _resnet('resnet34', BasicBlock, [3, 4, 6, 3], pretrained, progress,
233 **kwargs)
234
235
236 def resnet50(pretrained=False, progress=True, **kwargs):
237 """Constructs a ResNet-50 model.
238 Args:
239 pretrained (bool): If True, returns a model pre-trained on ImageNet
240 progress (bool): If True, displays a progress bar of the download to stderr
241 """
242 return _resnet('resnet50', Bottleneck, [3, 4, 6, 3], pretrained, progress,
243 **kwargs)
244
245
246 def resnet101(pretrained=False, progress=True, **kwargs):
247 """Constructs a ResNet-101 model.
248 Args:
249 pretrained (bool): If True, returns a model pre-trained on ImageNet
250 progress (bool): If True, displays a progress bar of the download to stderr
251 """
252 return _resnet('resnet101', Bottleneck, [3, 4, 23, 3], pretrained, progress,
253 **kwargs)
254
255
256 def resnet152(pretrained=False, progress=True, **kwargs):
257 """Constructs a ResNet-152 model.
258 Args:
259 pretrained (bool): If True, returns a model pre-trained on ImageNet
260 progress (bool): If True, displays a progress bar of the download to stderr
261 """
262 return _resnet('resnet152', Bottleneck, [3, 8, 36, 3], pretrained, progress,
263 **kwargs)
264
265
266 def resnext50_32x4d(**kwargs):
267 kwargs['groups'] = 32
268 kwargs['width_per_group'] = 4
269 return _resnet('resnext50_32x4d', Bottleneck, [3, 4, 6, 3],
270 pretrained=False, progress=True, **kwargs)
271
272
273 def resnext101_32x8d(**kwargs):
274 kwargs['groups'] = 32
275 kwargs['width_per_group'] = 8
276 return _resnet('resnext101_32x8d', Bottleneck, [3, 4, 23, 3],
277 pretrained=False, progress=True, **kwargs)参考
https://github.com/pytorch/vision/tree/master/torchvision/models
原文出处：https://www.cnblogs.com/ys99/p/10872262.html
来源：oschina
链接：https://my.oschina.net/u/4413313/blog/3263782