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)