1 import warnings
  2 from collections import namedtuple
  3 import torch
  4 import torch.nn as nn
  5 import torch.nn.functional as F
  6 from torch.utils.model_zoo import load_url as load_state_dict_from_url
  7 from torchsummary import summary
  8 
  9 __all__ = ['GoogLeNet', 'googlenet']
 10 
 11 model_urls = {
 12     # GoogLeNet ported from TensorFlow
 13     'googlenet': 'https://download.pytorch.org/models/googlenet-1378be20.pth',
 14 }
 15 
 16 _GoogLeNetOuputs = namedtuple('GoogLeNetOuputs', ['logits', 'aux_logits2', 'aux_logits1'])
 17 
 18 
 19 def googlenet(pretrained=False, progress=True, **kwargs):
 20     r"""GoogLeNet (Inception v1) model architecture from
 21     `"Going Deeper with Convolutions" <http://arxiv.org/abs/1409.4842>`_.
 22     Args:
 23         pretrained (bool): If True, returns a model pre-trained on ImageNet
 24         progress (bool): If True, displays a progress bar of the download to stderr
 25         aux_logits (bool): If True, adds two auxiliary branches that can improve training.
 26             Default: *False* when pretrained is True otherwise *True*
 27         transform_input (bool): If True, preprocesses the input according to the method with which it
 28             was trained on ImageNet. Default: *False*
 29     """
 30     if pretrained:
 31         if 'transform_input' not in kwargs:
 32             kwargs['transform_input'] = True
 33         if 'aux_logits' not in kwargs:
 34             kwargs['aux_logits'] = False
 35         if kwargs['aux_logits']:
 36             warnings.warn('auxiliary heads in the pretrained googlenet model are NOT pretrained, '
 37                           'so make sure to train them')
 38         original_aux_logits = kwargs['aux_logits']
 39         kwargs['aux_logits'] = True
 40         kwargs['init_weights'] = False
 41         model = GoogLeNet(**kwargs)
 42         state_dict = load_state_dict_from_url(model_urls['googlenet'],
 43                                               progress=progress)
 44         model.load_state_dict(state_dict)
 45         if not original_aux_logits:
 46             model.aux_logits = False
 47             del model.aux1, model.aux2
 48         return model
 49 
 50     return GoogLeNet(**kwargs)
 51 
 52 
 53 class GoogLeNet(nn.Module):
 54 
 55     def __init__(self, num_classes=1000, aux_logits=True, transform_input=False, init_weights=True):
 56         super(GoogLeNet, self).__init__()
 57         self.aux_logits = aux_logits
 58         self.transform_input = transform_input
 59 
 60         self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
 61         self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)   #向上取整
 62         self.conv2 = BasicConv2d(64, 64, kernel_size=1)
 63         self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
 64         self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
 65 
 66         self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
 67         self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
 68         self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)
 69 
 70         self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
 71         self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
 72         self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
 73         self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
 74         self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
 75         self.maxpool4 = nn.MaxPool2d(2, stride=2, ceil_mode=True)
 76 
 77         self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
 78         self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)
 79 
 80         if aux_logits:
 81             self.aux1 = InceptionAux(512, num_classes)
 82             self.aux2 = InceptionAux(528, num_classes)
 83 
 84         self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
 85         self.dropout = nn.Dropout(0.2)
 86         self.fc = nn.Linear(1024, num_classes)
 87 
 88         if init_weights:
 89             self._initialize_weights()
 90 
 91     def _initialize_weights(self):
 92         for m in self.modules():
 93             if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
 94                 import scipy.stats as stats
 95                 X = stats.truncnorm(-2, 2, scale=0.01)
 96                 values = torch.as_tensor(X.rvs(m.weight.numel()), dtype=m.weight.dtype)
 97                 values = values.view(m.weight.size())
 98                 with torch.no_grad():
 99                     m.weight.copy_(values)
100             elif isinstance(m, nn.BatchNorm2d):
101                 nn.init.constant_(m.weight, 1)
102                 nn.init.constant_(m.bias, 0)
103 
104     def forward(self, x):
105         if self.transform_input:
106             x_ch0 = torch.unsqueeze(x[:, 0], 1) * (0.229 / 0.5) + (0.485 - 0.5) / 0.5
107             x_ch1 = torch.unsqueeze(x[:, 1], 1) * (0.224 / 0.5) + (0.456 - 0.5) / 0.5
108             x_ch2 = torch.unsqueeze(x[:, 2], 1) * (0.225 / 0.5) + (0.406 - 0.5) / 0.5
109             x = torch.cat((x_ch0, x_ch1, x_ch2), 1)
110 
111         # N x 3 x 224 x 224
112         x = self.conv1(x)
113         # N x 64 x 112 x 112
114         x = self.maxpool1(x)
115         # N x 64 x 56 x 56
116         x = self.conv2(x)
117         # N x 64 x 56 x 56
118         x = self.conv3(x)
119         # N x 192 x 56 x 56
120         x = self.maxpool2(x)
121 
122         # N x 192 x 28 x 28
123         x = self.inception3a(x)
124         # N x 256 x 28 x 28
125         x = self.inception3b(x)
126         # N x 480 x 28 x 28
127         x = self.maxpool3(x)
128         # N x 480 x 14 x 14
129         x = self.inception4a(x)
130         # N x 512 x 14 x 14
131         if self.training and self.aux_logits:
132             aux1 = self.aux1(x)
133 
134         x = self.inception4b(x)
135         # N x 512 x 14 x 14
136         x = self.inception4c(x)
137         # N x 512 x 14 x 14
138         x = self.inception4d(x)
139         # N x 528 x 14 x 14
140         if self.training and self.aux_logits:
141             aux2 = self.aux2(x)
142 
143         x = self.inception4e(x)
144         # N x 832 x 14 x 14
145         x = self.maxpool4(x)
146         # N x 832 x 7 x 7
147         x = self.inception5a(x)
148         # N x 832 x 7 x 7
149         x = self.inception5b(x)
150         # N x 1024 x 7 x 7
151 
152         x = self.avgpool(x)
153         # N x 1024 x 1 x 1
154         x = x.view(x.size(0), -1)
155         # N x 1024
156         x = self.dropout(x)
157         x = self.fc(x)
158         # N x 1000 (num_classes)
159         if self.training and self.aux_logits:
160             return _GoogLeNetOuputs(x, aux2, aux1)
161         return x
162 
163 
164 class Inception(nn.Module):     #Inception模块
165 
166     def __init__(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
167         super(Inception, self).__init__()
168 
169         self.branch1 = BasicConv2d(in_channels, ch1x1, kernel_size=1)
170 
171         self.branch2 = nn.Sequential(
172             BasicConv2d(in_channels, ch3x3red, kernel_size=1),
173             BasicConv2d(ch3x3red, ch3x3, kernel_size=3, padding=1)
174         )
175 
176         self.branch3 = nn.Sequential(
177             BasicConv2d(in_channels, ch5x5red, kernel_size=1),
178             BasicConv2d(ch5x5red, ch5x5, kernel_size=3, padding=1)
179         )
180 
181         self.branch4 = nn.Sequential(
182             nn.MaxPool2d(kernel_size=3, stride=1, padding=1, ceil_mode=True),
183             BasicConv2d(in_channels, pool_proj, kernel_size=1)
184         )
185 
186     def forward(self, x):
187         branch1 = self.branch1(x)
188         branch2 = self.branch2(x)
189         branch3 = self.branch3(x)
190         branch4 = self.branch4(x)
191 
192         outputs = [branch1, branch2, branch3, branch4]
193         return torch.cat(outputs, 1)
194 
195 
196 class InceptionAux(nn.Module):      #辅助分支
197 
198     def __init__(self, in_channels, num_classes):
199         super(InceptionAux, self).__init__()
200         self.conv = BasicConv2d(in_channels, 128, kernel_size=1)
201 
202         self.fc1 = nn.Linear(2048, 1024)
203         self.fc2 = nn.Linear(1024, num_classes)
204 
205     def forward(self, x):
206         # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
207         x = F.adaptive_avg_pool2d(x, (4, 4))
208         # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
209         x = self.conv(x)
210         # N x 128 x 4 x 4
211         x = x.view(x.size(0), -1)
212         # N x 2048
213         x = F.relu(self.fc1(x), inplace=True)
214         # N x 1024
215         x = F.dropout(x, 0.7, training=self.training)
216         # N x 1024
217         x = self.fc2(x)
218         # N x num_classes
219 
220         return x
221 
222 
223 class BasicConv2d(nn.Module):       #Conv2d+BN+Relu
224 
225     def __init__(self, in_channels, out_channels, **kwargs):
226         super(BasicConv2d, self).__init__()
227         self.conv = nn.Conv2d(in_channels, out_channels, bias=False, **kwargs)
228         self.bn = nn.BatchNorm2d(out_channels, eps=0.001)
229 
230     def forward(self, x):
231         x = self.conv(x)
232         x = self.bn(x)
233         return F.relu(x, inplace=True)
234 
235 
236 if __name__=="__main__":
237     model=googlenet()
238     print(model,(3,224,224))