import torch.nn as nn
import torch
import torch.nn.functional as F


class GoogLeNet(nn.Module):
 def init(self, num_classes=1000, aux_logits=True, init_weights=False):
 super(GoogLeNet, self).init()

 self.aux_logits = aux_logits

 # 7*7,stride=2
 self.conv1 = BasicConv2d(3, 64, kernel_size=7, stride=2, padding=3)
 self.maxpool1 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

 # 1*1+3*3+maxpool
 self.conv2 = BasicConv2d(64, 64, kernel_size=1)
 self.conv3 = BasicConv2d(64, 192, kernel_size=3, padding=1)
 self.maxpool2 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

 # 枝丫a
 self.inception3a = Inception(192, 64, 96, 128, 16, 32, 32)
 # 枝丫a
 self.inception3b = Inception(256, 128, 128, 192, 32, 96, 64)
 self.maxpool3 = nn.MaxPool2d(3, stride=2, ceil_mode=True)


 self.inception4a = Inception(480, 192, 96, 208, 16, 48, 64)
 self.inception4b = Inception(512, 160, 112, 224, 24, 64, 64)
 self.inception4c = Inception(512, 128, 128, 256, 24, 64, 64)
 self.inception4d = Inception(512, 112, 144, 288, 32, 64, 64)
 self.inception4e = Inception(528, 256, 160, 320, 32, 128, 128)
 self.maxpool4 = nn.MaxPool2d(3, stride=2, ceil_mode=True)

 self.inception5a = Inception(832, 256, 160, 320, 32, 128, 128)
 self.inception5b = Inception(832, 384, 192, 384, 48, 128, 128)

 # 辅助分类器
 if self.aux_logits:
 self.aux1 = InceptionAux(512, num_classes)
 self.aux2 = InceptionAux(528, num_classes)

 # 平均池化
 self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
 self.dropout = nn.Dropout(0.4)
 self.fc = nn.Linear(1024, num_classes)
 if init_weights:
 self._initialize_weights()

 def forward(self, x):
 # N x 3 x 224 x 224
 x = self.conv1(x)
 # N x 64 x 112 x 112
 x = self.maxpool1(x)
 # N x 64 x 56 x 56
 x = self.conv2(x)
 # N x 64 x 56 x 56
 x = self.conv3(x)
 # N x 192 x 56 x 56
 x = self.maxpool2(x)

 # N x 192 x 28 x 28
 x = self.inception3a(x)
 # N x 256 x 28 x 28
 x = self.inception3b(x)
 # N x 480 x 28 x 28
 x = self.maxpool3(x)
 # N x 480 x 14 x 14
 x = self.inception4a(x)
 # N x 512 x 14 x 14
 if self.training and self.aux_logits: # eval model lose this layer
 aux1 = self.aux1(x)

 x = self.inception4b(x)
 # N x 512 x 14 x 14
 x = self.inception4c(x)
 # N x 512 x 14 x 14
 x = self.inception4d(x)
 # N x 528 x 14 x 14

 if self.training and self.aux_logits: # eval model lose this layer
 aux2 = self.aux2(x)

 x = self.inception4e(x)
 # N x 832 x 14 x 14
 x = self.maxpool4(x)
 # N x 832 x 7 x 7
 x = self.inception5a(x)
 # N x 832 x 7 x 7
 x = self.inception5b(x)
 # N x 1024 x 7 x 7

 x = self.avgpool(x)
 # N x 1024 x 1 x 1
 x = torch.flatten(x, 1)
 # N x 1024
 x = self.dropout(x)
 x = self.fc(x)
 # N x 1000 (num_classes)
 if self.training and self.aux_logits: # eval model lose this layer
 return x, aux2, aux1
 return x

 def _initialize_weights(self):

 for m in self.modules():
 if isinstance(m, nn.Conv2d):
 nn.init.kaimingnormal(m.weight, mode=‘fanout’, nonlinearity=‘relu’)
 if m.bias is not None:
 nn.init.constant

(m.bias, 0)
 elif isinstance(m, nn.Linear):
 nn.init.normal(m.weight, 0, 0.01)
 nn.init.constant
(m.bias, 0)


class Inception(nn.Module):
 def init(self, in_channels, ch1x1, ch3x3red, ch3x3, ch5x5red, ch5x5, pool_proj):
 “’

 :param in_channels: 输入的通道数
 :param ch1x1: 1x1 卷积核通道数
 :param ch3x3red:
 :param ch3x3:
 :param ch5x5red:
 :param ch5x5:
 :param pool_proj: 池化输出通道
 “’
 super(Inception, self).init()

 self.branch1x1 = BasicConv2d(in_channels=in_channels,out_channels= ch1x1, kernel_size=1)

 self.branch3x3 = nn.Sequential(
 BasicConv2d(in_channels=in_channels, out_channels=ch3x3red, kernel_size=1),
 BasicConv2d(in_channels= ch3x3red, out_channels=ch3x3, kernel_size=3, padding=1) # 保证输出大小等于输入大小
 )

 self.branch5x5 = nn.Sequential(
 BasicConv2d(in_channels=in_channels, out_channels=ch5x5red, kernel_size=1),
 BasicConv2d(in_channels=ch5x5red, out_channels=ch5x5, kernel_size=5, padding=2) # 保证输出大小等于输入大小
 )

 self.pool = nn.Sequential(
 nn.MaxPool2d(kernel_size=3, stride=1, padding=1),
 BasicConv2d(in_channels=in_channels, out_channels=pool_proj, kernel_size=1)
 )

 def forward(self, x):
 branch1 = self.branch1x1(x)
 branch2 = self.branch3x3(x)
 branch3 = self.branch5x5(x)
 branch4 = self.pool(x)

 outputs = [branch1, branch2, branch3, branch4]
 return torch.cat(outputs, 1)


class InceptionAux(nn.Module):
 def init(self, in_channels, num_classes):
 super(InceptionAux, self).init()
 self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
 self.conv = BasicConv2d(in_channels, 128, kernel_size=1) # output[batch, 128, 4, 4]

 self.fc1 = nn.Linear(2048, 1024)
 self.fc2 = nn.Linear(1024, num_classes)

 def forward(self, x):
 # aux1: N x 512 x 14 x 14, aux2: N x 528 x 14 x 14
 x = self.averagePool(x)
 # aux1: N x 512 x 4 x 4, aux2: N x 528 x 4 x 4
 x = self.conv(x)
 # N x 128 x 4 x 4
 x = torch.flatten(x, 1)
 x = F.dropout(x, 0.5, training=self.training)
 # N x 2048
 x = F.relu(self.fc1(x), inplace=True)
 x = F.dropout(x, 0.5, training=self.training)
 # N x 1024
 x = self.fc2(x)
 # N x num_classes
 return x


class BasicConv2d(nn.Module):

 def init(self, in_channels, out_channels, kwargs):
 super(BasicConv2d, self).init()
 self.conv = nn.Conv2d(in_channels, out_channels, kwargs)
 self.relu = nn.ReLU(inplace=True)

 def forward(self, x):

 x = self.conv(x)
 x = self.relu(x)
 return x


if name == ‘main’:
 model = GoogLeNet()
 print(model)

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