import numpy as npimport torchfrom torch import nnfrom torch.autograd import Variablefrom torchvision.datasets import CIFAR10def conv_relu(in_channel, out_channel, kernel, stride=1, padding=0): layer = nn.Sequential( nn.Conv2d(in_channel, out_channel, kernel, stride, padding), nn.BatchNorm2d(out_channel, eps=1e-3), nn.ReLU(True) ) return layer class inception(nn.Module): def __init__(self,in_channel,out1_1,out2_1,out2_3,out3_1,out3_5,out4_1): super(inception,self).__init__() self.branch1x1=conv_relu(in_channel,out1_1,1) self.branch3x3=nn.Sequential( conv_relu(in_channel,out2_1,1), conv_relu(out2_1,out2_3,3,padding=1) ) self.branch5x5=nn.Sequential( conv_relu(in_channel,out3_1,1), conv_relu(out3_1,out3_5,5,padding=2) ) self.branch_pool=nn.Sequential( nn.MaxPool2d(3,stride=1,padding=1), conv_relu(in_channel,out4_1,1) ) def forward(self,x): f1 = self.branch1x1(x) f2 = self.branch3x3(x) f3 = self.branch5x5(x) f4 = self.branch_pool(x) output = torch.cat((f1, f2, f3, f4), dim=1) return output class GoogLeNet(nn.Module): def __init__(self, in_channel, num_classes): super(GoogLeNet, self).__init__() self.block1 = nn.Sequential( conv_relu(in_channel, out_channel=64, kernel=7, stride=2, padding=3), nn.MaxPool2d(3, 2) ) self.block2 = nn.Sequential( conv_relu(64, 64, kernel=1), conv_relu(64, 192, kernel=3, padding=1), nn.MaxPool2d(3, 2) ) self.block3 = nn.Sequential( inception(192, 64, 96, 128, 16, 32, 32), inception(256, 128, 128, 192, 32, 96, 64), nn.MaxPool2d(3, 2) ) self.block4 = nn.Sequential( inception(480, 192, 96, 208, 16, 48, 64), inception(512, 160, 112, 224, 24, 64, 64), inception(512, 128, 128, 256, 24, 64, 64), inception(512, 112, 144, 288, 32, 64, 64), inception(528, 256, 160, 320, 32, 128, 128), nn.MaxPool2d(3, 2) ) self.block5 = nn.Sequential( inception(832, 256, 160, 320, 32, 128, 128), inception(832, 384, 182, 384, 48, 128, 128), nn.AvgPool2d(2) ) self.classifier = nn.Linear(1024, num_classes) def forward(self, x): x = self.block1(x) x = self.block2(x) x = self.block3(x) x = self.block4(x) x = self.block5(x) x = x.view(x.shape[0], -1) x = self.classifier(x) return x net=GoogLeNet(3,10)data=torch.randn(64,3,96,96)print(net(data).shape)#torch.Size([64, 10])
