GoogLeNet

In [ ]:

import torch
from torch import nn, optim
import torch.nn.functional as F
class Inception(nn.Module):
    # c1 - c4为每条线路里的层的输出通道数
    def __init__(self, in_c, c1, c2, c3, c4):
        super(Inception, self).__init__()
        # 线路1，单1 x 1卷积层
        self.p1_1 = nn.Conv2d(in_c, c1, kernel_size=1)
        # 线路2，1 x 1卷积层后接3 x 3卷积层
        self.p2_1 = nn.Conv2d(in_c, c2[0], kernel_size=1)
        self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
        # 线路3，1 x 1卷积层后接5 x 5卷积层
        self.p3_1 = nn.Conv2d(in_c, c3[0], kernel_size=1)
        self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
        # 线路4，3 x 3最大池化层后接1 x 1卷积层
        self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.p4_2 = nn.Conv2d(in_c, c4, kernel_size=1)
 
    def forward(self, x):
        p1 = F.relu(self.p1_1(x))
        p2 = F.relu(self.p2_2(F.relu(self.p2_1(x))))
        p3 = F.relu(self.p3_2(F.relu(self.p3_1(x))))
        p4 = F.relu(self.p4_2(self.p4_1(x)))
        return torch.cat((p1, p2, p3, p4), dim=1)  # 在通道维上连结输出

import torch from torch import nn, optim import torch.nn.functional as F class Inception(nn.Module): # c1 - c4为每条线路里的层的输出通道数 def __init__(self, in_c, c1, c2, c3, c4): super(Inception, self).__init__() # 线路1，单1 x 1卷积层 self.p1_1 = nn.Conv2d(in_c, c1, kernel_size=1) # 线路2，1 x 1卷积层后接3 x 3卷积层 self.p2_1 = nn.Conv2d(in_c, c2[0], kernel_size=1) self.p2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1) # 线路3，1 x 1卷积层后接5 x 5卷积层 self.p3_1 = nn.Conv2d(in_c, c3[0], kernel_size=1) self.p3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2) # 线路4，3 x 3最大池化层后接1 x 1卷积层 self.p4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1) self.p4_2 = nn.Conv2d(in_c, c4, kernel_size=1) def forward(self, x): p1 = F.relu(self.p1_1(x)) p2 = F.relu(self.p2_2(F.relu(self.p2_1(x)))) p3 = F.relu(self.p3_2(F.relu(self.p3_1(x)))) p4 = F.relu(self.p4_2(self.p4_1(x))) return torch.cat((p1, p2, p3, p4), dim=1) # 在通道维上连结输出

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class GlobalAvgPool2d(nn.Module):
    # 全局平均池化层可通过将池化窗口形状设置成输入的高和宽实现
    def __init__(self):
        super(GlobalAvgPool2d, self).__init__()
    def forward(self, x):
        return F.avg_pool2d(x, kernel_size=x.size()[2:])

class GlobalAvgPool2d(nn.Module): # 全局平均池化层可通过将池化窗口形状设置成输入的高和宽实现 def __init__(self): super(GlobalAvgPool2d, self).__init__() def forward(self, x): return F.avg_pool2d(x, kernel_size=x.size()[2:])

In [ ]:

class FlattenLayer(torch.nn.Module):
    def __init__(self):
        super(FlattenLayer, self).__init__()
    def forward(self, x): # x shape: (batch, *, *, ...)
        return x.view(x.shape[0], -1)

class FlattenLayer(torch.nn.Module): def __init__(self): super(FlattenLayer, self).__init__() def forward(self, x): # x shape: (batch, *, *, ...) return x.view(x.shape[0], -1)

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class AuxClassifier(nn.Module):
    def __init__(self, in_channels, num_classes):
        super(AuxClassifier, self).__init__()
        self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3)
        self.conv = nn.Conv2d(in_channels, 128, kernel_size=1)

        self.fc1 = nn.Linear(2048, 1024)
        self.fc2 = nn.Linear(1024, num_classes)
    def forward(self, x):
        x = self.averagePool(x)
        x = self.conv(x)

        x = torch.flatten(x, 1)
        x = F.dropout(x, 0.4, training=self.training)
        x = F.relu(self.fc1(x), inplace=True)
        x = F.dropout(x, 0.4, training=self.training)
        x = self.fc2(x)
        return x

class AuxClassifier(nn.Module): def __init__(self, in_channels, num_classes): super(AuxClassifier, self).__init__() self.averagePool = nn.AvgPool2d(kernel_size=5, stride=3) self.conv = nn.Conv2d(in_channels, 128, kernel_size=1) self.fc1 = nn.Linear(2048, 1024) self.fc2 = nn.Linear(1024, num_classes) def forward(self, x): x = self.averagePool(x) x = self.conv(x) x = torch.flatten(x, 1) x = F.dropout(x, 0.4, training=self.training) x = F.relu(self.fc1(x), inplace=True) x = F.dropout(x, 0.4, training=self.training) x = self.fc2(x) return x

In [ ]:

class GoogLeNet(nn.Module):
    def __init__(self, num_classes=10):
        super(GoogLeNet, self).__init__()
        
        self.b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3),
                           nn.ReLU(),
                           nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

        self.b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1),
                           nn.Conv2d(64, 192, kernel_size=3, padding=1),
                           nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

        self.b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32),
                           Inception(256, 128, (128, 192), (32, 96), 64),
                           nn.MaxPool2d(kernel_size=3, stride=2, padding=1))

        self.b4 = 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(kernel_size=3, stride=2, padding=1))

        self.b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
                           Inception(832, 384, (192, 384), (48, 128), 128),
                           GlobalAvgPool2d())
        self.output=nn.Sequential(FlattenLayer(),
                                  nn.Dropout(p=0.4),
                                  nn.Linear(1024, num_classes))
        
    def forward(self, x):
        x=self.b1(x)
        x=self.b2(x)
        x=self.b3(x)
        x=self.b4(x)
        x=self.b5(x)
        x=self.output(x)
        return x

class GoogLeNet(nn.Module): def __init__(self, num_classes=10): super(GoogLeNet, self).__init__() self.b1 = nn.Sequential(nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3), nn.ReLU(), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) self.b2 = nn.Sequential(nn.Conv2d(64, 64, kernel_size=1), nn.Conv2d(64, 192, kernel_size=3, padding=1), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) self.b3 = nn.Sequential(Inception(192, 64, (96, 128), (16, 32), 32), Inception(256, 128, (128, 192), (32, 96), 64), nn.MaxPool2d(kernel_size=3, stride=2, padding=1)) self.b4 = 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(kernel_size=3, stride=2, padding=1)) self.b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128), Inception(832, 384, (192, 384), (48, 128), 128), GlobalAvgPool2d()) self.output=nn.Sequential(FlattenLayer(), nn.Dropout(p=0.4), nn.Linear(1024, num_classes)) def forward(self, x): x=self.b1(x) x=self.b2(x) x=self.b3(x) x=self.b4(x) x=self.b5(x) x=self.output(x) return x