GoogleNet实现

Inception 块

 

 

 

 Inception块中可以自定义的超参数是每个层的输出通道数，我们以此来控制模型复杂度

import time
import torch
from torch import nn,optim
import torch.nn.functional as F

import sys
sys.path.append("./Dive-into-DL-PyTorch-master/Dive-into-DL-PyTorch-master/code/") 
import d2lzh_pytorch as d2l
device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')

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1×1卷积层
        self.p1_1 = nn.Conv2d(in_c,c1,kernel_size=1)
        # 线路2 1×1卷积层后接3×3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)))
        
        # 在通道维上连接输出
        return torch.cat((p1,p2,p3,p4),dim=1)

GoogLeNet模型

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))

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))

b3 = 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))

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))

b5 = nn.Sequential(Inception(832, 256, (160, 320), (32, 128), 128),
                   Inception(832, 384, (192, 384), (48, 128), 128),
                   d2l.GlobalAvgPool2d())

net = nn.Sequential(b1, b2, b3, b4, b5, 
                    d2l.FlattenLayer(), nn.Linear(1024, 10))

net = nn.Sequential(b1, b2, b3, b4, b5, d2l.FlattenLayer(), nn.Linear(1024, 10))
X = torch.rand(1, 1, 96, 96)
for blk in net.children(): 
    X = blk(X)
    print('output shape: ', X.shape)