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machinelearning_notebook

 
			
			   
				 
					
						
						
							
							# -*- coding: utf-8 -*-
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# # GoogLeNet
# 前面我们讲的 VGG 是 2014 年 ImageNet 比赛的亚军，那么冠军是谁呢？就是我们马上要讲的 GoogLeNet，这是 Google 的研究人员提出的网络结构，在当时取得了非常大的影响，因为网络的结构变得前所未有，它颠覆了大家对卷积网络的串联的印象和固定做法，采用了一种非常有效的 inception 模块，得到了比 VGG 更深的网络结构，但是却比 VGG 的参数更少，因为其去掉了后面的全连接层，所以参数大大减少，同时有了很高的计算效率。
#
# ![](https://ws2.sinaimg.cn/large/006tNc79ly1fmprhdocouj30qb08vac3.jpg)
#
# 这是 googlenet 的网络示意图，下面我们介绍一下其作为创新的 inception 模块。

# ## Inception 模块
# 在上面的网络中，我们看到了多个四个并行卷积的层，这些四个卷积并行的层就是 inception 模块，可视化如下
#
# ![](https://ws4.sinaimg.cn/large/006tNc79gy1fmprivb2hxj30dn09dwef.jpg)
#

# 一个 inception 模块的四个并行线路如下：
# 1.一个 1 x 1 的卷积，一个小的感受野进行卷积提取特征
# 2.一个 1 x 1 的卷积加上一个 3 x 3 的卷积，1 x 1 的卷积降低输入的特征通道，减少参数计算量，然后接一个 3 x 3 的卷积做一个较大感受野的卷积
# 3.一个 1 x 1 的卷积加上一个 5 x 5 的卷积，作用和第二个一样
# 4.一个 3 x 3 的最大池化加上 1 x 1 的卷积，最大池化改变输入的特征排列，1 x 1 的卷积进行特征提取
#
# 最后将四个并行线路得到的特征在通道这个维度上拼接在一起，下面我们可以实现一下

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:05.427292Z", "start_time": "2017-12-22T12:51:04.924747Z"}}
import sys
sys.path.append('..')

import numpy as np
import torch
from torch import nn
from torch.autograd import Variable
from torchvision.datasets import CIFAR10

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:08.890890Z", "start_time": "2017-12-22T12:51:08.876313Z"}}
# 定义一个卷积加一个 relu 激活函数和一个 batchnorm 作为一个基本的层结构
def 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

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:09.671474Z", "start_time": "2017-12-22T12:51:09.587337Z"}}
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__()
        # 第一条线路
        self.branch1x1 = conv_relu(in_channel, out1_1, 1)
        
        # 第二条线路
        self.branch3x3 = nn.Sequential( 
            conv_relu(in_channel, out2_1, 1),
            conv_relu(out2_1, out2_3, 3, padding=1)
        )
        
        # 第三条线路
        self.branch5x5 = nn.Sequential(
            conv_relu(in_channel, out3_1, 1),
            conv_relu(out3_1, out3_5, 5, padding=2)
        )
        
        # 第四条线路
        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

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:10.948630Z", "start_time": "2017-12-22T12:51:10.757903Z"}}
test_net = inception(3, 64, 48, 64, 64, 96, 32)
test_x = Variable(torch.zeros(1, 3, 96, 96))
print('input shape: {} x {} x {}'.format(test_x.shape[1], test_x.shape[2], test_x.shape[3]))
test_y = test_net(test_x)
print('output shape: {} x {} x {}'.format(test_y.shape[1], test_y.shape[2], test_y.shape[3]))
# -

# 可以看到输入经过了 inception 模块之后，大小没有变化，通道的维度变多了

# 下面我们定义 GoogLeNet，GoogLeNet 可以看作是很多个 inception 模块的串联，注意，原论文中使用了多个输出来解决梯度消失的问题，这里我们只定义一个简单版本的 GoogLeNet，简化为一个输出

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:13.149380Z", "start_time": "2017-12-22T12:51:12.934110Z"}}
class googlenet(nn.Module):
    def __init__(self, in_channel, num_classes, verbose=False):
        super(googlenet, self).__init__()
        self.verbose = verbose
        
        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)
        if self.verbose:
            print('block 1 output: {}'.format(x.shape))
        x = self.block2(x)
        if self.verbose:
            print('block 2 output: {}'.format(x.shape))
        x = self.block3(x)
        if self.verbose:
            print('block 3 output: {}'.format(x.shape))
        x = self.block4(x)
        if self.verbose:
            print('block 4 output: {}'.format(x.shape))
        x = self.block5(x)
        if self.verbose:
            print('block 5 output: {}'.format(x.shape))
        x = x.view(x.shape[0], -1)
        x = self.classifier(x)
        return x

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:13.614936Z", "start_time": "2017-12-22T12:51:13.428383Z"}}
test_net = googlenet(3, 10, True)
test_x = Variable(torch.zeros(1, 3, 96, 96))
test_y = test_net(test_x)
print('output: {}'.format(test_y.shape))
# -

# 可以看到输入的尺寸不断减小，通道的维度不断增加

# + {"ExecuteTime": {"end_time": "2017-12-22T12:51:16.387778Z", "start_time": "2017-12-22T12:51:15.121350Z"}}
from utils import train

def data_tf(x):
    x = x.resize((96, 96), 2) # 将图片放大到 96 x 96
    x = np.array(x, dtype='float32') / 255
    x = (x - 0.5) / 0.5 # 标准化，这个技巧之后会讲到
    x = x.transpose((2, 0, 1)) # 将 channel 放到第一维，只是 pytorch 要求的输入方式
    x = torch.from_numpy(x)
    return x
     
train_set = CIFAR10('./data', train=True, transform=data_tf)
train_data = torch.utils.data.DataLoader(train_set, batch_size=64, shuffle=True)
test_set = CIFAR10('./data', train=False, transform=data_tf)
test_data = torch.utils.data.DataLoader(test_set, batch_size=128, shuffle=False)

net = googlenet(3, 10)
optimizer = torch.optim.SGD(net.parameters(), lr=0.01)
criterion = nn.CrossEntropyLoss()

# + {"ExecuteTime": {"end_time": "2017-12-22T13:17:25.310685Z", "start_time": "2017-12-22T12:51:16.389607Z"}}
train(net, train_data, test_data, 20, optimizer, criterion)
# -

# GoogLeNet 加入了更加结构化的 Inception 块使得我们能够使用更大的通道，更多的层，同时也控制了计算量。
#
# **小练习：GoogLeNet 有很多后续的版本，尝试看看论文，看看有什么不同，实现一下：  
# v1：最早的版本  
# v2：加入 batch normalization 加快训练  
# v3：对 inception 模块做了调整  
# v4：基于 ResNet 加入了 残差连接  **