VGG

AlexNet在Lenet的基础上增加了几个卷积层,改变了卷积核大小,每一层输出通道数目等,并且取得了很好的效果.但是并没有提出一个简单有效的思路.
VGG做到了这一点,提出了可以通过重复使⽤简单的基础块来构建深度学习模型的思路.

论文地址:https://arxiv.org/abs/1409.1556

vgg的结构如下所示:

上图给出了不同层数的vgg的结构.也就是常说的vgg16,vgg19等等.

VGG BLOCK

vgg的设计思路是,通过不断堆叠3x3的卷积核,不断加深模型深度.vgg net证明了加深模型深度对提高模型的学习能力是一个很有效的手段.

看上图就能发现,连续的2个3x3卷积,感受野和一个5x5卷积是一样的,但是前者有两次非线性变换,后者只有一次!,这就是连续堆叠小卷积核能提高
模型特征学习的关键.此外,2个3x3的参数数量也比一个5x5少.(2x3x3 < 5x5)

vgg的基础组成模块,每一个卷积层都由n个3x3卷积后面接2x2的最大池化.池化层的步幅为2.从而卷积层卷积后,宽高不变,池化后,宽高减半.
我们可以有以下代码:

def make_layers(in_channels,cfg):
    layers = []
    previous_channel = in_channels #上一层的输出的channel数量
    for v in cfg:
        if v == 'M':
            layers.append(nn.MaxPool2d(kernel_size=2,stride=2))
        else:
            layers.append(nn.Conv2d(previous_channel,v,kernel_size=3,padding=1))
            layers.append(nn.ReLU())

            previous_channel = v

    conv = nn.Sequential(*layers)
    return conv


cfgs = {
    'A': [64, 'M', 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'B': [64, 64, 'M', 128, 128, 'M', 256, 256, 'M', 512, 512, 'M', 512, 512, 'M'],
    'D': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 'M', 512, 512, 512, 'M', 512, 512, 512, 'M'],
    'E': [64, 64, 'M', 128, 128, 'M', 256, 256, 256, 256, 'M', 512, 512, 512, 512, 'M', 512, 512, 512, 512, 'M'],
}

cfgs定义了不同的vgg模型的结构,比如'A'代表vgg11．　数字代表卷积后的channel数. 'M'代表Maxpool

我们可以给出模型定义

class VGG(nn.Module):
    def __init__(self,input_channels,cfg,num_classes=10, init_weights=True):
        super(VGG, self).__init__()
        self.conv = make_layers(input_channels,cfg) # torch.Size([1, 512, 7, 7])
        self.fc = nn.Sequential(
            nn.Linear(512*7*7,4096),
            nn.ReLU(),
            nn.Linear(4096,4096),
            nn.ReLU(),
            nn.Linear(4096,num_classes)
        )
    
    def forward(self, img):
        feature = self.conv(img)
        output = self.fc(feature.view(img.shape[0], -1))
        return output

卷积层的输出可由以下测试代码得出

# conv = make_layers(1,cfgs['A'])
# X = torch.randn((1,1,224,224))
# out = conv(X)
# #print(out.shape)

加载数据

batch_size,num_workers=4,4
train_iter,test_iter = learntorch_utils.load_data(batch_size,num_workers,resize=224)

这里batch_size调到8我的显存就不够了...

定义模型

net = VGG(1,cfgs['A']).cuda()

定义损失函数

loss = nn.CrossEntropyLoss()

定义优化器　

opt = torch.optim.Adam(net.parameters(),lr=0.001)

定义评估函数

def test():
    acc_sum = 0
    batch = 0
    for X,y in test_iter:
        X,y = X.cuda(),y.cuda()
        y_hat = net(X)
        acc_sum += (y_hat.argmax(dim=1) == y).float().sum().item()
        batch += 1
    #print('acc_sum %d,batch %d' % (acc_sum,batch))

    return 1.0*acc_sum/(batch*batch_size)

训练

num_epochs = 3
def train():
    for epoch in range(num_epochs):
        train_l_sum,batch,acc_sum = 0,0,0
        start = time.time()
        for X,y in train_iter:
            # start_batch_begin = time.time()
            X,y = X.cuda(),y.cuda()
            y_hat = net(X)
            acc_sum += (y_hat.argmax(dim=1) == y).float().sum().item()

            l = loss(y_hat,y)
            opt.zero_grad()
            l.backward()

            opt.step()
            train_l_sum += l.item()

            batch += 1

            mean_loss = train_l_sum/(batch*batch_size) #计算平均到每张图片的loss
            start_batch_end = time.time()
            time_batch = start_batch_end - start

            print('epoch %d,batch %d,train_loss %.3f,time %.3f' % 
                (epoch,batch,mean_loss,time_batch))

        print('***************************************')
        mean_loss = train_l_sum/(batch*batch_size) #计算平均到每张图片的loss
        train_acc = acc_sum/(batch*batch_size)     #计算训练准确率
        test_acc = test()                           #计算测试准确率
        end = time.time()
        time_per_epoch =  end - start
        print('epoch %d,train_loss %f,train_acc %f,test_acc %f,time %f' % 
                (epoch + 1,mean_loss,train_acc,test_acc,time_per_epoch))

train()

4G的GTX 1050显卡,训练一个epoch大概一个多小时.
完整代码:https://github.com/sdu2011/learn_pytorch