python 特征拼接融合 pytorch三个特征层融合

第11讲 卷积神经网络高级篇 Advanced CNN

pytorch学习视频——B站视频链接：《PyTorch深度学习实践》完结合集_哔哩哔哩_bilibili

以下是视频内容笔记以及源码，笔记纯属个人理解，如有错误欢迎路过的大佬指出 。

1. GoogleNet

网络结构如图所示，

GoogleNet，常被用作基础主干网络，图中红色圈出的一个部分称为Inception块。

2. Inception Module解析

不知道选取什么kernel，将卷积核都使用一遍，对效果比较好的卷积核赋予更高的权重，将各个卷积核得到的结果拼接起来。

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• 1 × 1卷积核解析
  可以看成是在做信息融合，最终得到的特征图的一个像素值融合了前面三个通道的值。
  信息融合最简单的例子——考试对各科分数求总分进行比较分数高低，在多个维度下不太好比较。
  此处就是在做一个通道的变换，原通道数为3，新的通道数是卷积核的个数，高度和宽度不变。

• 作用
  可以用来减少计算量，如图所示：
  如果直接对原图像做卷积，计算量很大，如果先用1×1的卷积核做卷积减少通道数，再用卷积核进行操作，可以减少很多计算量，如图所示加上1×1的卷积层后，计算量是原来的1/10。

3. Inception Module的实现

如图所示的Inception块的4个分支

实现——advanced_cnn.py

import torch
from torch import nn
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ), (0.3081, ))
])

train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               # 指定数据用transform来处理
                               transform=transform)
train_loader = DataLoader(dataset=train_dataset,
                          batch_size=batch_size,
                          shuffle=True)

test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(dataset=test_dataset,
                         batch_size=batch_size,
                         shuffle=False)

# 定义一个Inception类，在网络里会用到
class InceptionA(nn.Module):
    def __init__(self, in_channels):
        super(InceptionA, self).__init__()
        self.branch1X1 = nn.Conv2d(in_channels, 16, kernel_size=1)
		
        # 设置padding保证各个分支输出的高度和宽度保持不变
        self.branch5X5_1 = nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch5X5_2 = nn.Conv2d(16, 24, kernel_size=5, padding=2)

        self.branch3X3_1 = nn.Conv2d(in_channels, 16, kernel_size=1)
        self.branch3X3_2 = nn.Conv2d(16, 24, kernel_size=3, padding=1)
        self.branch3X3_3 = nn.Conv2d(24, 24, kernel_size=3, padding=1)

        self.branch_pool = nn.Conv2d(in_channels, 24, kernel_size=1)

    def forward(self, x):
        branch1X1 = self.branch1X1(x)

        branch5X5 = self.branch5X5_1(x)
        branch5X5 = self.branch5X5_2(branch5X5)

        branch3X3 = self.branch3X3_1(x)
        branch3X3 = self.branch3X3_2(branch3X3)
        branch3X3 = self.branch3X3_3(branch3X3)

        branch_pool = F.avg_pool2d(x, kernel_size=3, stride=1, padding=1)
        branch_pool = self.branch_pool(branch_pool)

        outputs = [branch1X1, branch5X5, branch3X3, branch_pool]
        # （b, c, w, h），dim=1——以第一个维度channel来拼接
        return torch.cat(outputs, dim=1)

# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(88, 20, kernel_size=5)

        self.incep1 = InceptionA(in_channels=10)
        self.incep2 = InceptionA(in_channels=20)

        self.mp = nn.MaxPool2d(2)
        self.fc = nn.Linear(1408, 10)

    def forward(self, x):
        in_size = x.size(0)
        x = F.relu(self.mp(self.conv1(x)))
        x = self.incep1(x)
        x = F.relu(self.mp(self.conv2(x)))
        x = self.incep2(x)
        x = x.view(in_size, -1)
        x = self.fc(x)
        return x



model = Net()
# 将模型迁移到GPU上运行，cuda:0表示第0块显卡
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(torch.cuda.is_available())
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


# 将训练和测试过程分别封装在两个函数当中
def train(epoch):
    running_loss = 0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        # 将要计算的张量也迁移到GPU上——输入和输出
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()

        # 前馈 反馈 更新
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0

accuracy = []
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            # 测试中的张量也迁移到GPU上
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            correct += (predicted == labels).sum().item()
    print('Accuracy on test  set: %d %%' % (100 * correct / total))
    accuracy.append(100 * correct / total)


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()
    print(accuracy)
    plt.plot(range(10), accuracy)
    plt.xlabel("epoch")
    plt.ylabel("Accuracy")
    plt.show()

结果：

4. 残差网络

普通网络与残差网络的区别，残差网络在做完卷积激活之前，将该层的输入加上输出一起作为整个的输出来激活。

定义：

实现——residual.py

import torch
from torch import nn
from torchvision import transforms
from torchvision import datasets
from torch.utils.data import DataLoader
import torch.nn.functional as F
import torch.optim as optim
import matplotlib.pyplot as plt

batch_size = 64
transform = transforms.Compose([
    transforms.ToTensor(),
    transforms.Normalize((0.1307, ), (0.3081, ))
])

train_dataset = datasets.MNIST(root='../dataset/mnist',
                               train=True,
                               download=True,
                               # 指定数据用transform来处理
                               transform=transform)
train_loader = DataLoader(dataset=train_dataset,
                          batch_size=batch_size,
                          shuffle=True)

test_dataset = datasets.MNIST(root='../dataset/mnist',
                              train=False,
                              download=True,
                              transform=transform)
test_loader = DataLoader(dataset=test_dataset,
                         batch_size=batch_size,
                         shuffle=False)


# 定义一个残差模块类
class ResidualBlock(nn.Module):
    def __init__(self, channels):
        super(ResidualBlock, self).__init__()
        self.channels = channels
        self.conv1 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)
        self.conv2 = nn.Conv2d(channels, channels, kernel_size=3, padding=1)

    def forward(self, x):
        y = F.relu(self.conv1(x))
        y = self.conv2(y)
        return F.relu(x + y)


# 定义模型
class Net(nn.Module):
    def __init__(self):
        super(Net, self).__init__()
        self.conv1 = nn.Conv2d(1,16, kernel_size=5)
        self.conv2 = nn.Conv2d(16, 32, kernel_size=5)
        self.mp = nn.MaxPool2d(2)

        self.rblock1 = ResidualBlock(16)
        self.rblock2 = ResidualBlock(32)

        self.fc = nn.Linear(512, 10)

    def forward(self, x):
        in_size = x.size(0)
        x = self.mp(F.relu(self.conv1(x)))
        x = self.rblock1(x)
        x = self.mp(F.relu(self.conv2(x)))
        x = self.rblock2(x)
        x = x.view(in_size, -1)
        x = self.fc(x)
        return x



model = Net()
# 将模型迁移到GPU上运行，cuda:0表示第0块显卡
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print(torch.cuda.is_available())
model.to(device)

criterion = torch.nn.CrossEntropyLoss()
optimizer = optim.SGD(model.parameters(), lr=0.01, momentum=0.5)


# 将训练和测试过程分别封装在两个函数当中
def train(epoch):
    running_loss = 0
    for batch_idx, data in enumerate(train_loader, 0):
        inputs, target = data
        # 将要计算的张量也迁移到GPU上——输入和输出
        inputs, target = inputs.to(device), target.to(device)
        optimizer.zero_grad()

        # 前馈 反馈 更新
        outputs = model(inputs)
        loss = criterion(outputs, target)
        loss.backward()
        optimizer.step()

        running_loss += loss.item()
        if batch_idx % 300 == 299:
            print('[%d, %5d] loss: %.3f' % (epoch + 1, batch_idx + 1, running_loss / 300))
            running_loss = 0

accuracy = []
def test():
    correct = 0
    total = 0
    with torch.no_grad():
        for data in test_loader:
            images, labels = data
            # 测试中的张量也迁移到GPU上
            images, labels = images.to(device), labels.to(device)
            outputs = model(images)
            _, predicted = torch.max(outputs.data, dim=1)
            total += labels.size(0)
            # ？？怎么比较的相等
            # print(predicted)
            # print(labels)
            # print('predicted == labels', predicted == labels)
            # 两个张量比较，得出的是其中相等的元素的个数（即一个批次中预测正确的个数）
            correct += (predicted == labels).sum().item()
            # print('correct______', correct)
    print('Accuracy on test  set: %d %%' % (100 * correct / total))
    accuracy.append(100 * correct / total)


if __name__ == '__main__':
    for epoch in range(10):
        train(epoch)
        test()
    print(accuracy)
    plt.plot(range(10), accuracy)
    plt.xlabel("epoch")
    plt.ylabel("Accuracy")
    plt.show()

结果：