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PyTorch详解经典网络种含并行连结的网络GoogLeNet实现流程

2024-04-02 19:55

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1. Inception块

Inception块是 GoogLeNet 的基本组成单元。Inception 块由四条并行的路径组成，每个路径使用不同大小的卷积核：

路径1：使用 1×1 卷积层；

路径2：先对输出执行 1×1 卷积层，来减少通道数，降低模型复杂性，然后接 3×3 卷积层；

路径3：先对输出执行 1×1 卷积层，然后接 5×5 卷积层；

路径4：使用 3×3 最大汇聚层，然后使用 1×1 卷积层；

在各自路径中使用合适的 padding ，使得各个路径的输出拥有相同的高和宽，然后将每条路径的输出在通道维度上做连结，作为 Inception 块的最终输出.

class Inception(nn.Module):
    def __init__(self, in_channels, out_channels):
        super(Inception, self).__init__()
        # 路径1
        c1, c2, c3, c4 = out_channels
        self.route1_1 = nn.Conv2d(in_channels, c1, kernel_size=1)
        # 路径2
        self.route2_1 = nn.Conv2d(in_channels, c2[0], kernel_size=1)
        self.route2_2 = nn.Conv2d(c2[0], c2[1], kernel_size=3, padding=1)
        # 路径3
        self.route3_1 = nn.Conv2d(in_channels, c3[0], kernel_size=1)
        self.route3_2 = nn.Conv2d(c3[0], c3[1], kernel_size=5, padding=2)
        # 路径4
        self.route4_1 = nn.MaxPool2d(kernel_size=3, stride=1, padding=1)
        self.route4_2 = nn.Conv2d(in_channels, c4, kernel_size=1)
    def forward(self, x):
        x1 = F.relu(self.route1_1(x))
        x2 = F.relu(self.route2_2(F.relu(self.route2_1(x))))
        x3 = F.relu(self.route3_2(F.relu(self.route3_1(x))))
        x4 = F.relu(self.route4_2(self.route4_1(x)))
        return torch.cat((x1, x2, x3, x4), dim=1)

2. 构造 GoogLeNet 网络

顺序定义 GoogLeNet 的模块。

第一个模块，顺序使用三个卷积层。

# 模型的第一个模块
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),
    nn.Conv2d(64, 64, kernel_size=1),
    nn.ReLU(),
    nn.Conv2d(64, 192, kernel_size=3, padding=1),
    nn.ReLU(),
    nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
                   )

第二个模块，使用两个Inception模块。

# Inception组成的第二个模块
b2 = 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)
                    )

第三个模块，串联五个Inception模块。

# Inception组成的第三个模块
b3 = 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)
                    )

第四个模块，传来两个Inception模块。

GoogLeNet使用 avg pooling layer 代替了 fully-connected layer。一方面降低了维度，另一方面也可以视为对低层特征的组合。

# Inception组成的第四个模块
b4 = nn.Sequential(
    Inception(832, (256, (160, 320), (32, 128), 128)),
    Inception(832, (384, (192, 384), (48, 128), 128)),
    nn.AdaptiveAvgPool2d((1, 1)),
    nn.Flatten()
                    )

net = nn.Sequential(b1, b2, b3, b4, nn.Linear(1024, 10))
x = torch.randn(1, 1, 96, 96)
for layer in net:
    x = layer(x)
    print(layer.__class__.__name__, "output shape: ", x.shape)

输出：

Sequential output shape: torch.Size([1, 192, 28, 28])
Sequential output shape: torch.Size([1, 480, 14, 14])
Sequential output shape: torch.Size([1, 832, 7, 7])
Sequential output shape: torch.Size([1, 1024])
Linear output shape: torch.Size([1, 10])

3. FashionMNIST训练测试

def load_datasets_Cifar10(batch_size, resize=None):
    trans = [transforms.ToTensor()]
    if resize:
        transform = trans.insert(0, transforms.Resize(resize))
    trans = transforms.Compose(trans)
    train_data = torchvision.datasets.CIFAR10(root="../data", train=True, transform=trans, download=True)
    test_data = torchvision.datasets.CIFAR10(root="../data", train=False, transform=trans, download=True)
    print("Cifar10 下载完成...")
    return (torch.utils.data.DataLoader(train_data, batch_size, shuffle=True),
            torch.utils.data.DataLoader(test_data, batch_size, shuffle=False))
def load_datasets_FashionMNIST(batch_size, resize=None):
    trans = [transforms.ToTensor()]
    if resize:
        transform = trans.insert(0, transforms.Resize(resize))
    trans = transforms.Compose(trans)
    train_data = torchvision.datasets.FashionMNIST(root="../data", train=True, transform=trans, download=True)
    test_data = torchvision.datasets.FashionMNIST(root="../data", train=False, transform=trans, download=True)
    print("FashionMNIST 下载完成...")
    return (torch.utils.data.DataLoader(train_data, batch_size, shuffle=True),
            torch.utils.data.DataLoader(test_data, batch_size, shuffle=False))
def load_datasets(dataset, batch_size, resize):
    if dataset == "Cifar10":
        return load_datasets_Cifar10(batch_size, resize=resize)
    else:
        return load_datasets_FashionMNIST(batch_size, resize=resize)
train_iter, test_iter = load_datasets("", 128, 96) # Cifar10

训练结果：