alexnet.py

import torch
from torch import nn


class AlexNet(nn.Module):
    def __init__(self, num_classes=1000):
        super(AlexNet, self).__init__()
        self.net = nn.Sequential(
            # 为了和论文的图像输入尺寸保持一致以及下一层的55对应，这里对图像进行了padding
            nn.Conv2d(in_channels=3, out_channels=96, kernel_size=11, stride=4, padding=2),
            nn.ReLU(),
            nn.LocalResponseNorm(size=5, alpha=10e-4, beta=0.75, k=2),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, padding=2),
            nn.ReLU(),
            nn.LocalResponseNorm(size=5, alpha=10e-4, beta=0.75, k=2),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Conv2d(in_channels=256, out_channels=384, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=384, out_channels=384, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1),
            nn.ReLU(),
            nn.MaxPool2d(kernel_size=3, stride=2),
            nn.Flatten(),
            nn.Dropout(p=0.5),
            nn.Linear(in_features=256 * 6 * 6, out_features=4096),
            nn.ReLU(),
            nn.Dropout(p=0.5),
            nn.Linear(in_features=4096, out_features=4096),
            nn.ReLU(),
            nn.Linear(in_features=4096, out_features=num_classes)
        )
        self.init_weights()

    def init_weights(self):
        for layer in self.net:
            # 先一致初始化
            if isinstance(layer, nn.Conv2d):
                nn.init.kaiming_normal_(layer.weight, mode='fan_out', nonlinearity='relu')
                # nn.init.normal_(layer.weight, mean=0, std=0.01) # 论文权重初始化策略
                nn.init.constant_(layer.bias, 0)
            elif isinstance(layer, nn.Linear):
                nn.init.normal_(layer.weight, mean=0, std=0.01)
                nn.init.constant_(layer.bias, 1)
            # 单独对论文网络中的2、4、5卷积层的偏置进行初始化
            nn.init.constant_(self.net[4].bias, 1)
            nn.init.constant_(self.net[10].bias, 1)
            nn.init.constant_(self.net[12].bias, 1)

    def forward(self, x):
        return self.net(x)

    def test_output_shape(self):
        test_img = torch.rand(size=(1, 3, 227, 227), dtype=torch.float32)
        for layer in self.net:
            test_img = layer(test_img)
            print(layer.__class__.__name__, 'output shape: \t', test_img.shape)

# alexnet = AlexNet()
# alexnet.test_output_shape()