|
|
import torch.nn as nn
|
|
|
import torch
|
|
|
import torch
|
|
|
import torch.nn as nn
|
|
|
import torch.nn.functional as F
|
|
|
|
|
|
class ImprovedAlexNet(nn.Module):
|
|
|
def __init__(self, num_classes=1000):
|
|
|
super(ImprovedAlexNet, self).__init__()
|
|
|
self.features = nn.Sequential( # 卷积层提取图像特征
|
|
|
nn.Conv2d(3, 48, kernel_size=11, stride=4, padding=2), # input[3, 224, 224] output[48, 55, 55]
|
|
|
nn.ReLU(inplace=True), # 直接修改覆盖原值，节省运算内存
|
|
|
nn.MaxPool2d(kernel_size=3, stride=2), # output[48, 27, 27]
|
|
|
nn.Conv2d(48, 128, kernel_size=5, padding=2), # output[128, 27, 27]
|
|
|
nn.ReLU(inplace=True),
|
|
|
nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 13, 13]
|
|
|
nn.Conv2d(128, 192, kernel_size=3, padding=1), # output[192, 13, 13]
|
|
|
nn.ReLU(inplace=True),
|
|
|
nn.Conv2d(192, 192, kernel_size=3, padding=1), # output[192, 13, 13]
|
|
|
nn.ReLU(inplace=True),
|
|
|
nn.Conv2d(192, 128, kernel_size=3, padding=1), # output[128, 13, 13]
|
|
|
nn.ReLU(inplace=True),
|
|
|
nn.MaxPool2d(kernel_size=3, stride=2), # output[128, 6, 6]
|
|
|
)
|
|
|
self.classifier = nn.Sequential( # 全连接层对图像分类
|
|
|
nn.Dropout(p=0.5), # Dropout 随机失活神经元，默认比例为0.5
|
|
|
nn.Linear(128 * 6 * 6, 2048),
|
|
|
nn.ReLU(inplace=True),
|
|
|
nn.Dropout(p=0.5),
|
|
|
nn.Linear(2048, 2048),
|
|
|
nn.ReLU(inplace=True),
|
|
|
nn.Linear(2048, num_classes),
|
|
|
)
|
|
|
|
|
|
def forward(self, x):
|
|
|
x = self.features(x)#进行卷积操作
|
|
|
x = torch.flatten(x, start_dim=1)#展平
|
|
|
x = self.classifier(x)#输出
|
|
|
return x
|
|
|
|
