File size: 2,951 Bytes
bdbd148 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 |
'''
AlexNet in Pytorch
'''
import torch
import torch.nn as nn
class AlexNet(nn.Module): # 训练 ALexNet
'''
AlexNet模型
'''
def __init__(self,num_classes=10):
super(AlexNet,self).__init__()
# 五个卷积层 输入 32 * 32 * 3
self.conv1 = nn.Sequential(
nn.Conv2d(in_channels=3, out_channels=6, kernel_size=3, stride=1, padding=1), # (32-3+2)/1+1 = 32
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (32-2)/2+1 = 16
)
self.conv2 = nn.Sequential( # 输入 16 * 16 * 6
nn.Conv2d(in_channels=6, out_channels=16, kernel_size=3, stride=1, padding=1), # (16-3+2)/1+1 = 16
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (16-2)/2+1 = 8
)
self.conv3 = nn.Sequential( # 输入 8 * 8 * 16
nn.Conv2d(in_channels=16, out_channels=32, kernel_size=3, stride=1, padding=1), # (8-3+2)/1+1 = 8
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (8-2)/2+1 = 4
)
self.conv4 = nn.Sequential( # 输入 4 * 4 * 64
nn.Conv2d(in_channels=32, out_channels=64, kernel_size=3, stride=1, padding=1), # (4-3+2)/1+1 = 4
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (4-2)/2+1 = 2
)
self.conv5 = nn.Sequential( # 输入 2 * 2 * 128
nn.Conv2d(in_channels=64, out_channels=128, kernel_size=3, stride=1, padding=1),# (2-3+2)/1+1 = 2
nn.ReLU(),
nn.MaxPool2d(kernel_size=2, stride=2, padding=0) # (2-2)/2+1 = 1
) # 最后一层卷积层，输出 1 * 1 * 128
# 全连接层
self.dense = nn.Sequential(
nn.Linear(128,120),
nn.ReLU(),
nn.Linear(120,84),
nn.ReLU(),
nn.Linear(84,num_classes)
)
# 初始化权重
self._initialize_weights()
def forward(self,x):
x = self.conv1(x)
x = self.conv2(x)
x = self.conv3(x)
x = self.conv4(x)
x = self.conv5(x)
x = x.view(x.size()[0],-1)
x = self.dense(x)
return x
def _initialize_weights(self):
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
if m.bias is not None:
nn.init.constant_(m.bias, 0)
elif isinstance(m, nn.Linear):
nn.init.normal_(m.weight, 0, 0.01)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def test():
net = AlexNet()
x = torch.randn(2,3,32,32)
y = net(x)
print(y.size())
from torchinfo import summary
device = 'cuda' if torch.cuda.is_available() else 'cpu'
net = net.to(device)
summary(net,(3,32,32)) |