import torch
from torch import nn
from torch.autograd import Variable
class AlexNet(nn.Module):
def __init__(self):
super(AlexNet, self).__init__()
self.conv_unit = nn.Sequential(
nn.Conv2d(3, 96, kernel_size=11, stride=4),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(96, 256, kernel_size=5, padding=2),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2),
nn.Conv2d(256, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 384, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.Conv2d(384, 256, kernel_size=3, padding=1),
nn.ReLU(inplace=True),
nn.MaxPool2d(kernel_size=3, stride=2))
# self.fc=nn.Sequential(
# nn.Linear(???, 4096))
def forward(self, x):
x = self.conv_unit(x)
print(x.size())
if __name__ == '__main__':
net = AlexNet()
data_input = Variable(torch.randn([1, 3, 96, 96])) # 這裏假設輸入圖片是96x96
print(data_input.size())
net(data_input)