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alexnetModel.py
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alexnetModel.py
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# #!/usr/bin/env python
# # coding: utf-8
# import torch
# import os
# import torch.nn as nn
# import torch.nn.functional as F
# from torch import optim
##Model by Ma
# class AlexNet(nn.Module):
# def __init__(self,num_class=5):
# super().__init__()
# #（1,f=200,t=960*x）
# self.block1=nn.Sequential(
# nn.Conv2d(in_channels=1, out_channels=96, kernel_size=11, stride=4),
# nn.ReLU(inplace=True),
# nn.LocalResponseNorm(size=2, alpha=2e-5, beta=0.75, k=1.0),
# nn.MaxPool2d(kernel_size=3, stride=2)
# )
# self.block2 = nn.Sequential(
# nn.Conv2d(in_channels=96, out_channels=256, kernel_size=5, stride=1, padding=2,groups=2),
# nn.ReLU(inplace=True),
# nn.LocalResponseNorm(size=2, alpha=2e-5, beta=0.75, k=1.0),
# nn.MaxPool2d(kernel_size=3, stride=2)
# )
# self.block3 = nn.Sequential(
# nn.Conv2d(in_channels=256, out_channels=384, padding=1, kernel_size=3,stride=1),
# nn.ReLU(inplace=True)
# )
# self.block4 = nn.Sequential(
# nn.Conv2d(in_channels=384, out_channels=384, kernel_size=3, padding=1,stride=1),
# nn.ReLU(inplace=True)
# )
# self.block5 = nn.Sequential(
# nn.Conv2d(in_channels=384, out_channels=256, kernel_size=3, padding=1,stride=1),
# nn.ReLU(inplace=True),
# nn.MaxPool2d(kernel_size=3, stride=2)
# )
# #(256,F,T)
# self.MLP = nn.Sequential(
# nn.Linear(in_features=256, out_features=256),
# nn.Tanh()
# )
# self.ei=nn.Linear(in_features=256,out_features=256,bias=False)
# self.classification=nn.Linear(in_features=256,out_features=num_class)
# def forward(self, x):
# lbda=0.3
# x=self.block1(x)
# x=self.block2(x)
# x=self.block3(x)
# x=self.block4(x)
# x=self.block5(x)
# x=x.permute([0,3,2,1])
# tmp=x.size()[1]*x.size()[2]
# x=x.contiguous().view([-1,tmp,256])
# e=self.ei(self.MLP(x))
# e=F.softmax(torch.mul(e, lbda),dim=1)
# x=x.mul(e)
# x=torch.sum(x, dim=-2)
# x=F.softmax(self.classification(x))
# return x
# #!/usr/bin/env python
# # coding: utf-8
#
##Model by Saurav
import torch.nn as nn
import torchvision.models as models
import os
import torch
import torch.nn.functional as F
num_class = 5
class MLP(nn.Module):
def __init__(self):
super(MLP, self).__init__()
self.mlp = nn.Sequential(
nn.Linear(in_features=256, out_features=256),
nn.Tanh()
)
self.ei = nn.Linear(in_features=256, out_features=256, bias=False)
def forward(self, x):
#print(x.size())
lbda = 0.3
x = x.permute([0, 3, 2, 1])
tmp = x.size()[1] * x.size()[2]
x = x.contiguous().view([-1, tmp, 256])
e = self.ei(self.mlp(x))
e = F.softmax(torch.mul(e, lbda), dim=1)
x = x.mul(e)
# print("before" , x.size())
return x
class Classification(nn.Module):
def __init__(self):
super(Classification, self).__init__()
self.classification = nn.Linear(in_features=256, out_features=num_class)
def forward(self, x):
x = torch.sum(x, dim=-2)
x = F.softmax(self.classification(x))
return x
def AlexNet():
os.environ['TORCH_HOME'] = '/home/zzhang/test/'
alexnet = models.alexnet(pretrained=True)
alexnet.features[0] = nn.Conv2d(1, 96, kernel_size=(11, 11), stride=(4, 4))
alexnet.features[1] = nn.Sequential(
nn.ReLU(inplace=True),
nn.LocalResponseNorm(size=2, alpha=2e-5, beta=0.75, k=1.0))
alexnet.features[3] = nn.Conv2d(96, 256, kernel_size=(5, 5), stride=(1, 1), padding=(2,2), groups= 2)
alexnet.features[4] = nn.Sequential(
nn.ReLU(inplace=True),
nn.LocalResponseNorm(size=2, alpha=2e-5, beta=0.75, k=1.0))
alexnet.features[6] = nn.Conv2d(256, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1,1))
alexnet.features[8] = nn.Conv2d(384, 384, kernel_size=(3, 3), stride=(1, 1), padding=(1,1))
alexnet.features[10] = nn.Conv2d(384, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1,1))
alexnet.avgpool = MLP()
alexnet.classifier = Classification()
return alexnet