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DensenetModels.py
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DensenetModels.py
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import os
import numpy as np
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
import torchvision.transforms as transforms
from torch.utils.data import DataLoader
from sklearn.metrics import roc_auc_score
import torchvision
import se_densenet
class ResNet50(nn.Module):
def __init__(self, classCount, isTrained):
super(ResNet50, self).__init__()
self.resnet50 = torchvision.models.resnet50(pretrained=isTrained)
kernelCount = self.resnet50.fc.in_features
self.resnet50.fc = nn.Linear(kernelCount, classCount)
def forward(self, x):
x = self.resnet50(x)
return x
import se_resnet
class SE_ResNet50(nn.Module):
def __init__(self, classCount, isTrained):
super(SE_ResNet50, self).__init__()
self.se_resnet50 = se_resnet.se_resnet50(num_classes = classCount,pretrained=isTrained)
def forward(self, x):
x = self.se_resnet50(x)
return x
class DenseNet121(nn.Module):
def __init__(self, classCount, isTrained):
super(DenseNet121, self).__init__()
self.densenet121 = torchvision.models.densenet121(pretrained=isTrained)
kernelCount = self.densenet121.classifier.in_features
self.densenet121.classifier = nn.Sequential(nn.Linear(kernelCount, classCount), nn.Sigmoid())
def forward(self, x):
x = self.densenet121(x)
return x
class SE_DenseNet121(nn.Module):
def __init__(self, classCount, isTrained):
super(SE_DenseNet121, self).__init__()
self.densenet121 = se_densenet.densenet121(pretrained=isTrained)
kernelCount = self.densenet121.classifier.in_features
self.densenet121.classifier = nn.Sequential(nn.Linear(kernelCount, classCount), nn.Sigmoid())
def forward(self, x):
x = self.densenet121(x)
return x
class DenseNet169(nn.Module):
def __init__(self, classCount, isTrained):
super(DenseNet169, self).__init__()
self.densenet169 = torchvision.models.densenet169(pretrained=isTrained)
kernelCount = self.densenet169.classifier.in_features
self.densenet169.classifier = nn.Sequential(nn.Linear(kernelCount, classCount), nn.Sigmoid())
def forward (self, x):
x = self.densenet169(x)
return x