DensenetModels.py

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

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 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
    
class DenseNet201(nn.Module):
    
    def __init__ (self, classCount, isTrained):
        
        super(DenseNet201, self).__init__()
        
        self.densenet201 = torchvision.models.densenet201(pretrained=isTrained)
        
        kernelCount = self.densenet201.classifier.in_features
        
        self.densenet201.classifier = nn.Sequential(nn.Linear(kernelCount, classCount), nn.Sigmoid())
        
    def forward (self, x):
        x = self.densenet201(x)
        return x