baseline_model.py

import torch.nn as nn
import torch

class SELayer(nn.Module):
    ''' Squeeze-and-Excitation block'''
    def __init__(self, channel, reduction=16):
        super(SELayer, self).__init__()
        self.avg_pool = nn.AdaptiveAvgPool1d(1)
        self.fc = nn.Sequential(
            nn.Linear(channel, channel // reduction, bias=False),
            nn.ReLU(inplace=True),
            nn.Linear(channel // reduction, channel, bias=False),
            nn.Sigmoid()
        )

    def forward(self, x):
        b, c, _ = x.size()
        y = self.avg_pool(x).view(b, c)
        y = self.fc(y).view(b, c, 1)
        return x * y.expand_as(x)


def conv3x1(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=7, stride=stride,
                     padding=3, bias=False)


def conv1x1(in_planes, out_planes, stride=1):
    """1x1 convolution"""
    return nn.Conv1d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)


class BasicBlock(nn.Module):
    '''The convolutional block implementation for Resnet 
    as its architecture uses the CNN blocks multiple times
    '''
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x1(inplanes, planes, stride) # 3x3 padding
        self.bn1 = nn.BatchNorm1d(planes) # 
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x1(planes, planes)
        self.bn2 = nn.BatchNorm1d(planes)
        self.se = SELayer(planes)
        self.downsample = downsample
        self.stride = stride
        self.dropout = nn.Dropout(.2)
        
    def forward(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        out = self.dropout(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.se(out)
        if self.downsample is not None:
            identity = self.downsample(x)

        out += identity
        out = self.relu(out)

        return out


class ResNet(nn.Module):
    '''Class for implementing the ResNet architecture
    
    "The improved ResNet can be decomposed into....
     1) Feature extraction: 1 convolutional layers followed by a batch normalization layer, 
                            a ReLU activation function, a max pooling layer, 
                            N=8 residual blocks (= 2 convolutional layers, an SE block and an average pooling layer)
     2) Feature fusion: concatenates deep features from the previous part and 
                        the additional age and gender information
     3) Classifier: constitutes a fully connected layer and Sigmoid layer, 
                    outputs of the probabilities of belonging to a disease class" 
     (Zhao et al. 2022)
    '''

    def __init__(self, block, layers, in_channel=1, out_channel=10, zero_init_residual=False):
        super(ResNet, self).__init__()
        self.inplanes = 64
        self.conv1 = nn.Conv1d(in_channel, 64, kernel_size=15, stride=2, padding=7, bias=False)
        self.bn1 = nn.BatchNorm1d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, 64, layers[0]) 
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AdaptiveAvgPool1d(1)
        self.fc1 = nn.Linear(3, 10) # AGE AND GENDER LAYER - input size the same with the array size of attributes
        self.fc = nn.Linear(512 * block.expansion + 10, out_channel)

        for m in self.modules():
            if isinstance(m, nn.Conv1d):
                nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm1d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

        if zero_init_residual:
            for m in self.modules():
                if isinstance(m, Bottleneck):
                    nn.init.constant_(m.bn3.weight, 0)
                elif isinstance(m, BasicBlock):
                    nn.init.constant_(m.bn2.weight, 0)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                conv1x1(self.inplanes, planes * block.expansion, stride),
                nn.BatchNorm1d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for _ in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def forward(self, x, ag):
        x = self.conv1(x) 
        x = self.bn1(x) 
        x = self.relu(x) 
        x = self.maxpool(x) 

        x = self.layer1(x) 
        x = self.layer2(x) 
        x = self.layer3(x) 
        x = self.layer4(x)

        x = self.avgpool(x) 
        x = x.view(x.size(0), -1)
        ag = self.fc1(ag)  
        x = torch.cat((ag, x), dim=1)
        x = self.fc(x) 

        return x


def resnet18(**kwargs):
    """Constructing a ResNet-18 model.
    """
    model = ResNet(BasicBlock, [2, 2, 2, 2], **kwargs)
    return model