model.py

import torch.nn as nn
import torch


class ResidualBlock(nn.Module):
    def __init__(self, in_channels, out_channels, stride=1):
        super().__init__()
        self.conv1 = nn.Conv2d(in_channels, out_channels, 
                               3, stride, padding=1, bias=False)
        self.bn1 = nn.BatchNorm2d(out_channels)
        self.conv2 = nn.Conv2d(out_channels, out_channels, 
                               3, 1, padding=1, bias=False)
        
        self.bn2 = nn.BatchNorm2d(out_channels)
        
        self.shortcut = nn.Sequential()
        self.use_shortcut = stride!=1 or in_channels != out_channels
        
        if self.use_shortcut:
            self.shortcut = nn.Sequential(
                nn.Conv2d(in_channels, out_channels, 1, stride=stride, bias=False),
                nn.BatchNorm2d(out_channels)
            )
    
    def forward(self, x):
        out = self.conv1(x)
        out = self.bn1(out)
        out = torch.relu(out)
        
        out = self.conv2(out)
        out = self.bn2(out)
        
        shortcut = self.shortcut(x) if self.use_shortcut else x

        out_add = out + shortcut
        out = torch.relu(out_add)
        
        return out
    
class AudioCNN(nn.Module):
    def __init__(self, num_classes=50):
        super(
            ).__init__()
        self.conv1 = nn.Sequential( 
            nn.Conv2d(1, 64 , 7, stride=2, padding=3, bias=False),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True),
            nn.MaxPool2d(3, stride=2, padding =1)
        
            )
        self.layer1 = nn.ModuleList([
            ResidualBlock(64, 64) for i in range(3)
        ])
        self.layer2 = nn.ModuleList([
            ResidualBlock(64 if i == 0 else 128, 128, stride=2 if i ==0 else 1) for i in range(4)
        ])
        self.layer3 = nn.ModuleList([
            ResidualBlock(128 if i == 0 else 256, 256, stride=2 if i ==0 else 1) for i in range(6)
        ])
        self.layer4 = nn.ModuleList([
            ResidualBlock(256 if i == 0 else 512, 512, stride=2 if i ==0 else 1) for i in range(3)
        ])
        self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
        self.dropout= nn.Dropout(0.5)
        self.fc = nn.Linear(512, num_classes)
        
    def forward(self, x, return_feature_maps=False):
        feature_maps = {}

        x = self.conv1(x)
        feature_maps['conv1'] = x

        for i, block in enumerate(self.layer1):
            x = block(x)
            feature_maps[f'layer1.{i}'] = x

        for i, block in enumerate(self.layer2):
            x = block(x)
            feature_maps[f'layer2.{i}'] = x

        for i, block in enumerate(self.layer3):
            x = block(x)
            feature_maps[f'layer3.{i}'] = x

        for i, block in enumerate(self.layer4):
            x = block(x)
            feature_maps[f'layer4.{i}'] = x

        x = self.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.dropout(x)
        x = self.fc(x)

        if return_feature_maps:
            return x, feature_maps
        return x