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models.py

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+import torchvision.models as models
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+class BasicBlock1D(nn.Module):
+    expansion = 1
+
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
+        super(BasicBlock1D, self).__init__()
+        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=3, 
+                              stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm1d(out_channels)
+        self.relu = nn.ReLU(inplace=True)
+        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, 
+                              stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm1d(out_channels)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class Bottleneck1D(nn.Module):
+    expansion = 4
+
+    def __init__(self, in_channels, out_channels, stride=1, downsample=None):
+        super(Bottleneck1D, self).__init__()
+        self.conv1 = nn.Conv1d(in_channels, out_channels, kernel_size=1, bias=False)
+        self.bn1 = nn.BatchNorm1d(out_channels)
+        self.conv2 = nn.Conv1d(out_channels, out_channels, kernel_size=3, stride=stride,
+                              padding=1, bias=False)
+        self.bn2 = nn.BatchNorm1d(out_channels)
+        self.conv3 = nn.Conv1d(out_channels, out_channels * self.expansion, kernel_size=1, bias=False)
+        self.bn3 = nn.BatchNorm1d(out_channels * self.expansion)
+        self.relu = nn.ReLU(inplace=True)
+        self.downsample = downsample
+        self.stride = stride
+
+    def forward(self, x):
+        identity = x
+
+        out = self.conv1(x)
+        out = self.bn1(out)
+        out = self.relu(out)
+
+        out = self.conv2(out)
+        out = self.bn2(out)
+        out = self.relu(out)
+
+        out = self.conv3(out)
+        out = self.bn3(out)
+
+        if self.downsample is not None:
+            identity = self.downsample(x)
+
+        out += identity
+        out = self.relu(out)
+
+        return out
+
+
+class AudioResNet(nn.Module):
+    def __init__(self, block, layers, num_classes=7):
+        """
+        构建用于音频分类的1D ResNet
+        
+        参数:
+            block: 使用的残差块类型(BasicBlock1D or Bottleneck1D)
+            layers: 每个层的块数量的列表
+            num_classes: 分类的类别数量，默认为7种情感
+        """
+        super(AudioResNet, self).__init__()
+        self.in_channels = 64
+        
+        # 初始卷积层，缩减序列长度
+        self.conv1 = nn.Conv1d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        self.bn1 = nn.BatchNorm1d(64)
+        self.relu = nn.ReLU(inplace=True)
+        self.maxpool = nn.MaxPool1d(kernel_size=3, stride=2, padding=1)
+        
+        # 残差块堆叠
+        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)
+        
+        # 全局平均池化和分类器
+        self.avgpool = nn.AdaptiveAvgPool1d(1)
+        self.fc = nn.Linear(512 * block.expansion, num_classes)
+        
+        # 权重初始化
+        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)
+
+    def _make_layer(self, block, out_channels, blocks, stride=1):
+        downsample = None
+        if stride != 1 or self.in_channels != out_channels * block.expansion:
+            downsample = nn.Sequential(
+                nn.Conv1d(self.in_channels, out_channels * block.expansion,
+                          kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm1d(out_channels * block.expansion),
+            )
+
+        layers = []
+        layers.append(block(self.in_channels, out_channels, stride, downsample))
+        self.in_channels = out_channels * block.expansion
+        for _ in range(1, blocks):
+            layers.append(block(self.in_channels, out_channels))
+
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        # 输入 x 形状: [batch_size, 1, 24000]
+        
+        x = self.conv1(x)       # [batch_size, 64, 12000]
+        x = self.bn1(x)
+        x = self.relu(x)
+        x = self.maxpool(x)     # [batch_size, 64, 6000]
+
+        x = self.layer1(x)      # [batch_size, 64*expansion, 6000]
+        x = self.layer2(x)      # [batch_size, 128*expansion, 3000]
+        x = self.layer3(x)      # [batch_size, 256*expansion, 1500]
+        x = self.layer4(x)      # [batch_size, 512*expansion, 750]
+
+        x = self.avgpool(x)     # [batch_size, 512*expansion, 1]
+        x = torch.flatten(x, 1) # [batch_size, 512*expansion]
+        x = self.fc(x)          # [batch_size, num_classes]
+
+        return x
+
+
+# 定义不同规模的ResNet模型
+def waveform_resnet18(num_classes=7):
+    """
+    构建类似ResNet18的音频分类模型
+    """
+    return AudioResNet(BasicBlock1D, [2, 2, 2, 2], num_classes)
+
+def waveform_resnet34(num_classes=7):
+    """
+    构建类似ResNet34的音频分类模型
+    """
+    return AudioResNet(BasicBlock1D, [3, 4, 6, 3], num_classes)
+
+def waveform_resnet50(num_classes=7):
+    """
+    构建类似ResNet50的音频分类模型
+    """
+    return AudioResNet(Bottleneck1D, [3, 4, 6, 3], num_classes)
+
+def waveform_resnet101(num_classes=7):
+    """
+    构建类似ResNet101的音频分类模型
+    """
+    return AudioResNet(Bottleneck1D, [3, 4, 23, 3], num_classes)
+
+
+class SpectrogramResNet(nn.Module):
+    """
+    使用预训练的ResNet模型对音频频谱图进行情感分类
+    """
+    def __init__(self, model_name='resnet18', num_classes=6, pretrained=True):
+        """
+        初始化频谱图ResNet分类模型
+        
+        参数:
+            model_name: 使用的ResNet版本 ('resnet18', 'resnet34', 'resnet50', 'resnet101')
+            num_classes: 情感类别数量
+            pretrained: 是否使用预训练权重
+        """
+        super(SpectrogramResNet, self).__init__()
+        
+        # 选择预训练的ResNet模型
+        if model_name == 'resnet18':
+            base_model = models.resnet18(pretrained=pretrained)
+        elif model_name == 'resnet34':
+            base_model = models.resnet34(pretrained=pretrained)
+        elif model_name == 'resnet50':
+            base_model = models.resnet50(pretrained=pretrained)
+        elif model_name == 'resnet101':
+            base_model = models.resnet101(pretrained=pretrained)
+        else:
+            raise ValueError(f"不支持的模型名称: {model_name}")
+        
+        # 修改第一个卷积层以接受单通道输入
+        self.conv1 = nn.Conv2d(1, 64, kernel_size=7, stride=2, padding=3, bias=False)
+        
+        # 使用预训练权重初始化第一层（如果可用）
+        if pretrained:
+            # 将预训练的三通道权重平均为单通道权重
+            with torch.no_grad():
+                self.conv1.weight.data = base_model.conv1.weight.data.mean(dim=1, keepdim=True)
+        
+        # 使用其余的预训练层
+        self.bn1 = base_model.bn1
+        self.relu = base_model.relu
+        self.maxpool = base_model.maxpool
+        self.layer1 = base_model.layer1
+        self.layer2 = base_model.layer2
+        self.layer3 = base_model.layer3
+        self.layer4 = base_model.layer4
+        self.avgpool = base_model.avgpool
+        
+        # 修改全连接层以匹配目标类别数
+        in_features = base_model.fc.in_features
+        self.fc = nn.Linear(in_features, num_classes)
+        
+    def forward(self, x):
+        """
+        前向传播
+        
+        参数:
+            x: 形状为 [batch_size, 1, 128, 128] 的频谱图
+            
+        返回:
+            形状为 [batch_size, num_classes] 的类别预测
+        """
+        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 = torch.flatten(x, 1)
+        x = self.fc(x)
+        
+        return x
+
+# 便捷函数用于创建不同版本的模型
+def spectrogram_resnet18(num_classes=6, pretrained=True):
+    return SpectrogramResNet('resnet18', num_classes, pretrained)
+
+def spectrogram_resnet34(num_classes=6, pretrained=True):
+    return SpectrogramResNet('resnet34', num_classes, pretrained)
+
+def spectrogram_resnet50(num_classes=6, pretrained=True):
+    return SpectrogramResNet('resnet50', num_classes, pretrained)
+
+def spectrogram_resnet101(num_classes=6, pretrained=True):
+    return SpectrogramResNet('resnet101', num_classes, pretrained)
+