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)