93 lines
3.4 KiB
Python
93 lines
3.4 KiB
Python
import torch
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import torch.nn as nn
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import torch.nn.functional as F
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class VideoClassifierV6_0(nn.Module):
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def __init__(self, embedding_dim=256, seq_length=1024, hidden_dim=512, output_dim=3):
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super().__init__()
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self.num_channels = 4
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self.channel_names = ['title', 'description', 'tags', 'author_info']
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# CNN特征提取层
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self.conv_layers = nn.Sequential(
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# 第一层卷积
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nn.Conv2d(self.num_channels, 64, kernel_size=(3, 3), padding=1),
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nn.BatchNorm2d(64),
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nn.GELU(),
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nn.MaxPool2d(kernel_size=(2, 2)),
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# 第二层卷积
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nn.Conv2d(64, 128, kernel_size=(3, 3), padding=1),
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nn.BatchNorm2d(128),
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nn.GELU(),
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nn.MaxPool2d(kernel_size=(2, 2)),
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# 第三层卷积
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nn.Conv2d(128, 256, kernel_size=(3, 3), padding=1),
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nn.BatchNorm2d(256),
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nn.GELU(),
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# 全局平均池化层
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# 输出形状为 [batch_size, 256, 1, 1]
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nn.AdaptiveAvgPool2d((1, 1))
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)
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# 全局池化后的特征维度固定为 256
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self.feature_dim = 256
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# 全连接层
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self.fc = nn.Sequential(
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nn.Linear(self.feature_dim, hidden_dim),
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nn.BatchNorm1d(hidden_dim),
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nn.Dropout(0.2),
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nn.GELU(),
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nn.Linear(hidden_dim, output_dim)
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)
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self._init_weights()
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def _init_weights(self):
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for module in self.modules():
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if isinstance(module, nn.Conv2d) or isinstance(module, nn.Linear):
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nn.init.kaiming_normal_(module.weight, nonlinearity='relu')
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if module.bias is not None:
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nn.init.zeros_(module.bias)
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def forward(self, channel_features: torch.Tensor):
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"""
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输入格式: [batch_size, num_channels, seq_length, embedding_dim]
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输出格式: [batch_size, output_dim]
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"""
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# CNN特征提取
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conv_features = self.conv_layers(channel_features)
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# 展平特征（全局池化后形状为 [batch_size, 256, 1, 1]）
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flat_features = conv_features.view(conv_features.size(0), -1) # [batch_size, 256]
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# 全连接层分类
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return self.fc(flat_features)
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# 损失函数保持不变
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class AdaptiveRecallLoss(nn.Module):
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def __init__(self, class_weights, alpha=0.8, gamma=2.0, fp_penalty=0.5):
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super().__init__()
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self.class_weights = class_weights
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self.alpha = alpha
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self.gamma = gamma
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self.fp_penalty = fp_penalty
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def forward(self, logits, targets):
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ce_loss = F.cross_entropy(logits, targets, weight=self.class_weights, reduction='none')
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pt = torch.exp(-ce_loss)
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focal_loss = ((1 - pt) ** self.gamma) * ce_loss
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class_mask = F.one_hot(targets, num_classes=len(self.class_weights))
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class_weights = (self.alpha + (1 - self.alpha) * pt.unsqueeze(-1)) * class_mask
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recall_loss = (class_weights * focal_loss.unsqueeze(-1)).sum(dim=1)
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probs = F.softmax(logits, dim=1)
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fp_mask = (targets != 0) & (torch.argmax(logits, dim=1) == 0)
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fp_loss = self.fp_penalty * probs[:, 0][fp_mask].pow(2).sum()
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total_loss = recall_loss.mean() + fp_loss / len(targets)
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return total_loss |