update: filter model v3.8
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@ -16,3 +16,6 @@ Note
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0229: V3.3-test3 # 重新回到V3迭代
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0316: V3.4-test3 # 模型架构修改，自定义Loss与FC层修改
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0324: V3.5-test3 # 用回3.2的FC层试试
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0331: V3.6-test3 # 3.5不太行，我试着调下超参
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0335: V3.7-test3 # 3.6还行，再调超参试试看
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0352: V3.8-test3 # 3.7不行，从3.6的基础重新调
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@ -1,109 +0,0 @@
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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 VideoClassifierV3_5(nn.Module):
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def __init__(self, embedding_dim=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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# 可学习温度系数
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self.temperature = nn.Parameter(torch.tensor(1.7))
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# 带约束的通道权重（使用Sigmoid替代Softmax）
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self.channel_weights = nn.Parameter(torch.ones(self.num_channels))
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# 增强的非线性层
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self.fc = nn.Sequential(
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nn.Linear(embedding_dim * self.num_channels, hidden_dim*2),
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nn.BatchNorm1d(hidden_dim*2),
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nn.Dropout(0.1),
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nn.ReLU(),
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nn.Linear(hidden_dim*2, hidden_dim),
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nn.LayerNorm(hidden_dim),
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nn.Linear(hidden_dim, output_dim)
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)
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# 权重初始化
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self._init_weights()
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def _init_weights(self):
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for layer in self.fc:
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if isinstance(layer, nn.Linear):
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# 使用ReLU的初始化参数（GELU的近似）
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nn.init.kaiming_normal_(layer.weight, nonlinearity='relu') # 修改这里
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# 或者使用Xavier初始化（更适合通用场景）
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# nn.init.xavier_normal_(layer.weight, gain=nn.init.calculate_gain('relu'))
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nn.init.zeros_(layer.bias)
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def forward(self, input_texts, sentence_transformer):
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# 合并文本进行批量编码
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all_texts = [text for channel in self.channel_names for text in input_texts[channel]]
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# 冻结的文本编码
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with torch.no_grad():
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embeddings = torch.tensor(
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sentence_transformer.encode(all_texts),
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device=next(self.parameters()).device
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)
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# 分割并加权通道特征
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split_sizes = [len(input_texts[name]) for name in self.channel_names]
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channel_features = torch.split(embeddings, split_sizes, dim=0)
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channel_features = torch.stack(channel_features, dim=1)
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# 自适应通道权重（Sigmoid约束）
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weights = torch.sigmoid(self.channel_weights) # [0,1]范围
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weighted_features = channel_features * weights.unsqueeze(0).unsqueeze(-1)
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# 特征拼接
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combined = weighted_features.view(weighted_features.size(0), -1)
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return self.fc(combined)
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def get_channel_weights(self):
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"""获取各通道权重（带温度调节）"""
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return torch.softmax(self.channel_weights / self.temperature, dim=0).detach().cpu().numpy()
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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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"""
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Args:
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class_weights (torch.Tensor): 类别权重
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alpha (float): 召回率调节因子（0-1）
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gamma (float): Focal Loss参数
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fp_penalty (float): 类别0假阳性惩罚强度
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"""
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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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# 基础交叉熵损失
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ce_loss = F.cross_entropy(logits, targets, weight=self.class_weights, reduction='none')
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# Focal Loss组件
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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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# 召回率增强（对困难样本加权）
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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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# 类别0假阳性惩罚
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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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# 总损失
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total_loss = recall_loss.mean() + fp_loss / len(targets)
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return total_loss
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@ -4,7 +4,7 @@ import numpy as np
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from torch.utils.data import DataLoader
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import torch.optim as optim
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from dataset import MultiChannelDataset
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from modelV3_5 import VideoClassifierV3_5, AdaptiveRecallLoss
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from modelV3_4 import VideoClassifierV3_4, AdaptiveRecallLoss
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from sentence_transformers import SentenceTransformer
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from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report
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import os
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@ -52,18 +52,18 @@ class_weights = torch.tensor(
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# 初始化模型和SentenceTransformer
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sentence_transformer = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024")
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model = VideoClassifierV3_5()
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checkpoint_name = './filter/checkpoints/best_model_V3.5.pt'
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model = VideoClassifierV3_4()
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checkpoint_name = './filter/checkpoints/best_model_V3.8.pt'
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# 模型保存路径
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os.makedirs('./filter/checkpoints', exist_ok=True)
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# 优化器
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optimizer = optim.AdamW(model.parameters(), lr=2e-3)
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optimizer = optim.AdamW(model.parameters(), lr=4e-4)
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criterion = AdaptiveRecallLoss(
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class_weights=class_weights,
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alpha=0.7, # 召回率权重
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gamma=1.5, # 困难样本聚焦
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alpha=0.9, # 召回率权重
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gamma=1.6, # 困难样本聚焦
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fp_penalty=0.8 # 假阳性惩罚强度
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)
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