diff --git a/filter/RunningLogs.txt b/filter/RunningLogs.txt new file mode 100644 index 0000000..c2bf03f --- /dev/null +++ b/filter/RunningLogs.txt @@ -0,0 +1,15 @@ +Note +2028: V1 +2233: V2 +2255: V1 +2319: V2.5-test2 # V2.x效果太差，代码已删除 +0006: V1.5-test3 # test3代表测试集第3个版本 +0011: V3-test3 +0043: V1.5-test3 +0048: V3.1-test3 +0056: V3.2-test3 +0116: V4-test3 # V4 尝试删除了author_info 通道 +0125: V4.1-test3 +0133: V4.2-test3 +0138: V4.3-test3 +0155: V5-test3 # V4 的效果也不是特别好 \ No newline at end of file diff --git a/filter/modelV5.py b/filter/modelV5.py new file mode 100644 index 0000000..5cc597d --- /dev/null +++ b/filter/modelV5.py @@ -0,0 +1,62 @@ +import torch +import torch.nn as nn + +class VideoClassifierV5(nn.Module): + def __init__(self, embedding_dim=1024, hidden_dim=640, output_dim=3): + super().__init__() + self.num_channels = 4 + self.channel_names = ['title', 'description', 'tags', 'author_info'] + + # 改进1：带温度系数的通道权重（比原始固定权重更灵活） + self.channel_weights = nn.Parameter(torch.ones(self.num_channels)) + self.temperature = 1.4 # 可调节的平滑系数 + + # 改进2：更稳健的全连接结构 + self.fc = nn.Sequential( + nn.Linear(embedding_dim * self.num_channels, hidden_dim*2), + nn.BatchNorm1d(hidden_dim*2), + nn.Dropout(0.1), + nn.ReLU(), + nn.Linear(hidden_dim*2, hidden_dim), + nn.LayerNorm(hidden_dim), + nn.Linear(hidden_dim, output_dim) + ) + + # 改进3：输出层初始化 + nn.init.xavier_uniform_(self.fc[-1].weight) + nn.init.zeros_(self.fc[-1].bias) + + def forward(self, input_texts, sentence_transformer): + # 合并所有通道文本进行批量编码 + all_texts = [text for channel in self.channel_names for text in input_texts[channel]] + + # 使用SentenceTransformer生成嵌入（保持冻结） + with torch.no_grad(): + task = "classification" + embeddings = torch.tensor( + sentence_transformer.encode(all_texts, task=task), + device=next(self.parameters()).device + ) + + # 分割嵌入并加权 + split_sizes = [len(input_texts[name]) for name in self.channel_names] + channel_features = torch.split(embeddings, split_sizes, dim=0) + channel_features = torch.stack(channel_features, dim=1) # [batch, 4, 1024] + + # 改进4：带温度系数的softmax加权 + weights = torch.softmax(self.channel_weights / self.temperature, dim=0) + weighted_features = channel_features * weights.unsqueeze(0).unsqueeze(-1) + + # 拼接特征 + combined = weighted_features.view(weighted_features.size(0), -1) + + # 全连接层 + return self.fc(combined) + + def get_channel_weights(self): + """获取各通道权重（带温度调节）""" + return torch.softmax(self.channel_weights / self.temperature, dim=0).detach().cpu().numpy() + + def set_temperature(self, temperature): + """设置温度值""" + self.temperature = temperature \ No newline at end of file diff --git a/filter/train.py b/filter/train.py index 9bdf44f..5f644f4 100644 --- a/filter/train.py +++ b/filter/train.py @@ -3,7 +3,7 @@ os.environ["PYTORCH_ENABLE_MPS_FALLBACK"]="1" from torch.utils.data import DataLoader import torch.optim as optim from dataset import MultiChannelDataset -from modelV3_2 import VideoClassifierV3_2 +from modelV5 import VideoClassifierV5 from sentence_transformers import SentenceTransformer import torch.nn as nn from sklearn.metrics import f1_score, recall_score, precision_score, accuracy_score, classification_report @@ -39,8 +39,8 @@ test_loader = DataLoader(test_dataset, batch_size=24, shuffle=False) # 初始化模型和SentenceTransformer sentence_transformer = SentenceTransformer("Thaweewat/jina-embedding-v3-m2v-1024") -model = VideoClassifierV3_2() -checkpoint_name = './filter/checkpoints/best_model_V3.2.pt' +model = VideoClassifierV5() +checkpoint_name = './filter/checkpoints/best_model_V5.pt' # 模型保存路径 os.makedirs('./filter/checkpoints', exist_ok=True) @@ -80,15 +80,23 @@ print(f"Trainable parameters: {count_trainable_parameters(model)}") # 训练循环 best_f1 = 0 -total_step = 0 +step = 0 eval_interval = 50 +num_epochs = 8 -for epoch in range(8): +total_steps = num_epochs * len(train_loader) # 总训练步数 +T_max = 1.4 # 初始温度 +T_min = 0.15 # 最终温度 + +for epoch in range(num_epochs): model.train() epoch_loss = 0 # 训练阶段 for batch_idx, batch in enumerate(train_loader): + temperature = T_max - (T_max - T_min) * (step / total_steps) + model.set_temperature(temperature) + optimizer.zero_grad() # 传入文本字典和sentence_transformer @@ -100,18 +108,18 @@ for epoch in range(8): epoch_loss += loss.item() # 记录训练损失 - writer.add_scalar('Train/Loss', loss.item(), total_step) - total_step += 1 + writer.add_scalar('Train/Loss', loss.item(), step) + step += 1 # 每隔 eval_interval 步执行验证 - if total_step % eval_interval == 0: + if step % eval_interval == 0: eval_f1, eval_recall, eval_precision, eval_accuracy, eval_class_report = evaluate(model, eval_loader) - writer.add_scalar('Eval/F1', eval_f1, total_step) - writer.add_scalar('Eval/Recall', eval_recall, total_step) - writer.add_scalar('Eval/Precision', eval_precision, total_step) - writer.add_scalar('Eval/Accuracy', eval_accuracy, total_step) + writer.add_scalar('Eval/F1', eval_f1, step) + writer.add_scalar('Eval/Recall', eval_recall, step) + writer.add_scalar('Eval/Precision', eval_precision, step) + writer.add_scalar('Eval/Accuracy', eval_accuracy, step) - print(f"Step {total_step}") + print(f"Step {step}") print(f" Eval F1: {eval_f1:.4f} | Eval Recall: {eval_recall:.4f} | Eval Precision: {eval_precision:.4f} | Eval Accuracy: {eval_accuracy:.4f}") print(" Eval Class Report:") for cls, metrics in eval_class_report.items(): @@ -123,6 +131,7 @@ for epoch in range(8): best_f1 = eval_f1 torch.save(model.state_dict(), checkpoint_name) print(" Saved best model") + print("Channel weights: ", model.get_channel_weights()) # 记录每个 epoch 的平均训练损失 avg_epoch_loss = epoch_loss / len(train_loader) @@ -159,17 +168,17 @@ for epoch in range(8): print("\nTesting...") model.load_state_dict(torch.load(checkpoint_name)) test_f1, test_recall, test_precision, test_accuracy, test_class_report = evaluate(model, test_loader) -writer.add_scalar('Test/F1', test_f1, total_step) -writer.add_scalar('Test/Recall', test_recall, total_step) -writer.add_scalar('Test/Precision', test_precision, total_step) -writer.add_scalar('Test/Accuracy', test_accuracy, total_step) +writer.add_scalar('Test/F1', test_f1, step) +writer.add_scalar('Test/Recall', test_recall, step) +writer.add_scalar('Test/Precision', test_precision, step) +writer.add_scalar('Test/Accuracy', test_accuracy, step) print(f"Test F1: {test_f1:.4f} | Test Recall: {test_recall:.4f} | Test Precision: {test_precision:.4f} | Test Accuracy: {test_accuracy:.4f}") print(" Test Class Report:") for cls, metrics in test_class_report.items(): if cls.isdigit(): # 只打印类别的指标 print(f" Class {cls}: Precision: {metrics['precision']:.4f}, Recall: {metrics['recall']:.4f}, F1: {metrics['f1-score']:.4f}, Support: {metrics['support']}") - writer.add_scalar(f'Test/Class_{cls}_Precision', metrics['precision'], total_step) - writer.add_scalar(f'Test/Class_{cls}_Recall', metrics['recall'], total_step) - writer.add_scalar(f'Test/Class_{cls}_F1', metrics['f1-score'], total_step) + writer.add_scalar(f'Test/Class_{cls}_Precision', metrics['precision'], step) + writer.add_scalar(f'Test/Class_{cls}_Recall', metrics['recall'], step) + writer.add_scalar(f'Test/Class_{cls}_F1', metrics['f1-score'], step) # 关闭 TensorBoard writer.close()