219 lines
9.3 KiB
Python
219 lines
9.3 KiB
Python
import os
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"]="1"
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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 filter.modelV3_10 import VideoClassifierV3_10
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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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import torch
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from torch.utils.tensorboard import SummaryWriter
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import time
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from embedding import prepare_batch
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import torch.nn as nn
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run_name = f"run_{time.strftime('%Y%m%d_%H%M')}"
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log_dir = os.path.join('./filter/runs', run_name)
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# 初始化 SummaryWriter
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writer = SummaryWriter(log_dir=log_dir)
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# 创建数据集
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train_dataset = MultiChannelDataset('./data/filter/labeled_data.jsonl', mode='train')
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eval_dataset = MultiChannelDataset('./data/filter/labeled_data.jsonl', mode='eval')
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# 加载test数据集
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test_file = './data/filter/test.jsonl'
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if not os.path.exists(test_file):
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# 如果test文件不存在，先创建
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_ = MultiChannelDataset('./data/filter/labeled_data.jsonl', mode='train')
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test_dataset = MultiChannelDataset(test_file, mode='test')
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# 创建DataLoader
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train_loader = DataLoader(train_dataset, batch_size=24, shuffle=True)
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eval_loader = DataLoader(eval_dataset, batch_size=24, shuffle=False)
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test_loader = DataLoader(test_dataset, batch_size=24, shuffle=False)
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train_labels = []
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for batch in train_loader:
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train_labels.extend(batch['label'].tolist())
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# 计算自适应类别权重
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class_counts = np.bincount(train_labels)
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median_freq = np.median(class_counts)
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class_weights = torch.tensor(
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[median_freq / count for count in class_counts],
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dtype=torch.float32,
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device='cpu'
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)
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# 初始化模型和SentenceTransformer
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model1 = VideoClassifierV3_10(output_dim=2, hidden_dim=384)
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model2 = VideoClassifierV3_10(output_dim=2, hidden_dim=384)
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checkpoint1_name = './filter/checkpoints/best_model_V3.14-part1.pt'
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checkpoint2_name = './filter/checkpoints/best_model_V3.14-part2.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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optimizer1 = optim.AdamW(model1.parameters(), lr=4e-4)
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optimizer2 = optim.AdamW(model2.parameters(), lr=4e-4)
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# Cross entropy loss
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criterion1 = nn.CrossEntropyLoss()
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criterion2 = nn.CrossEntropyLoss()
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def count_trainable_parameters(model):
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return sum(p.numel() for p in model.parameters() if p.requires_grad)
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def evaluate(model1, model2, dataloader):
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model1.eval()
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model2.eval()
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all_preds = []
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all_labels = []
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with torch.no_grad():
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for batch in dataloader:
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batch_tensor = prepare_batch(batch['texts'])
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logits1 = model1(batch_tensor)
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logits2 = model2(batch_tensor)
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preds1 = torch.argmax(logits1, dim=1)
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preds2 = torch.argmax(logits2, dim=1)
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# 如果preds1输出为0，那么预测结果为0，否则使用preds2的结果加上1
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preds = torch.where(preds1 == 0, preds1, preds2 + 1)
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all_preds.extend(preds.cpu().numpy())
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all_labels.extend(batch['label'].cpu().numpy())
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# 计算每个类别的 F1、Recall、Precision 和 Accuracy
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f1 = f1_score(all_labels, all_preds, average='weighted')
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recall = recall_score(all_labels, all_preds, average='weighted')
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precision = precision_score(all_labels, all_preds, average='weighted')
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accuracy = accuracy_score(all_labels, all_preds)
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# 获取每个类别的详细指标
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class_report = classification_report(all_labels, all_preds, output_dict=True)
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return f1, recall, precision, accuracy, class_report
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print(f"Trainable parameters: {count_trainable_parameters(model1) + count_trainable_parameters(model2)}")
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# 训练循环
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best_f1 = 0
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step = 0
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eval_interval = 20
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num_epochs = 8
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for epoch in range(num_epochs):
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model1.train()
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model2.train()
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epoch_loss_1 = 0
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epoch_loss_2 = 0
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# 训练阶段
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for batch_idx, batch in enumerate(train_loader):
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optimizer1.zero_grad()
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optimizer2.zero_grad()
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batch_tensor = prepare_batch(batch['texts'])
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batch_tensor_1 = batch_tensor
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mask = batch['label'] != 0
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batch_tensor_2 = batch_tensor_1[mask]
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logits1 = model1(batch_tensor_1)
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logits2 = model2(batch_tensor_2)
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label1 = torch.where(batch['label'] == 0, 0, 1)
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label2 = torch.where(batch['label'][mask] == 1, 0, 1)
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loss1 = criterion1(logits1, label1)
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loss1.backward()
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loss2 = criterion2(logits2, label2)
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loss2.backward()
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optimizer1.step()
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optimizer2.step()
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epoch_loss_1 += loss1.item()
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epoch_loss_2 += loss2.item()
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# 记录训练损失
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writer.add_scalar('Train/Loss-1', loss1.item(), step)
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writer.add_scalar('Train/Loss-2', loss2.item(), step)
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step += 1
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# 每隔 eval_interval 步执行验证
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if step % eval_interval == 0:
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eval_f1, eval_recall, eval_precision, eval_accuracy, eval_class_report = evaluate(model1, model2, eval_loader)
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writer.add_scalar('Eval/F1', eval_f1, step)
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writer.add_scalar('Eval/Recall', eval_recall, step)
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writer.add_scalar('Eval/Precision', eval_precision, step)
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writer.add_scalar('Eval/Accuracy', eval_accuracy, step)
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print(f"Step {step}")
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print(f" Eval F1: {eval_f1:.4f} | Eval Recall: {eval_recall:.4f} | Eval Precision: {eval_precision:.4f} | Eval Accuracy: {eval_accuracy:.4f}")
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print(" Eval Class Report:")
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for cls, metrics in eval_class_report.items():
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if cls.isdigit(): # 只打印类别的指标
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print(f" Class {cls}: Precision: {metrics['precision']:.4f}, Recall: {metrics['recall']:.4f}, F1: {metrics['f1-score']:.4f}, Support: {metrics['support']}")
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# 保存最佳模型
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if eval_f1 > best_f1:
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best_f1 = eval_f1
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torch.save(model1.state_dict(), checkpoint1_name)
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torch.save(model2.state_dict(), checkpoint2_name)
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print(" Saved best model")
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print("Channel weights: ", model1.get_channel_weights())
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print("Channel weights: ", model2.get_channel_weights())
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# 记录每个 epoch 的平均训练损失
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avg_epoch_loss = (epoch_loss_1 + epoch_loss_2) / 2 / len(train_loader)
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writer.add_scalar('Train/Epoch_Loss', avg_epoch_loss, epoch)
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# 每个 epoch 结束后执行一次完整验证
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train_f1, train_recall, train_precision, train_accuracy, train_class_report = evaluate(model1, model2, train_loader)
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eval_f1, eval_recall, eval_precision, eval_accuracy, eval_class_report = evaluate(model1, model2, eval_loader)
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writer.add_scalar('Train/Epoch_F1', train_f1, epoch)
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writer.add_scalar('Train/Epoch_Recall', train_recall, epoch)
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writer.add_scalar('Train/Epoch_Precision', train_precision, epoch)
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writer.add_scalar('Train/Epoch_Accuracy', train_accuracy, epoch)
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writer.add_scalar('Eval/Epoch_F1', eval_f1, epoch)
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writer.add_scalar('Eval/Epoch_Recall', eval_recall, epoch)
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writer.add_scalar('Eval/Epoch_Precision', eval_precision, epoch)
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writer.add_scalar('Eval/Epoch_Accuracy', eval_accuracy, epoch)
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print(f"Epoch {epoch+1}")
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print(f" Train Loss: {avg_epoch_loss:.4f}")
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print(f" Train F1: {train_f1:.4f} | Train Recall: {train_recall:.4f} | Train Precision: {train_precision:.4f} | Train Accuracy: {train_accuracy:.4f}")
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print(" Train Class Report:")
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for cls, metrics in train_class_report.items():
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if cls.isdigit(): # 只打印类别的指标
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print(f" Class {cls}: Precision: {metrics['precision']:.4f}, Recall: {metrics['recall']:.4f}, F1: {metrics['f1-score']:.4f}, Support: {metrics['support']}")
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print(f" Eval F1: {eval_f1:.4f} | Eval Recall: {eval_recall:.4f} | Eval Precision: {eval_precision:.4f} | Eval Accuracy: {eval_accuracy:.4f}")
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print(" Eval Class Report:")
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for cls, metrics in eval_class_report.items():
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if cls.isdigit(): # 只打印类别的指标
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print(f" Class {cls}: Precision: {metrics['precision']:.4f}, Recall: {metrics['recall']:.4f}, F1: {metrics['f1-score']:.4f}, Support: {metrics['support']}")
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# 测试阶段
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print("\nTesting...")
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model1.load_state_dict(torch.load(checkpoint1_name))
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model2.load_state_dict(torch.load(checkpoint2_name))
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test_f1, test_recall, test_precision, test_accuracy, test_class_report = evaluate(model1, model2, test_loader)
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writer.add_scalar('Test/F1', test_f1, step)
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writer.add_scalar('Test/Recall', test_recall, step)
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writer.add_scalar('Test/Precision', test_precision, step)
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writer.add_scalar('Test/Accuracy', test_accuracy, step)
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print(f"Test F1: {test_f1:.4f} | Test Recall: {test_recall:.4f} | Test Precision: {test_precision:.4f} | Test Accuracy: {test_accuracy:.4f}")
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print(" Test Class Report:")
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for cls, metrics in test_class_report.items():
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if cls.isdigit(): # 只打印类别的指标
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print(f" Class {cls}: Precision: {metrics['precision']:.4f}, Recall: {metrics['recall']:.4f}, F1: {metrics['f1-score']:.4f}, Support: {metrics['support']}")
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writer.add_scalar(f'Test/Class_{cls}_Precision', metrics['precision'], step)
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writer.add_scalar(f'Test/Class_{cls}_Recall', metrics['recall'], step)
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writer.add_scalar(f'Test/Class_{cls}_F1', metrics['f1-score'], step)
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# 关闭 TensorBoard
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writer.close()
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