174 lines
7.6 KiB
Python
174 lines
7.6 KiB
Python
import os
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os.environ["PYTORCH_ENABLE_MPS_FALLBACK"]="1"
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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 model import VideoClassifier
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from sentence_transformers import SentenceTransformer
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import torch.nn as nn
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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 # 引入 TensorBoard
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import time
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import numpy as np
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# 动态生成子目录名称
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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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# 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 = VideoClassifier()
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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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criterion = 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(model, dataloader):
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model.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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# 传入文本字典和sentence_transformer
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logits = model(input_texts=batch['texts'], sentence_transformer=sentence_transformer)
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preds = torch.argmax(logits, dim=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(model)}")
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# 训练循环
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best_f1 = 0
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global_step = 0 # 全局步数计数器
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eval_interval = 50 # 每隔 10 步执行一次验证
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for epoch in range(8):
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model.train()
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epoch_loss = 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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optimizer.zero_grad()
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# 传入文本字典和sentence_transformer
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logits = model(input_texts=batch['texts'], sentence_transformer=sentence_transformer)
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loss = criterion(logits, batch['label'])
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loss.backward()
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optimizer.step()
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epoch_loss += loss.item()
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# 记录训练损失
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writer.add_scalar('Train/Loss', loss.item(), global_step)
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global_step += 1
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# 每隔 eval_interval 步执行验证
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if global_step % eval_interval == 0:
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eval_f1, eval_recall, eval_precision, eval_accuracy, eval_class_report = evaluate(model, eval_loader)
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writer.add_scalar('Eval/F1', eval_f1, global_step)
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writer.add_scalar('Eval/Recall', eval_recall, global_step)
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writer.add_scalar('Eval/Precision', eval_precision, global_step)
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writer.add_scalar('Eval/Accuracy', eval_accuracy, global_step)
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print(f"Step {global_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(model.state_dict(), f'./filter/checkpoints/best_model.pt')
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print(" Saved best model")
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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 / 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(model, train_loader)
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eval_f1, eval_recall, eval_precision, eval_accuracy, eval_class_report = evaluate(model, 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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model.load_state_dict(torch.load('./filter/checkpoints/best_model.pt'))
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test_f1, test_recall, test_precision, test_accuracy, test_class_report = evaluate(model, test_loader)
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writer.add_scalar('Test/F1', test_f1, global_step)
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writer.add_scalar('Test/Recall', test_recall, global_step)
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writer.add_scalar('Test/Precision', test_precision, global_step)
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writer.add_scalar('Test/Accuracy', test_accuracy, global_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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# 关闭 TensorBoard
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writer.close()
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