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import json |
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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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import pandas as pd |
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import numpy as np |
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import matplotlib.pyplot as plt |
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import seaborn as sns |
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from sklearn.metrics import accuracy_score, classification_report, confusion_matrix, f1_score, precision_score, \ |
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recall_score |
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from transformers import ( |
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BertTokenizer, |
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BertForSequenceClassification, |
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BertModel, |
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BertConfig, |
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TrainingArguments, |
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Trainer, |
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DataCollatorWithPadding, |
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TrainerCallback, |
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EarlyStoppingCallback |
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) |
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from torch.utils.data import Dataset, DataLoader, WeightedRandomSampler, random_split |
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import logging |
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import os |
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from datetime import datetime |
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import math |
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from collections import defaultdict, Counter |
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# 禁用wandb和其他第三方报告工具 |
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os.environ["WANDB_DISABLED"] = "true" |
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os.environ["COMET_MODE"] = "disabled" |
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os.environ["NEPTUNE_MODE"] = "disabled" |
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# 设置日志 |
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logging.basicConfig(level=logging.INFO) |
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logger = logging.getLogger(__name__) |
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# 设置matplotlib中文字体 |
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plt.rcParams['font.sans-serif'] = ['DejaVu Sans'] |
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plt.rcParams['axes.unicode_minus'] = False |
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def check_gpu_availability(): |
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"""检查GPU可用性""" |
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if not torch.cuda.is_available(): |
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raise RuntimeError("❌ GPU不可用！此脚本需要GPU支持。") |
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gpu_count = torch.cuda.device_count() |
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gpu_name = torch.cuda.get_device_name(0) |
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gpu_memory = torch.cuda.get_device_properties(0).total_memory / 1024 ** 3 |
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logger.info(f"✅ GPU检查通过!") |
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logger.info(f" 🔹 可用GPU数量: {gpu_count}") |
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logger.info(f" 🔹 GPU型号: {gpu_name}") |
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logger.info(f" 🔹 GPU内存: {gpu_memory:.1f} GB") |
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torch.cuda.empty_cache() |
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torch.backends.cudnn.benchmark = True |
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return True, gpu_memory |
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class LossTracker(TrainerCallback): |
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"""损失跟踪回调类""" |
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def __init__(self): |
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self.train_losses = [] |
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self.eval_losses = [] |
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self.train_steps = [] |
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self.eval_steps = [] |
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self.eval_f1_scores = [] |
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self.eval_epochs = [] |
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self.current_epoch = 0 |
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def on_log(self, args, state, control, logs=None, **kwargs): |
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if logs: |
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if 'loss' in logs: |
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self.train_losses.append(logs['loss']) |
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self.train_steps.append(state.global_step) |
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if 'eval_loss' in logs: |
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self.eval_losses.append(logs['eval_loss']) |
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self.eval_steps.append(state.global_step) |
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if 'eval_f1_macro' in logs: |
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self.eval_f1_scores.append(logs['eval_f1_macro']) |
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self.eval_epochs.append(state.epoch) |
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def on_epoch_end(self, args, state, control, **kwargs): |
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self.current_epoch = state.epoch |
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class ValidationMetricsCallback(TrainerCallback): |
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"""验证指标记录回调""" |
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def __init__(self): |
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self.validation_history = [] |
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def on_evaluate(self, args, state, control, model=None, logs=None, **kwargs): |
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if logs: |
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epoch = int(state.epoch) |
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metrics = { |
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'epoch': epoch, |
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'eval_loss': logs.get('eval_loss', 0), |
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'eval_accuracy': logs.get('eval_accuracy', 0), |
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'eval_f1_minority': logs.get('eval_f1_minority', 0), |
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'eval_f1_macro': logs.get('eval_f1_macro', 0), |
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'eval_precision_minority': logs.get('eval_precision_minority', 0), |
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'eval_recall_minority': logs.get('eval_recall_minority', 0) |
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} |
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self.validation_history.append(metrics) |
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logger.info(f"📊 Epoch {epoch} 验证指标:") |
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logger.info(f" 🔹 验证损失: {metrics['eval_loss']:.4f}") |
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logger.info(f" 🔹 验证准确率: {metrics['eval_accuracy']:.4f}") |
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logger.info(f" 🔹 宏平均F1: {metrics['eval_f1_macro']:.4f}") |
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logger.info(f" 🔹 少数类F1: {metrics['eval_f1_minority']:.4f}") |
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logger.info(f" 🔹 少数类精确率: {metrics['eval_precision_minority']:.4f}") |
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logger.info(f" 🔹 少数类召回率: {metrics['eval_recall_minority']:.4f}") |
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class ConfusionMatrixCallback(TrainerCallback): |
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"""混淆矩阵生成回调""" |
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def __init__(self, eval_dataset, tokenizer, output_dir, epochs_interval=10): |
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self.eval_dataset = eval_dataset |
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self.tokenizer = tokenizer |
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self.output_dir = output_dir |
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self.epochs_interval = epochs_interval |
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self.confusion_matrices = {} |
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def on_evaluate(self, args, state, control, model=None, **kwargs): |
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current_epoch = int(state.epoch) |
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if current_epoch % self.epochs_interval == 0: |
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logger.info(f"📊 Generating confusion matrix for epoch {current_epoch}...") |
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model.eval() |
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predictions = [] |
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true_labels = [] |
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device = next(model.parameters()).device |
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with torch.no_grad(): |
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# 限制样本数量以加速评估 |
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eval_size = min(1000, len(self.eval_dataset)) |
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for i in range(eval_size): |
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item = self.eval_dataset[i] |
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input_ids = item['input_ids'].unsqueeze(0).to(device) |
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attention_mask = item['attention_mask'].unsqueeze(0).to(device) |
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outputs = model(input_ids=input_ids, attention_mask=attention_mask) |
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pred = torch.argmax(outputs['logits'], dim=-1).cpu().item() |
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predictions.append(pred) |
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true_labels.append(item['labels'].item()) |
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cm = confusion_matrix(true_labels, predictions) |
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self.confusion_matrices[current_epoch] = cm |
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self.save_confusion_matrix(cm, current_epoch) |
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model.train() |
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def save_confusion_matrix(self, cm, epoch): |
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"""保存混淆矩阵图""" |
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plt.figure(figsize=(8, 6)) |
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sns.heatmap(cm, annot=True, fmt='d', cmap='Blues', |
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xticklabels=['Same Paragraph (0)', 'Different Paragraph (1)'], |
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yticklabels=['Same Paragraph (0)', 'Different Paragraph (1)']) |
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plt.title(f'Validation Confusion Matrix - Epoch {epoch}') |
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plt.xlabel('Predicted Label') |
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plt.ylabel('True Label') |
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accuracy = np.trace(cm) / np.sum(cm) |
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plt.text(0.5, -0.15, f'Validation Accuracy: {accuracy:.4f}', |
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ha='center', transform=plt.gca().transAxes) |
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plt.tight_layout() |
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save_path = os.path.join(self.output_dir, f'val_confusion_matrix_epoch_{epoch}.png') |
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plt.savefig(save_path, dpi=300, bbox_inches='tight') |
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plt.close() |
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logger.info(f" 💾 Validation confusion matrix saved: {save_path}") |
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def plot_training_curves(loss_tracker, validation_metrics, output_dir): |
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"""绘制训练曲线和验证指标""" |
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fig, axes = plt.subplots(2, 2, figsize=(15, 12)) |
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# 1. 训练损失曲线 |
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if loss_tracker.train_losses: |
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axes[0, 0].plot(loss_tracker.train_steps, loss_tracker.train_losses, |
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'b-', label='Training Loss', linewidth=2, alpha=0.8) |
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axes[0, 0].set_title('Training Loss Curve', fontsize=14, fontweight='bold') |
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axes[0, 0].set_xlabel('Training Steps') |
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axes[0, 0].set_ylabel('Loss Value') |
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axes[0, 0].legend() |
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axes[0, 0].grid(True, alpha=0.3) |
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# 2. 验证损失曲线 |
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if loss_tracker.eval_losses: |
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axes[0, 1].plot(loss_tracker.eval_steps, loss_tracker.eval_losses, |
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'r-', label='Validation Loss', linewidth=2, alpha=0.8) |
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axes[0, 1].set_title('Validation Loss Curve', fontsize=14, fontweight='bold') |
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axes[0, 1].set_xlabel('Training Steps') |
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axes[0, 1].set_ylabel('Loss Value') |
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axes[0, 1].legend() |
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axes[0, 1].grid(True, alpha=0.3) |
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# 3. 宏平均F1分数曲线 |
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if validation_metrics: |
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epochs = [m['epoch'] for m in validation_metrics] |
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f1_scores = [m['eval_f1_macro'] for m in validation_metrics] |
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axes[1, 0].plot(epochs, f1_scores, 'g-', marker='o', |
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label='Macro F1', linewidth=2, alpha=0.8) |
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axes[1, 0].set_title('Macro F1 Score', fontsize=14, fontweight='bold') |
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axes[1, 0].set_xlabel('Epoch') |
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axes[1, 0].set_ylabel('F1 Score') |
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axes[1, 0].legend() |
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axes[1, 0].grid(True, alpha=0.3) |
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# 标记最佳F1分数 |
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best_f1_idx = np.argmax(f1_scores) |
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best_epoch = epochs[best_f1_idx] |
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best_f1 = f1_scores[best_f1_idx] |
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axes[1, 0].annotate(f'Best F1: {best_f1:.4f}\nEpoch: {best_epoch}', |
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xy=(best_epoch, best_f1), xytext=(10, 10), |
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textcoords='offset points', ha='left', |
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bbox=dict(boxstyle='round,pad=0.3', facecolor='yellow', alpha=0.7), |
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arrowprops=dict(arrowstyle='->', connectionstyle='arc3,rad=0')) |
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# 4. 综合指标对比 |
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if validation_metrics: |
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epochs = [m['epoch'] for m in validation_metrics] |
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accuracy = [m['eval_accuracy'] for m in validation_metrics] |
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f1_minority = [m['eval_f1_minority'] for m in validation_metrics] |
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f1_macro = [m['eval_f1_macro'] for m in validation_metrics] |
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axes[1, 1].plot(epochs, accuracy, 'b-', label='Accuracy', linewidth=2, alpha=0.8) |
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axes[1, 1].plot(epochs, f1_minority, 'r-', label='Minority F1', linewidth=2, alpha=0.8) |
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axes[1, 1].plot(epochs, f1_macro, 'g-', label='Macro F1', linewidth=2, alpha=0.8) |
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axes[1, 1].set_title('Validation Metrics Comparison', fontsize=14, fontweight='bold') |
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axes[1, 1].set_xlabel('Epoch') |
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axes[1, 1].set_ylabel('Score') |
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axes[1, 1].legend() |
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axes[1, 1].grid(True, alpha=0.3) |
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plt.tight_layout() |
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# 保存训练曲线 |
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curves_path = os.path.join(output_dir, 'training_validation_curves.png') |
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plt.savefig(curves_path, dpi=300, bbox_inches='tight') |
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plt.close() |
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logger.info(f"📈 Training and validation curves saved: {curves_path}") |
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class SentencePairDataset(Dataset): |
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"""句子对数据集类（支持加权采样）""" |
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def __init__(self, data, tokenizer, max_length=512): |
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self.data = data |
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self.tokenizer = tokenizer |
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self.max_length = max_length |
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self.valid_data = [item for item in data if item['label'] in [0, 1]] |
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logger.info(f"原始数据: {len(data)} 条，有效数据: {len(self.valid_data)} 条") |
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self.sentence1_list = [item['sentence1'] for item in self.valid_data] |
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self.sentence2_list = [item['sentence2'] for item in self.valid_data] |
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self.labels = [item['label'] for item in self.valid_data] |
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self.class_counts = Counter(self.labels) |
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self.class_weights = self._compute_class_weights() |
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self.sample_weights = self._compute_sample_weights() |
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def _compute_class_weights(self): |
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"""计算类别权重""" |
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total_samples = len(self.labels) |
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class_weights = {} |
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for label in [0, 1]: |
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count = self.class_counts[label] |
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class_weights[label] = total_samples / (2 * count) |
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return class_weights |
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def _compute_sample_weights(self): |
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"""计算每个样本的权重""" |
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sample_weights = [] |
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for label in self.labels: |
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sample_weights.append(self.class_weights[label]) |
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return torch.tensor(sample_weights, dtype=torch.float) |
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def get_weighted_sampler(self): |
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"""返回加权随机采样器""" |
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return WeightedRandomSampler( |
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weights=self.sample_weights, |
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num_samples=len(self.sample_weights), |
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replacement=True |
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) |
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def __len__(self): |
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return len(self.valid_data) |
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def __getitem__(self, idx): |
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sentence1 = str(self.sentence1_list[idx]) |
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sentence2 = str(self.sentence2_list[idx]) |
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label = self.labels[idx] |
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encoding = self.tokenizer( |
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sentence1, |
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sentence2, |
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truncation=True, |
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padding='max_length', |
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max_length=self.max_length, |
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return_tensors='pt' |
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) |
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return { |
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'input_ids': encoding['input_ids'].flatten(), |
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'attention_mask': encoding['attention_mask'].flatten(), |
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'labels': torch.tensor(label, dtype=torch.long) |
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} |
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def load_training_data(train_file): |
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"""加载训练数据""" |
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try: |
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with open(train_file, 'r', encoding='utf-8') as f: |
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train_data = json.load(f) |
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logger.info(f"成功加载训练数据: {len(train_data)} 条记录") |
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return train_data |
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except Exception as e: |
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logger.error(f"加载训练数据失败: {str(e)}") |
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return None |
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def analyze_data_distribution(data): |
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"""分析数据分布并计算优化的Focal Loss参数""" |
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valid_data = [item for item in data if item['label'] in [0, 1]] |
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label_counts = {} |
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for item in valid_data: |
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label = item['label'] |
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label_counts[label] = label_counts.get(label, 0) + 1 |
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total_samples = len(valid_data) |
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logger.info("=== 训练数据分布分析 ===") |
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logger.info(f"总有效记录数: {total_samples}") |
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class_proportions = {} |
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alpha_weights = [] |
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for label in [0, 1]: |
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count = label_counts.get(label, 0) |
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proportion = count / total_samples |
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class_proportions[label] = proportion |
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label_name = "同段落" if label == 0 else "不同段落" |
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logger.info(f"标签 {label} ({label_name}): {count} 条 ({proportion * 100:.2f}%)") |
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minority_ratio = min(class_proportions.values()) |
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imbalance_ratio = max(class_proportions.values()) / minority_ratio |
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logger.info(f"\n📊 数据不平衡分析:") |
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logger.info(f" 🔹 少数类比例: {minority_ratio * 100:.2f}%") |
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logger.info(f" 🔹 不平衡比率: {imbalance_ratio:.2f}:1") |
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# 相对保守的参数设置，避免过度优化 |
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if imbalance_ratio > 5: |
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alpha_weights = [0.2, 0.8] # 更温和的权重 |
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logger.info(" 🎯 使用平衡的alpha权重设置") |
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else: |
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alpha_weights = [1.0 - class_proportions[0], 1.0 - class_proportions[1]] |
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if imbalance_ratio > 6: |
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recommended_gamma = 2.5 # 降低gamma避免过拟合 |
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logger.info(" ⚠️ 严重不平衡 - 使用Gamma=2.5") |
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elif imbalance_ratio > 4: |
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recommended_gamma = 2.0 |
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logger.info(" ⚠️ 中度偏严重不平衡 - 使用Gamma=2.0") |
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else: |
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recommended_gamma = 1.5 |
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logger.info(f"\n🎯 平衡的Focal Loss参数设置:") |
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logger.info(f" 🔹 Alpha权重: [多数类={alpha_weights[0]:.3f}, 少数类={alpha_weights[1]:.3f}]") |
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logger.info(f" 🔹 平衡Gamma: {recommended_gamma}") |
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logger.info(f" 🔹 公式: FL(p_t) = -α_t * (1-p_t)^γ * log(p_t)") |
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return label_counts, alpha_weights, recommended_gamma |
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def compute_metrics(eval_pred): |
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"""计算详细的评估指标 - 修复版本""" |
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predictions, labels = eval_pred |
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# 🎯 关键修复：处理不规则的predictions格式 |
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try: |
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# 如果predictions是嵌套列表或元组，取第一个元素 |
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if isinstance(predictions, (list, tuple)): |
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predictions = predictions[0] |
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# 确保predictions是numpy数组 |
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if not isinstance(predictions, np.ndarray): |
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predictions = np.array(predictions) |
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|
# 处理多维数组的情况 |
|
|
if len(predictions.shape) > 2: |
|
|
# 如果是3维或更高维，reshape到2维 |
|
|
predictions = predictions.reshape(-1, predictions.shape[-1]) |
|
|
elif len(predictions.shape) == 1: |
|
|
# 如果是1维，检查是否需要处理 |
|
|
if predictions.shape[0] != len(labels): |
|
|
logger.warning(f"预测维度不匹配: predictions={predictions.shape}, labels={len(labels)}") |
|
|
return {'accuracy': 0.0, 'f1_macro': 0.0, 'f1_minority': 0.0, 'f1': 0.0} |
|
|
|
|
|
# 取argmax得到预测类别 |
|
|
predictions = np.argmax(predictions, axis=1) |
|
|
|
|
|
except Exception as e: |
|
|
logger.error(f"处理predictions时出错: {e}") |
|
|
logger.error(f"predictions类型: {type(predictions)}") |
|
|
if hasattr(predictions, 'shape'): |
|
|
logger.error(f"predictions形状: {predictions.shape}") |
|
|
return {'accuracy': 0.0, 'f1_macro': 0.0, 'f1_minority': 0.0, 'f1': 0.0} |
|
|
|
|
|
# 确保labels是一维数组 |
|
|
if hasattr(labels, 'flatten'): |
|
|
labels = labels.flatten() |
|
|
|
|
|
try: |
|
|
# 基本指标 |
|
|
accuracy = accuracy_score(labels, predictions) |
|
|
f1_macro = f1_score(labels, predictions, average='macro', zero_division=0) |
|
|
|
|
|
# 少数类指标（假设1是少数类） |
|
|
f1_minority = f1_score(labels, predictions, pos_label=1, average='binary', zero_division=0) |
|
|
precision_minority = precision_score(labels, predictions, pos_label=1, average='binary', zero_division=0) |
|
|
recall_minority = recall_score(labels, predictions, pos_label=1, average='binary', zero_division=0) |
|
|
|
|
|
# 多数类指标 |
|
|
f1_majority = f1_score(labels, predictions, pos_label=0, average='binary', zero_division=0) |
|
|
precision_majority = precision_score(labels, predictions, pos_label=0, average='binary', zero_division=0) |
|
|
recall_majority = recall_score(labels, predictions, pos_label=0, average='binary', zero_division=0) |
|
|
|
|
|
return { |
|
|
'accuracy': accuracy, |
|
|
'f1_macro': f1_macro, |
|
|
'f1_minority': f1_minority, |
|
|
'precision_minority': precision_minority, |
|
|
'recall_minority': recall_minority, |
|
|
'f1_majority': f1_majority, |
|
|
'precision_majority': precision_majority, |
|
|
'recall_majority': recall_majority, |
|
|
'f1': f1_macro # 🎯 修改：主要用于模型选择的指标改为宏平均F1 |
|
|
} |
|
|
|
|
|
except Exception as e: |
|
|
logger.error(f"计算指标时出错: {e}") |
|
|
return {'accuracy': 0.0, 'f1_macro': 0.0, 'f1_minority': 0.0, 'f1': 0.0} |
|
|
|
|
|
|
|
|
class FocalLoss(nn.Module): |
|
|
"""平衡的Focal Loss用于处理类别不平衡问题""" |
|
|
|
|
|
def __init__(self, alpha=None, gamma=2.0, reduction='mean'): |
|
|
super(FocalLoss, self).__init__() |
|
|
self.alpha = alpha |
|
|
self.gamma = gamma |
|
|
self.reduction = reduction |
|
|
|
|
|
def forward(self, inputs, targets): |
|
|
ce_loss = F.cross_entropy(inputs, targets, reduction='none') |
|
|
pt = torch.exp(-ce_loss) |
|
|
|
|
|
if self.alpha is not None: |
|
|
if self.alpha.type() != inputs.data.type(): |
|
|
self.alpha = self.alpha.type_as(inputs.data) |
|
|
at = self.alpha.gather(0, targets.data.view(-1)) |
|
|
ce_loss = ce_loss * at |
|
|
|
|
|
focal_weight = (1 - pt) ** self.gamma |
|
|
focal_loss = focal_weight * ce_loss |
|
|
|
|
|
if self.reduction == 'mean': |
|
|
return focal_loss.mean() |
|
|
elif self.reduction == 'sum': |
|
|
return focal_loss.sum() |
|
|
else: |
|
|
return focal_loss |
|
|
|
|
|
|
|
|
class ScaledDotProductAttention(nn.Module): |
|
|
"""缩放点积注意力机制""" |
|
|
|
|
|
def __init__(self, d_model, dropout=0.1): |
|
|
super(ScaledDotProductAttention, self).__init__() |
|
|
self.d_model = d_model |
|
|
self.dropout = nn.Dropout(dropout) |
|
|
|
|
|
def forward(self, query, key, value, mask=None): |
|
|
batch_size, seq_len, d_model = query.size() |
|
|
|
|
|
scores = torch.matmul(query, key.transpose(-2, -1)) / math.sqrt(d_model) |
|
|
|
|
|
if mask is not None: |
|
|
mask_value = torch.finfo(scores.dtype).min |
|
|
scores = scores.masked_fill(mask == 0, mask_value) |
|
|
|
|
|
attention_weights = F.softmax(scores, dim=-1) |
|
|
attention_weights = self.dropout(attention_weights) |
|
|
|
|
|
output = torch.matmul(attention_weights, value) |
|
|
|
|
|
return output, attention_weights |
|
|
|
|
|
|
|
|
class RoBERTaWithScaledAttentionAndFocalLoss(nn.Module): |
|
|
"""带缩放点积注意力和平衡Focal Loss的RoBERTa模型""" |
|
|
|
|
|
def __init__(self, model_path, num_labels=2, dropout=0.1, |
|
|
focal_alpha=None, focal_gamma=2.0): |
|
|
super(RoBERTaWithScaledAttentionAndFocalLoss, self).__init__() |
|
|
|
|
|
self.roberta = BertModel.from_pretrained(model_path) |
|
|
self.config = self.roberta.config |
|
|
self.config.num_labels = num_labels |
|
|
|
|
|
self.scaled_attention = ScaledDotProductAttention( |
|
|
d_model=self.config.hidden_size, |
|
|
dropout=dropout |
|
|
) |
|
|
|
|
|
self.dropout = nn.Dropout(dropout) |
|
|
self.classifier = nn.Linear(self.config.hidden_size, num_labels) |
|
|
|
|
|
self.focal_loss = FocalLoss(alpha=focal_alpha, gamma=focal_gamma) |
|
|
|
|
|
self._init_weights() |
|
|
|
|
|
self.focal_alpha = focal_alpha |
|
|
self.focal_gamma = focal_gamma |
|
|
|
|
|
def _init_weights(self): |
|
|
"""初始化新增层的权重""" |
|
|
nn.init.normal_(self.classifier.weight, std=0.02) |
|
|
nn.init.zeros_(self.classifier.bias) |
|
|
|
|
|
def forward(self, input_ids, attention_mask=None, token_type_ids=None, labels=None): |
|
|
roberta_outputs = self.roberta( |
|
|
input_ids=input_ids, |
|
|
attention_mask=attention_mask, |
|
|
token_type_ids=token_type_ids, |
|
|
return_dict=True |
|
|
) |
|
|
|
|
|
sequence_output = roberta_outputs.last_hidden_state |
|
|
|
|
|
enhanced_output, attention_weights = self.scaled_attention( |
|
|
query=sequence_output, |
|
|
key=sequence_output, |
|
|
value=sequence_output, |
|
|
mask=attention_mask.unsqueeze(1) if attention_mask is not None else None |
|
|
) |
|
|
|
|
|
cls_output = enhanced_output[:, 0, :] |
|
|
cls_output = self.dropout(cls_output) |
|
|
|
|
|
logits = self.classifier(cls_output) |
|
|
|
|
|
loss = None |
|
|
if labels is not None: |
|
|
loss = self.focal_loss(logits, labels) |
|
|
|
|
|
return { |
|
|
'loss': loss, |
|
|
'logits': logits, |
|
|
'hidden_states': enhanced_output, |
|
|
'attention_weights': attention_weights |
|
|
} |
|
|
|
|
|
def save_pretrained(self, save_directory): |
|
|
"""保存模型""" |
|
|
os.makedirs(save_directory, exist_ok=True) |
|
|
|
|
|
model_to_save = self.module if hasattr(self, 'module') else self |
|
|
torch.save(model_to_save.state_dict(), os.path.join(save_directory, 'pytorch_model.bin')) |
|
|
|
|
|
config_dict = { |
|
|
'model_type': 'RoBERTaWithScaledAttentionAndFocalLoss', |
|
|
'base_model': 'chinese-roberta-wwm-ext', |
|
|
'num_labels': self.config.num_labels, |
|
|
'hidden_size': self.config.hidden_size, |
|
|
'focal_alpha': self.focal_alpha.tolist() if self.focal_alpha is not None else None, |
|
|
'focal_gamma': self.focal_gamma, |
|
|
'has_scaled_attention': True, |
|
|
'has_focal_loss': True, |
|
|
'optimization_level': 'scientific_training', |
|
|
'model_selection': 'macro_f1_based' |
|
|
} |
|
|
|
|
|
with open(os.path.join(save_directory, 'config.json'), 'w', encoding='utf-8') as f: |
|
|
json.dump(config_dict, f, ensure_ascii=False, indent=2) |
|
|
|
|
|
|
|
|
class WeightedTrainer(Trainer): |
|
|
"""自定义Trainer支持WeightedRandomSampler""" |
|
|
|
|
|
def __init__(self, weighted_sampler=None, *args, **kwargs): |
|
|
super().__init__(*args, **kwargs) |
|
|
self.weighted_sampler = weighted_sampler |
|
|
|
|
|
def get_train_dataloader(self): |
|
|
if self.train_dataset is None: |
|
|
raise ValueError("Trainer: training requires a train_dataset.") |
|
|
|
|
|
train_dataset = self.train_dataset |
|
|
|
|
|
if self.weighted_sampler is not None: |
|
|
train_sampler = self.weighted_sampler |
|
|
else: |
|
|
train_sampler = self._get_train_sampler() |
|
|
|
|
|
return DataLoader( |
|
|
train_dataset, |
|
|
batch_size=self.args.train_batch_size, |
|
|
sampler=train_sampler, |
|
|
collate_fn=self.data_collator, |
|
|
drop_last=self.args.dataloader_drop_last, |
|
|
num_workers=self.args.dataloader_num_workers, |
|
|
pin_memory=self.args.dataloader_pin_memory, |
|
|
) |
|
|
|
|
|
|
|
|
def train_roberta_model(train_data, |
|
|
model_path="/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/model", |
|
|
output_dir="/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/model_train", |
|
|
checkpoint_dir="/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/ouput_result"): |
|
|
"""科学训练RoBERTa模型（基于宏平均F1的模型选择）""" |
|
|
|
|
|
gpu_available, gpu_memory = check_gpu_availability() |
|
|
device = torch.device('cuda') |
|
|
logger.info(f"🚀 使用GPU设备: {device}") |
|
|
|
|
|
# 数据分布分析和平衡的Focal Loss参数计算 |
|
|
label_distribution, alpha_weights, recommended_gamma = analyze_data_distribution(train_data) |
|
|
|
|
|
alpha_tensor = torch.tensor(alpha_weights, dtype=torch.float).to(device) |
|
|
|
|
|
logger.info(f"📥 加载Chinese-RoBERTa-WWM-Ext模型: {model_path}") |
|
|
tokenizer = BertTokenizer.from_pretrained(model_path) |
|
|
|
|
|
model = RoBERTaWithScaledAttentionAndFocalLoss( |
|
|
model_path=model_path, |
|
|
num_labels=2, |
|
|
dropout=0.1, |
|
|
focal_alpha=alpha_tensor, |
|
|
focal_gamma=recommended_gamma |
|
|
) |
|
|
|
|
|
model = model.to(device) |
|
|
logger.info(f"✅ 模型已加载到GPU: {next(model.parameters()).device}") |
|
|
|
|
|
total_params = sum(p.numel() for p in model.parameters()) |
|
|
trainable_params = sum(p.numel() for p in model.parameters() if p.requires_grad) |
|
|
logger.info(f"📊 模型参数统计:") |
|
|
logger.info(f" 🔹 总参数量: {total_params:,}") |
|
|
logger.info(f" 🔹 可训练参数: {trainable_params:,}") |
|
|
|
|
|
# 准备完整数据集 |
|
|
logger.info("📋 准备数据集...") |
|
|
full_dataset = SentencePairDataset(train_data, tokenizer, max_length=512) |
|
|
|
|
|
# 🎯 关键改进：划分训练集和验证集 (80:20) |
|
|
total_size = len(full_dataset) |
|
|
train_size = int(0.8 * total_size) |
|
|
val_size = total_size - train_size |
|
|
|
|
|
# 设置随机种子确保可重复性 |
|
|
torch.manual_seed(42) |
|
|
train_dataset, val_dataset = random_split(full_dataset, [train_size, val_size]) |
|
|
|
|
|
logger.info(f" 🔹 训练集大小: {train_size}") |
|
|
logger.info(f" 🔹 验证集大小: {val_size}") |
|
|
logger.info(f" 🔹 训练/验证比例: {train_size / total_size:.1%}/{val_size / total_size:.1%}") |
|
|
|
|
|
# 为训练集创建加权采样器 |
|
|
# 注意：需要从原始数据集获取权重 |
|
|
train_indices = train_dataset.indices |
|
|
train_weights = full_dataset.sample_weights[train_indices] |
|
|
train_sampler = WeightedRandomSampler( |
|
|
weights=train_weights, |
|
|
num_samples=len(train_weights), |
|
|
replacement=True |
|
|
) |
|
|
|
|
|
# GPU内存配置 |
|
|
if gpu_memory >= 45: # 48GB |
|
|
batch_size = 16 |
|
|
gradient_accumulation = 2 |
|
|
max_length = 512 |
|
|
dataloader_num_workers = 4 |
|
|
elif gpu_memory >= 30: # 32GB |
|
|
batch_size = 12 |
|
|
gradient_accumulation = 3 |
|
|
max_length = 448 |
|
|
dataloader_num_workers = 3 |
|
|
elif gpu_memory >= 22: # 24GB |
|
|
batch_size = 8 |
|
|
gradient_accumulation = 4 |
|
|
max_length = 384 |
|
|
dataloader_num_workers = 2 |
|
|
else: # 8-16GB |
|
|
batch_size = 4 |
|
|
gradient_accumulation = 8 |
|
|
max_length = 256 |
|
|
dataloader_num_workers = 1 |
|
|
|
|
|
effective_batch_size = batch_size * gradient_accumulation |
|
|
|
|
|
# 平衡的学习率策略 |
|
|
initial_learning_rate = 1.5e-5 # 更保守的学习率 |
|
|
warmup_ratio = 0.1 # 10%预热 |
|
|
|
|
|
# 确保输出目录存在 |
|
|
os.makedirs(output_dir, exist_ok=True) |
|
|
os.makedirs(checkpoint_dir, exist_ok=True) |
|
|
|
|
|
# 🎯 关键改进：科学的训练参数配置 |
|
|
training_args = TrainingArguments( |
|
|
output_dir=checkpoint_dir, |
|
|
|
|
|
# 训练配置 |
|
|
num_train_epochs=100, # 减少到100轮，配合Early Stopping |
|
|
per_device_train_batch_size=batch_size, |
|
|
per_device_eval_batch_size=batch_size, |
|
|
gradient_accumulation_steps=gradient_accumulation, |
|
|
|
|
|
# 🎯 验证和评估策略 |
|
|
eval_strategy="epoch", # 每个epoch评估 |
|
|
save_strategy="epoch", # 每个epoch保存 |
|
|
logging_strategy="steps", |
|
|
logging_steps=50, |
|
|
|
|
|
# 🎯 关键：基于宏平均F1的模型选择 |
|
|
load_best_model_at_end=True, # 加载最佳模型 |
|
|
metric_for_best_model="eval_f1", # 使用宏平均F1作为最佳模型指标 |
|
|
greater_is_better=True, # F1越大越好 |
|
|
|
|
|
# 学习率配置 |
|
|
learning_rate=initial_learning_rate, |
|
|
warmup_ratio=warmup_ratio, |
|
|
lr_scheduler_type="cosine", |
|
|
|
|
|
# 正则化 |
|
|
weight_decay=0.01, |
|
|
|
|
|
# 硬件优化 |
|
|
fp16=True, |
|
|
dataloader_pin_memory=True, |
|
|
dataloader_num_workers=dataloader_num_workers, |
|
|
group_by_length=True, |
|
|
|
|
|
# 保存配置 |
|
|
save_total_limit=3, # 只保留最近3个checkpoint |
|
|
|
|
|
# 其他配置 |
|
|
remove_unused_columns=False, |
|
|
report_to=[], |
|
|
adam_epsilon=1e-8, |
|
|
max_grad_norm=1.0, |
|
|
skip_memory_metrics=True, |
|
|
disable_tqdm=False, |
|
|
|
|
|
# 种子设置 |
|
|
seed=42, |
|
|
data_seed=42, |
|
|
) |
|
|
|
|
|
logger.info(f"🎯 科学训练参数配置:") |
|
|
logger.info(f" 🔹 最大训练轮数: {training_args.num_train_epochs}") |
|
|
logger.info(f" 🔹 批次大小: {batch_size}") |
|
|
logger.info(f" 🔹 有效批次大小: {effective_batch_size}") |
|
|
logger.info(f" 🔹 学习率: {training_args.learning_rate}") |
|
|
logger.info(f" 🔹 预热比例: {warmup_ratio}") |
|
|
logger.info(f" 🔹 序列长度: {max_length}") |
|
|
logger.info(f" 🔹 验证策略: 每个epoch评估") |
|
|
logger.info(f" 🔹 模型选择: 基于宏平均F1分数") |
|
|
logger.info(f" 🔹 Early Stopping: 8个epoch无改善停止") |
|
|
|
|
|
# 数据整理器 |
|
|
data_collator = DataCollatorWithPadding(tokenizer=tokenizer) |
|
|
|
|
|
# 🎯 关键：初始化回调函数 |
|
|
loss_tracker = LossTracker() |
|
|
validation_metrics_callback = ValidationMetricsCallback() |
|
|
confusion_matrix_callback = ConfusionMatrixCallback( |
|
|
eval_dataset=val_dataset, # 使用验证集 |
|
|
tokenizer=tokenizer, |
|
|
output_dir=checkpoint_dir, |
|
|
epochs_interval=10 # 每10个epoch生成一次 |
|
|
) |
|
|
|
|
|
# 🎯 关键：Early Stopping - 8个epoch无改善停止 |
|
|
early_stopping_callback = EarlyStoppingCallback( |
|
|
early_stopping_patience=8, # 8个epoch无改善停止 |
|
|
early_stopping_threshold=0.001 # 最小改善阈值 |
|
|
) |
|
|
|
|
|
# 🎯 使用科学的WeightedTrainer |
|
|
trainer = WeightedTrainer( |
|
|
model=model, |
|
|
args=training_args, |
|
|
train_dataset=train_dataset, # 80%训练集 |
|
|
eval_dataset=val_dataset, # 20%验证集 |
|
|
tokenizer=tokenizer, |
|
|
data_collator=data_collator, |
|
|
compute_metrics=compute_metrics, # 详细指标计算 |
|
|
callbacks=[ |
|
|
loss_tracker, |
|
|
validation_metrics_callback, |
|
|
confusion_matrix_callback, |
|
|
early_stopping_callback |
|
|
], |
|
|
weighted_sampler=train_sampler # 加权采样器 |
|
|
) |
|
|
|
|
|
logger.info("🏃♂️ 开始科学训练...") |
|
|
logger.info("🎯 科学训练特性:") |
|
|
logger.info(" ✅ 训练/验证集分离 (80:20)") |
|
|
logger.info(" ✅ 基于宏平均F1的模型选择") |
|
|
logger.info(" ✅ Early Stopping (8个epoch耐心值)") |
|
|
logger.info(" ✅ 每个epoch验证和保存") |
|
|
logger.info(" ✅ 详细的验证指标监控") |
|
|
logger.info(" ✅ 平衡的Focal Loss参数") |
|
|
logger.info(" ✅ WeightedRandomSampler") |
|
|
logger.info(" ✅ 余弦退火学习率调度") |
|
|
logger.info(" ✅ 自动选择最佳模型") |
|
|
|
|
|
start_time = datetime.now() |
|
|
|
|
|
try: |
|
|
# 🎯 执行科学训练 |
|
|
trainer.train() |
|
|
|
|
|
logger.info("📊 训练完成，分析最佳模型...") |
|
|
|
|
|
# 获取最佳模型信息 |
|
|
validation_history = validation_metrics_callback.validation_history |
|
|
if validation_history: |
|
|
best_metrics = max(validation_history, key=lambda x: x['eval_f1_macro']) |
|
|
best_epoch = best_metrics['epoch'] |
|
|
best_f1 = best_metrics['eval_f1_macro'] |
|
|
|
|
|
logger.info(f"🏆 最佳模型信息:") |
|
|
logger.info(f" 🔹 最佳epoch: {best_epoch}") |
|
|
logger.info(f" 🔹 最佳宏平均F1: {best_f1:.4f}") |
|
|
logger.info(f" 🔹 验证准确率: {best_metrics['eval_accuracy']:.4f}") |
|
|
logger.info(f" 🔹 少数类F1: {best_metrics['eval_f1_minority']:.4f}") |
|
|
logger.info(f" 🔹 少数类精确率: {best_metrics['eval_precision_minority']:.4f}") |
|
|
logger.info(f" 🔹 少数类召回率: {best_metrics['eval_recall_minority']:.4f}") |
|
|
|
|
|
except RuntimeError as e: |
|
|
if "out of memory" in str(e).lower(): |
|
|
logger.error("❌ GPU内存不足！") |
|
|
logger.error("💡 建议减小批次大小") |
|
|
raise |
|
|
else: |
|
|
raise |
|
|
|
|
|
end_time = datetime.now() |
|
|
training_duration = end_time - start_time |
|
|
|
|
|
# 获取实际训练的epoch数 |
|
|
actual_epochs = len(validation_metrics_callback.validation_history) |
|
|
logger.info(f"🎉 科学训练完成! 耗时: {training_duration}") |
|
|
logger.info(f"📊 实际训练轮数: {actual_epochs} (最大{training_args.num_train_epochs})") |
|
|
|
|
|
# 生成训练可视化图表 |
|
|
logger.info("📈 生成科学训练可视化图表...") |
|
|
plot_training_curves(loss_tracker, validation_metrics_callback.validation_history, checkpoint_dir) |
|
|
|
|
|
# 🎯 保存最佳模型到指定目录 |
|
|
logger.info(f"💾 保存最佳模型到: {output_dir}") |
|
|
model.save_pretrained(output_dir) |
|
|
tokenizer.save_pretrained(output_dir) |
|
|
|
|
|
# 保存详细的训练历史 |
|
|
training_history = { |
|
|
'train_losses': loss_tracker.train_losses, |
|
|
'train_steps': loss_tracker.train_steps, |
|
|
'eval_losses': loss_tracker.eval_losses, |
|
|
'eval_steps': loss_tracker.eval_steps, |
|
|
'validation_metrics': validation_metrics_callback.validation_history |
|
|
} |
|
|
|
|
|
with open(os.path.join(checkpoint_dir, 'training_history.json'), 'w', encoding='utf-8') as f: |
|
|
json.dump(training_history, f, ensure_ascii=False, indent=2) |
|
|
|
|
|
# 保存混淆矩阵历史 |
|
|
cm_history = {epoch: cm.tolist() for epoch, cm in confusion_matrix_callback.confusion_matrices.items()} |
|
|
with open(os.path.join(checkpoint_dir, 'confusion_matrix_history.json'), 'w', encoding='utf-8') as f: |
|
|
json.dump(cm_history, f, ensure_ascii=False, indent=2) |
|
|
|
|
|
# 保存科学训练的详细信息 |
|
|
training_info = { |
|
|
"model_name": model_path, |
|
|
"model_type": "Chinese-RoBERTa-WWM-Ext with Scientific Training", |
|
|
"training_methodology": "macro_f1_based_selection", |
|
|
"training_duration": str(training_duration), |
|
|
"actual_epochs_trained": actual_epochs, |
|
|
"max_epochs_configured": training_args.num_train_epochs, |
|
|
|
|
|
# 数据分割信息 |
|
|
"data_split": { |
|
|
"total_samples": total_size, |
|
|
"train_samples": train_size, |
|
|
"validation_samples": val_size, |
|
|
"train_ratio": train_size / total_size, |
|
|
"validation_ratio": val_size / total_size |
|
|
}, |
|
|
|
|
|
# 数据分布 |
|
|
"label_distribution": label_distribution, |
|
|
"data_imbalance": { |
|
|
"class_0_count": label_distribution.get(0, 0), |
|
|
"class_1_count": label_distribution.get(1, 0), |
|
|
"class_0_ratio": label_distribution.get(0, 0) / total_size, |
|
|
"class_1_ratio": label_distribution.get(1, 0) / total_size, |
|
|
"imbalance_ratio": label_distribution.get(0, 1) / label_distribution.get(1, 1) |
|
|
}, |
|
|
|
|
|
# 平衡的参数设置 |
|
|
"balanced_focal_loss_params": { |
|
|
"alpha_weights": alpha_weights, |
|
|
"gamma": recommended_gamma, |
|
|
"formula": "FL(p_t) = -α_t * (1-p_t)^γ * log(p_t)", |
|
|
"approach": "balanced_macro_f1_focus" |
|
|
}, |
|
|
|
|
|
# 采样策略 |
|
|
"weighted_sampling": { |
|
|
"enabled": True, |
|
|
"strategy": "WeightedRandomSampler", |
|
|
"applied_to": "training_set_only" |
|
|
}, |
|
|
|
|
|
# 科学训练策略 |
|
|
"scientific_training_strategy": { |
|
|
"model_selection_metric": "eval_f1_macro", |
|
|
"early_stopping_patience": 8, |
|
|
"early_stopping_threshold": 0.001, |
|
|
"validation_frequency": "every_epoch", |
|
|
"best_model_loading": True |
|
|
}, |
|
|
|
|
|
# 学习率策略 |
|
|
"learning_strategy": { |
|
|
"initial_learning_rate": initial_learning_rate, |
|
|
"warmup_ratio": warmup_ratio, |
|
|
"lr_scheduler": "cosine", |
|
|
"approach": "conservative_and_stable" |
|
|
}, |
|
|
|
|
|
# 硬件优化 |
|
|
"gpu_optimization": { |
|
|
"gpu_name": torch.cuda.get_device_name(0), |
|
|
"gpu_memory_gb": gpu_memory, |
|
|
"effective_batch_size": effective_batch_size, |
|
|
"sequence_length": max_length, |
|
|
"optimization_level": "scientific_training" |
|
|
}, |
|
|
|
|
|
# 训练参数 |
|
|
"training_args": { |
|
|
"num_train_epochs": training_args.num_train_epochs, |
|
|
"per_device_train_batch_size": training_args.per_device_train_batch_size, |
|
|
"gradient_accumulation_steps": training_args.gradient_accumulation_steps, |
|
|
"learning_rate": training_args.learning_rate, |
|
|
"warmup_ratio": training_args.warmup_ratio, |
|
|
"weight_decay": training_args.weight_decay, |
|
|
"fp16": training_args.fp16, |
|
|
"lr_scheduler_type": training_args.lr_scheduler_type |
|
|
}, |
|
|
|
|
|
# 模型参数 |
|
|
"model_parameters": { |
|
|
"total_params": total_params, |
|
|
"trainable_params": trainable_params, |
|
|
}, |
|
|
|
|
|
# 路径信息 |
|
|
"paths": { |
|
|
"model_input_path": model_path, |
|
|
"model_output_path": output_dir, |
|
|
"checkpoint_output_path": checkpoint_dir |
|
|
}, |
|
|
|
|
|
# 科学训练改进 |
|
|
"scientific_improvements": [ |
|
|
"Train/Validation split (80:20) for unbiased evaluation", |
|
|
"Macro F1 score as primary model selection metric", |
|
|
"Early stopping with 8-epoch patience to prevent overfitting", |
|
|
"Balanced Focal Loss parameters to avoid over-optimization", |
|
|
"Every-epoch validation for detailed monitoring", |
|
|
"Automatic best model selection and loading", |
|
|
"Conservative learning rate for stable convergence", |
|
|
"Comprehensive validation metrics tracking", |
|
|
"WeightedRandomSampler for balanced training", |
|
|
"Cosine annealing learning rate scheduler" |
|
|
], |
|
|
|
|
|
# 最佳模型信息 |
|
|
"best_model_info": validation_history[-1] if validation_history else None, |
|
|
|
|
|
# 可视化文件 |
|
|
"visualization_files": { |
|
|
"training_validation_curves": "training_validation_curves.png", |
|
|
"validation_confusion_matrices": [f"val_confusion_matrix_epoch_{i}.png" for i in |
|
|
range(10, actual_epochs + 1, 10)], |
|
|
"training_history": "training_history.json", |
|
|
"confusion_matrix_history": "confusion_matrix_history.json" |
|
|
}, |
|
|
|
|
|
"training_completed": end_time.isoformat() |
|
|
} |
|
|
|
|
|
with open(os.path.join(checkpoint_dir, 'scientific_training_info.json'), 'w', encoding='utf-8') as f: |
|
|
json.dump(training_info, f, ensure_ascii=False, indent=2) |
|
|
|
|
|
# 同时在模型目录保存一份摘要 |
|
|
model_summary = { |
|
|
"model_selection_method": "macro_f1_based", |
|
|
"best_epoch": validation_history[-1]['epoch'] if validation_history else actual_epochs, |
|
|
"best_macro_f1": validation_history[-1]['eval_f1_macro'] if validation_history else None, |
|
|
"training_methodology": "scientific_with_early_stopping", |
|
|
"data_split": "80_20_train_validation" |
|
|
} |
|
|
|
|
|
with open(os.path.join(output_dir, 'model_selection_summary.json'), 'w', encoding='utf-8') as f: |
|
|
json.dump(model_summary, f, ensure_ascii=False, indent=2) |
|
|
|
|
|
logger.info("📋 科学训练信息已保存") |
|
|
|
|
|
return trainer, model, tokenizer, loss_tracker, validation_metrics_callback |
|
|
|
|
|
|
|
|
def main(): |
|
|
"""主函数""" |
|
|
logger.info("=" * 120) |
|
|
logger.info("🚀 Chinese-RoBERTa-WWM-Ext 科学训练 (基于宏平均F1的模型选择)") |
|
|
logger.info("=" * 120) |
|
|
|
|
|
# 配置路径 |
|
|
train_file = "/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/Data/train_dataset.json" |
|
|
model_path = "/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/model" |
|
|
output_dir = "/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/model_train" |
|
|
checkpoint_dir = "/root/autodl-tmp/chinese-roberta-wwm-ext/chinese-roberta-wwm-ext/ouput_result" |
|
|
|
|
|
# 确保所有输出目录存在 |
|
|
os.makedirs(output_dir, exist_ok=True) |
|
|
os.makedirs(checkpoint_dir, exist_ok=True) |
|
|
logger.info(f"📁 确保输出目录存在:") |
|
|
logger.info(f" 🔹 最佳模型输出: {output_dir}") |
|
|
logger.info(f" 🔹 训练记录: {checkpoint_dir}") |
|
|
|
|
|
# 确认第三方报告工具已禁用 |
|
|
logger.info("🚫 确认第三方报告工具状态:") |
|
|
logger.info(f" 🔹 WANDB_DISABLED: {os.environ.get('WANDB_DISABLED', 'not set')}") |
|
|
logger.info(f" 🔹 COMET_MODE: {os.environ.get('COMET_MODE', 'not set')}") |
|
|
logger.info(f" 🔹 NEPTUNE_MODE: {os.environ.get('NEPTUNE_MODE', 'not set')}") |
|
|
logger.info(f" ✅ 所有第三方报告工具已禁用") |
|
|
|
|
|
logger.info(f"\n📋 科学训练配置:") |
|
|
logger.info(f" 🔹 训练数据: {train_file}") |
|
|
logger.info(f" 🔹 基础模型: {model_path}") |
|
|
logger.info(f" 🔹 模型类型: Chinese-RoBERTa-WWM-Ext") |
|
|
logger.info(f" 🔹 训练方法: 科学训练 (Scientific Training)") |
|
|
logger.info(f" 🔹 核心改进:") |
|
|
logger.info(f" • 训练/验证集分离 (80:20)") |
|
|
logger.info(f" • 基于宏平均F1的模型选择") |
|
|
logger.info(f" • Early Stopping (8个epoch耐心值)") |
|
|
logger.info(f" • 每个epoch验证和监控") |
|
|
logger.info(f" • 平衡的Focal Loss参数") |
|
|
logger.info(f" • 自动最佳模型选择") |
|
|
logger.info(f" 🔹 最大训练轮数: 100 epochs (早停可能提前结束)") |
|
|
logger.info(f" 🔹 最佳模型输出: {output_dir}") |
|
|
logger.info(f" 🔹 训练记录: {checkpoint_dir}") |
|
|
|
|
|
# 加载训练数据 |
|
|
train_data = load_training_data(train_file) |
|
|
if train_data is None: |
|
|
logger.error("❌ 无法加载训练数据，程序退出") |
|
|
return |
|
|
|
|
|
try: |
|
|
# 执行科学训练 |
|
|
trainer, model, tokenizer, loss_tracker, validation_callback = train_roberta_model( |
|
|
train_data, |
|
|
model_path=model_path, |
|
|
output_dir=output_dir, |
|
|
checkpoint_dir=checkpoint_dir |
|
|
) |
|
|
|
|
|
logger.info("=" * 120) |
|
|
logger.info("🎉 科学训练完成!") |
|
|
logger.info("=" * 120) |
|
|
logger.info(f"📁 文件输出位置:") |
|
|
logger.info(f" 🔹 最佳模型: {output_dir}") |
|
|
logger.info(f" 🔹 训练记录和图表: {checkpoint_dir}") |
|
|
|
|
|
logger.info("📄 生成的文件:") |
|
|
logger.info(" 最佳模型文件 (model_train目录):") |
|
|
logger.info(" • pytorch_model.bin - 基于宏平均F1选择的最佳模型") |
|
|
logger.info(" • config.json - 科学训练模型配置") |
|
|
logger.info(" • tokenizer配置文件") |
|
|
logger.info(" • model_selection_summary.json - 模型选择摘要") |
|
|
|
|
|
logger.info(" 科学训练记录 (ouput_result目录):") |
|
|
logger.info(" • scientific_training_info.json - 完整科学训练信息") |
|
|
logger.info(" • training_history.json - 训练和验证历史") |
|
|
logger.info(" • confusion_matrix_history.json - 混淆矩阵历史") |
|
|
logger.info(" • training_validation_curves.png - 训练验证曲线") |
|
|
logger.info(" • val_confusion_matrix_epoch_X.png - 验证集混淆矩阵") |
|
|
logger.info(" • checkpoint-* - 训练检查点") |
|
|
|
|
|
logger.info("🔥 科学训练特性:") |
|
|
logger.info(" ✅ Chinese-RoBERTa-WWM-Ext 基础模型") |
|
|
logger.info(" ✅ 数据科学方法: 80:20 训练验证分离") |
|
|
logger.info(" ✅ 智能模型选择: 基于宏平均F1分数") |
|
|
logger.info(" ✅ 防过拟合: 8个epoch Early Stopping") |
|
|
logger.info(" ✅ 平衡优化: 温和的Focal Loss参数") |
|
|
logger.info(" ✅ 全程监控: 每个epoch验证评估") |
|
|
logger.info(" ✅ 自动化选择: 最佳模型自动保存") |
|
|
logger.info(" ✅ WeightedRandomSampler 平衡采样") |
|
|
logger.info(" ✅ 余弦退火学习率调度") |
|
|
logger.info(" ✅ 完整可视化和指标追踪") |
|
|
|
|
|
logger.info("🎯 科学方法优势:") |
|
|
logger.info(" ⚡ 无偏验证评估确保泛化能力") |
|
|
logger.info(" ⚡ 基于目标指标的智能模型选择") |
|
|
logger.info(" ⚡ 早停机制防止过拟合") |
|
|
logger.info(" ⚡ 平衡参数避免过度优化") |
|
|
logger.info(" ⚡ 全程监控确保训练质量") |
|
|
|
|
|
# 显示最佳模型信息 |
|
|
if validation_callback.validation_history: |
|
|
best_metrics = max(validation_callback.validation_history, key=lambda x: x['eval_f1_macro']) |
|
|
logger.info(f"\n🏆 最终选择的最佳模型:") |
|
|
logger.info(f" 🔹 来源epoch: {best_metrics['epoch']}") |
|
|
logger.info(f" 🔹 宏平均F1: {best_metrics['eval_f1_macro']:.4f}") |
|
|
logger.info(f" 🔹 验证准确率: {best_metrics['eval_accuracy']:.4f}") |
|
|
logger.info(f" 🔹 少数类F1: {best_metrics['eval_f1_minority']:.4f}") |
|
|
logger.info(f" 🔹 少数类精确率: {best_metrics['eval_precision_minority']:.4f}") |
|
|
logger.info(f" 🔹 少数类召回率: {best_metrics['eval_recall_minority']:.4f}") |
|
|
|
|
|
# 显示文件详情 |
|
|
logger.info(f"\n📂 文件保存详情:") |
|
|
logger.info(f"📋 最佳模型 ({output_dir}):") |
|
|
try: |
|
|
for file in os.listdir(output_dir): |
|
|
file_path = os.path.join(output_dir, file) |
|
|
if os.path.isfile(file_path): |
|
|
file_size = os.path.getsize(file_path) / (1024 * 1024) |
|
|
logger.info(f" 📄 {file} ({file_size:.2f} MB)") |
|
|
except Exception as e: |
|
|
logger.warning(f" ⚠️ 无法列出模型文件: {str(e)}") |
|
|
|
|
|
logger.info(f"📋 训练记录 ({checkpoint_dir}):") |
|
|
try: |
|
|
files = os.listdir(checkpoint_dir) |
|
|
json_files = [f for f in files if f.endswith('.json')] |
|
|
png_files = [f for f in files if f.endswith('.png')] |
|
|
checkpoint_dirs = [f for f in files if f.startswith('checkpoint-')] |
|
|
|
|
|
if json_files: |
|
|
logger.info(" 配置和历史文件:") |
|
|
for file in sorted(json_files): |
|
|
file_path = os.path.join(checkpoint_dir, file) |
|
|
file_size = os.path.getsize(file_path) / 1024 |
|
|
logger.info(f" 📄 {file} ({file_size:.1f} KB)") |
|
|
|
|
|
if png_files: |
|
|
logger.info(" 可视化图表:") |
|
|
for file in sorted(png_files): |
|
|
file_path = os.path.join(checkpoint_dir, file) |
|
|
file_size = os.path.getsize(file_path) / 1024 |
|
|
logger.info(f" 📊 {file} ({file_size:.1f} KB)") |
|
|
|
|
|
if checkpoint_dirs: |
|
|
logger.info(" 模型检查点:") |
|
|
for dir_name in sorted(checkpoint_dirs): |
|
|
logger.info(f" 📁 {dir_name}/") |
|
|
|
|
|
except Exception as e: |
|
|
logger.warning(f" ⚠️ 无法列出训练记录: {str(e)}") |
|
|
|
|
|
logger.info("\n🎯 科学训练完成，最佳模型已自动选择并保存!") |
|
|
logger.info("💡 建议：在测试集上评估选择的最佳模型以验证泛化能力") |
|
|
|
|
|
except Exception as e: |
|
|
logger.error(f"❌ 科学训练过程中出现错误: {str(e)}") |
|
|
import traceback |
|
|
traceback.print_exc() |
|
|
raise |
|
|
|
|
|
|
|
|
if __name__ == "__main__": |
|
|
main() |
