import math import os import time import copy from tqdm import tqdm import torch from utils.logger import get_logger from utils.loss_function import all_metrics from utils.training_stats import TrainingStats class Trainer: def __init__( self, model, loss, optimizer, train_loader, val_loader, test_loader, scaler, args, lr_scheduler=None, ): # 设备和基本参数 self.device = args["basic"]["device"] train_args = args["train"] # 模型和训练相关组件 self.model = model self.loss = loss self.optimizer = optimizer self.lr_scheduler = lr_scheduler # 数据加载器 self.train_loader = train_loader self.val_loader = val_loader self.test_loader = test_loader # 数据处理工具 self.scaler = scaler self.args = train_args self.batches_seen = 0 # 统计信息 self.train_per_epoch = len(train_loader) self.val_per_epoch = len(val_loader) if val_loader else 0 # 初始化路径、日志和统计 self._initialize_paths(train_args) self._initialize_logger(train_args) self._initialize_stats() def _initialize_paths(self, args): """初始化模型保存路径""" self.best_path = os.path.join(args["log_dir"], "best_model.pth") self.best_test_path = os.path.join(args["log_dir"], "best_test_model.pth") self.loss_figure_path = os.path.join(args["log_dir"], "loss.png") def _initialize_logger(self, args): """初始化日志记录器""" if not os.path.isdir(args["log_dir"]) and not args["debug"]: os.makedirs(args["log_dir"], exist_ok=True) self.logger = get_logger( args["log_dir"], name=self.model.__class__.__name__, debug=args["debug"] ) self.logger.info(f"Experiment log path in: {args['log_dir']}") def _initialize_stats(self): """初始化统计信息记录器""" self.stats = TrainingStats(device=self.device) def _run_epoch(self, epoch, dataloader, mode): """运行一个训练/验证/测试epoch""" # 设置模型模式和是否进行优化 if mode == "train": self.model.train() optimizer_step = True else: self.model.eval() optimizer_step = False # 初始化变量 total_loss = 0 epoch_time = time.time() y_pred, y_true = [], [] with torch.set_grad_enabled(optimizer_step): progress_bar = tqdm( enumerate(dataloader), total=len(dataloader), desc=f"{mode.capitalize()} Epoch {epoch}" ) for batch_idx, (data, target) in progress_bar: start_time = time.time() self.batches_seen += 1 label = target[..., : self.args["output_dim"]].clone() # 前向传播 if mode == "train": output = self.model(data, target, self.batches_seen).to(self.device) else: output = self.model(data, target).to(self.device) # 计算原始loss loss = self.loss(output, label) # 检查output和label的shape是否一致 if output.shape == label.shape: print(f"✓ Test passed: output shape {output.shape} matches label shape {label.shape}") import sys sys.exit(0) else: print(f"✗ Test failed: output shape {output.shape} does not match label shape {label.shape}") import sys sys.exit(1) # 反归一化 d_output = self.scaler.inverse_transform(output) d_label = self.scaler.inverse_transform(label) # 反归一化的loss d_loss = self.loss(d_output, d_label) # 反向传播和优化（仅在训练模式） if optimizer_step and self.optimizer is not None: self.optimizer.zero_grad() loss.backward() if self.args["grad_norm"]: torch.nn.utils.clip_grad_norm_( self.model.parameters(), self.args["max_grad_norm"] ) self.optimizer.step() # 记录步骤时间 step_time = time.time() - start_time self.stats.record_step_time(step_time, mode) total_loss += d_loss.item() # 累积预测结果 y_pred.append(d_output.detach().cpu()) y_true.append(d_label.detach().cpu()) if mode == "train" and (batch_idx + 1) % self.args["log_step"] == 0: self.logger.info( f"Train Epoch {epoch}: {batch_idx + 1}/{len(dataloader)} Loss: {d_loss.item():.6f}" ) # 更新 tqdm 的进度 progress_bar.update(1) progress_bar.set_postfix(loss=d_loss.item()) # 合并所有批次的预测结果 y_pred = torch.cat(y_pred, dim=0) y_true = torch.cat(y_true, dim=0) # 计算平均损失 avg_loss = total_loss / len(dataloader) # 计算并记录指标 mae, rmse, mape = all_metrics( y_pred, y_true, self.args["mae_thresh"], self.args["mape_thresh"] ) self.logger.info( f"Epoch #{epoch:02d}: {mode.capitalize():<5} MAE:{mae:5.2f} | RMSE:{rmse:5.2f} | MAPE:{mape:7.4f} | Time: {time.time() - epoch_time:.2f} s" ) # 记录内存使用情况 self.stats.record_memory_usage() return avg_loss def train_epoch(self, epoch): return self._run_epoch(epoch, self.train_loader, "train") def val_epoch(self, epoch): return self._run_epoch(epoch, self.val_loader or self.test_loader, "val") def test_epoch(self, epoch): return self._run_epoch(epoch, self.test_loader, "test") def train(self): """执行完整的训练流程""" # 初始化最佳模型和损失记录 best_model, best_test_model = None, None best_loss, best_test_loss = float("inf"), float("inf") not_improved_count = 0 # 开始训练 self.stats.start_training() self.logger.info("Training process started") # 训练循环 for epoch in range(1, self.args["epochs"] + 1): # 训练、验证和测试一个epoch train_epoch_loss = self.train_epoch(epoch) val_epoch_loss = self.val_epoch(epoch) test_epoch_loss = self.test_epoch(epoch) # 检查梯度爆炸 if train_epoch_loss > 1e6: self.logger.warning("Gradient explosion detected. Ending...") break # 更新最佳验证模型 if val_epoch_loss < best_loss: best_loss = val_epoch_loss not_improved_count = 0 best_model = copy.deepcopy(self.model.state_dict()) self.logger.info("Best validation model saved!") else: not_improved_count += 1 # 检查早停条件 if self._should_early_stop(not_improved_count): break # 更新最佳测试模型 if test_epoch_loss < best_test_loss: best_test_loss = test_epoch_loss best_test_model = copy.deepcopy(self.model.state_dict()) # 保存最佳模型 if not self.args["debug"]: self._save_best_models(best_model, best_test_model) # 结束训练并输出统计信息 self.stats.end_training() self.stats.report(self.logger) # 输出模型参数量 self._log_model_params() # 最终评估 self._finalize_training(best_model, best_test_model) def _should_early_stop(self, not_improved_count): """检查是否满足早停条件""" if ( self.args["early_stop"] and not_improved_count == self.args["early_stop_patience"] ): self.logger.info( f"Validation performance didn't improve for {self.args['early_stop_patience']} epochs. Training stops." ) return True return False def _save_best_models(self, best_model, best_test_model): """保存最佳模型到文件""" torch.save(best_model, self.best_path) torch.save(best_test_model, self.best_test_path) self.logger.info( f"Best models saved at {self.best_path} and {self.best_test_path}" ) def _log_model_params(self): """输出模型可训练参数数量""" total_params = sum( p.numel() for p in self.model.parameters() if p.requires_grad) self.logger.info(f"Trainable params: {total_params}") def _finalize_training(self, best_model, best_test_model): self.model.load_state_dict(best_model) self.logger.info("Testing on best validation model") self.test(self.model, self.args, self.test_loader, self.scaler, self.logger) self.model.load_state_dict(best_test_model) self.logger.info("Testing on best test model") self.test(self.model, self.args, self.test_loader, self.scaler, self.logger) @staticmethod def test(model, args, data_loader, scaler, logger, path=None): """对模型进行评估并输出性能指标""" # 加载模型检查点（如果提供了路径） if path: checkpoint = torch.load(path) model.load_state_dict(checkpoint["state_dict"]) model.to(args["basic"]["device"]) # 设置为评估模式 model.eval() # 收集预测和真实标签 y_pred, y_true = [], [] # 不计算梯度的情况下进行预测 with torch.no_grad(): for data, target in data_loader: label = target[..., : args["output_dim"]].clone() output = model(data, target) y_pred.append(output.detach().cpu()) y_true.append(label.detach().cpu()) # 反归一化 d_y_pred = scaler.inverse_transform(torch.cat(y_pred, dim=0)) d_y_true = scaler.inverse_transform(torch.cat(y_true, dim=0)) # 计算并记录每个时间步的指标 for t in range(d_y_true.shape[1]): mae, rmse, mape = all_metrics( d_y_pred[:, t, ...], d_y_true[:, t, ...], args["mae_thresh"], args["mape_thresh"], ) logger.info(f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}") # 计算并记录平均指标 mae, rmse, mape = all_metrics(d_y_pred, d_y_true, args["mae_thresh"], args["mape_thresh"]) logger.info( f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}") @staticmethod def _compute_sampling_threshold(global_step, k): return k / (k + math.exp(global_step / k))