577 lines
24 KiB
Python
577 lines
24 KiB
Python
import math
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import os
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import time
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import copy
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import pandas as pd
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import numpy as np
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from tqdm import tqdm
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import torch
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class Trainer:
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def __init__(self, config, model, loss, optimizer, train_loader, val_loader, test_loader,
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scalers, logger, lr_scheduler=None):
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self.model = model
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self.loss = loss
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self.optimizer = optimizer
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self.train_loader = train_loader
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self.val_loader = val_loader
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self.test_loader = test_loader
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self.scalers = scalers # 现在是多个标准化器的列表
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self.args = config['train']
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self.logger = logger
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self.args['device'] = config['basic']['device']
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self.lr_scheduler = lr_scheduler
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self.train_per_epoch = len(train_loader)
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self.val_per_epoch = len(val_loader) if val_loader else 0
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self.best_path = os.path.join(logger.dir_path, 'best_model.pth')
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self.best_test_path = os.path.join(logger.dir_path, 'best_test_model.pth')
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self.loss_figure_path = os.path.join(logger.dir_path, 'loss.png')
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# 用于收集nfe数据
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self.c = []
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self.res, self.keys = [], []
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def _run_epoch(self, epoch, dataloader, mode):
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if mode == 'train':
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self.model.train()
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optimizer_step = True
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else:
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self.model.eval()
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optimizer_step = False
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total_loss = 0
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epoch_time = time.time()
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# 清空nfe数据收集
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if mode == 'train':
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self.c.clear()
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with torch.set_grad_enabled(optimizer_step):
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with tqdm(total=len(dataloader), desc=f'{mode.capitalize()} Epoch {epoch}') as pbar:
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for batch_idx, (data, target) in enumerate(dataloader):
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label = target[..., :self.args['output_dim']]
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output, fe = self.model(data)
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if self.args['real_value']:
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# 只对输出维度进行反归一化
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output = self._inverse_transform_output(output)
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loss = self.loss(output, label)
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# 收集nfe数据（仅在训练模式下）
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if mode == 'train':
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self.c.append([*fe, loss.item()])
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# 记录FE信息
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self.logger.logger.debug("FE: number - {}, time - {:.3f} s, err - {:.3f}".format(*fe, loss.item()))
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if optimizer_step and self.optimizer is not None:
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self.optimizer.zero_grad()
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loss.backward()
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if self.args['grad_norm']:
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torch.nn.utils.clip_grad_norm_(self.model.parameters(), self.args['max_grad_norm'])
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self.optimizer.step()
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total_loss += loss.item()
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if mode == 'train' and (batch_idx + 1) % self.args['log_step'] == 0:
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self.logger.info(
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f'Train Epoch {epoch}: {batch_idx + 1}/{len(dataloader)} Loss: {loss.item():.6f}')
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# 更新 tqdm 的进度
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pbar.update(1)
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pbar.set_postfix(loss=loss.item())
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avg_loss = total_loss / len(dataloader)
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self.logger.logger.info(
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f'{mode.capitalize()} Epoch {epoch}: average Loss: {avg_loss:.6f}, time: {time.time() - epoch_time:.2f} s')
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# 收集nfe数据（仅在训练模式下）
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if mode == 'train':
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self.res.append(pd.DataFrame(self.c, columns=['nfe', 'time', 'err']))
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self.keys.append(epoch)
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return avg_loss
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def _inverse_transform_output(self, output):
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"""
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只对输出维度进行反归一化
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假设输出数据形状为 [batch, horizon, nodes, features]
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只对前output_dim个特征进行反归一化
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"""
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if not self.args['real_value']:
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return output
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# 获取输出维度的数量
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output_dim = self.args['output_dim']
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# 如果输出特征数小于等于标准化器数量，直接使用对应的标准化器
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if output_dim <= len(self.scalers):
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# 对每个输出特征分别进行反归一化
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for feature_idx in range(output_dim):
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if feature_idx < len(self.scalers):
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output[..., feature_idx:feature_idx+1] = self.scalers[feature_idx].inverse_transform(
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output[..., feature_idx:feature_idx+1]
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)
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else:
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# 如果输出特征数大于标准化器数量，只对前len(scalers)个特征进行反归一化
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for feature_idx in range(len(self.scalers)):
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output[..., feature_idx:feature_idx+1] = self.scalers[feature_idx].inverse_transform(
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output[..., feature_idx:feature_idx+1]
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)
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return output
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def train_epoch(self, epoch):
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return self._run_epoch(epoch, self.train_loader, 'train')
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def val_epoch(self, epoch):
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return self._run_epoch(epoch, self.val_loader or self.test_loader, 'val')
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def test_epoch(self, epoch):
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return self._run_epoch(epoch, self.test_loader, 'test')
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def train(self):
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best_model, best_test_model = None, None
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best_loss, best_test_loss = float('inf'), float('inf')
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not_improved_count = 0
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self.logger.logger.info("Training process started")
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for epoch in range(1, self.args['epochs'] + 1):
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train_epoch_loss = self.train_epoch(epoch)
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val_epoch_loss = self.val_epoch(epoch)
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test_epoch_loss = self.test_epoch(epoch)
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if train_epoch_loss > 1e6:
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self.logger.logger.warning('Gradient explosion detected. Ending...')
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break
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if val_epoch_loss < best_loss:
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best_loss = val_epoch_loss
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not_improved_count = 0
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best_model = copy.deepcopy(self.model.state_dict())
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torch.save(best_model, self.best_path)
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self.logger.logger.info('Best validation model saved!')
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else:
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not_improved_count += 1
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if self.args['early_stop'] and not_improved_count == self.args['early_stop_patience']:
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self.logger.logger.info(
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f"Validation performance didn't improve for {self.args['early_stop_patience']} epochs. Training stops.")
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break
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if test_epoch_loss < best_test_loss:
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best_test_loss = test_epoch_loss
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best_test_model = copy.deepcopy(self.model.state_dict())
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torch.save(best_test_model, self.best_test_path)
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# 保存nfe数据（如果启用）
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if hasattr(self.args, 'nfe') and bool(self.args.get('nfe', False)):
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self._save_nfe_data()
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if not self.args['debug']:
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torch.save(best_model, self.best_path)
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torch.save(best_test_model, self.best_test_path)
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self.logger.logger.info(f"Best models saved at {self.best_path} and {self.best_test_path}")
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self._finalize_training(best_model, best_test_model)
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def _finalize_training(self, best_model, best_test_model):
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self.model.load_state_dict(best_model)
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self.logger.logger.info("Testing on best validation model")
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self.test(self.model, self.args, self.test_loader, self.scalers, self.logger, generate_viz=False)
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self.model.load_state_dict(best_test_model)
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self.logger.logger.info("Testing on best test model")
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self.test(self.model, self.args, self.test_loader, self.scalers, self.logger, generate_viz=True)
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@staticmethod
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def test(model, args, data_loader, scalers, logger, path=None, generate_viz=True):
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if path:
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checkpoint = torch.load(path)
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model.load_state_dict(checkpoint['state_dict'])
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model.to(args['device'])
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model.eval()
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y_pred, y_true = [], []
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# 用于收集nfe数据
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c = []
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with torch.no_grad():
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for data, target in data_loader:
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label = target[..., :args['output_dim']]
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output, fe = model(data)
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y_pred.append(output)
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y_true.append(label)
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# 收集nfe数据
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c.append([*fe, 0.0]) # 测试时没有loss，设为0
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if args['real_value']:
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# 只对输出维度进行反归一化
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y_pred = Trainer._inverse_transform_output_static(torch.cat(y_pred, dim=0), args, scalers)
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else:
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y_pred = torch.cat(y_pred, dim=0)
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y_true = torch.cat(y_true, dim=0)
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# 计算每个时间步的指标
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for t in range(y_true.shape[1]):
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mae, rmse, mape = logger.all_metrics(y_pred[:, t, ...], y_true[:, t, ...],
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args['mae_thresh'], args['mape_thresh'])
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logger.logger.info(f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
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mae, rmse, mape = logger.all_metrics(y_pred, y_true, args['mae_thresh'], args['mape_thresh'])
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logger.logger.info(f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
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# 保存nfe数据（如果启用）
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if hasattr(args, 'nfe') and bool(args.get('nfe', False)):
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Trainer._save_nfe_data_static(c, model, logger)
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# 只在需要时生成可视化图片
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if generate_viz:
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save_dir = logger.dir_path if hasattr(logger, 'dir_path') else './logs'
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Trainer._generate_node_visualizations(y_pred, y_true, logger, save_dir)
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Trainer._generate_input_output_comparison(y_pred, y_true, data_loader, logger, save_dir,
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target_node=1, num_samples=10, scalers=scalers)
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@staticmethod
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def _inverse_transform_output_static(output, args, scalers):
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"""
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静态方法：只对输出维度进行反归一化
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"""
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if not args['real_value']:
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return output
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# 获取输出维度的数量
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output_dim = args['output_dim']
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# 如果输出特征数小于等于标准化器数量，直接使用对应的标准化器
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if output_dim <= len(scalers):
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# 对每个输出特征分别进行反归一化
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for feature_idx in range(output_dim):
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if feature_idx < len(scalers):
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output[..., feature_idx:feature_idx+1] = scalers[feature_idx].inverse_transform(
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output[..., feature_idx:feature_idx+1]
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)
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else:
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# 如果输出特征数大于标准化器数量，只对前len(scalers)个特征进行反归一化
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for feature_idx in range(len(scalers)):
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output[..., feature_idx:feature_idx+1] = scalers[feature_idx].inverse_transform(
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output[..., feature_idx:feature_idx+1]
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)
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return output
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@staticmethod
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def _generate_node_visualizations(y_pred, y_true, logger, save_dir):
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"""
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生成节点预测可视化图片
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Args:
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y_pred: 预测值
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y_true: 真实值
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logger: 日志记录器
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save_dir: 保存目录
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import matplotlib
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from tqdm import tqdm
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# 设置matplotlib配置，减少字体查找输出
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matplotlib.set_loglevel('error') # 只显示错误信息
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plt.rcParams['font.family'] = 'DejaVu Sans' # 使用默认字体
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# 检查数据有效性
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if y_pred is None or y_true is None:
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return
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# 创建pic文件夹
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pic_dir = os.path.join(save_dir, 'pic')
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os.makedirs(pic_dir, exist_ok=True)
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# 固定生成10张图片
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num_nodes_to_plot = 10
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# 生成单个节点的详细图
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with tqdm(total=num_nodes_to_plot, desc="Generating node visualizations") as pbar:
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for node_id in range(num_nodes_to_plot):
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# 获取对应节点的数据
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if len(y_pred.shape) > 2 and y_pred.shape[-2] > node_id:
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# 数据格式: [time_step, seq_len, num_node, dim]
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node_pred = y_pred[:, 12, node_id, 0].cpu().numpy() # t=1时刻，指定节点，第一个特征
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node_true = y_true[:, 12, node_id, 0].cpu().numpy()
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else:
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# 如果数据不足10个节点，只处理实际存在的节点
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if node_id >= y_pred.shape[-2]:
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pbar.update(1)
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continue
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else:
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node_pred = y_pred[:, 0, node_id, 0].cpu().numpy()
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node_true = y_true[:, 0, node_id, 0].cpu().numpy()
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# 检查数据有效性
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if np.isnan(node_pred).any() or np.isnan(node_true).any():
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pbar.update(1)
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continue
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# 取前500个时间步
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max_steps = min(500, len(node_pred))
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if max_steps <= 0:
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pbar.update(1)
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continue
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node_pred_500 = node_pred[:max_steps]
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node_true_500 = node_true[:max_steps]
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# 创建时间轴
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time_steps = np.arange(max_steps)
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# 绘制对比图
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plt.figure(figsize=(12, 6))
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plt.plot(time_steps, node_true_500, 'b-', label='True Values', linewidth=2, alpha=0.8)
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plt.plot(time_steps, node_pred_500, 'r-', label='Predictions', linewidth=2, alpha=0.8)
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plt.xlabel('Time Steps')
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plt.ylabel('Values')
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plt.title(f'Node {node_id + 1}: True vs Predicted Values (First {max_steps} Time Steps)')
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plt.legend()
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plt.grid(True, alpha=0.3)
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# 保存图片，使用不同的命名
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save_path = os.path.join(pic_dir, f'node{node_id + 1:02d}_prediction_first500.png')
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plt.savefig(save_path, dpi=300, bbox_inches='tight')
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plt.close()
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pbar.update(1)
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# 生成所有节点的对比图（前100个时间步，便于观察）
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# 选择前100个时间步
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plot_steps = min(100, y_pred.shape[0])
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if plot_steps <= 0:
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return
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# 创建子图
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fig, axes = plt.subplots(2, 5, figsize=(20, 8))
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axes = axes.flatten()
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for node_id in range(num_nodes_to_plot):
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if len(y_pred.shape) > 2 and y_pred.shape[-2] > node_id:
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# 数据格式: [time_step, seq_len, num_node, dim]
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node_pred = y_pred[:plot_steps, 0, node_id, 0].cpu().numpy()
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node_true = y_true[:plot_steps, 0, node_id, 0].cpu().numpy()
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else:
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# 如果数据不足10个节点，只处理实际存在的节点
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if node_id >= y_pred.shape[-2]:
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axes[node_id].text(0.5, 0.5, f'Node {node_id + 1}\nNo Data',
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ha='center', va='center', transform=axes[node_id].transAxes)
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continue
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else:
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node_pred = y_pred[:plot_steps, 0, node_id, 0].cpu().numpy()
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node_true = y_true[:plot_steps, 0, node_id, 0].cpu().numpy()
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# 检查数据有效性
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if np.isnan(node_pred).any() or np.isnan(node_true).any():
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axes[node_id].text(0.5, 0.5, f'Node {node_id + 1}\nNo Data',
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ha='center', va='center', transform=axes[node_id].transAxes)
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continue
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time_steps = np.arange(plot_steps)
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axes[node_id].plot(time_steps, node_true, 'b-', label='True', linewidth=1.5, alpha=0.8)
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axes[node_id].plot(time_steps, node_pred, 'r-', label='Pred', linewidth=1.5, alpha=0.8)
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axes[node_id].set_title(f'Node {node_id + 1}')
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axes[node_id].grid(True, alpha=0.3)
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axes[node_id].legend(fontsize=8)
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if node_id >= 5: # 下面一行添加x轴标签
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axes[node_id].set_xlabel('Time Steps')
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if node_id % 5 == 0: # 左边一列添加y轴标签
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axes[node_id].set_ylabel('Values')
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plt.tight_layout()
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summary_path = os.path.join(pic_dir, 'all_nodes_summary.png')
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plt.savefig(summary_path, dpi=300, bbox_inches='tight')
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plt.close()
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@staticmethod
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def _generate_input_output_comparison(y_pred, y_true, data_loader, logger, save_dir,
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target_node=1, num_samples=10, scalers=None):
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"""
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生成输入-输出样本比较图
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Args:
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y_pred: 预测值
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y_true: 真实值
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data_loader: 数据加载器，用于获取输入数据
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logger: 日志记录器
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save_dir: 保存目录
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target_node: 目标节点ID（从1开始）
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num_samples: 要比较的样本数量
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scalers: 标准化器列表，用于反归一化输入数据
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"""
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import matplotlib.pyplot as plt
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import numpy as np
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import os
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import matplotlib
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from tqdm import tqdm
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# 设置matplotlib配置
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matplotlib.set_loglevel('error')
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plt.rcParams['font.family'] = 'DejaVu Sans'
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# 创建compare文件夹
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compare_dir = os.path.join(save_dir, 'pic', 'compare')
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os.makedirs(compare_dir, exist_ok=True)
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# 获取输入数据
|
||
input_data = []
|
||
for batch_idx, (data, target) in enumerate(data_loader):
|
||
if batch_idx >= num_samples:
|
||
break
|
||
input_data.append(data.cpu().numpy())
|
||
|
||
if not input_data:
|
||
return
|
||
|
||
# 获取目标节点的索引（从0开始）
|
||
node_idx = target_node - 1
|
||
|
||
# 检查节点索引是否有效
|
||
if node_idx >= y_pred.shape[-2]:
|
||
return
|
||
|
||
# 为每个样本生成比较图
|
||
with tqdm(total=min(num_samples, len(input_data)), desc="Generating input-output comparisons") as pbar:
|
||
for sample_idx in range(min(num_samples, len(input_data))):
|
||
# 获取输入序列（假设输入形状为 [batch, seq_len, nodes, features]）
|
||
input_seq = input_data[sample_idx][0, :, node_idx, 0] # 第一个batch，所有时间步，目标节点，第一个特征
|
||
|
||
# 对输入数据进行反归一化
|
||
if scalers is not None and len(scalers) > 0:
|
||
# 使用第一个标准化器对输入进行反归一化（假设输入特征使用第一个标准化器）
|
||
input_seq = scalers[0].inverse_transform(input_seq.reshape(-1, 1)).flatten()
|
||
|
||
# 获取对应的预测值和真实值
|
||
pred_seq = y_pred[sample_idx, :, node_idx, 0].cpu().numpy() # 所有horizon，目标节点，第一个特征
|
||
true_seq = y_true[sample_idx, :, node_idx, 0].cpu().numpy()
|
||
|
||
# 检查数据有效性
|
||
if (np.isnan(input_seq).any() or np.isnan(pred_seq).any() or np.isnan(true_seq).any()):
|
||
pbar.update(1)
|
||
continue
|
||
|
||
# 创建时间轴 - 输入和输出连续
|
||
total_time = np.arange(len(input_seq) + len(pred_seq))
|
||
|
||
# 创建合并的图形 - 输入和输出在同一个图中
|
||
plt.figure(figsize=(14, 8))
|
||
|
||
# 绘制完整的真实值曲线（输入 + 真实输出）
|
||
true_combined = np.concatenate([input_seq, true_seq])
|
||
plt.plot(total_time, true_combined, 'b', label='True Values (Input + Output)',
|
||
linewidth=2.5, alpha=0.9, linestyle='-')
|
||
|
||
# 绘制预测值曲线（只绘制输出部分）
|
||
output_time = np.arange(len(input_seq), len(input_seq) + len(pred_seq))
|
||
plt.plot(output_time, pred_seq, 'r', label='Predicted Values',
|
||
linewidth=2, alpha=0.8, linestyle='-')
|
||
|
||
# 添加垂直线分隔输入和输出
|
||
plt.axvline(x=len(input_seq)-0.5, color='gray', linestyle=':', alpha=0.7,
|
||
label='Input/Output Boundary')
|
||
|
||
# 设置图形属性
|
||
plt.xlabel('Time Steps')
|
||
plt.ylabel('Values')
|
||
plt.title(f'Sample {sample_idx + 1}: Input-Output Comparison (Node {target_node})')
|
||
plt.legend()
|
||
plt.grid(True, alpha=0.3)
|
||
|
||
# 调整布局
|
||
plt.tight_layout()
|
||
|
||
# 保存图片
|
||
save_path = os.path.join(compare_dir, f'sample{sample_idx + 1:02d}_node{target_node:02d}_comparison.png')
|
||
plt.savefig(save_path, dpi=300, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
pbar.update(1)
|
||
|
||
# 生成汇总图（所有样本的预测值对比）
|
||
|
||
fig, axes = plt.subplots(2, 5, figsize=(20, 8))
|
||
axes = axes.flatten()
|
||
|
||
for sample_idx in range(min(num_samples, len(input_data))):
|
||
if sample_idx >= 10: # 最多显示10个子图
|
||
break
|
||
|
||
ax = axes[sample_idx]
|
||
|
||
# 获取输入序列和预测值、真实值
|
||
input_seq = input_data[sample_idx][0, :, node_idx, 0]
|
||
if scalers is not None and len(scalers) > 0:
|
||
input_seq = scalers[0].inverse_transform(input_seq.reshape(-1, 1)).flatten()
|
||
|
||
pred_seq = y_pred[sample_idx, :, node_idx, 0].cpu().numpy()
|
||
true_seq = y_true[sample_idx, :, node_idx, 0].cpu().numpy()
|
||
|
||
# 检查数据有效性
|
||
if np.isnan(input_seq).any() or np.isnan(pred_seq).any() or np.isnan(true_seq).any():
|
||
ax.text(0.5, 0.5, f'Sample {sample_idx + 1}\nNo Data',
|
||
ha='center', va='center', transform=ax.transAxes)
|
||
continue
|
||
|
||
# 绘制对比图 - 输入和输出连续显示
|
||
total_time = np.arange(len(input_seq) + len(pred_seq))
|
||
true_combined = np.concatenate([input_seq, true_seq])
|
||
output_time = np.arange(len(input_seq), len(input_seq) + len(pred_seq))
|
||
|
||
ax.plot(total_time, true_combined, 'b', label='True', linewidth=2, alpha=0.9, linestyle='-')
|
||
ax.plot(output_time, pred_seq, 'r', label='Pred', linewidth=1.5, alpha=0.8, linestyle='-')
|
||
ax.axvline(x=len(input_seq)-0.5, color='gray', linestyle=':', alpha=0.5)
|
||
ax.set_title(f'Sample {sample_idx + 1}')
|
||
ax.grid(True, alpha=0.3)
|
||
ax.legend(fontsize=8)
|
||
|
||
if sample_idx >= 5: # 下面一行添加x轴标签
|
||
ax.set_xlabel('Time Steps')
|
||
if sample_idx % 5 == 0: # 左边一列添加y轴标签
|
||
ax.set_ylabel('Values')
|
||
|
||
# 隐藏多余的子图
|
||
for i in range(min(num_samples, len(input_data)), 10):
|
||
axes[i].set_visible(False)
|
||
|
||
plt.tight_layout()
|
||
summary_path = os.path.join(compare_dir, f'all_samples_node{target_node:02d}_summary.png')
|
||
plt.savefig(summary_path, dpi=300, bbox_inches='tight')
|
||
plt.close()
|
||
|
||
def _save_nfe_data(self):
|
||
"""保存nfe数据到文件"""
|
||
if not self.res:
|
||
return
|
||
|
||
res = pd.concat(self.res, keys=self.keys)
|
||
res.index.names = ['epoch', 'iter']
|
||
|
||
# 获取模型配置参数
|
||
filter_type = getattr(self.model, 'filter_type', 'unknown')
|
||
atol = getattr(self.model, 'atol', 1e-5)
|
||
rtol = getattr(self.model, 'rtol', 1e-5)
|
||
|
||
# 保存nfe数据
|
||
nfe_file = os.path.join(
|
||
self.logger.dir_path,
|
||
'nfe_{}_a{}_r{}.pkl'.format(filter_type, int(atol*1e5), int(rtol*1e5)))
|
||
res.to_pickle(nfe_file)
|
||
self.logger.logger.info(f"NFE data saved to {nfe_file}")
|
||
|
||
@staticmethod
|
||
def _compute_sampling_threshold(global_step, k):
|
||
return k / (k + math.exp(global_step / k))
|