262 lines
11 KiB
Python
262 lines
11 KiB
Python
import math
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import os
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import sys
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import time
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import copy
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import torch.nn.functional as F
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import torch
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from torch import nn
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from tqdm import tqdm
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from lib.logger import get_logger
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from lib.loss_function import all_metrics
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from model.STMLP.STMLP import STMLP
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class Trainer:
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def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader,
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scaler, args, 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.scaler = scaler
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self.args = args['train']
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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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# Paths for saving models and logs
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self.best_path = os.path.join(self.args['log_dir'], 'best_model.pth')
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self.best_test_path = os.path.join(self.args['log_dir'], 'best_test_model.pth')
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self.loss_figure_path = os.path.join(self.args['log_dir'], 'loss.png')
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self.pretrain_dir = f'./pre-train/{args["model"]["type"]}/{args["data"]["type"]}'
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self.pretrain_path = os.path.join(self.pretrain_dir, 'best_model.pth')
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self.pretrain_best_path = os.path.join(self.pretrain_dir, 'best_test_model.pth')
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# Initialize logger
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if not os.path.isdir(self.args['log_dir']) and not self.args['debug']:
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os.makedirs(self.args['log_dir'], exist_ok=True)
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if not os.path.isdir(self.pretrain_dir) and not self.args['debug']:
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os.makedirs(self.pretrain_dir, exist_ok=True)
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self.logger = get_logger(self.args['log_dir'], name=self.model.__class__.__name__, debug=self.args['debug'])
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self.logger.info(f"Experiment log path in: {self.args['log_dir']}")
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if self.args['teacher_stu']:
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self.tmodel = self.loadTeacher(args)
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else:
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self.logger.info(f"当前使用预训练模式，预训练后请移动教师模型到"
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f"./pre-train/{args['model']['type']}/{args['data']['type']}/best_model.pth"
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f"然后在config中配置train.teacher_stu模式为True开启蒸馏模式")
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def _run_epoch(self, epoch, dataloader, mode):
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# self.tmodel.eval()
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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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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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if self.args['teacher_stu']:
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label = target[..., :self.args['output_dim']]
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output, out_, _ = self.model(data)
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gout, tout, sout = self.tmodel(data)
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if self.args['real_value']:
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output = self.scaler.inverse_transform(output)
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loss1 = self.loss(output, label)
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scl = self.loss_cls(out_, sout)
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kl_loss = nn.KLDivLoss(reduction="batchmean", log_target=True).cuda()
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gout = F.log_softmax(gout, dim=-1).cuda()
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mlp_emb_ = F.log_softmax(output, dim=-1).cuda()
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tkloss = kl_loss(mlp_emb_.cuda().float(), gout.cuda().float())
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loss = loss1 + 10 * tkloss + 1 * scl
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else:
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label = target[..., :self.args['output_dim']]
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output, out_, _ = self.model(data)
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if self.args['real_value']:
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output = self.scaler.inverse_transform(output)
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loss = self.loss(output, label)
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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.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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return avg_loss
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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.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.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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torch.save(best_model, self.pretrain_path)
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self.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.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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torch.save(best_model, self.pretrain_best_path)
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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.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.info("Testing on best validation model")
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self.test(self.model, self.args, self.test_loader, self.scaler, self.logger)
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self.model.load_state_dict(best_test_model)
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self.logger.info("Testing on best test model")
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self.test(self.model, self.args, self.test_loader, self.scaler, self.logger)
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def loadTeacher(self, args):
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model_path = f'./pre-train/{args["model"]["type"]}/{args["data"]["type"]}/best_model.pth'
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try:
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# 尝试加载教师模型权重
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state_dict = torch.load(model_path)
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self.logger.info(f"成功加载教师模型权重: {model_path}")
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# 初始化并返回教师模型
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args['model']['model_type'] = 'teacher'
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tmodel = STMLP(args['model'])
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tmodel = tmodel.to(args['device'])
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tmodel.load_state_dict(state_dict, strict=False)
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return tmodel
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except FileNotFoundError:
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# 如果找不到权重文件，记录日志并修改 args
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self.logger.error(
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f"未找到教师模型权重文件: {model_path}。切换到预训练模式训练老师权重。\n"
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f"在预训练完成后，再次启动模型则为蒸馏模式")
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self.args['teacher_stu'] = False
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return None
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def loss_cls(self, x1, x2):
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temperature = 0.05
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x1 = F.normalize(x1, p=2, dim=-1)
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x2 = F.normalize(x2, p=2, dim=-1)
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weight = F.cosine_similarity(x1, x2, dim=-1)
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batch_size = x1.size()[0]
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# neg score
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out = torch.cat([x1, x2], dim=0)
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neg = torch.exp(torch.matmul(out, out.transpose(2, 3).contiguous()) / temperature)
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pos = torch.exp(torch.sum(x1 * x2, dim=-1) * weight / temperature)
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# pos = torch.exp(torch.sum(x1 * x2, dim=-1) / temperature)
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pos = torch.cat([pos, pos], dim=0).sum(dim=1)
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Ng = neg.sum(dim=-1).sum(dim=1)
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loss = (- torch.log(pos / (pos + Ng))).mean()
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return loss
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@staticmethod
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def test(model, args, data_loader, scaler, logger, path=None):
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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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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, _, _ = model(data)
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y_pred.append(output)
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y_true.append(label)
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if args['real_value']:
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y_pred = scaler.inverse_transform(torch.cat(y_pred, dim=0))
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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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# 你在这里需要把y_pred和y_true保存下来
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# torch.save(y_pred, "./test/PEMS07/y_pred_D.pt") # [3566,12,170,1]
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# torch.save(y_true, "./test/PEMSD8/y_true.pt") # [3566,12,170,1]
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for t in range(y_true.shape[1]):
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mae, rmse, mape = all_metrics(y_pred[:, t, ...], y_true[:, t, ...],
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args['mae_thresh'], args['mape_thresh'])
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logger.info(f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
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mae, rmse, mape = all_metrics(y_pred, y_true, args['mae_thresh'], args['mape_thresh'])
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logger.info(f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}")
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@staticmethod
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def _compute_sampling_threshold(global_step, k):
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return k / (k + math.exp(global_step / k))
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