230 lines
8.1 KiB
Python
Executable File
230 lines
8.1 KiB
Python
Executable File
import math
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import os
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import time
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import copy
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from tqdm import tqdm
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import torch
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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 lib.training_stats import TrainingStats
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class Trainer:
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def __init__(
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self,
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model,
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loss,
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optimizer,
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train_loader,
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val_loader,
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test_loader,
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scaler,
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args,
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lr_scheduler=None,
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):
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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
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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(args["log_dir"], "best_model.pth")
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self.best_test_path = os.path.join(args["log_dir"], "best_test_model.pth")
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self.loss_figure_path = os.path.join(args["log_dir"], "loss.png")
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# Initialize logger
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if not os.path.isdir(args["log_dir"]) and not args["debug"]:
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os.makedirs(args["log_dir"], exist_ok=True)
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self.logger = get_logger(
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args["log_dir"], name=self.model.__class__.__name__, debug=args["debug"]
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)
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self.logger.info(f"Experiment log path in: {args['log_dir']}")
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# Stats tracker
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self.stats = TrainingStats(device=args["device"])
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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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with torch.set_grad_enabled(optimizer_step):
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with tqdm(
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total=len(dataloader), desc=f"{mode.capitalize()} Epoch {epoch}"
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) as pbar:
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for batch_idx, (data, target) in enumerate(dataloader):
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start_time = time.time()
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label = target[..., : self.args["output_dim"]]
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output = self.model(data).to(self.args["device"])
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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_(
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self.model.parameters(), self.args["max_grad_norm"]
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)
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self.optimizer.step()
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step_time = time.time() - start_time
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self.stats.record_step_time(step_time, mode)
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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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)
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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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)
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# 记录内存
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self.stats.record_memory_usage()
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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.stats.start_training()
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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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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 (
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self.args["early_stop"]
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and not_improved_count == self.args["early_stop_patience"]
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):
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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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)
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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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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(
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f"Best models saved at {self.best_path} and {self.best_test_path}"
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)
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# 输出统计与参数
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self.stats.end_training()
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self.stats.report(self.logger)
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try:
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total_params = sum(
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p.numel() for p in self.model.parameters() if p.requires_grad
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)
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self.logger.info(f"Trainable params: {total_params}")
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except Exception:
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pass
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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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@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/PEMS08/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(
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y_pred[:, t, ...],
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y_true[:, t, ...],
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args["mae_thresh"],
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args["mape_thresh"],
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)
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logger.info(
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f"Horizon {t + 1:02d}, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}"
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)
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mae, rmse, mape = all_metrics(
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y_pred, y_true, args["mae_thresh"], args["mape_thresh"]
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)
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logger.info(
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f"Average Horizon, MAE: {mae:.4f}, RMSE: {rmse:.4f}, MAPE: {mape:.4f}"
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)
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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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