175 lines
6.9 KiB
Python
Executable File
175 lines
6.9 KiB
Python
Executable File
import math
|
|
import os
|
|
import time
|
|
import copy
|
|
from tqdm import tqdm
|
|
|
|
import torch
|
|
from lib.logger import get_logger
|
|
from lib.loss_function import all_metrics
|
|
|
|
|
|
class Trainer:
|
|
def __init__(self, model, loss, optimizer, train_loader, val_loader, test_loader,
|
|
scaler, args, lr_scheduler=None):
|
|
self.model = model
|
|
self.loss = loss
|
|
self.optimizer = optimizer
|
|
self.train_loader = train_loader
|
|
self.val_loader = val_loader
|
|
self.test_loader = test_loader
|
|
self.scaler = scaler
|
|
self.args = args
|
|
self.lr_scheduler = lr_scheduler
|
|
self.train_per_epoch = len(train_loader)
|
|
self.val_per_epoch = len(val_loader) if val_loader else 0
|
|
self.batches_seen = 0
|
|
|
|
# Paths for saving models and logs
|
|
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')
|
|
|
|
# Initialize logger
|
|
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 _run_epoch(self, epoch, dataloader, mode):
|
|
if mode == 'train':
|
|
self.model.train()
|
|
optimizer_step = True
|
|
else:
|
|
self.model.eval()
|
|
optimizer_step = False
|
|
|
|
total_loss = 0
|
|
epoch_time = time.time()
|
|
|
|
with torch.set_grad_enabled(optimizer_step):
|
|
with tqdm(total=len(dataloader), desc=f'{mode.capitalize()} Epoch {epoch}') as pbar:
|
|
for batch_idx, (data, target) in enumerate(dataloader):
|
|
self.batches_seen += 1
|
|
label = target[..., :self.args['output_dim']].clone()
|
|
output = self.model(data, target, self.batches_seen).to(self.args['device'])
|
|
|
|
if self.args['real_value']:
|
|
output = self.scaler.inverse_transform(output)
|
|
|
|
loss = self.loss(output, label)
|
|
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()
|
|
|
|
total_loss += loss.item()
|
|
|
|
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: {loss.item():.6f}')
|
|
|
|
# 更新 tqdm 的进度
|
|
pbar.update(1)
|
|
pbar.set_postfix(loss=loss.item())
|
|
|
|
avg_loss = total_loss / len(dataloader)
|
|
self.logger.info(
|
|
f'{mode.capitalize()} Epoch {epoch}: average Loss: {avg_loss:.6f}, time: {time.time() - epoch_time:.2f} s')
|
|
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):
|
|
best_model, best_test_model = None, None
|
|
best_loss, best_test_loss = float('inf'), float('inf')
|
|
not_improved_count = 0
|
|
|
|
self.logger.info("Training process started")
|
|
for epoch in range(1, self.args['epochs'] + 1):
|
|
train_epoch_loss = self.train_epoch(epoch)
|
|
val_epoch_loss = self.val_epoch(epoch)
|
|
test_epoch_loss = self.test_epoch(epoch)
|
|
|
|
if train_epoch_loss > 1e6:
|
|
self.logger.warning('Gradient explosion detected. Ending...')
|
|
break
|
|
|
|
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
|
|
|
|
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.")
|
|
break
|
|
|
|
if test_epoch_loss < best_test_loss:
|
|
best_test_loss = test_epoch_loss
|
|
best_test_model = copy.deepcopy(self.model.state_dict())
|
|
|
|
if not self.args['debug']:
|
|
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}")
|
|
|
|
self._finalize_training(best_model, best_test_model)
|
|
|
|
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):
|
|
if path:
|
|
checkpoint = torch.load(path)
|
|
model.load_state_dict(checkpoint['state_dict'])
|
|
model.to(args['device'])
|
|
|
|
model.eval()
|
|
y_pred, y_true = [], []
|
|
|
|
with torch.no_grad():
|
|
for data, target in data_loader:
|
|
label = target[..., :args['output_dim']].clone()
|
|
output = model(data, target)
|
|
y_pred.append(output)
|
|
y_true.append(label)
|
|
|
|
if args['real_value']:
|
|
y_pred = scaler.inverse_transform(torch.cat(y_pred, dim=0))
|
|
else:
|
|
y_pred = torch.cat(y_pred, dim=0)
|
|
y_true = torch.cat(y_true, dim=0)
|
|
|
|
for t in range(y_true.shape[1]):
|
|
mae, rmse, mape = all_metrics(y_pred[:, t, ...], 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}")
|
|
|
|
mae, rmse, mape = all_metrics(y_pred, 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))
|