mirror of
https://github.com/KimMeen/Time-LLM.git
synced 2024-11-27 07:49:53 +08:00
233 lines
8.1 KiB
Python
233 lines
8.1 KiB
Python
import numpy as np
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import torch
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import matplotlib.pyplot as plt
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import shutil
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from tqdm import tqdm
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plt.switch_backend('agg')
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def adjust_learning_rate(accelerator, optimizer, scheduler, epoch, args, printout=True):
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if args.lradj == 'type1':
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lr_adjust = {epoch: args.learning_rate * (0.5 ** ((epoch - 1) // 1))}
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elif args.lradj == 'type2':
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lr_adjust = {
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2: 5e-5, 4: 1e-5, 6: 5e-6, 8: 1e-6,
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10: 5e-7, 15: 1e-7, 20: 5e-8
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}
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elif args.lradj == 'type3':
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lr_adjust = {epoch: args.learning_rate if epoch < 3 else args.learning_rate * (0.9 ** ((epoch - 3) // 1))}
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elif args.lradj == 'PEMS':
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lr_adjust = {epoch: args.learning_rate * (0.95 ** (epoch // 1))}
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elif args.lradj == 'TST':
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lr_adjust = {epoch: scheduler.get_last_lr()[0]}
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elif args.lradj == 'constant':
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lr_adjust = {epoch: args.learning_rate}
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if epoch in lr_adjust.keys():
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lr = lr_adjust[epoch]
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for param_group in optimizer.param_groups:
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param_group['lr'] = lr
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if printout:
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if accelerator is not None:
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accelerator.print('Updating learning rate to {}'.format(lr))
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else:
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print('Updating learning rate to {}'.format(lr))
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class EarlyStopping:
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def __init__(self, accelerator=None, patience=7, verbose=False, delta=0, save_mode=True):
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self.accelerator = accelerator
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self.patience = patience
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self.verbose = verbose
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self.counter = 0
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self.best_score = None
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self.early_stop = False
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self.val_loss_min = np.Inf
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self.delta = delta
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self.save_mode = save_mode
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def __call__(self, val_loss, model, path):
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score = -val_loss
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if self.best_score is None:
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self.best_score = score
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if self.save_mode:
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self.save_checkpoint(val_loss, model, path)
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elif score < self.best_score + self.delta:
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self.counter += 1
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if self.accelerator is None:
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print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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else:
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self.accelerator.print(f'EarlyStopping counter: {self.counter} out of {self.patience}')
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if self.counter >= self.patience:
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self.early_stop = True
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else:
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self.best_score = score
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if self.save_mode:
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self.save_checkpoint(val_loss, model, path)
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self.counter = 0
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def save_checkpoint(self, val_loss, model, path):
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if self.verbose:
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if self.accelerator is not None:
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self.accelerator.print(
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f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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else:
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print(
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f'Validation loss decreased ({self.val_loss_min:.6f} --> {val_loss:.6f}). Saving model ...')
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if self.accelerator is not None:
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model = self.accelerator.unwrap_model(model)
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torch.save(model.state_dict(), path + '/' + 'checkpoint')
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else:
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torch.save(model.state_dict(), path + '/' + 'checkpoint')
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self.val_loss_min = val_loss
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class dotdict(dict):
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"""dot.notation access to dictionary attributes"""
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__getattr__ = dict.get
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__setattr__ = dict.__setitem__
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__delattr__ = dict.__delitem__
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class StandardScaler():
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def __init__(self, mean, std):
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self.mean = mean
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self.std = std
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def transform(self, data):
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return (data - self.mean) / self.std
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def inverse_transform(self, data):
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return (data * self.std) + self.mean
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def adjustment(gt, pred):
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anomaly_state = False
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for i in range(len(gt)):
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if gt[i] == 1 and pred[i] == 1 and not anomaly_state:
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anomaly_state = True
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for j in range(i, 0, -1):
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if gt[j] == 0:
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break
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else:
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if pred[j] == 0:
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pred[j] = 1
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for j in range(i, len(gt)):
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if gt[j] == 0:
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break
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else:
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if pred[j] == 0:
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pred[j] = 1
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elif gt[i] == 0:
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anomaly_state = False
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if anomaly_state:
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pred[i] = 1
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return gt, pred
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def cal_accuracy(y_pred, y_true):
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return np.mean(y_pred == y_true)
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def del_files(dir_path):
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shutil.rmtree(dir_path)
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def vali(args, accelerator, model, vali_data, vali_loader, criterion, mae_metric):
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total_loss = []
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total_mae_loss = []
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model.eval()
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with torch.no_grad():
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in tqdm(enumerate(vali_loader)):
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batch_x = batch_x.float().to(accelerator.device)
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batch_y = batch_y.float()
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batch_x_mark = batch_x_mark.float().to(accelerator.device)
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batch_y_mark = batch_y_mark.float().to(accelerator.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -args.pred_len:, :]).float()
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dec_inp = torch.cat([batch_y[:, :args.label_len, :], dec_inp], dim=1).float().to(
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accelerator.device)
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# encoder - decoder
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if args.use_amp:
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with torch.cuda.amp.autocast():
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if args.output_attention:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
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else:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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else:
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if args.output_attention:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)[0]
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else:
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outputs = model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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outputs, batch_y = accelerator.gather_for_metrics((outputs, batch_y))
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f_dim = -1 if args.features == 'MS' else 0
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outputs = outputs[:, -args.pred_len:, f_dim:]
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batch_y = batch_y[:, -args.pred_len:, f_dim:].to(accelerator.device)
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pred = outputs.detach()
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true = batch_y.detach()
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loss = criterion(pred, true)
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mae_loss = mae_metric(pred, true)
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total_loss.append(loss.item())
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total_mae_loss.append(mae_loss.item())
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total_loss = np.average(total_loss)
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total_mae_loss = np.average(total_mae_loss)
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model.train()
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return total_loss, total_mae_loss
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def test(args, accelerator, model, train_loader, vali_loader, criterion):
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x, _ = train_loader.dataset.last_insample_window()
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y = vali_loader.dataset.timeseries
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x = torch.tensor(x, dtype=torch.float32).to(accelerator.device)
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x = x.unsqueeze(-1)
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model.eval()
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with torch.no_grad():
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B, _, C = x.shape
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dec_inp = torch.zeros((B, args.pred_len, C)).float().to(accelerator.device)
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dec_inp = torch.cat([x[:, -args.label_len:, :], dec_inp], dim=1)
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outputs = torch.zeros((B, args.pred_len, C)).float().to(accelerator.device)
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id_list = np.arange(0, B, args.eval_batch_size)
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id_list = np.append(id_list, B)
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for i in range(len(id_list) - 1):
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outputs[id_list[i]:id_list[i + 1], :, :] = model(
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x[id_list[i]:id_list[i + 1]],
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None,
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dec_inp[id_list[i]:id_list[i + 1]],
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None
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)
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accelerator.wait_for_everyone()
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outputs = accelerator.gather_for_metrics(outputs)
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f_dim = -1 if args.features == 'MS' else 0
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outputs = outputs[:, -args.pred_len:, f_dim:]
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pred = outputs
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true = torch.from_numpy(np.array(y)).to(accelerator.device)
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batch_y_mark = torch.ones(true.shape).to(accelerator.device)
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true = accelerator.gather_for_metrics(true)
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batch_y_mark = accelerator.gather_for_metrics(batch_y_mark)
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loss = criterion(x[:, :, 0], args.frequency_map, pred[:, :, 0], true, batch_y_mark)
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model.train()
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return loss
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def load_content(args):
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if 'ETT' in args.data:
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file = 'ETT'
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else:
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file = args.data
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with open('./dataset/prompt_bank/{0}.txt'.format(file), 'r') as f:
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content = f.read()
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return content |