mirror of
https://github.com/wassname/ETSformer.git
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wassname
241 lines
8.9 KiB
Python
241 lines
8.9 KiB
Python
from models import ETSformer
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from data_provider.data_factory import data_provider
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from exp.exp_basic import Exp_Basic
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from utils.tools import EarlyStopping, adjust_learning_rate
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from utils.metrics import metric
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from utils.Adam import Adam
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import numpy as np
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import torch
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import torch.nn as nn
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import os
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import time
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import warnings
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import numpy as np
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warnings.filterwarnings('ignore')
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class Exp_Main(Exp_Basic):
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def __init__(self, args):
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super(Exp_Main, self).__init__(args)
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def _build_model(self):
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model_dict = {
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'ETSformer': ETSformer,
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}
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model = model_dict[self.args.model](self.args).float()
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if self.args.use_multi_gpu and self.args.use_gpu:
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model = nn.DataParallel(model, device_ids=self.args.device_ids)
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return model
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def _get_data(self, flag):
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data_set, data_loader = data_provider(self.args, flag)
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return data_set, data_loader
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def _select_optimizer(self):
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if 'warmup' in self.args.lradj:
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lr = self.args.min_lr
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else:
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lr = self.args.learning_rate
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if self.args.smoothing_learning_rate > 0:
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smoothing_lr = self.args.smoothing_learning_rate
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else:
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smoothing_lr = 100 * self.args.learning_rate
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if self.args.damping_learning_rate > 0:
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damping_lr = self.args.damping_learning_rate
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else:
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damping_lr = 100 * self.args.learning_rate
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nn_params = []
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smoothing_params = []
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damping_params = []
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for k, v in self.model.named_parameters():
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if k[-len('_smoothing_weight'):] == '_smoothing_weight':
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smoothing_params.append(v)
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elif k[-len('_damping_factor'):] == '_damping_factor':
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damping_params.append(v)
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else:
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nn_params.append(v)
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model_optim = Adam([
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{'params': nn_params, 'lr': lr, 'name': 'nn'},
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{'params': smoothing_params, 'lr': smoothing_lr, 'name': 'smoothing'},
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{'params': damping_params, 'lr': damping_lr, 'name': 'damping'},
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])
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return model_optim
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def _select_criterion(self):
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criterion = nn.MSELoss()
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return criterion
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def vali(self, vali_data, vali_loader, criterion):
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total_loss = []
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self.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 enumerate(vali_loader):
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batch_x = batch_x.float().to(self.device)
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batch_y = batch_y.float()
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batch_x_mark = batch_x_mark.float().to(self.device)
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batch_y_mark = batch_y_mark.float().to(self.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
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dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
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# encoder - decoder
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outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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f_dim = -1 if self.args.features == 'MS' else 0
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batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
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pred = outputs.detach().cpu()
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true = batch_y.detach().cpu()
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loss = criterion(pred, true)
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total_loss.append(loss)
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total_loss = np.average(total_loss)
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self.model.train()
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return total_loss
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def train(self, setting):
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train_data, train_loader = self._get_data(flag='train')
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vali_data, vali_loader = self._get_data(flag='val')
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test_data, test_loader = self._get_data(flag='test')
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path = os.path.join(self.args.checkpoints, setting)
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if not os.path.exists(path):
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os.makedirs(path)
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time_now = time.time()
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train_steps = len(train_loader)
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early_stopping = EarlyStopping(patience=self.args.patience, verbose=True)
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model_optim = self._select_optimizer()
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criterion = self._select_criterion()
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for epoch in range(self.args.train_epochs):
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iter_count = 0
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train_loss = []
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self.model.train()
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epoch_time = time.time()
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for i, (batch_x, batch_y, batch_x_mark, batch_y_mark) in enumerate(train_loader):
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iter_count += 1
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model_optim.zero_grad()
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batch_x = batch_x.float().to(self.device)
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batch_y = batch_y.float().to(self.device)
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batch_x_mark = batch_x_mark.float().to(self.device)
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batch_y_mark = batch_y_mark.float().to(self.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
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dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
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# encoder - decoder
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outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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f_dim = -1 if self.args.features == 'MS' else 0
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batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
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loss = criterion(outputs, batch_y)
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train_loss.append(loss.item())
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if (i + 1) % 100 == 0:
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print("\titers: {0}, epoch: {1} | loss: {2:.7f}".format(i + 1, epoch + 1, loss.item()))
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speed = (time.time() - time_now) / iter_count
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left_time = speed * ((self.args.train_epochs - epoch) * train_steps - i)
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print('\tspeed: {:.4f}s/iter; left time: {:.4f}s'.format(speed, left_time))
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iter_count = 0
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time_now = time.time()
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loss.backward()
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torch.nn.utils.clip_grad_norm(self.model.parameters(), 1.0)
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model_optim.step()
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print("Epoch: {} cost time: {}".format(epoch + 1, time.time() - epoch_time))
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train_loss = np.average(train_loss)
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vali_loss = self.vali(vali_data, vali_loader, criterion)
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test_loss = self.vali(test_data, test_loader, criterion)
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print("Epoch: {0}, Steps: {1} | Train Loss: {2:.7f} Vali Loss: {3:.7f} Test Loss: {4:.7f}".format(
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epoch + 1, train_steps, train_loss, vali_loss, test_loss))
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early_stopping(vali_loss, self.model, path)
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if early_stopping.early_stop:
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print("Early stopping")
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break
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adjust_learning_rate(model_optim, epoch + 1, self.args)
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best_model_path = path + '/' + 'checkpoint.pth'
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self.model.load_state_dict(torch.load(best_model_path))
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return self.model
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def test(self, setting, data, save_vals=False):
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"""data - 'val' or 'test' """
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test_data, test_loader = self._get_data(flag=data)
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print('loading model')
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self.model.load_state_dict(torch.load(os.path.join('./checkpoints/' + setting, 'checkpoint.pth')))
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preds = []
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trues = []
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self.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 enumerate(test_loader):
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batch_x = batch_x.float().to(self.device)
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batch_y = batch_y.float().to(self.device)
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batch_x_mark = batch_x_mark.float().to(self.device)
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batch_y_mark = batch_y_mark.float().to(self.device)
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# decoder input
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dec_inp = torch.zeros_like(batch_y[:, -self.args.pred_len:, :]).float()
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dec_inp = torch.cat([batch_y[:, :self.args.label_len, :], dec_inp], dim=1).float().to(self.device)
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# encoder - decoder
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outputs = self.model(batch_x, batch_x_mark, dec_inp, batch_y_mark)
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f_dim = -1 if self.args.features == 'MS' else 0
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outputs = outputs[:, -self.args.pred_len:, f_dim:]
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batch_y = batch_y[:, -self.args.pred_len:, f_dim:].to(self.device)
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outputs = outputs.detach().cpu().numpy()
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batch_y = batch_y.detach().cpu().numpy()
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pred = outputs
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true = batch_y
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preds.append(pred)
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trues.append(true)
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preds = np.array(preds)
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trues = np.array(trues)
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print('test shape:', preds.shape, trues.shape)
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preds = preds.reshape(-1, preds.shape[-2], preds.shape[-1])
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trues = trues.reshape(-1, trues.shape[-2], trues.shape[-1])
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print('test shape:', preds.shape, trues.shape)
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# result save
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folder_path = './results/' + setting + '/'
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if not os.path.exists(folder_path):
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os.makedirs(folder_path)
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mae, mse, rmse, mape, mspe = metric(preds, trues)
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print('mse:{}, mae:{}'.format(mse, mae))
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np.save(folder_path + f'{data}_metrics.npy', np.array([mae, mse, rmse, mape, mspe]))
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if save_vals:
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np.save(folder_path + 'pred.npy', preds)
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np.save(folder_path + 'true.npy', trues)
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return preds, trues
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