mirror of
https://github.com/wassname/DeepTime.git
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wassname
227 lines
8.0 KiB
Python
227 lines
8.0 KiB
Python
import os
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from os.path import join
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import math
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import logging
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from typing import Callable, Optional, Union, Dict, Tuple
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import gin
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from fire import Fire
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import numpy as np
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import torch
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from torch.utils.data import DataLoader
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from torch import optim
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from torch import nn
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from experiments.base import Experiment
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from data.datasets import ForecastDataset
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from models import get_model
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from utils.checkpoint import Checkpoint
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from utils.ops import default_device, to_tensor
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from utils.losses import get_loss_fn
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from utils.metrics import calc_metrics
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class ForecastExperiment(Experiment):
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@gin.configurable()
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def instance(self,
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model_type: str,
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save_vals: Optional[bool] = True,):
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# load datasets, model, checkpointer
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train_set, train_loader = get_data(flag='train')
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val_set, val_loader = get_data(flag='val')
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test_set, test_loader = get_data(flag='test')
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model = get_model(model_type,
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dim_size=train_set.data_x.shape[1],
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datetime_feats=train_set.timestamps.shape[-1]).to(default_device())
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checkpoint = Checkpoint(self.root)
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# train forecasting task
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model = train(model, checkpoint, train_loader, val_loader, test_loader)
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# testing
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val_metrics = validate(model, loader=val_loader, report_metrics=True)
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test_metrics = validate(model, loader=test_loader, report_metrics=True,
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save_path=self.root if save_vals else None)
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np.save(join(self.root, 'metrics.npy'), {'val': val_metrics, 'test': test_metrics})
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val_metrics = {f'ValMetric/{k}': v for k, v in val_metrics.items()}
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test_metrics = {f'TestMetric/{k}': v for k, v in test_metrics.items()}
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checkpoint.close({**val_metrics, **test_metrics})
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@gin.configurable()
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def get_optimizer(model: nn.Module,
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lr: Optional[float] = 1e-3,
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lambda_lr: Optional[float] = 1.,
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weight_decay: Optional[float] = 1e-2) -> optim.Optimizer:
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group1 = [] # lambda
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group2 = [] # no decay
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group3 = [] # decay
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no_decay_list = ('bias', 'norm',)
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for param_name, param in model.named_parameters():
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if '_lambda' in param_name:
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group1.append(param)
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elif any([mod in param_name for mod in no_decay_list]):
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group2.append(param)
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else:
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group3.append(param)
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optimizer = optim.Adam([
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{'params': group1, 'weight_decay': 0, 'lr': lambda_lr, 'scheduler': 'cosine_annealing'},
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{'params': group2, 'weight_decay': 0, 'scheduler': 'cosine_annealing_with_linear_warmup'},
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{'params': group3, 'scheduler': 'cosine_annealing_with_linear_warmup'}
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], lr=lr, weight_decay=weight_decay)
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return optimizer
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@gin.configurable()
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def get_scheduler(optimizer: optim.Optimizer,
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T_max: int,
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warmup_epochs: int,
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eta_min: Optional[float] = 0.) -> optim.lr_scheduler.LambdaLR:
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scheduler_fns = []
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for param_group in optimizer.param_groups:
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scheduler = param_group['scheduler']
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if scheduler == 'none':
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fn = lambda T_cur: 1
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elif scheduler == 'cosine_annealing':
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lr = eta_max = param_group['lr']
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fn = lambda T_cur: (eta_min + 0.5 * (eta_max - eta_min) * (
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1.0 + math.cos((T_cur - warmup_epochs) / (T_max - warmup_epochs) * math.pi))) / lr
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elif scheduler == 'cosine_annealing_with_linear_warmup':
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lr = eta_max = param_group['lr']
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# https://blog.csdn.net/qq_36560894/article/details/114004799
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fn = lambda T_cur: T_cur / warmup_epochs if T_cur < warmup_epochs else (eta_min + 0.5 * (
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eta_max - eta_min) * (1.0 + math.cos(
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(T_cur - warmup_epochs) / (T_max - warmup_epochs) * math.pi))) / lr
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else:
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raise ValueError(f'No such scheduler, {scheduler}')
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scheduler_fns.append(fn)
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scheduler = optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=scheduler_fns)
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return scheduler
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@gin.configurable()
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def get_data(flag: bool,
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batch_size: int) -> Tuple[ForecastDataset, DataLoader]:
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if flag in ('val', 'test'):
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shuffle = False
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drop_last = False
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elif flag == 'train':
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shuffle = True
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drop_last = True
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else:
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raise ValueError(f'no such flag {flag}')
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dataset = ForecastDataset(flag)
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data_loader = DataLoader(dataset,
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batch_size=batch_size,
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shuffle=shuffle,
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drop_last=drop_last)
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return dataset, data_loader
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@gin.configurable()
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def train(model: nn.Module,
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checkpoint: Checkpoint,
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train_loader: DataLoader,
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val_loader: DataLoader,
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test_loader: DataLoader,
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loss_name: str,
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epochs: int,
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clip: float) -> nn.Module:
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optimizer = get_optimizer(model)
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scheduler = get_scheduler(optimizer=optimizer, T_max=epochs)
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training_loss_fn = get_loss_fn(loss_name)
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for epoch in range(epochs):
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train_loss = []
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model.train()
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for it, data in enumerate(train_loader):
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optimizer.zero_grad()
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x, y, x_time, y_time = map(to_tensor, data)
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forecast = model(x, x_time, y_time)
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if isinstance(forecast, tuple):
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# for models which require reconstruction + forecast loss
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loss = training_loss_fn(forecast[0], x) + \
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training_loss_fn(forecast[1], y)
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else:
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loss = training_loss_fn(forecast, y)
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loss.backward()
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nn.utils.clip_grad_norm_(model.parameters(), clip)
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optimizer.step()
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train_loss.append(loss.item())
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if (it + 1) % 100 == 0:
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logging.info(f"epochs: {epoch + 1}, iters: {it + 1} | training loss: {loss.item():.2f}")
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scheduler.step()
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train_loss = np.average(train_loss)
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val_loss = validate(model, loader=val_loader, loss_fn=training_loss_fn)
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test_loss = validate(model, loader=test_loader, loss_fn=training_loss_fn)
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scalars = {'Loss/Train': train_loss,
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'Loss/Val': val_loss,
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'Loss/Test': test_loss}
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checkpoint(epoch + 1, model, scalars=scalars)
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if checkpoint.early_stop:
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logging.info("Early stopping")
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break
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if epochs > 0:
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model.load_state_dict(torch.load(checkpoint.model_path))
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return model
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@torch.no_grad()
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def validate(model: nn.Module,
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loader: DataLoader,
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loss_fn: Optional[Callable] = None,
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report_metrics: Optional[bool] = False,
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save_path: Optional[str] = None) -> Union[Dict[str, float], float]:
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model.eval()
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preds = []
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trues = []
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inps = []
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total_loss = []
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for it, data in enumerate(loader):
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x, y, x_time, y_time = map(to_tensor, data)
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if x.shape[0] == 1:
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# skip final batch if batch_size == 1
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# due to bug in torch.linalg.solve which raises error when batch_size == 1
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continue
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forecast = model(x, x_time, y_time)
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if report_metrics:
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preds.append(forecast)
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trues.append(y)
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if save_path is not None:
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inps.append(x)
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else:
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loss = loss_fn(forecast, y, reduction='none')
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total_loss.append(loss)
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if report_metrics:
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preds = torch.cat(preds, dim=0).detach().cpu().numpy()
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trues = torch.cat(trues, dim=0).detach().cpu().numpy()
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if save_path is not None:
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inps = torch.cat(inps, dim=0).detach().cpu().numpy()
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np.save(join(save_path, 'inps.npy'), inps)
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np.save(join(save_path, 'preds.npy'), preds)
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np.save(join(save_path, 'trues.npy'), trues)
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metrics = calc_metrics(preds, trues)
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return metrics
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total_loss = torch.cat(total_loss, dim=0).cpu()
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return np.average(total_loss)
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if __name__ == '__main__':
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logging.root.setLevel(logging.INFO)
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Fire(ForecastExperiment)
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