| r""" Helper functions """ | |
| import random | |
| import torch | |
| import numpy as np | |
| def fix_randseed(seed): | |
| r""" Set random seeds for reproducibility """ | |
| if seed is None: | |
| seed = int(random.random() * 1e5) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| torch.backends.cudnn.benchmark = False | |
| torch.backends.cudnn.deterministic = True | |
| def mean(x): | |
| return sum(x) / len(x) if len(x) > 0 else 0.0 | |
| def to_cuda(batch): | |
| for key, value in batch.items(): | |
| if isinstance(value, torch.Tensor): | |
| batch[key] = value.cuda() | |
| return batch | |
| def to_cpu(tensor): | |
| return tensor.detach().clone().cpu() | |