| import numpy as np |
| import random |
| import torch |
|
|
|
|
| def set_seed(seed: int, deterministic: bool = False): |
| """ |
| Helper function for reproducible behavior to set the seed in `random`, `numpy`, `torch`. |
| |
| Args: |
| seed (`int`): |
| The seed to set. |
| deterministic (`bool`, *optional*, defaults to `False`): |
| Whether to use deterministic algorithms where available. Can slow down training. |
| """ |
| random.seed(seed) |
| np.random.seed(seed) |
| torch.manual_seed(seed) |
| torch.cuda.manual_seed_all(seed) |
|
|
| if deterministic: |
| torch.use_deterministic_algorithms(True) |
|
|
|
|
| def merge_dict_list(dict_list): |
| if len(dict_list) == 1: |
| return dict_list[0] |
|
|
| merged_dict = {} |
| for k, v in dict_list[0].items(): |
| if isinstance(v, torch.Tensor): |
| if v.ndim == 0: |
| merged_dict[k] = torch.stack([d[k] for d in dict_list], dim=0) |
| else: |
| merged_dict[k] = torch.cat([d[k] for d in dict_list], dim=0) |
| else: |
| |
| merged_dict[k] = v |
| return merged_dict |
|
|
