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| 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(seed) | |
| torch.cuda.manual_seed_all(seed) | |
| if deterministic: | |
| torch.use_deterministic_algorithms(True, warn_only=True) | |
| torch.backends.cudnn.benchmark = False | |
| 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: | |
| # for non-tensor values, we just copy the value from the first item | |
| merged_dict[k] = v | |
| return merged_dict | |
| def format_dict(dict_obj, indent: int = 4, indent_per_level: int = 4) -> str: | |
| # format a dict into string for one item per line | |
| formatted_str = "" | |
| for k, v in dict_obj.items(): | |
| if isinstance(v, dict): | |
| formatted_str += f"{' ' * indent}{k}:\n{format_dict(v, indent=indent+indent_per_level, indent_per_level=indent_per_level)}" | |
| else: | |
| formatted_str += f"{' ' * indent}{k}: {v}\n" | |
| formatted_str = "{\n" + formatted_str + " " * (indent - indent_per_level) + "}\n" | |
| return formatted_str | |