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| # Provides methods to summarize the information of data, giving a brief overview in text. | |
| import torch | |
| import numpy as np | |
| from typing import Optional | |
| from .log import get_logger | |
| def look_tensor( | |
| x : torch.Tensor, | |
| prompt : Optional[str] = None, | |
| silent : bool = False, | |
| ): | |
| ''' | |
| Summarize the information of a tensor, including its shape, value range (min, max, mean, std), and dtype. | |
| Then return a string containing the information. | |
| ### Args | |
| - x: torch.Tensor | |
| - silent: bool, default `False` | |
| - If not silent, the function will print the message itself. The information string will always be returned. | |
| - prompt: Optional[str], default `None` | |
| - If have prompt, it will be printed at the very beginning. | |
| ### Returns | |
| - str | |
| ''' | |
| info_list = [] if prompt is None else [prompt] | |
| # Convert to float to calculate the statistics. | |
| x_num = x.float() | |
| info_list.append(f'📐 [{x_num.min():06f} -> {x_num.max():06f}] ~ ({x_num.mean():06f}, {x_num.std():06f})') | |
| info_list.append(f'📦 {tuple(x.shape)}') | |
| info_list.append(f'🏷️ {x.dtype}') | |
| info_list.append(f'🖥️ {x.device}') | |
| # Generate the final information and print it if necessary. | |
| ret = '\t'.join(info_list) | |
| if not silent: | |
| get_logger().info(ret) | |
| return ret | |
| def look_ndarray( | |
| x : np.ndarray, | |
| silent : bool = False, | |
| prompt : Optional[str] = None, | |
| ): | |
| ''' | |
| Summarize the information of a numpy array, including its shape, value range (min, max, mean, std), and dtype. | |
| Then return a string containing the information. | |
| ### Args | |
| - x: np.ndarray | |
| - silent: bool, default `False` | |
| - If not silent, the function will print the message itself. The information string will always be returned. | |
| - prompt: Optional[str], default `None` | |
| - If have prompt, it will be printed at the very beginning. | |
| ### Returns | |
| - str | |
| ''' | |
| info_list = [] if prompt is None else [prompt] | |
| # Convert to float to calculate the statistics. | |
| x_num = x.astype(np.float32) | |
| info_list.append(f'📐 [ {x_num.min():06f} -> {x_num.max():06f} ] ~ ( {x_num.mean():06f}, {x_num.std():06f} )') | |
| info_list.append(f'📦 {tuple(x.shape)}') | |
| info_list.append(f'🏷️ {x.dtype}') | |
| # Generate the final information and print it if necessary. | |
| ret = '\t'.join(info_list) | |
| if not silent: | |
| get_logger().info(ret) | |
| return ret | |
| def look_dict( | |
| d : dict, | |
| silent : bool = False, | |
| ): | |
| ''' | |
| Summarize the information of a dictionary, including the keys and the information of the values. | |
| Then return a string containing the information. | |
| ### Args | |
| - d: dict | |
| - silent: bool, default `False` | |
| - If not silent, the function will print the message itself. The information string will always be returned. | |
| ### Returns | |
| - str | |
| ''' | |
| info_list = ['{'] | |
| for k, v in d.items(): | |
| if isinstance(v, torch.Tensor): | |
| info_list.append(f'{k} : tensor: {look_tensor(v, silent=True)}') | |
| elif isinstance(v, np.ndarray): | |
| info_list.append(f'{k} : ndarray: {look_ndarray(v, silent=True)}') | |
| elif isinstance(v, str): | |
| info_list.append(f'{k} : {v[:32]}') | |
| else: | |
| info_list.append(f'{k} : {type(v)}') | |
| info_list.append('}') | |
| ret = '\n'.join(info_list) | |
| if not silent: | |
| get_logger().info(ret) | |
| return ret |