Source code for mmedit.datasets.base_generation_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from pathlib import Path

from mmcv import scandir

from .base_dataset import BaseDataset

IMG_EXTENSIONS = ('.jpg', '.JPG', '.jpeg', '.JPEG', '.png', '.PNG', '.ppm',
                  '.PPM', '.bmp', '.BMP', '.tif', '.TIF', '.tiff', '.TIFF')

[docs]class BaseGenerationDataset(BaseDataset): """Base class for generation datasets."""
[docs] @staticmethod def scan_folder(path): """Obtain image path list (including sub-folders) from a given folder. Args: path (str | :obj:`Path`): Folder path. Returns: list[str]: Image list obtained from the given folder. """ if isinstance(path, (str, Path)): path = str(path) else: raise TypeError("'path' must be a str or a Path object, " f'but received {type(path)}.') images = scandir(path, suffix=IMG_EXTENSIONS, recursive=True) images = [osp.join(path, v) for v in images] assert images, f'{path} has no valid image file.' return images
[docs] def evaluate(self, results, logger=None): """Evaluating with saving generated images. (needs no metrics) Args: results (list[tuple]): The output of forward_test() of the model. Return: dict: Evaluation results dict. """ if not isinstance(results, list): raise TypeError(f'results must be a list, but got {type(results)}') assert len(results) == len(self), ( 'The length of results is not equal to the dataset len: ' f'{len(results)} != {len(self)}') results = [res['saved_flag'] for res in results] saved_num = 0 for flag in results: if flag: saved_num += 1 # make a dict to show eval_result = {'val_saved_number': saved_num} return eval_result
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