Source code for mmedit.datasets.generation_paired_dataset

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

from .base_generation_dataset import BaseGenerationDataset
from .registry import DATASETS

[docs]@DATASETS.register_module() class GenerationPairedDataset(BaseGenerationDataset): """General paired image folder dataset for image generation. It assumes that the training directory is '/path/to/data/train'. During test time, the directory is '/path/to/data/test'. '/path/to/data' can be initialized by args 'dataroot'. Each sample contains a pair of images concatenated in the w dimension (A|B). Args: dataroot (str | :obj:`Path`): Path to the folder root of paired images. pipeline (List[dict | callable]): A sequence of data transformations. test_mode (bool): Store `True` when building test dataset. Default: `False`. """ def __init__(self, dataroot, pipeline, test_mode=False): super().__init__(pipeline, test_mode) phase = 'test' if test_mode else 'train' self.dataroot = osp.join(str(dataroot), phase) self.data_infos = self.load_annotations()
[docs] def load_annotations(self): """Load paired image paths. Returns: list[dict]: List that contains paired image paths. """ data_infos = [] pair_paths = sorted(self.scan_folder(self.dataroot)) for pair_path in pair_paths: data_infos.append(dict(pair_path=pair_path)) return data_infos
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