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Source code for mmedit.datasets.sr_vimeo90k_multiple_gt_dataset

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

from .base_sr_dataset import BaseSRDataset
from .registry import DATASETS


[docs]@DATASETS.register_module() class SRVimeo90KMultipleGTDataset(BaseSRDataset): """Vimeo90K dataset for video super resolution for recurrent networks. The dataset loads several LQ (Low-Quality) frames and GT (Ground-Truth) frames. Then it applies specified transforms and finally returns a dict containing paired data and other information. It reads Vimeo90K keys from the txt file. Each line contains: 1. video frame folder 2. image shape Examples: :: 00001/0266 (256,448,3) 00001/0268 (256,448,3) Args: lq_folder (str | :obj:`Path`): Path to a lq folder. gt_folder (str | :obj:`Path`): Path to a gt folder. ann_file (str | :obj:`Path`): Path to the annotation file. pipeline (list[dict | callable]): A sequence of data transformations. scale (int): Upsampling scale ratio. num_input_frames (int): Number of frames in each training sequence. Default: 7. test_mode (bool): Store `True` when building test dataset. Default: `False`. """ def __init__(self, lq_folder, gt_folder, ann_file, pipeline, scale, num_input_frames=7, test_mode=False): super().__init__(pipeline, scale, test_mode) self.lq_folder = str(lq_folder) self.gt_folder = str(gt_folder) self.ann_file = str(ann_file) self.num_input_frames = num_input_frames self.data_infos = self.load_annotations()
[docs] def load_annotations(self): """Load annoations for Vimeo-90K dataset. Returns: dict: Returned dict for LQ and GT pairs. """ # get keys with open(self.ann_file, 'r') as fin: keys = [line.strip().split(' ')[0] for line in fin] data_infos = [] for key in keys: lq_paths = [ osp.join(self.lq_folder, key, f'im{i}.png') for i in range(1, self.num_input_frames + 1) ] gt_paths = [ osp.join(self.gt_folder, key, f'im{i}.png') for i in range(1, self.num_input_frames + 1) ] data_infos.append( dict(lq_path=lq_paths, gt_path=gt_paths, key=key)) return data_infos
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