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Source code for mmedit.datasets.sr_folder_video_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os
import os.path as osp
import re
from collections import defaultdict

import mmcv
import numpy as np

from .base_sr_dataset import BaseSRDataset
from .registry import DATASETS


[docs]@DATASETS.register_module() class SRFolderVideoDataset(BaseSRDataset): """General dataset for video SR, used for sliding-window framework. The dataset loads several LQ (Low-Quality) frames and one GT (Ground-Truth) frames. Then it applies specified transforms and finally returns a dict containing paired data and other information. This dataset takes an annotation file specifying the sequences used in training or test. If no annotation file is provided, it assumes all video sequences under the root directory are used for training or test. In the annotation file (.txt), each line contains: 1. image name (no file extension); 2. number of frames in the sequence (in the same folder) Examples: :: calendar/00000000 41 calendar/00000001 41 ... calendar/00000040 41 city/00000000 34 ... Args: lq_folder (str | :obj:`Path`): Path to a lq folder. gt_folder (str | :obj:`Path`): Path to a gt folder. num_input_frames (int): Window size for input frames. pipeline (list[dict | callable]): A sequence of data transformations. scale (int): Upsampling scale ratio. ann_file (str): The path to the annotation file. If None, we assume that all sequences in the folder is used. Default: None. filename_tmpl (str): Template for each filename. Note that the template excludes the file extension. Default: '{:08d}'. start_idx (int): The index corresponds to the first frame in the sequence. Default: 0. metric_average_mode (str): The way to compute the average metric. If 'clip', we first compute an average value for each clip, and then average the values from different clips. If 'all', we compute the average of all frames. Default: 'clip'. test_mode (bool): Store `True` when building test dataset. Default: `True`. """ def __init__(self, lq_folder, gt_folder, num_input_frames, pipeline, scale, ann_file=None, filename_tmpl='{:08d}', start_idx=0, metric_average_mode='clip', test_mode=True): super().__init__(pipeline, scale, test_mode) assert num_input_frames % 2 == 1, ( f'num_input_frames should be odd numbers, ' f'but received {num_input_frames }.') if metric_average_mode not in ['clip', 'all']: raise ValueError('metric_average_mode can only be "clip" or ' f'"all", but got {metric_average_mode}.') self.lq_folder = str(lq_folder) self.gt_folder = str(gt_folder) self.num_input_frames = num_input_frames self.ann_file = ann_file self.filename_tmpl = filename_tmpl self.start_idx = start_idx self.metric_average_mode = metric_average_mode self.data_infos = self.load_annotations() def _load_annotations_from_file(self): self.folders = {} data_infos = [] ann_list = mmcv.list_from_file(self.ann_file) for ann in ann_list: key, max_frame_num = ann.strip().rsplit(' ', 1) key = key.replace('/', os.sep) sequence = osp.basename(key) if sequence not in self.folders: self.folders[sequence] = int(max_frame_num) data_infos.append( dict( lq_path=self.lq_folder, gt_path=self.gt_folder, key=key, num_input_frames=self.num_input_frames, max_frame_num=int(max_frame_num))) return data_infos
[docs] def load_annotations(self): """Load annotations for the dataset. Returns: list[dict]: A list of dicts for paired paths and other information. """ if self.ann_file: return self._load_annotations_from_file() self.folders = {} data_infos = [] sequences = sorted(glob.glob(osp.join(self.lq_folder, '*'))) sequences = [re.split(r'[\\/]', s)[-1] for s in sequences] for sequence in sequences: seq_dir = osp.join(self.lq_folder, sequence) max_frame_num = len(list(mmcv.utils.scandir(seq_dir))) self.folders[sequence] = max_frame_num for i in range(self.start_idx, max_frame_num + self.start_idx): data_infos.append( dict( lq_path=self.lq_folder, gt_path=self.gt_folder, key=osp.join(sequence, self.filename_tmpl.format(i)), num_input_frames=self.num_input_frames, max_frame_num=max_frame_num)) return data_infos
[docs] def evaluate(self, results, logger=None): """Evaluate with different 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['eval_result'] for res in results] # a list of dict eval_result = defaultdict(list) # a dict of list for res in results: for metric, val in res.items(): eval_result[metric].append(val) for metric, val_list in eval_result.items(): assert len(val_list) == len(self), ( f'Length of evaluation result of {metric} is {len(val_list)}, ' f'should be {len(self)}') # average the results if self.metric_average_mode == 'clip': for metric, values in eval_result.items(): start_idx = 0 metric_avg = 0 for _, num_img in self.folders.items(): end_idx = start_idx + num_img folder_values = values[start_idx:end_idx] metric_avg += np.mean(folder_values) start_idx = end_idx eval_result[metric] = metric_avg / len(self.folders) else: eval_result = { metric: sum(values) / len(self) for metric, values in eval_result.items() } return eval_result
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