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Source code for mmedit.datasets.base_vfi_dataset

# Copyright (c) OpenMMLab. All rights reserved.
import copy
from collections import defaultdict

from .base_dataset import BaseDataset

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


[docs]class BaseVFIDataset(BaseDataset): """Base class for video frame interpolation datasets. """ def __init__(self, pipeline, folder, ann_file, test_mode=False): super().__init__(pipeline, test_mode) self.folder = str(folder) self.ann_file = str(ann_file) def __getitem__(self, idx): """Get item at each call. Args: idx (int): Index for getting each item. """ results = copy.deepcopy(self.data_infos[idx]) results['folder'] = self.folder results['ann_file'] = self.ann_file return self.pipeline(results)
[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 eval_result = { metric: sum(values) / len(self) for metric, values in eval_result.items() } return eval_result
[docs] def load_annotations(self): """Abstract function for loading annotation. All subclasses should overwrite this function """ pass
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