Source code for mmedit.datasets.base_matting_dataset

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

from .base_dataset import BaseDataset
from .registry import DATASETS

[docs]@DATASETS.register_module() class BaseMattingDataset(BaseDataset): """Base image matting dataset. """ def __init__(self, ann_file, pipeline, data_prefix=None, test_mode=False): super().__init__(pipeline, test_mode) self.ann_file = str(ann_file) self.data_prefix = str(data_prefix) self.data_infos = self.load_annotations()
[docs] def evaluate(self, results, logger=None): """Evaluating 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 ' f'dataset len: {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
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