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Source code for mmcls.datasets.multi_label

# Copyright (c) OpenMMLab. All rights reserved.
from typing import List

import numpy as np

from mmcls.core import average_performance, mAP
from .base_dataset import BaseDataset


[docs]class MultiLabelDataset(BaseDataset): """Multi-label Dataset.""" def get_cat_ids(self, idx: int) -> List[int]: """Get category ids by index. Args: idx (int): Index of data. Returns: cat_ids (List[int]): Image categories of specified index. """ gt_labels = self.data_infos[idx]['gt_label'] cat_ids = np.where(gt_labels == 1)[0].tolist() return cat_ids def evaluate(self, results, metric='mAP', metric_options=None, indices=None, logger=None): """Evaluate the dataset. Args: results (list): Testing results of the dataset. metric (str | list[str]): Metrics to be evaluated. Default value is 'mAP'. Options are 'mAP', 'CP', 'CR', 'CF1', 'OP', 'OR' and 'OF1'. metric_options (dict, optional): Options for calculating metrics. Allowed keys are 'k' and 'thr'. Defaults to None logger (logging.Logger | str, optional): Logger used for printing related information during evaluation. Defaults to None. Returns: dict: evaluation results """ if metric_options is None or metric_options == {}: metric_options = {'thr': 0.5} if isinstance(metric, str): metrics = [metric] else: metrics = metric allowed_metrics = ['mAP', 'CP', 'CR', 'CF1', 'OP', 'OR', 'OF1'] eval_results = {} results = np.vstack(results) gt_labels = self.get_gt_labels() if indices is not None: gt_labels = gt_labels[indices] num_imgs = len(results) assert len(gt_labels) == num_imgs, 'dataset testing results should '\ 'be of the same length as gt_labels.' invalid_metrics = set(metrics) - set(allowed_metrics) if len(invalid_metrics) != 0: raise ValueError(f'metric {invalid_metrics} is not supported.') if 'mAP' in metrics: mAP_value = mAP(results, gt_labels) eval_results['mAP'] = mAP_value if len(set(metrics) - {'mAP'}) != 0: performance_keys = ['CP', 'CR', 'CF1', 'OP', 'OR', 'OF1'] performance_values = average_performance(results, gt_labels, **metric_options) for k, v in zip(performance_keys, performance_values): if k in metrics: eval_results[k] = v return eval_results
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