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import numpy as np |
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from mmengine.logging import print_log |
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from terminaltables import AsciiTable |
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def fast_hist(preds, labels, num_classes): |
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"""Compute the confusion matrix for every batch. |
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Args: |
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preds (np.ndarray): Prediction labels of points with shape of |
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(num_points, ). |
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labels (np.ndarray): Ground truth labels of points with shape of |
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(num_points, ). |
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num_classes (int): number of classes |
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Returns: |
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np.ndarray: Calculated confusion matrix. |
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""" |
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k = (labels >= 0) & (labels < num_classes) |
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bin_count = np.bincount( |
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num_classes * labels[k].astype(int) + preds[k], |
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minlength=num_classes**2) |
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return bin_count[:num_classes**2].reshape(num_classes, num_classes) |
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def per_class_iou(hist): |
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"""Compute the per class iou. |
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Args: |
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hist(np.ndarray): Overall confusion martix |
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(num_classes, num_classes ). |
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Returns: |
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np.ndarray: Calculated per class iou |
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""" |
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return np.diag(hist) / (hist.sum(1) + hist.sum(0) - np.diag(hist)) |
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def get_acc(hist): |
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"""Compute the overall accuracy. |
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Args: |
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hist(np.ndarray): Overall confusion martix |
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(num_classes, num_classes ). |
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Returns: |
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float: Calculated overall acc |
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""" |
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return np.diag(hist).sum() / hist.sum() |
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def get_acc_cls(hist): |
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"""Compute the class average accuracy. |
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Args: |
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hist(np.ndarray): Overall confusion martix |
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(num_classes, num_classes ). |
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Returns: |
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float: Calculated class average acc |
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""" |
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return np.nanmean(np.diag(hist) / hist.sum(axis=1)) |
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def seg_eval(gt_labels, seg_preds, label2cat, ignore_index, logger=None): |
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"""Semantic Segmentation Evaluation. |
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Evaluate the result of the Semantic Segmentation. |
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Args: |
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gt_labels (list[torch.Tensor]): Ground truth labels. |
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seg_preds (list[torch.Tensor]): Predictions. |
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label2cat (dict): Map from label to category name. |
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ignore_index (int): Index that will be ignored in evaluation. |
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logger (logging.Logger | str, optional): The way to print the mAP |
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summary. See `mmdet.utils.print_log()` for details. Default: None. |
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Returns: |
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dict[str, float]: Dict of results. |
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""" |
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assert len(seg_preds) == len(gt_labels) |
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num_classes = len(label2cat) |
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hist_list = [] |
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for i in range(len(gt_labels)): |
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gt_seg = gt_labels[i].astype(np.int64) |
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pred_seg = seg_preds[i].astype(np.int64) |
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pred_seg[gt_seg == ignore_index] = -1 |
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gt_seg[gt_seg == ignore_index] = -1 |
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hist_list.append(fast_hist(pred_seg, gt_seg, num_classes)) |
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iou = per_class_iou(sum(hist_list)) |
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if ignore_index < len(iou): |
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iou[ignore_index] = np.nan |
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miou = np.nanmean(iou) |
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acc = get_acc(sum(hist_list)) |
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acc_cls = get_acc_cls(sum(hist_list)) |
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header = ['classes'] |
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for i in range(len(label2cat)): |
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header.append(label2cat[i]) |
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header.extend(['miou', 'acc', 'acc_cls']) |
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ret_dict = dict() |
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table_columns = [['results']] |
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for i in range(len(label2cat)): |
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ret_dict[label2cat[i]] = float(iou[i]) |
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table_columns.append([f'{iou[i]:.4f}']) |
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ret_dict['miou'] = float(miou) |
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ret_dict['acc'] = float(acc) |
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ret_dict['acc_cls'] = float(acc_cls) |
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table_columns.append([f'{miou:.4f}']) |
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table_columns.append([f'{acc:.4f}']) |
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table_columns.append([f'{acc_cls:.4f}']) |
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table_data = [header] |
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table_rows = list(zip(*table_columns)) |
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table_data += table_rows |
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table = AsciiTable(table_data) |
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table.inner_footing_row_border = True |
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print_log('\n' + table.table, logger=logger) |
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return ret_dict |
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