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mmseg.evaluation.metrics.iou_metric 源代码

# Copyright (c) OpenMMLab. All rights reserved.
import os.path as osp
from collections import OrderedDict
from typing import Dict, List, Optional, Sequence

import numpy as np
import torch
from mmengine.dist import is_main_process
from mmengine.evaluator import BaseMetric
from mmengine.logging import MMLogger, print_log
from mmengine.utils import mkdir_or_exist
from PIL import Image
from prettytable import PrettyTable

from mmseg.registry import METRICS


[文档]@METRICS.register_module() class IoUMetric(BaseMetric): """IoU evaluation metric. Args: ignore_index (int): Index that will be ignored in evaluation. Default: 255. iou_metrics (list[str] | str): Metrics to be calculated, the options includes 'mIoU', 'mDice' and 'mFscore'. nan_to_num (int, optional): If specified, NaN values will be replaced by the numbers defined by the user. Default: None. beta (int): Determines the weight of recall in the combined score. Default: 1. collect_device (str): Device name used for collecting results from different ranks during distributed training. Must be 'cpu' or 'gpu'. Defaults to 'cpu'. output_dir (str): The directory for output prediction. Defaults to None. format_only (bool): Only format result for results commit without perform evaluation. It is useful when you want to save the result to a specific format and submit it to the test server. Defaults to False. prefix (str, optional): The prefix that will be added in the metric names to disambiguate homonymous metrics of different evaluators. If prefix is not provided in the argument, self.default_prefix will be used instead. Defaults to None. """ def __init__(self, ignore_index: int = 255, iou_metrics: List[str] = ['mIoU'], nan_to_num: Optional[int] = None, beta: int = 1, collect_device: str = 'cpu', output_dir: Optional[str] = None, format_only: bool = False, prefix: Optional[str] = None, **kwargs) -> None: super().__init__(collect_device=collect_device, prefix=prefix) self.ignore_index = ignore_index self.metrics = iou_metrics self.nan_to_num = nan_to_num self.beta = beta self.output_dir = output_dir if self.output_dir and is_main_process(): mkdir_or_exist(self.output_dir) self.format_only = format_only
[文档] def process(self, data_batch: dict, data_samples: Sequence[dict]) -> None: """Process one batch of data and data_samples. The processed results should be stored in ``self.results``, which will be used to compute the metrics when all batches have been processed. Args: data_batch (dict): A batch of data from the dataloader. data_samples (Sequence[dict]): A batch of outputs from the model. """ num_classes = len(self.dataset_meta['classes']) for data_sample in data_samples: pred_label = data_sample['pred_sem_seg']['data'].squeeze() # format_only always for test dataset without ground truth if not self.format_only: label = data_sample['gt_sem_seg']['data'].squeeze().to( pred_label) self.results.append( self.intersect_and_union(pred_label, label, num_classes, self.ignore_index)) # format_result if self.output_dir is not None: basename = osp.splitext(osp.basename( data_sample['img_path']))[0] png_filename = osp.abspath( osp.join(self.output_dir, f'{basename}.png')) output_mask = pred_label.cpu().numpy() # The index range of official ADE20k dataset is from 0 to 150. # But the index range of output is from 0 to 149. # That is because we set reduce_zero_label=True. if data_sample.get('reduce_zero_label', False): output_mask = output_mask + 1 output = Image.fromarray(output_mask.astype(np.uint8)) output.save(png_filename)
[文档] def compute_metrics(self, results: list) -> Dict[str, float]: """Compute the metrics from processed results. Args: results (list): The processed results of each batch. Returns: Dict[str, float]: The computed metrics. The keys are the names of the metrics, and the values are corresponding results. The key mainly includes aAcc, mIoU, mAcc, mDice, mFscore, mPrecision, mRecall. """ logger: MMLogger = MMLogger.get_current_instance() if self.format_only: logger.info(f'results are saved to {osp.dirname(self.output_dir)}') return OrderedDict() # convert list of tuples to tuple of lists, e.g. # [(A_1, B_1, C_1, D_1), ..., (A_n, B_n, C_n, D_n)] to # ([A_1, ..., A_n], ..., [D_1, ..., D_n]) results = tuple(zip(*results)) assert len(results) == 4 total_area_intersect = sum(results[0]) total_area_union = sum(results[1]) total_area_pred_label = sum(results[2]) total_area_label = sum(results[3]) ret_metrics = self.total_area_to_metrics( total_area_intersect, total_area_union, total_area_pred_label, total_area_label, self.metrics, self.nan_to_num, self.beta) class_names = self.dataset_meta['classes'] # summary table ret_metrics_summary = OrderedDict({ ret_metric: np.round(np.nanmean(ret_metric_value) * 100, 2) for ret_metric, ret_metric_value in ret_metrics.items() }) metrics = dict() for key, val in ret_metrics_summary.items(): if key == 'aAcc': metrics[key] = val else: metrics['m' + key] = val # each class table ret_metrics.pop('aAcc', None) ret_metrics_class = OrderedDict({ ret_metric: np.round(ret_metric_value * 100, 2) for ret_metric, ret_metric_value in ret_metrics.items() }) ret_metrics_class.update({'Class': class_names}) ret_metrics_class.move_to_end('Class', last=False) class_table_data = PrettyTable() for key, val in ret_metrics_class.items(): class_table_data.add_column(key, val) print_log('per class results:', logger) print_log('\n' + class_table_data.get_string(), logger=logger) return metrics
[文档] @staticmethod def intersect_and_union(pred_label: torch.tensor, label: torch.tensor, num_classes: int, ignore_index: int): """Calculate Intersection and Union. Args: pred_label (torch.tensor): Prediction segmentation map or predict result filename. The shape is (H, W). label (torch.tensor): Ground truth segmentation map or label filename. The shape is (H, W). num_classes (int): Number of categories. ignore_index (int): Index that will be ignored in evaluation. Returns: torch.Tensor: The intersection of prediction and ground truth histogram on all classes. torch.Tensor: The union of prediction and ground truth histogram on all classes. torch.Tensor: The prediction histogram on all classes. torch.Tensor: The ground truth histogram on all classes. """ mask = (label != ignore_index) pred_label = pred_label[mask] label = label[mask] intersect = pred_label[pred_label == label] area_intersect = torch.histc( intersect.float(), bins=(num_classes), min=0, max=num_classes - 1).cpu() area_pred_label = torch.histc( pred_label.float(), bins=(num_classes), min=0, max=num_classes - 1).cpu() area_label = torch.histc( label.float(), bins=(num_classes), min=0, max=num_classes - 1).cpu() area_union = area_pred_label + area_label - area_intersect return area_intersect, area_union, area_pred_label, area_label
[文档] @staticmethod def total_area_to_metrics(total_area_intersect: np.ndarray, total_area_union: np.ndarray, total_area_pred_label: np.ndarray, total_area_label: np.ndarray, metrics: List[str] = ['mIoU'], nan_to_num: Optional[int] = None, beta: int = 1): """Calculate evaluation metrics Args: total_area_intersect (np.ndarray): The intersection of prediction and ground truth histogram on all classes. total_area_union (np.ndarray): The union of prediction and ground truth histogram on all classes. total_area_pred_label (np.ndarray): The prediction histogram on all classes. total_area_label (np.ndarray): The ground truth histogram on all classes. metrics (List[str] | str): Metrics to be evaluated, 'mIoU' and 'mDice'. nan_to_num (int, optional): If specified, NaN values will be replaced by the numbers defined by the user. Default: None. beta (int): Determines the weight of recall in the combined score. Default: 1. Returns: Dict[str, np.ndarray]: per category evaluation metrics, shape (num_classes, ). """ def f_score(precision, recall, beta=1): """calculate the f-score value. Args: precision (float | torch.Tensor): The precision value. recall (float | torch.Tensor): The recall value. beta (int): Determines the weight of recall in the combined score. Default: 1. Returns: [torch.tensor]: The f-score value. """ score = (1 + beta**2) * (precision * recall) / ( (beta**2 * precision) + recall) return score if isinstance(metrics, str): metrics = [metrics] allowed_metrics = ['mIoU', 'mDice', 'mFscore'] if not set(metrics).issubset(set(allowed_metrics)): raise KeyError(f'metrics {metrics} is not supported') all_acc = total_area_intersect.sum() / total_area_label.sum() ret_metrics = OrderedDict({'aAcc': all_acc}) for metric in metrics: if metric == 'mIoU': iou = total_area_intersect / total_area_union acc = total_area_intersect / total_area_label ret_metrics['IoU'] = iou ret_metrics['Acc'] = acc elif metric == 'mDice': dice = 2 * total_area_intersect / ( total_area_pred_label + total_area_label) acc = total_area_intersect / total_area_label ret_metrics['Dice'] = dice ret_metrics['Acc'] = acc elif metric == 'mFscore': precision = total_area_intersect / total_area_pred_label recall = total_area_intersect / total_area_label f_value = torch.tensor([ f_score(x[0], x[1], beta) for x in zip(precision, recall) ]) ret_metrics['Fscore'] = f_value ret_metrics['Precision'] = precision ret_metrics['Recall'] = recall ret_metrics = { metric: value.numpy() for metric, value in ret_metrics.items() } if nan_to_num is not None: ret_metrics = OrderedDict({ metric: np.nan_to_num(metric_value, nan=nan_to_num) for metric, metric_value in ret_metrics.items() }) return ret_metrics