def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
"""Evaluate the dataset.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
Returns:
dict[str, float]: Default metrics.
"""
if not isinstance(metric, str):
assert len(metric) == 1
metric = metric[0]
allowed_metrics = ['mIoU']
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
eval_results = {}
gt_seg_maps = self.get_gt_seg_maps()
if self.CLASSES is None:
num_classes = len(
reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
else:
num_classes = len(self.CLASSES)
all_acc, acc, iou = mean_iou(
results, gt_seg_maps, num_classes, ignore_index=self.ignore_index)
summary_str = ''
summary_str += 'per class results:\n'
line_format = '{:<15} {:>10} {:>10}\n'
summary_str += line_format.format('Class', 'IoU', 'Acc')
if self.CLASSES is None:
class_names = tuple(range(num_classes))
else:
class_names = self.CLASSES
for i in range(num_classes):
iou_str = '{:.2f}'.format(iou[i] * 100)
acc_str = '{:.2f}'.format(acc[i] * 100)
summary_str += line_format.format(class_names[i], iou_str, acc_str)
summary_str += 'Summary:\n'
line_format = '{:<15} {:>10} {:>10} {:>10}\n'
summary_str += line_format.format('Scope', 'mIoU', 'mAcc', 'aAcc')
iou_str = '{:.2f}'.format(np.nanmean(iou) * 100)
acc_str = '{:.2f}'.format(np.nanmean(acc) * 100)
all_acc_str = '{:.2f}'.format(all_acc * 100)
summary_str += line_format.format('global', iou_str, acc_str,
all_acc_str)
print_log(summary_str, logger)
eval_results['mIoU'] = np.nanmean(iou)
eval_results['mAcc'] = np.nanmean(acc)
eval_results['aAcc'] = all_acc
return eval_results
def compute_metrics(
seg_pred,
seg_gt,
n_cls,
ignore_index=None,
ret_cat_iou=False,
tmp_dir=None,
distributed=False,
):
ret_metrics_mean = torch.zeros(3, dtype=float, device=ptu.device)
if ptu.dist_rank == 0:
list_seg_pred = []
list_seg_gt = []
keys = sorted(seg_pred.keys())
for k in keys:
list_seg_pred.append(np.asarray(seg_pred[k]))
list_seg_gt.append(np.asarray(seg_gt[k]))
ret_metrics = mean_iou(
results=list_seg_pred,
gt_seg_maps=list_seg_gt,
num_classes=n_cls,
ignore_index=ignore_index,
)
ret_metrics = [
ret_metrics["aAcc"], ret_metrics["Acc"], ret_metrics["IoU"]
]
ret_metrics_mean = torch.tensor(
[
np.round(np.nanmean(ret_metric.astype(np.float)) * 100, 2)
for ret_metric in ret_metrics
],
dtype=float,
device=ptu.device,
)
cat_iou = ret_metrics[2]
# broadcast metrics from 0 to all nodes
if distributed:
dist.broadcast(ret_metrics_mean, 0)
pix_acc, mean_acc, miou = ret_metrics_mean
ret = dict(pixel_accuracy=pix_acc, mean_accuracy=mean_acc, mean_iou=miou)
if ret_cat_iou and ptu.dist_rank == 0:
ret["cat_iou"] = cat_iou
return ret
def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
"""Evaluate the dataset.
Args:
results (list[list]): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
Returns:
dict[str, float]: Default metrics.
"""
if not isinstance(metric, str):
assert len(metric) == 1
metric = metric[0]
allowed_metrics = ['mIoU']
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
eval_results = {}
gt_seg_maps_0, gt_seg_maps_1, gt_seg_maps_2 = self.get_gt_seg_maps()
results_0 = results[0]
results_1 = results[1]
results_2 = results[2]
if self.CLASSES is None:
num_classes_0 = 3
num_classes_1 = 3
num_classes_2 = 9
else:
# num_classes = len(self.CLASSES) # TODO fix
pass
num_classes = [3, 3, 9]
all_acc_0, acc_0, iou_0 = mean_iou(results_0,
gt_seg_maps_0,
num_classes_0,
ignore_index=self.ignore_index)
all_acc_1, acc_1, iou_1 = mean_iou(results_1,
gt_seg_maps_1,
num_classes_1,
ignore_index=self.ignore_index)
all_acc_2, acc_2, iou_2 = mean_iou(results_2,
gt_seg_maps_2,
num_classes_2,
ignore_index=self.ignore_index)
all_acc = [all_acc_0, all_acc_1, all_acc_2]
acc = [acc_0, acc_1, acc_2]
iou = [iou_0, iou_1, iou_2]
summary_str = ''
summary_str += 'per class results:\n'
line_format = '{:<15} {:>10} {:>10}\n'
summary_str += line_format.format('Class', 'IoU', 'Acc')
if self.CLASSES is None:
class_names = [
tuple(range(num_classes_i)) for num_classes_i in num_classes
]
else:
# class_names = self.CLASSES #TODO fix
pass
for idx in range(3):
for i in range(num_classes[idx]):
iou_str = '{:.2f}'.format(iou[idx][i] * 100)
acc_str = '{:.2f}'.format(acc[idx][i] * 100)
summary_str += line_format.format(
LABEL_NAMES[idx] + '_' + str(class_names[idx][i]), iou_str,
acc_str)
summary_str += 'Summary:\n'
line_format = '{:<15} {:>10} {:>10} {:>10}\n'
summary_str += line_format.format('Scope', 'mIoU', 'mAcc', 'aAcc')
for idx in range(3):
iou_str = '{:.2f}'.format(np.nanmean([idx]) * 100)
acc_str = '{:.2f}'.format(np.nanmean(acc[idx]) * 100)
all_acc_str = '{:.2f}'.format(all_acc[idx] * 100)
summary_str += line_format.format(LABEL_NAMES[idx], iou_str,
acc_str, all_acc_str)
print_log(summary_str, logger)
for idx in range(3):
eval_results[LABEL_NAMES[idx] + '_' + 'mIoU'] = np.nanmean(
iou[idx])
eval_results[LABEL_NAMES[idx] + '_' + 'mAcc'] = np.nanmean(
acc[idx])
eval_results[LABEL_NAMES[idx] + '_' + 'aAcc'] = all_acc[idx]
return eval_results
def evaluate(self, results, metric='mIoU', logger=None, **kwargs):
"""Evaluate the dataset.
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
Returns:
dict[str, float]: Default metrics.
"""
if not isinstance(metric, str):
assert len(metric) == 1
metric = metric[0]
allowed_metrics = ['mIoU']
if metric not in allowed_metrics:
raise KeyError('metric {} is not supported'.format(metric))
eval_results = {}
gt_seg_maps = self.get_gt_seg_maps()
if self.CLASSES is None:
num_classes = len(
reduce(np.union1d, [np.unique(_) for _ in gt_seg_maps]))
else:
num_classes = len(self.CLASSES)
all_acc, TP, FP, TN, FN, acc, iou, dice = mean_iou(
results, gt_seg_maps, num_classes, ignore_index=self.ignore_index)
summary_str = ''
summary_str += 'per class results:\n'
line_format = '{:<15} {:>10} {:>10} {:>10}\n'
summary_str += line_format.format('Class', 'IoU', 'Dice', 'Acc')
if self.CLASSES is None:
class_names = tuple(range(num_classes))
else:
class_names = self.CLASSES
# to record class-wise dice scores
dice_pos_str = ''
dice_neg_str = ''
pl_dice = 0
bg_dice = 0
for i in range(num_classes):
iou_str = '{:.2f}'.format(iou[i] * 100)
acc_str = '{:.2f}'.format(acc[i] * 100)
dice_score = dice[i] * 100
dice_str = '{:.2f}'.format(dice_score)
if i == 0: #background class
dice_neg_str = '{:.2f}'.format(dice_score)
bg_dice = dice_score
else: #positive class
dice_pos_str = '{:.2f}'.format(dice_score)
pl_dice = dice_score
summary_str += line_format.format(class_names[i], iou_str,
dice_str, acc_str)
summary_str += 'Summary:\n'
line_format = '{:<15} {:>10} {:>10} {:>10} {:>20} {:>20} {:>10} {:>10} {:>10} {:>10} {:>10}\n'
summary_str += line_format.format('Scope', 'mIoU', 'mDice', 'mAcc',
'Powerline Dice', 'Background Dice',
'aAcc', 'TPR', 'TNR', 'FDR',
'Precision')
iou_str = '{:.2f}'.format(np.nanmean(iou) * 100)
dice_str = '{:.2f}'.format(np.nanmean(dice) * 100)
acc_str = '{:.2f}'.format(np.nanmean(acc) * 100)
all_acc_str = '{:.2f}'.format(all_acc * 100)
TPR = (TP / (TP + FN)) if (TP + FN) != 0 else 0
TNR = (TN / (TN + FP)) if (TN + FP) != 0 else 0
FDR = (FP / (FP + TP)) if (FP + TP) != 0 else 0
precision = (TP / (TP + FP)) if (TP + FP) != 0 else 0
TPR_str = '{:.2f}'.format(TPR * 100)
TNR_str = '{:.2f}'.format(TNR * 100)
FDR_str = '{:.2f}'.format(FDR * 100)
precision_str = '{:.2f}'.format(precision * 100)
summary_str += line_format.format('global', iou_str, dice_str, acc_str,
dice_pos_str, dice_neg_str,
all_acc_str, TPR_str, TNR_str,
FDR_str, precision_str)
print_log(summary_str, logger)
eval_results['mIoU'] = np.nanmean(iou)
eval_results['mDice'] = np.nanmean(dice)
eval_results['mAcc'] = np.nanmean(acc)
eval_results['plDice'] = pl_dice
eval_results['bgDice'] = bg_dice
eval_results['aAcc'] = all_acc
eval_results['TPR'] = TPR
eval_results['TNR'] = TNR
eval_results['FDR'] = FDR
eval_results['Precision'] = precision
return eval_results
