mirrors/mmsegmentation
1
0
Fork 0
mirror of https://github.com/open-mmlab/mmsegmentation.git synced 2025-06-03 22:03:48 +08:00
Code Issues Projects Releases Wiki Activity

add metric mFscore (#509)

Browse Source 
* add mFscore and refactor the metrics return value

* fix linting

* some docstring and name fix
This commit is contained in:
sshuair 2021-05-01 02:34:57 +08:00 committed by GitHub
parent cf2cb542f7
commit e16e0e303b
No known key found for this signature in database
GPG Key ID: 4AEE18F83AFDEB23
7 changed files with 319 additions and 86 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

6
mmseg/core/evaluation/__init__.py
View File

@ -1,8 +1,8 @@
from .class_names import get_classes, get_palette
from .eval_hooks import DistEvalHook, EvalHook
from .metrics import eval_metrics, mean_dice, mean_iou
from .metrics import eval_metrics, mean_dice, mean_fscore, mean_iou
__all__ = [
'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'eval_metrics',
'get_classes', 'get_palette'
'EvalHook', 'DistEvalHook', 'mean_dice', 'mean_iou', 'mean_fscore',
'eval_metrics', 'get_classes', 'get_palette'
]

129
mmseg/core/evaluation/metrics.py
View File

@ -1,8 +1,27 @@
from collections import OrderedDict
import mmcv
import numpy as np
import torch
def f_score(precision, recall, beta=1):
"""calcuate 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: False.
Returns:
[torch.tensor]: The f-score value.
"""
score = (1 + beta**2) * (precision * recall) / (
(beta**2 * precision) + recall)
return score
def intersect_and_union(pred_label,
label,
num_classes,
@ -133,11 +152,12 @@ def mean_iou(results,
reduce_zero_label (bool): Wether ignore zero label. Default: False.
Returns:
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category IoU, shape (num_classes, ).
dict[str, float | ndarray]:
<aAcc> float: Overall accuracy on all images.
<Acc> ndarray: Per category accuracy, shape (num_classes, ).
<IoU> ndarray: Per category IoU, shape (num_classes, ).
"""
all_acc, acc, iou = eval_metrics(
iou_result = eval_metrics(
results=results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
@ -146,7 +166,7 @@ def mean_iou(results,
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label)
return all_acc, acc, iou
return iou_result
def mean_dice(results,
@ -171,12 +191,13 @@ def mean_dice(results,
reduce_zero_label (bool): Wether ignore zero label. Default: False.
Returns:
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category dice, shape (num_classes, ).
dict[str, float | ndarray]: Default metrics.
<aAcc> float: Overall accuracy on all images.
<Acc> ndarray: Per category accuracy, shape (num_classes, ).
<Dice> ndarray: Per category dice, shape (num_classes, ).
"""
all_acc, acc, dice = eval_metrics(
dice_result = eval_metrics(
results=results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
@ -185,7 +206,52 @@ def mean_dice(results,
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label)
return all_acc, acc, dice
return dice_result
def mean_fscore(results,
gt_seg_maps,
num_classes,
ignore_index,
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False,
beta=1):
"""Calculate Mean Intersection and Union (mIoU)
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
maps or list of prediction result filenames.
gt_seg_maps (list[ndarray] | list[str]): list of ground truth
segmentation maps or list of label filenames.
num_classes (int): Number of categories.
ignore_index (int): Index that will be ignored in evaluation.
nan_to_num (int, optional): If specified, NaN values will be replaced
by the numbers defined by the user. Default: None.
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Wether ignore zero label. Default: False.
beta (int): Determines the weight of recall in the combined score.
Default: False.
Returns:
dict[str, float | ndarray]: Default metrics.
<aAcc> float: Overall accuracy on all images.
<Fscore> ndarray: Per category recall, shape (num_classes, ).
<Precision> ndarray: Per category precision, shape (num_classes, ).
<Recall> ndarray: Per category f-score, shape (num_classes, ).
"""
fscore_result = eval_metrics(
results=results,
gt_seg_maps=gt_seg_maps,
num_classes=num_classes,
ignore_index=ignore_index,
metrics=['mFscore'],
nan_to_num=nan_to_num,
label_map=label_map,
reduce_zero_label=reduce_zero_label,
beta=beta)
return fscore_result
def eval_metrics(results,
@ -195,7 +261,8 @@ def eval_metrics(results,
metrics=['mIoU'],
nan_to_num=None,
label_map=dict(),
reduce_zero_label=False):
reduce_zero_label=False,
beta=1):
"""Calculate evaluation metrics
Args:
results (list[ndarray] | list[str]): List of prediction segmentation
@ -210,13 +277,13 @@ def eval_metrics(results,
label_map (dict): Mapping old labels to new labels. Default: dict().
reduce_zero_label (bool): Wether ignore zero label. Default: False.
Returns:
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category evaluation metrics, shape (num_classes, ).
float: Overall accuracy on all images.
ndarray: Per category accuracy, shape (num_classes, ).
ndarray: Per category evaluation metrics, shape (num_classes, ).
"""
if isinstance(metrics, str):
metrics = [metrics]
allowed_metrics = ['mIoU', 'mDice']
allowed_metrics = ['mIoU', 'mDice', 'mFscore']
if not set(metrics).issubset(set(allowed_metrics)):
raise KeyError('metrics {} is not supported'.format(metrics))
@ -225,19 +292,35 @@ def eval_metrics(results,
results, gt_seg_maps, num_classes, ignore_index, label_map,
reduce_zero_label)
all_acc = total_area_intersect.sum() / total_area_label.sum()
acc = total_area_intersect / total_area_label
ret_metrics = [all_acc, acc]
ret_metrics = OrderedDict({'aAcc': all_acc})
for metric in metrics:
if metric == 'mIoU':
iou = total_area_intersect / total_area_union
ret_metrics.append(iou)
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)
ret_metrics.append(dice)
ret_metrics = [metric.numpy() for metric in ret_metrics]
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 = [
np.nan_to_num(metric, nan=nan_to_num) for metric in ret_metrics
]
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

86
mmseg/datasets/custom.py
View File

@ -1,11 +1,12 @@
import os
import os.path as osp
from collections import OrderedDict
from functools import reduce
import mmcv
import numpy as np
from mmcv.utils import print_log
from terminaltables import AsciiTable
from prettytable import PrettyTable
from torch.utils.data import Dataset
from mmseg.core import eval_metrics
@ -312,8 +313,8 @@ class CustomDataset(Dataset):
Args:
results (list): Testing results of the dataset.
metric (str | list[str]): Metrics to be evaluated. 'mIoU' and
'mDice' are supported.
metric (str | list[str]): Metrics to be evaluated. 'mIoU',
'mDice' and 'mFscore' are supported.
logger (logging.Logger | None | str): Logger used for printing
related information during evaluation. Default: None.
@ -323,7 +324,7 @@ class CustomDataset(Dataset):
if isinstance(metric, str):
metric = [metric]
allowed_metrics = ['mIoU', 'mDice']
allowed_metrics = ['mIoU', 'mDice', 'mFscore']
if not set(metric).issubset(set(allowed_metrics)):
raise KeyError('metric {} is not supported'.format(metric))
eval_results = {}
@ -341,42 +342,57 @@ class CustomDataset(Dataset):
metric,
label_map=self.label_map,
reduce_zero_label=self.reduce_zero_label)
class_table_data = [['Class'] + [m[1:] for m in metric] + ['Acc']]
if self.CLASSES is None:
class_names = tuple(range(num_classes))
else:
class_names = self.CLASSES
ret_metrics_round = [
np.round(ret_metric * 100, 2) for ret_metric in ret_metrics
]
for i in range(num_classes):
class_table_data.append([class_names[i]] +
[m[i] for m in ret_metrics_round[2:]] +
[ret_metrics_round[1][i]])
summary_table_data = [['Scope'] +
['m' + head
for head in class_table_data[0][1:]] + ['aAcc']]
ret_metrics_mean = [
np.round(np.nanmean(ret_metric) * 100, 2)
for ret_metric in ret_metrics
]
summary_table_data.append(['global'] + ret_metrics_mean[2:] +
[ret_metrics_mean[1]] +
[ret_metrics_mean[0]])
print_log('per class results:', logger)
table = AsciiTable(class_table_data)
print_log('\n' + table.table, logger=logger)
print_log('Summary:', logger)
table = AsciiTable(summary_table_data)
print_log('\n' + table.table, logger=logger)
for i in range(1, len(summary_table_data[0])):
eval_results[summary_table_data[0]
[i]] = summary_table_data[1][i] / 100.0
for idx, sub_metric in enumerate(class_table_data[0][1:], 1):
for item in class_table_data[1:]:
eval_results[str(sub_metric) + '.' +
str(item[0])] = item[idx] / 100.0
# 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()
})
# 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)
# for logger
class_table_data = PrettyTable()
for key, val in ret_metrics_class.items():
class_table_data.add_column(key, val)
summary_table_data = PrettyTable()
for key, val in ret_metrics_summary.items():
if key == 'aAcc':
summary_table_data.add_column(key, [val])
else:
summary_table_data.add_column('m' + key, [val])
print_log('per class results:', logger)
print_log('\n' + class_table_data.get_string(), logger=logger)
print_log('Summary:', logger)
print_log('\n' + summary_table_data.get_string(), logger=logger)
# each metric dict
for key, value in ret_metrics_summary.items():
if key == 'aAcc':
eval_results[key] = value / 100.0
else:
eval_results['m' + key] = value / 100.0
ret_metrics_class.pop('Class', None)
for key, value in ret_metrics_class.items():
eval_results.update({
key + '.' + str(name): value[idx] / 100.0
for idx, name in enumerate(class_names)
})
if mmcv.is_list_of(results, str):
for file_name in results:

2
requirements/runtime.txt
View File

@ -1,3 +1,3 @@
matplotlib
numpy
terminaltables
prettytable

2
setup.cfg
View File

@ -8,6 +8,6 @@ line_length = 79
multi_line_output = 0
known_standard_library = setuptools
known_first_party = mmseg
known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,oss2,pytest,scipy,seaborn,terminaltables,torch
known_third_party = PIL,cityscapesscripts,cv2,detail,matplotlib,mmcv,numpy,onnxruntime,oss2,prettytable,pytest,scipy,seaborn,torch
no_lines_before = STDLIB,LOCALFOLDER
default_section = THIRDPARTY

14
tests/test_data/test_dataset.py
View File

@ -159,7 +159,7 @@ def test_custom_dataset():
for gt_seg_map in gt_seg_maps:
h, w = gt_seg_map.shape
pseudo_results.append(np.random.randint(low=0, high=7, size=(h, w)))
eval_results = train_dataset.evaluate(pseudo_results, metric='mIoU')
eval_results = train_dataset.evaluate(pseudo_results, metric=['mIoU'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mAcc' in eval_results
@ -193,13 +193,23 @@ def test_custom_dataset():
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(pseudo_results, metric='mFscore')
assert isinstance(eval_results, dict)
assert 'mRecall' in eval_results
assert 'mPrecision' in eval_results
assert 'mFscore' in eval_results
assert 'aAcc' in eval_results
eval_results = train_dataset.evaluate(
pseudo_results, metric=['mIoU', 'mDice'])
pseudo_results, metric=['mIoU', 'mDice', 'mFscore'])
assert isinstance(eval_results, dict)
assert 'mIoU' in eval_results
assert 'mDice' in eval_results
assert 'mAcc' in eval_results
assert 'aAcc' in eval_results
assert 'mFscore' in eval_results
assert 'mPrecision' in eval_results
assert 'mRecall' in eval_results
@patch('mmseg.datasets.CustomDataset.load_annotations', MagicMock)

166
tests/test_metrics.py
View File

@ -1,6 +1,8 @@
import numpy as np
from mmseg.core.evaluation import eval_metrics, mean_dice, mean_iou
from mmseg.core.evaluation import (eval_metrics, mean_dice, mean_fscore,
mean_iou)
from mmseg.core.evaluation.metrics import f_score
def get_confusion_matrix(pred_label, label, num_classes, ignore_index):
@ -58,6 +60,28 @@ def legacy_mean_dice(results, gt_seg_maps, num_classes, ignore_index):
return all_acc, acc, dice
# This func is deprecated since it's not memory efficient
def legacy_mean_fscore(results,
gt_seg_maps,
num_classes,
ignore_index,
beta=1):
num_imgs = len(results)
assert len(gt_seg_maps) == num_imgs
total_mat = np.zeros((num_classes, num_classes), dtype=np.float)
for i in range(num_imgs):
mat = get_confusion_matrix(
results[i], gt_seg_maps[i], num_classes, ignore_index=ignore_index)
total_mat += mat
all_acc = np.diag(total_mat).sum() / total_mat.sum()
recall = np.diag(total_mat) / total_mat.sum(axis=1)
precision = np.diag(total_mat) / total_mat.sum(axis=0)
fv = np.vectorize(f_score)
fscore = fv(precision, recall, beta=beta)
return all_acc, recall, precision, fscore
def test_metrics():
pred_size = (10, 30, 30)
num_classes = 19
@ -69,63 +93,113 @@ def test_metrics():
label[:, 2, 5:10] = ignore_index
# Test the correctness of the implementation of mIoU calculation.
all_acc, acc, iou = eval_metrics(
ret_metrics = eval_metrics(
results, label, num_classes, ignore_index, metrics='mIoU')
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'IoU']
all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
# Test the correctness of the implementation of mDice calculation.
all_acc, acc, dice = eval_metrics(
ret_metrics = eval_metrics(
results, label, num_classes, ignore_index, metrics='mDice')
all_acc, acc, dice = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'Dice']
all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(dice, dice_l)
# Test the correctness of the implementation of mDice calculation.
ret_metrics = eval_metrics(
results, label, num_classes, ignore_index, metrics='mFscore')
all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
all_acc_l, recall_l, precision_l, fscore_l = legacy_mean_fscore(
results, label, num_classes, ignore_index)
assert all_acc == all_acc_l
assert np.allclose(recall, recall_l)
assert np.allclose(precision, precision_l)
assert np.allclose(fscore, fscore_l)
# Test the correctness of the implementation of joint calculation.
all_acc, acc, iou, dice = eval_metrics(
results, label, num_classes, ignore_index, metrics=['mIoU', 'mDice'])
ret_metrics = eval_metrics(
results,
label,
num_classes,
ignore_index,
metrics=['mIoU', 'mDice', 'mFscore'])
all_acc, acc, iou, dice, precision, recall, fscore = ret_metrics[
'aAcc'], ret_metrics['Acc'], ret_metrics['IoU'], ret_metrics[
'Dice'], ret_metrics['Precision'], ret_metrics[
'Recall'], ret_metrics['Fscore']
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
assert np.allclose(dice, dice_l)
assert np.allclose(precision, precision_l)
assert np.allclose(recall, recall_l)
assert np.allclose(fscore, fscore_l)
# Test the correctness of calculation when arg: num_classes is larger
# than the maximum value of input maps.
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
all_acc, acc, iou = eval_metrics(
ret_metrics = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics='mIoU',
nan_to_num=-1)
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'IoU']
assert acc[-1] == -1
assert iou[-1] == -1
all_acc, acc, dice = eval_metrics(
ret_metrics = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics='mDice',
nan_to_num=-1)
all_acc, acc, dice = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'Dice']
assert acc[-1] == -1
assert dice[-1] == -1
all_acc, acc, dice, iou = eval_metrics(
ret_metrics = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics=['mDice', 'mIoU'],
metrics='mFscore',
nan_to_num=-1)
all_acc, precision, recall, fscore = ret_metrics['aAcc'], ret_metrics[
'Precision'], ret_metrics['Recall'], ret_metrics['Fscore']
assert precision[-1] == -1
assert recall[-1] == -1
assert fscore[-1] == -1
ret_metrics = eval_metrics(
results,
label,
num_classes,
ignore_index=255,
metrics=['mDice', 'mIoU', 'mFscore'],
nan_to_num=-1)
all_acc, acc, iou, dice, precision, recall, fscore = ret_metrics[
'aAcc'], ret_metrics['Acc'], ret_metrics['IoU'], ret_metrics[
'Dice'], ret_metrics['Precision'], ret_metrics[
'Recall'], ret_metrics['Fscore']
assert acc[-1] == -1
assert dice[-1] == -1
assert iou[-1] == -1
assert precision[-1] == -1
assert recall[-1] == -1
assert fscore[-1] == -1
# Test the bug which is caused by torch.histc.
# torch.histc: https://pytorch.org/docs/stable/generated/torch.histc.html
@ -134,8 +208,10 @@ def test_metrics():
results = np.array([np.repeat(31, 59)])
label = np.array([np.arange(59)])
num_classes = 59
all_acc, acc, iou = eval_metrics(
ret_metrics = eval_metrics(
results, label, num_classes, ignore_index=255, metrics='mIoU')
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'IoU']
assert not np.any(np.isnan(iou))
@ -146,7 +222,9 @@ def test_mean_iou():
results = np.random.randint(0, num_classes, size=pred_size)
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
all_acc, acc, iou = mean_iou(results, label, num_classes, ignore_index)
ret_metrics = mean_iou(results, label, num_classes, ignore_index)
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'IoU']
all_acc_l, acc_l, iou_l = legacy_mean_iou(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
@ -155,10 +233,12 @@ def test_mean_iou():
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
all_acc, acc, iou = mean_iou(
ret_metrics = mean_iou(
results, label, num_classes, ignore_index=255, nan_to_num=-1)
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'IoU']
assert acc[-1] == -1
assert acc[-1] == -1
assert iou[-1] == -1
def test_mean_dice():
@ -168,19 +248,62 @@ def test_mean_dice():
results = np.random.randint(0, num_classes, size=pred_size)
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
all_acc, acc, iou = mean_dice(results, label, num_classes, ignore_index)
all_acc_l, acc_l, iou_l = legacy_mean_dice(results, label, num_classes,
ignore_index)
ret_metrics = mean_dice(results, label, num_classes, ignore_index)
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'Dice']
all_acc_l, acc_l, dice_l = legacy_mean_dice(results, label, num_classes,
ignore_index)
assert all_acc == all_acc_l
assert np.allclose(acc, acc_l)
assert np.allclose(iou, iou_l)
assert np.allclose(iou, dice_l)
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
all_acc, acc, iou = mean_dice(
ret_metrics = mean_dice(
results, label, num_classes, ignore_index=255, nan_to_num=-1)
all_acc, acc, dice = ret_metrics['aAcc'], ret_metrics['Acc'], ret_metrics[
'Dice']
assert acc[-1] == -1
assert iou[-1] == -1
assert dice[-1] == -1
def test_mean_fscore():
pred_size = (10, 30, 30)
num_classes = 19
ignore_index = 255
results = np.random.randint(0, num_classes, size=pred_size)
label = np.random.randint(0, num_classes, size=pred_size)
label[:, 2, 5:10] = ignore_index
ret_metrics = mean_fscore(results, label, num_classes, ignore_index)
all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
all_acc_l, recall_l, precision_l, fscore_l = legacy_mean_fscore(
results, label, num_classes, ignore_index)
assert all_acc == all_acc_l
assert np.allclose(recall, recall_l)
assert np.allclose(precision, precision_l)
assert np.allclose(fscore, fscore_l)
ret_metrics = mean_fscore(
results, label, num_classes, ignore_index, beta=2)
all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
all_acc_l, recall_l, precision_l, fscore_l = legacy_mean_fscore(
results, label, num_classes, ignore_index, beta=2)
assert all_acc == all_acc_l
assert np.allclose(recall, recall_l)
assert np.allclose(precision, precision_l)
assert np.allclose(fscore, fscore_l)
results = np.random.randint(0, 5, size=pred_size)
label = np.random.randint(0, 4, size=pred_size)
ret_metrics = mean_fscore(
results, label, num_classes, ignore_index=255, nan_to_num=-1)
all_acc, recall, precision, fscore = ret_metrics['aAcc'], ret_metrics[
'Recall'], ret_metrics['Precision'], ret_metrics['Fscore']
assert recall[-1] == -1
assert precision[-1] == -1
assert fscore[-1] == -1
def test_filename_inputs():
@ -211,13 +334,14 @@ def test_filename_inputs():
result_files = save_arr(results, 'pred', False, temp_dir)
label_files = save_arr(labels, 'label', True, temp_dir)
all_acc, acc, iou = eval_metrics(
ret_metrics = eval_metrics(
result_files,
label_files,
num_classes,
ignore_index,
metrics='mIoU')
all_acc, acc, iou = ret_metrics['aAcc'], ret_metrics[
'Acc'], ret_metrics['IoU']
all_acc_l, acc_l, iou_l = legacy_mean_iou(results, labels, num_classes,
ignore_index)
assert all_acc == all_acc_l