本文整理匯總了Python中object_detection.utils.metrics.compute_cor_loc方法的典型用法代碼示例。如果您正苦於以下問題：Python metrics.compute_cor_loc方法的具體用法？Python metrics.compute_cor_loc怎麽用？Python metrics.compute_cor_loc使用的例子？那麽, 這裏精選的方法代碼示例或許可以為您提供幫助。您也可以進一步了解該方法所在類object_detection.utils.metrics的用法示例。
在下文中一共展示了metrics.compute_cor_loc方法的5個代碼示例，這些例子默認根據受歡迎程度排序。您可以為喜歡或者感覺有用的代碼點讚，您的評價將有助於係統推薦出更棒的Python代碼示例。
示例1: test_compute_cor_loc
# 需要導入模塊: from object_detection.utils import metrics [as 別名]
# 或者: from object_detection.utils.metrics import compute_cor_loc [as 別名]
def test_compute_cor_loc(self):
num_gt_imgs_per_class = np.array([100, 1, 5, 1, 1], dtype=int)
num_images_correctly_detected_per_class = np.array([10, 0, 1, 0, 0],
dtype=int)
corloc = metrics.compute_cor_loc(num_gt_imgs_per_class,
num_images_correctly_detected_per_class)
expected_corloc = np.array([0.1, 0, 0.2, 0, 0], dtype=float)
self.assertTrue(np.allclose(corloc, expected_corloc)) 示例2: test_compute_cor_loc_nans
# 需要導入模塊: from object_detection.utils import metrics [as 別名]
# 或者: from object_detection.utils.metrics import compute_cor_loc [as 別名]
def test_compute_cor_loc_nans(self):
num_gt_imgs_per_class = np.array([100, 0, 0, 1, 1], dtype=int)
num_images_correctly_detected_per_class = np.array([10, 0, 1, 0, 0],
dtype=int)
corloc = metrics.compute_cor_loc(num_gt_imgs_per_class,
num_images_correctly_detected_per_class)
expected_corloc = np.array([0.1, np.nan, np.nan, 0, 0], dtype=float)
self.assertAllClose(corloc, expected_corloc) 示例3: evaluate
# 需要導入模塊: from object_detection.utils import metrics [as 別名]
# 或者: from object_detection.utils.metrics import compute_cor_loc [as 別名]
def evaluate(self):
"""Compute evaluation result.
Returns:
average_precision_per_class: float numpy array of average precision for
each class.
mean_ap: mean average precision of all classes, float scalar
precisions_per_class: List of precisions, each precision is a float numpy
array
recalls_per_class: List of recalls, each recall is a float numpy array
corloc_per_class: numpy float array
mean_corloc: Mean CorLoc score for each class, float scalar
"""
if (self.num_gt_instances_per_class == 0).any():
logging.warn(
'The following classes have no ground truth examples: %s',
np.squeeze(np.argwhere(self.num_gt_instances_per_class == 0)))
for class_index in range(self.num_class):
if self.num_gt_instances_per_class[class_index] == 0:
continue
scores = np.concatenate(self.scores_per_class[class_index])
tp_fp_labels = np.concatenate(self.tp_fp_labels_per_class[class_index])
precision, recall = metrics.compute_precision_recall(
scores, tp_fp_labels, self.num_gt_instances_per_class[class_index])
self.precisions_per_class.append(precision)
self.recalls_per_class.append(recall)
average_precision = metrics.compute_average_precision(precision, recall)
self.average_precision_per_class[class_index] = average_precision
self.corloc_per_class = metrics.compute_cor_loc(
self.num_gt_imgs_per_class,
self.num_images_correctly_detected_per_class)
mean_ap = np.nanmean(self.average_precision_per_class)
mean_corloc = np.nanmean(self.corloc_per_class)
return (self.average_precision_per_class, mean_ap,
self.precisions_per_class, self.recalls_per_class,
self.corloc_per_class, mean_corloc) 示例4: test_compute_cor_loc
# 需要導入模塊: from object_detection.utils import metrics [as 別名]
# 或者: from object_detection.utils.metrics import compute_cor_loc [as 別名]
def test_compute_cor_loc(self):
num_gt_imgs_per_class = np.array([100, 1, 5, 1, 1], dtype=int)
num_images_correctly_detected_per_class = np.array(
[10, 0, 1, 0, 0], dtype=int)
corloc = metrics.compute_cor_loc(num_gt_imgs_per_class,
num_images_correctly_detected_per_class)
expected_corloc = np.array([0.1, 0, 0.2, 0, 0], dtype=float)
self.assertTrue(np.allclose(corloc, expected_corloc)) 示例5: test_compute_cor_loc_nans
# 需要導入模塊: from object_detection.utils import metrics [as 別名]
# 或者: from object_detection.utils.metrics import compute_cor_loc [as 別名]
def test_compute_cor_loc_nans(self):
num_gt_imgs_per_class = np.array([100, 0, 0, 1, 1], dtype=int)
num_images_correctly_detected_per_class = np.array(
[10, 0, 1, 0, 0], dtype=int)
corloc = metrics.compute_cor_loc(num_gt_imgs_per_class,
num_images_correctly_detected_per_class)
expected_corloc = np.array([0.1, np.nan, np.nan, 0, 0], dtype=float)
self.assertAllClose(corloc, expected_corloc)