Python object_detection.utils.metrics.compute_recall_at_k() Examples
The following are 19
code examples of object_detection.utils.metrics.compute_recall_at_k().
You can vote up the ones you like or vote down the ones you don't like,
and go to the original project or source file by following the links above each example.
You may also want to check out all available functions/classes of the module
object_detection.utils.metrics
, or try the search function
.
.
Example #1
| Source File: metrics_test.py From Person-Detection-and-Tracking with MIT License | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #2
| Source File: metrics_test.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #3
| Source File: metrics_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #4
| Source File: metrics_test.py From ros_tensorflow with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #5
| Source File: metrics_test.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #6
| Source File: metrics_test.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #7
| Source File: metrics_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #8
| Source File: metrics_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #9
| Source File: metrics_test.py From models with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #10
| Source File: metrics_test.py From open-solution-googleai-object-detection with MIT License | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #11
| Source File: metrics_test.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def test_compute_recall_at_k(self):
num_gt = 4
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 1], dtype=float),
np.array([0, 0, 0, 0, 0], dtype=float)
]
tp_fp_bool = [
np.array([True, False, False], dtype=bool),
np.array([False, True], dtype=float),
np.array([False, False, False, False, False], dtype=float)
]
recall_1 = metrics.compute_recall_at_k(tp_fp, num_gt, 1)
recall_3 = metrics.compute_recall_at_k(tp_fp, num_gt, 3)
recall_5 = metrics.compute_recall_at_k(tp_fp, num_gt, 5)
recall_3_bool = metrics.compute_recall_at_k(tp_fp_bool, num_gt, 3)
self.assertAlmostEqual(recall_1, 0.25)
self.assertAlmostEqual(recall_3, 0.5)
self.assertAlmostEqual(recall_3_bool, 0.5)
self.assertAlmostEqual(recall_5, 0.5)
Example #12
| Source File: vrd_evaluation.py From Live-feed-object-device-identification-using-Tensorflow-and-OpenCV with Apache License 2.0 | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warning('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (six.iteritems(
self._num_gt_instances_per_relationship)):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(
list(self._average_precisions.values()))
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #13
| Source File: vrd_evaluation.py From multilabel-image-classification-tensorflow with MIT License | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #14
| Source File: vrd_evaluation.py From models with Apache License 2.0 | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warning('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (six.iteritems(
self._num_gt_instances_per_relationship)):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(
list(self._average_precisions.values()))
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #15
| Source File: vrd_evaluation.py From g-tensorflow-models with Apache License 2.0 | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #16
| Source File: vrd_evaluation.py From MAX-Object-Detector with Apache License 2.0 | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #17
| Source File: vrd_evaluation.py From BMW-TensorFlow-Training-GUI with Apache License 2.0 | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #18
| Source File: vrd_evaluation.py From Person-Detection-and-Tracking with MIT License | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
Example #19
| Source File: vrd_evaluation.py From vehicle_counting_tensorflow with MIT License | 4 votes |
|
def evaluate(self):
"""Computes evaluation result.
Returns:
A named tuple with the following fields -
average_precision: a float number corresponding to average precision.
precisions: an array of precisions.
recalls: an array of recalls.
recall@50: recall computed on 50 top-scoring samples.
recall@100: recall computed on 100 top-scoring samples.
median_rank@50: median rank computed on 50 top-scoring samples.
median_rank@100: median rank computed on 100 top-scoring samples.
"""
if self._num_gt_instances == 0:
logging.warn('No ground truth instances')
if not self._scores:
scores = np.array([], dtype=float)
tp_fp_labels = np.array([], dtype=bool)
else:
scores = np.concatenate(self._scores)
tp_fp_labels = np.concatenate(self._tp_fp_labels)
relation_field_values = np.concatenate(self._relation_field_values)
for relation_field_value, _ in (
self._num_gt_instances_per_relationship.iteritems()):
precisions, recalls = metrics.compute_precision_recall(
scores[relation_field_values == relation_field_value],
tp_fp_labels[relation_field_values == relation_field_value],
self._num_gt_instances_per_relationship[relation_field_value])
self._average_precisions[
relation_field_value] = metrics.compute_average_precision(
precisions, recalls)
self._mean_average_precision = np.mean(self._average_precisions.values())
self._precisions, self._recalls = metrics.compute_precision_recall(
scores, tp_fp_labels, self._num_gt_instances)
self._weighted_average_precision = metrics.compute_average_precision(
self._precisions, self._recalls)
self._recall_50 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
50))
self._median_rank_50 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 50))
self._recall_100 = (
metrics.compute_recall_at_k(self._tp_fp_labels, self._num_gt_instances,
100))
self._median_rank_100 = (
metrics.compute_median_rank_at_k(self._tp_fp_labels, 100))
return VRDDetectionEvalMetrics(
self._weighted_average_precision, self._mean_average_precision,
self._average_precisions, self._precisions, self._recalls,
self._recall_50, self._recall_100, self._median_rank_50,
self._median_rank_100)
