Python object_detection.utils.metrics.compute_median_rank_at_k() Examples
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code examples of object_detection.utils.metrics.compute_median_rank_at_k().
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Example #1
| Source File: metrics_test.py From Person-Detection-and-Tracking with MIT License | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #2
| Source File: metrics_test.py From multilabel-image-classification-tensorflow with MIT License | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #3
| Source File: metrics_test.py From Gun-Detector with Apache License 2.0 | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #4
| Source File: metrics_test.py From ros_tensorflow with Apache License 2.0 | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_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_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_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_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #7
| Source File: metrics_test.py From vehicle_counting_tensorflow with MIT License | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #8
| Source File: metrics_test.py From MAX-Object-Detector with Apache License 2.0 | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #9
| Source File: metrics_test.py From models with Apache License 2.0 | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_5, 0.5)
Example #10
| Source File: metrics_test.py From open-solution-googleai-object-detection with MIT License | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEqual(median_ranks_1, 0)
self.assertEqual(median_ranks_3, 0.5)
self.assertEqual(median_ranks_3_bool, 0.5)
self.assertEqual(median_ranks_5, 0.5)
Example #11
| Source File: metrics_test.py From g-tensorflow-models with Apache License 2.0 | 6 votes |
|
def test_compute_median_rank_at_k(self):
tp_fp = [
np.array([1, 0, 0], dtype=float),
np.array([0, 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)
]
median_ranks_1 = metrics.compute_median_rank_at_k(tp_fp, 1)
median_ranks_3 = metrics.compute_median_rank_at_k(tp_fp, 3)
median_ranks_5 = metrics.compute_median_rank_at_k(tp_fp, 5)
median_ranks_3_bool = metrics.compute_median_rank_at_k(tp_fp_bool, 3)
self.assertEquals(median_ranks_1, 0)
self.assertEquals(median_ranks_3, 0.5)
self.assertEquals(median_ranks_3_bool, 0.5)
self.assertEquals(median_ranks_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)
