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per_image_vrd_evaluation_test.py
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# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""Tests for object_detection.utils.per_image_vrd_evaluation."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
import tensorflow as tf
from object_detection.utils import per_image_vrd_evaluation
class SingleClassPerImageVrdEvaluationTest(tf.test.TestCase):
def setUp(self):
matching_iou_threshold = 0.5
self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
matching_iou_threshold)
box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
self.detected_box_tuples = np.array(
[([0, 0, 1.1, 1], [1, 1, 2, 2]), ([0, 0, 1, 1], [1, 1, 2, 2]),
([1, 1, 2, 2], [0, 0, 1.1, 1])],
dtype=box_data_type)
self.detected_scores = np.array([0.8, 0.2, 0.1], dtype=float)
self.groundtruth_box_tuples = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2])], dtype=box_data_type)
def test_tp_fp_eval(self):
tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_box_tuples, self.groundtruth_box_tuples)
expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
def test_tp_fp_eval_empty_gt(self):
box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
self.detected_box_tuples, np.array([], dtype=box_data_type))
expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
class MultiClassPerImageVrdEvaluationTest(tf.test.TestCase):
def setUp(self):
matching_iou_threshold = 0.5
self.eval = per_image_vrd_evaluation.PerImageVRDEvaluation(
matching_iou_threshold)
box_data_type = np.dtype([('subject', 'f4', (4,)), ('object', 'f4', (4,))])
label_data_type = np.dtype([('subject', 'i4'), ('object', 'i4'),
('relation', 'i4')])
self.detected_box_tuples = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2]), ([0, 0, 1.1, 1], [1, 1, 2, 2]),
([1, 1, 2, 2], [0, 0, 1.1, 1]), ([0, 0, 1, 1], [3, 4, 5, 6])],
dtype=box_data_type)
self.detected_class_tuples = np.array(
[(1, 2, 3), (1, 2, 3), (1, 2, 3), (1, 4, 5)], dtype=label_data_type)
self.detected_scores = np.array([0.2, 0.8, 0.1, 0.5], dtype=float)
self.groundtruth_box_tuples = np.array(
[([0, 0, 1, 1], [1, 1, 2, 2]), ([1, 1, 2, 2], [0, 0, 1.1, 1]),
([0, 0, 1, 1], [3, 4, 5, 5.5])],
dtype=box_data_type)
self.groundtruth_class_tuples = np.array(
[(1, 2, 3), (1, 7, 3), (1, 4, 5)], dtype=label_data_type)
def test_tp_fp_eval(self):
scores, tp_fp_labels, mapping = self.eval.compute_detection_tp_fp(
self.detected_box_tuples, self.detected_scores,
self.detected_class_tuples, self.groundtruth_box_tuples,
self.groundtruth_class_tuples)
expected_scores = np.array([0.8, 0.5, 0.2, 0.1], dtype=float)
expected_tp_fp_labels = np.array([True, True, False, False], dtype=bool)
expected_mapping = np.array([1, 3, 0, 2])
self.assertTrue(np.allclose(expected_scores, scores))
self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
self.assertTrue(np.allclose(expected_mapping, mapping))
if __name__ == '__main__':
tf.test.main()