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824 lines
35 KiB
824 lines
35 KiB
# Copyright 2017 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for object_detection.utils.object_detection_evaluation."""
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from absl.testing import parameterized
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import numpy as np
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import tensorflow as tf
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from object_detection import eval_util
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from object_detection.core import standard_fields
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from object_detection.utils import object_detection_evaluation
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class OpenImagesV2EvaluationTest(tf.test.TestCase):
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def test_returns_correct_metric_values(self):
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categories = [{
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'id': 1,
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'name': 'cat'
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}, {
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'id': 2,
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'name': 'dog'
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}, {
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'id': 3,
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'name': 'elephant'
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}]
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oiv2_evaluator = object_detection_evaluation.OpenImagesDetectionEvaluator(
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categories)
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image_key1 = 'img1'
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groundtruth_boxes1 = np.array(
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[[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], dtype=float)
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groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
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oiv2_evaluator.add_single_ground_truth_image_info(image_key1, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes1,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels1,
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standard_fields.InputDataFields.groundtruth_group_of:
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np.array([], dtype=bool)
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})
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image_key2 = 'img2'
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groundtruth_boxes2 = np.array(
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[[10, 10, 11, 11], [500, 500, 510, 510], [10, 10, 12, 12]], dtype=float)
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groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
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groundtruth_is_group_of_list2 = np.array([False, True, False], dtype=bool)
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oiv2_evaluator.add_single_ground_truth_image_info(image_key2, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes2,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels2,
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standard_fields.InputDataFields.groundtruth_group_of:
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groundtruth_is_group_of_list2
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})
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image_key3 = 'img3'
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groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
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groundtruth_class_labels3 = np.array([2], dtype=int)
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oiv2_evaluator.add_single_ground_truth_image_info(image_key3, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes3,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels3
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})
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# Add detections
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image_key = 'img2'
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detected_boxes = np.array(
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[[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
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dtype=float)
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detected_class_labels = np.array([1, 1, 3], dtype=int)
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detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
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oiv2_evaluator.add_single_detected_image_info(image_key, {
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standard_fields.DetectionResultFields.detection_boxes:
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detected_boxes,
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standard_fields.DetectionResultFields.detection_scores:
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detected_scores,
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standard_fields.DetectionResultFields.detection_classes:
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detected_class_labels
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})
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metrics = oiv2_evaluator.evaluate()
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self.assertAlmostEqual(
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metrics['OpenImagesV2_PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
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self.assertAlmostEqual(
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metrics['OpenImagesV2_PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
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self.assertAlmostEqual(
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metrics['OpenImagesV2_PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
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self.assertAlmostEqual(metrics['OpenImagesV2_Precision/mAP@0.5IOU'],
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0.05555555)
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oiv2_evaluator.clear()
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self.assertFalse(oiv2_evaluator._image_ids)
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class OpenImagesDetectionChallengeEvaluatorTest(tf.test.TestCase):
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def test_returns_correct_metric_values(self):
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categories = [{
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'id': 1,
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'name': 'cat'
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}, {
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'id': 2,
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'name': 'dog'
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}, {
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'id': 3,
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'name': 'elephant'
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}]
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oivchallenge_evaluator = (
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object_detection_evaluation.OpenImagesDetectionChallengeEvaluator(
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categories, group_of_weight=0.5))
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image_key = 'img1'
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groundtruth_boxes = np.array(
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[[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]], dtype=float)
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groundtruth_class_labels = np.array([1, 3, 1], dtype=int)
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groundtruth_is_group_of_list = np.array([False, False, True], dtype=bool)
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groundtruth_verified_labels = np.array([1, 2, 3], dtype=int)
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oivchallenge_evaluator.add_single_ground_truth_image_info(
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image_key, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels,
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standard_fields.InputDataFields.groundtruth_group_of:
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groundtruth_is_group_of_list,
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standard_fields.InputDataFields.groundtruth_image_classes:
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groundtruth_verified_labels,
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})
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image_key = 'img2'
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groundtruth_boxes = np.array(
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[[10, 10, 11, 11], [500, 500, 510, 510], [10, 10, 12, 12]], dtype=float)
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groundtruth_class_labels = np.array([1, 1, 3], dtype=int)
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groundtruth_is_group_of_list = np.array([False, False, True], dtype=bool)
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oivchallenge_evaluator.add_single_ground_truth_image_info(
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image_key, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels,
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standard_fields.InputDataFields.groundtruth_group_of:
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groundtruth_is_group_of_list
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})
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image_key = 'img3'
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groundtruth_boxes = np.array([[0, 0, 1, 1]], dtype=float)
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groundtruth_class_labels = np.array([2], dtype=int)
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oivchallenge_evaluator.add_single_ground_truth_image_info(
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image_key, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels
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})
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image_key = 'img1'
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detected_boxes = np.array(
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[[10, 10, 11, 11], [100, 100, 120, 120]], dtype=float)
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detected_class_labels = np.array([2, 2], dtype=int)
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detected_scores = np.array([0.7, 0.8], dtype=float)
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oivchallenge_evaluator.add_single_detected_image_info(
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image_key, {
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standard_fields.DetectionResultFields.detection_boxes:
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detected_boxes,
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standard_fields.DetectionResultFields.detection_scores:
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detected_scores,
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standard_fields.DetectionResultFields.detection_classes:
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detected_class_labels
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})
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image_key = 'img2'
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detected_boxes = np.array(
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[[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220],
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[10, 10, 11, 11]],
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dtype=float)
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detected_class_labels = np.array([1, 1, 2, 3], dtype=int)
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detected_scores = np.array([0.7, 0.8, 0.5, 0.9], dtype=float)
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oivchallenge_evaluator.add_single_detected_image_info(
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image_key, {
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standard_fields.DetectionResultFields.detection_boxes:
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detected_boxes,
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standard_fields.DetectionResultFields.detection_scores:
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detected_scores,
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standard_fields.DetectionResultFields.detection_classes:
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detected_class_labels
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})
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image_key = 'img3'
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detected_boxes = np.array([[0, 0, 1, 1]], dtype=float)
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detected_class_labels = np.array([2], dtype=int)
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detected_scores = np.array([0.5], dtype=float)
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oivchallenge_evaluator.add_single_detected_image_info(
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image_key, {
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standard_fields.DetectionResultFields.detection_boxes:
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detected_boxes,
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standard_fields.DetectionResultFields.detection_scores:
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detected_scores,
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standard_fields.DetectionResultFields.detection_classes:
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detected_class_labels
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})
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metrics = oivchallenge_evaluator.evaluate()
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self.assertAlmostEqual(
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metrics['OpenImagesChallenge2018_PerformanceByCategory/AP@0.5IOU/dog'],
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0.3333333333)
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self.assertAlmostEqual(
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metrics[
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'OpenImagesChallenge2018_PerformanceByCategory/AP@0.5IOU/elephant'],
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0.333333333333)
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self.assertAlmostEqual(
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metrics['OpenImagesChallenge2018_PerformanceByCategory/AP@0.5IOU/cat'],
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0.142857142857)
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self.assertAlmostEqual(
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metrics['OpenImagesChallenge2018_Precision/mAP@0.5IOU'], 0.269841269)
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oivchallenge_evaluator.clear()
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self.assertFalse(oivchallenge_evaluator._image_ids)
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class PascalEvaluationTest(tf.test.TestCase):
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def test_returns_correct_metric_values_on_boxes(self):
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categories = [{'id': 1, 'name': 'cat'},
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{'id': 2, 'name': 'dog'},
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{'id': 3, 'name': 'elephant'}]
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# Add groundtruth
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pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
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categories)
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image_key1 = 'img1'
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groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
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dtype=float)
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groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
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pascal_evaluator.add_single_ground_truth_image_info(
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image_key1,
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{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels1,
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standard_fields.InputDataFields.groundtruth_difficult:
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np.array([], dtype=bool)})
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image_key2 = 'img2'
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groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
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[10, 10, 12, 12]], dtype=float)
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groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
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groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
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pascal_evaluator.add_single_ground_truth_image_info(
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image_key2,
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{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels2,
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standard_fields.InputDataFields.groundtruth_difficult:
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groundtruth_is_difficult_list2})
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image_key3 = 'img3'
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groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
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groundtruth_class_labels3 = np.array([2], dtype=int)
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pascal_evaluator.add_single_ground_truth_image_info(
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image_key3,
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{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes3,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels3})
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# Add detections
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image_key = 'img2'
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detected_boxes = np.array(
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[[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
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dtype=float)
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detected_class_labels = np.array([1, 1, 3], dtype=int)
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detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
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pascal_evaluator.add_single_detected_image_info(
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image_key,
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{standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
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standard_fields.DetectionResultFields.detection_scores:
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detected_scores,
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standard_fields.DetectionResultFields.detection_classes:
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detected_class_labels})
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metrics = pascal_evaluator.evaluate()
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self.assertAlmostEqual(
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metrics['PascalBoxes_PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
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self.assertAlmostEqual(
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metrics['PascalBoxes_PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
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self.assertAlmostEqual(
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metrics['PascalBoxes_PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
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self.assertAlmostEqual(metrics['PascalBoxes_Precision/mAP@0.5IOU'],
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0.05555555)
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pascal_evaluator.clear()
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self.assertFalse(pascal_evaluator._image_ids)
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def test_returns_correct_metric_values_on_masks(self):
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categories = [{'id': 1, 'name': 'cat'},
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{'id': 2, 'name': 'dog'},
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{'id': 3, 'name': 'elephant'}]
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# Add groundtruth
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pascal_evaluator = (
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object_detection_evaluation.PascalInstanceSegmentationEvaluator(
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categories))
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image_key1 = 'img1'
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groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
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dtype=float)
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groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
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groundtruth_masks_1_0 = np.array([[1, 0, 0, 0],
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[1, 0, 0, 0],
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[1, 0, 0, 0]], dtype=np.uint8)
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groundtruth_masks_1_1 = np.array([[0, 0, 1, 0],
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[0, 0, 1, 0],
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[0, 0, 1, 0]], dtype=np.uint8)
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groundtruth_masks_1_2 = np.array([[0, 1, 0, 0],
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[0, 1, 0, 0],
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[0, 1, 0, 0]], dtype=np.uint8)
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groundtruth_masks1 = np.stack(
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[groundtruth_masks_1_0, groundtruth_masks_1_1, groundtruth_masks_1_2],
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axis=0)
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pascal_evaluator.add_single_ground_truth_image_info(
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image_key1, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes1,
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standard_fields.InputDataFields.groundtruth_instance_masks:
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groundtruth_masks1,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels1,
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standard_fields.InputDataFields.groundtruth_difficult:
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np.array([], dtype=bool)
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})
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image_key2 = 'img2'
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groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
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[10, 10, 12, 12]], dtype=float)
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groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
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groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
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groundtruth_masks_2_0 = np.array([[1, 1, 1, 1],
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[0, 0, 0, 0],
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[0, 0, 0, 0]], dtype=np.uint8)
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groundtruth_masks_2_1 = np.array([[0, 0, 0, 0],
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[1, 1, 1, 1],
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[0, 0, 0, 0]], dtype=np.uint8)
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groundtruth_masks_2_2 = np.array([[0, 0, 0, 0],
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[0, 0, 0, 0],
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[1, 1, 1, 1]], dtype=np.uint8)
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groundtruth_masks2 = np.stack(
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[groundtruth_masks_2_0, groundtruth_masks_2_1, groundtruth_masks_2_2],
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axis=0)
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pascal_evaluator.add_single_ground_truth_image_info(
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image_key2, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes2,
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standard_fields.InputDataFields.groundtruth_instance_masks:
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groundtruth_masks2,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels2,
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standard_fields.InputDataFields.groundtruth_difficult:
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groundtruth_is_difficult_list2
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})
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image_key3 = 'img3'
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groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
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groundtruth_class_labels3 = np.array([2], dtype=int)
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groundtruth_masks_3_0 = np.array([[1, 1, 1, 1],
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[1, 1, 1, 1],
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[1, 1, 1, 1]], dtype=np.uint8)
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groundtruth_masks3 = np.stack([groundtruth_masks_3_0], axis=0)
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pascal_evaluator.add_single_ground_truth_image_info(
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image_key3, {
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standard_fields.InputDataFields.groundtruth_boxes:
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groundtruth_boxes3,
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standard_fields.InputDataFields.groundtruth_instance_masks:
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groundtruth_masks3,
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standard_fields.InputDataFields.groundtruth_classes:
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groundtruth_class_labels3
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})
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# Add detections
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image_key = 'img2'
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detected_boxes = np.array(
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[[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
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dtype=float)
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detected_class_labels = np.array([1, 1, 3], dtype=int)
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detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
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detected_masks_0 = np.array([[1, 1, 1, 1],
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[0, 0, 1, 0],
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[0, 0, 0, 0]], dtype=np.uint8)
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detected_masks_1 = np.array([[1, 0, 0, 0],
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[1, 1, 0, 0],
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[0, 0, 0, 0]], dtype=np.uint8)
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detected_masks_2 = np.array([[0, 1, 0, 0],
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[0, 1, 1, 0],
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[0, 1, 0, 0]], dtype=np.uint8)
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detected_masks = np.stack(
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[detected_masks_0, detected_masks_1, detected_masks_2], axis=0)
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pascal_evaluator.add_single_detected_image_info(
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image_key, {
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standard_fields.DetectionResultFields.detection_boxes:
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detected_boxes,
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standard_fields.DetectionResultFields.detection_masks:
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detected_masks,
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standard_fields.DetectionResultFields.detection_scores:
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detected_scores,
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standard_fields.DetectionResultFields.detection_classes:
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detected_class_labels
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})
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metrics = pascal_evaluator.evaluate()
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self.assertAlmostEqual(
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metrics['PascalMasks_PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
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self.assertAlmostEqual(
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metrics['PascalMasks_PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
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self.assertAlmostEqual(
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metrics['PascalMasks_PerformanceByCategory/AP@0.5IOU/cat'], 0.16666666)
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self.assertAlmostEqual(metrics['PascalMasks_Precision/mAP@0.5IOU'],
|
|
0.05555555)
|
|
pascal_evaluator.clear()
|
|
self.assertFalse(pascal_evaluator._image_ids)
|
|
|
|
def test_value_error_on_duplicate_images(self):
|
|
categories = [{'id': 1, 'name': 'cat'},
|
|
{'id': 2, 'name': 'dog'},
|
|
{'id': 3, 'name': 'elephant'}]
|
|
# Add groundtruth
|
|
pascal_evaluator = object_detection_evaluation.PascalDetectionEvaluator(
|
|
categories)
|
|
image_key1 = 'img1'
|
|
groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
|
|
dtype=float)
|
|
groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
|
|
pascal_evaluator.add_single_ground_truth_image_info(
|
|
image_key1,
|
|
{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels1})
|
|
with self.assertRaises(ValueError):
|
|
pascal_evaluator.add_single_ground_truth_image_info(
|
|
image_key1,
|
|
{standard_fields.InputDataFields.groundtruth_boxes:
|
|
groundtruth_boxes1,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels1})
|
|
|
|
|
|
class WeightedPascalEvaluationTest(tf.test.TestCase):
|
|
|
|
def setUp(self):
|
|
self.categories = [{'id': 1, 'name': 'cat'},
|
|
{'id': 2, 'name': 'dog'},
|
|
{'id': 3, 'name': 'elephant'}]
|
|
|
|
def create_and_add_common_ground_truth(self):
|
|
# Add groundtruth
|
|
self.wp_eval = (
|
|
object_detection_evaluation.WeightedPascalDetectionEvaluator(
|
|
self.categories))
|
|
|
|
image_key1 = 'img1'
|
|
groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
|
|
dtype=float)
|
|
groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
|
|
self.wp_eval.add_single_ground_truth_image_info(
|
|
image_key1,
|
|
{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels1})
|
|
# add 'img2' separately
|
|
image_key3 = 'img3'
|
|
groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
|
|
groundtruth_class_labels3 = np.array([2], dtype=int)
|
|
self.wp_eval.add_single_ground_truth_image_info(
|
|
image_key3,
|
|
{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes3,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels3})
|
|
|
|
def add_common_detected(self):
|
|
image_key = 'img2'
|
|
detected_boxes = np.array(
|
|
[[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
|
|
dtype=float)
|
|
detected_class_labels = np.array([1, 1, 3], dtype=int)
|
|
detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
|
|
self.wp_eval.add_single_detected_image_info(
|
|
image_key,
|
|
{standard_fields.DetectionResultFields.detection_boxes: detected_boxes,
|
|
standard_fields.DetectionResultFields.detection_scores:
|
|
detected_scores,
|
|
standard_fields.DetectionResultFields.detection_classes:
|
|
detected_class_labels})
|
|
|
|
def test_returns_correct_metric_values(self):
|
|
self.create_and_add_common_ground_truth()
|
|
image_key2 = 'img2'
|
|
groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
|
|
[10, 10, 12, 12]], dtype=float)
|
|
groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
|
|
self.wp_eval.add_single_ground_truth_image_info(
|
|
image_key2,
|
|
{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels2
|
|
})
|
|
self.add_common_detected()
|
|
|
|
metrics = self.wp_eval.evaluate()
|
|
self.assertAlmostEqual(
|
|
metrics[self.wp_eval._metric_prefix +
|
|
'PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
|
|
self.assertAlmostEqual(
|
|
metrics[self.wp_eval._metric_prefix +
|
|
'PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
|
|
self.assertAlmostEqual(
|
|
metrics[self.wp_eval._metric_prefix +
|
|
'PerformanceByCategory/AP@0.5IOU/cat'], 0.5 / 4)
|
|
self.assertAlmostEqual(metrics[self.wp_eval._metric_prefix +
|
|
'Precision/mAP@0.5IOU'],
|
|
1. / (4 + 1 + 2) / 3)
|
|
self.wp_eval.clear()
|
|
self.assertFalse(self.wp_eval._image_ids)
|
|
|
|
def test_returns_correct_metric_values_with_difficult_list(self):
|
|
self.create_and_add_common_ground_truth()
|
|
image_key2 = 'img2'
|
|
groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
|
|
[10, 10, 12, 12]], dtype=float)
|
|
groundtruth_class_labels2 = np.array([1, 1, 3], dtype=int)
|
|
groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
|
|
self.wp_eval.add_single_ground_truth_image_info(
|
|
image_key2,
|
|
{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes2,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels2,
|
|
standard_fields.InputDataFields.groundtruth_difficult:
|
|
groundtruth_is_difficult_list2
|
|
})
|
|
self.add_common_detected()
|
|
|
|
metrics = self.wp_eval.evaluate()
|
|
self.assertAlmostEqual(
|
|
metrics[self.wp_eval._metric_prefix +
|
|
'PerformanceByCategory/AP@0.5IOU/dog'], 0.0)
|
|
self.assertAlmostEqual(
|
|
metrics[self.wp_eval._metric_prefix +
|
|
'PerformanceByCategory/AP@0.5IOU/elephant'], 0.0)
|
|
self.assertAlmostEqual(
|
|
metrics[self.wp_eval._metric_prefix +
|
|
'PerformanceByCategory/AP@0.5IOU/cat'], 0.5 / 3)
|
|
self.assertAlmostEqual(metrics[self.wp_eval._metric_prefix +
|
|
'Precision/mAP@0.5IOU'],
|
|
1. / (3 + 1 + 2) / 3)
|
|
self.wp_eval.clear()
|
|
self.assertFalse(self.wp_eval._image_ids)
|
|
|
|
def test_value_error_on_duplicate_images(self):
|
|
# Add groundtruth
|
|
self.wp_eval = (
|
|
object_detection_evaluation.WeightedPascalDetectionEvaluator(
|
|
self.categories))
|
|
image_key1 = 'img1'
|
|
groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
|
|
dtype=float)
|
|
groundtruth_class_labels1 = np.array([1, 3, 1], dtype=int)
|
|
self.wp_eval.add_single_ground_truth_image_info(
|
|
image_key1,
|
|
{standard_fields.InputDataFields.groundtruth_boxes: groundtruth_boxes1,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels1})
|
|
with self.assertRaises(ValueError):
|
|
self.wp_eval.add_single_ground_truth_image_info(
|
|
image_key1,
|
|
{standard_fields.InputDataFields.groundtruth_boxes:
|
|
groundtruth_boxes1,
|
|
standard_fields.InputDataFields.groundtruth_classes:
|
|
groundtruth_class_labels1})
|
|
|
|
|
|
class ObjectDetectionEvaluationTest(tf.test.TestCase):
|
|
|
|
def setUp(self):
|
|
num_groundtruth_classes = 3
|
|
self.od_eval = object_detection_evaluation.ObjectDetectionEvaluation(
|
|
num_groundtruth_classes)
|
|
|
|
image_key1 = 'img1'
|
|
groundtruth_boxes1 = np.array([[0, 0, 1, 1], [0, 0, 2, 2], [0, 0, 3, 3]],
|
|
dtype=float)
|
|
groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
|
|
self.od_eval.add_single_ground_truth_image_info(
|
|
image_key1, groundtruth_boxes1, groundtruth_class_labels1)
|
|
image_key2 = 'img2'
|
|
groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
|
|
[10, 10, 12, 12]], dtype=float)
|
|
groundtruth_class_labels2 = np.array([0, 0, 2], dtype=int)
|
|
groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
|
|
groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
|
|
self.od_eval.add_single_ground_truth_image_info(
|
|
image_key2, groundtruth_boxes2, groundtruth_class_labels2,
|
|
groundtruth_is_difficult_list2, groundtruth_is_group_of_list2)
|
|
|
|
image_key3 = 'img3'
|
|
groundtruth_boxes3 = np.array([[0, 0, 1, 1]], dtype=float)
|
|
groundtruth_class_labels3 = np.array([1], dtype=int)
|
|
self.od_eval.add_single_ground_truth_image_info(
|
|
image_key3, groundtruth_boxes3, groundtruth_class_labels3)
|
|
|
|
image_key = 'img2'
|
|
detected_boxes = np.array(
|
|
[[10, 10, 11, 11], [100, 100, 120, 120], [100, 100, 220, 220]],
|
|
dtype=float)
|
|
detected_class_labels = np.array([0, 0, 2], dtype=int)
|
|
detected_scores = np.array([0.7, 0.8, 0.9], dtype=float)
|
|
self.od_eval.add_single_detected_image_info(
|
|
image_key, detected_boxes, detected_scores, detected_class_labels)
|
|
|
|
def test_value_error_on_zero_classes(self):
|
|
with self.assertRaises(ValueError):
|
|
object_detection_evaluation.ObjectDetectionEvaluation(
|
|
num_groundtruth_classes=0)
|
|
|
|
def test_add_single_ground_truth_image_info(self):
|
|
expected_num_gt_instances_per_class = np.array([3, 1, 1], dtype=int)
|
|
expected_num_gt_imgs_per_class = np.array([2, 1, 2], dtype=int)
|
|
self.assertTrue(np.array_equal(expected_num_gt_instances_per_class,
|
|
self.od_eval.num_gt_instances_per_class))
|
|
self.assertTrue(np.array_equal(expected_num_gt_imgs_per_class,
|
|
self.od_eval.num_gt_imgs_per_class))
|
|
groundtruth_boxes2 = np.array([[10, 10, 11, 11], [500, 500, 510, 510],
|
|
[10, 10, 12, 12]], dtype=float)
|
|
self.assertTrue(np.allclose(self.od_eval.groundtruth_boxes['img2'],
|
|
groundtruth_boxes2))
|
|
groundtruth_is_difficult_list2 = np.array([False, True, False], dtype=bool)
|
|
self.assertTrue(np.allclose(
|
|
self.od_eval.groundtruth_is_difficult_list['img2'],
|
|
groundtruth_is_difficult_list2))
|
|
groundtruth_is_group_of_list2 = np.array([False, False, True], dtype=bool)
|
|
self.assertTrue(
|
|
np.allclose(self.od_eval.groundtruth_is_group_of_list['img2'],
|
|
groundtruth_is_group_of_list2))
|
|
|
|
groundtruth_class_labels1 = np.array([0, 2, 0], dtype=int)
|
|
self.assertTrue(np.array_equal(self.od_eval.groundtruth_class_labels[
|
|
'img1'], groundtruth_class_labels1))
|
|
|
|
def test_add_single_detected_image_info(self):
|
|
expected_scores_per_class = [[np.array([0.8, 0.7], dtype=float)], [],
|
|
[np.array([0.9], dtype=float)]]
|
|
expected_tp_fp_labels_per_class = [[np.array([0, 1], dtype=bool)], [],
|
|
[np.array([0], dtype=bool)]]
|
|
expected_num_images_correctly_detected_per_class = np.array([0, 0, 0],
|
|
dtype=int)
|
|
for i in range(self.od_eval.num_class):
|
|
for j in range(len(expected_scores_per_class[i])):
|
|
self.assertTrue(np.allclose(expected_scores_per_class[i][j],
|
|
self.od_eval.scores_per_class[i][j]))
|
|
self.assertTrue(np.array_equal(expected_tp_fp_labels_per_class[i][
|
|
j], self.od_eval.tp_fp_labels_per_class[i][j]))
|
|
self.assertTrue(np.array_equal(
|
|
expected_num_images_correctly_detected_per_class,
|
|
self.od_eval.num_images_correctly_detected_per_class))
|
|
|
|
def test_evaluate(self):
|
|
(average_precision_per_class, mean_ap, precisions_per_class,
|
|
recalls_per_class, corloc_per_class,
|
|
mean_corloc) = self.od_eval.evaluate()
|
|
expected_precisions_per_class = [np.array([0, 0.5], dtype=float),
|
|
np.array([], dtype=float),
|
|
np.array([0], dtype=float)]
|
|
expected_recalls_per_class = [
|
|
np.array([0, 1. / 3.], dtype=float), np.array([], dtype=float),
|
|
np.array([0], dtype=float)
|
|
]
|
|
expected_average_precision_per_class = np.array([1. / 6., 0, 0],
|
|
dtype=float)
|
|
expected_corloc_per_class = np.array([0, np.divide(0, 0), 0], dtype=float)
|
|
expected_mean_ap = 1. / 18
|
|
expected_mean_corloc = 0.0
|
|
for i in range(self.od_eval.num_class):
|
|
self.assertTrue(np.allclose(expected_precisions_per_class[i],
|
|
precisions_per_class[i]))
|
|
self.assertTrue(np.allclose(expected_recalls_per_class[i],
|
|
recalls_per_class[i]))
|
|
self.assertTrue(np.allclose(expected_average_precision_per_class,
|
|
average_precision_per_class))
|
|
self.assertTrue(np.allclose(expected_corloc_per_class, corloc_per_class))
|
|
self.assertAlmostEqual(expected_mean_ap, mean_ap)
|
|
self.assertAlmostEqual(expected_mean_corloc, mean_corloc)
|
|
|
|
|
|
class ObjectDetectionEvaluatorTest(tf.test.TestCase, parameterized.TestCase):
|
|
|
|
def setUp(self):
|
|
self.categories = [{
|
|
'id': 1,
|
|
'name': 'person'
|
|
}, {
|
|
'id': 2,
|
|
'name': 'dog'
|
|
}, {
|
|
'id': 3,
|
|
'name': 'cat'
|
|
}]
|
|
self.od_eval = object_detection_evaluation.ObjectDetectionEvaluator(
|
|
categories=self.categories)
|
|
|
|
def _make_evaluation_dict(self,
|
|
resized_groundtruth_masks=False,
|
|
batch_size=1,
|
|
max_gt_boxes=None,
|
|
scale_to_absolute=False):
|
|
input_data_fields = standard_fields.InputDataFields
|
|
detection_fields = standard_fields.DetectionResultFields
|
|
|
|
image = tf.zeros(shape=[batch_size, 20, 20, 3], dtype=tf.uint8)
|
|
if batch_size == 1:
|
|
key = tf.constant('image1')
|
|
else:
|
|
key = tf.constant([str(i) for i in range(batch_size)])
|
|
detection_boxes = tf.concat([
|
|
tf.tile(
|
|
tf.constant([[[0., 0., 1., 1.]]]), multiples=[batch_size - 1, 1, 1
|
|
]),
|
|
tf.constant([[[0., 0., 0.5, 0.5]]])
|
|
],
|
|
axis=0)
|
|
detection_scores = tf.concat([
|
|
tf.tile(tf.constant([[0.5]]), multiples=[batch_size - 1, 1]),
|
|
tf.constant([[0.8]])
|
|
],
|
|
axis=0)
|
|
detection_classes = tf.tile(tf.constant([[0]]), multiples=[batch_size, 1])
|
|
detection_masks = tf.tile(
|
|
tf.ones(shape=[1, 2, 20, 20], dtype=tf.float32),
|
|
multiples=[batch_size, 1, 1, 1])
|
|
groundtruth_boxes = tf.constant([[0., 0., 1., 1.]])
|
|
groundtruth_classes = tf.constant([1])
|
|
groundtruth_instance_masks = tf.ones(shape=[1, 20, 20], dtype=tf.uint8)
|
|
num_detections = tf.ones([batch_size])
|
|
if resized_groundtruth_masks:
|
|
groundtruth_instance_masks = tf.ones(shape=[1, 10, 10], dtype=tf.uint8)
|
|
|
|
if batch_size > 1:
|
|
groundtruth_boxes = tf.tile(
|
|
tf.expand_dims(groundtruth_boxes, 0), multiples=[batch_size, 1, 1])
|
|
groundtruth_classes = tf.tile(
|
|
tf.expand_dims(groundtruth_classes, 0), multiples=[batch_size, 1])
|
|
groundtruth_instance_masks = tf.tile(
|
|
tf.expand_dims(groundtruth_instance_masks, 0),
|
|
multiples=[batch_size, 1, 1, 1])
|
|
|
|
detections = {
|
|
detection_fields.detection_boxes: detection_boxes,
|
|
detection_fields.detection_scores: detection_scores,
|
|
detection_fields.detection_classes: detection_classes,
|
|
detection_fields.detection_masks: detection_masks,
|
|
detection_fields.num_detections: num_detections
|
|
}
|
|
groundtruth = {
|
|
input_data_fields.groundtruth_boxes:
|
|
groundtruth_boxes,
|
|
input_data_fields.groundtruth_classes:
|
|
groundtruth_classes,
|
|
input_data_fields.groundtruth_instance_masks:
|
|
groundtruth_instance_masks,
|
|
}
|
|
if batch_size > 1:
|
|
return eval_util.result_dict_for_batched_example(
|
|
image,
|
|
key,
|
|
detections,
|
|
groundtruth,
|
|
scale_to_absolute=scale_to_absolute,
|
|
max_gt_boxes=max_gt_boxes)
|
|
else:
|
|
return eval_util.result_dict_for_single_example(
|
|
image,
|
|
key,
|
|
detections,
|
|
groundtruth,
|
|
scale_to_absolute=scale_to_absolute)
|
|
|
|
@parameterized.parameters({
|
|
'batch_size': 1,
|
|
'expected_map': 0,
|
|
'max_gt_boxes': None,
|
|
'scale_to_absolute': True
|
|
}, {
|
|
'batch_size': 8,
|
|
'expected_map': 0.765625,
|
|
'max_gt_boxes': [1],
|
|
'scale_to_absolute': True
|
|
}, {
|
|
'batch_size': 1,
|
|
'expected_map': 0,
|
|
'max_gt_boxes': None,
|
|
'scale_to_absolute': False
|
|
}, {
|
|
'batch_size': 8,
|
|
'expected_map': 0.765625,
|
|
'max_gt_boxes': [1],
|
|
'scale_to_absolute': False
|
|
})
|
|
def test_get_estimator_eval_metric_ops(self,
|
|
batch_size=1,
|
|
expected_map=1,
|
|
max_gt_boxes=None,
|
|
scale_to_absolute=False):
|
|
|
|
eval_dict = self._make_evaluation_dict(
|
|
batch_size=batch_size,
|
|
max_gt_boxes=max_gt_boxes,
|
|
scale_to_absolute=scale_to_absolute)
|
|
tf.logging.info('eval_dict: {}'.format(eval_dict))
|
|
metric_ops = self.od_eval.get_estimator_eval_metric_ops(eval_dict)
|
|
_, update_op = metric_ops['Precision/mAP@0.5IOU']
|
|
|
|
with self.test_session() as sess:
|
|
metrics = {}
|
|
for key, (value_op, _) in metric_ops.iteritems():
|
|
metrics[key] = value_op
|
|
sess.run(update_op)
|
|
metrics = sess.run(metrics)
|
|
self.assertAlmostEqual(expected_map, metrics['Precision/mAP@0.5IOU'])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
tf.test.main()
|