Merge pull request #3973 from pkulzc/master

Object detection internal changes

research/object_detection/utils/per_image_vrd_evaluation_test.py

+# 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."""
+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, 2, 2]), ([0, 0, 1.1, 1], [1, 1, 2, 2]),
+         ([1, 1, 2, 2], [0, 0, 1.1, 1])],
+        dtype=box_data_type)
+    self.detected_scores = np.array([0.2, 0.8, 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):
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_box_tuples, self.detected_scores,
+        self.groundtruth_box_tuples)
+    expected_scores = np.array([0.8, 0.2, 0.1], dtype=float)
+    expected_tp_fp_labels = np.array([True, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    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,))])
+
+    scores, tp_fp_labels = self.eval._compute_tp_fp_for_single_class(
+        self.detected_box_tuples, self.detected_scores,
+        np.array([], dtype=box_data_type))
+    expected_scores = np.array([0.8, 0.2, 0.1], dtype=float)
+    expected_tp_fp_labels = np.array([False, False, False], dtype=bool)
+    self.assertTrue(np.allclose(expected_scores, scores))
+    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 = 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)
+
+    self.assertTrue(np.allclose(expected_scores, scores))
+    self.assertTrue(np.allclose(expected_tp_fp_labels, tp_fp_labels))
+
+
+if __name__ == '__main__':
+  tf.test.main()