research/deeplab/evaluation/panoptic_quality_test.py
# Lint as: python2, python3
# Copyright 2019 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 Panoptic Quality metric."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
from absl.testing import absltest
import numpy as np
import six
from deeplab.evaluation import panoptic_quality
from deeplab.evaluation import test_utils
# See the definition of the color names at:
# https://en.wikipedia.org/wiki/Web_colors.
_CLASS_COLOR_MAP = {
(0, 0, 0): 0,
(0, 0, 255): 1, # Person (blue).
(255, 0, 0): 2, # Bear (red).
(0, 255, 0): 3, # Tree (lime).
(255, 0, 255): 4, # Bird (fuchsia).
(0, 255, 255): 5, # Sky (aqua).
(255, 255, 0): 6, # Cat (yellow).
}
class PanopticQualityTest(absltest.TestCase):
def test_perfect_match(self):
categories = np.zeros([6, 6], np.uint16)
instances = np.array([
[1, 1, 1, 1, 1, 1],
[1, 2, 2, 2, 2, 1],
[1, 2, 2, 2, 2, 1],
[1, 2, 2, 2, 2, 1],
[1, 2, 2, 1, 1, 1],
[1, 2, 1, 1, 1, 1],
],
dtype=np.uint16)
pq = panoptic_quality.PanopticQuality(
num_categories=1,
ignored_label=2,
max_instances_per_category=16,
offset=16)
pq.compare_and_accumulate(categories, instances, categories, instances)
np.testing.assert_array_equal(pq.iou_per_class, [2.0])
np.testing.assert_array_equal(pq.tp_per_class, [2])
np.testing.assert_array_equal(pq.fn_per_class, [0])
np.testing.assert_array_equal(pq.fp_per_class, [0])
np.testing.assert_array_equal(pq.result_per_category(), [1.0])
self.assertEqual(pq.result(), 1.0)
def test_totally_wrong(self):
det_categories = np.array([
[0, 0, 0, 0, 0, 0],
[0, 1, 0, 0, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 1, 1, 1, 1, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
],
dtype=np.uint16)
gt_categories = 1 - det_categories
instances = np.zeros([6, 6], np.uint16)
pq = panoptic_quality.PanopticQuality(
num_categories=2,
ignored_label=2,
max_instances_per_category=1,
offset=16)
pq.compare_and_accumulate(gt_categories, instances, det_categories,
instances)
np.testing.assert_array_equal(pq.iou_per_class, [0.0, 0.0])
np.testing.assert_array_equal(pq.tp_per_class, [0, 0])
np.testing.assert_array_equal(pq.fn_per_class, [1, 1])
np.testing.assert_array_equal(pq.fp_per_class, [1, 1])
np.testing.assert_array_equal(pq.result_per_category(), [0.0, 0.0])
self.assertEqual(pq.result(), 0.0)
def test_matches_by_iou(self):
good_det_labels = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 2, 2, 2, 2, 1],
[1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
gt_labels = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 2, 2, 2, 1],
[1, 2, 2, 2, 2, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
pq = panoptic_quality.PanopticQuality(
num_categories=1,
ignored_label=2,
max_instances_per_category=16,
offset=16)
pq.compare_and_accumulate(
np.zeros_like(gt_labels), gt_labels, np.zeros_like(good_det_labels),
good_det_labels)
# iou(1, 1) = 28/30
# iou(2, 2) = 6/8
np.testing.assert_array_almost_equal(pq.iou_per_class, [28 / 30 + 6 / 8])
np.testing.assert_array_equal(pq.tp_per_class, [2])
np.testing.assert_array_equal(pq.fn_per_class, [0])
np.testing.assert_array_equal(pq.fp_per_class, [0])
self.assertAlmostEqual(pq.result(), (28 / 30 + 6 / 8) / 2)
bad_det_labels = np.array(
[
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 2, 2, 1],
[1, 1, 1, 2, 2, 1],
[1, 1, 1, 2, 2, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
pq.reset()
pq.compare_and_accumulate(
np.zeros_like(gt_labels), gt_labels, np.zeros_like(bad_det_labels),
bad_det_labels)
# iou(1, 1) = 27/32
np.testing.assert_array_almost_equal(pq.iou_per_class, [27 / 32])
np.testing.assert_array_equal(pq.tp_per_class, [1])
np.testing.assert_array_equal(pq.fn_per_class, [1])
np.testing.assert_array_equal(pq.fp_per_class, [1])
self.assertAlmostEqual(pq.result(), (27 / 32) * (1 / 2))
def test_wrong_instances(self):
categories = np.array([
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 2, 2, 1, 2, 2],
[1, 2, 2, 1, 2, 2],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
predicted_instances = np.array([
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
],
dtype=np.uint16)
groundtruth_instances = np.zeros([6, 6], dtype=np.uint16)
pq = panoptic_quality.PanopticQuality(
num_categories=3,
ignored_label=0,
max_instances_per_category=10,
offset=100)
pq.compare_and_accumulate(categories, groundtruth_instances, categories,
predicted_instances)
np.testing.assert_array_equal(pq.iou_per_class, [0.0, 1.0, 0.0])
np.testing.assert_array_equal(pq.tp_per_class, [0, 1, 0])
np.testing.assert_array_equal(pq.fn_per_class, [0, 0, 1])
np.testing.assert_array_equal(pq.fp_per_class, [0, 0, 2])
np.testing.assert_array_equal(pq.result_per_category(), [0, 1, 0])
self.assertAlmostEqual(pq.result(), 0.5)
def test_instance_order_is_arbitrary(self):
categories = np.array([
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 2, 2, 1, 2, 2],
[1, 2, 2, 1, 2, 2],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
predicted_instances = np.array([
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 1, 1],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
],
dtype=np.uint16)
groundtruth_instances = np.array([
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 1, 1, 0, 0, 0],
[0, 1, 1, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
[0, 0, 0, 0, 0, 0],
],
dtype=np.uint16)
pq = panoptic_quality.PanopticQuality(
num_categories=3,
ignored_label=0,
max_instances_per_category=10,
offset=100)
pq.compare_and_accumulate(categories, groundtruth_instances, categories,
predicted_instances)
np.testing.assert_array_equal(pq.iou_per_class, [0.0, 1.0, 2.0])
np.testing.assert_array_equal(pq.tp_per_class, [0, 1, 2])
np.testing.assert_array_equal(pq.fn_per_class, [0, 0, 0])
np.testing.assert_array_equal(pq.fp_per_class, [0, 0, 0])
np.testing.assert_array_equal(pq.result_per_category(), [0, 1, 1])
self.assertAlmostEqual(pq.result(), 1.0)
def test_matches_expected(self):
pred_classes = test_utils.read_segmentation_with_rgb_color_map(
'team_pred_class.png', _CLASS_COLOR_MAP)
pred_instances = test_utils.read_test_image(
'team_pred_instance.png', mode='L')
instance_class_map = {
0: 0,
47: 1,
97: 1,
133: 1,
150: 1,
174: 1,
198: 2,
215: 1,
244: 1,
255: 1,
}
gt_instances, gt_classes = test_utils.panoptic_segmentation_with_class_map(
'team_gt_instance.png', instance_class_map)
pq = panoptic_quality.PanopticQuality(
num_categories=3,
ignored_label=0,
max_instances_per_category=256,
offset=256 * 256)
pq.compare_and_accumulate(gt_classes, gt_instances, pred_classes,
pred_instances)
np.testing.assert_array_almost_equal(
pq.iou_per_class, [2.06104, 5.26827, 0.54069], decimal=4)
np.testing.assert_array_equal(pq.tp_per_class, [1, 7, 1])
np.testing.assert_array_equal(pq.fn_per_class, [0, 1, 0])
np.testing.assert_array_equal(pq.fp_per_class, [0, 0, 0])
np.testing.assert_array_almost_equal(pq.result_per_category(),
[2.061038, 0.702436, 0.54069])
self.assertAlmostEqual(pq.result(), 0.62156287)
def test_merge_accumulates_all_across_instances(self):
categories = np.zeros([6, 6], np.uint16)
good_det_labels = np.array([
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 2, 2, 2, 2, 1],
[1, 2, 2, 2, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
gt_labels = np.array([
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 2, 2, 2, 1],
[1, 2, 2, 2, 2, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
good_pq = panoptic_quality.PanopticQuality(
num_categories=1,
ignored_label=2,
max_instances_per_category=16,
offset=16)
for _ in six.moves.range(2):
good_pq.compare_and_accumulate(categories, gt_labels, categories,
good_det_labels)
bad_det_labels = np.array([
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 1, 1, 1],
[1, 1, 1, 2, 2, 1],
[1, 1, 1, 2, 2, 1],
[1, 1, 1, 2, 2, 1],
[1, 1, 1, 1, 1, 1],
],
dtype=np.uint16)
bad_pq = panoptic_quality.PanopticQuality(
num_categories=1,
ignored_label=2,
max_instances_per_category=16,
offset=16)
for _ in six.moves.range(2):
bad_pq.compare_and_accumulate(categories, gt_labels, categories,
bad_det_labels)
good_pq.merge(bad_pq)
np.testing.assert_array_almost_equal(
good_pq.iou_per_class, [2 * (28 / 30 + 6 / 8) + 2 * (27 / 32)])
np.testing.assert_array_equal(good_pq.tp_per_class, [2 * 2 + 2])
np.testing.assert_array_equal(good_pq.fn_per_class, [2])
np.testing.assert_array_equal(good_pq.fp_per_class, [2])
self.assertAlmostEqual(good_pq.result(), 0.63177083)
if __name__ == '__main__':
absltest.main()