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# coding=utf-8
# Copyright 2021 The Deeplab2 Authors.
#
# 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 metrics."""
import collections
from absl import logging
import numpy as np
import tensorflow as tf
from deeplab2.evaluation import panoptic_quality
from deeplab2.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).
}
def combine_maps(semantic_map, instance_map, label_divisor):
combined_map = instance_map + semantic_map * label_divisor
return tf.cast(combined_map, tf.int32)
class PanopticQualityMetricTest(tf.test.TestCase):
def test_streaming_metric_on_single_image(self):
max_instances_per_category = 1000
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)
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', image_format='L')
pq_obj = panoptic_quality.PanopticQuality(
num_classes=3,
max_instances_per_category=max_instances_per_category,
ignored_label=0, offset=256*256)
y_true = combine_maps(gt_classes, gt_instances, max_instances_per_category)
y_pred = combine_maps(pred_classes, pred_instances,
max_instances_per_category)
pq_obj.update_state(y_true, y_pred)
result = pq_obj.result().numpy()
self.assertAlmostEqual(result[0], 0.62156284, places=4)
self.assertAlmostEqual(result[1], 0.64664984, places=4)
self.assertAlmostEqual(result[2], 0.9666667, places=4)
self.assertEqual(result[3], 4.)
self.assertAlmostEqual(result[4], 0.5)
self.assertEqual(result[5], 0.)
def test_streaming_metric_on_multiple_images(self):
num_classes = 7
bird_gt_instance_class_map = {
92: 5,
176: 3,
255: 4,
}
cat_gt_instance_class_map = {
0: 0,
255: 6,
}
team_gt_instance_class_map = {
0: 0,
47: 1,
97: 1,
133: 1,
150: 1,
174: 1,
198: 2,
215: 1,
244: 1,
255: 1,
}
max_instances_per_category = 256
test_image = collections.namedtuple(
'TestImage',
['gt_class_map', 'gt_path', 'pred_inst_path', 'pred_class_path'])
test_images = [
test_image(bird_gt_instance_class_map, 'bird_gt.png',
'bird_pred_instance.png', 'bird_pred_class.png'),
test_image(cat_gt_instance_class_map, 'cat_gt.png',
'cat_pred_instance.png', 'cat_pred_class.png'),
test_image(team_gt_instance_class_map, 'team_gt_instance.png',
'team_pred_instance.png', 'team_pred_class.png'),
]
gt_classes = []
gt_instances = []
pred_classes = []
pred_instances = []
for test_image in test_images:
(image_gt_instances,
image_gt_classes) = test_utils.panoptic_segmentation_with_class_map(
test_image.gt_path, test_image.gt_class_map)
gt_classes.append(image_gt_classes)
gt_instances.append(image_gt_instances)
pred_classes.append(
test_utils.read_segmentation_with_rgb_color_map(
test_image.pred_class_path, _CLASS_COLOR_MAP))
pred_instances.append(
test_utils.read_test_image(test_image.pred_inst_path,
image_format='L'))
pq_obj = panoptic_quality.PanopticQuality(
num_classes=num_classes,
max_instances_per_category=max_instances_per_category,
ignored_label=0, offset=256*256)
for pred_class, pred_instance, gt_class, gt_instance in zip(
pred_classes, pred_instances, gt_classes, gt_instances):
y_true = combine_maps(gt_class, gt_instance, max_instances_per_category)
y_pred = combine_maps(pred_class, pred_instance,
max_instances_per_category)
pq_obj.update_state(y_true, y_pred)
result = pq_obj.result().numpy()
self.assertAlmostEqual(result[0], 0.76855499, places=4)
self.assertAlmostEqual(result[1], 0.7769174, places=4)
self.assertAlmostEqual(result[2], 0.98888892, places=4)
self.assertEqual(result[3], 2.)
self.assertAlmostEqual(result[4], 1. / 6, places=4)
self.assertEqual(result[5], 0.)
def test_predicted_non_contiguous_ignore_label(self):
max_instances_per_category = 256
pq_obj = panoptic_quality.PanopticQuality(
num_classes=3,
max_instances_per_category=max_instances_per_category,
ignored_label=9,
offset=256 * 256)
gt_class = [
[0, 9, 9],
[1, 2, 2],
[1, 9, 9],
]
gt_instance = [
[0, 2, 2],
[1, 0, 0],
[1, 0, 0],
]
y_true = combine_maps(
np.array(gt_class), np.array(gt_instance), max_instances_per_category)
logging.info('y_true=\n%s', y_true)
pred_class = [
[0, 0, 9],
[1, 1, 1],
[1, 9, 9],
]
pred_instance = [
[0, 0, 0],
[0, 1, 1],
[0, 1, 1],
]
y_pred = combine_maps(
np.array(pred_class), np.array(pred_instance),
max_instances_per_category)
logging.info('y_pred=\n%s', y_pred)
pq_obj.update_state(y_true, y_pred)
result = pq_obj.result().numpy()
# pq
self.assertAlmostEqual(result[0], 2. / 9, places=4)
# sq
self.assertAlmostEqual(result[1], 1. / 3, places=4)
# rq
self.assertAlmostEqual(result[2], 2. / 9, places=4)
# tp
self.assertAlmostEqual(result[3], 1. / 3, places=4)
# fn
self.assertAlmostEqual(result[4], 2. / 3, places=4)
# fp
self.assertAlmostEqual(result[5], 2. / 3, places=4)
if __name__ == '__main__':
tf.test.main()
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