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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.
"""Test for vip_deeplab.py."""
import numpy as np
import tensorflow as tf
from deeplab2.model.post_processor import vip_deeplab
class PostProcessingTest(tf.test.TestCase):
def test_stitch_video_panoptic_prediction(self):
concat_semantic = np.array(
[[[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 2, 2, 0],
[2, 2, 3, 3]]], dtype=np.int32)
concat_instance = np.array(
[[[1, 1, 2, 2],
[1, 0, 0, 2],
[1, 1, 1, 2],
[2, 2, 1, 1]]], dtype=np.int32)
next_semantic = np.array(
[[[0, 1, 1, 0],
[0, 1, 1, 0],
[0, 2, 2, 0],
[2, 2, 3, 3]]], dtype=np.int32)
next_instance = np.array(
[[[2, 0, 0, 1],
[2, 0, 0, 1],
[2, 4, 4, 1],
[5, 5, 3, 3]]], dtype=np.int32)
label_divisor = 1000
concat_panoptic = concat_semantic * label_divisor + concat_instance
next_panoptic = next_semantic * label_divisor + next_instance
new_panoptic = vip_deeplab.stitch_video_panoptic_prediction(
concat_panoptic,
next_panoptic,
label_divisor)
# The expected instance is manually computed. It should receive the IDs
# propagated from concat_instance by IoU matching between concat_panoptic
# and next_panoptic.
expected_semantic = next_semantic
expected_instance = np.array(
[[[1, 0, 0, 2],
[1, 0, 0, 2],
[1, 1, 1, 2],
[2, 2, 1, 1]]], dtype=np.int32)
expected_panoptic = expected_semantic * label_divisor + expected_instance
np.testing.assert_array_equal(expected_panoptic, new_panoptic)
if __name__ == '__main__':
tf.test.main()
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