Spaces:

akhaliq
/

deeplab2

Runtime error

deeplab2 / model /post_processor /vip_deeplab_test.py

akhaliq3

spaces demo

506da10 almost 3 years ago

No virus

2.26 kB

	# 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()