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deeplab2 / model /post_processor /panoptic_deeplab_test.py
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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 panoptic_deeplab.py."""
import numpy as np
import tensorflow as tf
from deeplab2.model.post_processor import panoptic_deeplab
class PostProcessingTest(tf.test.TestCase):
def test_py_func_merge_semantic_and_instance_maps_can_run(self):
batch = 1
height = 5
width = 5
semantic_prediction = tf.random.uniform((batch, height, width),
minval=0,
maxval=20,
dtype=tf.int32)
instance_maps = tf.random.uniform((batch, height, width),
minval=0,
maxval=3,
dtype=tf.int32)
thing_class_ids = tf.convert_to_tensor([1, 2, 3])
label_divisor = 256
stuff_area_limit = 3
void_label = 255
panoptic_prediction = panoptic_deeplab._merge_semantic_and_instance_maps(
semantic_prediction, instance_maps, thing_class_ids, label_divisor,
stuff_area_limit, void_label)
self.assertListEqual(semantic_prediction.get_shape().as_list(),
panoptic_prediction.get_shape().as_list())
def test_merge_semantic_and_instance_maps_with_a_simple_example(self):
semantic_prediction = tf.convert_to_tensor(
[[[0, 0, 0, 0],
[0, 1, 1, 0],
[0, 2, 2, 0],
[2, 2, 3, 3]]], dtype=tf.int32)
instance_maps = tf.convert_to_tensor(
[[[0, 0, 0, 0],
[0, 0, 0, 0],
[0, 1, 1, 0],
[2, 2, 3, 3]]], dtype=tf.int32)
thing_class_ids = tf.convert_to_tensor([2, 3])
label_divisor = 256
stuff_area_limit = 3
void_label = 255
# The expected_panoptic_prediction is computed as follows.
# For `thing` segmentation, instance 1, 2, and 3 are kept, but instance 3
# will have a new instance ID 1, since it is the first instance in its
# own semantic label.
# For `stuff` segmentation, class-0 region is kept, while class-1 region
# is re-labeled as `void_label * label_divisor` since its area is smaller
# than stuff_area_limit.
expected_panoptic_prediction = tf.convert_to_tensor(
[[[0, 0, 0, 0],
[0, void_label * label_divisor, void_label * label_divisor, 0],
[0, 2 * label_divisor + 1, 2 * label_divisor + 1, 0],
[2 * label_divisor + 2, 2 * label_divisor + 2, 3 * label_divisor + 1,
3 * label_divisor + 1]]], dtype=tf.int32)
panoptic_prediction = panoptic_deeplab._merge_semantic_and_instance_maps(
semantic_prediction, instance_maps, thing_class_ids, label_divisor,
stuff_area_limit, void_label)
np.testing.assert_equal(expected_panoptic_prediction.numpy(),
panoptic_prediction.numpy())
def test_gets_panoptic_predictions_with_score(self):
batch = 1
height = 5
width = 5
classes = 3
semantic_logits = tf.random.uniform((batch, 1, 1, classes))
semantic_logits = tf.tile(semantic_logits, (1, height, width, 1))
center_heatmap = tf.convert_to_tensor([
[1.0, 0.0, 0.0, 0.0, 0.0],
[0.8, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.0, 0.0],
[0.0, 0.0, 0.0, 0.1, 0.7],
[0.0, 0.0, 0.0, 0.0, 0.2],
],
dtype=tf.float32)
center_heatmap = tf.expand_dims(center_heatmap, 0)
center_heatmap = tf.expand_dims(center_heatmap, 3)
center_offsets = tf.zeros((batch, height, width, 2))
center_threshold = 0.0
thing_class_ids = tf.range(classes) # No "stuff" classes.
label_divisor = 256
stuff_area_limit = 16
void_label = classes
nms_kernel_size = 3
keep_k_centers = 2
merge_semantic_and_instance_with_tf_op = True
result = panoptic_deeplab._get_panoptic_predictions(
semantic_logits, center_heatmap, center_offsets, center_threshold,
thing_class_ids, label_divisor, stuff_area_limit, void_label,
nms_kernel_size, keep_k_centers, merge_semantic_and_instance_with_tf_op)
instance_maps = result[2].numpy()
instance_scores = result[4].numpy()
self.assertSequenceEqual(instance_maps.shape, (batch, height, width))
expected_instances = [[
[1, 1, 1, 1, 2],
[1, 1, 1, 2, 2],
[1, 1, 2, 2, 2],
[1, 2, 2, 2, 2],
[1, 2, 2, 2, 2],
]]
np.testing.assert_array_equal(instance_maps, expected_instances)
self.assertSequenceEqual(instance_scores.shape, (batch, height, width))
expected_instance_scores = [[
[1.0, 1.0, 1.0, 1.0, 0.7],
[1.0, 1.0, 1.0, 0.7, 0.7],
[1.0, 1.0, 0.7, 0.7, 0.7],
[1.0, 0.7, 0.7, 0.7, 0.7],
[1.0, 0.7, 0.7, 0.7, 0.7],
]]
np.testing.assert_array_almost_equal(instance_scores,
expected_instance_scores)
if __name__ == '__main__':
tf.test.main()