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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 max_deeplab.py."""
import numpy as np
import tensorflow as tf
from deeplab2.model.post_processor import max_deeplab
class PostProcessingTest(tf.test.TestCase):
def test_filter_by_count(self):
input_index_map = tf.convert_to_tensor(
[[[1, 1, 1, 1],
[1, 2, 2, 1],
[3, 3, 3, 3],
[4, 5, 5, 5]],
[[4, 5, 5, 5],
[3, 3, 3, 3],
[1, 2, 2, 1],
[1, 1, 1, 1]]], dtype=tf.float32)
area_limit = 3
filtered_index_map, mask = max_deeplab._filter_by_count(
input_index_map, area_limit)
expected_filtered_index_map = tf.convert_to_tensor(
[[[1, 1, 1, 1],
[1, 0, 0, 1],
[3, 3, 3, 3],
[0, 5, 5, 5]],
[[0, 5, 5, 5],
[3, 3, 3, 3],
[1, 0, 0, 1],
[1, 1, 1, 1]]], dtype=tf.float32)
np.testing.assert_equal(filtered_index_map.numpy(),
expected_filtered_index_map.numpy())
expected_mask = tf.convert_to_tensor(
[[[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 1, 1],
[0, 1, 1, 1]],
[[0, 1, 1, 1],
[1, 1, 1, 1],
[1, 0, 0, 1],
[1, 1, 1, 1]]], dtype=tf.float32)
np.testing.assert_equal(mask.numpy(), expected_mask.numpy())
def test_get_mask_id_and_semantic_maps(self):
height = 21
width = 21
num_mask_slots = 5
num_thing_stuff_classes = 19
thing_class_ids = list(range(11, 19))
stuff_class_ids = list(range(0, 11))
pixel_space_mask_logits = tf.random.uniform(
(height, width, num_mask_slots), minval=-10, maxval=10)
# Class scores are normalized beforehand (softmax-ed beforehand).
transformer_class_probs = tf.random.uniform(
(num_mask_slots, num_thing_stuff_classes + 1), minval=0, maxval=1)
input_shape = [41, 41]
pixel_confidence_threshold = 0.4
transformer_class_confidence_threshold = 0.7
pieces = 2
mask_id_map, semantic_map, thing_mask, stuff_mask = (
max_deeplab._get_mask_id_and_semantic_maps(
thing_class_ids, stuff_class_ids, pixel_space_mask_logits,
transformer_class_probs, input_shape, pixel_confidence_threshold,
transformer_class_confidence_threshold, pieces)
)
self.assertListEqual(mask_id_map.get_shape().as_list(), input_shape)
self.assertListEqual(semantic_map.get_shape().as_list(), input_shape)
self.assertListEqual(thing_mask.get_shape().as_list(), input_shape)
self.assertListEqual(stuff_mask.get_shape().as_list(), input_shape)
def test_merge_mask_id_and_semantic_maps(self):
mask_id_maps = tf.convert_to_tensor(
[[[1, 1, 1, 1],
[1, 2, 2, 1],
[3, 3, 4, 4],
[5, 5, 6, 6]]], dtype=tf.int32)
semantic_maps = tf.convert_to_tensor(
[[[0, 0, 0, 0],
[0, 1, 1, 0],
[2, 2, 2, 2],
[2, 2, 3, 3]]], dtype=tf.int32)
thing_masks = tf.convert_to_tensor(
[[[0, 0, 0, 0],
[0, 0, 0, 0],
[1, 1, 1, 1],
[1, 0, 1, 1]]], dtype=tf.float32) # thing_class_ids = [2, 3]
stuff_masks = tf.convert_to_tensor(
[[[1, 1, 1, 0],
[1, 1, 1, 1],
[0, 0, 0, 0],
[0, 0, 0, 0]]], dtype=tf.float32) # stuff_class_ids = [0, 1]
batch_size = 3
mask_id_maps = tf.repeat(mask_id_maps, repeats=batch_size, axis=0)
semantic_maps = tf.repeat(semantic_maps, repeats=batch_size, axis=0)
thing_masks = tf.repeat(thing_masks, repeats=batch_size, axis=0)
stuff_masks = tf.repeat(stuff_masks, repeats=batch_size, axis=0)
label_divisor = 100
stuff_area_limit = 3
void_label = 255
thing_area_limit = 2
# The expected_panoptic_prediction is computed as follows.
# All un-certain regions will be labeled as `void_label * label_divisor`.
# For `thing` segmentation, instance 3, 4, and 6 are kept, but instance 5
# is re-labeled as `void_label * label_divisor` since its area had been
# reduced by `confident_regions` and is then filtered by thing_area_limit.
# 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, void_label * label_divisor],
[0, void_label * label_divisor, void_label * label_divisor, 0],
[2 * label_divisor + 3, 2 * label_divisor + 3, 2 * label_divisor + 4,
2 * label_divisor + 4],
[void_label * label_divisor, void_label * label_divisor,
3 * label_divisor + 6, 3 * label_divisor + 6]]],
dtype=tf.int32)
expected_panoptic_prediction = tf.repeat(
expected_panoptic_prediction, repeats=batch_size, axis=0)
panoptic_prediction = (
max_deeplab._merge_mask_id_and_semantic_maps(
mask_id_maps, semantic_maps, thing_masks, stuff_masks, void_label,
label_divisor, thing_area_limit, stuff_area_limit))
np.testing.assert_equal(expected_panoptic_prediction.numpy(),
panoptic_prediction.numpy())
def test_get_panoptic_predictions(self):
batch = 1
height = 5
width = 5
num_thing_stuff_classes = 2
thing_class_ids = list(range(1, num_thing_stuff_classes + 1)) # [1, 2]
label_divisor = 10
stuff_area_limit = 3
void_label = 0 # `class-0` is `void`
o, x = 10, -10
pixel_space_mask_logits = tf.convert_to_tensor(
[[[[o, o, o, o, o], # instance-1 mask
[o, x, x, o, o],
[x, x, x, x, x],
[x, x, x, x, x],
[x, x, x, x, x]],
[[x, x, x, x, x], # instance-2 mask
[x, o, o, x, x],
[x, o, o, x, x],
[x, o, o, x, x],
[x, x, x, x, x]],
[[x, x, x, x, x], # instance-3 mask
[x, x, x, x, x],
[o, x, x, o, o],
[o, x, x, o, o],
[o, o, o, o, o]]]],
dtype=tf.float32)
pixel_space_mask_logits = tf.transpose(pixel_space_mask_logits,
perm=[0, 2, 3, 1]) # b, h, w, c
# class scores are 0-1 normalized beforehand.
# 3-rd column (class-2) represents `void` class scores.
transformer_class_logits = tf.convert_to_tensor(
[[
[o, x, x], # instance-1 -- class-0
[o, x, x], # instance-2 -- class-0
[x, o, x], # instance-3 -- class-1
]], dtype=tf.float32)
input_shape = [5, 5]
pixel_confidence_threshold = 0.4
transformer_class_confidence_threshold = 0.7
thing_area_limit = 3
pieces = 1 # No piece-wise operation used.
panoptic_maps, mask_id_maps, semantic_maps = (
max_deeplab._get_panoptic_predictions(
pixel_space_mask_logits, transformer_class_logits, thing_class_ids,
void_label, label_divisor, thing_area_limit, stuff_area_limit,
input_shape, pixel_confidence_threshold,
transformer_class_confidence_threshold, pieces)
)
self.assertSequenceEqual(panoptic_maps.shape, (batch, height, width))
self.assertSequenceEqual(semantic_maps.shape, (batch, height, width))
self.assertSequenceEqual(mask_id_maps.shape, (batch, height, width))
expected_panoptic_maps = [[ # label_divisor = 10
[11, 11, 11, 11, 11], # 11: semantic_id=1, instance_id=1
[11, 12, 12, 11, 11], # 12: semantic_id=1, instance_id=2
[23, 12, 12, 23, 23], # 23: semantic_id=2, instance_id=3
[23, 12, 12, 23, 23],
[23, 23, 23, 23, 23],
]]
np.testing.assert_array_equal(panoptic_maps, expected_panoptic_maps)
expected_mask_id_maps = [[
[1, 1, 1, 1, 1],
[1, 2, 2, 1, 1],
[3, 2, 2, 3, 3],
[3, 2, 2, 3, 3],
[3, 3, 3, 3, 3],
]]
np.testing.assert_array_equal(mask_id_maps, expected_mask_id_maps)
expected_semantic_maps = [[
[1, 1, 1, 1, 1],
[1, 1, 1, 1, 1],
[2, 1, 1, 2, 2],
[2, 1, 1, 2, 2],
[2, 2, 2, 2, 2],
]]
np.testing.assert_array_equal(semantic_maps, expected_semantic_maps)
if __name__ == '__main__':
tf.test.main()
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