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# Copyright 2023 The TensorFlow Authors. All Rights Reserved.
#
# 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 segmentation_heads.py."""
# Import libraries
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.vision.modeling.heads import segmentation_heads
class SegmentationHeadTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(2, 'pyramid_fusion', None, None),
(3, 'pyramid_fusion', None, None),
(2, 'panoptic_fpn_fusion', 2, 5),
(2, 'panoptic_fpn_fusion', 2, 6),
(3, 'panoptic_fpn_fusion', 3, 5),
(3, 'panoptic_fpn_fusion', 3, 6),
(3, 'deeplabv3plus', 3, 6),
(3, 'deeplabv3plus_sum_to_merge', 3, 6))
def test_forward(self, level, feature_fusion,
decoder_min_level, decoder_max_level):
backbone_features = {
'3': np.random.rand(2, 128, 128, 16),
'4': np.random.rand(2, 64, 64, 16),
'5': np.random.rand(2, 32, 32, 16),
}
decoder_features = {
'3': np.random.rand(2, 128, 128, 64),
'4': np.random.rand(2, 64, 64, 64),
'5': np.random.rand(2, 32, 32, 64),
'6': np.random.rand(2, 16, 16, 64),
}
if feature_fusion == 'panoptic_fpn_fusion':
backbone_features['2'] = np.random.rand(2, 256, 256, 16)
decoder_features['2'] = np.random.rand(2, 256, 256, 64)
head = segmentation_heads.SegmentationHead(
num_classes=10,
level=level,
low_level=decoder_min_level,
low_level_num_filters=64,
feature_fusion=feature_fusion,
decoder_min_level=decoder_min_level,
decoder_max_level=decoder_max_level,
num_decoder_filters=64)
logits = head((backbone_features, decoder_features))
if str(level) in decoder_features:
self.assertAllEqual(logits.numpy().shape, [
2, decoder_features[str(level)].shape[1],
decoder_features[str(level)].shape[2], 10
])
def test_serialize_deserialize(self):
head = segmentation_heads.SegmentationHead(num_classes=10, level=3)
config = head.get_config()
new_head = segmentation_heads.SegmentationHead.from_config(config)
self.assertAllEqual(head.get_config(), new_head.get_config())
class MaskScoringHeadTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(1, 1, 64, [4, 4]),
(2, 1, 64, [4, 4]),
(3, 1, 64, [4, 4]),
(1, 2, 32, [8, 8]),
(2, 2, 32, [8, 8]),
(3, 2, 32, [8, 8]),)
def test_forward(self, num_convs, num_fcs,
num_filters, fc_input_size):
features = np.random.rand(2, 64, 64, 16)
head = segmentation_heads.MaskScoring(
num_classes=2,
num_convs=num_convs,
num_filters=num_filters,
fc_dims=128,
num_fcs=num_fcs,
fc_input_size=fc_input_size)
scores = head(features)
self.assertAllEqual(scores.numpy().shape, [2, 2])
def test_serialize_deserialize(self):
head = segmentation_heads.MaskScoring(
num_classes=2, fc_input_size=[4, 4], fc_dims=128)
config = head.get_config()
new_head = segmentation_heads.MaskScoring.from_config(config)
self.assertAllEqual(head.get_config(), new_head.get_config())
if __name__ == '__main__':
tf.test.main()
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