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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.
"""Tests for max_deeplab."""
import tensorflow as tf
from deeplab2 import common
from deeplab2 import config_pb2
from deeplab2.model.decoder import max_deeplab
def _create_max_deeplab_example_proto(num_non_void_classes=19):
semantic_decoder = config_pb2.DecoderOptions(
feature_key='feature_semantic', atrous_rates=[6, 12, 18])
auxiliary_semantic_head = config_pb2.HeadOptions(
output_channels=num_non_void_classes, head_channels=256)
pixel_space_head = config_pb2.HeadOptions(
output_channels=128, head_channels=256)
max_deeplab_options = config_pb2.ModelOptions.MaXDeepLabOptions(
pixel_space_head=pixel_space_head,
auxiliary_semantic_head=auxiliary_semantic_head)
# Add features from lowest to highest.
max_deeplab_options.auxiliary_low_level.add(
feature_key='res3', channels_project=64)
max_deeplab_options.auxiliary_low_level.add(
feature_key='res2', channels_project=32)
return config_pb2.ModelOptions(
decoder=semantic_decoder, max_deeplab=max_deeplab_options)
class MaXDeeplabTest(tf.test.TestCase):
def test_max_deeplab_decoder_output_shape(self):
num_non_void_classes = 19
num_mask_slots = 127
model_options = _create_max_deeplab_example_proto(
num_non_void_classes=num_non_void_classes)
decoder = max_deeplab.MaXDeepLab(
max_deeplab_options=model_options.max_deeplab,
ignore_label=255,
decoder_options=model_options.decoder)
input_dict = {
'res2':
tf.random.uniform([2, 17, 17, 256]),
'res3':
tf.random.uniform([2, 9, 9, 512]),
'transformer_class_feature':
tf.random.uniform([2, num_mask_slots, 256]),
'transformer_mask_feature':
tf.random.uniform([2, num_mask_slots, 256]),
'feature_panoptic':
tf.random.uniform([2, 17, 17, 256]),
'feature_semantic':
tf.random.uniform([2, 5, 5, 2048])
}
resulting_dict = decoder(input_dict)
self.assertListEqual(
resulting_dict[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(),
[2, 17, 17, 19]) # Stride 4
self.assertListEqual(
resulting_dict[
common.PRED_PIXEL_SPACE_NORMALIZED_FEATURE_KEY].shape.as_list(),
[2, 17, 17, 128]) # Stride 4
self.assertListEqual(
resulting_dict[
common.PRED_TRANSFORMER_CLASS_LOGITS_KEY].shape.as_list(),
# Non-void classes and a void class.
[2, num_mask_slots, num_non_void_classes + 1])
self.assertListEqual(
resulting_dict[common.PRED_PIXEL_SPACE_MASK_LOGITS_KEY].shape.as_list(),
[2, 17, 17, num_mask_slots]) # Stride 4.
if __name__ == '__main__':
tf.test.main()
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