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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 deeplabv3plus."""
import numpy as np
import tensorflow as tf
from deeplab2 import common
from deeplab2 import config_pb2
from deeplab2.model.decoder import deeplabv3plus
from deeplab2.utils import test_utils
def _create_deeplabv3plus_model(high_level_feature_name, low_level_feature_name,
low_level_channels_project,
aspp_output_channels, decoder_output_channels,
atrous_rates, num_classes, **kwargs):
decoder_options = config_pb2.DecoderOptions(
feature_key=high_level_feature_name,
decoder_channels=decoder_output_channels,
aspp_channels=aspp_output_channels,
atrous_rates=atrous_rates)
deeplabv3plus_options = config_pb2.ModelOptions.DeeplabV3PlusOptions(
low_level=config_pb2.LowLevelOptions(
feature_key=low_level_feature_name,
channels_project=low_level_channels_project),
num_classes=num_classes)
return deeplabv3plus.DeepLabV3Plus(decoder_options, deeplabv3plus_options,
**kwargs)
class Deeplabv3PlusTest(tf.test.TestCase):
def test_deeplabv3plus_feature_key_not_present(self):
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='not_in_features_dict',
low_level_feature_name='in_feature_dict',
low_level_channels_project=128,
aspp_output_channels=64,
decoder_output_channels=64,
atrous_rates=[6, 12, 18],
num_classes=80)
input_dict = dict()
input_dict['in_feature_dict'] = tf.random.uniform(shape=(2, 65, 65, 32))
with self.assertRaises(KeyError):
_ = deeplabv3plus_decoder(input_dict)
def test_deeplabv3plus_output_shape(self):
list_of_num_classes = [2, 19, 133]
for num_classes in list_of_num_classes:
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='high',
low_level_feature_name='low',
low_level_channels_project=128,
aspp_output_channels=64,
decoder_output_channels=128,
atrous_rates=[6, 12, 18],
num_classes=num_classes)
input_dict = dict()
input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32))
input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16))
expected_shape = [2, 129, 129, num_classes]
logit_tensor = deeplabv3plus_decoder(input_dict)
self.assertListEqual(
logit_tensor[common.PRED_SEMANTIC_LOGITS_KEY].shape.as_list(),
expected_shape)
def test_deeplabv3plus_feature_extraction_consistency(self):
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='high',
low_level_feature_name='low',
low_level_channels_project=128,
aspp_output_channels=96,
decoder_output_channels=64,
atrous_rates=[6, 12, 18],
num_classes=80)
input_dict = dict()
input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32))
input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16))
reference_logits_tensor = deeplabv3plus_decoder(
input_dict, training=False)
logits_tensor_to_compare = deeplabv3plus_decoder(input_dict, training=False)
np.testing.assert_equal(
reference_logits_tensor[common.PRED_SEMANTIC_LOGITS_KEY].numpy(),
logits_tensor_to_compare[common.PRED_SEMANTIC_LOGITS_KEY].numpy())
def test_deeplabv3plus_pool_size_setter(self):
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='high',
low_level_feature_name='low',
low_level_channels_project=128,
aspp_output_channels=96,
decoder_output_channels=64,
atrous_rates=[6, 12, 18],
num_classes=80)
pool_size = (10, 10)
deeplabv3plus_decoder.set_pool_size(pool_size)
self.assertTupleEqual(deeplabv3plus_decoder._aspp._aspp_pool._pool_size,
pool_size)
@test_utils.test_all_strategies
def test_deeplabv3plus_sync_bn(self, strategy):
input_dict = dict()
input_dict['high'] = tf.random.uniform(shape=(2, 65, 65, 32))
input_dict['low'] = tf.random.uniform(shape=(2, 129, 129, 16))
with strategy.scope():
for bn_layer in test_utils.NORMALIZATION_LAYERS:
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='high',
low_level_feature_name='low',
low_level_channels_project=128,
aspp_output_channels=96,
decoder_output_channels=64,
atrous_rates=[6, 12, 18],
num_classes=80,
bn_layer=bn_layer)
_ = deeplabv3plus_decoder(input_dict)
def test_deeplabv3plus_pool_size_resetter(self):
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='high',
low_level_feature_name='low',
low_level_channels_project=128,
aspp_output_channels=96,
decoder_output_channels=64,
atrous_rates=[6, 12, 18],
num_classes=80)
pool_size = (None, None)
deeplabv3plus_decoder.reset_pooling_layer()
self.assertTupleEqual(deeplabv3plus_decoder._aspp._aspp_pool._pool_size,
pool_size)
def test_deeplabv3plus_ckpt_items(self):
deeplabv3plus_decoder = _create_deeplabv3plus_model(
high_level_feature_name='high',
low_level_feature_name='low',
low_level_channels_project=128,
aspp_output_channels=96,
decoder_output_channels=64,
atrous_rates=[6, 12, 18],
num_classes=80)
ckpt_dict = deeplabv3plus_decoder.checkpoint_items
self.assertIn(common.CKPT_DEEPLABV3PLUS_ASPP, ckpt_dict)
self.assertIn(common.CKPT_DEEPLABV3PLUS_PROJECT_CONV_BN_ACT, ckpt_dict)
self.assertIn(common.CKPT_DEEPLABV3PLUS_FUSE, ckpt_dict)
self.assertIn(common.CKPT_SEMANTIC_LAST_LAYER, ckpt_dict)
if __name__ == '__main__':
tf.test.main()
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