File size: 2,442 Bytes
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#
# 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 basnet network."""
from absl.testing import parameterized
import numpy as np
import tensorflow as tf, tf_keras
from official.projects.basnet.modeling import basnet_model
from official.projects.basnet.modeling import refunet
class BASNetNetworkTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(256),
(512),
)
def test_basnet_network_creation(
self, input_size):
"""Test for creation of a segmentation network."""
inputs = np.random.rand(2, input_size, input_size, 3)
tf_keras.backend.set_image_data_format('channels_last')
backbone = basnet_model.BASNetEncoder()
decoder = basnet_model.BASNetDecoder()
refinement = refunet.RefUnet()
model = basnet_model.BASNetModel(
backbone=backbone,
decoder=decoder,
refinement=refinement
)
sigmoids = model(inputs)
levels = sorted(sigmoids.keys())
self.assertAllEqual(
[2, input_size, input_size, 1],
sigmoids[levels[-1]].numpy().shape)
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
backbone = basnet_model.BASNetEncoder()
decoder = basnet_model.BASNetDecoder()
refinement = refunet.RefUnet()
model = basnet_model.BASNetModel(
backbone=backbone,
decoder=decoder,
refinement=refinement
)
config = model.get_config()
new_model = basnet_model.BASNetModel.from_config(config)
# Validate that the config can be forced to JSON.
_ = new_model.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(model.get_config(), new_model.get_config())
if __name__ == '__main__':
tf.test.main()
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