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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 NAS-FPN."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.vision.modeling.backbones import resnet
from official.vision.modeling.decoders import nasfpn
class NASFPNTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(256, 3, 7, False),
(256, 3, 7, True),
)
def test_network_creation(self, input_size, min_level, max_level,
use_separable_conv):
"""Test creation of NAS-FPN."""
tf_keras.backend.set_image_data_format('channels_last')
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
num_filters = 256
backbone = resnet.ResNet(model_id=50)
network = nasfpn.NASFPN(
input_specs=backbone.output_specs,
min_level=min_level,
max_level=max_level,
num_filters=num_filters,
use_separable_conv=use_separable_conv)
endpoints = backbone(inputs)
feats = network(endpoints)
for level in range(min_level, max_level + 1):
self.assertIn(str(level), feats)
self.assertAllEqual(
[1, input_size // 2**level, input_size // 2**level, num_filters],
feats[str(level)].shape.as_list())
if __name__ == '__main__':
tf.test.main()
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