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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 EfficientNet."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.vision.modeling.backbones import efficientnet
class EfficientNetTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(32, 224)
def test_network_creation(self, input_size):
"""Test creation of EfficientNet family models."""
tf_keras.backend.set_image_data_format('channels_last')
network = efficientnet.EfficientNet(model_id='b0')
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
endpoints = network(inputs)
self.assertAllEqual([1, input_size / 2**2, input_size / 2**2, 24],
endpoints['2'].shape.as_list())
self.assertAllEqual([1, input_size / 2**3, input_size / 2**3, 40],
endpoints['3'].shape.as_list())
self.assertAllEqual([1, input_size / 2**4, input_size / 2**4, 112],
endpoints['4'].shape.as_list())
self.assertAllEqual([1, input_size / 2**5, input_size / 2**5, 320],
endpoints['5'].shape.as_list())
@parameterized.parameters('b0', 'b3', 'b6')
def test_network_scaling(self, model_id):
"""Test compound scaling."""
efficientnet_params = {
'b0': 4049564,
'b3': 10783528,
'b6': 40960136,
}
tf_keras.backend.set_image_data_format('channels_last')
input_size = 32
network = efficientnet.EfficientNet(model_id=model_id, se_ratio=0.25)
self.assertEqual(network.count_params(), efficientnet_params[model_id])
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
_ = network(inputs)
@parameterized.parameters(1, 3)
def test_input_specs(self, input_dim):
"""Test different input feature dimensions."""
tf_keras.backend.set_image_data_format('channels_last')
input_specs = tf_keras.layers.InputSpec(shape=[None, None, None, input_dim])
network = efficientnet.EfficientNet(model_id='b0', input_specs=input_specs)
inputs = tf_keras.Input(shape=(128, 128, input_dim), batch_size=1)
_ = network(inputs)
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
model_id='b0',
se_ratio=0.25,
stochastic_depth_drop_rate=None,
use_sync_bn=False,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
activation='relu',
norm_momentum=0.99,
norm_epsilon=0.001,
)
network = efficientnet.EfficientNet(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = efficientnet.EfficientNet.from_config(network.get_config())
# Validate that the config can be forced to JSON.
_ = new_network.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(network.get_config(), new_network.get_config())
if __name__ == '__main__':
tf.test.main()
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