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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 SpineNet."""
# Import libraries
from absl.testing import parameterized
import tensorflow as tf, tf_keras
from official.vision.modeling.backbones import spinenet
class SpineNetTest(parameterized.TestCase, tf.test.TestCase):
@parameterized.parameters(
(128, 0.65, 1, 0.5, 128, 4, 6),
(256, 1.0, 1, 0.5, 256, 3, 6),
(384, 1.0, 2, 0.5, 256, 4, 7),
(512, 1.0, 3, 1.0, 256, 3, 7),
(640, 1.3, 4, 1.0, 384, 3, 7),
)
def test_network_creation(self, input_size, filter_size_scale, block_repeats,
resample_alpha, endpoints_num_filters, min_level,
max_level):
"""Test creation of SpineNet models."""
tf_keras.backend.set_image_data_format('channels_last')
input_specs = tf_keras.layers.InputSpec(
shape=[None, input_size, input_size, 3])
model = spinenet.SpineNet(
input_specs=input_specs,
min_level=min_level,
max_level=max_level,
endpoints_num_filters=endpoints_num_filters,
resample_alpha=resample_alpha,
block_repeats=block_repeats,
filter_size_scale=filter_size_scale,
init_stochastic_depth_rate=0.2,
)
inputs = tf_keras.Input(shape=(input_size, input_size, 3), batch_size=1)
endpoints = model(inputs)
for l in range(min_level, max_level + 1):
self.assertIn(str(l), endpoints.keys())
self.assertAllEqual(
[1, input_size / 2**l, input_size / 2**l, endpoints_num_filters],
endpoints[str(l)].shape.as_list())
@parameterized.parameters(
((128, 128), (128, 128)),
((128, 128), (256, 256)),
((640, 640), (896, 1664)),
)
def test_load_from_different_input_specs(self, input_size_1, input_size_2):
"""Test loading checkpoints with different input size."""
def build_spinenet(input_size):
tf_keras.backend.set_image_data_format('channels_last')
input_specs = tf_keras.layers.InputSpec(
shape=[None, input_size[0], input_size[1], 3])
model = spinenet.SpineNet(
input_specs=input_specs,
min_level=3,
max_level=7,
endpoints_num_filters=384,
resample_alpha=1.0,
block_repeats=2,
filter_size_scale=0.5)
return model
model_1 = build_spinenet(input_size_1)
model_2 = build_spinenet(input_size_2)
ckpt_1 = tf.train.Checkpoint(backbone=model_1)
ckpt_2 = tf.train.Checkpoint(backbone=model_2)
ckpt_path = self.get_temp_dir() + '/ckpt'
ckpt_1.write(ckpt_path)
ckpt_2.restore(ckpt_path).expect_partial()
def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
min_level=3,
max_level=7,
endpoints_num_filters=256,
resample_alpha=0.5,
block_repeats=1,
filter_size_scale=1.0,
init_stochastic_depth_rate=0.2,
use_sync_bn=False,
activation='relu',
norm_momentum=0.99,
norm_epsilon=0.001,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
)
network = spinenet.SpineNet(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = spinenet.SpineNet.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())
@parameterized.parameters(
('relu', tf.nn.relu),
('swish', tf.nn.swish)
)
def test_activation(self, activation, activation_fn):
model = spinenet.SpineNet(activation=activation)
self.assertEqual(model._activation_fn, activation_fn)
def test_invalid_activation_raises_valurerror(self):
with self.assertRaises(ValueError):
spinenet.SpineNet(activation='invalid_activation_name')
if __name__ == '__main__':
tf.test.main()
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