def test_network_creation(self, input_size, filter_size_scale,
block_repeats, resample_alpha,
endpoints_num_filters):
"""Test creation of SpineNet models."""
min_level = 3
max_level = 7
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,
)
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())
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,
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())
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