def test_serialize_deserialize(self):
# Create a network object that sets all of its config options.
kwargs = dict(
model_id=50,
temporal_strides=[1, 1, 1, 1],
temporal_kernel_sizes=[(3, 3, 3), (3, 1, 3, 1), (3, 1, 3, 1, 3, 1),
(1, 3, 1)],
stem_type='v0',
stem_conv_temporal_kernel_size=5,
stem_conv_temporal_stride=2,
stem_pool_temporal_stride=2,
se_ratio=0.0,
use_self_gating=None,
init_stochastic_depth_rate=0.0,
use_sync_bn=False,
activation='relu',
norm_momentum=0.99,
norm_epsilon=0.001,
kernel_initializer='VarianceScaling',
kernel_regularizer=None,
bias_regularizer=None,
)
network = resnet_3d.ResNet3D(**kwargs)
expected_config = dict(kwargs)
self.assertEqual(network.get_config(), expected_config)
# Create another network object from the first object's config.
new_network = resnet_3d.ResNet3D.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 test_network_creation(self, input_size, model_id,
endpoint_filter_scale):
"""Test creation of ResNet3D family models."""
tf.keras.backend.set_image_data_format('channels_last')
temporal_strides = [1, 1, 1, 1]
temporal_kernel_sizes = [(3, 3, 3), (3, 1, 3, 1), (3, 1, 3, 1, 3, 1),
(1, 3, 1)]
use_self_gating = [True, False, True, False]
network = resnet_3d.ResNet3D(
model_id=model_id,
temporal_strides=temporal_strides,
temporal_kernel_sizes=temporal_kernel_sizes,
use_self_gating=use_self_gating,
)
inputs = tf.keras.Input(shape=(8, input_size, input_size, 3),
batch_size=1)
endpoints = network(inputs)
self.assertAllEqual([
1, 2, input_size / 2**2, input_size / 2**2,
64 * endpoint_filter_scale
], endpoints['2'].shape.as_list())
self.assertAllEqual([
1, 2, input_size / 2**3, input_size / 2**3,
128 * endpoint_filter_scale
], endpoints['3'].shape.as_list())
self.assertAllEqual([
1, 2, input_size / 2**4, input_size / 2**4,
256 * endpoint_filter_scale
], endpoints['4'].shape.as_list())
self.assertAllEqual([
1, 2, input_size / 2**5, input_size / 2**5,
512 * endpoint_filter_scale
], endpoints['5'].shape.as_list())