def test_unet_3d_decoder_creation(self, model_id):
"""Test creation of UNet 3D decoder."""
# Create test input for decoders based on input model_id.
input_specs = {}
for level in range(model_id):
input_specs[str(level + 1)] = tf.TensorShape([
1, 128 // (2**level), 128 // (2**level), 128 // (2**level), 1
])
network = decoders.UNet3DDecoder(model_id=model_id,
input_specs=input_specs,
use_sync_bn=True,
use_batch_normalization=True,
use_deconvolution=True)
model_config = semantic_segmentation_3d_exp.SemanticSegmentationModel3D(
)
model_config.num_classes = 2
model_config.num_channels = 1
model_config.input_size = [None, None, None]
model_config.decoder = decoders_cfg.Decoder(
type='unet_3d_decoder',
unet_3d_decoder=decoders_cfg.UNet3DDecoder(model_id=model_id))
factory_network = factory.build_decoder(input_specs=input_specs,
model_config=model_config)
network_config = network.get_config()
factory_network_config = factory_network.get_config()
print(network_config)
print(factory_network_config)
self.assertEqual(network_config, factory_network_config)
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
num_classes = 3
backbone = backbones.UNet3D(model_id=4)
decoder = decoders.UNet3DDecoder(
model_id=4, input_specs=backbone.output_specs)
head = segmentation_heads_3d.SegmentationHead3D(
num_classes, level=1, num_convs=0)
model = segmentation_model.SegmentationModel(
backbone=backbone, decoder=decoder, head=head)
config = model.get_config()
new_model = segmentation_model.SegmentationModel.from_config(config)
# Validate that the config can be forced to JSON.
_ = new_model.to_json()
# If the serialization was successful, the new config should match the old.
self.assertAllEqual(model.get_config(), new_model.get_config())
def test_segmentation_network_unet3d_creation(self, input_size, depth):
"""Test for creation of a segmentation network."""
num_classes = 2
inputs = np.random.rand(2, input_size[0], input_size[0], input_size[1], 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.UNet3D(model_id=depth)
decoder = decoders.UNet3DDecoder(
model_id=depth, input_specs=backbone.output_specs)
head = segmentation_heads_3d.SegmentationHead3D(
num_classes, level=1, num_convs=0)
model = segmentation_model.SegmentationModel(
backbone=backbone, decoder=decoder, head=head)
outputs = model(inputs)
self.assertAllEqual(
[2, input_size[0], input_size[0], input_size[1], num_classes],
outputs['logits'].numpy().shape)