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 build_segmentation_model_3d(
input_specs: tf.keras.layers.InputSpec,
model_config: hyperparams.Config,
l2_regularizer: tf.keras.regularizers.Regularizer = None) -> tf.keras.Model: # pytype: disable=annotation-type-mismatch # typed-keras
"""Builds Segmentation model."""
norm_activation_config = model_config.norm_activation
backbone = backbone_factory.build_backbone(
input_specs=input_specs,
backbone_config=model_config.backbone,
norm_activation_config=norm_activation_config,
l2_regularizer=l2_regularizer)
decoder = decoder_factory.build_decoder(
input_specs=backbone.output_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
head_config = model_config.head
head = segmentation_heads_3d.SegmentationHead3D(
num_classes=model_config.num_classes,
level=head_config.level,
num_convs=head_config.num_convs,
num_filters=head_config.num_filters,
upsample_factor=head_config.upsample_factor,
activation=norm_activation_config.activation,
use_sync_bn=norm_activation_config.use_sync_bn,
norm_momentum=norm_activation_config.norm_momentum,
norm_epsilon=norm_activation_config.norm_epsilon,
use_batch_normalization=head_config.use_batch_normalization,
kernel_regularizer=l2_regularizer,
output_logits=head_config.output_logits)
model = segmentation_model.SegmentationModel(backbone, decoder, head)
return model
def test_identity_creation(self):
"""Test creation of identity decoder."""
model_config = semantic_segmentation_3d_exp.SemanticSegmentationModel3D(
)
model_config.num_classes = 2
model_config.num_channels = 3
model_config.input_size = [None, None, None]
model_config.decoder = decoders_cfg.Decoder(
type='identity', identity=decoders_cfg.Identity())
factory_network = factory.build_decoder(input_specs=None,
model_config=model_config)
self.assertIsNone(factory_network)