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 build_segmentation_model(
input_specs: tf.keras.layers.InputSpec,
model_config: segmentation_cfg.SemanticSegmentationModel,
l2_regularizer: Optional[tf.keras.regularizers.Regularizer] = None,
backbone: Optional[tf.keras.regularizers.Regularizer] = None,
decoder: Optional[tf.keras.regularizers.Regularizer] = None
) -> tf.keras.Model:
"""Builds Segmentation model."""
norm_activation_config = model_config.norm_activation
if not backbone:
backbone = backbones.factory.build_backbone(
input_specs=input_specs,
backbone_config=model_config.backbone,
norm_activation_config=norm_activation_config,
l2_regularizer=l2_regularizer)
if not decoder:
decoder = decoders.factory.build_decoder(
input_specs=backbone.output_specs,
model_config=model_config,
l2_regularizer=l2_regularizer)
head_config = model_config.head
head = segmentation_heads.SegmentationHead(
num_classes=model_config.num_classes,
level=head_config.level,
num_convs=head_config.num_convs,
prediction_kernel_size=head_config.prediction_kernel_size,
num_filters=head_config.num_filters,
use_depthwise_convolution=head_config.use_depthwise_convolution,
upsample_factor=head_config.upsample_factor,
feature_fusion=head_config.feature_fusion,
low_level=head_config.low_level,
low_level_num_filters=head_config.low_level_num_filters,
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,
kernel_regularizer=l2_regularizer)
mask_scoring_head = None
if model_config.mask_scoring_head:
mask_scoring_head = segmentation_heads.MaskScoring(
num_classes=model_config.num_classes,
**model_config.mask_scoring_head.as_dict(),
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,
kernel_regularizer=l2_regularizer)
model = segmentation_model.SegmentationModel(
backbone, decoder, head, mask_scoring_head=mask_scoring_head)
return model
def test_serialize_deserialize(self):
"""Validate the network can be serialized and deserialized."""
num_classes = 3
backbone = backbones.ResNet(model_id=50)
decoder = fpn.FPN(
input_specs=backbone.output_specs, min_level=3, max_level=7)
head = segmentation_heads.SegmentationHead(num_classes, level=3)
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_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)
def test_segmentation_network_creation(
self, input_size, level):
"""Test for creation of a segmentation network."""
num_classes = 10
inputs = np.random.rand(2, input_size, input_size, 3)
tf.keras.backend.set_image_data_format('channels_last')
backbone = backbones.ResNet(model_id=50)
decoder = fpn.FPN(
input_specs=backbone.output_specs, min_level=2, max_level=7)
head = segmentation_heads.SegmentationHead(num_classes, level=level)
model = segmentation_model.SegmentationModel(
backbone=backbone,
decoder=decoder,
head=head,
mask_scoring_head=None,
)
outputs = model(inputs)
self.assertAllEqual(
[2, input_size // (2**level), input_size // (2**level), num_classes],
outputs['logits'].numpy().shape)