def test_backbone(self):
registry = Registry()
key = "new_backbone"
value = 0
registry.register_backbone(key, value)
got = registry.get_backbone(key)
assert got == value
def build_backbone(
image_size: tuple,
out_channels: int,
config: dict,
method_name: str,
registry: Registry = Registry(),
) -> tf.keras.Model:
"""
Backbone model accepts a single input of shape (batch, dim1, dim2, dim3, ch_in)
and returns a single output of shape (batch, dim1, dim2, dim3, ch_out).
:param image_size: tuple, dims of image, (dim1, dim2, dim3)
:param out_channels: int, number of out channels, ch_out
:param method_name: str, one of ddf, dvf and conditional
:param config: dict, backbone configuration
:param registry: the registry object having all backbone classes
:return: tf.keras.Model
"""
if not ((isinstance(image_size, tuple) or isinstance(image_size, list))
and len(image_size) == 3):
raise ValueError(
f"image_size must be tuple of length 3, got {image_size}")
if method_name not in ["ddf", "dvf", "conditional", "affine"]:
raise ValueError(
f"method name has to be one of ddf/dvf/conditional/affine in build_backbone, "
f"got {method_name}")
if method_name in ["ddf", "dvf"]:
out_activation = None
# TODO try random init with smaller number
out_kernel_initializer = "zeros" # to ensure small ddf and dvf
elif method_name in ["conditional"]:
out_activation = "sigmoid" # output is probability
out_kernel_initializer = "glorot_uniform"
elif method_name in ["affine"]:
out_activation = None
out_kernel_initializer = "zeros"
backbone_cls = registry.get_backbone(key=config["name"])
return backbone_cls(
image_size=image_size,
out_channels=out_channels,
out_kernel_initializer=out_kernel_initializer,
out_activation=out_activation,
**config,
)
