def __init__(
self,
image_size: tuple,
out_channels: int,
num_channel_initial: int,
depth: int,
out_kernel_initializer: str,
out_activation: str,
pooling: bool = True,
concat_skip: bool = False,
name: str = "Unet",
**kwargs,
):
"""
Initialise UNet.
:param image_size: (dim1, dim2, dim3), dims of input image.
:param out_channels: number of channels for the output
:param num_channel_initial: number of initial channels
:param depth: input is at level 0, bottom is at level depth
:param out_kernel_initializer: kernel initializer for the last layer
:param out_activation: activation at the last layer
:param pooling: for downsampling, use non-parameterized
pooling if true, otherwise use conv3d
:param concat_skip: when upsampling, concatenate skipped
tensor if true, otherwise use addition
:param name: name of the backbone.
:param kwargs: additional arguments.
"""
super().__init__(
image_size=image_size,
out_channels=out_channels,
num_channel_initial=num_channel_initial,
out_kernel_initializer=out_kernel_initializer,
out_activation=out_activation,
name=name,
**kwargs,
)
# init layer variables
num_channels = [num_channel_initial * (2**d) for d in range(depth + 1)]
self._num_channel_initial = num_channel_initial
self._depth = depth
self._downsample_blocks = [
layer.DownSampleResnetBlock(filters=num_channels[d],
pooling=pooling) for d in range(depth)
]
self._bottom_conv3d = layer.Conv3dBlock(filters=num_channels[depth])
self._bottom_res3d = layer.Residual3dBlock(filters=num_channels[depth])
self._upsample_blocks = [
layer.UpSampleResnetBlock(filters=num_channels[d],
concat=concat_skip) for d in range(depth)
]
self._output_conv3d = layer.Conv3dWithResize(
output_shape=image_size,
filters=out_channels,
kernel_initializer=out_kernel_initializer,
activation=out_activation,
)
def __init__(self,
image_size, out_channels,
num_channel_initial, depth,
out_kernel_initializer, out_activation,
pooling=True, concat_skip=False, **kwargs):
"""
:param image_size: [f_dim1, f_dim2, f_dim3]
:param out_channels: number of channels for the output
:param num_channel_initial:
:param depth: input is at level 0, bottom is at level depth
:param out_kernel_initializer:
:param out_activation:
:param pooling: true if use pooling to down sample
:param kwargs:
"""
super(UNet, self).__init__(**kwargs)
# init layer variables
nc = [num_channel_initial * (2 ** d) for d in range(depth + 1)]
self._num_channel_initial = num_channel_initial
self._depth = depth
self._downsample_blocks = [layer.DownSampleResnetBlock(filters=nc[d], pooling=pooling)
for d in range(depth)]
self._bottom_conv3d = layer.Conv3dBlock(filters=nc[depth])
self._bottom_res3d = layer.Residual3dBlock(filters=nc[depth])
self._upsample_blocks = [layer.UpSampleResnetBlock(filters=nc[d], concat=concat_skip)
for d in range(depth)]
self._output_conv3d = layer.Conv3dWithResize(output_shape=image_size, filters=out_channels,
kernel_initializer=out_kernel_initializer,
activation=out_activation)
def __init__(
self,
image_size,
out_channels,
num_channel_initial,
depth,
out_kernel_initializer,
out_activation,
pooling=True,
concat_skip=False,
**kwargs,
):
"""
Initialise UNet.
:param image_size: list, [f_dim1, f_dim2, f_dim3], dims of input image.
:param out_channels: int, number of channels for the output
:param num_channel_initial: int, number of initial channels
:param depth: int, input is at level 0, bottom is at level depth
:param out_kernel_initializer: str, which kernel to use as initialiser
:param out_activation: str, activation at last layer
:param pooling: Boolean, for downsampling, use non-parameterized
pooling if true, otherwise use conv3d
:param concat_skip: Boolean, when upsampling, concatenate skipped
tensor if true, otherwise use addition
:param kwargs:
"""
super(UNet, self).__init__(**kwargs)
# init layer variables
num_channels = [num_channel_initial * (2 ** d) for d in range(depth + 1)]
self._num_channel_initial = num_channel_initial
self._depth = depth
self._downsample_blocks = [
layer.DownSampleResnetBlock(filters=num_channels[d], pooling=pooling)
for d in range(depth)
]
self._bottom_conv3d = layer.Conv3dBlock(filters=num_channels[depth])
self._bottom_res3d = layer.Residual3dBlock(filters=num_channels[depth])
self._upsample_blocks = [
layer.UpSampleResnetBlock(filters=num_channels[d], concat=concat_skip)
for d in range(depth)
]
self._output_conv3d = layer.Conv3dWithResize(
output_shape=image_size,
filters=out_channels,
kernel_initializer=out_kernel_initializer,
activation=out_activation,
)
def test_init_UNet():
"""
Testing init of UNet as expected
"""
local_test = u.UNet(
image_size=[1, 2, 3],
out_channels=3,
num_channel_initial=3,
depth=5,
out_kernel_initializer="he_normal",
out_activation="softmax",
)
# Asserting num channels initial is the same, Pass
assert local_test._num_channel_initial == 3
# Asserting depth is the same, Pass
assert local_test._depth == 5
# Assert downsample blocks type is correct, Pass
assert all(
isinstance(item, type(layer.DownSampleResnetBlock(12)))
for item in local_test._downsample_blocks
)
# Assert number of downsample blocks is correct (== depth), Pass
assert len(local_test._downsample_blocks) == 5
# Assert bottom_conv3d type is correct, Pass
assert isinstance(local_test._bottom_conv3d, type(layer.Conv3dBlock(5)))
# Assert bottom res3d type is correct, Pass
assert isinstance(local_test._bottom_res3d, type(layer.Residual3dBlock(5)))
# Assert upsample blocks type is correct, Pass
assert all(
isinstance(item, type(layer.UpSampleResnetBlock(12)))
for item in local_test._upsample_blocks
)
# Assert number of upsample blocks is correct (== depth), Pass
assert len(local_test._upsample_blocks) == 5
# Assert output_conv3d is correct type, Pass
assert isinstance(
local_test._output_conv3d, type(layer.Conv3dWithResize([1, 2, 3], 3))
)
def test_upsample_resnet_block():
"""
Test the layer.UpSampleResnetBlock class and its default attributes.
"""
batch_size = 5
channels = 4
input_size = (32, 32, 16)
output_size = (64, 64, 32)
input_tensor_size = (batch_size,) + input_size + (channels,)
skip_tensor_size = (batch_size,) + output_size + (channels // 2,)
model = layer.UpSampleResnetBlock(8)
model.build([input_tensor_size, skip_tensor_size])
assert model._filters == 8
assert model._concat is False
assert isinstance(model._conv3d_block, layer.Conv3dBlock)
assert isinstance(model._residual_block, layer.Residual3dBlock)
assert isinstance(model._deconv3d_block, layer.Deconv3dBlock)
def __init__(
self,
image_size: tuple,
out_channels: int,
num_channel_initial: int,
depth: int,
out_kernel_initializer: str,
out_activation: str,
pooling: bool = True,
concat_skip: bool = False,
control_points: (tuple, None) = None,
**kwargs,
):
"""
Initialise UNet.
:param image_size: tuple, (dim1, dim2, dim3), dims of input image.
:param out_channels: int, number of channels for the output
:param num_channel_initial: int, number of initial channels
:param depth: int, input is at level 0, bottom is at level depth
:param out_kernel_initializer: str, which kernel to use as initializer
:param out_activation: str, activation at last layer
:param pooling: Boolean, for downsampling, use non-parameterized
pooling if true, otherwise use conv3d
:param concat_skip: Boolean, when upsampling, concatenate skipped
tensor if true, otherwise use addition
:param control_points: (tuple, None), specify the distance between control points (in voxels).
:param kwargs:
"""
super(UNet, self).__init__(**kwargs)
# init layer variables
num_channels = [num_channel_initial * (2 ** d) for d in range(depth + 1)]
self._num_channel_initial = num_channel_initial
self._depth = depth
self._downsample_blocks = [
layer.DownSampleResnetBlock(filters=num_channels[d], pooling=pooling)
for d in range(depth)
]
self._bottom_conv3d = layer.Conv3dBlock(filters=num_channels[depth])
self._bottom_res3d = layer.Residual3dBlock(filters=num_channels[depth])
self._upsample_blocks = [
layer.UpSampleResnetBlock(filters=num_channels[d], concat=concat_skip)
for d in range(depth)
]
self._output_conv3d = layer.Conv3dWithResize(
output_shape=image_size,
filters=out_channels,
kernel_initializer=out_kernel_initializer,
activation=out_activation,
)
self.resize = (
layer.ResizeCPTransform(control_points)
if control_points is not None
else False
)
self.interpolate = (
layer.BSplines3DTransform(control_points, image_size)
if control_points is not None
else False
)