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# coding=utf-8
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from typing import List, Optional, Tuple, Union
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import numpy as np
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import tensorflow as tf
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import tensorflow.keras.layers as tfkl
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from deepreg.model import layer_util
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from deepreg.model.backbone.u_net import UNet
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from deepreg.registry import REGISTRY
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class AffineHead(tfkl.Layer):
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def __init__(
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self,
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image_size: tuple,
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name: str = "AffineHead",
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):
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"""
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Init.
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:param image_size: such as (dim1, dim2, dim3)
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:param name: name of the layer
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"""
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super().__init__(name=name)
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self.reference_grid = layer_util.get_reference_grid(image_size)
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self.transform_initial = tf.constant_initializer(
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value=list(np.eye(4, 3).reshape((-1)))
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)
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self._flatten = tfkl.Flatten()
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self._dense = tfkl.Dense(units=12, bias_initializer=self.transform_initial)
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def call(
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self, inputs: Union[tf.Tensor, List], **kwargs
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) -> Tuple[tf.Tensor, tf.Tensor]:
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"""
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:param inputs: a tensor or a list of tensor with length 1
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:param kwargs: additional args
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:return: ddf and theta
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- ddf has shape (batch, dim1, dim2, dim3, 3)
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- theta has shape (batch, 4, 3)
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"""
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if isinstance(inputs, list):
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inputs = inputs[0]
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theta = self._dense(self._flatten(inputs))
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theta = tf.reshape(theta, shape=(-1, 4, 3))
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# warp the reference grid with affine parameters to output a ddf
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grid_warped = layer_util.warp_grid(self.reference_grid, theta)
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ddf = grid_warped - self.reference_grid
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return ddf, theta
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def get_config(self):
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"""Return the config dictionary for recreating this class."""
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config = super().get_config()
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config.update(image_size=self.reference_grid.shape[:3])
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return config
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@REGISTRY.register_backbone(name="global")
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class GlobalNet(UNet):
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"""
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Build GlobalNet for image registration.
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GlobalNet is a special UNet where the decoder for up-sampling is skipped.
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The network's outputs come from the bottom layer from the encoder directly.
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Reference:
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- Hu, Yipeng, et al.
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"Label-driven weakly-supervised learning
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for multimodal deformable image registration,"
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https://arxiv.org/abs/1711.01666
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"""
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def __init__(
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self,
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image_size: tuple,
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num_channel_initial: int,
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extract_levels: Optional[Tuple[int, ...]] = None,
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depth: Optional[int] = None,
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name: str = "GlobalNet",
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**kwargs,
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):
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"""
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Image is encoded gradually, i from level 0 to E.
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Then, a densely-connected layer outputs an affine
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transformation.
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:param image_size: tuple, such as (dim1, dim2, dim3)
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:param num_channel_initial: int, number of initial channels
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:param extract_levels: list, which levels from net to extract, deprecated.
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If depth is not given, depth = max(extract_levels) will be used.
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:param depth: depth of the encoder. If given, extract_levels is not used.
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:param name: name of the backbone.
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:param kwargs: additional arguments.
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"""
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if depth is None:
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if extract_levels is None:
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raise ValueError(
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"GlobalNet requires `depth` or `extract_levels` "
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"to define the depth of encoder. "
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"If `depth` is not given, "
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"the maximum value of `extract_levels` will be used."
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"However the argument `extract_levels` is deprecated "
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"and will be removed in future release."
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)
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depth = max(extract_levels)
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super().__init__(
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image_size=image_size,
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num_channel_initial=num_channel_initial,
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depth=depth,
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extract_levels=(depth,),
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name=name,
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**kwargs,
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)
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def build_output_block(
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self,
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image_size: Tuple[int, ...],
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extract_levels: Tuple[int, ...],
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out_channels: int,
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out_kernel_initializer: str,
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out_activation: str,
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) -> Union[tf.keras.Model, tfkl.Layer]:
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"""
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Build a block for output.
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The input to this block is a list of length 1.
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The output has two tensors.
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:param image_size: such as (dim1, dim2, dim3)
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:param extract_levels: not used
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:param out_channels: not used
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:param out_kernel_initializer: not used
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:param out_activation: not used
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:return: a block consists of one or multiple layers
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"""
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return AffineHead(image_size=image_size)
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DeepRegNet /
DeepReg
