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"""Provide helper functions or classes for defining loss or metrics.""" |
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from typing import List, Optional, Union |
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import tensorflow as tf |
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from deepreg.loss.kernel import cauchy_kernel1d |
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from deepreg.loss.kernel import gaussian_kernel1d_sigma as gaussian_kernel1d |
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class MultiScaleMixin(tf.keras.losses.Loss): |
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""" |
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Mixin class for multi-scale loss. |
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It applies the loss at different scales (gaussian or cauchy smoothing). |
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It is assumed that loss values are between 0 and 1. |
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""" |
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kernel_fn_dict = dict(gaussian=gaussian_kernel1d, cauchy=cauchy_kernel1d) |
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def __init__( |
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self, |
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scales: Optional[Union[List, float, int]] = None, |
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kernel: str = "gaussian", |
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**kwargs, |
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): |
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""" |
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Init. |
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:param scales: list of scalars or None, if None, do not apply any scaling. |
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:param kernel: gaussian or cauchy. |
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:param kwargs: additional arguments. |
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""" |
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super().__init__(**kwargs) |
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if kernel not in self.kernel_fn_dict: |
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raise ValueError( |
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f"Kernel {kernel} is not supported." |
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f"Supported kernels are {list(self.kernel_fn_dict.keys())}" |
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) |
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if scales is not None and not isinstance(scales, list): |
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scales = [scales] |
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self.scales = scales |
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self.kernel = kernel |
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def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: |
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""" |
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Use super().call to calculate loss at different scales. |
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:param y_true: ground-truth tensor, shape = (batch, dim1, dim2, dim3). |
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:param y_pred: predicted tensor, shape = (batch, dim1, dim2, dim3). |
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:return: multi-scale loss, shape = (batch, ). |
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""" |
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if self.scales is None: |
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return super().call(y_true=y_true, y_pred=y_pred) |
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kernel_fn = self.kernel_fn_dict[self.kernel] |
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losses = [] |
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for s in self.scales: |
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if s == 0: |
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# no smoothing |
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losses.append( |
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super().call( |
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y_true=y_true, |
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y_pred=y_pred, |
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) |
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) |
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else: |
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losses.append( |
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super().call( |
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y_true=separable_filter( |
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tf.expand_dims(y_true, axis=4), kernel_fn(s) |
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)[..., 0], |
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y_pred=separable_filter( |
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tf.expand_dims(y_pred, axis=4), kernel_fn(s) |
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)[..., 0], |
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) |
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) |
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loss = tf.add_n(losses) |
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loss = loss / len(self.scales) |
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return loss |
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def get_config(self) -> dict: |
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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["scales"] = self.scales |
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config["kernel"] = self.kernel |
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return config |
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class NegativeLossMixin(tf.keras.losses.Loss): |
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"""Mixin class to revert the sign of the loss value.""" |
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def __init__(self, **kwargs): |
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""" |
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Init without required arguments. |
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:param kwargs: additional arguments. |
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""" |
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super().__init__(**kwargs) |
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self.name = self.name + "Loss" |
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def call(self, y_true: tf.Tensor, y_pred: tf.Tensor) -> tf.Tensor: |
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""" |
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Revert the sign of loss. |
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:param y_true: ground-truth tensor. |
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:param y_pred: predicted tensor. |
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:return: negated loss. |
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""" |
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return -super().call(y_true=y_true, y_pred=y_pred) |
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111
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112
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def separable_filter(tensor: tf.Tensor, kernel: tf.Tensor) -> tf.Tensor: |
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""" |
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Create a 3d separable filter. |
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116
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Here `tf.nn.conv3d` accepts the `filters` argument of shape |
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(filter_depth, filter_height, filter_width, in_channels, out_channels), |
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where the first axis of `filters` is the depth not batch, |
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and the input to `tf.nn.conv3d` is of shape |
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(batch, in_depth, in_height, in_width, in_channels). |
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:param tensor: shape = (batch, dim1, dim2, dim3, 1) |
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:param kernel: shape = (dim4,) |
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:return: shape = (batch, dim1, dim2, dim3, 1) |
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""" |
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strides = [1, 1, 1, 1, 1] |
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kernel = tf.cast(kernel, dtype=tensor.dtype) |
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129
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tensor = tf.nn.conv3d( |
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tf.nn.conv3d( |
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tf.nn.conv3d( |
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tensor, |
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filters=tf.reshape(kernel, [-1, 1, 1, 1, 1]), |
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strides=strides, |
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padding="SAME", |
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), |
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filters=tf.reshape(kernel, [1, -1, 1, 1, 1]), |
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strides=strides, |
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padding="SAME", |
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), |
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filters=tf.reshape(kernel, [1, 1, -1, 1, 1]), |
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strides=strides, |
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padding="SAME", |
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) |
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return tensor |
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146
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DeepRegNet /
DeepReg
