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Separating ashpy Executors and custom Keras losses
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| # Copyright 2019 Zuru Tech HK Limited. All Rights Reserved. | ||
| # | ||
| # Licensed under the Apache License, Version 2.0 (the "License"); | ||
| # you may not use this file except in compliance with the License. | ||
| # You may obtain a copy of the License at | ||
| # | ||
| # http://www.apache.org/licenses/LICENSE-2.0 | ||
| # | ||
| # Unless required by applicable law or agreed to in writing, software | ||
| # distributed under the License is distributed on an "AS IS" BASIS, | ||
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | ||
| # See the License for the specific language governing permissions and | ||
| # limitations under the License. | ||
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| """Custom Keras losses, used by the AshPy executors.""" | ||
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| import tensorflow as tf | ||
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| class L1(tf.keras.losses.Loss): | ||
| """L1 Loss implementation as :py:class:`tf.keras.losses.Loss`.""" | ||
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| def __init__(self) -> None: | ||
| """Initialize the Loss.""" | ||
| super().__init__() | ||
| self._reduction = tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE | ||
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| @property | ||
| def reduction(self) -> tf.keras.losses.Reduction: | ||
| """Return the current `reduction` for this type of loss.""" | ||
| return self._reduction | ||
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| @reduction.setter | ||
| def reduction(self, value: tf.keras.losses.Reduction) -> None: | ||
| """ | ||
| Set the `reduction`. | ||
| Args: | ||
| value (:py:class:`tf.keras.losses.Reduction`): Reduction to use for the loss. | ||
| """ | ||
| self._reduction = value | ||
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| def call(self, x: tf.Tensor, y: tf.Tensor) -> tf.Tensor: | ||
| """Compute the mean of the l1 between x and y.""" | ||
| if self._reduction == tf.keras.losses.Reduction.SUM_OVER_BATCH_SIZE: | ||
| axis = None | ||
| elif self._reduction == tf.keras.losses.Reduction.NONE: | ||
| axis = (1, 2, 3) | ||
| else: | ||
| raise ValueError("L1Loss: unhandled reduction type") | ||
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| return tf.reduce_mean(tf.abs(x - y), axis=axis) | ||
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| class DMinMax(tf.keras.losses.Loss): | ||
| r"""Implementation of MinMax Discriminator loss as :py:class:`tf.keras.losses.Loss`. | ||
| .. math:: | ||
| L_{D} = - \frac{1}{2} E [\log(D(x)) + \log (1 - D(G(z))] | ||
| """ | ||
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| def __init__(self, from_logits: bool = True, label_smoothing: float = 0.0) -> None: | ||
| """Initialize the loss.""" | ||
| self._positive_bce = tf.keras.losses.BinaryCrossentropy( | ||
| from_logits=from_logits, | ||
| label_smoothing=label_smoothing, | ||
| reduction=tf.keras.losses.Reduction.NONE, | ||
| ) | ||
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| self._negative_bce = tf.keras.losses.BinaryCrossentropy( | ||
| from_logits=from_logits, | ||
| label_smoothing=0.0, | ||
| reduction=tf.keras.losses.Reduction.NONE, | ||
| ) | ||
| super().__init__() | ||
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| @property | ||
| def reduction(self) -> tf.keras.losses.Reduction: | ||
| """ | ||
| Return the reduction type of this loss. | ||
| Returns: | ||
| :py:classes:`tf.keras.losses.Reduction`: Reduction. | ||
| """ | ||
| return self._positive_bce.reduction | ||
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| @reduction.setter | ||
| def reduction(self, value: tf.keras.losses.Reduction) -> None: | ||
| self._positive_bce.reduction = value | ||
| self._negative_bce.reduction = value | ||
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| def call(self, d_real: tf.Tensor, d_fake: tf.Tensor) -> tf.Tensor: | ||
| """ | ||
| Compute the MinMax Loss. | ||
| Play the DiscriminatorMinMax game between the discriminator | ||
| computed in real and the discriminator compute with fake inputs. | ||
| Args: | ||
| d_real (:py:class:`tf.Tensor`): Real data. | ||
| d_fake (:py:class:`tf.Tensor`): Fake (generated) data. | ||
| Returns: | ||
| :py:class:`tf.Tensor`: Output Tensor. | ||
| """ | ||
| return 0.5 * ( | ||
| self._positive_bce(tf.ones_like(d_real), d_real) | ||
| + self._negative_bce(tf.zeros_like(d_fake), d_fake) | ||
| ) | ||
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| class DLeastSquare(tf.keras.losses.Loss): | ||
| """Discriminator Least Square Loss as :py:class:`tf.keras.losses.Loss`.""" | ||
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| def __init__(self) -> None: | ||
| """Least square Loss for Discriminator. | ||
| Reference: Least Squares Generative Adversarial Networks [1]_ . | ||
| Basically the Mean Squared Error between | ||
| the discriminator output when evaluated in fake samples and 0 | ||
| and the discriminator output when evaluated in real samples and 1: | ||
| For the unconditioned case this is: | ||
| .. math:: | ||
| L_{D} = \frac{1}{2} E[(D(x) - 1)^2 + (0 - D(G(z))^2] | ||
| where x are real samples and z is the latent vector. | ||
| For the conditioned case this is: | ||
| .. math:: | ||
| L_{D} = \frac{1}{2} E[(D(x, c) - 1)^2 + (0 - D(G(c), c)^2] | ||
| where c is the condition and x are real samples. | ||
| .. [1] https://arxiv.org/abs/1611.04076 | ||
| """ | ||
| self._positive_mse = tf.keras.losses.MeanSquaredError( | ||
| reduction=tf.keras.losses.Reduction.NONE | ||
| ) | ||
| self._negative_mse = tf.keras.losses.MeanSquaredError( | ||
| reduction=tf.keras.losses.Reduction.NONE | ||
| ) | ||
| super().__init__() | ||
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| @property | ||
| def reduction(self) -> tf.keras.losses.Reduction: | ||
| """ | ||
| Return the reduction type for this loss. | ||
| Returns: | ||
| :py:class:`tf.keras.losses.Reduction`: Reduction. | ||
| """ | ||
| return self._positive_mse.reduction | ||
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| @reduction.setter | ||
| def reduction(self, value) -> None: | ||
| self._positive_mse.reduction = value | ||
| self._negative_mse.reduction = value | ||
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| def call(self, d_real: tf.Tensor, d_fake: tf.Tensor) -> tf.Tensor: | ||
| """ | ||
| Compute the Least Square Loss. | ||
| Args: | ||
| d_real (:py:class:`tf.Tensor`): Discriminator evaluated in real samples. | ||
| d_fake (:py:class:`tf.Tensor`): Discriminator evaluated in fake samples. | ||
| Returns: | ||
| :py:class:`tf.Tensor`: Loss. | ||
| """ | ||
| return 0.5 * ( | ||
| self._positive_mse(tf.ones_like(d_real), d_real) | ||
| + self._negative_mse(tf.zeros_like(d_fake), d_fake) | ||
| ) |
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