# gan¶

GAN losses.

Functions

 get_adversarial_loss_discriminator Return the correct loss fot the Discriminator. get_adversarial_loss_generator Return the correct loss for the Generator.

Classes

 AdversarialLossType Enumeration for Adversarial Losses. CategoricalCrossEntropy Categorical Cross Entropy between generator output and target. DiscriminatorAdversarialLoss Base class for the adversarial loss of the discriminator. DiscriminatorHingeLoss Hinge loss for the Discriminator. DiscriminatorLSGAN Least square Loss for discriminator. DiscriminatorMinMax The min-max game played by the discriminator. EncoderBCE The Binary Cross Entropy computed among the encoder and the 0 label. FeatureMatchingLoss Conditional GAN Feature matching loss. GANExecutor Executor for GANs. GeneratorAdversarialLoss Base class for the adversarial loss of the generator. GeneratorBCE The Binary CrossEntropy computed among the generator and the 1 label. GeneratorHingeLoss Hinge loss for the Generator. GeneratorL1 L1 loss between the generator output and the target. GeneratorLSGAN Least Square GAN Loss for generator. Pix2PixLoss Pix2Pix Loss. Pix2PixLossSemantic Semantic Pix2Pix Loss.
class ashpy.losses.gan.AdversarialLossType[source]

Bases: enum.Enum

Enumeration for Adversarial Losses. Implemented: GAN and LSGAN.

class ashpy.losses.gan.CategoricalCrossEntropy[source]

Categorical Cross Entropy between generator output and target.

Useful when the output of the generator is a distribution over classes.

..note::
The target must be represented in one hot notation.
__init__()[source]

Initialize the Categorical Cross Entropy Executor.

Return type: None
class ashpy.losses.gan.DiscriminatorAdversarialLoss(loss_fn=None)[source]

Base class for the adversarial loss of the discriminator.

__init__(loss_fn=None)[source]

Initialize the Executor.

Parameters: loss_fn (tf.keras.losses.Loss) – Loss function call passing d_fake) ((d_real,) – None
class ashpy.losses.gan.DiscriminatorHingeLoss[source]

Hinge loss for the Discriminator.

See Geometric GAN [1]_ for more details.

 [1] Geometric GAN https://arxiv.org/abs/1705.02894
__init__()[source]

Initialize the Least Square Loss for the Generator.

Return type: None
class ashpy.losses.gan.DiscriminatorLSGAN[source]

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:

$L_{D} = \frac{1}{2} E[(D(x) - 1)^2 + (0 - D(G(z))^2]$

where x are real samples and z is the latent vector.

For the conditioned case this is:

$L_{D} = \frac{1}{2} E[(D(x, c) - 1)^2 + (0 - D(G(c), c)^2]$

where c is the condition and x are real samples.

 [1] Least Squares Generative Adversarial Networks https://arxiv.org/abs/1611.04076
__init__()[source]

Initialize loss.

Return type: None
class ashpy.losses.gan.DiscriminatorMinMax(from_logits=True, label_smoothing=0.0)[source]

The min-max game played by the discriminator.

$L_{D} = - \frac{1}{2} E [\log(D(x)) + \log (1 - D(G(z))]$
__init__(from_logits=True, label_smoothing=0.0)[source]

Initialize Loss.

class ashpy.losses.gan.EncoderBCE(from_logits=True)[source]

The Binary Cross Entropy computed among the encoder and the 0 label.

__init__(from_logits=True)[source]

Initialize the Executor.

Return type: None
class ashpy.losses.gan.FeatureMatchingLoss[source]

Conditional GAN Feature matching loss.

The loss is computed for each example and it’s the L1 (MAE) of the feature difference. Implementation of pix2pix HD: https://github.com/NVIDIA/pix2pixHD

$\text{FM} = \sum_{i=0}^N \frac{1}{M_i} ||D_i(x, c) - D_i(G(c), c) ||_1$

Where:

• D_i is the i-th layer of the discriminator
• N is the total number of layer of the discriminator
• M_i is the number of components for the i-th layer
• x is the target image
• c is the condition
• G(c) is the generated image from the condition c
• || ||_1 stands for norm 1.

This is for a single example: basically for each layer of the discriminator we compute the absolute error between the layer evaluated in real examples and in fake examples. Then we average along the batch. In the case where D_i is a multidimensional tensor we simply calculate the mean over the axis 1,2,3.

__init__()[source]

Initialize the Executor.

Return type: None
class ashpy.losses.gan.GANExecutor(fn=None)[source]

Executor for GANs.

Implements the basic functions needed by the GAN losses.

call(context, **kwargs)[source]

Execute the function, using the information provided by the context.

Parameters: context (ashpy.contexts.Context) – The function execution Context. tf.Tensor – Output Tensor.
static get_discriminator_inputs(context, fake_or_real, condition, training)[source]

Return the discriminator inputs. If needed it uses the encoder.

The current implementation uses the number of inputs to determine whether the discriminator is conditioned or not.

Parameters: context (ashpy.contexts.gan.GANContext) – Context for GAN models. fake_or_real (tf.Tensor) – Discriminator input tensor, it can be fake (generated) or real. condition (tf.Tensor) – Discriminator condition (it can also be generator noise). training (bool) – whether is training phase or not Union[Tensor, List[Tensor]] The discriminator inputs.
class ashpy.losses.gan.GeneratorAdversarialLoss(loss_fn=None)[source]

Base class for the adversarial loss of the generator.

__init__(loss_fn=None)[source]

Initialize the Executor.

Parameters: loss_fn (tf.keras.losses.Loss) – Keras Loss function to call passing (tf.ones_like(d_fake_i), d_fake_i). None
class ashpy.losses.gan.GeneratorBCE(from_logits=True)[source]

The Binary CrossEntropy computed among the generator and the 1 label.

$L_{G} = E [\log (D( G(z))]$
__init__(from_logits=True)[source]

Initialize the BCE Loss for the Generator.

Return type: None
class ashpy.losses.gan.GeneratorHingeLoss[source]

Hinge loss for the Generator.

See Geometric GAN [1]_ for more details.

 [1] Geometric GAN https://arxiv.org/abs/1705.02894
__init__()[source]

Initialize the Least Square Loss for the Generator.

Return type: None
class ashpy.losses.gan.GeneratorL1[source]

L1 loss between the generator output and the target.

$L_G = E ||x - G(z)||_1$

Where x is the target and G(z) is generated image.

__init__()[source]

Initialize the Executor.

Return type: None
class ashpy.losses.gan.GeneratorLSGAN[source]

Least Square GAN Loss for generator.

Reference: https://arxiv.org/abs/1611.04076

Note

Basically the Mean Squared Error between the discriminator output when evaluated in fake and 1.

$L_{G} = \frac{1}{2} E [(1 - D(G(z))^2]$
__init__()[source]

Initialize the Least Square Loss for the Generator.

Return type: None
class ashpy.losses.gan.Pix2PixLoss(l1_loss_weight=100.0, adversarial_loss_weight=1.0, feature_matching_weight=10.0, adversarial_loss_type=<AdversarialLossType.GAN: 1>, use_feature_matching_loss=False)[source]

Pix2Pix Loss.

Weighted sum of ashpy.losses.gan.GeneratorL1, ashpy.losses.gan.AdversarialLossG and ashpy.losses.gan.FeatureMatchingLoss.

Used by Pix2Pix [1] and Pix2PixHD [2]

 [1] Image-to-Image Translation with Conditional Adversarial Networks https://arxiv.org/abs/1611.07004
 [2] High-Resolution Image Synthesis and Semantic Manipulation with Conditional GANs https://arxiv.org/abs/1711.11585
__init__(l1_loss_weight=100.0, adversarial_loss_weight=1.0, feature_matching_weight=10.0, adversarial_loss_type=<AdversarialLossType.GAN: 1>, use_feature_matching_loss=False)[source]

Initialize the loss.

Weighted sum of ashpy.losses.gan.GeneratorL1, ashpy.losses.gan.AdversarialLossG and ashpy.losses.gan.FeatureMatchingLoss.

Parameters: l1_loss_weight (ashpy.ashtypes.TWeight) – Weight of L1 loss. adversarial_loss_weight (ashpy.ashtypes.TWeight) – Weight of adversarial loss. feature_matching_weight (ashpy.ashtypes.TWeight) – Weight of the feature matching loss. adversarial_loss_type (ashpy.losses.gan.AdversarialLossType) – Adversarial loss type (ashpy.losses.gan.AdversarialLossType.GAN or ashpy.losses.gan.AdversarialLossType.LSGAN). use_feature_matching_loss (bool) – if True use also uses ashpy.losses.gan.FeatureMatchingLoss. None
class ashpy.losses.gan.Pix2PixLossSemantic(cross_entropy_weight=100.0, adversarial_loss_weight=1.0, feature_matching_weight=10.0, adversarial_loss_type=<AdversarialLossType.GAN: 1>, use_feature_matching_loss=False)[source]

Semantic Pix2Pix Loss.

Weighted sum of ashpy.losses.gan.CategoricalCrossEntropy, ashpy.losses.gan.AdversarialLossG and ashpy.losses.gan.FeatureMatchingLoss.

__init__(cross_entropy_weight=100.0, adversarial_loss_weight=1.0, feature_matching_weight=10.0, adversarial_loss_type=<AdversarialLossType.GAN: 1>, use_feature_matching_loss=False)[source]

Initialize the Executor.

Weighted sum of ashpy.losses.gan.CategoricalCrossEntropy, ashpy.losses.gan.AdversarialLossG and ashpy.losses.gan.FeatureMatchingLoss

Parameters: cross_entropy_weight (ashpy.ashtypes.TWeight) – Weight of the categorical cross entropy loss. adversarial_loss_weight (ashpy.ashtypes.TWeight) – Weight of the adversarial loss. feature_matching_weight (ashpy.ashtypes.TWeight) – Weight of the feature matching loss. adversarial_loss_type (ashpy.losses.gan.AdversarialLossType) – type of adversarial loss, see ashpy.losses.gan.AdversarialLossType use_feature_matching_loss (bool) – whether to use feature matching loss or not
ashpy.losses.gan.get_adversarial_loss_discriminator(adversarial_loss_type=<AdversarialLossType.GAN: 1>)[source]

Return the correct loss fot the Discriminator.

Parameters: adversarial_loss_type (ashpy.losses.gan.AdversarialLossType) – Type of loss (ashpy.losses.gan.AdversarialLossType.GAN or ashpy.losses.gan.AdversarialLossType.LSGAN) Type[Executor] The correct (ashpy.losses.executor.Executor) (to be instantiated).
ashpy.losses.gan.get_adversarial_loss_generator(adversarial_loss_type=<AdversarialLossType.GAN: 1>)[source]

Return the correct loss for the Generator.

Parameters: adversarial_loss_type (ashpy.losses.AdversarialLossType) – Type of loss (ashpy.losses.AdversarialLossType.GAN or ashpy.losses.AdversarialLossType.LSGAN). Type[Executor] The correct (ashpy.losses.executor.Executor) (to be instantiated).