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

Bases: enum.Enum

Enumeration for Adversarial Losses. Implemented: GAN and LSGAN.

class ashpy.losses.gan.CategoricalCrossEntropy

Bases: ashpy.losses.executor.Executor

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__()

Initialize the Categorical Cross Entropy Executor.

Return type:None

class ashpy.losses.gan.DiscriminatorAdversarialLoss(loss_fn=None)

Bases: ashpy.losses.gan.GANExecutor

Base class for the adversarial loss of the discriminator.

__init__(loss_fn=None)

Initialize the Executor.

Parameters:

• loss_fn (tf.keras.losses.Loss) – Loss function call passing
• d_fake) ((d_real,) –

Return type:

None

class ashpy.losses.gan.DiscriminatorHingeLoss

Bases: ashpy.losses.gan.DiscriminatorAdversarialLoss

Hinge loss for the Discriminator.

See Geometric GAN [1]_ for more details.

[1]	Geometric GAN https://arxiv.org/abs/1705.02894

__init__()

Initialize the Least Square Loss for the Generator.

Return type:None

class ashpy.losses.gan.DiscriminatorLSGAN

Bases: ashpy.losses.gan.DiscriminatorAdversarialLoss

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__()

Initialize loss.

Return type:None

class ashpy.losses.gan.DiscriminatorMinMax(from_logits=True, label_smoothing=0.0)

Bases: ashpy.losses.gan.DiscriminatorAdversarialLoss

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)

Initialize Loss.

class ashpy.losses.gan.EncoderBCE(from_logits=True)

Bases: ashpy.losses.executor.Executor

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

__init__(from_logits=True)

Initialize the Executor.

Return type:None

class ashpy.losses.gan.FeatureMatchingLoss

Bases: ashpy.losses.gan.GANExecutor

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__()

Initialize the Executor.

Return type:None

class ashpy.losses.gan.GANExecutor(fn=None)

Bases: ashpy.losses.executor.Executor, abc.ABC

Executor for GANs.

Implements the basic functions needed by the GAN losses.

call(context, **kwargs)

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

Parameters:context (ashpy.contexts.Context) – The function execution Context. Returns:tf.Tensor – Output Tensor.

static get_discriminator_inputs(context, fake_or_real, condition, training)

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

Return type:

Union[Tensor, List[Tensor]]

Returns:

The discriminator inputs.

class ashpy.losses.gan.GeneratorAdversarialLoss(loss_fn=None)

Bases: ashpy.losses.gan.GANExecutor

Base class for the adversarial loss of the generator.

__init__(loss_fn=None)

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). Return type:None

class ashpy.losses.gan.GeneratorBCE(from_logits=True)

Bases: ashpy.losses.gan.GeneratorAdversarialLoss

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

\[L_{G} = E [\log (D( G(z))]\]

__init__(from_logits=True)

Initialize the BCE Loss for the Generator.

Return type:None

class ashpy.losses.gan.GeneratorHingeLoss

Bases: ashpy.losses.gan.GeneratorAdversarialLoss

Hinge loss for the Generator.

See Geometric GAN [1]_ for more details.

[1]	Geometric GAN https://arxiv.org/abs/1705.02894

__init__()

Initialize the Least Square Loss for the Generator.

Return type:None

class ashpy.losses.gan.GeneratorL1

Bases: ashpy.losses.gan.GANExecutor

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__()

Initialize the Executor.

Return type:None

class ashpy.losses.gan.GeneratorLSGAN

Bases: ashpy.losses.gan.GeneratorAdversarialLoss

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__()

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)

Bases: ashpy.losses.executor.SumExecutor

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)

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.

Return type:

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)

Bases: ashpy.losses.executor.SumExecutor

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)

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>)

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) Return type:Type[Executor] Returns:The correct (ashpy.losses.executor.Executor) (to be instantiated).

ashpy.losses.gan.get_adversarial_loss_generator(adversarial_loss_type=<AdversarialLossType.GAN: 1>)

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). Return type:Type[Executor] Returns:The correct (ashpy.losses.executor.Executor) (to be instantiated).