def call(
self,
context: GANContext,
*,
fake: tf.Tensor,
real: tf.Tensor,
condition: tf.Tensor,
training: bool,
**kwargs,
) -> tf.Tensor:
"""
Configure the discriminator inputs and calls `loss_fn`.
Args:
context (:py:class:`ashpy.contexts.GANContext`): GAN Context.
fake (): Fake data.
real (): Real data.
condition (): Generator conditioning.
training (bool): If training or evaluation.
Returns:
:py:class:`tf.Tensor`: The loss for each example.
"""
fake_inputs = self.get_discriminator_inputs(context,
fake_or_real=fake,
condition=condition,
training=training)
real_inputs = self.get_discriminator_inputs(context,
fake_or_real=real,
condition=condition,
training=training)
_, features_fake = context.discriminator_model(fake_inputs,
training=training,
return_features=True)
_, features_real = context.discriminator_model(real_inputs,
training=training,
return_features=True)
# for each feature the L1 between the real and the fake
# every call to fn should return [batch_size, 1] that is the mean L1
feature_loss = [
self._fn(feat_real_i, feat_fake_i)
for feat_real_i, feat_fake_i in zip(features_real, features_fake)
]
mae = tf.add_n(feature_loss)
return mae
def call(
self,
context: GANContext,
*,
fake: tf.Tensor,
real: tf.Tensor,
condition: tf.Tensor,
training: bool,
**kwargs,
):
r"""
Call: setup the discriminator inputs and calls `loss_fn`.
Args:
context (:py:class:`ashpy.contexts.GANContext`): GAN Context.
fake (:py:class:`tf.Tensor`): Fake images corresponding to the condition G(c).
real (:py:class:`tf.Tensor`): Real images corresponding to the condition x(c).
condition (:py:class:`tf.Tensor`): Condition for the generator and discriminator.
training (bool): if training or evaluation
Returns:
:py:class:`tf.Tensor`: The loss for each example.
"""
fake_inputs = self.get_discriminator_inputs(context,
fake_or_real=fake,
condition=condition,
training=training)
real_inputs = self.get_discriminator_inputs(context,
fake_or_real=real,
condition=condition,
training=training)
d_fake = context.discriminator_model(fake_inputs, training=training)
d_real = context.discriminator_model(real_inputs, training=training)
if isinstance(d_fake, list):
value = tf.add_n([
tf.reduce_mean(self._fn(d_real_i, d_fake_i), axis=[1, 2])
for d_real_i, d_fake_i in zip(d_real, d_fake)
])
return value
value = self._fn(d_real, d_fake)
value = tf.cond(
tf.equal(tf.rank(d_fake), tf.constant(4)),
lambda: value,
lambda: tf.expand_dims(tf.expand_dims(value, axis=-1), axis=-1),
)
return tf.reduce_mean(value, axis=[1, 2])
def call(
self,
context: GANContext,
*,
fake: tf.Tensor,
condition: tf.Tensor,
training: bool,
**kwargs,
) -> tf.Tensor:
r"""
Configure the discriminator inputs and calls `loss_fn`.
Args:
context (:py:class:`ashpy.contexts.GANContext`): GAN Context.
fake (:py:class:`tf.Tensor`): Fake images.
condition (:py:class:`tf.Tensor`): Generator conditioning.
training (bool): If training or evaluation.
Returns:
:py:class:`tf.Tensor`: The loss for each example.
"""
fake_inputs = self.get_discriminator_inputs(
context=context, fake_or_real=fake, condition=condition, training=training
)
d_fake = context.discriminator_model(fake_inputs, training=training)
# Support for Multiscale discriminator
# TODO: Improve
if isinstance(d_fake, list):
value = tf.add_n(
[
tf.reduce_mean(
self._fn(tf.ones_like(d_fake_i), d_fake_i), axis=[1, 2]
)
for d_fake_i in d_fake
]
)
return value
value = self._fn(tf.ones_like(d_fake), d_fake)
value = tf.cond(
tf.equal(tf.rank(d_fake), tf.constant(4)),
lambda: value,
lambda: tf.expand_dims(tf.expand_dims(value, axis=-1), axis=-1),
)
return tf.reduce_mean(value, axis=[1, 2])