def _log_fn(self, context: GANEncoderContext):
"""
Log output of the generator to Tensorboard.
Logs G(E(x)).
Args:
context (:py:class:`ashpy.contexts.gan.GanEncoderContext`): current context.
"""
if context.log_eval_mode == LogEvalMode.TEST:
generator_of_encoder = context.generator_model(
context.encoder_model(context.encoder_inputs, training=False),
training=False,
)
elif context.log_eval_mode == LogEvalMode.TRAIN:
generator_of_encoder = context.generator_of_encoder
else:
raise ValueError("Invalid LogEvalMode")
# Tensorboard 2.0 does not support float images in [-1, 1]
# Only in [0,1]
if generator_of_encoder.dtype == tf.float32:
# The hypothesis is that image are in [-1,1] how to check?
generator_of_encoder = (generator_of_encoder + 1.0) / 2
log("generator_of_encoder", generator_of_encoder, context.global_step)
def call(self, context: GANEncoderContext, *, real: tf.Tensor,
training: bool, **kwargs):
"""
Compute the Encoder BCE.
Args:
context (:py:class:`ashpy.contexts.GANEncoderContext`): GAN Context
with Encoder support.
real (:py:class:`tf.Tensor`): Real images.
training (bool): If training or evaluation.
Returns:
:py:class:`tf.Tensor`: The loss for each example.
"""
encode = context.encoder_model(real, training=training)
d_real = context.discriminator_model([real, encode], training=training)
return self._fn(tf.zeros_like(d_real), d_real)