def update_state(self, context: GANContext) -> None:
"""
Update the internal state of the metric, using the information from the context object.
Args:
context (:py:class:`ashpy.contexts.GANContext`): An AshPy Context Object that carries
all the information the Metric needs.
"""
for real_xy, noise in context.dataset:
real_x, real_y = real_xy
g_inputs = noise
if len(context.generator_model.inputs) == 2:
g_inputs = [noise, real_y]
fake = context.generator_model(
g_inputs, training=context.log_eval_mode == LogEvalMode.TRAIN)
loss = context.discriminator_loss(
context,
fake=fake,
real=real_x,
condition=real_y,
training=context.log_eval_mode == LogEvalMode.TRAIN,
)
self._distribute_strategy.experimental_run_v2(
lambda: self._metric.update_state(loss))
def update_state(self, context: GANContext) -> None:
"""
Update the internal state of the metric, using the information from the context object.
Args:
context (:py:class:`ashpy.contexts.gan.GANContext`): An AshPy Context Object
that carries all the information the Metric needs.
"""
updater = lambda value: lambda: self._metric.update_state(value)
for real_xy, noise in context.dataset:
_, real_y = real_xy
g_inputs = noise
if len(context.generator_model.inputs) == 2:
g_inputs = [noise, real_y]
fake = context.generator_model(
g_inputs, training=context.log_eval_mode == LogEvalMode.TRAIN)
img1, img2 = self.split_batch(fake)
ssim_multiscale = tf.image.ssim_multiscale(
img1,
img2,
max_val=self.max_val,
power_factors=self.power_factors,
filter_sigma=self.filter_sigma,
filter_size=self.filter_size,
k1=self.k1,
k2=self.k2,
)
self._distribute_strategy.experimental_run_v2(
updater(ssim_multiscale))
def update_state(self, context: GANContext) -> None:
"""
Update the internal state of the metric, using the information from the context object.
Args:
context (:py:class:`ashpy.contexts.gan.GANContext`): An AshPy Context Object
that carries all the information the Metric needs.
"""
updater = lambda value: lambda: self._metric.update_state(value)
for real_xy, noise in context.dataset:
real_x, real_y = real_xy
g_inputs = noise
if len(context.generator_model.inputs) == 2:
g_inputs = [noise, real_y]
fake = context.generator_model(
g_inputs, training=context.log_eval_mode == LogEvalMode.TRAIN)
# check the resolution is the same as the one passed as input
resolution = real_x.shape[1]
if resolution != self.resolution:
raise ValueError(
"Image resolution is not the same as the input resolution."
)
scores = sliced_wasserstein_distance(
real_x,
fake,
resolution_min=self.resolution_min,
patches_per_image=self.patches_per_image,
use_svd=self.use_svd,
patch_size=self.patch_size,
random_projection_dim=self.random_projection_dim,
random_sampling_count=self.random_sampling_count,
)
fake_scores = []
for i, couple in enumerate(scores):
self.children_real_fake[i][0].update_state(context, couple[0])
self.children_real_fake[i][1].update_state(context, couple[1])
fake_scores.append(tf.expand_dims(couple[1], axis=0))
fake_scores = tf.concat(fake_scores, axis=0)
self._distribute_strategy.experimental_run_v2(updater(fake_scores))
def update_state(self, context: GANContext) -> None:
"""
Update the internal state of the metric, using the information from the context object.
Args:
context (:py:class:`ashpy.contexts.ClassifierContext`): An AshPy Context
holding all the information the Metric needs.
"""
updater = lambda value: lambda: self._metric.update_state(value)
# Generate the images created with the AshPy Context's generator
for real_xy, noise in context.dataset:
_, real_y = real_xy
g_inputs = noise
if len(context.generator_model.inputs) == 2:
g_inputs = [noise, real_y]
fake = context.generator_model(
g_inputs, training=context.log_eval_mode == LogEvalMode.TRAIN)
# rescale images between 0 and 1
fake = (fake + 1.0) / 2.0
# Resize images to 299x299
fake = tf.image.resize(fake, (299, 299))
try:
fake = tf.image.grayscale_to_rgb(fake)
except ValueError:
# Images are already RGB
pass
# Calculate the inception score
inception_score_per_batch = self.inception_score(fake)
# Update the Mean metric created for this context
# self._metric.update_state(mean)
self._distribute_strategy.experimental_run_v2(
updater(inception_score_per_batch))
def _log_fn(self, context: GANContext) -> None:
"""
Log output of the generator to Tensorboard.
Args:
context (:py:class:`ashpy.contexts.gan.GANContext`): current context.
"""
if context.log_eval_mode == LogEvalMode.TEST:
out = context.generator_model(context.generator_inputs,
training=False)
elif context.log_eval_mode == LogEvalMode.TRAIN:
out = context.fake_samples
else:
raise ValueError("Invalid LogEvalMode")
# tensorboard 2.0 does not support float images in [-1, 1]
# only in [0,1]
if out.dtype == tf.float32:
# The hypothesis is that image are in [-1,1] how to check?
out = (out + 1.0) / 2
log("generator", out, context.global_step)