def update_state(self, context: ClassifierContext) -> 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.
"""
# Generate the images created with the AshPy Context's generator
generated_images = [
context.generator_model(
noise, training=context.log_eval_mode == LogEvalMode.TRAIN)
for noise in context.noise_dataset # FIXME: ?
]
rescaled_images = [((generate_image * 0.5) + 0.5)
for generate_image in generated_images]
# Resize images to 299x299
resized_images = [
tf.image.resize(rescaled_image, (299, 299))
for rescaled_image in rescaled_images
]
try:
resized_images[:] = [
tf.image.grayscale_to_rgb(images) for images in resized_images
]
except ValueError:
# Images are already RGB
pass
# Instead of using multiple batches of 'batch_size' each (that causes OOM).
# Unravel the dataset and then create small batches, each with 2 images at most.
dataset = tf.unstack(
tf.reshape(tf.stack(resized_images), (-1, 1, 299, 299, 3)))
# Calculate the inception score
mean, _ = self.inception_score(dataset)
# Update the Mean metric created for this context
# self._metric.update_state(mean)
self._distribute_strategy.experimental_run(
lambda: self._metric.update_state(mean))
def update_state(self, context: ClassifierContext) -> 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)
for features, labels in context.dataset:
loss = context.loss(
context,
features=features,
labels=labels,
training=context.log_eval_mode == LogEvalMode.TRAIN,
)
self._distribute_strategy.experimental_run_v2(updater(loss))
def update_state(self, context: ClassifierContext) -> 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.
"""
for features, labels in context.dataset:
predictions = context.classifier_model(
features, training=context.log_eval_mode == LogEvalMode.TRAIN)
self._distribute_strategy.experimental_run(
lambda: self._metric.update_state(
labels,
self._processing_predictions["fn"]
(predictions, **self._processing_predictions["kwargs"]),
))
def call(self, context: ClassifierContext, *, features: tf.Tensor,
labels: tf.Tensor, training: bool, **kwargs) -> tf.Tensor:
r"""
Compute the classifier loss.
Args:
context (:py:class:`ashpy.ClassifierContext`): Context for classification.
features (:py:class:`tf.Tensor`): Inputs for the classifier model.
labels (:py:class:`tf.Tensor`): Target for the classifier model.
training (bool): Whether is training or not.
**kwargs:
Returns:
:py:class:`tf.Tensor`: Loss value.
"""
predictions = context.classifier_model(features, training=training)
loss = self._fn(labels, predictions)
loss = tf.cond(
tf.equal(tf.rank(loss), tf.constant(4)),
lambda: loss,
lambda: tf.expand_dims(tf.expand_dims(loss, axis=-1), axis=-1),
)
return tf.reduce_mean(loss, axis=[1, 2])