def mixup(batch_size, alpha, same_mix_weight_per_batch, use_truncated_beta,
use_random_shuffling, images, labels):
"""Applies Mixup regularization to a batch of images and labels.
[1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
Mixup: Beyond Empirical Risk Minimization.
ICLR'18, https://arxiv.org/abs/1710.09412
Arguments:
batch_size: The input batch size for images and labels.
alpha: Float that controls the strength of Mixup regularization.
same_mix_weight_per_batch: whether to use the same mix coef over the batch.
use_truncated_beta: whether to sample from Beta_[0,1](alpha, alpha) or from
the truncated distribution Beta_[1/2, 1](alpha, alpha).
use_random_shuffling: Whether to pair images by random shuffling
(default is a deterministic pairing by reversing the batch).
images: A batch of images of shape [batch_size, ...]
labels: A batch of labels of shape [batch_size, num_classes]
Returns:
A tuple of (images, labels) with the same dimensions as the input with
Mixup regularization applied.
"""
if same_mix_weight_per_batch:
mix_weight = ed.Beta(alpha, alpha, sample_shape=[1, 1])
mix_weight = tf.tile(mix_weight, [batch_size, 1])
else:
mix_weight = ed.Beta(alpha, alpha, sample_shape=[batch_size, 1])
if use_truncated_beta:
mix_weight = tf.maximum(mix_weight, 1. - mix_weight)
images_mix_weight = tf.reshape(mix_weight, [batch_size, 1, 1, 1])
images_mix_weight = tf.cast(images_mix_weight, images.dtype)
if not use_random_shuffling:
# Mixup on a single batch is implemented by taking a weighted sum with the
# same batch in reverse.
mixup_index = tf.reverse(tf.range(batch_size), axis=[0])
else:
mixup_index = tf.random.shuffle(tf.range(batch_size))
images_mix = (images * images_mix_weight + tf.gather(images, mixup_index) *
(1. - images_mix_weight))
mix_weight = tf.cast(mix_weight, labels.dtype)
labels_mix = labels * mix_weight + tf.gather(
labels, mixup_index) * (1. - mix_weight)
return images_mix, labels_mix
def mixup(self, batch_size, alpha, images, labels):
"""Applies Mixup regularization to a batch of images and labels.
[1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
Mixup: Beyond Empirical Risk Minimization.
ICLR'18, https://arxiv.org/abs/1710.09412
Arguments:
batch_size: The input batch size for images and labels.
alpha: Float that controls the strength of Mixup regularization.
images: A batch of images of shape [batch_size, ...]
labels: A batch of labels of shape [batch_size, num_classes]
Returns:
A tuple of (images, labels) with the same dimensions as the input with
Mixup regularization applied.
"""
mix_weight = ed.Beta(alpha, alpha, sample_shape=[batch_size, 1])
mix_weight = tf.maximum(mix_weight, 1. - mix_weight)
images_mix_weight = tf.reshape(mix_weight, [batch_size, 1, 1, 1])
images_mix_weight = tf.cast(images_mix_weight, images.dtype)
# Mixup on a single batch is implemented by taking a weighted sum with the
# same batch in reverse.
images_mix = (images * images_mix_weight + images[::-1] *
(1. - images_mix_weight))
mix_weight = tf.cast(mix_weight, labels.dtype)
labels_mix = labels * mix_weight + labels[::-1] * (1. - mix_weight)
return images_mix, labels_mix
def mixup(batch_size, aug_params, images, labels):
"""Applies Mixup regularization to a batch of images and labels.
[1] Hongyi Zhang, Moustapha Cisse, Yann N. Dauphin, David Lopez-Paz
Mixup: Beyond Empirical Risk Minimization.
ICLR'18, https://arxiv.org/abs/1710.09412
`aug_params` can have the follwing fields:
augmix: whether or not to run AugMix.
mixup_alpha: the alpha to use in the Beta distribution.
aug_count: the number of augmentations to use in AugMix.
same_mix_weight_per_batch: whether to use the same mix coef over the batch.
use_truncated_beta: whether to sample from Beta_[0,1](alpha, alpha) or from
the truncated distribution Beta_[1/2, 1](alpha, alpha).
use_random_shuffling: Whether to pair images by random shuffling
(default is a deterministic pairing by reversing the batch).
Arguments:
batch_size: The input batch size for images and labels.
aug_params: Dict of data augmentation hyper parameters.
images: A batch of images of shape [batch_size, ...]
labels: A batch of labels of shape [batch_size, num_classes]
Returns:
A tuple of (images, labels) with the same dimensions as the input with
Mixup regularization applied.
"""
augmix = aug_params.get('augmix', False)
alpha = aug_params.get('mixup_alpha', 0.)
aug_count = aug_params.get('aug_count', 3)
same_mix_weight_per_batch = aug_params.get('same_mix_weight_per_batch',
False)
use_truncated_beta = aug_params.get('use_truncated_beta', True)
use_random_shuffling = aug_params.get('use_random_shuffling', False)
if augmix and same_mix_weight_per_batch:
raise ValueError(
'Can only set one of `augmix` or `same_mix_weight_per_batch`.')
# 4 is hard-coding to aug_count=3. Fix this later!
if augmix:
mix_weight = ed.Beta(alpha,
alpha,
sample_shape=[batch_size, aug_count + 1, 1])
elif same_mix_weight_per_batch:
mix_weight = ed.Beta(alpha, alpha, sample_shape=[1, 1])
mix_weight = tf.tile(mix_weight, [batch_size, 1])
else:
mix_weight = ed.Beta(alpha, alpha, sample_shape=[batch_size, 1])
if use_truncated_beta:
mix_weight = tf.maximum(mix_weight, 1. - mix_weight)
if augmix:
images_mix_weight = tf.reshape(mix_weight,
[batch_size, aug_count + 1, 1, 1, 1])
else:
images_mix_weight = tf.reshape(mix_weight, [batch_size, 1, 1, 1])
images_mix_weight = tf.cast(images_mix_weight, images.dtype)
if use_random_shuffling:
mixup_index = tf.random.shuffle(tf.range(batch_size))
else:
# Mixup on a single batch is implemented by taking a weighted sum with the
# same batch in reverse.
mixup_index = tf.reverse(tf.range(batch_size), axis=[0])
images_mix = (images * images_mix_weight + tf.gather(images, mixup_index) *
(1. - images_mix_weight))
mix_weight = tf.cast(mix_weight, labels.dtype)
if augmix:
labels = tf.reshape(tf.tile(labels, [1, aug_count + 1]),
[batch_size, aug_count + 1, -1])
labels_mix = (labels * mix_weight + tf.gather(labels, mixup_index) *
(1. - mix_weight))
labels_mix = tf.reshape(tf.transpose(labels_mix, [1, 0, 2]),
[batch_size * (aug_count + 1), -1])
else:
labels_mix = (labels * mix_weight + tf.gather(labels, mixup_index) *
(1. - mix_weight))
return images_mix, labels_mix