def mixup_and_cutmix(features, labels):
augmenter = augment.MixupAndCutmix(
mixup_alpha=params.mixup_and_cutmix.mixup_alpha,
cutmix_alpha=params.mixup_and_cutmix.cutmix_alpha,
prob=params.mixup_and_cutmix.prob,
label_smoothing=params.mixup_and_cutmix.label_smoothing,
num_classes=self._get_num_classes())
features['image'], labels = augmenter(features['image'],
labels)
return features, labels
def build_inputs(
self,
params: exp_cfg.DataConfig,
input_context: Optional[tf.distribute.InputContext] = None
) -> tf.data.Dataset:
"""Builds classification input."""
num_classes = self.task_config.model.num_classes
input_size = self.task_config.model.input_size
image_field_key = self.task_config.train_data.image_field_key
label_field_key = self.task_config.train_data.label_field_key
is_multilabel = self.task_config.train_data.is_multilabel
if params.tfds_name:
decoder = tfds_factory.get_classification_decoder(params.tfds_name)
else:
decoder = classification_input.Decoder(
image_field_key=image_field_key, label_field_key=label_field_key,
is_multilabel=is_multilabel)
parser = classification_input.Parser(
output_size=input_size[:2],
num_classes=num_classes,
image_field_key=image_field_key,
label_field_key=label_field_key,
decode_jpeg_only=params.decode_jpeg_only,
aug_rand_hflip=params.aug_rand_hflip,
aug_crop=params.aug_crop,
aug_type=params.aug_type,
color_jitter=params.color_jitter,
random_erasing=params.random_erasing,
is_multilabel=is_multilabel,
dtype=params.dtype)
postprocess_fn = None
if params.mixup_and_cutmix:
postprocess_fn = augment.MixupAndCutmix(
mixup_alpha=params.mixup_and_cutmix.mixup_alpha,
cutmix_alpha=params.mixup_and_cutmix.cutmix_alpha,
prob=params.mixup_and_cutmix.prob,
label_smoothing=params.mixup_and_cutmix.label_smoothing,
num_classes=num_classes)
reader = input_reader_factory.input_reader_generator(
params,
dataset_fn=dataset_fn.pick_dataset_fn(params.file_type),
decoder_fn=decoder.decode,
parser_fn=parser.parse_fn(params.is_training),
postprocess_fn=postprocess_fn)
dataset = reader.read(input_context=input_context)
return dataset
def test_mixup_and_cutmix_smoothes_labels(self):
batch_size = 12
num_classes = 1000
label_smoothing = 0.1
images = tf.random.normal((batch_size, 224, 224, 3), dtype=tf.float32)
labels = tf.range(batch_size)
augmenter = augment.MixupAndCutmix(num_classes=num_classes,
label_smoothing=label_smoothing)
aug_images, aug_labels = augmenter.distort(images, labels)
self.assertEqual(images.shape, aug_images.shape)
self.assertEqual(images.dtype, aug_images.dtype)
self.assertEqual([batch_size, num_classes], aug_labels.shape)
self.assertAllLessEqual(aug_labels, 1. - label_smoothing +
2. / num_classes) # With tolerance
self.assertAllGreaterEqual(aug_labels, label_smoothing / num_classes -
1e4) # With tolerance
def test_cutmix_changes_image(self):
batch_size = 12
num_classes = 1000
label_smoothing = 0.1
images = tf.random.normal((batch_size, 224, 224, 3), dtype=tf.float32)
labels = tf.range(batch_size)
augmenter = augment.MixupAndCutmix(mixup_alpha=0.,
cutmix_alpha=1.,
num_classes=num_classes)
aug_images, aug_labels = augmenter.distort(images, labels)
self.assertEqual(images.shape, aug_images.shape)
self.assertEqual(images.dtype, aug_images.dtype)
self.assertEqual([batch_size, num_classes], aug_labels.shape)
self.assertAllLessEqual(aug_labels, 1. - label_smoothing +
2. / num_classes) # With tolerance
self.assertAllGreaterEqual(aug_labels, label_smoothing / num_classes -
1e4) # With tolerance
self.assertFalse(tf.math.reduce_all(images == aug_images))