def wrapper_map_probe_v2(self, tfrecord):
"""tf.data.Dataset map function for probe data v2.
Args:
tfrecord: serilized by tf.data.Dataset.
Returns:
A map function
"""
def _extract_fn(tfrecord):
"""Extracts the functions."""
features = {
'image/encoded': tf.FixedLenFeature([], tf.string),
'image/label': tf.FixedLenFeature([], tf.int64)
}
example = tf.parse_single_example(tfrecord, features)
image, label = example['image/encoded'], tf.cast(
example['image/label'], dtype=tf.int32)
return [image, label]
image_bytes, label = _extract_fn(tfrecord)
label = tf.cast(label, tf.int64)
image = imagenet_preprocess_image(image_bytes,
is_training=True,
image_size=self.image_size)
return image, label
def _func(data):
image, label = data['image'], data['label']
image_bytes = tf.image.encode_jpeg(image, name='encode_jpeg')
image = imagenet_preprocess_image(image_bytes,
is_training=train,
image_size=self.image_size)
return image, label, data['id'], label # Last field is useless
def _func(data):
img, label = data['image'], data['label']
image_bytes = tf.image.encode_jpeg(img)
image_1 = imagenet_preprocess_image(image_bytes,
is_training=train,
image_size=self.image_size)
if train:
image_2 = imagenet_preprocess_image(image_bytes,
is_training=train,
image_size=self.image_size,
autoaugment_name='v0',
use_cutout=True)
images = tf.concat(
[tf.expand_dims(image_1, 0),
tf.expand_dims(image_2, 0)],
axis=0)
else:
images = image_1
return images, label