def transform(image, label, training):
image = pad(image, 2)
image, label = to_tensor(image, label)
image = normalize(image, [0.1307], [0.3081])
label = tf.one_hot(label, 10)
return image, label
def transform(image, label, training):
if training:
image = random_crop(image, (32, 32), (4, 4))
image = tf.image.random_flip_left_right(image)
image, label = to_tensor(image, label)
image = normalize(image, [0.491, 0.482, 0.447], [0.247, 0.243, 0.262])
label = tf.one_hot(label, 100)
return image, label
def transform(image, label, training):
if training:
image = random_crop(image, (32, 32), (4, 4))
image = tf.image.random_flip_left_right(image)
image = autoaugment(image, "CIFAR10")
image, label = to_tensor(image, label)
image = normalize(image, [0.491, 0.482, 0.447], [0.247, 0.243, 0.262])
if training:
image = random_apply(cutout(length=16), params['cutout_prob'], image)
label = tf.one_hot(label, 100)
return image, label
def transform(image, label, training):
if training:
image = random_crop(image, (32, 32), (4, 4))
image = tf.image.random_flip_left_right(image)
# image = autoaugment(image, "CIFAR10")
image, label = to_tensor(image, label)
image = normalize(image, [0.491, 0.482, 0.447], [0.247, 0.243, 0.262])
# if training:
# image = cutout(image, 16)
label = tf.one_hot(label, 10)
return image, label
def transform(image, label, training):
if training:
image = random_resized_crop(image,
160,
scale=(0.05, 1.0),
ratio=(0.75, 1.33))
image = tf.image.random_flip_left_right(image)
else:
image = resize(image, int(256 / 224 * 224))
image = center_crop(image, 224)
image, label = to_tensor(image, label, label_offset=1)
image = normalize(image, [0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
label = tf.one_hot(label, 1000)
return image, label
def transform(x, y, training):
x, y = to_tensor(x, y, vmax=16)
x = normalize(x, [4.8842], [6.0168])
return x, y
