def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
if self.normalize is None and hp.Boolean(NORMALIZE):
with hp.conditional_scope(NORMALIZE, [True]):
output_node = preprocessing.Normalization().build(hp, output_node)
elif self.normalize:
output_node = preprocessing.Normalization().build(hp, output_node)
if self.augment is None and hp.Boolean(AUGMENT):
with hp.conditional_scope(AUGMENT, [True]):
output_node = preprocessing.ImageAugmentation().build(
hp, output_node
)
elif self.augment:
output_node = preprocessing.ImageAugmentation().build(hp, output_node)
if self.block_type is None:
block_type = hp.Choice(BLOCK_TYPE, [RESNET, XCEPTION, VANILLA])
with hp.conditional_scope(BLOCK_TYPE, [block_type]):
output_node = self._build_block(hp, output_node, block_type)
else:
output_node = self._build_block(hp, output_node, self.block_type)
return output_node
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
output_node = input_node
block_type = self.block_type or hp.Choice(
'block_type', ['resnet', 'xception', 'vanilla'], default='vanilla')
normalize = self.normalize
if normalize is None:
normalize = hp.Boolean('normalize', default=False)
augment = self.augment
if augment is None:
augment = hp.Boolean('augment', default=False)
if normalize:
output_node = preprocessing.Normalization().build(hp, output_node)
if augment:
output_node = preprocessing.ImageAugmentation().build(
hp, output_node)
if block_type == 'resnet':
output_node = basic.ResNetBlock().build(hp, output_node)
elif block_type == 'xception':
output_node = basic.XceptionBlock().build(hp, output_node)
elif block_type == 'vanilla':
output_node = basic.ConvBlock().build(hp, output_node)
return output_node
def test_image_augmentation():
utils.block_basic_exam(
preprocessing.ImageAugmentation(),
tf.keras.Input(shape=(32, 32, 3), dtype=tf.float32),
['vertical_flip', 'horizontal_flip'],
)