autokeras/blocks/wrapper.py
# Copyright 2020 The AutoKeras Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from typing import Optional
import tree
from autokeras.blocks import basic
from autokeras.blocks import preprocessing
from autokeras.blocks import reduction
from autokeras.engine import block as block_module
from autokeras.utils import utils
BLOCK_TYPE = "block_type"
RESNET = "resnet"
XCEPTION = "xception"
VANILLA = "vanilla"
EFFICIENT = "efficient"
NORMALIZE = "normalize"
AUGMENT = "augment"
class ImageBlock(block_module.Block):
"""Block for image data.
The image blocks is a block choosing from ResNetBlock, XceptionBlock,
ConvBlock, which is controlled by a hyperparameter, 'block_type'.
# Arguments
block_type: String. 'resnet', 'xception', 'vanilla'. The type of Block
to use. If unspecified, it will be tuned automatically.
normalize: Boolean. Whether to channel-wise normalize the images.
If unspecified, it will be tuned automatically.
augment: Boolean. Whether to do image augmentation. If unspecified,
it will be tuned automatically.
"""
def __init__(
self,
block_type: Optional[str] = None,
normalize: Optional[bool] = None,
augment: Optional[bool] = None,
**kwargs
):
super().__init__(**kwargs)
self.block_type = block_type
self.normalize = normalize
self.augment = augment
def get_config(self):
config = super().get_config()
config.update(
{
BLOCK_TYPE: self.block_type,
NORMALIZE: self.normalize,
AUGMENT: self.augment,
}
)
return config
def _build_block(self, hp, output_node, block_type):
if block_type == RESNET:
return basic.ResNetBlock().build(hp, output_node)
elif block_type == XCEPTION:
return basic.XceptionBlock().build(hp, output_node)
elif block_type == VANILLA:
return basic.ConvBlock().build(hp, output_node)
elif block_type == EFFICIENT:
return basic.EfficientNetBlock().build(hp, output_node)
def build(self, hp, inputs=None):
input_node = tree.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, EFFICIENT]
)
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
class TextBlock(block_module.Block):
"""Block for text data."""
def __init__(self, **kwargs):
super().__init__(**kwargs)
def build(self, hp, inputs=None):
input_node = tree.flatten(inputs)[0]
output_node = input_node
output_node = self._build_block(hp, output_node)
return output_node
def _build_block(self, hp, output_node):
output_node = basic.BertBlock().build(hp, output_node)
return output_node
class GeneralBlock(block_module.Block):
"""A general neural network block when the input type is unknown.
When the input type is unknown. The GeneralBlock would search in a large
space for a good model.
# Arguments
name: String.
"""
def build(self, hp, inputs=None):
inputs = tree.flatten(inputs)
utils.validate_num_inputs(inputs, 1)
input_node = inputs[0]
output_node = input_node
output_node = reduction.Flatten().build(hp, output_node)
output_node = basic.DenseBlock().build(hp, output_node)
return output_node