815 lines
30 KiB
Python
815 lines
30 KiB
Python
# Copyright 2018 The TensorFlow Authors. All Rights Reserved.
|
|
#
|
|
# 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.
|
|
# ==============================================================================
|
|
# pylint: disable=invalid-name
|
|
"""NASNet-A models for Keras.
|
|
|
|
NASNet refers to Neural Architecture Search Network, a family of models
|
|
that were designed automatically by learning the model architectures
|
|
directly on the dataset of interest.
|
|
|
|
Here we consider NASNet-A, the highest performance model that was found
|
|
for the CIFAR-10 dataset, and then extended to ImageNet 2012 dataset,
|
|
obtaining state of the art performance on CIFAR-10 and ImageNet 2012.
|
|
Only the NASNet-A models, and their respective weights, which are suited
|
|
for ImageNet 2012 are provided.
|
|
|
|
The below table describes the performance on ImageNet 2012:
|
|
--------------------------------------------------------------------------------
|
|
Architecture | Top-1 Acc | Top-5 Acc | Multiply-Adds | Params (M)
|
|
--------------------------------------------------------------------------------
|
|
| NASNet-A (4 @ 1056) | 74.0 % | 91.6 % | 564 M | 5.3 |
|
|
| NASNet-A (6 @ 4032) | 82.7 % | 96.2 % | 23.8 B | 88.9 |
|
|
--------------------------------------------------------------------------------
|
|
|
|
Reference:
|
|
- [Learning Transferable Architectures for Scalable Image Recognition](
|
|
https://arxiv.org/abs/1707.07012) (CVPR 2018)
|
|
"""
|
|
from __future__ import absolute_import
|
|
from __future__ import division
|
|
from __future__ import print_function
|
|
|
|
from tensorflow.python.keras import backend
|
|
from tensorflow.python.keras.applications import imagenet_utils
|
|
from tensorflow.python.keras.engine import training
|
|
from tensorflow.python.keras.layers import VersionAwareLayers
|
|
from tensorflow.python.keras.utils import data_utils
|
|
from tensorflow.python.keras.utils import layer_utils
|
|
from tensorflow.python.lib.io import file_io
|
|
from tensorflow.python.platform import tf_logging as logging
|
|
from tensorflow.python.util.tf_export import keras_export
|
|
|
|
|
|
BASE_WEIGHTS_PATH = ('https://storage.googleapis.com/tensorflow/'
|
|
'keras-applications/nasnet/')
|
|
NASNET_MOBILE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-mobile.h5'
|
|
NASNET_MOBILE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-mobile-no-top.h5'
|
|
NASNET_LARGE_WEIGHT_PATH = BASE_WEIGHTS_PATH + 'NASNet-large.h5'
|
|
NASNET_LARGE_WEIGHT_PATH_NO_TOP = BASE_WEIGHTS_PATH + 'NASNet-large-no-top.h5'
|
|
|
|
layers = VersionAwareLayers()
|
|
|
|
|
|
def NASNet(input_shape=None,
|
|
penultimate_filters=4032,
|
|
num_blocks=6,
|
|
stem_block_filters=96,
|
|
skip_reduction=True,
|
|
filter_multiplier=2,
|
|
include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
pooling=None,
|
|
classes=1000,
|
|
default_size=None,
|
|
classifier_activation='softmax'):
|
|
"""Instantiates a NASNet model.
|
|
|
|
Reference:
|
|
- [Learning Transferable Architectures for Scalable Image Recognition](
|
|
https://arxiv.org/abs/1707.07012) (CVPR 2018)
|
|
|
|
Optionally loads weights pre-trained on ImageNet.
|
|
Note that the data format convention used by the model is
|
|
the one specified in your Keras config at `~/.keras/keras.json`.
|
|
|
|
Arguments:
|
|
input_shape: Optional shape tuple, the input shape
|
|
is by default `(331, 331, 3)` for NASNetLarge and
|
|
`(224, 224, 3)` for NASNetMobile.
|
|
It should have exactly 3 input channels,
|
|
and width and height should be no smaller than 32.
|
|
E.g. `(224, 224, 3)` would be one valid value.
|
|
penultimate_filters: Number of filters in the penultimate layer.
|
|
NASNet models use the notation `NASNet (N @ P)`, where:
|
|
- N is the number of blocks
|
|
- P is the number of penultimate filters
|
|
num_blocks: Number of repeated blocks of the NASNet model.
|
|
NASNet models use the notation `NASNet (N @ P)`, where:
|
|
- N is the number of blocks
|
|
- P is the number of penultimate filters
|
|
stem_block_filters: Number of filters in the initial stem block
|
|
skip_reduction: Whether to skip the reduction step at the tail
|
|
end of the network.
|
|
filter_multiplier: Controls the width of the network.
|
|
- If `filter_multiplier` < 1.0, proportionally decreases the number
|
|
of filters in each layer.
|
|
- If `filter_multiplier` > 1.0, proportionally increases the number
|
|
of filters in each layer.
|
|
- If `filter_multiplier` = 1, default number of filters from the
|
|
paper are used at each layer.
|
|
include_top: Whether to include the fully-connected
|
|
layer at the top of the network.
|
|
weights: `None` (random initialization) or
|
|
`imagenet` (ImageNet weights)
|
|
input_tensor: Optional Keras tensor (i.e. output of
|
|
`layers.Input()`)
|
|
to use as image input for the model.
|
|
pooling: Optional pooling mode for feature extraction
|
|
when `include_top` is `False`.
|
|
- `None` means that the output of the model
|
|
will be the 4D tensor output of the
|
|
last convolutional block.
|
|
- `avg` means that global average pooling
|
|
will be applied to the output of the
|
|
last convolutional block, and thus
|
|
the output of the model will be a
|
|
2D tensor.
|
|
- `max` means that global max pooling will
|
|
be applied.
|
|
classes: Optional number of classes to classify images
|
|
into, only to be specified if `include_top` is True, and
|
|
if no `weights` argument is specified.
|
|
default_size: Specifies the default image size of the model
|
|
classifier_activation: A `str` or callable. The activation function to use
|
|
on the "top" layer. Ignored unless `include_top=True`. Set
|
|
`classifier_activation=None` to return the logits of the "top" layer.
|
|
|
|
Returns:
|
|
A `keras.Model` instance.
|
|
|
|
Raises:
|
|
ValueError: In case of invalid argument for `weights`,
|
|
invalid input shape or invalid `penultimate_filters` value.
|
|
ValueError: if `classifier_activation` is not `softmax` or `None` when
|
|
using a pretrained top layer.
|
|
"""
|
|
if not (weights in {'imagenet', None} or file_io.file_exists_v2(weights)):
|
|
raise ValueError('The `weights` argument should be either '
|
|
'`None` (random initialization), `imagenet` '
|
|
'(pre-training on ImageNet), '
|
|
'or the path to the weights file to be loaded.')
|
|
|
|
if weights == 'imagenet' and include_top and classes != 1000:
|
|
raise ValueError('If using `weights` as `"imagenet"` with `include_top` '
|
|
'as true, `classes` should be 1000')
|
|
|
|
if (isinstance(input_shape, tuple) and None in input_shape and
|
|
weights == 'imagenet'):
|
|
raise ValueError('When specifying the input shape of a NASNet'
|
|
' and loading `ImageNet` weights, '
|
|
'the input_shape argument must be static '
|
|
'(no None entries). Got: `input_shape=' +
|
|
str(input_shape) + '`.')
|
|
|
|
if default_size is None:
|
|
default_size = 331
|
|
|
|
# Determine proper input shape and default size.
|
|
input_shape = imagenet_utils.obtain_input_shape(
|
|
input_shape,
|
|
default_size=default_size,
|
|
min_size=32,
|
|
data_format=backend.image_data_format(),
|
|
require_flatten=True,
|
|
weights=weights)
|
|
|
|
if backend.image_data_format() != 'channels_last':
|
|
logging.warning('The NASNet family of models is only available '
|
|
'for the input data format "channels_last" '
|
|
'(width, height, channels). '
|
|
'However your settings specify the default '
|
|
'data format "channels_first" (channels, width, height).'
|
|
' You should set `image_data_format="channels_last"` '
|
|
'in your Keras config located at ~/.keras/keras.json. '
|
|
'The model being returned right now will expect inputs '
|
|
'to follow the "channels_last" data format.')
|
|
backend.set_image_data_format('channels_last')
|
|
old_data_format = 'channels_first'
|
|
else:
|
|
old_data_format = None
|
|
|
|
if input_tensor is None:
|
|
img_input = layers.Input(shape=input_shape)
|
|
else:
|
|
if not backend.is_keras_tensor(input_tensor):
|
|
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
|
|
else:
|
|
img_input = input_tensor
|
|
|
|
if penultimate_filters % (24 * (filter_multiplier**2)) != 0:
|
|
raise ValueError(
|
|
'For NASNet-A models, the `penultimate_filters` must be a multiple '
|
|
'of 24 * (`filter_multiplier` ** 2). Current value: %d' %
|
|
penultimate_filters)
|
|
|
|
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
|
|
filters = penultimate_filters // 24
|
|
|
|
x = layers.Conv2D(
|
|
stem_block_filters, (3, 3),
|
|
strides=(2, 2),
|
|
padding='valid',
|
|
use_bias=False,
|
|
name='stem_conv1',
|
|
kernel_initializer='he_normal')(
|
|
img_input)
|
|
|
|
x = layers.BatchNormalization(
|
|
axis=channel_dim, momentum=0.9997, epsilon=1e-3, name='stem_bn1')(
|
|
x)
|
|
|
|
p = None
|
|
x, p = _reduction_a_cell(
|
|
x, p, filters // (filter_multiplier**2), block_id='stem_1')
|
|
x, p = _reduction_a_cell(
|
|
x, p, filters // filter_multiplier, block_id='stem_2')
|
|
|
|
for i in range(num_blocks):
|
|
x, p = _normal_a_cell(x, p, filters, block_id='%d' % (i))
|
|
|
|
x, p0 = _reduction_a_cell(
|
|
x, p, filters * filter_multiplier, block_id='reduce_%d' % (num_blocks))
|
|
|
|
p = p0 if not skip_reduction else p
|
|
|
|
for i in range(num_blocks):
|
|
x, p = _normal_a_cell(
|
|
x, p, filters * filter_multiplier, block_id='%d' % (num_blocks + i + 1))
|
|
|
|
x, p0 = _reduction_a_cell(
|
|
x,
|
|
p,
|
|
filters * filter_multiplier**2,
|
|
block_id='reduce_%d' % (2 * num_blocks))
|
|
|
|
p = p0 if not skip_reduction else p
|
|
|
|
for i in range(num_blocks):
|
|
x, p = _normal_a_cell(
|
|
x,
|
|
p,
|
|
filters * filter_multiplier**2,
|
|
block_id='%d' % (2 * num_blocks + i + 1))
|
|
|
|
x = layers.Activation('relu')(x)
|
|
|
|
if include_top:
|
|
x = layers.GlobalAveragePooling2D()(x)
|
|
imagenet_utils.validate_activation(classifier_activation, weights)
|
|
x = layers.Dense(classes, activation=classifier_activation,
|
|
name='predictions')(x)
|
|
else:
|
|
if pooling == 'avg':
|
|
x = layers.GlobalAveragePooling2D()(x)
|
|
elif pooling == 'max':
|
|
x = layers.GlobalMaxPooling2D()(x)
|
|
|
|
# Ensure that the model takes into account
|
|
# any potential predecessors of `input_tensor`.
|
|
if input_tensor is not None:
|
|
inputs = layer_utils.get_source_inputs(input_tensor)
|
|
else:
|
|
inputs = img_input
|
|
|
|
model = training.Model(inputs, x, name='NASNet')
|
|
|
|
# Load weights.
|
|
if weights == 'imagenet':
|
|
if default_size == 224: # mobile version
|
|
if include_top:
|
|
weights_path = data_utils.get_file(
|
|
'nasnet_mobile.h5',
|
|
NASNET_MOBILE_WEIGHT_PATH,
|
|
cache_subdir='models',
|
|
file_hash='020fb642bf7360b370c678b08e0adf61')
|
|
else:
|
|
weights_path = data_utils.get_file(
|
|
'nasnet_mobile_no_top.h5',
|
|
NASNET_MOBILE_WEIGHT_PATH_NO_TOP,
|
|
cache_subdir='models',
|
|
file_hash='1ed92395b5b598bdda52abe5c0dbfd63')
|
|
model.load_weights(weights_path)
|
|
elif default_size == 331: # large version
|
|
if include_top:
|
|
weights_path = data_utils.get_file(
|
|
'nasnet_large.h5',
|
|
NASNET_LARGE_WEIGHT_PATH,
|
|
cache_subdir='models',
|
|
file_hash='11577c9a518f0070763c2b964a382f17')
|
|
else:
|
|
weights_path = data_utils.get_file(
|
|
'nasnet_large_no_top.h5',
|
|
NASNET_LARGE_WEIGHT_PATH_NO_TOP,
|
|
cache_subdir='models',
|
|
file_hash='d81d89dc07e6e56530c4e77faddd61b5')
|
|
model.load_weights(weights_path)
|
|
else:
|
|
raise ValueError('ImageNet weights can only be loaded with NASNetLarge'
|
|
' or NASNetMobile')
|
|
elif weights is not None:
|
|
model.load_weights(weights)
|
|
|
|
if old_data_format:
|
|
backend.set_image_data_format(old_data_format)
|
|
|
|
return model
|
|
|
|
|
|
@keras_export('keras.applications.nasnet.NASNetMobile',
|
|
'keras.applications.NASNetMobile')
|
|
def NASNetMobile(input_shape=None,
|
|
include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
pooling=None,
|
|
classes=1000):
|
|
"""Instantiates a Mobile NASNet model in ImageNet mode.
|
|
|
|
Reference:
|
|
- [Learning Transferable Architectures for Scalable Image Recognition](
|
|
https://arxiv.org/abs/1707.07012) (CVPR 2018)
|
|
|
|
Optionally loads weights pre-trained on ImageNet.
|
|
Note that the data format convention used by the model is
|
|
the one specified in your Keras config at `~/.keras/keras.json`.
|
|
|
|
Note: each Keras Application expects a specific kind of input preprocessing.
|
|
For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
|
|
inputs before passing them to the model.
|
|
|
|
Arguments:
|
|
input_shape: Optional shape tuple, only to be specified
|
|
if `include_top` is False (otherwise the input shape
|
|
has to be `(224, 224, 3)` for NASNetMobile
|
|
It should have exactly 3 inputs channels,
|
|
and width and height should be no smaller than 32.
|
|
E.g. `(224, 224, 3)` would be one valid value.
|
|
include_top: Whether to include the fully-connected
|
|
layer at the top of the network.
|
|
weights: `None` (random initialization) or
|
|
`imagenet` (ImageNet weights)
|
|
For loading `imagenet` weights, `input_shape` should be (224, 224, 3)
|
|
input_tensor: Optional Keras tensor (i.e. output of
|
|
`layers.Input()`)
|
|
to use as image input for the model.
|
|
pooling: Optional pooling mode for feature extraction
|
|
when `include_top` is `False`.
|
|
- `None` means that the output of the model
|
|
will be the 4D tensor output of the
|
|
last convolutional layer.
|
|
- `avg` means that global average pooling
|
|
will be applied to the output of the
|
|
last convolutional layer, and thus
|
|
the output of the model will be a
|
|
2D tensor.
|
|
- `max` means that global max pooling will
|
|
be applied.
|
|
classes: Optional number of classes to classify images
|
|
into, only to be specified if `include_top` is True, and
|
|
if no `weights` argument is specified.
|
|
|
|
Returns:
|
|
A Keras model instance.
|
|
|
|
Raises:
|
|
ValueError: In case of invalid argument for `weights`,
|
|
or invalid input shape.
|
|
RuntimeError: If attempting to run this model with a
|
|
backend that does not support separable convolutions.
|
|
"""
|
|
return NASNet(
|
|
input_shape,
|
|
penultimate_filters=1056,
|
|
num_blocks=4,
|
|
stem_block_filters=32,
|
|
skip_reduction=False,
|
|
filter_multiplier=2,
|
|
include_top=include_top,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
default_size=224)
|
|
|
|
|
|
@keras_export('keras.applications.nasnet.NASNetLarge',
|
|
'keras.applications.NASNetLarge')
|
|
def NASNetLarge(input_shape=None,
|
|
include_top=True,
|
|
weights='imagenet',
|
|
input_tensor=None,
|
|
pooling=None,
|
|
classes=1000):
|
|
"""Instantiates a NASNet model in ImageNet mode.
|
|
|
|
Reference:
|
|
- [Learning Transferable Architectures for Scalable Image Recognition](
|
|
https://arxiv.org/abs/1707.07012) (CVPR 2018)
|
|
|
|
Optionally loads weights pre-trained on ImageNet.
|
|
Note that the data format convention used by the model is
|
|
the one specified in your Keras config at `~/.keras/keras.json`.
|
|
|
|
Note: each Keras Application expects a specific kind of input preprocessing.
|
|
For NASNet, call `tf.keras.applications.nasnet.preprocess_input` on your
|
|
inputs before passing them to the model.
|
|
|
|
Arguments:
|
|
input_shape: Optional shape tuple, only to be specified
|
|
if `include_top` is False (otherwise the input shape
|
|
has to be `(331, 331, 3)` for NASNetLarge.
|
|
It should have exactly 3 inputs channels,
|
|
and width and height should be no smaller than 32.
|
|
E.g. `(224, 224, 3)` would be one valid value.
|
|
include_top: Whether to include the fully-connected
|
|
layer at the top of the network.
|
|
weights: `None` (random initialization) or
|
|
`imagenet` (ImageNet weights)
|
|
For loading `imagenet` weights, `input_shape` should be (331, 331, 3)
|
|
input_tensor: Optional Keras tensor (i.e. output of
|
|
`layers.Input()`)
|
|
to use as image input for the model.
|
|
pooling: Optional pooling mode for feature extraction
|
|
when `include_top` is `False`.
|
|
- `None` means that the output of the model
|
|
will be the 4D tensor output of the
|
|
last convolutional layer.
|
|
- `avg` means that global average pooling
|
|
will be applied to the output of the
|
|
last convolutional layer, and thus
|
|
the output of the model will be a
|
|
2D tensor.
|
|
- `max` means that global max pooling will
|
|
be applied.
|
|
classes: Optional number of classes to classify images
|
|
into, only to be specified if `include_top` is True, and
|
|
if no `weights` argument is specified.
|
|
|
|
Returns:
|
|
A Keras model instance.
|
|
|
|
Raises:
|
|
ValueError: in case of invalid argument for `weights`,
|
|
or invalid input shape.
|
|
RuntimeError: If attempting to run this model with a
|
|
backend that does not support separable convolutions.
|
|
"""
|
|
return NASNet(
|
|
input_shape,
|
|
penultimate_filters=4032,
|
|
num_blocks=6,
|
|
stem_block_filters=96,
|
|
skip_reduction=True,
|
|
filter_multiplier=2,
|
|
include_top=include_top,
|
|
weights=weights,
|
|
input_tensor=input_tensor,
|
|
pooling=pooling,
|
|
classes=classes,
|
|
default_size=331)
|
|
|
|
|
|
def _separable_conv_block(ip,
|
|
filters,
|
|
kernel_size=(3, 3),
|
|
strides=(1, 1),
|
|
block_id=None):
|
|
"""Adds 2 blocks of [relu-separable conv-batchnorm].
|
|
|
|
Arguments:
|
|
ip: Input tensor
|
|
filters: Number of output filters per layer
|
|
kernel_size: Kernel size of separable convolutions
|
|
strides: Strided convolution for downsampling
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
A Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
|
|
|
|
with backend.name_scope('separable_conv_block_%s' % block_id):
|
|
x = layers.Activation('relu')(ip)
|
|
if strides == (2, 2):
|
|
x = layers.ZeroPadding2D(
|
|
padding=imagenet_utils.correct_pad(x, kernel_size),
|
|
name='separable_conv_1_pad_%s' % block_id)(x)
|
|
conv_pad = 'valid'
|
|
else:
|
|
conv_pad = 'same'
|
|
x = layers.SeparableConv2D(
|
|
filters,
|
|
kernel_size,
|
|
strides=strides,
|
|
name='separable_conv_1_%s' % block_id,
|
|
padding=conv_pad,
|
|
use_bias=False,
|
|
kernel_initializer='he_normal')(
|
|
x)
|
|
x = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name='separable_conv_1_bn_%s' % (block_id))(
|
|
x)
|
|
x = layers.Activation('relu')(x)
|
|
x = layers.SeparableConv2D(
|
|
filters,
|
|
kernel_size,
|
|
name='separable_conv_2_%s' % block_id,
|
|
padding='same',
|
|
use_bias=False,
|
|
kernel_initializer='he_normal')(
|
|
x)
|
|
x = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name='separable_conv_2_bn_%s' % (block_id))(
|
|
x)
|
|
return x
|
|
|
|
|
|
def _adjust_block(p, ip, filters, block_id=None):
|
|
"""Adjusts the input `previous path` to match the shape of the `input`.
|
|
|
|
Used in situations where the output number of filters needs to be changed.
|
|
|
|
Arguments:
|
|
p: Input tensor which needs to be modified
|
|
ip: Input tensor whose shape needs to be matched
|
|
filters: Number of output filters to be matched
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
Adjusted Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
|
|
img_dim = 2 if backend.image_data_format() == 'channels_first' else -2
|
|
|
|
ip_shape = backend.int_shape(ip)
|
|
|
|
if p is not None:
|
|
p_shape = backend.int_shape(p)
|
|
|
|
with backend.name_scope('adjust_block'):
|
|
if p is None:
|
|
p = ip
|
|
|
|
elif p_shape[img_dim] != ip_shape[img_dim]:
|
|
with backend.name_scope('adjust_reduction_block_%s' % block_id):
|
|
p = layers.Activation('relu', name='adjust_relu_1_%s' % block_id)(p)
|
|
p1 = layers.AveragePooling2D((1, 1),
|
|
strides=(2, 2),
|
|
padding='valid',
|
|
name='adjust_avg_pool_1_%s' % block_id)(
|
|
p)
|
|
p1 = layers.Conv2D(
|
|
filters // 2, (1, 1),
|
|
padding='same',
|
|
use_bias=False,
|
|
name='adjust_conv_1_%s' % block_id,
|
|
kernel_initializer='he_normal')(
|
|
p1)
|
|
|
|
p2 = layers.ZeroPadding2D(padding=((0, 1), (0, 1)))(p)
|
|
p2 = layers.Cropping2D(cropping=((1, 0), (1, 0)))(p2)
|
|
p2 = layers.AveragePooling2D((1, 1),
|
|
strides=(2, 2),
|
|
padding='valid',
|
|
name='adjust_avg_pool_2_%s' % block_id)(
|
|
p2)
|
|
p2 = layers.Conv2D(
|
|
filters // 2, (1, 1),
|
|
padding='same',
|
|
use_bias=False,
|
|
name='adjust_conv_2_%s' % block_id,
|
|
kernel_initializer='he_normal')(
|
|
p2)
|
|
|
|
p = layers.concatenate([p1, p2], axis=channel_dim)
|
|
p = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name='adjust_bn_%s' % block_id)(
|
|
p)
|
|
|
|
elif p_shape[channel_dim] != filters:
|
|
with backend.name_scope('adjust_projection_block_%s' % block_id):
|
|
p = layers.Activation('relu')(p)
|
|
p = layers.Conv2D(
|
|
filters, (1, 1),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='adjust_conv_projection_%s' % block_id,
|
|
use_bias=False,
|
|
kernel_initializer='he_normal')(
|
|
p)
|
|
p = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name='adjust_bn_%s' % block_id)(
|
|
p)
|
|
return p
|
|
|
|
|
|
def _normal_a_cell(ip, p, filters, block_id=None):
|
|
"""Adds a Normal cell for NASNet-A (Fig. 4 in the paper).
|
|
|
|
Arguments:
|
|
ip: Input tensor `x`
|
|
p: Input tensor `p`
|
|
filters: Number of output filters
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
A Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
|
|
|
|
with backend.name_scope('normal_A_block_%s' % block_id):
|
|
p = _adjust_block(p, ip, filters, block_id)
|
|
|
|
h = layers.Activation('relu')(ip)
|
|
h = layers.Conv2D(
|
|
filters, (1, 1),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='normal_conv_1_%s' % block_id,
|
|
use_bias=False,
|
|
kernel_initializer='he_normal')(
|
|
h)
|
|
h = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name='normal_bn_1_%s' % block_id)(
|
|
h)
|
|
|
|
with backend.name_scope('block_1'):
|
|
x1_1 = _separable_conv_block(
|
|
h, filters, kernel_size=(5, 5), block_id='normal_left1_%s' % block_id)
|
|
x1_2 = _separable_conv_block(
|
|
p, filters, block_id='normal_right1_%s' % block_id)
|
|
x1 = layers.add([x1_1, x1_2], name='normal_add_1_%s' % block_id)
|
|
|
|
with backend.name_scope('block_2'):
|
|
x2_1 = _separable_conv_block(
|
|
p, filters, (5, 5), block_id='normal_left2_%s' % block_id)
|
|
x2_2 = _separable_conv_block(
|
|
p, filters, (3, 3), block_id='normal_right2_%s' % block_id)
|
|
x2 = layers.add([x2_1, x2_2], name='normal_add_2_%s' % block_id)
|
|
|
|
with backend.name_scope('block_3'):
|
|
x3 = layers.AveragePooling2D((3, 3),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='normal_left3_%s' % (block_id))(
|
|
h)
|
|
x3 = layers.add([x3, p], name='normal_add_3_%s' % block_id)
|
|
|
|
with backend.name_scope('block_4'):
|
|
x4_1 = layers.AveragePooling2D((3, 3),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='normal_left4_%s' % (block_id))(
|
|
p)
|
|
x4_2 = layers.AveragePooling2D((3, 3),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='normal_right4_%s' % (block_id))(
|
|
p)
|
|
x4 = layers.add([x4_1, x4_2], name='normal_add_4_%s' % block_id)
|
|
|
|
with backend.name_scope('block_5'):
|
|
x5 = _separable_conv_block(
|
|
h, filters, block_id='normal_left5_%s' % block_id)
|
|
x5 = layers.add([x5, h], name='normal_add_5_%s' % block_id)
|
|
|
|
x = layers.concatenate([p, x1, x2, x3, x4, x5],
|
|
axis=channel_dim,
|
|
name='normal_concat_%s' % block_id)
|
|
return x, ip
|
|
|
|
|
|
def _reduction_a_cell(ip, p, filters, block_id=None):
|
|
"""Adds a Reduction cell for NASNet-A (Fig. 4 in the paper).
|
|
|
|
Arguments:
|
|
ip: Input tensor `x`
|
|
p: Input tensor `p`
|
|
filters: Number of output filters
|
|
block_id: String block_id
|
|
|
|
Returns:
|
|
A Keras tensor
|
|
"""
|
|
channel_dim = 1 if backend.image_data_format() == 'channels_first' else -1
|
|
|
|
with backend.name_scope('reduction_A_block_%s' % block_id):
|
|
p = _adjust_block(p, ip, filters, block_id)
|
|
|
|
h = layers.Activation('relu')(ip)
|
|
h = layers.Conv2D(
|
|
filters, (1, 1),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='reduction_conv_1_%s' % block_id,
|
|
use_bias=False,
|
|
kernel_initializer='he_normal')(
|
|
h)
|
|
h = layers.BatchNormalization(
|
|
axis=channel_dim,
|
|
momentum=0.9997,
|
|
epsilon=1e-3,
|
|
name='reduction_bn_1_%s' % block_id)(
|
|
h)
|
|
h3 = layers.ZeroPadding2D(
|
|
padding=imagenet_utils.correct_pad(h, 3),
|
|
name='reduction_pad_1_%s' % block_id)(
|
|
h)
|
|
|
|
with backend.name_scope('block_1'):
|
|
x1_1 = _separable_conv_block(
|
|
h,
|
|
filters, (5, 5),
|
|
strides=(2, 2),
|
|
block_id='reduction_left1_%s' % block_id)
|
|
x1_2 = _separable_conv_block(
|
|
p,
|
|
filters, (7, 7),
|
|
strides=(2, 2),
|
|
block_id='reduction_right1_%s' % block_id)
|
|
x1 = layers.add([x1_1, x1_2], name='reduction_add_1_%s' % block_id)
|
|
|
|
with backend.name_scope('block_2'):
|
|
x2_1 = layers.MaxPooling2D((3, 3),
|
|
strides=(2, 2),
|
|
padding='valid',
|
|
name='reduction_left2_%s' % block_id)(
|
|
h3)
|
|
x2_2 = _separable_conv_block(
|
|
p,
|
|
filters, (7, 7),
|
|
strides=(2, 2),
|
|
block_id='reduction_right2_%s' % block_id)
|
|
x2 = layers.add([x2_1, x2_2], name='reduction_add_2_%s' % block_id)
|
|
|
|
with backend.name_scope('block_3'):
|
|
x3_1 = layers.AveragePooling2D((3, 3),
|
|
strides=(2, 2),
|
|
padding='valid',
|
|
name='reduction_left3_%s' % block_id)(
|
|
h3)
|
|
x3_2 = _separable_conv_block(
|
|
p,
|
|
filters, (5, 5),
|
|
strides=(2, 2),
|
|
block_id='reduction_right3_%s' % block_id)
|
|
x3 = layers.add([x3_1, x3_2], name='reduction_add3_%s' % block_id)
|
|
|
|
with backend.name_scope('block_4'):
|
|
x4 = layers.AveragePooling2D((3, 3),
|
|
strides=(1, 1),
|
|
padding='same',
|
|
name='reduction_left4_%s' % block_id)(
|
|
x1)
|
|
x4 = layers.add([x2, x4])
|
|
|
|
with backend.name_scope('block_5'):
|
|
x5_1 = _separable_conv_block(
|
|
x1, filters, (3, 3), block_id='reduction_left4_%s' % block_id)
|
|
x5_2 = layers.MaxPooling2D((3, 3),
|
|
strides=(2, 2),
|
|
padding='valid',
|
|
name='reduction_right5_%s' % block_id)(
|
|
h3)
|
|
x5 = layers.add([x5_1, x5_2], name='reduction_add4_%s' % block_id)
|
|
|
|
x = layers.concatenate([x2, x3, x4, x5],
|
|
axis=channel_dim,
|
|
name='reduction_concat_%s' % block_id)
|
|
return x, ip
|
|
|
|
|
|
@keras_export('keras.applications.nasnet.preprocess_input')
|
|
def preprocess_input(x, data_format=None):
|
|
return imagenet_utils.preprocess_input(x, data_format=data_format, mode='tf')
|
|
|
|
|
|
@keras_export('keras.applications.nasnet.decode_predictions')
|
|
def decode_predictions(preds, top=5):
|
|
return imagenet_utils.decode_predictions(preds, top=top)
|
|
|
|
|
|
preprocess_input.__doc__ = imagenet_utils.PREPROCESS_INPUT_DOC.format(
|
|
mode='',
|
|
ret=imagenet_utils.PREPROCESS_INPUT_RET_DOC_TF,
|
|
error=imagenet_utils.PREPROCESS_INPUT_ERROR_DOC)
|
|
decode_predictions.__doc__ = imagenet_utils.decode_predictions.__doc__
|