345 lines
13 KiB
Python
345 lines
13 KiB
Python
|
# Copyright 2016 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
|
||
|
# pylint: disable=unused-import
|
||
|
"""Xception V1 model for Keras.
|
||
|
|
||
|
On ImageNet, this model gets to a top-1 validation accuracy of 0.790
|
||
|
and a top-5 validation accuracy of 0.945.
|
||
|
|
||
|
Do note that the input image format for this model is different than for
|
||
|
the VGG16 and ResNet models (299x299 instead of 224x224),
|
||
|
and that the input preprocessing function
|
||
|
is also different (same as Inception V3).
|
||
|
|
||
|
Also do note that this model is only available for the TensorFlow backend,
|
||
|
due to its reliance on `SeparableConvolution` layers.
|
||
|
|
||
|
# Reference
|
||
|
|
||
|
- [Xception: Deep Learning with Depthwise Separable
|
||
|
Convolutions](https://arxiv.org/abs/1610.02357)
|
||
|
|
||
|
"""
|
||
|
from __future__ import absolute_import
|
||
|
from __future__ import division
|
||
|
from __future__ import print_function
|
||
|
|
||
|
import os
|
||
|
|
||
|
from tensorflow.python.keras import backend as K
|
||
|
from tensorflow.python.keras import layers
|
||
|
from tensorflow.python.keras.applications import imagenet_utils
|
||
|
from tensorflow.python.keras.applications.imagenet_utils import _obtain_input_shape
|
||
|
from tensorflow.python.keras.applications.imagenet_utils import decode_predictions
|
||
|
from tensorflow.python.keras.layers import Activation
|
||
|
from tensorflow.python.keras.layers import BatchNormalization
|
||
|
from tensorflow.python.keras.layers import Conv2D
|
||
|
from tensorflow.python.keras.layers import Dense
|
||
|
from tensorflow.python.keras.layers import GlobalAveragePooling2D
|
||
|
from tensorflow.python.keras.layers import GlobalMaxPooling2D
|
||
|
from tensorflow.python.keras.layers import Input
|
||
|
from tensorflow.python.keras.layers import MaxPooling2D
|
||
|
from tensorflow.python.keras.layers import SeparableConv2D
|
||
|
from tensorflow.python.keras.models import Model
|
||
|
from tensorflow.python.keras.utils import layer_utils
|
||
|
from tensorflow.python.keras.utils.data_utils import get_file
|
||
|
from tensorflow.python.platform import tf_logging as logging
|
||
|
from tensorflow.python.util.tf_export import tf_export
|
||
|
|
||
|
|
||
|
TF_WEIGHTS_PATH = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels.h5'
|
||
|
TF_WEIGHTS_PATH_NO_TOP = 'https://github.com/fchollet/deep-learning-models/releases/download/v0.4/xception_weights_tf_dim_ordering_tf_kernels_notop.h5'
|
||
|
|
||
|
|
||
|
@tf_export('keras.applications.Xception',
|
||
|
'keras.applications.xception.Xception')
|
||
|
def Xception(include_top=True,
|
||
|
weights='imagenet',
|
||
|
input_tensor=None,
|
||
|
input_shape=None,
|
||
|
pooling=None,
|
||
|
classes=1000):
|
||
|
"""Instantiates the Xception architecture.
|
||
|
|
||
|
Optionally loads weights pre-trained
|
||
|
on ImageNet. This model is available for TensorFlow only,
|
||
|
and can only be used with inputs following the TensorFlow
|
||
|
data format `(width, height, channels)`.
|
||
|
You should set `image_data_format='channels_last'` in your Keras config
|
||
|
located at ~/.keras/keras.json.
|
||
|
|
||
|
Note that the default input image size for this model is 299x299.
|
||
|
|
||
|
Arguments:
|
||
|
include_top: whether to include the fully-connected
|
||
|
layer at the top of the network.
|
||
|
weights: one of `None` (random initialization),
|
||
|
'imagenet' (pre-training on ImageNet),
|
||
|
or the path to the weights file to be loaded.
|
||
|
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
|
||
|
to use as image input for the model.
|
||
|
input_shape: optional shape tuple, only to be specified
|
||
|
if `include_top` is False (otherwise the input shape
|
||
|
has to be `(299, 299, 3)`.
|
||
|
It should have exactly 3 inputs channels,
|
||
|
and width and height should be no smaller than 71.
|
||
|
E.g. `(150, 150, 3)` would be one valid value.
|
||
|
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.
|
||
|
"""
|
||
|
if not (weights in {'imagenet', None} or os.path.exists(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 K.image_data_format() != 'channels_last':
|
||
|
logging.warning(
|
||
|
'The Xception model 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.')
|
||
|
K.set_image_data_format('channels_last')
|
||
|
old_data_format = 'channels_first'
|
||
|
else:
|
||
|
old_data_format = None
|
||
|
|
||
|
# Determine proper input shape
|
||
|
input_shape = _obtain_input_shape(
|
||
|
input_shape,
|
||
|
default_size=299,
|
||
|
min_size=71,
|
||
|
data_format=K.image_data_format(),
|
||
|
require_flatten=False,
|
||
|
weights=weights)
|
||
|
|
||
|
if input_tensor is None:
|
||
|
img_input = Input(shape=input_shape)
|
||
|
else:
|
||
|
if not K.is_keras_tensor(input_tensor):
|
||
|
img_input = Input(tensor=input_tensor, shape=input_shape)
|
||
|
else:
|
||
|
img_input = input_tensor
|
||
|
|
||
|
x = Conv2D(
|
||
|
32, (3, 3), strides=(2, 2), use_bias=False, name='block1_conv1')(
|
||
|
img_input)
|
||
|
x = BatchNormalization(name='block1_conv1_bn')(x)
|
||
|
x = Activation('relu', name='block1_conv1_act')(x)
|
||
|
x = Conv2D(64, (3, 3), use_bias=False, name='block1_conv2')(x)
|
||
|
x = BatchNormalization(name='block1_conv2_bn')(x)
|
||
|
x = Activation('relu', name='block1_conv2_act')(x)
|
||
|
|
||
|
residual = Conv2D(
|
||
|
128, (1, 1), strides=(2, 2), padding='same', use_bias=False)(
|
||
|
x)
|
||
|
residual = BatchNormalization()(residual)
|
||
|
|
||
|
x = SeparableConv2D(
|
||
|
128, (3, 3), padding='same', use_bias=False, name='block2_sepconv1')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block2_sepconv1_bn')(x)
|
||
|
x = Activation('relu', name='block2_sepconv2_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
128, (3, 3), padding='same', use_bias=False, name='block2_sepconv2')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block2_sepconv2_bn')(x)
|
||
|
|
||
|
x = MaxPooling2D(
|
||
|
(3, 3), strides=(2, 2), padding='same', name='block2_pool')(
|
||
|
x)
|
||
|
x = layers.add([x, residual])
|
||
|
|
||
|
residual = Conv2D(
|
||
|
256, (1, 1), strides=(2, 2), padding='same', use_bias=False)(
|
||
|
x)
|
||
|
residual = BatchNormalization()(residual)
|
||
|
|
||
|
x = Activation('relu', name='block3_sepconv1_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
256, (3, 3), padding='same', use_bias=False, name='block3_sepconv1')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block3_sepconv1_bn')(x)
|
||
|
x = Activation('relu', name='block3_sepconv2_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
256, (3, 3), padding='same', use_bias=False, name='block3_sepconv2')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block3_sepconv2_bn')(x)
|
||
|
|
||
|
x = MaxPooling2D(
|
||
|
(3, 3), strides=(2, 2), padding='same', name='block3_pool')(
|
||
|
x)
|
||
|
x = layers.add([x, residual])
|
||
|
|
||
|
residual = Conv2D(
|
||
|
728, (1, 1), strides=(2, 2), padding='same', use_bias=False)(
|
||
|
x)
|
||
|
residual = BatchNormalization()(residual)
|
||
|
|
||
|
x = Activation('relu', name='block4_sepconv1_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
728, (3, 3), padding='same', use_bias=False, name='block4_sepconv1')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block4_sepconv1_bn')(x)
|
||
|
x = Activation('relu', name='block4_sepconv2_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
728, (3, 3), padding='same', use_bias=False, name='block4_sepconv2')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block4_sepconv2_bn')(x)
|
||
|
|
||
|
x = MaxPooling2D(
|
||
|
(3, 3), strides=(2, 2), padding='same', name='block4_pool')(
|
||
|
x)
|
||
|
x = layers.add([x, residual])
|
||
|
|
||
|
for i in range(8):
|
||
|
residual = x
|
||
|
prefix = 'block' + str(i + 5)
|
||
|
|
||
|
x = Activation('relu', name=prefix + '_sepconv1_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv1')(
|
||
|
x)
|
||
|
x = BatchNormalization(name=prefix + '_sepconv1_bn')(x)
|
||
|
x = Activation('relu', name=prefix + '_sepconv2_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv2')(
|
||
|
x)
|
||
|
x = BatchNormalization(name=prefix + '_sepconv2_bn')(x)
|
||
|
x = Activation('relu', name=prefix + '_sepconv3_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
728, (3, 3), padding='same', use_bias=False, name=prefix + '_sepconv3')(
|
||
|
x)
|
||
|
x = BatchNormalization(name=prefix + '_sepconv3_bn')(x)
|
||
|
|
||
|
x = layers.add([x, residual])
|
||
|
|
||
|
residual = Conv2D(
|
||
|
1024, (1, 1), strides=(2, 2), padding='same', use_bias=False)(
|
||
|
x)
|
||
|
residual = BatchNormalization()(residual)
|
||
|
|
||
|
x = Activation('relu', name='block13_sepconv1_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
728, (3, 3), padding='same', use_bias=False, name='block13_sepconv1')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block13_sepconv1_bn')(x)
|
||
|
x = Activation('relu', name='block13_sepconv2_act')(x)
|
||
|
x = SeparableConv2D(
|
||
|
1024, (3, 3), padding='same', use_bias=False, name='block13_sepconv2')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block13_sepconv2_bn')(x)
|
||
|
|
||
|
x = MaxPooling2D(
|
||
|
(3, 3), strides=(2, 2), padding='same', name='block13_pool')(
|
||
|
x)
|
||
|
x = layers.add([x, residual])
|
||
|
|
||
|
x = SeparableConv2D(
|
||
|
1536, (3, 3), padding='same', use_bias=False, name='block14_sepconv1')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block14_sepconv1_bn')(x)
|
||
|
x = Activation('relu', name='block14_sepconv1_act')(x)
|
||
|
|
||
|
x = SeparableConv2D(
|
||
|
2048, (3, 3), padding='same', use_bias=False, name='block14_sepconv2')(
|
||
|
x)
|
||
|
x = BatchNormalization(name='block14_sepconv2_bn')(x)
|
||
|
x = Activation('relu', name='block14_sepconv2_act')(x)
|
||
|
|
||
|
if include_top:
|
||
|
x = GlobalAveragePooling2D(name='avg_pool')(x)
|
||
|
x = Dense(classes, activation='softmax', name='predictions')(x)
|
||
|
else:
|
||
|
if pooling == 'avg':
|
||
|
x = GlobalAveragePooling2D()(x)
|
||
|
elif pooling == 'max':
|
||
|
x = 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
|
||
|
# Create model.
|
||
|
model = Model(inputs, x, name='xception')
|
||
|
|
||
|
# load weights
|
||
|
if weights == 'imagenet':
|
||
|
if include_top:
|
||
|
weights_path = get_file(
|
||
|
'xception_weights_tf_dim_ordering_tf_kernels.h5',
|
||
|
TF_WEIGHTS_PATH,
|
||
|
cache_subdir='models',
|
||
|
file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
|
||
|
else:
|
||
|
weights_path = get_file(
|
||
|
'xception_weights_tf_dim_ordering_tf_kernels_notop.h5',
|
||
|
TF_WEIGHTS_PATH_NO_TOP,
|
||
|
cache_subdir='models',
|
||
|
file_hash='b0042744bf5b25fce3cb969f33bebb97')
|
||
|
model.load_weights(weights_path)
|
||
|
elif weights is not None:
|
||
|
model.load_weights(weights)
|
||
|
|
||
|
if old_data_format:
|
||
|
K.set_image_data_format(old_data_format)
|
||
|
return model
|
||
|
|
||
|
|
||
|
@tf_export('keras.applications.xception.preprocess_input')
|
||
|
def preprocess_input(x):
|
||
|
"""Preprocesses a numpy array encoding a batch of images.
|
||
|
|
||
|
Arguments:
|
||
|
x: a 4D numpy array consists of RGB values within [0, 255].
|
||
|
|
||
|
Returns:
|
||
|
Preprocessed array.
|
||
|
"""
|
||
|
return imagenet_utils.preprocess_input(x, mode='tf')
|