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Xception encoder #419

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4a9b0f4
Added xception encoder
ArneSchulzTUBS Dec 5, 2020
96aaccd
fixed keras imports
ArneSchulzTUBS Dec 13, 2020
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7 changes: 6 additions & 1 deletion segmentation_models/backbones/backbones_factory.py
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Original file line number Diff line number Diff line change
Expand Up @@ -4,6 +4,7 @@

from . import inception_resnet_v2 as irv2
from . import inception_v3 as iv3
from . import xception as xc


class BackbonesFactory(ModelsFactory):
Expand Down Expand Up @@ -70,6 +71,8 @@ class BackbonesFactory(ModelsFactory):
'efficientnetb7': ('block6a_expand_activation', 'block4a_expand_activation',
'block3a_expand_activation', 'block2a_expand_activation'),

# Xception
'xception': (32, 22, 12, 4),
}

_models_update = {
Expand All @@ -84,11 +87,13 @@ class BackbonesFactory(ModelsFactory):
'efficientnetb5': [eff.EfficientNetB5, eff.preprocess_input],
'efficientnetb6': [eff.EfficientNetB6, eff.preprocess_input],
'efficientnetb7': [eff.EfficientNetB7, eff.preprocess_input],

'xception': [xc.Xception, xc.preprocess_input],
}

# currently not supported
_models_delete = ['resnet50v2', 'resnet101v2', 'resnet152v2',
'nasnetlarge', 'nasnetmobile', 'xception']
'nasnetlarge', 'nasnetmobile']

@property
def models(self):
Expand Down
321 changes: 321 additions & 0 deletions segmentation_models/backbones/xception.py
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Original file line number Diff line number Diff line change
@@ -0,0 +1,321 @@
"""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).

# Reference

- [Xception: Deep Learning with Depthwise Separable Convolutions](
https://arxiv.org/abs/1610.02357) (CVPR 2017)

"""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
from keras_applications import imagenet_utils
from keras_applications import get_submodules_from_kwargs


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')

backend = None
layers = None
models = None
keras_utils = None

def Xception(include_top=True,
weights='imagenet',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
**kwargs):
"""Instantiates the Xception architecture.

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 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 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.

# 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.
"""
global backend, layers, models, keras_utils
backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)

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')

# Determine proper input shape
input_shape = imagenet_utils._obtain_input_shape(input_shape,
default_size=299,
min_size=71,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)

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

channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1

x = layers.Conv2D(32, (3, 3),
padding='same',
strides=(2, 2),
use_bias=False,
name='block1_conv1')(img_input)
x = layers.BatchNormalization(axis=channel_axis, name='block1_conv1_bn')(x)
x = layers.Activation('relu', name='block1_conv1_act')(x)
x = layers.Conv2D(64, (3, 3), padding='same', use_bias=False, name='block1_conv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block1_conv2_bn')(x)
x = layers.Activation('relu', name='block1_conv2_act')(x)

residual = layers.Conv2D(128, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)

x = layers.SeparableConv2D(128, (3, 3),
padding='same',
use_bias=False,
name='block2_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv1_bn')(x)
x = layers.Activation('relu', name='block2_sepconv2_act')(x)
x = layers.SeparableConv2D(128, (3, 3),
padding='same',
use_bias=False,
name='block2_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block2_sepconv2_bn')(x)

x = layers.MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block2_pool')(x)
x = layers.add([x, residual])

residual = layers.Conv2D(256, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)

x = layers.Activation('relu', name='block3_sepconv1_act')(x)
x = layers.SeparableConv2D(256, (3, 3),
padding='same',
use_bias=False,
name='block3_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv1_bn')(x)
x = layers.Activation('relu', name='block3_sepconv2_act')(x)
x = layers.SeparableConv2D(256, (3, 3),
padding='same',
use_bias=False,
name='block3_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block3_sepconv2_bn')(x)

x = layers.MaxPooling2D((3, 3), strides=(2, 2),
padding='same',
name='block3_pool')(x)
x = layers.add([x, residual])

residual = layers.Conv2D(728, (1, 1),
strides=(2, 2),
padding='same',
use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)

x = layers.Activation('relu', name='block4_sepconv1_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv1_bn')(x)
x = layers.Activation('relu', name='block4_sepconv2_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block4_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block4_sepconv2_bn')(x)

x = layers.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 = layers.Activation('relu', name=prefix + '_sepconv1_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv1_bn')(x)
x = layers.Activation('relu', name=prefix + '_sepconv2_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv2_bn')(x)
x = layers.Activation('relu', name=prefix + '_sepconv3_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name=prefix + '_sepconv3')(x)
x = layers.BatchNormalization(axis=channel_axis,
name=prefix + '_sepconv3_bn')(x)

x = layers.add([x, residual])

residual = layers.Conv2D(1024, (1, 1), strides=(2, 2),
padding='same', use_bias=False)(x)
residual = layers.BatchNormalization(axis=channel_axis)(residual)

x = layers.Activation('relu', name='block13_sepconv1_act')(x)
x = layers.SeparableConv2D(728, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block13_sepconv1_bn')(x)
x = layers.Activation('relu', name='block13_sepconv2_act')(x)
x = layers.SeparableConv2D(1024, (3, 3),
padding='same',
use_bias=False,
name='block13_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block13_sepconv2_bn')(x)

x = layers.MaxPooling2D((3, 3),
strides=(2, 2),
padding='same',
name='block13_pool')(x)
x = layers.add([x, residual])

x = layers.SeparableConv2D(1536, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv1')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block14_sepconv1_bn')(x)
x = layers.Activation('relu', name='block14_sepconv1_act')(x)

x = layers.SeparableConv2D(2048, (3, 3),
padding='same',
use_bias=False,
name='block14_sepconv2')(x)
x = layers.BatchNormalization(axis=channel_axis, name='block14_sepconv2_bn')(x)
x = layers.Activation('relu', name='block14_sepconv2_act')(x)

if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Dense(classes, activation='softmax', 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 = keras_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = models.Model(inputs, x, name='xception')

# Load weights.
if weights == 'imagenet':
if include_top:
weights_path = keras_utils.get_file(
'xception_weights_tf_dim_ordering_tf_kernels.h5',
TF_WEIGHTS_PATH,
cache_subdir='models',
file_hash='0a58e3b7378bc2990ea3b43d5981f1f6')
else:
weights_path = keras_utils.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)
# if backend.backend() == 'theano':
# convert_all_kernels_in_model(model)
elif weights is not None:
model.load_weights(weights)

return model


def preprocess_input(x, **kwargs):
"""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', **kwargs)