def senet_conv_block(input_tensor,
kernel_size,
filters,
stage,
block,
bias=False,
lpf_size=1,
strides=(2, 2)):
se = senet_residual_block(input_tensor, kernel_size, filters, stage, block,
bias, lpf_size, strides)
if len(filters) > 2:
conv1_proj_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_proj"
shortcut = LPFConv2D(filters[2], (1, 1),
use_bias=bias,
strides=strides,
name=conv1_proj_name,
lpf_size=lpf_size)(input_tensor)
shortcut = BatchNormalization(axis=bn_axis,
name=conv1_proj_name + "/bn",
epsilon=bn_eps)(shortcut)
m = layers.add([se, shortcut])
m = Activation('relu')(m)
else:
m = se
return m
def get_residual(img_input, lpf_size, resnet_type=50):
conv = senet_conv_block
identity = senet_identity_block
x = LPFConv2D(64, (7, 7),
use_bias=False,
strides=(2, 2),
padding='same',
name='conv1/7x7_s2',
lpf_size=lpf_size)(img_input)
x = BatchNormalization(axis=bn_axis,
name='conv1/7x7_s2/bn',
epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = LPFMaxPooling2D((3, 3), strides=(2, 2), lpf_size=lpf_size)(x)
s = [64, 64, 256]
if resnet_type < 50: s = s[:-1]
x = conv(x, 3, s, stage=2, block=1, strides=(1, 1), lpf_size=lpf_size)
x = identity(x, 3, s, stage=2, block=2)
if resnet_type > 18:
x = identity(x, 3, s, stage=2, block=3)
s = [128, 128, 512]
if resnet_type < 50: s = s[:-1]
x = conv(x, 3, s, stage=3, block=1, lpf_size=lpf_size)
x = identity(x, 3, s, stage=3, block=2)
if resnet_type > 18:
x = identity(x, 3, s, stage=3, block=3)
x = identity(x, 3, s, stage=3, block=4)
s = [256, 256, 1024]
if resnet_type < 50: s = s[:-1]
x = conv(x, 3, s, stage=4, block=1, lpf_size=lpf_size)
x = identity(x, 3, s, stage=4, block=2)
if resnet_type > 18:
x = identity(x, 3, s, stage=4, block=3)
x = identity(x, 3, s, stage=4, block=4)
x = identity(x, 3, s, stage=4, block=5)
x = identity(x, 3, s, stage=4, block=6)
s = [512, 512, 2048]
if resnet_type < 50: s = s[:-1]
x = conv(x, 3, s, stage=5, block=1, lpf_size=lpf_size)
x = identity(x, 3, s, stage=5, block=2)
if resnet_type > 18:
x = identity(x, 3, s, stage=5, block=3)
x = AveragePooling2D((7, 7), name='avg_pool')(x)
return x
def senet_residual_block(input_tensor,
kernel_size,
filters,
stage,
block,
bias=False,
lpf_size=0,
strides=(1, 1)):
conv1_reduce_name = 'conv' + str(stage) + "_" + str(block) + "_1x1_reduce"
conv1_increase_name = 'conv' + str(stage) + "_" + str(
block) + "_1x1_increase"
conv3_name = 'conv' + str(stage) + "_" + str(block) + "_3x3"
x = LPFConv2D(filters[0], (1, 1) if len(filters) > 2 else kernel_size,
padding='same',
use_bias=bias,
strides=strides,
name=conv1_reduce_name,
lpf_size=lpf_size)(input_tensor)
x = BatchNormalization(axis=bn_axis,
name=conv1_reduce_name + "/bn",
epsilon=bn_eps)(x)
x = Activation('relu')(x)
x = Conv2D(filters[1],
kernel_size,
padding='same',
use_bias=bias,
name=conv3_name)(x)
x = BatchNormalization(axis=bn_axis,
name=conv3_name + "/bn",
epsilon=bn_eps)(x)
x = Activation('relu')(x)
if len(filters) > 2:
x = Conv2D(filters[2], (1, 1), name=conv1_increase_name,
use_bias=bias)(x)
x = BatchNormalization(axis=bn_axis,
name=conv1_increase_name + "/bn",
epsilon=bn_eps)(x)
se = senet_se_block(x, stage=stage, block=block, bias=True)
return se
def Xception(include_top=True,
weights='vggface2',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
lpf_size=1,
**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.
"""
backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
if not (weights in {'imagenet', 'vggface2', 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 = _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 = LPFConv2D(32, (3, 3),
strides=(2, 2),
lpf_size=lpf_size,
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), 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 = LPFConv2D(128, (1, 1),
strides=(2, 2),
lpf_size=lpf_size,
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 = LPFMaxPooling2D((3, 3),
strides=(2, 2),
lpf_size=lpf_size,
padding='same',
name='block2_pool')(x)
x = layers.add([x, residual])
residual = LPFConv2D(256, (1, 1), strides=(2, 2), lpf_size=lpf_size,
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 = LPFMaxPooling2D((3, 3), strides=(2, 2),
lpf_size=lpf_size,
padding='same',
name='block3_pool')(x)
x = layers.add([x, residual])
residual = LPFConv2D(728, (1, 1),
strides=(2, 2),
lpf_size=lpf_size,
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 = LPFMaxPooling2D((3, 3), strides=(2, 2),
lpf_size=lpf_size,
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 = LPFConv2D(1024, (1, 1), strides=(2, 2),
lpf_size=lpf_size,
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 = LPFMaxPooling2D((3, 3),
strides=(2, 2),
lpf_size=lpf_size,
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%s'%('' if classes=='' else str(classes)) )(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':
keras_utils.convert_all_kernels_in_model(model)
elif weights == 'vggface2':
weights_path = keras_utils.get_file(
basename(TF_VGGFACE_WEIGHTS_PATH),
TF_VGGFACE_WEIGHTS_PATH,
cache_subdir='models',
file_hash='ca2c965451caa231c68baac819512f1f')
elif weights is not None:
model.load_weights(weights, by_name=True)
return model
def DenseNet(blocks,
include_top=True,
weights='vggface2',
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
lpf_size=1,
**kwargs):
"""Instantiates the DenseNet 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`.
# Arguments
blocks: numbers of building blocks for the four dense layers.
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 `(224, 224, 3)` (with `'channels_last'` data format)
or `(3, 224, 224)` (with `'channels_first'` data format).
It should have exactly 3 inputs channels,
and width and height should be no smaller than 32.
pooling: optional pooling mode for feature extraction
E.g. `(200, 200, 3)` would be one valid value.
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.
"""
global backend, layers, models, keras_utils
backend, layers, models, keras_utils = get_submodules_from_kwargs(kwargs)
if not (weights in {'imagenet', 'vggface2', 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 = _obtain_input_shape(input_shape,
default_size=224,
min_size=32,
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
bn_axis = 3 if backend.image_data_format() == 'channels_last' else 1
x = layers.ZeroPadding2D(padding=((3, 3), (3, 3)))(img_input)
x = LPFConv2D(64,
7,
strides=2,
use_bias=False,
name='conv1/conv',
lpf_size=lpf_size)(x)
x = layers.BatchNormalization(axis=bn_axis,
epsilon=1.001e-5,
name='conv1/bn')(x)
x = layers.Activation('relu', name='conv1/relu')(x)
x = layers.ZeroPadding2D(padding=((1, 1), (1, 1)))(x)
x = LPFMaxPooling2D(3, strides=2, name='pool1', lpf_size=lpf_size)(x)
x = dense_block(x, blocks[0], name='conv2')
x = transition_block(x, 0.5, name='pool2', lpf_size=lpf_size)
x = dense_block(x, blocks[1], name='conv3')
x = transition_block(x, 0.5, name='pool3', lpf_size=lpf_size)
x = dense_block(x, blocks[2], name='conv4')
x = transition_block(x, 0.5, name='pool4', lpf_size=lpf_size)
x = dense_block(x, blocks[3], name='conv5')
x = layers.BatchNormalization(axis=bn_axis, epsilon=1.001e-5, name='bn')(x)
x = layers.Activation('relu', name='relu')(x)
if include_top:
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
x = layers.Dense(classes, activation='softmax',
name='fc%d' % classes)(x)
else:
if pooling == 'avg':
x = layers.GlobalAveragePooling2D(name='avg_pool')(x)
elif pooling == 'max':
x = layers.GlobalMaxPooling2D(name='max_pool')(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.
if blocks == [6, 12, 24, 16]:
model = models.Model(inputs, x, name='densenet121')
elif blocks == [6, 12, 32, 32]:
model = models.Model(inputs, x, name='densenet169')
elif blocks == [6, 12, 48, 32]:
model = models.Model(inputs, x, name='densenet201')
else:
model = models.Model(inputs, x, name='densenet')
# Load weights.
if weights == 'imagenet':
if include_top:
if blocks == [6, 12, 24, 16]:
weights_path = keras_utils.get_file(
'densenet121_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET121_WEIGHT_PATH,
cache_subdir='models',
file_hash='9d60b8095a5708f2dcce2bca79d332c7')
elif blocks == [6, 12, 32, 32]:
weights_path = keras_utils.get_file(
'densenet169_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET169_WEIGHT_PATH,
cache_subdir='models',
file_hash='d699b8f76981ab1b30698df4c175e90b')
elif blocks == [6, 12, 48, 32]:
weights_path = keras_utils.get_file(
'densenet201_weights_tf_dim_ordering_tf_kernels.h5',
DENSENET201_WEIGHT_PATH,
cache_subdir='models',
file_hash='1ceb130c1ea1b78c3bf6114dbdfd8807')
else:
if blocks == [6, 12, 24, 16]:
weights_path = keras_utils.get_file(
'densenet121_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET121_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='30ee3e1110167f948a6b9946edeeb738')
elif blocks == [6, 12, 32, 32]:
weights_path = keras_utils.get_file(
'densenet169_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET169_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='b8c4d4c20dd625c148057b9ff1c1176b')
elif blocks == [6, 12, 48, 32]:
weights_path = keras_utils.get_file(
'densenet201_weights_tf_dim_ordering_tf_kernels_notop.h5',
DENSENET201_WEIGHT_PATH_NO_TOP,
cache_subdir='models',
file_hash='c13680b51ded0fb44dff2d8f86ac8bb1')
model.load_weights(weights_path)
elif weights == 'vggface2' and blocks == [6, 12, 24, 16]:
weights_path = keras_utils.get_file(
basename(DENSENET121_VGGFACE_WEIGHT_PATH),
DENSENET121_VGGFACE_WEIGHT_PATH,
cache_subdir='models',
file_hash='8bb1ea084bc86ca5c1c55c8417da32b0')
model.load_weights(weights_path, by_name=True)
elif weights is not None:
model.load_weights(weights, by_name=True)
return model