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mobilenet_v2_keras.py
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mobilenet_v2_keras.py
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"""MobileNet v2 models for Keras.
# Reference
- [Inverted Residuals and Linear Bottlenecks Mobile Networks for
Classification, Detection and Segmentation]
(https://arxiv.org/abs/1801.04381)
"""
from keras.models import Model
from keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dropout
from keras.layers import Activation, BatchNormalization, add, Reshape
from keras.layers import DepthwiseConv2D
#from keras_applications.mobilenet import relu6
#from keras.applications.mobilenet import relu6
from keras.utils.vis_utils import plot_model
from keras.utils.generic_utils import CustomObjectScope
from keras import backend as K
def relu6(x):
return K.relu(x, max_value=6)
def _conv_block(inputs, filters, kernel, strides, use_bias=True):
"""Convolution Block
This function defines a 2D convolution operation with BN and relu6.
# Arguments
inputs: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
# Returns
Output tensor.
"""
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
if nlay<0 or nlay>16:
basename = 'conv_%d' % (nlay+1)
else:
basename = 'expanded_conv_%d_expand'%nlay
x = Conv2D(filters, kernel, padding='same', strides=strides, name=basename, use_bias=use_bias)(inputs)
x = BatchNormalization(axis=channel_axis, name=basename+'_batch_normalization')(x)
return Activation(relu6, name=basename+'_activation')(x)
def _bottleneck(inputs, filters, kernel, t, s, r=False):
"""Bottleneck
This function defines a basic bottleneck structure.
# Arguments
inputs: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
t: Integer, expansion factor.
t is always applied to the input size.
s: An integer or tuple/list of 2 integers,specifying the strides
of the convolution along the width and height.Can be a single
integer to specify the same value for all spatial dimensions.
r: Boolean, Whether to use the residuals.
# Returns
Output tensor.
"""
global nlay
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
# Create expansions layer only if needed (expansion factor >1)
if t > 1:
tchannel = K.int_shape(inputs)[channel_axis] * t
x = _conv_block(inputs, tchannel, (1, 1), (1, 1), use_bias=False)
else:
x = inputs
x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same', name='expanded_conv_%d_depthwise'%nlay, use_bias=False)(x)
x = BatchNormalization(axis=channel_axis, name='expanded_conv_%d_depthwise_batch_normalization'%nlay)(x)
x = Activation(relu6, name='expanded_conv_%d_depthwise_activation'%nlay)(x)
x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same', name='expanded_conv_%d_project'%nlay, use_bias=False)(x)
x = BatchNormalization(axis=channel_axis, name='expanded_conv_%d_project_batch_normalization'%nlay)(x)
if r:
x = add([x, inputs], name="expanded_conv_%d_add"%nlay)
nlay +=1
return x
def _inverted_residual_block(inputs, filters, kernel, t, strides, n):
"""Inverted Residual Block
This function defines a sequence of 1 or more identical layers.
# Arguments
inputs: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
t: Integer, expansion factor.
t is always applied to the input size.
s: An integer or tuple/list of 2 integers,specifying the strides
of the convolution along the width and height.Can be a single
integer to specify the same value for all spatial dimensions.
n: Integer, layer repeat times.
# Returns
Output tensor.
"""
x = _bottleneck(inputs, filters, kernel, t, strides)
for i in range(1, n):
x = _bottleneck(x, filters, kernel, t, 1, True)
return x
def roundup(n):
x = (n+6)//8
return x*8
def MobileNetv2(input_shape, k, width_multiplier=1.0):
"""MobileNetv2
This function defines a MobileNetv2 architectures.
# Arguments
input_shape: An integer or tuple/list of 3 integers, shape
of input tensor.
k: Integer, number of classes.
# Returns
MobileNetv2 model.
"""
global nlay
nlay = -1
inputs = Input(shape=input_shape)
x = _conv_block(inputs, roundup(int(32*width_multiplier)), (3, 3), strides=(2, 2), use_bias=False)
nlay+=1
fix = 0
if width_multiplier - 1.3 < 0.01:
fix = -2
x = _inverted_residual_block(x, roundup(int(16*width_multiplier)), (3, 3), t=1, strides=1, n=1)
x = _inverted_residual_block(x, roundup(int(24*width_multiplier)), (3, 3), t=6, strides=2, n=2)
x = _inverted_residual_block(x, roundup(int(32*width_multiplier)), (3, 3), t=6, strides=2, n=3)
x = _inverted_residual_block(x, roundup(int(64*width_multiplier)+fix), (3, 3), t=6, strides=2, n=4)
x = _inverted_residual_block(x, roundup(int(96*width_multiplier)), (3, 3), t=6, strides=1, n=3)
x = _inverted_residual_block(x, roundup(int(160*width_multiplier)), (3, 3), t=6, strides=2, n=3)
x = _inverted_residual_block(x, roundup(int(320*width_multiplier)), (3, 3), t=6, strides=1, n=1)
last_conv_size = max(1280, int(1280*width_multiplier))
x = _conv_block(x, last_conv_size, (1, 1), strides=(1, 1), use_bias=False)
x = GlobalAveragePooling2D()(x)
x = Reshape((1, 1, last_conv_size))(x)
x = Dropout(0.3, name='Dropout')(x)
x = Conv2D(k, (1, 1), padding='same', name='logits', use_bias=True)(x)
x = Activation('softmax', name='softmax')(x)
output = Reshape((k,), name='out')(x)
model = Model(inputs, output)
plot_model(model, to_file='MobileNetv2.png', show_shapes=True)
return model
if __name__ == '__main__':
MobileNetv2((224, 224, 3), 100)