"""MobileNet v3 small models for Keras.
# Reference
[Searching for MobileNetV3](https://arxiv.org/abs/1905.02244?context=cs)
"""
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Conv2D, GlobalAveragePooling2D, Reshape
#from keras.utils.vis_utils import plot_model
from mobilenet_base import MobileNetBase
class MobileNetV3_Small(MobileNetBase):
def __init__(self, shape, n_class):
"""Init.
# Arguments
input_shape: An integer or tuple/list of 3 integers, shape
of input tensor.
n_class: Integer, number of classes.
# Returns
MobileNetv3 model.
"""
super(MobileNetV3_Small, self).__init__(shape, n_class)
def build(self, plot=False):
"""build MobileNetV3 Small.
# Arguments
plot: Boolean, weather to plot model.
# Returns
model: Model, model.
"""
inputs = Input(shape=self.shape)
x = self._conv_block(inputs, 16, (3, 3), strides=(2, 2), nl='HS')
x = self._bottleneck(x, 16, (3, 3), e=16, s=2, squeeze=True, nl='RE')
x = self._bottleneck(x, 24, (3, 3), e=72, s=2, squeeze=False, nl='RE')
x = self._bottleneck(x, 24, (3, 3), e=88, s=1, squeeze=False, nl='RE')
x = self._bottleneck(x, 40, (5, 5), e=96, s=2, squeeze=True, nl='HS')
x = self._bottleneck(x, 40, (5, 5), e=240, s=1, squeeze=True, nl='HS')
x = self._bottleneck(x, 40, (5, 5), e=240, s=1, squeeze=True, nl='HS')
x = self._bottleneck(x, 48, (5, 5), e=120, s=1, squeeze=True, nl='HS')
x = self._bottleneck(x, 48, (5, 5), e=144, s=1, squeeze=True, nl='HS')
x = self._bottleneck(x, 96, (5, 5), e=288, s=2, squeeze=True, nl='HS')
x = self._bottleneck(x, 96, (5, 5), e=576, s=1, squeeze=True, nl='HS')
x = self._bottleneck(x, 96, (5, 5), e=576, s=1, squeeze=True, nl='HS')
x = self._conv_block(x, 576, (1, 1), strides=(1, 1), nl='HS')
x = GlobalAveragePooling2D()(x)
x = Reshape((1, 1, 576))(x)
x = Conv2D(1280, (1, 1), padding='same')(x)
x = self._return_activation(x, 'HS')
x = Conv2D(self.n_class, (1, 1), padding='same', activation='softmax')(x)
output = Reshape((self.n_class,))(x)
model = Model(inputs, output)
#if plot:
# plot_model(model, to_file='images/MobileNetv3_small.png', show_shapes=True)
return model
