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MobileFaceNet.py
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import tensorflow
bias_initializer = None
conv2d_regularizer = None
depthwise_regularizer = None
alpha_regularizer = None
dense_regularizer = None
def MobileNetV2Block(x, stride, t, output_channel):
y = tensorflow.keras.layers.Conv2D(
filters=int(t * x.shape.as_list()[3]),
kernel_size=1,
strides=1,
padding='same',
bias_initializer=bias_initializer,
kernel_regularizer=conv2d_regularizer,
bias_regularizer=conv2d_regularizer)(x)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=conv2d_regularizer,
gamma_regularizer=conv2d_regularizer)(y)
y = tensorflow.keras.layers.PReLU(
alpha_initializer=tensorflow.keras.initializers.Constant(0.25),
alpha_regularizer=alpha_regularizer,
shared_axes=[1, 2])(y)
y = tensorflow.keras.layers.DepthwiseConv2D(
kernel_size=3,
strides=stride,
padding='same',
bias_initializer=bias_initializer,
kernel_regularizer=depthwise_regularizer,
bias_regularizer=depthwise_regularizer)(y)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=depthwise_regularizer,
gamma_regularizer=depthwise_regularizer)(y)
y = tensorflow.keras.layers.PReLU(
alpha_initializer=tensorflow.keras.initializers.Constant(0.25),
alpha_regularizer=alpha_regularizer,
shared_axes=[1, 2])(y)
y = tensorflow.keras.layers.Conv2D(
filters=output_channel,
kernel_size=1,
strides=1,
padding='same',
bias_initializer=bias_initializer,
kernel_regularizer=conv2d_regularizer,
bias_regularizer=conv2d_regularizer)(y)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=conv2d_regularizer,
gamma_regularizer=conv2d_regularizer)(y)
if x.shape.as_list() == y.shape.as_list():
y = tensorflow.keras.layers.Add()([x, y])
return y
def MobileFaceNet(num_classes):
inputs = tensorflow.keras.Input(shape=(112, 112, 3))
y = tensorflow.keras.layers.Conv2D(
filters=64,
kernel_size=3,
strides=2,
padding='same',
bias_initializer=bias_initializer,
kernel_regularizer=conv2d_regularizer,
bias_regularizer=conv2d_regularizer)(inputs)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=conv2d_regularizer,
gamma_regularizer=conv2d_regularizer)(y)
y = tensorflow.keras.layers.PReLU(
alpha_initializer=tensorflow.keras.initializers.Constant(0.25),
alpha_regularizer=alpha_regularizer,
shared_axes=[1, 2])(y)
y = tensorflow.keras.layers.DepthwiseConv2D(
kernel_size=3,
strides=1,
padding='same',
bias_initializer=bias_initializer,
kernel_regularizer=depthwise_regularizer,
bias_regularizer=depthwise_regularizer)(y)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=depthwise_regularizer,
gamma_regularizer=depthwise_regularizer)(y)
y = tensorflow.keras.layers.PReLU(
alpha_initializer=tensorflow.keras.initializers.Constant(0.25),
alpha_regularizer=alpha_regularizer,
shared_axes=[1, 2])(y)
y = MobileNetV2Block(y, 2, 2, 64)
y = MobileNetV2Block(y, 1, 2, 64)
y = MobileNetV2Block(y, 1, 2, 64)
y = MobileNetV2Block(y, 1, 2, 64)
y = MobileNetV2Block(y, 1, 2, 64)
y = MobileNetV2Block(y, 2, 4, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 2, 4, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = MobileNetV2Block(y, 1, 2, 128)
y = tensorflow.keras.layers.Conv2D(
filters=512,
kernel_size=1,
strides=1,
padding='same',
bias_initializer=bias_initializer,
kernel_regularizer=conv2d_regularizer,
bias_regularizer=conv2d_regularizer)(y)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=conv2d_regularizer,
gamma_regularizer=conv2d_regularizer)(y)
y = tensorflow.keras.layers.PReLU(
alpha_initializer=tensorflow.keras.initializers.Constant(0.25),
alpha_regularizer=alpha_regularizer,
shared_axes=[1, 2])(y)
y = tensorflow.keras.layers.DepthwiseConv2D(
kernel_size=7,
strides=1,
bias_initializer=bias_initializer,
kernel_regularizer=depthwise_regularizer,
bias_regularizer=depthwise_regularizer)(y)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=depthwise_regularizer,
gamma_regularizer=depthwise_regularizer)(y)
y = tensorflow.keras.layers.Conv2D(
filters=128,
kernel_size=1,
strides=1,
bias_initializer=bias_initializer,
kernel_regularizer=conv2d_regularizer,
bias_regularizer=conv2d_regularizer)(y)
y = tensorflow.keras.layers.BatchNormalization(
beta_regularizer=conv2d_regularizer,
gamma_regularizer=conv2d_regularizer)(y)
embed = tensorflow.keras.layers.Flatten()(y)
predict = tensorflow.keras.layers.Dense(
units=num_classes,
use_bias=False,
name='last_layer',
kernel_regularizer=dense_regularizer)(embed)
model = tensorflow.keras.Model(inputs=inputs, outputs=[embed, predict])
return model
if __name__ == '__main__':
tensorflow.enable_eager_execution(config=tensorflow.ConfigProto(allow_soft_placement=True, gpu_options=tensorflow.GPUOptions(allow_growth=True)))
data = tensorflow.random.uniform([4, 112, 112, 3])
model = MobileFaceNet(10)
embed = model(data)