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MobileNetV3.py
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import tensorflow
def swish(x):
y = x * tensorflow.nn.sigmoid(x)
return y
def hard_swish(x):
y = x * (tensorflow.nn.relu6(x + 3)) / 6
return y
class MobileNetV3Block(tensorflow.keras.layers.Layer):
def __init__(self, input_shape, kernel_size, exp_size, output_shape, use_se, activation, stride):
super(MobileNetV3Block, self).__init__()
self.in_shape = input_shape
self.stride = stride
self.kernel_size = kernel_size
self.exp_size = exp_size
self.use_se = use_se
self.out_shape = output_shape
if activation == 're':
self.active = tensorflow.nn.relu6
elif activation == 'hs':
self.active = hard_swish
self.Conv2D_1 = tensorflow.keras.layers.Conv2D(
filters=self.exp_size,
kernel_size=1,
strides=1,
padding='same')
self.BatchNormalization_1 = tensorflow.keras.layers.BatchNormalization()
self.DepthwiseConv2D_2 = tensorflow.keras.layers.DepthwiseConv2D(
kernel_size=self.kernel_size,
strides=self.stride,
padding='same')
self.BatchNormalization_2 = tensorflow.keras.layers.BatchNormalization()
if self.use_se == True:
self.AveragePooling2D_3 = tensorflow.keras.layers.AveragePooling2D(
pool_size=[x // self.stride for x in self.in_shape[:2]],
strides=1)
self.Conv2D_4 = tensorflow.keras.layers.Conv2D(
filters=self.exp_size // 4,
kernel_size=1,
strides=1,
activation=tensorflow.nn.relu)
self.Conv2D_5 = tensorflow.keras.layers.Conv2D(
filters=self.exp_size,
kernel_size=1,
strides=1,
activation=tensorflow.keras.activations.hard_sigmoid)
self.Conv2D_6 = tensorflow.keras.layers.Conv2D(
filters=self.out_shape[2],
kernel_size=1,
strides=1,
padding='same')
self.BatchNormalization_6 = tensorflow.keras.layers.BatchNormalization()
def call(self, x):
y = self.Conv2D_1(x)
y = self.BatchNormalization_1(y)
y = self.active(y)
y = self.DepthwiseConv2D_2(y)
y = self.BatchNormalization_2(y)
y = self.active(y)
if self.use_se == True:
z = self.AveragePooling2D_3(y)
z = self.Conv2D_4(z)
z = self.Conv2D_5(z)
y = tensorflow.multiply(y, z)
y = self.Conv2D_6(y)
y = self.BatchNormalization_6(y)
if self.in_shape == self.out_shape:
y = y + x
return y
class MobileNetV3Large(tensorflow.keras.Model):
def __init__(self, num_classes=1000):
super(MobileNetV3Large, self).__init__(name='MobileNetV3Large')
self.Conv2D_1 = tensorflow.keras.layers.Conv2D(
filters=16,
kernel_size=3,
strides=2,
padding='same')
self.BatchNormalization_1 = tensorflow.keras.layers.BatchNormalization()
self.hard_swish_1 = hard_swish
self.MobileNetV3Block_2 = MobileNetV3Block([112, 112, 16], 3, 16, [112, 112, 16], False, 're', 1)
self.MobileNetV3Block_3 = MobileNetV3Block([112, 112, 16], 3, 64, [56, 56, 24], False, 're', 2)
self.MobileNetV3Block_4 = MobileNetV3Block([56, 56, 24], 3, 72, [56, 56, 24], False, 're', 1)
self.MobileNetV3Block_5 = MobileNetV3Block([56, 56, 24], 5, 72, [28, 28, 40], True, 're', 2)
self.MobileNetV3Block_6 = MobileNetV3Block([28, 28, 40], 5, 120, [28, 28, 40], True, 're', 1)
self.MobileNetV3Block_7 = MobileNetV3Block([28, 28, 40], 5, 120, [28, 28, 40], True, 're', 1)
self.MobileNetV3Block_8 = MobileNetV3Block([28, 28, 40], 3, 240, [14, 14, 80], False, 'hs', 2)
self.MobileNetV3Block_9 = MobileNetV3Block([14, 14, 80], 3, 200, [14, 14, 80], False, 'hs', 1)
self.MobileNetV3Block_10 = MobileNetV3Block([14, 14, 80], 3, 184, [14, 14, 80], False, 'hs', 1)
self.MobileNetV3Block_11 = MobileNetV3Block([14, 14, 80], 3, 184, [14, 14, 80], False, 'hs', 1)
self.MobileNetV3Block_12 = MobileNetV3Block([14, 14, 80], 3, 480, [14, 14, 112], True, 'hs', 1)
self.MobileNetV3Block_13 = MobileNetV3Block([14, 14, 112], 3, 672, [14, 14, 112], True, 'hs', 1)
self.MobileNetV3Block_14 = MobileNetV3Block([14, 14, 112], 5, 672, [7, 7, 160], True, 'hs', 2)
self.MobileNetV3Block_15 = MobileNetV3Block([7, 7, 160], 5, 960, [7, 7, 160], True, 'hs', 1)
self.MobileNetV3Block_16 = MobileNetV3Block([7, 7, 160], 5, 960, [7, 7, 160], True, 'hs', 1)
self.Conv2D_17 = tensorflow.keras.layers.Conv2D(
filters=960,
kernel_size=1,
strides=1,
padding='same')
self.BatchNormalization_17 = tensorflow.keras.layers.BatchNormalization()
self.hard_swish_17 = hard_swish
self.AveragePooling2D_18 = tensorflow.keras.layers.AveragePooling2D(
pool_size=7,
strides=1)
self.Conv2D_19 = tensorflow.keras.layers.Conv2D(
filters=1280,
kernel_size=1,
strides=1,
padding='same',
activation=hard_swish)
self.Conv2D_20 = tensorflow.keras.layers.Conv2D(
filters=num_classes,
kernel_size=1,
strides=1,
padding='same')
def call(self, x):
x = self.Conv2D_1(x)
x = self.BatchNormalization_1(x)
x = self.hard_swish_1(x)
x = self.MobileNetV3Block_2(x)
x = self.MobileNetV3Block_3(x)
x = self.MobileNetV3Block_4(x)
x = self.MobileNetV3Block_5(x)
x = self.MobileNetV3Block_6(x)
x = self.MobileNetV3Block_7(x)
x = self.MobileNetV3Block_8(x)
x = self.MobileNetV3Block_9(x)
x = self.MobileNetV3Block_10(x)
x = self.MobileNetV3Block_11(x)
x = self.MobileNetV3Block_12(x)
x = self.MobileNetV3Block_13(x)
x = self.MobileNetV3Block_14(x)
x = self.MobileNetV3Block_15(x)
x = self.MobileNetV3Block_16(x)
x = self.Conv2D_17(x)
x = self.BatchNormalization_17(x)
x = self.hard_swish_17(x)
x = self.AveragePooling2D_18(x)
x = self.Conv2D_19(x)
x = self.Conv2D_20(x)
return x
class MobileNetV3Small(tensorflow.keras.Model):
def __init__(self, num_classes=1000):
super(MobileNetV3Small, self).__init__(name='MobileNetV3Small')
self.Conv2D_1 = tensorflow.keras.layers.Conv2D(
filters=16,
kernel_size=3,
strides=2,
padding='same')
self.BatchNormalization_1 = tensorflow.keras.layers.BatchNormalization()
self.hard_swish_1 = hard_swish
self.MobileNetV3Block_2 = MobileNetV3Block([112, 112, 16], 3, 16, [56, 56, 16], True, 're', 2)
self.MobileNetV3Block_3 = MobileNetV3Block([56, 56, 16], 3, 72, [28, 28, 24], False, 're', 2)
self.MobileNetV3Block_4 = MobileNetV3Block([28, 28, 24], 3, 88, [28, 28, 24], False, 're', 1)
self.MobileNetV3Block_5 = MobileNetV3Block([28, 28, 24], 5, 96, [14, 14, 40], True, 'hs', 2)
self.MobileNetV3Block_6 = MobileNetV3Block([14, 14, 40], 5, 240, [14, 14, 40], True, 'hs', 1)
self.MobileNetV3Block_7 = MobileNetV3Block([14, 14, 40], 5, 240, [14, 14, 40], True, 'hs', 1)
self.MobileNetV3Block_8 = MobileNetV3Block([14, 14, 40], 5, 120, [14, 14, 48], True, 'hs', 1)
self.MobileNetV3Block_9 = MobileNetV3Block([14, 14, 48], 5, 144, [14, 14, 48], True, 'hs', 1)
self.MobileNetV3Block_10 = MobileNetV3Block([14, 14, 48], 5, 288, [7, 7, 96], True, 'hs', 2)
self.MobileNetV3Block_11 = MobileNetV3Block([7, 7, 96], 5, 576, [7, 7, 96], True, 'hs', 1)
self.MobileNetV3Block_12 = MobileNetV3Block([7, 7, 96], 5, 576, [7, 7, 96], True, 'hs', 1)
self.Conv2D_13 = tensorflow.keras.layers.Conv2D(
filters=576,
kernel_size=1,
strides=1,
padding='same')
self.BatchNormalization_13 = tensorflow.keras.layers.BatchNormalization()
self.hard_swish_13 = hard_swish
self.AveragePooling2D_14 = tensorflow.keras.layers.AveragePooling2D(
pool_size=7,
strides=1)
self.Conv2D_15 = tensorflow.keras.layers.Conv2D(
filters=1280,
kernel_size=1,
strides=1,
padding='same',
activation=hard_swish)
self.Conv2D_16 = tensorflow.keras.layers.Conv2D(
filters=num_classes,
kernel_size=1,
strides=1,
padding='same')
def call(self, x):
x = self.Conv2D_1(x)
x = self.BatchNormalization_1(x)
x = self.hard_swish_1(x)
x = self.MobileNetV3Block_2(x)
x = self.MobileNetV3Block_3(x)
x = self.MobileNetV3Block_4(x)
x = self.MobileNetV3Block_5(x)
x = self.MobileNetV3Block_6(x)
x = self.MobileNetV3Block_7(x)
x = self.MobileNetV3Block_8(x)
x = self.MobileNetV3Block_9(x)
x = self.MobileNetV3Block_10(x)
x = self.MobileNetV3Block_11(x)
x = self.MobileNetV3Block_12(x)
x = self.Conv2D_13(x)
x = self.BatchNormalization_13(x)
x = self.hard_swish_13(x)
x = self.AveragePooling2D_14(x)
x = self.Conv2D_15(x)
x = self.Conv2D_16(x)
return x
if __name__ == '__main__':
tensorflow.enable_eager_execution()
data = tensorflow.random.uniform([128, 224, 224, 3])
model = MobileNetV3Small()
y = model(data)