MobilenetBase.py

#MobileNetV3Base
from keras.layers import Conv2D, DepthwiseConv2D, Dense, GlobalAveragePooling2D
from keras.layers import Activation, BatchNormalization, Add, Multiply, Reshape
from keras import backend as K


class MobilenetBase:
    def __init__(self, shape, n_class, alpha=1.0):
        """Init
        # Arguments
            input_shape: An integer or tuple/list of 3 integers, shape
                of input tensor.
            n_class: Integer, number of classes.
            alpha: Integer, width multiplier.
        """
        self.shape = shape
        self.n_class = n_class
        self.alpha = alpha

    def _relu6(self, x): #ReLU6

        return K.relu(x, max_value=6.0)

    def _hard_swish(self, x): #Hard-swish

        return x * K.relu(x + 3.0, max_value=6.0) / 6.0

    def _return_activation(self, x, nl):
        """Convolution Block
        This function defines a activation choice.
        # Arguments
            x: Tensor, input tensor of conv layer.
            nl: String, nonlinearity activation type. #可参考MobileNetV3论文
        # Returns
            Output tensor.
        """
        if nl == 'HS':
            x = Activation(self._hard_swish)(x)
        if nl == 'RE':
            x = Activation(self._relu6)(x)

        return x

    def _conv_block(self, inputs, filters, kernel, strides, nl):
        """Convolution Block
        This function defines a 2D convolution operation with BN and activation.
        # 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.
            nl: String, nonlinearity activation type.
        # Returns
            Output tensor.
        """

        channel_axis = 1 if K.image_data_format() == 'channels_first' else -1

        x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)
        x = BatchNormalization(axis=channel_axis)(x)

        return self._return_activation(x, nl)

    def _squeeze(self, inputs): #SE-Net
        """Squeeze and Excitation.
        This function defines a squeeze structure.
        # Arguments
            inputs: Tensor, input tensor of conv layer.
        """
        input_channels = int(inputs.shape[-1])

        x = GlobalAveragePooling2D()(inputs)
        x = Dense(input_channels, activation='relu')(x)
        x = Dense(input_channels, activation='hard_sigmoid')(x)
        x = Reshape((1, 1, input_channels))(x)
        x = Multiply()([inputs, x])

        return x

    def _bottleneck(self, inputs, filters, kernel, e, s, squeeze, nl):
        """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.
            e: 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.
            squeeze: Boolean, Whether to use the squeeze.
            nl: String, nonlinearity activation type.
        # Returns
            Output tensor.
        """
        channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
        input_shape = K.int_shape(inputs)

        tchannel = int(e)
        cchannel = int(self.alpha * filters)

        r = s == 1 and input_shape[3] == filters

        x = self._conv_block(inputs, tchannel, (1, 1), (1, 1), nl)

        x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same')(x)
        x = BatchNormalization(axis=channel_axis)(x)
        x = self._return_activation(x, nl)

        if squeeze:
            x = self._squeeze(x)

        x = Conv2D(cchannel, (1, 1), strides=(1, 1), padding='same')(x)
        x = BatchNormalization(axis=channel_axis)(x)

        if r:
            x = Add()([x, inputs])
        return x

    def build(self):
        pass