python神经网络MobileNetV3 large模型的复现详解

from keras.layers import Conv2D, DepthwiseConv2D, Dense, GlobalAveragePooling2D,Input
from keras.layers import Activation, BatchNormalization, Add, Multiply, Reshape
from keras.models import Model
from keras import backend as K
alpha = 1
def relu6(x):
    # relu函数
    return K.relu(x, max_value=6.0)
def hard_swish(x):
    # 利用relu函数乘上x模拟sigmoid
    return x * K.relu(x + 3.0, max_value=6.0) / 6.0
def return_activation(x, nl):
    # 用于判断使用哪个激活函数
    if nl == 'HS':
        x = Activation(hard_swish)(x)
    if nl == 'RE':
        x = Activation(relu6)(x)
    return x
def conv_block(inputs, filters, kernel, strides, nl):
    # 一个卷积单元，也就是conv2d + batchnormalization + activation
    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 return_activation(x, nl)
def squeeze(inputs):
    # 注意力机制单元
    input_channels = int(inputs.shape[-1])
    x = GlobalAveragePooling2D()(inputs)
    x = Dense(int(input_channels/4))(x)
    x = Activation(relu6)(x)
    x = Dense(input_channels)(x)
    x = Activation(hard_swish)(x)
    x = Reshape((1, 1, input_channels))(x)
    x = Multiply()([inputs, x])
    return x
def bottleneck(inputs, filters, kernel, up_dim, stride, sq, nl):
    channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
    input_shape = K.int_shape(inputs)
    tchannel = int(up_dim)
    cchannel = int(alpha * filters)
    r = stride == 1 and input_shape[3] == filters
    # 1x1卷积调整通道数，通道数上升
    x = conv_block(inputs, tchannel, (1, 1), (1, 1), nl)
    # 进行3x3深度可分离卷积
    x = DepthwiseConv2D(kernel, strides=(stride, stride), depth_multiplier=1, padding='same')(x)
    x = BatchNormalization(axis=channel_axis)(x)
    x = return_activation(x, nl)
    # 引入注意力机制
    if sq:
        x = squeeze(x)
    # 下降通道数
    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 MobileNetv3_large(shape = (224,224,3),n_class = 1000):
    inputs = Input(shape)
    # 224,224,3 -> 112,112,16
    x = conv_block(inputs, 16, (3, 3), strides=(2, 2), nl='HS')
    x = bottleneck(x, 16, (3, 3), up_dim=16, stride=1, sq=False, nl='RE')
    # 112,112,16 -> 56,56,24
    x = bottleneck(x, 24, (3, 3), up_dim=64, stride=2, sq=False, nl='RE')
    x = bottleneck(x, 24, (3, 3), up_dim=72, stride=1, sq=False, nl='RE')
    # 56,56,24 -> 28,28,40
    x = bottleneck(x, 40, (5, 5), up_dim=72, stride=2, sq=True, nl='RE')
    x = bottleneck(x, 40, (5, 5), up_dim=120, stride=1, sq=True, nl='RE')
    x = bottleneck(x, 40, (5, 5), up_dim=120, stride=1, sq=True, nl='RE')
    # 28,28,40 -> 14,14,80
    x = bottleneck(x, 80, (3, 3), up_dim=240, stride=2, sq=False, nl='HS')
    x = bottleneck(x, 80, (3, 3), up_dim=200, stride=1, sq=False, nl='HS')
    x = bottleneck(x, 80, (3, 3), up_dim=184, stride=1, sq=False, nl='HS')
    x = bottleneck(x, 80, (3, 3), up_dim=184, stride=1, sq=False, nl='HS')
    # 14,14,80 -> 14,14,112
    x = bottleneck(x, 112, (3, 3), up_dim=480, stride=1, sq=True, nl='HS')
    x = bottleneck(x, 112, (3, 3), up_dim=672, stride=1, sq=True, nl='HS')
    # 14,14,112 -> 7,7,160
    x = bottleneck(x, 160, (5, 5), up_dim=672, stride=2, sq=True, nl='HS')
    x = bottleneck(x, 160, (5, 5), up_dim=960, stride=1, sq=True, nl='HS')
    x = bottleneck(x, 160, (5, 5), up_dim=960, stride=1, sq=True, nl='HS')
    # 7,7,160 -> 7,7,960
    x = conv_block(x, 960, (1, 1), strides=(1, 1), nl='HS')
    x = GlobalAveragePooling2D()(x)
    x = Reshape((1, 1, 960))(x)
    x = Conv2D(1280, (1, 1), padding='same')(x)
    x = return_activation(x, 'HS')
    x = Conv2D(n_class, (1, 1), padding='same', activation='softmax')(x)
    x = Reshape((n_class,))(x)
    model = Model(inputs, x)
    return model
if __name__ == "__main__":
    model = MobileNetv3_large()
    model.summary()