有点喜欢的mobileNet系列mobileNetv3
mobileNetV3介绍
mobileNet系统主要针对在手机上运能运行高性能低资源的网络。而mobileNetv3主要利用网络架构搜索（NAS），搜索出来的一种算法。它比v2版本的精度提高了ImageNet分类上提高了6%。
mobilev3主要由如下优点：
1.大量使用11和33的卷积代替55的卷积，减少了参数量。在v2中先使用33的卷积在使用11的卷积，而在v3中则先11在使用3*3。论文里说这样使用是保留了高维特征空间，减少了反向传播的延迟
2.引入残差块和轻量级的注意力机制，轻量级注意力模块
并激发成瓶颈结构，更好的提取特征。
3、采用了h-swish计算速度更快，在移动设备中提高了网络的准确性。
网络复现实现代码 mobilenetV3 large
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()
网络复现实现代码 mobilenetV3 small
small主要减少了通道数，降低了参数量
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_small(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')
# 112,112,16 -> 56,56,16
x = bottleneck(x, 16, (3, 3), up_dim=16, stride=2, sq=True, nl='RE')
# 56,56,16 -> 28,28,24
x = bottleneck(x, 24, (3, 3), up_dim=72, stride=2, sq=False, nl='RE')
x = bottleneck(x, 24, (3, 3), up_dim=88, stride=1, sq=False, nl='RE')
# 28,28,24 -> 14,14,40
x = bottleneck(x, 40, (5, 5), up_dim=96, stride=2, sq=True, nl='HS')
x = bottleneck(x, 40, (5, 5), up_dim=240, stride=1, sq=True, nl='HS')
x = bottleneck(x, 40, (5, 5), up_dim=240, stride=1, sq=True, nl='HS')
# 14,14,40 -> 14,14,48
x = bottleneck(x, 48, (5, 5), up_dim=120, stride=1, sq=True, nl='HS')
x = bottleneck(x, 48, (5, 5), up_dim=144, stride=1, sq=True, nl='HS')
# 14,14,48 -> 7,7,96
x = bottleneck(x, 96, (5, 5), up_dim=288, stride=2, sq=True, nl='HS')
x = bottleneck(x, 96, (5, 5), up_dim=576, stride=1, sq=True, nl='HS')
x = bottleneck(x, 96, (5, 5), up_dim=576, stride=1, sq=True, nl='HS')
x = conv_block(x, 576, (1, 1), strides=(1, 1), nl='HS')
x = GlobalAveragePooling2D()(x)
x = Reshape((1, 1, 576))(x)
x = Conv2D(1024, (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_small()
model.summary()