def channel_split(x, name=''):
    # 輸入進(jìn)來的通道數(shù)
    in_channles = x.shape.as_list()[-1]
    ip = in_channles // 2
    # 對(duì)通道數(shù)進(jìn)行分割
    c_hat = Lambda(lambda z: z[:, :, :, 0:ip], name='%s/sp%d_slice' % (name, 0))(x)
    c = Lambda(lambda z: z[:, :, :, ip:], name='%s/sp%d_slice' % (name, 1))(x)
    return c_hat, c
def channel_shuffle(x):
    height, width, channels = x.shape.as_list()[1:]
    channels_per_split = channels // 2
    # 通道交換
    x = K.reshape(x, [-1, height, width, 2, channels_per_split])
    x = K.permute_dimensions(x, (0,1,2,4,3))
    x = K.reshape(x, [-1, height, width, channels])
    return x
def shuffle_unit(inputs, out_channels, bottleneck_ratio, strides=2, stage=1, block=1):
    bn_axis = -1
    prefix = 'stage{}/block{}'.format(stage, block)
    # [116, 232, 464]
    bottleneck_channels = int(out_channels * bottleneck_ratio/2)
    if strides < 2:
        c_hat, c = channel_split(inputs, '{}/spl'.format(prefix))
        inputs = c
    # [116, 232, 464]
    x = Conv2D(bottleneck_channels, kernel_size=(1,1), strides=1, padding='same', name='{}/1x1conv_1'.format(prefix))(inputs)
    x = BatchNormalization(axis=bn_axis, name='{}/bn_1x1conv_1'.format(prefix))(x)
    x = Activation('relu', name='{}/relu_1x1conv_1'.format(prefix))(x)
    # 深度可分離卷積
    x = DepthwiseConv2D(kernel_size=3, strides=strides, padding='same', name='{}/3x3dwconv'.format(prefix))(x)
    x = BatchNormalization(axis=bn_axis, name='{}/bn_3x3dwconv'.format(prefix))(x)
    # [116, 232, 464]
    x = Conv2D(bottleneck_channels, kernel_size=1,strides=1,padding='same', name='{}/1x1conv_2'.format(prefix))(x)
    x = BatchNormalization(axis=bn_axis, name='{}/bn_1x1conv_2'.format(prefix))(x)
    x = Activation('relu', name='{}/relu_1x1conv_2'.format(prefix))(x)
    # 當(dāng)strides等于2的時(shí)候，殘差邊需要添加卷積
    if strides < 2:
        ret = Concatenate(axis=bn_axis, name='{}/concat_1'.format(prefix))([x, c_hat])
    else:
        s2 = DepthwiseConv2D(kernel_size=3, strides=2, padding='same', name='{}/3x3dwconv_2'.format(prefix))(inputs)
        s2 = BatchNormalization(axis=bn_axis, name='{}/bn_3x3dwconv_2'.format(prefix))(s2)
        s2 = Conv2D(bottleneck_channels, kernel_size=1,strides=1,padding='same', name='{}/1x1_conv_3'.format(prefix))(s2)
        s2 = BatchNormalization(axis=bn_axis, name='{}/bn_1x1conv_3'.format(prefix))(s2)
        s2 = Activation('relu', name='{}/relu_1x1conv_3'.format(prefix))(s2)
        ret = Concatenate(axis=bn_axis, name='{}/concat_2'.format(prefix))([x, s2])
    ret = Lambda(channel_shuffle, name='{}/channel_shuffle'.format(prefix))(ret)
    return ret
def block(x, channel_map, bottleneck_ratio, repeat=1, stage=1):
    x = shuffle_unit(x, out_channels=channel_map[stage-1],
                      strides=2,bottleneck_ratio=bottleneck_ratio,stage=stage,block=1)
    for i in range(1, repeat+1):
        x = shuffle_unit(x, out_channels=channel_map[stage-1],strides=1,
                          bottleneck_ratio=bottleneck_ratio,stage=stage, block=(1+i))
    return x

2、網(wǎng)絡(luò)整體結(jié)構(gòu)

網(wǎng)絡(luò)整體結(jié)構(gòu)如圖所示：

1、當(dāng)輸入進(jìn)來的圖片為224,224,3的時(shí)候，會(huì)經(jīng)過一次卷積壓縮+一次最大池化，此時(shí)網(wǎng)絡(luò)的shape由224,224,3->112,112,24->56,56,24。

2、經(jīng)過一次右邊的ShuffleNet模塊后進(jìn)行三次左邊的ShuffleNet模塊。此時(shí)網(wǎng)絡(luò)的shape由56,56,24->28,28,116。

3、經(jīng)過一次右邊的ShuffleNet模塊后進(jìn)行七次左邊的ShuffleNet模塊。此時(shí)網(wǎng)絡(luò)的shape由28,28,116->14,14,232。

4、經(jīng)過一次右邊的ShuffleNet模塊后進(jìn)行三次左邊的ShuffleNet模塊。此時(shí)網(wǎng)絡(luò)的shape由14,14,232->7,7,464。

5、卷積到1024，此時(shí)網(wǎng)絡(luò)的shape由7,7,464->7,7,1024。

6、全局池化后，進(jìn)行全連接，用于預(yù)測(cè)。

網(wǎng)絡(luò)實(shí)現(xiàn)代碼

ShuffleNetV2一共有4個(gè)scale，分別對(duì)應(yīng)不同大小的ShuffleNetV2。

import numpy as np
from keras.utils import plot_model
from keras.layers import Input, Conv2D, MaxPool2D
from keras.layers import Activation, Add, Concatenate, Conv2D
from keras.layers import GlobalAveragePooling2D, Dense
from keras.layers import MaxPool2D,AveragePooling2D, BatchNormalization, Lambda, DepthwiseConv2D
from keras.models import Model
import keras.backend as K
import numpy as np
def channel_split(x, name=''):
    # 輸入進(jìn)來的通道數(shù)
    in_channles = x.shape.as_list()[-1]
    ip = in_channles // 2
    # 對(duì)通道數(shù)進(jìn)行分割
    c_hat = Lambda(lambda z: z[:, :, :, 0:ip], name='%s/sp%d_slice' % (name, 0))(x)
    c = Lambda(lambda z: z[:, :, :, ip:], name='%s/sp%d_slice' % (name, 1))(x)
    return c_hat, c
def channel_shuffle(x):
    height, width, channels = x.shape.as_list()[1:]
    channels_per_split = channels // 2
    # 通道交換
    x = K.reshape(x, [-1, height, width, 2, channels_per_split])
    x = K.permute_dimensions(x, (0,1,2,4,3))
    x = K.reshape(x, [-1, height, width, channels])
    return x
def shuffle_unit(inputs, out_channels, bottleneck_ratio, strides=2, stage=1, block=1):
    bn_axis = -1
    prefix = 'stage{}/block{}'.format(stage, block)
    # [116, 232, 464]
    bottleneck_channels = int(out_channels * bottleneck_ratio/2)
    if strides < 2:
        c_hat, c = channel_split(inputs, '{}/spl'.format(prefix))
        inputs = c
    # [116, 232, 464]
    x = Conv2D(bottleneck_channels, kernel_size=(1,1), strides=1, padding='same', name='{}/1x1conv_1'.format(prefix))(inputs)
    x = BatchNormalization(axis=bn_axis, name='{}/bn_1x1conv_1'.format(prefix))(x)
    x = Activation('relu', name='{}/relu_1x1conv_1'.format(prefix))(x)
    # 深度可分離卷積
    x = DepthwiseConv2D(kernel_size=3, strides=strides, padding='same', name='{}/3x3dwconv'.format(prefix))(x)
    x = BatchNormalization(axis=bn_axis, name='{}/bn_3x3dwconv'.format(prefix))(x)
    # [116, 232, 464]
    x = Conv2D(bottleneck_channels, kernel_size=1,strides=1,padding='same', name='{}/1x1conv_2'.format(prefix))(x)
    x = BatchNormalization(axis=bn_axis, name='{}/bn_1x1conv_2'.format(prefix))(x)
    x = Activation('relu', name='{}/relu_1x1conv_2'.format(prefix))(x)
    # 當(dāng)strides等于2的時(shí)候，殘差邊需要添加卷積
    if strides < 2:
        ret = Concatenate(axis=bn_axis, name='{}/concat_1'.format(prefix))([x, c_hat])
    else:
        s2 = DepthwiseConv2D(kernel_size=3, strides=2, padding='same', name='{}/3x3dwconv_2'.format(prefix))(inputs)
        s2 = BatchNormalization(axis=bn_axis, name='{}/bn_3x3dwconv_2'.format(prefix))(s2)
        s2 = Conv2D(bottleneck_channels, kernel_size=1,strides=1,padding='same', name='{}/1x1_conv_3'.format(prefix))(s2)
        s2 = BatchNormalization(axis=bn_axis, name='{}/bn_1x1conv_3'.format(prefix))(s2)
        s2 = Activation('relu', name='{}/relu_1x1conv_3'.format(prefix))(s2)
        ret = Concatenate(axis=bn_axis, name='{}/concat_2'.format(prefix))([x, s2])
    ret = Lambda(channel_shuffle, name='{}/channel_shuffle'.format(prefix))(ret)
    return ret
def block(x, channel_map, bottleneck_ratio, repeat=1, stage=1):
    x = shuffle_unit(x, out_channels=channel_map[stage-1],
                      strides=2,bottleneck_ratio=bottleneck_ratio,stage=stage,block=1)
    for i in range(1, repeat+1):
        x = shuffle_unit(x, out_channels=channel_map[stage-1],strides=1,
                          bottleneck_ratio=bottleneck_ratio,stage=stage, block=(1+i))
    return x
def ShuffleNetV2(input_tensor=None,
                 pooling='max',
                 input_shape=(224,224,3),
                 num_shuffle_units=[3,7,3],
                 scale_factor=1,
                 bottleneck_ratio=1,
                 classes=1000):
    name = 'ShuffleNetV2_{}_{}_{}'.format(scale_factor, bottleneck_ratio, "".join([str(x) for x in num_shuffle_units]))
    out_dim_stage_two = {0.5:48, 1:116, 1.5:176, 2:244}
    out_channels_in_stage = np.array([1,1,2,4])
    out_channels_in_stage *= out_dim_stage_two[scale_factor]  #  calculate output channels for each stage
    out_channels_in_stage[0] = 24  # first stage has always 24 output channels
    out_channels_in_stage = out_channels_in_stage.astype(int)
    img_input = Input(shape=input_shape)
    x = Conv2D(filters=out_channels_in_stage[0], kernel_size=(3, 3), padding='same', use_bias=False, strides=(2, 2),
               activation='relu', name='conv1')(img_input)
    x = MaxPool2D(pool_size=(3, 3), strides=(2, 2), padding='same', name='maxpool1')(x)
    for stage in range(len(num_shuffle_units)):
        repeat = num_shuffle_units[stage]
        x = block(x, out_channels_in_stage,
                   repeat=repeat,
                   bottleneck_ratio=bottleneck_ratio,
                   stage=stage + 2)
    if scale_factor!=2:
        x = Conv2D(1024, kernel_size=1, padding='same', strides=1, name='1x1conv5_out', activation='relu')(x)
    else:
        x = Conv2D(2048, kernel_size=1, padding='same', strides=1, name='1x1conv5_out', activation='relu')(x)
    x = GlobalAveragePooling2D(name='global_avg_pool')(x)
    x = Dense(classes, name='fc')(x)
    x = Activation('softmax', name='softmax')(x)
    inputs = img_input
    model = Model(inputs, x, name=name)
    return model
if __name__ == '__main__':
    import os
    os.environ['CUDA_VISIBLE_DEVICES'] = ''
    model = ShuffleNetV2(input_shape=(224, 224, 3),scale_factor=1)
    model.summary()

以上就是python神經(jīng)網(wǎng)絡(luò)ShuffleNetV2模型復(fù)現(xiàn)詳解的詳細(xì)內(nèi)容，更多關(guān)于ShuffleNetV2模型復(fù)現(xiàn)的資料請(qǐng)關(guān)注腳本之家其它相關(guān)文章！

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