def _group_conv(x, filters, kernel, stride, groups):
 """
 Group convolution
 # Arguments
  x: Tensor, input tensor of with `channels_last` or 'channels_first' data format
  filters: Integer, number of output channels
  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.
  groups: Integer, number of groups per channel
  
 # Returns
  Output tensor
 """
 channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
 in_channels = K.int_shape(x)[channel_axis]
 
 # number of input channels per group
 nb_ig = in_channels // groups
 # number of output channels per group
 nb_og = filters // groups
 
 gc_list = []
 # Determine whether the number of filters is divisible by the number of groups
 assert filters % groups == 0
 
 for i in range(groups):
  if channel_axis == -1:
   x_group = Lambda(lambda z: z[:, :, :, i * nb_ig: (i + 1) * nb_ig])(x)
  else:
   x_group = Lambda(lambda z: z[:, i * nb_ig: (i + 1) * nb_ig, :, :])(x)
  gc_list.append(Conv2D(filters=nb_og, kernel_size=kernel, strides=stride, 
        padding='same', use_bias=False)(x_group))
  
 return Concatenate(axis=channel_axis)(gc_list)

> d = np.array([0,1,2,3,4,5,6,7,8]) 
> x = np.reshape(d, (3,3)) 
> x = np.transpose(x, [1,0]) # 转置
> x = np.reshape(x, (9,)) # 平铺
'[0 1 2 3 4 5 6 7 8] --> [0 3 6 1 4 7 2 5 8]' 

def _channel_shuffle(x, groups):
 """
 Channel shuffle layer
 
 # Arguments
  x: Tensor, input tensor of with `channels_last` or 'channels_first' data format
  groups: Integer, number of groups per channel
  
 # Returns
  Shuffled tensor
 """
 
 if K.image_data_format() == 'channels_last':
  height, width, in_channels = K.int_shape(x)[1:]
  channels_per_group = in_channels // groups
  pre_shape = [-1, height, width, groups, channels_per_group]
  dim = (0, 1, 2, 4, 3)
  later_shape = [-1, height, width, in_channels]
 else:
  in_channels, height, width = K.int_shape(x)[1:]
  channels_per_group = in_channels // groups
  pre_shape = [-1, groups, channels_per_group, height, width]
  dim = (0, 2, 1, 3, 4)
  later_shape = [-1, in_channels, height, width]
 
 x = Lambda(lambda z: K.reshape(z, pre_shape))(x)
 x = Lambda(lambda z: K.permute_dimensions(z, dim))(x) 
 x = Lambda(lambda z: K.reshape(z, later_shape))(x)
 
 return x

# -*- coding: utf-8 -*-
"""
Created on Thu Apr 25 18:26:41 2019
@author: zjn
"""
import numpy as np
from keras.callbacks import LearningRateScheduler
from keras.models import Model
from keras.layers import Input, Conv2D, Dropout, Dense, GlobalAveragePooling2D, Concatenate, AveragePooling2D
from keras.layers import Activation, BatchNormalization, add, Reshape, ReLU, DepthwiseConv2D, MaxPooling2D, Lambda
from keras.utils.vis_utils import plot_model
from keras import backend as K
from keras.optimizers import SGD
 
def _group_conv(x, filters, kernel, stride, groups):
 """
 Group convolution
 
 # Arguments
  x: Tensor, input tensor of with `channels_last` or 'channels_first' data format
  filters: Integer, number of output channels
  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.
  groups: Integer, number of groups per channel
  
 # Returns
  Output tensor
 """
 
 channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
 in_channels = K.int_shape(x)[channel_axis]
 
 # number of input channels per group
 nb_ig = in_channels // groups
 # number of output channels per group
 nb_og = filters // groups
 
 gc_list = []
 # Determine whether the number of filters is divisible by the number of groups
 assert filters % groups == 0
 
 for i in range(groups):
  if channel_axis == -1:
   x_group = Lambda(lambda z: z[:, :, :, i * nb_ig: (i + 1) * nb_ig])(x)
  else:
   x_group = Lambda(lambda z: z[:, i * nb_ig: (i + 1) * nb_ig, :, :])(x)
  gc_list.append(Conv2D(filters=nb_og, kernel_size=kernel, strides=stride, 
        padding='same', use_bias=False)(x_group))
  
 return Concatenate(axis=channel_axis)(gc_list)
def _channel_shuffle(x, groups):
 """
 Channel shuffle layer
 
 # Arguments
  x: Tensor, input tensor of with `channels_last` or 'channels_first' data format
  groups: Integer, number of groups per channel
  
 # Returns
  Shuffled tensor
 """
 if K.image_data_format() == 'channels_last':
  height, width, in_channels = K.int_shape(x)[1:]
  channels_per_group = in_channels // groups
  pre_shape = [-1, height, width, groups, channels_per_group]
  dim = (0, 1, 2, 4, 3)
  later_shape = [-1, height, width, in_channels]
 else:
  in_channels, height, width = K.int_shape(x)[1:]
  channels_per_group = in_channels // groups
  pre_shape = [-1, groups, channels_per_group, height, width]
  dim = (0, 2, 1, 3, 4)
  later_shape = [-1, in_channels, height, width]
 
 x = Lambda(lambda z: K.reshape(z, pre_shape))(x)
 x = Lambda(lambda z: K.permute_dimensions(z, dim))(x) 
 x = Lambda(lambda z: K.reshape(z, later_shape))(x)
 
 return x
 
def _shufflenet_unit(inputs, filters, kernel, stride, groups, stage, bottleneck_ratio=0.25):
 """
 ShuffleNet unit
 
 # Arguments
  inputs: Tensor, input tensor of with `channels_last` or 'channels_first' data format
  filters: Integer, number of output channels
  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.
  groups: Integer, number of groups per channel
  stage: Integer, stage number of ShuffleNet
  bottleneck_channels: Float, bottleneck ratio implies the ratio of bottleneck channels to output channels
   
 # Returns
  Output tensor
  
 # Note
  For Stage 2, we(authors of shufflenet) do not apply group convolution on the first pointwise layer 
  because the number of input channels is relatively small.
 """
 channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
 in_channels = K.int_shape(inputs)[channel_axis]
 bottleneck_channels = int(filters * bottleneck_ratio)
 
 if stage == 2:
  x = Conv2D(filters=bottleneck_channels, kernel_size=kernel, strides=1,
     padding='same', use_bias=False)(inputs)
 else:
  x = _group_conv(inputs, bottleneck_channels, (1, 1), 1, groups)
 x = BatchNormalization(axis=channel_axis)(x)
 x = ReLU()(x)
 
 x = _channel_shuffle(x, groups)
 x = DepthwiseConv2D(kernel_size=kernel, strides=stride, depth_multiplier=1, 
      padding='same', use_bias=False)(x)
 x = BatchNormalization(axis=channel_axis)(x)
  
 if stride == 2:
  x = _group_conv(x, filters - in_channels, (1, 1), 1, groups)
  x = BatchNormalization(axis=channel_axis)(x)
  avg = AveragePooling2D(pool_size=(3, 3), strides=2, padding='same')(inputs)
  x = Concatenate(axis=channel_axis)([x, avg])
 else:
  x = _group_conv(x, filters, (1, 1), 1, groups)
  x = BatchNormalization(axis=channel_axis)(x)
  x = add([x, inputs])
 return x
 
def _stage(x, filters, kernel, groups, repeat, stage):
 """
 Stage of ShuffleNet
 
 # Arguments
  x: Tensor, input tensor of with `channels_last` or 'channels_first' data format
  filters: Integer, number of output channels
  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.
  groups: Integer, number of groups per channel
  repeat: Integer, total number of repetitions for a shuffle unit in every stage
  stage: Integer, stage number of ShuffleNet
  
 # Returns
  Output tensor
 """
 x = _shufflenet_unit(x, filters, kernel, 2, groups, stage)
 
 for i in range(1, repeat):
  x = _shufflenet_unit(x, filters, kernel, 1, groups, stage)
 return x
 
def ShuffleNet(input_shape, classes):
 """
 ShuffleNet architectures
 
 # Arguments
  input_shape: An integer or tuple/list of 3 integers, shape
   of input tensor
  k: Integer, number of classes to predict
  
 # Returns
  A keras model
 """
 inputs = Input(shape=input_shape)
 
 x = Conv2D(24, (3, 3), strides=2, padding='same', use_bias=True, activation='relu')(inputs)
 x = MaxPooling2D(pool_size=(3, 3), strides=2, padding='same')(x)
 
 x = _stage(x, filters=384, kernel=(3, 3), groups=8, repeat=4, stage=2)
 x = _stage(x, filters=768, kernel=(3, 3), groups=8, repeat=8, stage=3)
 x = _stage(x, filters=1536, kernel=(3, 3), groups=8, repeat=4, stage=4)
 
 x = GlobalAveragePooling2D()(x)
 
 x = Dense(classes)(x)
 predicts = Activation('softmax')(x)
 model = Model(inputs, predicts)
 return model
 
if __name__ == '__main__':
 model = ShuffleNet((224, 224, 3), 1000)
 #plot_model(model, to_file='ShuffleNet.png', show_shapes=True)