本文主要是介绍自制efficientnet网络，希望对大家解决编程问题提供一定的参考价值，需要的开发者们随着小编来一起学习吧！
用到的技术cnn,残差连接，全局池化注意力机制,点卷积切换通道,深度卷积提取空间特征
import os
os.environ["KERAS_BACKEND"] = "tensorflow" # @param ["tensorflow", "jax", "torch"]
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import matplotlib.pyplot as plt
import numpy as np
import keras
import tensorflow as tf
from keras import layers
# MobileNetV2 的一个关键特性是使用了“扩张-压缩”模式，即首先通过 1x1 卷积（点卷积）增加通道数（扩张），
# 然后通过深度可分离卷积处理特征，最后再通过 1x1 卷积减少通道数（压缩）,越往下,扩张和压缩通道越大
# 并且在特征图到60x60以下逐渐加大提取特征力度,用残差网络
# MobileNetV2 中的瓶颈块会根据网络的深度重复不同的次数。您的代码中为不同的瓶颈块设置了不同的重复次数（
# 如 2 次、3 次），这通常是正确的
def activation_block(x): # 大多预训练模型都是先批次标准化,再激活函数
# 如果把激活函数放前面,在模型摘要里会先显示激活函数,但是这不是大多数模型的摘要信息
# 说明是先批次标准化,之后激活函数
x = layers.BatchNormalization()(x)
return layers.Activation(keras.activations.hard_swish)(x)
def relu_activation_block(x): # 大多预训练模型都是先批次标准化,再激活函数
# 如果把激活函数放前面,在模型摘要里会先显示激活函数,但是这不是大多数模型的摘要信息
# 说明是先批次标准化,之后激活函数
# relu6:如果输入 x 是正数，则输出 x，但不超过6；如果 x 是负数，则输出0；如果 x 大于6，
# 则输出6.而relu无限制
x = layers.BatchNormalization()(x)
return layers.Activation('relu6')(x)
def se(inputs,in_c,out_c):
multiply=inputs
# (n,c) 全局平均池化,获取样本的全局分类信息
x=layers.GlobalAveragePooling2D()(multiply)
# 变形
x=layers.Reshape([1,1,-1])(x)
# 这里用了截距,这里减少通道数是为了紧凑特征,同时可以减少模型过拟合
x=layers.Conv2D(in_c,1,padding='same')(x)
# 之后放大到原通道数,这里卷积核大小是1,是点卷积
x=layers.Conv2D(out_c,1,padding='same')(x)
x=layers.Multiply()([x,multiply])
return x
def depthwiseConv_block(inputs,filters,kernel_size=3,strides=1):
x=inputs
x=layers.Conv2D(filters,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(kernel_size,strides=strides,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
return x
def conv1_block(inputs,filters,dropout=None):
x=inputs
x=layers.Conv2D(filters,1,padding='same',use_bias=False)(x)
x = layers.BatchNormalization()(x)
if dropout:
x=layers.Dropout(dropout)(x)
return x
# 收缩点卷积:16--24--40--80--112--192
# 扩张点卷积:96--144--240--480--672--1152
# global注意力模块:4--6--10--20--28--48
def get_efficientnetb0_model(input_shape,num_classes):
inputs=keras.Input(shape=input_shape)
x=layers.Rescaling(1.0/255)(inputs)
x=layers.Normalization()(x)
x=layers.Rescaling(scale=[2.0896918976428642, 2.1128856368212916, 2.1081851067789197], offset=0.0)(x)
x=layers.Conv2D(32,3,strides=2,padding='same',use_bias=False)(x) # (112,112)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=se(x,8,32) # 有利于通道信息的重修订,减少过拟合,只关注重要的特征
x=conv1_block(x,16)
# 深度卷积模块,用来提取空间信息
x=depthwiseConv_block(x,96,strides=2) # (56,56)
x=se(x,4,96)
x=conv1_block(x,24)
x0=x
x=depthwiseConv_block(x0,144)
x=se(x,6,144)
x=conv1_block(x,24,0.025)
x=layers.add([x,x0])
# 注意:kernel_size=5,核大,视野就大
x=depthwiseConv_block(x,144,kernel_size=5,strides=2) # (28,28)
x=se(x,6,144)
x=conv1_block(x,40)
x0=x
x=depthwiseConv_block(x0,240,kernel_size=5)
x=se(x,10,240)
x=conv1_block(x,40,0.025)
x=layers.add([x,x0])
# 这个位置用的kernel_size=3
x=depthwiseConv_block(x,240,strides=2) # (14,14)
x=se(x,10,240)
x=conv1_block(x,80)
for i in range(2):
x0=x
x=depthwiseConv_block(x0,480)
x=se(x,20,480)
x=conv1_block(x,80,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,480,kernel_size=5)
x=se(x,20,480)
x=conv1_block(x,112)
for i in range(2):
x0=x
x=depthwiseConv_block(x0,672,kernel_size=5)
x=se(x,28,672)
x=conv1_block(x,112,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,672,kernel_size=5,strides=2) # (7,7)
x=se(x,28,672)
x=conv1_block(x,192)
for i in range(3):
x0=x
x=depthwiseConv_block(x0,1152,kernel_size=5)
x=se(x,48,1152)
x=conv1_block(x,192,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,1152,kernel_size=3)
x=se(x,48,1152)
x=conv1_block(x,320)
x=layers.Conv2D(1280,1,padding='same',use_bias=False)(x)
x=activation_block(x)
return keras.Model(inputs,x)
# 64--128--256--512--1024
# 没有残差,点卷积用来切换通道,便于深度卷积提取信息
def get_mobilenet_model(input_shape,num_classes):
inputs=keras.Input(shape=input_shape)
x=layers.Rescaling(1.0/255)(inputs)
x=layers.Conv2D(32,3,strides=2,padding='same',use_bias=False)(x) # (80,80,32)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=layers.Conv2D(64,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(3,strides=2,padding='same',use_bias=False)(x) # (40,40,64)
x=relu_activation_block(x)
for i in range(2): # 在40x40的特征图上深度卷积两次
x=layers.Conv2D(128,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
if i==0:
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
else:
x=layers.DepthwiseConv2D(3,strides=2,padding='same',use_bias=False)(x) # (20,20,128)
x=relu_activation_block(x)
for i in range(2): # 在20x20的特征图上深度卷积两次
x=layers.Conv2D(256,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
if i ==0:
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
else:
x=layers.DepthwiseConv2D(3,strides=2,padding='same',use_bias=False)(x) # (10,10,256)
x=relu_activation_block(x)
for i in range(6): # 在10x10的特征图上狠提特征
x=layers.Conv2D(512,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
if i !=5:
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
else:
x=layers.DepthwiseConv2D(3,strides=2,padding='same',use_bias=False)(x) # (5,5,512)
x=relu_activation_block(x)
x=layers.Conv2D(1024,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=layers.Conv2D(1024,1,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
return keras.Model(inputs,x)
def get_efficientnetb5_model(input_shape,num_classes):
inputs=keras.Input(shape=input_shape)
x=layers.Rescaling(1.0/255)(inputs)
x=layers.Normalization()(x)
x=layers.Rescaling(scale=[2.0896918976428642, 2.1128856368212916, 2.1081851067789197], offset=0.0)(x)
x=layers.Conv2D(48,3,strides=2,padding='same',use_bias=False)(x) # (112,112)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=se(x,12,48)
x=conv1_block(x,24)
# 其实想想都知道它加深网络干啥事,之前200多层的网络主要提取14x14,7x7这些特征图的特征
# 现在它有足够的层数,就加大了112x112,56x56特征图的提取力度
for i in range(2): # 在112x112的特征图上用了两次残差
x0=x
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x0)
x=relu_activation_block(x)
x=se(x,6,24)
x=conv1_block(x,24,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,144,strides=2) # (56,56)
x=se(x,6,144)
x=conv1_block(x,40)
for i in range(4): # 在56x56的特征图上用了四次残差
x0=x
x=depthwiseConv_block(x0,240)
x=se(x,10,240)
x=conv1_block(x,40,0.025)
x=layers.add([x,x0])
# 用5x5的核,可以加强感受野
x=depthwiseConv_block(x,240,strides=2,kernel_size=5) # (28,28)
x=se(x,10,240)
x=conv1_block(x,64)
for i in range(4): # 在28x28的特征图上用了四次残差
x0=x
x=depthwiseConv_block(x0,384,kernel_size=5)
x=se(x,16,384)
x=conv1_block(x,64,0.025)
x=layers.add([x,x0])
# 用3x3的核
x=depthwiseConv_block(x,384,kernel_size=3,strides=2) # (14,14)
x=se(x,16,384)
x=conv1_block(x,128)
for i in range(6):
x0=x
x=depthwiseConv_block(x0,768)
x=se(x,32,768)
x=conv1_block(x,128,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,768,kernel_size=5)
x=se(x,32,768)
x=conv1_block(x,176)
for i in range(6): # 在14x14的特征图上一共用了12次残差
x0=x
x=depthwiseConv_block(x0,1056,kernel_size=5)
x=se(x,44,1056)
x=conv1_block(x,176,0.025)
x=layers.add([x,x0])
# 深度卷积核大小:5x5
x=depthwiseConv_block(x,1056,kernel_size=5,strides=2) # (7,7)
x=se(x,44,1056)
x=conv1_block(x,304)
for i in range(8):
x0=x
x=depthwiseConv_block(x0,1824,kernel_size=5) # 核大小:5x5
x=se(x,76,1824)
x=conv1_block(x,304,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,1824,kernel_size=3)
x=se(x,76,1824)
x=conv1_block(x,512)
for i in range(2): # 在7x7的特征图上一共用了10次残差
x0=x
x=depthwiseConv_block(x0,3072)
x=se(x,128,3072)
x=conv1_block(x,512,0.025)
x=layers.add([x,x0])
x=layers.Conv2D(2048,1,padding='same',use_bias=False)(x)
x=activation_block(x)
return keras.Model(inputs,x)
# 上面的要是没看懂,这个就不要看了,这个一共800多层，作为目标最优秀的一类卷积残差网络
def get_efficientnetb7_model(input_shape,num_classes):
inputs=keras.Input(shape=input_shape)
x=layers.Rescaling(1.0/255)(inputs)
x=layers.Normalization()(x)
x=layers.Rescaling(scale=[2.0896918976428642, 2.1128856368212916, 2.1081851067789197], offset=0.0)(x)
x=layers.Conv2D(64,3,strides=2,padding='same',use_bias=False)(x) # (112,112)
x=relu_activation_block(x)
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x)
x=relu_activation_block(x)
x=se(x,16,64)
x=conv1_block(x,32)
# 其实想想都知道它加深网络干啥事,之前200多层的网络主要提取14x14,7x7这些特征图的特征
# 现在它有足够的层数,就加大了112x112,56x56特征图的提取力度
for i in range(3): # 在 112x112的特征图上用了3次残差
x0=x
x=layers.DepthwiseConv2D(3,padding='same',use_bias=False)(x0)
x=relu_activation_block(x)
x=se(x,8,32)
x=conv1_block(x,32,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,192,strides=2) # (56,56)
x=se(x,8,192)
x=conv1_block(x,48)
for i in range(6): # 在56x56的特征图上用了6次残差
x0=x
x=depthwiseConv_block(x0,288)
x=se(x,12,288)
x=conv1_block(x,48,0.025)
x=layers.add([x,x0])
# 用5x5的核,可以加强感受野
x=depthwiseConv_block(x,288,strides=2,kernel_size=5) # (28,28)
x=se(x,12,288)
x=conv1_block(x,80)
for i in range(6): # 在28x28的特征图上用了6次残差
x0=x
x=depthwiseConv_block(x0,480,kernel_size=5)
x=se(x,20,480)
x=conv1_block(x,80,0.025)
x=layers.add([x,x0])
# 用3x3的核
x=depthwiseConv_block(x,480,kernel_size=3,strides=2) # (14,14)
x=se(x,20,480)
x=conv1_block(x,160)
for i in range(9):
x0=x
x=depthwiseConv_block(x0,960)
x=se(x,40,960)
x=conv1_block(x,160,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,960,kernel_size=5)
x=se(x,40,960)
x=conv1_block(x,224)
for i in range(9): # 在14x14的特征图上一共用了18次残差
x0=x
x=depthwiseConv_block(x0,1344,kernel_size=5)
x=se(x,56,1344)
x=conv1_block(x,224,0.025)
x=layers.add([x,x0])
# 深度卷积核大小:5x5
x=depthwiseConv_block(x,1344,kernel_size=5,strides=2) # (7,7)
x=se(x,56,1344)
x=conv1_block(x,384)
for i in range(12):
x0=x
x=depthwiseConv_block(x0,2304,kernel_size=5) # 核大小:5x5
x=se(x,96,2304)
x=conv1_block(x,384,0.025)
x=layers.add([x,x0])
x=depthwiseConv_block(x,2304,kernel_size=3)
x=se(x,96,2304)
x=conv1_block(x,640)
for i in range(3): # 在7x7的特征图上一共用了15次残差
x0=x
x=depthwiseConv_block(x0,3840)
x=se(x,160,3840)
x=conv1_block(x,640,0.025)
x=layers.add([x,x0])
x=layers.Conv2D(2560,1,padding='same',use_bias=False)(x)
x=activation_block(x)
return keras.Model(inputs,x)
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