unet网络结构说明及keras实现详解

一时失言乱红尘 2023-07-18 03:16 182阅读 0赞

原文链接：https://blog.csdn.net/weixin\_38193906/article/details/83787569?depth\_1-utm\_source=distribute.pc\_relevant.none-task&utm\_source=distribute.pc\_relevant.none-task

# 论文地址： #

[下载链接][Link 1]

## 背景介绍 ##

unet网络常见于图像分割任务，本文从其网络结构出发，详细解释unet网络结构的实现过程。

## 网络结构 ##

![unet网络结构][unet]

## 网络结构说明 ##

unet网络可以简单看为先下采样，经过不同程度的卷积，学习了深层次的特征，在经过上采样回复为原图大小，上采样用反卷积实现。最后输出类别数量的特征图，如分割是两类（是或不是），典型unet也是输出两张图，最后要说明一下，原网络到此就结束了，其实在最后还要使用激活函数softmax将这两个类别转换为概率图，针对某个像素点，如输出是\[0.1，0.9\]，则判定这个像素点是第二类的概率更大。  
网络结构可以看成3个部分：

*  1.  **下采样**：网络的红色箭头部分，池化实现
 *  1.  **上采样**：网络的绿色箭头部分，反卷积实现
 *  1.  **最后层的softmax**：在网络结构中，最后输出两张fiture maps后，其实在最后还要做一次softmax，将其转换为概率图。

## keras实现详解 ##

from __future__ import division, print_function
    from keras.layers import Input, Conv2D, Conv2DTranspose
    from keras.layers import MaxPooling2D, Cropping2D, Concatenate
    from keras.layers import Lambda, Activation, BatchNormalization, Dropout
    from keras.models import Model
    from keras import backend as K
    def downsampling_block(input_tensor, filters, padding='valid',  #下采样部分
                           batchnorm=False, dropout=0.0):
        _, height, width, _ = K.int_shape(input_tensor)
        assert height % 2 == 0
        assert width % 2 == 0
        x = Conv2D(filters, kernel_size=(3,3), padding=padding)(input_tensor)
        x = BatchNormalization()(x) if batchnorm else x
        x = Activation('relu')(x)
        x = Dropout(dropout)(x) if dropout > 0 else x
        x = Conv2D(filters, kernel_size=(3,3), padding=padding)(x)
        x = BatchNormalization()(x) if batchnorm else x
        x = Activation('relu')(x)
        x = Dropout(dropout)(x) if dropout > 0 else x
        return MaxPooling2D(pool_size=(2,2))(x), x   #返回的是池化后的值和dropout后的值，这里dropout后的值用于上采样特征级联
    def upsampling_block(input_tensor, skip_tensor, filters, padding='valid',
                         batchnorm=False, dropout=0.0):    #下采样部分
        x = Conv2DTranspose(filters, kernel_size=(2,2), strides=(2,2))(input_tensor)
        _, x_height, x_width, _ = K.int_shape(x)
        _, s_height, s_width, _ = K.int_shape(skip_tensor)
        h_crop = s_height - x_height
        w_crop = s_width - x_width
        assert h_crop >= 0
        assert w_crop >= 0
        if h_crop == 0 and w_crop == 0:
            y = skip_tensor
        else:                       #使级联时像素大小一致
            cropping = ((h_crop//2, h_crop - h_crop//2), (w_crop//2, w_crop - w_crop//2))
            y = Cropping2D(cropping=cropping)(skip_tensor)
        x = Concatenate()([x, y])         #特征级联
        x = Conv2D(filters, kernel_size=(3,3), padding=padding)(x)
        x = BatchNormalization()(x) if batchnorm else x
        x = Activation('relu')(x)
        x = Dropout(dropout)(x) if dropout > 0 else x
        x = Conv2D(filters, kernel_size=(3,3), padding=padding)(x)
        x = BatchNormalization()(x) if batchnorm else x
        x = Activation('relu')(x)
        x = Dropout(dropout)(x) if dropout > 0 else x
        return x                   #返回dropout后的值
    def unet(height, width, channels, classes, features=64, depth=4,
             temperature=1.0, padding='valid', batchnorm=False, dropout=0.0):  #使用4个深度长的网络就是官网的典型网络
    x = Input(shape=(height, width, channels))
        inputs = x
        skips = []                   #用于存放下采样中，每个深度后，dropout后的值，以供之后级联使用
        for i in range(depth):
            x, x0 = downsampling_block(x, features, padding,
                                       batchnorm, dropout)
            skips.append(x0)
            features *= 2            #下采样过程中，每个深度往下，特征翻倍，即每次使用翻倍数目的滤波器
        x = Conv2D(filters=features, kernel_size=(3,3), padding=padding)(x)
        x = BatchNormalization()(x) if batchnorm else x
        x = Activation('relu')(x)
        x = Dropout(dropout)(x) if dropout > 0 else x
        x = Conv2D(filters=features, kernel_size=(3,3), padding=padding)(x)
        x = BatchNormalization()(x) if batchnorm else x
        x = Activation('relu')(x)
        x = Dropout(dropout)(x) if dropout > 0 else x
        for i in reversed(range(depth)):    #下采样过程中，深度从深到浅
            features //= 2                  #每个深度往上。特征减少一倍
            x = upsampling_block(x, skips[i], features, padding,
                                 batchnorm, dropout)
        x = Conv2D(filters=classes, kernel_size=(1,1))(x)
        logits = Lambda(lambda z: z/temperature)(x)      #简单的对x做一个变换
        probabilities = Activation('softmax')(logits)    #对输出的两类做softmax，转换为概率。形式如【0.1,0.9],则预测为第二类的概率更大。
        return Model(inputs=inputs, outputs=probabilities)

[Link 1]: https://arxiv.org/pdf/1505.04597.pdf
[unet]: /images/20230528/f059cb807af64b9eb99fcb5b1b6329c2.png