UNet++学习/实现笔记

不要麻酱2024-05-04 15:52

原理：

UNet++解读 + 它是如何对UNet改进 + 作者的研究态度和方式 - 知乎 (zhihu.com)

实现方法有两种：

①跳跃连接改为直接连接

②深度监督方式对于每个子网络输出端加一个1x1的卷积核，相当于监督了每个子网络

Keras实现

语义分割（三）Unet++_unet++网络结构-CSDN博客

拿L2子网络作为例子解释一下结构：

python 复制代码 
    ## l2
    pool10_20 = MaxPool2D(pool_size=(2, 2))(conv1_0)
    conv2_0 = conv_drop(inputs=pool10_20, filters=filters[2])
    up20_11 = upsampling(inputs=conv2_0, filters=filters[1])
    concat2_1 = concatenate([up20_11, conv1_0], axis=3)
    conv1_1 = conv_drop(inputs=concat2_1, filters=filters[1])
    up11_02 = upsampling(inputs=conv1_1, filters=filters[0])
    concat2_2 = concatenate([up11_02, conv0_0, conv0_1], axis=3)
    conv0_2 = conv_drop(inputs=concat2_2, filters=filters[0])

pool10_20 为从 x1,0 到 x2,0 的池化结果， 再对池化结果进行卷积得到 x2, 0的卷积结果 conv2_0

将 x2,0 的卷积结果 上采样得到 x2,0 到 x1,1 的上采样结果 up20_11，并连接 conv1_0 ，再对连接后的结果进行一次卷积得到conv1_1的结果

同理可得conv0_2的结果

总结起来是：

每一步下采样为先池化后卷积。

每一步上采样为 左下角结构的反卷积 连接 正左所有结构的卷积，将连接结果再做一次卷积。

总代码：

python 复制代码 
###########################
# Unet++ loss              #
###########################
from keras import *
from keras.src.layers import Conv2D, UpSampling2D, concatenate, Reshape, Activation
from keras.src.losses import binary_crossentropy
from keras.src.optimizers import Adam
from tensorflow.python.keras.layers import MaxPool2D

############################
# Dice系数，用于评估图像分割任务中预测结果和真实标签之间的相似度指标
############################
def dice_coef(y_true, y_pred):
    smooth = 1. # 平滑项，避免分母为零
    y_true_f = K.flatten(y_true)
    y_pred_f = K.flatten(y_pred)
    intersection = K.sum(y_true_f * y_pred_f) # 表示预测结果和真实标签的交集（即正确预测的正样本）
    return (2. * intersection + smooth) / (K.sum(y_true_f) + K.sum(y_pred_f) + smooth)

# 二进制交叉熵损失和Dice系数之间的线性组合
def bce_dice_loss(y_true, y_pred):
    return 0.5 * binary_crossentropy(y_true, y_pred) - dice_coef(y_true, y_pred)
    # return 0.5 * categorical_crossentropy(y_true, y_pred) - dice_coef(y_true, y_pred)


#############################
# Unet++ conv and upsampling #
#############################
# 下采样：两个3x3卷积
def conv_drop(inputs, filters):
    conv1 = Conv2D(filters=filters, kernel_size=3, padding='same', activation='relu',
                   kernel_initializer='he_normal')(inputs)
    # drop1 = Dropout(rate=0.5)(conv1)
    conv2 = Conv2D(filters=filters, kernel_size=3, padding='same', activation='relu',
                   kernel_initializer='he_normal')(conv1)
    # drop2 = Dropout(rate=0.5)(conv2)
    return conv2

# 上采样：进行2倍上采样，然后进行一个2x2的卷积
def upsampling(inputs, filters):
    up = UpSampling2D(size=(2, 2))(inputs)
    conv = Conv2D(filters=filters, kernel_size=2, activation='relu', padding='same',
                  kernel_initializer='he_normal')(up)
    return conv


###############################
# Unet++                      #
###############################
def Unet_plusplus(input_size=(224, 224, 1), n_class=2, filters=(32, 64, 128, 256, 512), re_shape=False):
    inputs = Input(shape=input_size)
    ## l1
    conv0_0 = conv_drop(inputs=inputs, filters=filters[0])
    pool00_10 = MaxPool2D(pool_size=(2, 2))(conv0_0)
    conv1_0 = conv_drop(inputs=pool00_10, filters=filters[1])
    up10_01 = upsampling(inputs=conv1_0, filters=filters[0])
    concat1_1 = concatenate([up10_01, conv0_0], axis=3)
    conv0_1 = conv_drop(inputs=concat1_1, filters=filters[0])
    ## l2
    pool10_20 = MaxPool2D(pool_size=(2, 2))(conv1_0)
    conv2_0 = conv_drop(inputs=pool10_20, filters=filters[2])
    up20_11 = upsampling(inputs=conv2_0, filters=filters[1])
    concat2_1 = concatenate([up20_11, conv1_0], axis=3)
    conv1_1 = conv_drop(inputs=concat2_1, filters=filters[1])
    up11_02 = upsampling(inputs=conv1_1, filters=filters[0])
    concat2_2 = concatenate([up11_02, conv0_0, conv0_1], axis=3)
    conv0_2 = conv_drop(inputs=concat2_2, filters=filters[0])
    ##l3
    pool20_30 = MaxPool2D(pool_size=(2, 2))(conv2_0)
    conv3_0 = conv_drop(inputs=pool20_30, filters=filters[3])
    up30_21 = upsampling(inputs=conv3_0, filters=filters[2])
    concat3_1 = concatenate([up30_21, conv2_0], axis=3)
    conv2_1 = conv_drop(inputs=concat3_1, filters=filters[2])
    up21_12 = upsampling(inputs=conv2_1, filters=filters[1])
    concat3_2 = concatenate([up21_12, conv1_0, conv1_1], axis=3)
    conv1_2 = conv_drop(inputs=concat3_2, filters=filters[1])
    up12_03 = upsampling(inputs=conv1_2, filters=filters[0])
    concat3_3 = concatenate([up12_03, conv0_0, conv0_1, conv0_2], axis=3)
    conv0_3 = conv_drop(inputs=concat3_3, filters=filters[0])
    ## l4
    pool30_40 = MaxPool2D(pool_size=(2, 2))(conv3_0)
    conv4_0 = conv_drop(inputs=pool30_40, filters=filters[4])
    up40_31 = upsampling(inputs=conv4_0, filters=filters[3])
    concat4_1 = concatenate([up40_31, conv3_0], axis=3)
    conv3_1 = conv_drop(inputs=concat4_1, filters=filters[3])
    up31_22 = upsampling(inputs=conv3_1, filters=filters[2])
    concat4_2 = concatenate([up31_22, conv2_0, conv2_1], axis=3)
    conv2_2 = conv_drop(inputs=concat4_2, filters=filters[2])
    up22_13 = upsampling(inputs=conv2_2, filters=filters[1])
    concat4_3 = concatenate([up22_13, conv1_0, conv1_1, conv1_2], axis=3)
    conv1_3 = conv_drop(inputs=concat4_3, filters=filters[1])
    up13_04 = upsampling(inputs=conv1_3, filters=filters[0])
    concat4_4 = concatenate([up13_04, conv0_0, conv0_1, conv0_2, conv0_3], axis=3)
    conv0_4 = conv_drop(inputs=concat4_4, filters=filters[0])
    ## output
    l1_conv_out = Conv2D(filters=n_class, kernel_size=1, padding='same', kernel_initializer='he_normal')(conv0_1)
    l2_conv_out = Conv2D(filters=n_class, kernel_size=1, padding='same', kernel_initializer='he_normal')(conv0_2)
    l3_conv_out = Conv2D(filters=n_class, kernel_size=1, padding='same', kernel_initializer='he_normal')(conv0_3)
    l4_conv_out = Conv2D(filters=n_class, kernel_size=1, padding='same', kernel_initializer='he_normal')(conv0_4)

    if re_shape == True:
        l1_conv_out = Reshape((input_size[0] * input_size[1], n_class))(l1_conv_out)
        l2_conv_out = Reshape((input_size[0] * input_size[1], n_class))(l2_conv_out)
        l3_conv_out = Reshape((input_size[0] * input_size[1], n_class))(l3_conv_out)
        l4_conv_out = Reshape((input_size[0] * input_size[1], n_class))(l4_conv_out)

    l1_out = Activation('sigmoid', name='l1_out')(l1_conv_out)
    l2_out = Activation('sigmoid', name='l2_out')(l2_conv_out)
    l3_out = Activation('sigmoid', name='l3_out')(l3_conv_out)
    l4_out = Activation('sigmoid', name='l4_out')(l4_conv_out)

    model = Model(input=inputs, output=[l1_out, l2_out, l3_out, l4_out])
    # model = Model(input=inputs, output=l4_out)
    model.summary()
    losses = {
        'l1_out': bce_dice_loss,
        'l2_out': bce_dice_loss,
        'l3_out': bce_dice_loss,
        'l4_out': bce_dice_loss,
    }
    model.compile(optimizer=Adam(lr=1e-4), loss=losses, metrics=['accuracy'])
    return model

pytorch实现

语义分割系列6-Unet++（pytorch实现）-CSDN博客

python 复制代码 
import torch
import torch.nn as nn


class ContinusParalleConv(nn.Module):
    # 一个连续的卷积模块，包含BatchNorm 在前 和 在后 两种模式
    def __init__(self, in_channels, out_channels, pre_Batch_Norm=True):
        super(ContinusParalleConv, self).__init__()
        self.in_channels = in_channels
        self.out_channels = out_channels

        if pre_Batch_Norm:
            self.Conv_forward = nn.Sequential(
                nn.BatchNorm2d(self.in_channels),
                nn.ReLU(),
                nn.Conv2d(self.in_channels, self.out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(),
                nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1))

        else:
            self.Conv_forward = nn.Sequential(
                nn.Conv2d(self.in_channels, self.out_channels, 3, padding=1),
                nn.BatchNorm2d(out_channels),
                nn.ReLU(),
                nn.Conv2d(self.out_channels, self.out_channels, 3, padding=1),
                nn.BatchNorm2d(self.out_channels),
                nn.ReLU())

    def forward(self, x):
        x = self.Conv_forward(x)
        return x


class UnetPlusPlus(nn.Module):
    def __init__(self, num_classes, deep_supervision=False):
        super(UnetPlusPlus, self).__init__()
        self.num_classes = num_classes
        self.deep_supervision = deep_supervision
        self.filters = [64, 128, 256, 512, 1024]

        self.CONV3_1 = ContinusParalleConv(512 * 2, 512, pre_Batch_Norm=True)

        self.CONV2_2 = ContinusParalleConv(256 * 3, 256, pre_Batch_Norm=True)
        self.CONV2_1 = ContinusParalleConv(256 * 2, 256, pre_Batch_Norm=True)

        self.CONV1_1 = ContinusParalleConv(128 * 2, 128, pre_Batch_Norm=True)
        self.CONV1_2 = ContinusParalleConv(128 * 3, 128, pre_Batch_Norm=True)
        self.CONV1_3 = ContinusParalleConv(128 * 4, 128, pre_Batch_Norm=True)

        self.CONV0_1 = ContinusParalleConv(64 * 2, 64, pre_Batch_Norm=True)
        self.CONV0_2 = ContinusParalleConv(64 * 3, 64, pre_Batch_Norm=True)
        self.CONV0_3 = ContinusParalleConv(64 * 4, 64, pre_Batch_Norm=True)
        self.CONV0_4 = ContinusParalleConv(64 * 5, 64, pre_Batch_Norm=True)

        self.stage_0 = ContinusParalleConv(3, 64, pre_Batch_Norm=False)
        self.stage_1 = ContinusParalleConv(64, 128, pre_Batch_Norm=False)
        self.stage_2 = ContinusParalleConv(128, 256, pre_Batch_Norm=False)
        self.stage_3 = ContinusParalleConv(256, 512, pre_Batch_Norm=False)
        self.stage_4 = ContinusParalleConv(512, 1024, pre_Batch_Norm=False)

        self.pool = nn.MaxPool2d(2)

        self.upsample_3_1 = nn.ConvTranspose2d(in_channels=1024, out_channels=512, kernel_size=4, stride=2, padding=1)

        self.upsample_2_1 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1)
        self.upsample_2_2 = nn.ConvTranspose2d(in_channels=512, out_channels=256, kernel_size=4, stride=2, padding=1)

        self.upsample_1_1 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1)
        self.upsample_1_2 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1)
        self.upsample_1_3 = nn.ConvTranspose2d(in_channels=256, out_channels=128, kernel_size=4, stride=2, padding=1)

        self.upsample_0_1 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
        self.upsample_0_2 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
        self.upsample_0_3 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)
        self.upsample_0_4 = nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1)

        # 分割头
        self.final_super_0_1 = nn.Sequential(
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, self.num_classes, 3, padding=1),
        )
        self.final_super_0_2 = nn.Sequential(
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, self.num_classes, 3, padding=1),
        )
        self.final_super_0_3 = nn.Sequential(
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, self.num_classes, 3, padding=1),
        )
        self.final_super_0_4 = nn.Sequential(
            nn.BatchNorm2d(64),
            nn.ReLU(),
            nn.Conv2d(64, self.num_classes, 3, padding=1),
        )

    def forward(self, x):
        x_0_0 = self.stage_0(x)
        x_1_0 = self.stage_1(self.pool(x_0_0))
        x_2_0 = self.stage_2(self.pool(x_1_0))
        x_3_0 = self.stage_3(self.pool(x_2_0))
        x_4_0 = self.stage_4(self.pool(x_3_0))

        x_0_1 = torch.cat([self.upsample_0_1(x_1_0), x_0_0], 1)
        x_0_1 = self.CONV0_1(x_0_1)

        x_1_1 = torch.cat([self.upsample_1_1(x_2_0), x_1_0], 1)
        x_1_1 = self.CONV1_1(x_1_1)

        x_2_1 = torch.cat([self.upsample_2_1(x_3_0), x_2_0], 1)
        x_2_1 = self.CONV2_1(x_2_1)

        x_3_1 = torch.cat([self.upsample_3_1(x_4_0), x_3_0], 1)
        x_3_1 = self.CONV3_1(x_3_1)

        x_2_2 = torch.cat([self.upsample_2_2(x_3_1), x_2_0, x_2_1], 1)
        x_2_2 = self.CONV2_2(x_2_2)

        x_1_2 = torch.cat([self.upsample_1_2(x_2_1), x_1_0, x_1_1], 1)
        x_1_2 = self.CONV1_2(x_1_2)

        x_1_3 = torch.cat([self.upsample_1_3(x_2_2), x_1_0, x_1_1, x_1_2], 1)
        x_1_3 = self.CONV1_3(x_1_3)

        x_0_2 = torch.cat([self.upsample_0_2(x_1_1), x_0_0, x_0_1], 1)
        x_0_2 = self.CONV0_2(x_0_2)

        x_0_3 = torch.cat([self.upsample_0_3(x_1_2), x_0_0, x_0_1, x_0_2], 1)
        x_0_3 = self.CONV0_3(x_0_3)

        x_0_4 = torch.cat([self.upsample_0_4(x_1_3), x_0_0, x_0_1, x_0_2, x_0_3], 1)
        x_0_4 = self.CONV0_4(x_0_4)

        if self.deep_supervision:
            out_put1 = self.final_super_0_1(x_0_1)
            out_put2 = self.final_super_0_2(x_0_2)
            out_put3 = self.final_super_0_3(x_0_3)
            out_put4 = self.final_super_0_4(x_0_4)
            return [out_put1, out_put2, out_put3, out_put4]
        else:
            return self.final_super_0_4(x_0_4)


if __name__ == "__main__":
    print("deep_supervision: False")
    deep_supervision = False
    device = torch.device('cpu')
    inputs = torch.randn((1, 3, 224, 224)).to(device)
    model = UnetPlusPlus(num_classes=3, deep_supervision=deep_supervision).to(device)
    outputs = model(inputs)
    print(outputs.shape)

    print("deep_supervision: True")
    deep_supervision = True
    model = UnetPlusPlus(num_classes=3, deep_supervision=deep_supervision).to(device)
    outputs = model(inputs)
    for out in outputs:
        print(out.shape)
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