DeepLabV3+语义分割实战

语义分割是计算机视觉的一项重要任务，本文使用Jittor框架实现了DeepLabV3+语义分割模型。

DeepLabV3+论文：https://arxiv.org/pdf/1802.02611.pdf

完整代码：https://github.com/Jittor/deeplab-jittor

1. 数据集

1.1 数据准备

VOC2012数据集是目标检测、语义分割等任务常用的数据集之一， 本文使用VOC数据集的2012 trainaug (train + sbd set)作为训练集，2012 val set作为测试集。

VOC数据集中的物体共包括20个前景类别：'aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car', 'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike', 'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor' 和背景类别

最终数据集的文件组织如下。

# 文件组织
根目录
|----voc_aug
|    |----datalist
|    |    |----train.txt
|    |    |----val.txt
|    |----images
|    |----annotations

1.2 数据加载

使用jittor.dataset.dataset的基类Dataset可以构造自己的数据集，需要实现__init__、__getitem__、函数。

  1. __init__: 定义数据路径，这里的data_root需设置为之前设定的 voc_aug, split 为 train val test 之一，表示选择训练集、验证集还是测试集。同时需要调用self.set_attr来指定数据集加载所需的参数batch_size，total_len、shuffle。
  2. __getitem__: 返回单个item的数据。
import numpy as np
import os
from PIL import Image
import matplotlib.pyplot as plt
from jittor.dataset.dataset import Dataset, dataset_root
import jittor as jt
import os
import os.path as osp
from PIL import Image, ImageOps, ImageFilter
import numpy as np
import scipy.io as sio
import random
 
def fetch(image_path, label_path):
    with open(image_path, 'rb') as fp:
        image = Image.open(fp).convert('RGB')
 
    with open(label_path, 'rb') as fp:
        label = Image.open(fp).convert('P')
 
    return image, label
 
 
def scale(image, label):
    SCALES = (0.5, 0.75, 1.0, 1.25, 1.5, 1.75, 2.0)
    ratio = np.random.choice(SCALES)
    w,h = image.size
    nw = (int)(w*ratio)
    nh = (int)(h*ratio)
 
    image = image.resize((nw, nh), Image.BILINEAR)
    label = label.resize((nw, nh), Image.NEAREST)
 
    return image, label
 
 
def pad(image, label):
    w,h = image.size
    crop_size = 513
    pad_h = max(crop_size - h, 0)
    pad_w = max(crop_size - w, 0)
    image = ImageOps.expand(image, border=(0, 0, pad_w, pad_h), fill=0)
    label = ImageOps.expand(label, border=(0, 0, pad_w, pad_h), fill=255)
 
    return image, label
 
 
def crop(image, label):
    w, h = image.size
    crop_size = 513
    x1 = random.randint(0, w - crop_size)
    y1 = random.randint(0, h - crop_size)
    image = image.crop((x1, y1, x1 + crop_size, y1 + crop_size))
    label = label.crop((x1, y1, x1 + crop_size, y1 + crop_size))
 
 
    return image, label
 
 
def normalize(image, label):
    mean = (0.485, 0.456, 0.40)
    std = (0.229, 0.224, 0.225)
    image = np.array(image).astype(np.float32)
    label = np.array(label).astype(np.float32)
 
    image /= 255.0
    image -= mean
    image /= std
    return image, label
 
 
def flip(image, label):
    if random.random() < 0.5:
        image = image.transpose(Image.FLIP_LEFT_RIGHT)
        label = label.transpose(Image.FLIP_LEFT_RIGHT)
    return image, label
 
 
class BaseDataset(Dataset):
    def __init__(self,  data_root='/voc/', split='train', batch_size=1, shuffle=False):
        super().__init__()
        ''' total_len , batch_size, shuffle must be set '''
        self.data_root = data_root
        self.split = split
        self.batch_size = batch_size
        self.shuffle = shuffle
 
        self.image_root = os.path.join(data_root, 'images')
        self.label_root = os.path.join(data_root, 'annotations')
 
        self.data_list_path = os.path.join(self.data_root,'/datalist/' + self.split + '.txt')
        self.image_path = []
        self.label_path = []
 
        with open(self.data_list_path, "r") as f:
            lines = f.read().splitlines()
 
        for idx, line in enumerate(lines):
            _img_path = os.path.join(self.image_root, line + '.jpg')
            _label_path = os.path.join(self.label_root, line + '.png')
 
            assert os.path.isfile(_img_path)
            assert os.path.isfile(_label_path)
            self.image_path.append(_img_path)
            self.label_path.append(_label_path)
        self.total_len = len(self.image_path)
 
        # set_attr must be called to set batch size total len and shuffle like __len__ function in pytorch
        self.set_attr(batch_size = self.batch_size, total_len = self.total_len, shuffle = self.shuffle) # bs , total_len, shuffle
 
 
    def __getitem__(self, image_id):
        return NotImplementedError
 
 
class TrainDataset(BaseDataset):
    def __init__(self,  data_root='/voc/', split='train', batch_size=1, shuffle=False):
        super(TrainDataset, self).__init__(data_root, split, batch_size, shuffle)
 
    def __getitem__(self, image_id):
        image_path = self.image_path[image_id]
        label_path = self.label_path[image_id]
        image, label = fetch(image_path, label_path)
        image, label = scale(image, label)
        image, label = pad(image, label)
        image, label = crop(image, label)
        image, label = flip(image, label)
        image, label = normalize(image, label)
        image = np.array(image).astype(np.float).transpose(2, 0, 1)
        image = jt.array(image)
        label = jt.array(np.array(label).astype(np.int))
        return image, label
 
 
class ValDataset(BaseDataset):
    def __init__(self,  data_root='/voc/', split='train', batch_size=1, shuffle=False):
        super(ValDataset, self).__init__(data_root, split, batch_size, shuffle)
        
    def __getitem__(self, image_id):
        image_path = self.image_path[image_id]
        label_path = self.label_path[image_id]
 
        image, label = fetch(image_path, label_path)
        image, label = normalize(image, label)
 
        image = np.array(image).astype(np.float).transpose(2, 0, 1)
        image = jt.array(image)
        label = jt.array(np.array(label).astype(np.int))
 
        return image, label
 

2. 模型定义

上图为DeepLabV3+论文给出的网络架构图。本文采用ResNe为backbone。输入图像尺寸为513*513。

整个网络可以分成 backbone aspp decoder 三个部分。

2.1 backbonb 这里使用最常见的ResNet，作为backbone并且在ResNet的最后两次使用空洞卷积来扩大感受野，其完整定义如下：

import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat, argmax_pool
import time
 
 
class Bottleneck(Module):
    expansion = 4
    def __init__(self, inplanes, planes, stride=1, dilation=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm(planes)
        self.conv2 = nn.Conv(planes, planes, kernel_size=3, stride=stride,
                               dilation=dilation, padding=dilation, bias=False)
        self.bn2 = nn.BatchNorm(planes)
        self.conv3 = nn.Conv(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm(planes * 4)
        self.relu = nn.ReLU()
        self.downsample = downsample
        self.stride = stride
        self.dilation = dilation
 
    def execute(self, x):
        residual = x
 
        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
 
        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)
 
        out = self.conv3(out)
        out = self.bn3(out)
 
        if self.downsample is not None:
            residual = self.downsample(x)
 
        out += residual
        out = self.relu(out)
 
        return out
 
 
class ResNet(Module):
    def __init__(self, block, layers, output_stride):
        super(ResNet, self).__init__()
        self.inplanes = 64
        blocks = [1, 2, 4]
        if output_stride == 16:
            strides = [1, 2, 2, 1]
            dilations = [1, 1, 1, 2]
        elif output_stride == 8:
            strides = [1, 2, 1, 1]
            dilations = [1, 1, 2, 4]
        else:
            raise NotImplementedError
 
        # Modules
        self.conv1 = nn.Conv(3, 64, kernel_size=7, stride=2, padding=3, bias=False)
        self.bn1 = nn.BatchNorm(64)
        self.relu = nn.ReLU()
        # self.maxpool = nn.Pool(kernel_size=3, stride=2, padding=1)
 
        self.layer1 = self._make_layer(block, 64, layers[0], stride=strides[0], dilation=dilations[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=strides[1], dilation=dilations[1])
        self.layer3 = self._make_layer(block, 256, layers[2], stride=strides[2], dilation=dilations[2])
        self.layer4 = self._make_MG_unit(block, 512, blocks=blocks, stride=strides[3], dilation=dilations[3])
 
 
    def _make_layer(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm(planes * block.expansion),
            )
 
        layers = []
        layers.append(block(self.inplanes, planes, stride, dilation, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes, dilation=dilation))
 
        return nn.Sequential(*layers)
 
    def _make_MG_unit(self, block, planes, blocks, stride=1, dilation=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv(self.inplanes, planes * block.expansion,
                          kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm(planes * block.expansion),
            )
 
        layers = []
        layers.append(block(self.inplanes, planes, stride, dilation=blocks[0]*dilation,
                            downsample=downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, len(blocks)):
            layers.append(block(self.inplanes, planes, stride=1,
                                dilation=blocks[i]*dilation))
 
        return nn.Sequential(*layers)
 
    def execute(self, input):
 
        x = self.conv1(input)
        x = self.bn1(x)
        x = self.relu(x)
        x = argmax_pool(x, 2, 2)
        x = self.layer1(x)
 
        low_level_feat = x
        x = self.layer2(x)
        x = self.layer3(x)
 
        x = self.layer4(x)
        return x, low_level_feat
 
def resnet50(output_stride):
    model = ResNet(Bottleneck, [3,4,6,3], output_stride)
    return model
 
def resnet101(output_stride):
    model = ResNet(Bottleneck, [3,4,23,3], output_stride)
    return model
 

2.2 ASPP 

即使用不同尺寸的 dilation conv 对 backbone 得到的 feature map 进行卷积，最后 concat 并整合得到新的特征。

import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat
 
 
class Single_ASPPModule(Module):
    def __init__(self, inplanes, planes, kernel_size, padding, dilation):
        super(Single_ASPPModule, self).__init__()
        self.atrous_conv = nn.Conv(inplanes, planes, kernel_size=kernel_size,
                                            stride=1, padding=padding, dilation=dilation, bias=False)
        self.bn = nn.BatchNorm(planes)
        self.relu = nn.ReLU()
 
    def execute(self, x):
        x = self.atrous_conv(x)
        x = self.bn(x)
        x = self.relu(x)
        return x
 
class ASPP(Module):
    def __init__(self, output_stride):
        super(ASPP, self).__init__()
        inplanes = 2048
        if output_stride == 16:
            dilations = [1, 6, 12, 18]
        elif output_stride == 8:
            dilations = [1, 12, 24, 36]
        else:
            raise NotImplementedError
 
        self.aspp1 = Single_ASPPModule(inplanes, 256, 1, padding=0, dilation=dilations[0])
        self.aspp2 = Single_ASPPModule(inplanes, 256, 3, padding=dilations[1], dilation=dilations[1])
        self.aspp3 = Single_ASPPModule(inplanes, 256, 3, padding=dilations[2], dilation=dilations[2])
        self.aspp4 = Single_ASPPModule(inplanes, 256, 3, padding=dilations[3], dilation=dilations[3])
        self.global_avg_pool = nn.Sequential(GlobalPooling(),
                                             nn.Conv(inplanes, 256, 1, stride=1, bias=False),
                                             nn.BatchNorm(256),
                                             nn.ReLU())
        self.conv1 = nn.Conv(1280, 256, 1, bias=False)
        
        self.bn1 = nn.BatchNorm(256)
        self.relu = nn.ReLU()
        self.dropout = nn.Dropout(0.5)
 
    def execute(self, x):
        x1 = self.aspp1(x)
        x2 = self.aspp2(x)
        x3 = self.aspp3(x)
        x4 = self.aspp4(x)
        x5 = self.global_avg_pool(x)
        x5 = x5.broadcast((1,1,x4.shape[2],x4.shape[3]))
        x = concat((x1, x2, x3, x4, x5), dim=1)
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.dropout(x)
        return x
 
class GlobalPooling (Module):
    def __init__(self):
        super(GlobalPooling, self).__init__()
    def execute (self, x):
        return jt.mean(x, dims=[2,3], keepdims=1)
 

2.3 Decoder:

Decoder 将 ASPP 的特征放大后与 ResNet 的中间特征一起 concat， 得到最后分割所用的特征。

import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat
import time
 
class Decoder(nn.Module):
    def __init__(self, num_classes):
        super(Decoder, self).__init__()
        low_level_inplanes = 256
 
        self.conv1 = nn.Conv(low_level_inplanes, 48, 1, bias=False)
        self.bn1 = nn.BatchNorm(48)
        self.relu = nn.ReLU()
        self.last_conv = nn.Sequential(nn.Conv(304, 256, kernel_size=3, stride=1, padding=1, bias=False),
                                       nn.BatchNorm(256),
                                       nn.ReLU(),
                                       nn.Dropout(0.5),
                                       nn.Conv(256, 256, kernel_size=3, stride=1, padding=1, bias=False),
                                       nn.BatchNorm(256),
                                       nn.ReLU(),
                                       nn.Dropout(0.1),
                                       nn.Conv(256, num_classes, kernel_size=1, stride=1, bias=True))
 
    def execute(self, x, low_level_feat):
        low_level_feat = self.conv1(low_level_feat)
        low_level_feat = self.bn1(low_level_feat)
        low_level_feat = self.relu(low_level_feat)
 
        x_inter = nn.resize(x, size=(low_level_feat.shape[2], low_level_feat.shape[3]) , mode='bilinear')
        x_concat = concat((x_inter, low_level_feat), dim=1)
        x = self.last_conv(x_concat)
        return x

2.4 完整的模型整合如下： 即将以上部分通过一个类连接起来。

import jittor as jt
from jittor import nn
from jittor import Module
from jittor import init
from jittor.contrib import concat
from decoder import Decoder
from aspp import ASPP
from backbone import resnet50, resnet101
 
class DeepLab(Module):
    def __init__(self, output_stride=16, num_classes=21):
        super(DeepLab, self).__init__()
        self.backbone = resnet101(output_stride=output_stride)
        self.aspp = ASPP(output_stride)
        self.decoder = Decoder(num_classes)
 
    def execute(self, input):
        x, low_level_feat = self.backbone(input)
        x = self.aspp(x)
        x = self.decoder(x, low_level_feat)
        x = nn.resize(x, size=(input.shape[2], input.shape[3]), mode='bilinear')
        return x
 

3. 模型训练

3.1 模型训练参数设定如下：

# Learning parameters
batch_size = 8
learning_rate = 0.005
momentum = 0.9
weight_decay = 1e-4
epochs = 50
 

3.2 定义模型、优化器、数据加载器。

model = DeepLab(output_stride=16, num_classes=21)
optimizer = nn.SGD(model.parameters(), 
                   lr,
                   momentum=momentum, 
                   weight_decay=weight_decay)
train_loader = TrainDataset(data_root='/vocdata/',
                            split='train',
                            batch_size=batch_size,
                            shuffle=True)
val_loader = ValDataset(data_root='/vocdata/',
                        split='val',
                        batch_size=1,
                        shuffle=False)
 

3.3 模型训练与验证

# lr scheduler
def poly_lr_scheduler(opt, init_lr, iter, epoch, max_iter, max_epoch):
    new_lr = init_lr * (1 - float(epoch * max_iter + iter) / (max_epoch * max_iter)) ** 0.9
    opt.lr = new_lr
 
# train function
def train(model, train_loader, optimizer, epoch, init_lr):
    model.train()
    max_iter = len(train_loader)
 
    for idx, (image, target) in enumerate(train_loader):
        poly_lr_scheduler(optimizer, init_lr, idx, epoch, max_iter, 50) # using poly_lr_scheduler 
        image = image.float32()
        pred = model(image)
        loss = nn.cross_entropy_loss(pred, target, ignore_index=255)
        optimizer.step (loss)
        print ('Training in epoch {} iteration {} loss = {}'.format(epoch, idx, loss.data[0]))
 
# val function
# we omit evaluator code and you can 
def val (model, val_loader, epoch, evaluator):
    model.eval()
    evaluator.reset()
    for idx, (image, target) in enumerate(val_loader):
        image = image.float32()
        output = model(image)
        pred = output.data
        target = target.data
        pred = np.argmax(pred, axis=1)
        evaluator.add_batch(target, pred)
        print ('Test in epoch {} iteration {}'.format(epoch, idx))
    Acc = evaluator.Pixel_Accuracy()
    Acc_class = evaluator.Pixel_Accuracy_Class()
    mIoU = evaluator.Mean_Intersection_over_Union()
    FWIoU = evaluator.Frequency_Weighted_Intersection_over_Union()
    best_miou = 0.0
 
    if (mIoU > best_miou):
        best_miou = mIoU
    print ('Testing result of epoch {} miou = {} Acc = {} Acc_class = {} \
                FWIoU = {} Best Miou = {}'.format(epoch, mIoU, Acc, Acc_class, FWIoU, best_miou))

3.4 evaluator 写法：使用混淆矩阵计算 Pixel accuracy 和 mIoU。

class Evaluator(object):
    def __init__(self, num_class):
        self.num_class = num_class
        self.confusion_matrix = np.zeros((self.num_class,)*2)
 
    def Pixel_Accuracy(self):
        Acc = np.diag(self.confusion_matrix).sum() / self.confusion_matrix.sum()
        return Acc
 
    def Pixel_Accuracy_Class(self):
        Acc = np.diag(self.confusion_matrix) / self.confusion_matrix.sum(axis=1)
        Acc = np.nanmean(Acc)
        return Acc
 
    def Mean_Intersection_over_Union(self):
        MIoU = np.diag(self.confusion_matrix) / (
                 np.sum(self.confusion_matrix, axis=1) + 
                 np.sum(self.confusion_matrix, axis=0)-
                 np.diag(self.confusion_matrix))
        MIoU = np.nanmean(MIoU)
        return MIoU
 
    def Frequency_Weighted_Intersection_over_Union(self):
        freq = np.sum(self.confusion_matrix, axis=1) / np.sum(self.confusion_matrix)
        iu = np.diag(self.confusion_matrix) / (
                    np.sum(self.confusion_matrix, axis=1) + np.sum(self.confusion_matrix, axis=0) -
                    np.diag(self.confusion_matrix))
 
        FWIoU = (freq[freq > 0] * iu[freq > 0]).sum()
        return FWIoU
 
    def _generate_matrix(self, gt_image, pre_image):
        mask = (gt_image >= 0) & (gt_image < self.num_class)
        label = self.num_class * gt_image[mask].astype('int') + pre_image[mask]
        count = np.bincount(label, minlength=self.num_class**2)
        confusion_matrix = count.reshape(self.num_class, self.num_class)
        return confusion_matrix
 
    def add_batch(self, gt_image, pre_image):
        assert gt_image.shape == pre_image.shape
        self.confusion_matrix += self._generate_matrix(gt_image, pre_image)
 
    def reset(self):
        self.confusion_matrix = np.zeros((self.num_class,) * 2)

3.5 训练入口函数

epochs = 50
evaluator = Evaluator(21)
train_loader = TrainDataset(data_root='/voc/data/path/', split='train', batch_size=8, shuffle=True)
val_loader = ValDataset(data_root='/voc/data/path/', split='val', batch_size=1, shuffle=False)
learning_rate = 0.005
momentum = 0.9
weight_decay = 1e-4
optimizer = nn.SGD(model.parameters(), learning_rate, momentum, weight_decay)
 
for epoch in range (epochs):
    train(model, train_loader, optimizer, epoch, learning_rate)
    val(model, val_loader, epoch, evaluator)

4. 参考

  1. pytorch-deeplab-xception
  2. Encoder-Decoder with Atrous Separable Convolution for Semantic Image Segmentation

DeepLabV3+语义分割实战的更多相关文章

  1. 人工智能必须要知道的语义分割模型：DeepLabv3+

    图像分割是计算机视觉中除了分类和检测外的另一项基本任务,它意味着要将图片根据内容分割成不同的块.相比图像分类和检测,分割是一项更精细的工作,因为需要对每个像素点分类,如下图的街景分割,由于对每个像素点 ...

  2. 全卷积网络（FCN）实战：使用FCN实现语义分割

    摘要:FCN对图像进行像素级的分类,从而解决了语义级别的图像分割问题. 本文分享自华为云社区<全卷积网络(FCN)实战:使用FCN实现语义分割>,作者: AI浩. FCN对图像进行像素级的 ...

  3. 使用LabVIEW实现基于pytorch的DeepLabv3图像语义分割

    前言 今天我们一起来看一下如何使用LabVIEW实现语义分割. 一.什么是语义分割 图像语义分割(semantic segmentation),从字面意思上理解就是让计算机根据图像的语义来进行分割,例 ...

  4. 自动网络搜索（NAS）在语义分割上的应用（二）

    前言: 本文将介绍如何基于ProxylessNAS搜索semantic segmentation模型,最终搜索得到的模型结构可在CPU上达到36 fps的测试结果,展示自动网络搜索(NAS)在语义分割 ...

  5. 语义分割的简单指南 A Simple Guide to Semantic Segmentation

    语义分割是将标签分配给图像中的每个像素的过程.这与分类形成鲜明对比,其中单个标签被分配给整个图片.语义分段将同一类的多个对象视为单个实体.另一方面,实例分段将同一类的多个对象视为不同的单个对象(或实例 ...

  6. 语义分割丨PSPNet源码解析「训练阶段」

    引言 之前一段时间在参与语义分割的项目,最近有时间了,正好把这段时间的所学总结一下. 在代码上,语义分割的框架会比目标检测简单很多,但其中也涉及了很多细节.在这篇文章中,我以PSPNet为例,解读一下 ...

  7. 语义分割丨DeepLab系列总结「v1、v2、v3、v3+」

    花了点时间梳理了一下DeepLab系列的工作,主要关注每篇工作的背景和贡献,理清它们之间的联系,而实验和部分细节并没有过多介绍,请见谅. DeepLabv1 Semantic image segmen ...

  8. Learning a Discriminative Feature Network for Semantic Segmentation（语义分割DFN，区别特征网络）

    1.介绍 语义分割通常有两个问题:类内不一致性(同一物体分成两类)和类间不确定性(不同物体分成同一类).本文从宏观角度,认为语义分割不是标记像素而是标记一个整体,提出了两个结构解决这两个问题,平滑网络 ...

  9. 自动网络搜索（NAS）在语义分割上的应用（一）

    [摘要]本文简单介绍了NAS的发展现况和在语义分割中的应用,并且详细解读了两篇流行的work:DARTS和Auto-DeepLab. 自动网络搜索 多数神经网络结构都是基于一些成熟的backbone, ...

随机推荐

  1. Win64 驱动内核编程-10.突破WIN7的PatchGuard

    突破WIN7的PatchGuard WIN64 有两个内核保护机制,KPP 和 DSE.KPP 阻止我们 PATCH 内核,DSE 拦截我们加载驱动.当然 KPP 和 DSE 并不是不可战胜的,WIN ...

  2. C++ 三消游戏基本实现

    最近在研究三消算法,我想试试在完全不借助网络资源的情况下搞定这个东西,所以有些地方可能不是最优的. 代码留此备忘. 1. 3x_desk_event.h 1 #pragma once 2 3 #ifn ...

  3. C++ 模板元编程简单小栗子

    最近看了看模板的元编程,感觉有点意思. 一些计算可以在编译过程就能够完成,榨干编译器的最后一点资源. stl中用的全是这些玩意. 当然,这增加了编译时长. 我记得貌似有"图灵完备" ...

  4. .NET Core with 微服务 - 什么是微服务

    微服务是这几年最流行的架构,说起架构不提微服务都不好意思跟人家打招呼.最近想要再梳理一下关于微服务的知识,并且结合本人的一些实践经验来做一些总结与分享.前面会分享一些概念性的东西,后面也会使用.net ...

  5. 鼠标右键添加vscode

    实现效果 右键文件夹,可以使用vscode打开 右键文件,可以使用vscode打开 右键空白处,可以使用vscode打开 进行实现 下载压缩包 为了方便操作,和减少错误,将.reg文件上传到网盘中,点 ...

  6. 日志框架整合报错Class path contains multiple SLF4J bindings.

    在进行SSM框架的日志框架统一管理时,报错Class path contains multiple SLF4J bindings 如下图 意思是类路径下包含重复的SLF4J绑定,然后给出了重复的两个全 ...

  7. Lombok Requires Annotation Processing Annotation processing seems to be disabled for the project "HelloWorld". For  plugin to function correctly, please enable it under "Settings > Build > Compiler >

    更多精彩详见微信公众号  在网上查找说是插件的问题,但是我安装类插件父级项目没有开启注解处理Annotation Processor,子项目都有开启,如图,顶级项目是demo,下面的都是子项目,把第一 ...

  8. PHP基础—PHP的数据类型与常量使用

  9. Linux下查看在线用户及用户进程

    #该服务器下的所有用户运行进程的情况 ps -ax -u #查看java程序下用户的进程情况 ps -ax -u |grep java   或  ps aux|grep java cat /etc/p ...

  10. 【js】Leetcode每日一题-子数组异或查询

    [js]Leetcode每日一题-子数组异或查询 [题目描述] 有一个正整数数组 arr,现给你一个对应的查询数组 queries,其中 queries[i] = [Li, Ri]. 对于每个查询 i ...