Pytorch 使用CNN图像分类的实现


Posted in Python onJune 16, 2020

需求

在4*4的图片中,比较外围黑色像素点和内圈黑色像素点个数的大小将图片分类

Pytorch 使用CNN图像分类的实现

如上图图片外围黑色像素点5个大于内圈黑色像素点1个分为0类反之1类

想法

  • 通过numpy、PIL构造4*4的图像数据集
  • 构造自己的数据集类
  • 读取数据集对数据集选取减少偏斜
  • cnn设计因为特征少,直接1*1卷积层
  • 或者在4*4外围添加padding成6*6,设计2*2的卷积核得出3*3再接上全连接层

代码

import torch
import torchvision
import torchvision.transforms as transforms
import numpy as np
from PIL import Image

构造数据集

import csv
import collections
import os
import shutil

def buildDataset(root,dataType,dataSize):
  """构造数据集
  构造的图片存到root/{dataType}Data
  图片地址和标签的csv文件存到 root/{dataType}DataInfo.csv
  Args:
    root:str
      项目目录
    dataType:str
      'train'或者‘test'
    dataNum:int
      数据大小
  Returns:
  """
  dataInfo = []
  dataPath = f'{root}/{dataType}Data'
  if not os.path.exists(dataPath):
    os.makedirs(dataPath)
  else:
    shutil.rmtree(dataPath)
    os.mkdir(dataPath)
    
  for i in range(dataSize):
    # 创建0,1 数组
    imageArray=np.random.randint(0,2,(4,4))
    # 计算0,1数量得到标签
    allBlackNum = collections.Counter(imageArray.flatten())[0]
    innerBlackNum = collections.Counter(imageArray[1:3,1:3].flatten())[0]
    label = 0 if (allBlackNum-innerBlackNum)>innerBlackNum else 1
    # 将图片保存
    path = f'{dataPath}/{i}.jpg'
    dataInfo.append([path,label])
    im = Image.fromarray(np.uint8(imageArray*255))
    im = im.convert('1') 
    im.save(path)
  # 将图片地址和标签存入csv文件
  filePath = f'{root}/{dataType}DataInfo.csv'
  with open(filePath, 'w') as f:
    writer = csv.writer(f)
    writer.writerows(dataInfo)
root=r'/Users/null/Documents/PythonProject/Classifier'

构造训练数据集

buildDataset(root,'train',20000)

构造测试数据集

buildDataset(root,'test',10000)

读取数据集

class MyDataset(torch.utils.data.Dataset):

  def __init__(self, root, datacsv, transform=None):
    super(MyDataset, self).__init__()
    with open(f'{root}/{datacsv}', 'r') as f:
      imgs = []
      # 读取csv信息到imgs列表
      for path,label in map(lambda line:line.rstrip().split(','),f):
        imgs.append((path, int(label)))
    self.imgs = imgs
    self.transform = transform if transform is not None else lambda x:x
    
  def __getitem__(self, index):
    path, label = self.imgs[index]
    img = self.transform(Image.open(path).convert('1'))
    return img, label

  def __len__(self):
    return len(self.imgs)
trainData=MyDataset(root = root,datacsv='trainDataInfo.csv', transform=transforms.ToTensor())
testData=MyDataset(root = root,datacsv='testDataInfo.csv', transform=transforms.ToTensor())

处理数据集使得数据集不偏斜

import itertools

def chooseData(dataset,scale):
  # 将类别为1的排序到前面
  dataset.imgs.sort(key=lambda x:x[1],reverse=True)
  # 获取类别1的数目 ,取scale倍的数组,得数据不那么偏斜
  trueNum =collections.Counter(itertools.chain.from_iterable(dataset.imgs))[1]
  end = min(trueNum*scale,len(dataset))
  dataset.imgs=dataset.imgs[:end]
scale = 4
chooseData(trainData,scale)
chooseData(testData,scale)
len(trainData),len(testData)
(2250, 1122)
import torch.utils.data as Data

# 超参数
batchSize = 50
lr = 0.1
numEpochs = 20

trainIter = Data.DataLoader(dataset=trainData, batch_size=batchSize, shuffle=True)
testIter = Data.DataLoader(dataset=testData, batch_size=batchSize)

定义模型

from torch import nn
from torch.autograd import Variable
from torch.nn import Module,Linear,Sequential,Conv2d,ReLU,ConstantPad2d
import torch.nn.functional as F
class Net(Module):  
  def __init__(self):
    super(Net, self).__init__()

    self.cnnLayers = Sequential(
      # padding添加1层常数1,设定卷积核为2*2
      ConstantPad2d(1, 1),
      Conv2d(1, 1, kernel_size=2, stride=2,bias=True)
    )
    self.linearLayers = Sequential(
      Linear(9, 2)
    )

  def forward(self, x):
    x = self.cnnLayers(x)
    x = x.view(x.shape[0], -1)
    x = self.linearLayers(x)
    return x
class Net2(Module):  
  def __init__(self):
    super(Net2, self).__init__()

    self.cnnLayers = Sequential(
      Conv2d(1, 1, kernel_size=1, stride=1,bias=True)
    )
    self.linearLayers = Sequential(
      ReLU(),
      Linear(16, 2)
    )

  def forward(self, x):
    x = self.cnnLayers(x)
    x = x.view(x.shape[0], -1)
    x = self.linearLayers(x)
    return x

定义损失函数

# 交叉熵损失函数
loss = nn.CrossEntropyLoss()
loss2 = nn.CrossEntropyLoss()

定义优化算法

net = Net()
optimizer = torch.optim.SGD(net.parameters(),lr = lr)
net2 = Net2()
optimizer2 = torch.optim.SGD(net2.parameters(),lr = lr)

训练模型

# 计算准确率
def evaluateAccuracy(dataIter, net):
  accSum, n = 0.0, 0
  with torch.no_grad():
    for X, y in dataIter:
      accSum += (net(X).argmax(dim=1) == y).float().sum().item()
      n += y.shape[0]
  return accSum / n
def train(net, trainIter, testIter, loss, numEpochs, batchSize,
       optimizer):
  for epoch in range(numEpochs):
    trainLossSum, trainAccSum, n = 0.0, 0.0, 0
    for X,y in trainIter:
      yHat = net(X)
      l = loss(yHat,y).sum()
      optimizer.zero_grad()
      l.backward()
      optimizer.step()
      # 计算训练准确度和loss
      trainLossSum += l.item()
      trainAccSum += (yHat.argmax(dim=1) == y).sum().item()
      n += y.shape[0]
    # 评估测试准确度
    testAcc = evaluateAccuracy(testIter, net)
    print('epoch {:d}, loss {:.4f}, train acc {:.3f}, test acc {:.3f}'.format(epoch + 1, trainLossSum / n, trainAccSum / n, testAcc))

Net模型训练

train(net, trainIter, testIter, loss, numEpochs, batchSize,optimizer)
epoch 1, loss 0.0128, train acc 0.667, test acc 0.667
epoch 2, loss 0.0118, train acc 0.683, test acc 0.760
epoch 3, loss 0.0104, train acc 0.742, test acc 0.807
epoch 4, loss 0.0093, train acc 0.769, test acc 0.772
epoch 5, loss 0.0085, train acc 0.797, test acc 0.745
epoch 6, loss 0.0084, train acc 0.798, test acc 0.807
epoch 7, loss 0.0082, train acc 0.804, test acc 0.816
epoch 8, loss 0.0078, train acc 0.816, test acc 0.812
epoch 9, loss 0.0077, train acc 0.818, test acc 0.817
epoch 10, loss 0.0074, train acc 0.824, test acc 0.826
epoch 11, loss 0.0072, train acc 0.836, test acc 0.819
epoch 12, loss 0.0075, train acc 0.823, test acc 0.829
epoch 13, loss 0.0071, train acc 0.839, test acc 0.797
epoch 14, loss 0.0067, train acc 0.849, test acc 0.824
epoch 15, loss 0.0069, train acc 0.848, test acc 0.843
epoch 16, loss 0.0064, train acc 0.864, test acc 0.851
epoch 17, loss 0.0062, train acc 0.867, test acc 0.780
epoch 18, loss 0.0060, train acc 0.871, test acc 0.864
epoch 19, loss 0.0057, train acc 0.881, test acc 0.890
epoch 20, loss 0.0055, train acc 0.885, test acc 0.897

Net2模型训练

# batchSize = 50 
# lr = 0.1
# numEpochs = 15 下得出的结果
train(net2, trainIter, testIter, loss2, numEpochs, batchSize,optimizer2)

epoch 1, loss 0.0119, train acc 0.638, test acc 0.676
epoch 2, loss 0.0079, train acc 0.823, test acc 0.986
epoch 3, loss 0.0046, train acc 0.987, test acc 0.977
epoch 4, loss 0.0030, train acc 0.983, test acc 0.973
epoch 5, loss 0.0023, train acc 0.981, test acc 0.976
epoch 6, loss 0.0019, train acc 0.980, test acc 0.988
epoch 7, loss 0.0016, train acc 0.984, test acc 0.984
epoch 8, loss 0.0014, train acc 0.985, test acc 0.986
epoch 9, loss 0.0013, train acc 0.987, test acc 0.992
epoch 10, loss 0.0011, train acc 0.989, test acc 0.993
epoch 11, loss 0.0010, train acc 0.989, test acc 0.996
epoch 12, loss 0.0010, train acc 0.992, test acc 0.994
epoch 13, loss 0.0009, train acc 0.993, test acc 0.994
epoch 14, loss 0.0008, train acc 0.995, test acc 0.996
epoch 15, loss 0.0008, train acc 0.994, test acc 0.998

测试

test = torch.Tensor([[[[0,0,0,0],[0,1,1,0],[0,1,1,0],[0,0,0,0]]],
         [[[1,1,1,1],[1,0,0,1],[1,0,0,1],[1,1,1,1]]],
         [[[0,1,0,1],[1,0,0,1],[1,0,0,1],[0,0,0,1]]],
         [[[0,1,1,1],[1,0,0,1],[1,0,0,1],[0,0,0,1]]],
         [[[0,0,1,1],[1,0,0,1],[1,0,0,1],[1,0,1,0]]],
         [[[0,0,1,0],[0,1,0,1],[0,0,1,1],[1,0,1,0]]],
         [[[1,1,1,0],[1,0,0,1],[1,0,1,1],[1,0,1,1]]]
         ])

target=torch.Tensor([0,1,0,1,1,0,1])
test
tensor([[[[0., 0., 0., 0.],
     [0., 1., 1., 0.],
     [0., 1., 1., 0.],
     [0., 0., 0., 0.]]],

​

    [[[1., 1., 1., 1.],
     [1., 0., 0., 1.],
     [1., 0., 0., 1.],
     [1., 1., 1., 1.]]],

​

    [[[0., 1., 0., 1.],
     [1., 0., 0., 1.],
     [1., 0., 0., 1.],
     [0., 0., 0., 1.]]],

​

    [[[0., 1., 1., 1.],
     [1., 0., 0., 1.],
     [1., 0., 0., 1.],
     [0., 0., 0., 1.]]],

​

    [[[0., 0., 1., 1.],
     [1., 0., 0., 1.],
     [1., 0., 0., 1.],
     [1., 0., 1., 0.]]],

​

    [[[0., 0., 1., 0.],
     [0., 1., 0., 1.],
     [0., 0., 1., 1.],
     [1., 0., 1., 0.]]],

​

    [[[1., 1., 1., 0.],
     [1., 0., 0., 1.],
     [1., 0., 1., 1.],
     [1., 0., 1., 1.]]]])



with torch.no_grad():
  output = net(test)
  output2 = net2(test)
predictions =output.argmax(dim=1)
predictions2 =output2.argmax(dim=1)
# 比较结果
print(f'Net测试结果{predictions.eq(target)}')
print(f'Net2测试结果{predictions2.eq(target)}')
Net测试结果tensor([ True, True, False, True, True, True, True])
Net2测试结果tensor([False, True, False, True, True, False, True])

到此这篇关于Pytorch 使用CNN图像分类的实现的文章就介绍到这了,更多相关Pytorch CNN图像分类内容请搜索三水点靠木以前的文章或继续浏览下面的相关文章希望大家以后多多支持三水点靠木!

Python 相关文章推荐
简单介绍Python中的len()函数的使用
Apr 07 Python
简单介绍Python的轻便web框架Bottle
Apr 08 Python
python中尾递归用法实例详解
Apr 28 Python
python中defaultdict的用法详解
Jun 07 Python
Python人脸识别初探
Dec 21 Python
TensorFlow的权值更新方法
Jun 14 Python
Python3爬虫教程之利用Python实现发送天气预报邮件
Dec 16 Python
python zip()函数使用方法解析
Oct 31 Python
Python线程指南分享
Nov 19 Python
Python实现图像去噪方式(中值去噪和均值去噪)
Dec 18 Python
tensorflow实现二维平面模拟三维数据教程
Feb 11 Python
python爬虫开发之使用python爬虫库requests,urllib与今日头条搜索功能爬取搜索内容实例
Mar 10 Python
利用python中的matplotlib打印混淆矩阵实例
Jun 16 #Python
Python SMTP配置参数并发送邮件
Jun 16 #Python
基于matplotlib中ion()和ioff()的使用详解
Jun 16 #Python
Python数据相关系数矩阵和热力图轻松实现教程
Jun 16 #Python
matplotlib.pyplot.matshow 矩阵可视化实例
Jun 16 #Python
使用python matploblib库绘制准确率,损失率折线图
Jun 16 #Python
为什么称python为胶水语言
Jun 16 #Python
You might like
探讨GDFONTPATH能否被winxp下的php支持
2013/06/21 PHP
深入解析php中的foreach函数
2013/08/31 PHP
PHP记录页面停留时间的方法
2016/03/30 PHP
读jQuery之二(两种扩展)
2011/06/11 Javascript
js图片延迟加载的实现方法及思路
2013/07/22 Javascript
解决JS中乘法的浮点错误的方法
2014/01/03 Javascript
js判断iframe内的网页是否滚动到底部触发事件
2014/03/18 Javascript
javascript跑马灯抽奖实例讲解
2020/04/17 Javascript
AngualrJS中的Directive制作一个菜单
2016/01/26 Javascript
BootStrap初学者对弹出框和进度条的使用感觉
2016/06/27 Javascript
jquery.Callbacks的实现详解
2016/11/30 Javascript
jQuery事件详解
2017/02/23 Javascript
js实现简单的手风琴效果
2017/02/27 Javascript
用node和express连接mysql实现登录注册的实现代码
2017/07/05 Javascript
vue解决跨域路由冲突问题思路解析
2017/11/03 Javascript
JS实现的合并多个数组去重算法示例
2018/04/11 Javascript
Koa 中的错误处理解析
2019/04/09 Javascript
Electron 调用命令行(cmd)
2019/09/23 Javascript
Vue如何获取数据列表展示
2019/12/11 Javascript
Nodejs文件上传、监听上传进度的代码
2020/03/27 NodeJs
javascript实现移动端红包雨页面
2020/06/23 Javascript
Python复制文件操作实例详解
2015/11/10 Python
python虚拟环境virualenv的安装与使用
2016/12/18 Python
Python爬虫JSON及JSONPath运行原理详解
2020/06/04 Python
英国骑行、跑步、游泳、铁人三项运动装备专卖店:Wiggle
2016/08/23 全球购物
德国2018年度最佳在线药房:Bodfeld Apotheke
2019/11/04 全球购物
英国最大的独立摄影零售商:Park Cameras
2019/11/27 全球购物
幼儿园运动会加油词
2014/02/14 职场文书
离婚协议书包括哪些内容
2014/10/16 职场文书
交通事故案件代理词
2015/05/23 职场文书
如何计划开一家便利店?
2019/07/31 职场文书
导游词之河北野三坡
2019/12/11 职场文书
MySQL pt-slave-restart工具的使用简介
2021/04/07 MySQL
MySQL创建高性能索引的全步骤
2021/05/02 MySQL
聊聊配置 Nginx 访问与错误日志的问题
2022/05/25 Servers
Python使用Web框架Flask开发项目
2022/06/01 Python