深度学习实战之垃圾分类

垃圾分类，指按一定规定或标准将垃圾分类储存、分类投放和分类搬运，从而转变成公共资源的一系列活动的总称。分类的目的是提高垃圾的资源价值和经济价值，力争物尽其用；然而我们在日常生活中认为对垃圾分类还是有些不知所措的，对干垃圾、湿垃圾……分的不是很清楚，由此我们就想到了使用深度学习的方法进行分类。

简介

本篇博文主要会带领大家进行数据的预处理、网络搭建、模型训练、模型测试

1. 获取数据集
这里笔者已经为大家提供了一个比较完整的数据集，所以大家不必再自己去收集数据了
数据集链接：https://pan.baidu.com/s/1OhA7yQt9evqNIP5CIPjdgw​
提取码：Z5A1
如下为数据集中的部分数据展示

这里就不过多的展示了，因为有些图片比较的不雅
ok,不说题外话了，我们继续

2.预处理数据集

import torch,visdom,time
import os,csv,random,glob
from PIL import Image
from torchvision import transforms
from torch.utils.data import DataLoader,Dataset

class Data(Dataset):
    def __init__(self,root,resize,model):
        super(Data, self).__init__()
        self.root=root
        self.resize=resize

        self.name2label={}   # {0,1,2……}
        for name in sorted(os.listdir(os.path.join(root))):
            if not os.path.isdir(os.path.join(root, name)):  # if path not exists that create
                continue
            self.name2label[name] = len(self.name2label.keys())

        self.images,self.labels=self.load_csv('images.csv')  # load data of label and images

        # dividing data
        if model=='train':
            self.images=self.images[:int(0.6*len(self.images))]
            self.labels=self.labels[:int(0.6*len(self.labels))]
        if model=='verify':
            self.images=self.images[int(0.6*len(self.images)):int(0.8*len(self.images))]
            self.labels=self.labels[int(0.6*len(self.labels)):int(0.8*len(self.labels))]
        else:
            self.images=self.images[int(0.8*len(self.images)):]
            self.labels=self.labels[int(0.8*len(self.labels)):]




    def load_csv(self,filename):
        if not os.path.exists(os.path.join(self.root, filename)):
            images = []
            for name in self.name2label.keys():
                images += glob.glob(os.path.join(self.root, name, '*.jpg'))
                # images += glob.glob(os.path.join(self.root, name, '*.png'))
                # images += glob.glob(os.path.join(self.root, name, '*.jpeg'))
            print(len(images), images)
            random.shuffle(images)
            with open(os.path.join(self.root, filename), mode='w', newline='') as f:
                writer = csv.writer(f)
                for img in images:
                    name = img.split(os.sep)[-2]
                    label = self.name2label[name]
                    writer.writerow([img, label])
                print('writen into csv file:', filename)
        images, labels = [], []
        with open(os.path.join(self.root, filename)) as f:
            reader = csv.reader(f)
            for row in reader:
                img, label = row
                label = int(label) # trans int for label
                images.append(img)
                labels.append(label)
        assert len(images) == len(labels)   # for len(images) and len(labels) is eq
        return images, labels

    def __len__(self):
        return len(self.images)

    def normalize(self, x_hat):
        mean = [0.485, 0.456, 0.406]
        std = [0.229, 0.224, 0.225]
        # x_hat = (x-mean)/std
        # x = x_hat*std = mean
        # x: [c, h, w]
        # mean: [3] => [3, 1, 1]
        mean = torch.tensor(mean).unsqueeze(1).unsqueeze(1)
        std = torch.tensor(std).unsqueeze(1).unsqueeze(1)
        # print(mean.shape, std.shape)
        x = x_hat * std + mean
        return x
    def __getitem__(self, idx):
        img, label = self.images[idx], self.labels[idx]
        tf = transforms.Compose([
            lambda x:Image.open(x).convert('RGB'), # string path= > image data
            transforms.Resize((int(self.resize*1.25), int(self.resize*1.25))),
            transforms.RandomRotation(15),
            transforms.CenterCrop(self.resize),
            transforms.ToTensor(),
            transforms.Normalize(mean=[0.485, 0.456, 0.406],
                                 std=[0.229, 0.224, 0.225])
        ])
        img = tf(img)
        label = torch.tensor(label)
        return img, label
def main():
    # show data
    viz = visdom.Visdom()
    db = Data('data', 64, 'train')

    x,y = next(iter(db))
    print('sample:', x.shape, y.shape, y)

    viz.image(db.normalize(x), win='sample_x', opts=dict(title='sample_x'))

    loader = DataLoader(db, batch_size=32, shuffle=True, num_workers=8)

    for x,y in loader:
        viz.images(db.normalize(x), nrow=8, win='batch', opts=dict(title='batch'))
        viz.text(str(y.numpy()), win='label', opts=dict(title='batch-y'))

        time.sleep(10)

if __name__ == '__main__':
    main()

最终处理后展示

说明一下，笔者这里使用可视化工具visdom将数据进行了可视化，因此得以以上述的方式显示出来

3. 搭建网络
   笔者这里使用的是resnet18的网络结构

import  torch
from    torch import  nn
from    torch.nn import functional as F

class ResBlk(nn.Module):
    """
    resnet block
    """

    def __init__(self, ch_in, ch_out, stride=1):
        """
        :param ch_in:
        :param ch_out:
        """
        super(ResBlk, self).__init__()

        self.conv1 = nn.Conv2d(ch_in, ch_out, kernel_size=3, stride=stride, padding=1)
        self.bn1 = nn.BatchNorm2d(ch_out)
        self.conv2 = nn.Conv2d(ch_out, ch_out, kernel_size=3, stride=1, padding=1)
        self.bn2 = nn.BatchNorm2d(ch_out)

        self.extra = nn.Sequential()
        if ch_out != ch_in:
            # [b, ch_in, h, w] => [b, ch_out, h, w]
            self.extra = nn.Sequential(
                nn.Conv2d(ch_in, ch_out, kernel_size=1, stride=stride),
                nn.BatchNorm2d(ch_out)
            )


    def forward(self, x):
        """
        :param x: [b, ch, h, w]
        :return:
        """
        out = F.relu(self.bn1(self.conv1(x)))
        out = self.bn2(self.conv2(out))
        # short cut.
        # extra module: [b, ch_in, h, w] => [b, ch_out, h, w]
        # element-wise add:
        out = self.extra(x) + out
        out = F.relu(out)

        return out




class ResNet18(nn.Module):

    def __init__(self, num_class):
        super(ResNet18, self).__init__()

        self.conv1 = nn.Sequential(
            nn.Conv2d(3, 16, kernel_size=3, stride=3, padding=0),
            nn.BatchNorm2d(16)
        )
        # followed 4 blocks
        # [b, 16, h, w] => [b, 32, h ,w]
        self.blk1 = ResBlk(16, 32, stride=3)
        # [b, 32, h, w] => [b, 64, h, w]
        self.blk2 = ResBlk(32, 64, stride=3)
        # # [b, 64, h, w] => [b, 128, h, w]
        self.blk3 = ResBlk(64, 128, stride=2)
        # # [b, 128, h, w] => [b, 256, h, w]
        self.blk4 = ResBlk(128, 256, stride=2)

        # [b, 256, 7, 7]
        self.outlayer = nn.Linear(256*3*3, num_class)

    def forward(self, x):
        """
        :param x:
        :return:
        """
        x = F.relu(self.conv1(x))

        # [b, 64, h, w] => [b, 1024, h, w]
        x = self.blk1(x)
        x = self.blk2(x)
        x = self.blk3(x)
        x = self.blk4(x)

        # print(x.shape)
        x = x.view(x.size(0), -1)
        x = self.outlayer(x)


        return x



def main():
    blk = ResBlk(64, 128)
    tmp = torch.randn(2, 64, 224, 224)
    out = blk(tmp)
    print('block:', out.shape)


    model = ResNet18(5)
    tmp = torch.randn(2, 3, 224, 224)
    out = model(tmp)
    print('resnet:', out.shape)

    p = sum(map(lambda p:p.numel(), model.parameters()))
    print('parameters size:', p)


if __name__ == '__main__':
    main()

4. 训练

import torch
from torch import optim, nn
import visdom
import torchvision
from torch.utils.data import DataLoader

from Data_Pre import Data
# from    resnet import ResNet18
from torchvision.models import resnet18

from utils import Flatten

batchsz = 32
lr = 1e-3
epochs = 20

device = torch.device('cuda')
torch.manual_seed(1234)

train_db = Data('data', 224, model='train')
val_db = Data('data', 224, model='verify')
test_db = Data('data', 224, model='test')
train_loader = DataLoader(train_db, batch_size=batchsz, shuffle=True,
                          num_workers=4)
val_loader = DataLoader(val_db, batch_size=batchsz, num_workers=2)
test_loader = DataLoader(test_db, batch_size=batchsz, num_workers=2)

viz = visdom.Visdom()


def evalute(model, loader):
    model.eval()

    correct = 0
    total = len(loader.dataset)

    for x, y in loader:
        x, y = x.to(device), y.to(device)
        with torch.no_grad():
            logits = model(x)
            pred = logits.argmax(dim=1)
        correct += torch.eq(pred, y).sum().float().item()

    return correct / total


def main():
    # model = ResNet18(5).to(device)
    trained_model = resnet18(pretrained=True)
    model = nn.Sequential(*list(trained_model.children())[:-1],  # [b, 512, 1, 1]
                          Flatten(),  # [b, 512, 1, 1] => [b, 512]
                          nn.Linear(512, 5)
                          ).to(device)
    # x = torch.randn(2, 3, 224, 224)
    # print(model(x).shape)

    optimizer = optim.Adam(model.parameters(), lr=lr)
    criteon = nn.CrossEntropyLoss()

    best_acc, best_epoch = 0, 0
    global_step = 0
    viz.line([0], [-1], win='loss', opts=dict(title='loss'))
    viz.line([0], [-1], win='val_acc', opts=dict(title='val_acc'))
    for epoch in range(epochs):

        for step, (x, y) in enumerate(train_loader):
            # x: [b, 3, 224, 224], y: [b]
            x, y = x.to(device), y.to(device)

            model.train()
            logits = model(x)
            loss = criteon(logits, y)

            optimizer.zero_grad()
            loss.backward()
            optimizer.step()

            viz.line([loss.item()], [global_step], win='loss', update='append')
            global_step += 1

        if epoch % 1 == 0:

            val_acc = evalute(model, val_loader)
            if val_acc > best_acc:
                best_epoch = epoch
                best_acc = val_acc

                torch.save(model.state_dict(), 'best.mdl')

                viz.line([val_acc], [global_step], win='val_acc', update='append')

    print('best acc:', best_acc, 'best epoch:', best_epoch)

    model.load_state_dict(torch.load('best.mdl'))
    print('loaded from ckpt!')

    test_acc = evalute(model, test_loader)
    print('test acc:', test_acc)


if __name__ == '__main__':
    main()

5. 测试模型
   
   项目完整代码：https://github.com/huzixuan1/classification
   有什么问题欢迎同笔者讨论：1017190168
   最后希望大家能够动手实践实践