PyTorch搭建卷积神经网络（ResNet-50网络）进行图像分类实战（附源码和数据集）

落日映苍穹つ 2023-09-30 16:39 35阅读 0赞

> **需要数据集和源码请点赞关注收藏后评论区留言~~~**

## 一、实验数据准备 ##

我们使用的是MIT67数据集，这是一个标准的室内场景检测数据集，一个有67个室内场景，每类包括80张训练图片和20张测试图片 读者可通过以下网址下载

但是数据集较大，下载花费时间较长，所以建议私信我发给你们

[数据集][Link 1]

将下载的数据集解压，主要使用Image文件夹，这个文件夹一共包含6700张图片，还有它们标签的txt文件

大体流程分为以下几步

## 二、数据预处理和准备 ##

### 1：数据集的读取 ###

### 2：重载data.Dataset类 ###

### 3：transforms数据预处理 ###

## 三、模型构建 ##

### 1：ResNet-50网络 ###

网络结构图如下

![40ce71161a124cb99afc9bdd11cf8b6d.png][]

### 2：bottleneck的实现 ###

结构图如下

![431e5ae075b94bbdb4d92c30e9680478.png][]

### 3：ResNet-50卷积层定义 ###

### 4：forward函数的实现 ###

### 5：预训练参数装载 ###

## 四、模型训练与结果评估 ##

### 1：训练类的实现 ###

### 2：优化器的定义 ###

### 3：学习率衰减 ###

### 4：训练 ###

训练过程如下

![d896df04b0764507917b89659fd24df2.png][]

最后 部分代码如下

## 1.py ##

import torch
    from torch.autograd import Variable as V
    import torchvision.models as models
    from torchvision import transforms as trn
    from torch.nn import functional as F
    import os
    import numpy as np
    from mymodels import *
    from PIL import Image
    import torch.utils.data as data
    from torch.utils.data import DataLoader
    from utils import *
    tmp_dir = '/home/yyh/tmpmit67'
    import torch.optim as optim
    import matplotlib.pyplot as plt
    import time
    import json
    def get_im_list(im_dir, file_path):
        im_list = []
        im_labels = []
        im_origin = []
        with open(file_path, 'r') as fi:
            for line in fi:
                im_list.append(im_dir + line.split()[0])
                im_labels.append(int(line.split()[-1]))
                im_origin.append(line.split()[0])
                array = line.split('/')
        return im_list, im_labels, im_origin
    ate(fi):
                sname = line.strip()
                sdict[sid] = sname
        return sdict
    _sdict = sun397_sdict()
    
    
    arch = 'resnet50'
    # load the pre-trained weights
    model_file = '%s_places365.pth.tar' % arch
    if not os.access(model_file, os.W_OK):
        weight_url = 'http://places2.csail.mit.edu/models_places365/' + model_file
        os.system('wget ' + weight_url)
    model = resnet50(num_classes=365)
    checkpoint = torch.load(model_file, map_location=lambda storage, loc: storage)
    state_dict = {str.replace(k,'module.',''): v for k,v in checkpoint['state_dict'].items()}
    model.load_state_dict(state_dict)
    model.fc = torch.nn.Linear(2048,67)
    model.eval()
    
    
    
    
    """
    model = resnet50(num_classes=67)
    pretrained = torch.load("/home/yyh/fineTune/mit67_place/model_epoch_30.pth").module
    state_dict = pretrained.state_dict()
    model.load_state_dict(state_dict)
    model.eval()
    
    """
    
    
    
    
    
    # load the image transformer
    transform_train = trn.Compose([
            trn.Scale(256),
            trn.RandomSizedCrop(224),
            trn.RandomHorizontalFlip(),
            trn.ToTensor(),
            trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    transform_test = trn.Compose([
            trn.Scale(256),
            trn.CenterCrop(224),
            trn.ToTensor(),
            trn.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
    ])
    # load the class label
    
    def default_loader(path):
        return Image.open(path).convert('RGB')
    class MyDataset(data.Dataset):
        def __init__(self, images, labels,loader=default_loader,transform=None):
            self.images = images
            self.labels = labels
            self.loader = loader
            self.transform = transform
        def __getitem__(self, index):
            img, target = self.images[index], self.labels[index]
            #print(img)
            img = self.loader(img)
            if self.transform is not None:
                img = self.transform(img)
            #print(img)
            return img, target
        def __len__(self):
            return len(self.images)
    
    imdir = r'C:\Users\Admin\Desktop\MIT67\Images/'
    train_file = 'C:\Users\Admin\Desktop\MIT67\TrainImages.label'
    test_file = 'C:\Users\Admin\Desktop\MIT67\TestImages.label'
    #train_file = test_file
    train_list, train_labels,img_path= get_im_list(imdir, train_file)
    test_list, test_labels ,img_path_2= get_im_list(imdir, test_file)
    batch_size = 16
    net = model
    net.cuda()
    
    
    
    #print(test_js)
    
    
    for i in range(0, len(train_list)):
        path = img_path[i]
        save = []
        print(path)
        json_name = (path.replace("/", "_")).replace(".jpg", ".json")
        f_train = open("C:\Users\Admin\Desktop\rgbd_data\annotated_area/" + json_name)
        train_js = json.load(f_train)
        if len(train_js) == 0:
            train_js.append( {"classname":"unknown","bbox":[0,0,223,223],"score":1})
        for j in range(0, len(train_js)):
            data, target = train_list[i], train_labels[i]
            data = Image.open(data).convert('RGB')
            json_data = train_js[j]["bbox"]
            data = data.resize((224, 224), Image.ANTIALIAS)
            print(json_data)
            data = data.crop([json_data[0], json_data[1], json_data[2], json_data[3]])
            data = data.resize((224, 224), Image.ANTIALIAS)
            data = transform_test(data)
            newdata = torch.zeros(1, 3, 224, 224)
            newdata[0] = data
            data = Variable(newdata).cuda()
            output, record = net(data)
            data = record.cpu().detach().numpy()
            save.append(data)
    
        data = save[0]
        for j in range(1, len(train_js)):
            data += save[j]
        data = data / len(train_js)
        # print(data)
        # target = Variable(target).cuda()
    
        # print(output)
        # print(output["avgpool"].cpu().shape)
        root = "/home/yyh/PycharmProjects/feature_extractor/loc_224_npy/" + path.split("/")[0]
        if not os.path.exists(root):
            os.makedirs(root)
        dir = "/home/yyh/PycharmProjects/feature_extractor/loc_224_npy/" + path.replace(".jpg",".npy")
        np.save(dir, data)
        print(i)
    
    
    for i in range(0, len(test_list)):
        path = img_path_2[i]
        save = []
        print(path)
        json_name = (path.replace("/", "_")).replace(".jpg", ".json")
        f_test = open("/home/yyh/rgbd_data/annotated_area/" + json_name)
        test_js = json.load(f_test)
        if len(test_js) == 0:
            test_js.append( {"classname":"unknown","bbox":[0,0,223,223],"score":1})
        for j in range(0, len(test_js)):
            data, target = test_list[i], test_labels[i]
            data = Image.open(data).convert('RGB')
            json_data = test_js[j]["bbox"]
            data = data.resize((224, 224), Image.ANTIALIAS)
            print(json_data)
            data = data.crop([json_data[0], json_data[1], json_data[2], json_data[3]])
            data = data.resize((224, 224), Image.ANTIALIAS)
            data = transform_test(data)
            newdata = torch.zeros(1, 3, 224, 224)
           tach().numpy()
            save.append(data)
    
    
        data = save[0]
        for j in range(1, len(test_js)):
            data += save[j]
        data = data / len(test_js)
        print(data)
        root = "/home/yyh/PycharmProjects/feature_extractor/loc_224_npy/" + path.split("/")[0]
        if not os.path.exists(root):
            os.makedirs(root)
        dir = "/home/yyh/PycharmProjects/feature_extractor/loc_224_npy/" + path.replace(".jpg",".npy")
        np.save(dir, data)
        print(i)
    
        #time.sleep(10)
    
    
    
    
    
    
    
    
    #print(net)
    #train_net = torch.nn.DataParallel(net, device_ids=[0])
    #optimizer = optim.SGD(params=train_net.parameters(), lr=0.001, momentum=0.9, weight_decay=1e-4)
    #scheduler = StepLR(optimizer, 30, gamma=0.1)
    #trainer = Trainer(train_net, optimizer, F.cross_entropy, save_dir="./mit67_imagenet_448")
    #trainer.loop(130, train_loader, test_loader, scheduler)

## 2.py ##

from pathlib import Path
    import torch
    from torch.autograd import Variable
    from torch.optim import Optimizer
    from torch import nn
    from tqdm import tqdm
    import torch.nn.functional as F
    
        expansion = 4
    
        def __init__(self, inplanes, planes, stride=1, downsample=None):
            super(Bottleneck, self).__init__()
            self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
            self.bn1 = nn.BatchNorm2d(planes)
            self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                                   padding=1, bias=False)
            self.bn2 = nn.BatchNorm2d(planes)
            self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1, bias=False)
            self.bn3 = nn.BatchNorm2d(planes * self.expansion)
            self.relu = nn.ReLU(inplace=True)
            self.downsample = downsample
            self.stride = stride
    
        def forward(self, x):
            residual = x
    
            out = self.conv1(x)
            out = self.bn1(out)
            out = self.relu(out)
    
            out = self.conv2(out)
            out = self.bn 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
    
    
        def __init__(self, block, layers, num_classes=1000):
            self.inplanes = 64
            super(ResNet, self).__init__()
            self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                                   bias=False)
            self.bn1 = nn.BatchNorm2d(64)
            self.relu = nn.ReLU(inplace=True)
            self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
            self.layer1 = self._make_layer(block, 64, layers[0])
            self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
            self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
            self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
            self.avgpool = nn.AvgPool2d(kernel_size=7, stride=1, padding=0)
            self.fc = nn.Linear(512 * block.expansion, num_classes)
    
            for m in self.modules():
                if isinstance(m, nn.Conv2d):
                    nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
                elif isant_(m.weight, 1)
                    nn.init.constant_(m.bias, 0)
    
        def _make_layer(self, block, planes, blocks, stride=1):
            downsample = None
            if stride != 1 or self.inplanes != planes * block.expansion:
                downsample = nn.Sequential(
                    nn.Conv2d(self.inplanes, planes * block.expansion,
                              kernel_size=1, stride=stride, bias=False),
                    nn.BatchNorm2d(planes * block.expansion),
                )
    
            layers = []
            layers.append(block(self.inplanes, planes, stride, downsample))
            self.inplanes = planes * block.expansion
            for i in range(1, blocks):
                layers.append(block(self.inplanes, planes))
    
            return nn.Sequential(*layers)
    
        def forward(self, x):
            record = dict()
            x = self.conv1(x)
            x = self.bn1(x)
            x = self.relu(x)
            record["maxpool"] = x
            x = self.maxpool(x)
            x = self.layer1(x)
            record["layer1"] = x
            x = self.layer2(x)
            record["layer2"] = x
            x = self.layer3(x)
            record["layer3"] = x
            x = self.layer4(x)
            record["layer4"] = x
            x = selpool"] = x
            x = self.fc(x)
            return x,record["avgpool"]
    
    
    def rined=False, **kwargs):
        """Constructs a ResNet-50 model.
        Args:
            pretrained (bool): If True, returns a model pre-trained on ImageNet
        """
        model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
        return model

## 3.py ##

from pathlib import Path
    import torch
    from torch.autograd import Variable
    from torch.optim import Optimizer
    from torch import nn
    from tqdm import tqdm
    
    
    a.is_available()
        torch.backends.cudnn.benchmark = True
    
        def __init__(self, model, optimizer, loss_f, save_dir=None, save_freq=10):
            self.model = model
            if self.cuda:
                model.cuda()
            self.optimizer = optimizer
            self= save_dir
            self.save_freq = save_freq
    
        def _iteration(self, data_loader, is_train=True):
            loop_loss = []
            accuracy = []
            for data, target in tqdm(data_loader, ncols=80):
                if self.cuda:
                    data, target = data.cuda(), target.cuda()
                output = self.model(data)
                loss = self.loss_f(output, target)
                loop_loss.append(loss.data.item() / len(data_loader))
                accuracy.append((output.data.max(1)[1] == target.data).sum().item())
                if is_train:
                    self.optimizer.zero_grad()
                    loss.backward()
                    self.optimizer.step()
            mode = "train" if is_train else "test"
            #print(">>>[{}] loss: {:.2f}/accuracy: {:.2%}").format(mode,sum(loop_loss),float(sum(accuracy)) / float(len(data_loader.dataset)))
            print(mode)
            print(sum(loop_loss))
            print(float(sum(accuracy)) / float(len(data_loader.dataset)))
            
            return loop_loss, accuracy
    
        def train(self, data_loader):
            self.model.train()
            with torch.enable_grad():
                loss, correct = self._iteration(data_loader)
    
        def test(self, data_loader):
            self.model.eval()
            with torch.no_grad():
                loss, correct = self._iteration(data_loader, is_train=False)
    
        def loop(self, epochs, train_data, test_data, scheduler=None):
            for ep in range(1, epochs + 1):
                if scheduler is not None:
                    scheduler.step()
                print("epochs: {}".format(ep))
                self.train(train_data)
                self.test(test_data)
                if ep % self.save_freq == 0:
                    self.save(ep)
    
        def save(self, epoch, **kwargs):
            if self.save_dir is not None:
                model_out_path = Path(self.save_dir)
                state = self.model
                if not model_out_path.exists():
                    model_out_path.mkdir()
                print(self.save_dir+ "model_epoch_{}.pth".format(epoch))
                torch.save(state, self.save_dir+ "/model_epoch_{}.pth".format(epoch))
    
    
    class _LRScheduler(object):
        def __init__(self, optimizer, last_epoch=-1):
            if not isinstance(optimizer, Optimizer):
                raise TypeError('{} is not an Optimizer'.format(
                        type(optimizer).__name__))
            self.optimizer = optimizer
            if last_epoch == -1:
                for group in optimizer.param_groups:
                    group.setdefault('initial_lr', group['lr'])
            else:
                for i, group in enumerate(optimizer.param_groups):
                    if 'initial_lr' not in group:
                        raise KeyError("param 'initial_lr' is not specified "
                                       "in param_groups[{}] when resuming an optimizer".format(i))
            self.base_lrs = list(map(lambda group: group['initial_lr'], optimizer.param_groups))
            self.step(last_epoch + 1)
            self.last_epoch = last_epoch
    
        def get_lr(self):
            raise NotImplementedError
    
        def step(self, epoch=None):
            if epoch is None:
            tep_size, gamma=0.1, last_epoch=-1):
            self.step_size = step_size
            self.gamma = gamma
            super(StepLR, self).__init__(optimizer, last_epoch)
    
        def get_lr(self):
            return [base_lr * self.gamma ** (self.last_epoch // self.step_size)
                    for base_lr in self.base_lrs]

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[Link 1]: http://web.mit.edu/torralba/www/indoor.html
[40ce71161a124cb99afc9bdd11cf8b6d.png]: https://img-blog.csdnimg.cn/40ce71161a124cb99afc9bdd11cf8b6d.png
[431e5ae075b94bbdb4d92c30e9680478.png]: https://img-blog.csdnimg.cn/431e5ae075b94bbdb4d92c30e9680478.png
[d896df04b0764507917b89659fd24df2.png]: https://img-blog.csdnimg.cn/d896df04b0764507917b89659fd24df2.png