PyTorch怎么实现对猫狗二分类训练集进行读取

这篇文章主要介绍"PyTorch怎么实现对猫狗二分类训练集进行读取"，在日常操作中，相信很多人在PyTorch怎么实现对猫狗二分类训练集进行读取问题上存在疑惑，小编查阅了各式资料，整理出简单好用的操作方法，希望对大家解答"PyTorch怎么实现对猫狗二分类训练集进行读取"的疑惑有所帮助！接下来，请跟着小编一起来学习吧！

从kaggle中下载猫狗二分类训练数据，自己编写一个DogCatDataset，使得pytorch可以对猫狗二分类训练集进行读取

猫狗大战代码

import osimport zipfilefor dirname, _, filenames in os.walk('/kaggle/input'):    for filename in filenames:        print(os.path.join(dirname, filename))        # unzip        print(os.getcwd())os.makedirs('data', exist_ok=True)with zipfile.ZipFile('../input/dogs-vs-cats-redux-kernels-edition/train.zip') as train_zip:    train_zip.extractall('data')    with zipfile.ZipFile('../input/dogs-vs-cats-redux-kernels-edition/test.zip') as test_zip:    test_zip.extractall('data')# show unzip dirtrain_dir = './data/train'test_dir = './data/test'print('len:', len(os.listdir(train_dir)), len(os.listdir(test_dir)))os.listdir(train_dir)[:5]os.listdir(test_dir)[:5]import numpy as np import pandas as pd import globimport osimport torchimport matplotlib.pyplot as pltfrom PIL import Imagefrom sklearn.model_selection import train_test_splitfrom torchvision import datasets, models, transformsimport torch.nn as nnimport torch.optim as optimbatch_size = 100device = 'cuda' if torch.cuda.is_available() else 'cpu'print(device)torch.manual_seed(1234)if device =='cuda':    torch.cuda.manual_seed_all(1234)    lr = 0.001train_list = glob.glob(os.path.join(train_dir,'*.jpg'))test_list = glob.glob(os.path.join(test_dir, '*.jpg'))print('show data:', len(train_list), train_list[:3])print('show data:', len(test_list), test_list[:3])fig = plt.figure()ax = fig.add_subplot(1,1,1)img = Image.open(train_list[0])plt.imshow(img)plt.axis('off')plt.show()print(type(img))img_np = np.asarray(img)print(img_np.shape)train_list, val_list = train_test_split(train_list, test_size=0.2)print(len(train_list), train_list[:3])print(len(val_list), val_list[:3])train_transforms = transforms.Compose([    transforms.Resize((224, 224)),#     transforms.RandomCrop(224),     transforms.ToTensor(),])val_transforms = transforms.Compose([    transforms.Resize((224, 224)),#     transforms.RandomCrop(224),     transforms.ToTensor(),])test_transforms = transforms.Compose([    transforms.Resize((224, 224)),#     transforms.RandomCrop(224),     transforms.ToTensor(),])class dataset(torch.utils.data.Dataset):    def __init__(self,file_list,now_transform):        self.file_list = file_list # list of path        self.transform = now_transform        def __len__(self):        self.filelength = len(self.file_list)        return self.filelength        def __getitem__(self,idx):        img_path = self.file_list[idx]        img = Image.open(img_path)#         print(img.size)        img_transformed = self.transform(img)                # test 没有标签?        label = img_path.split('/')[-1].split('.')[0]        if label == 'dog':            label=1        elif label == 'cat':            label=0        else:            assert False                    return img_transformed,labeltrain_data = dataset(train_list, train_transforms)val_data = dataset(val_list, test_transforms)# test_data = dataset(test_list, transform=test_transforms)train_loader = torch.utils.data.DataLoader(dataset = train_data, batch_size=batch_size, shuffle=True )val_loader = torch.utils.data.DataLoader(dataset = val_data, batch_size=batch_size, shuffle=True)# test_loader = torch.utils.data.DataLoader(dataset = test_data, batch_size=batch_size, shuffle=True)print(len(train_data), len(train_loader))print(len(val_data), len(val_loader))print(train_data, type(train_data))t1, t2 = train_data[7]print(t1, t2)print(type(t1))print(t1.shape)class CNN_STD(nn.Module):    def __init__(self):        super(CNN_STD,self).__init__()                self.layer1 = nn.Sequential(            nn.Conv2d(3,16,kernel_size=3, padding=0,stride=2),            nn.BatchNorm2d(16),            nn.ReLU(),            nn.MaxPool2d(2)        )                self.layer2 = nn.Sequential(            nn.Conv2d(16,32, kernel_size=3, padding=0, stride=2),            nn.BatchNorm2d(32),            nn.ReLU(),            nn.MaxPool2d(2)            )                self.layer3 = nn.Sequential(            nn.Conv2d(32,64, kernel_size=3, padding=0, stride=2),            nn.BatchNorm2d(64),            nn.ReLU(),            nn.MaxPool2d(2)        )                        self.fc1 = nn.Linear(3*3*64,10)        self.dropout = nn.Dropout(0.5)        self.fc2 = nn.Linear(10,2)        self.relu = nn.ReLU()                    def forward(self,x):        out = self.layer1(x)        out = self.layer2(out)        out = self.layer3(out)        out = out.view(out.size(0),-1)        out = self.relu(self.fc1(out))        out = self.fc2(out)        return outoptimizer = optim.Adam(params = model.parameters(),lr=lr)loss_f = nn.CrossEntropyLoss()epochs = 10print('start epoch iter, please wait...')for epoch in range(epochs):    epoch_loss = 0    epoch_accuracy = 0        for data, label in train_loader:        data = data.to(device)        label = label.to(device)                output = model(data)        loss = loss_f(output, label)                optimizer.zero_grad()        loss.backward()        optimizer.step()                acc = ((output.argmax(dim=1) == label).float().mean())        epoch_accuracy += acc/len(train_loader)        epoch_loss += loss/len(train_loader)                print('Epoch : {}, train accuracy : {}, train loss : {}'.format(epoch+1, epoch_accuracy,epoch_loss))            with torch.no_grad():        epoch_val_accuracy=0        epoch_val_loss =0        for data, label in val_loader:            data = data.to(device)            label = label.to(device)                        val_output = model(data)            val_loss = loss_f(val_output,label)                                    acc = ((val_output.argmax(dim=1) == label).float().mean())            epoch_val_accuracy += acc/ len(val_loader)            epoch_val_loss += val_loss/ len(val_loader)                    print('Epoch : {}, val_accuracy : {}, val_loss : {}'.format(epoch+1, epoch_val_accuracy,epoch_val_loss))

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