几种读取图片和标签的方法

经常用，保存一下，方便查看

class Data(Dataset):
    def __init__(self, img_path, txt_path, dataset = '', data_transforms=None, loader = default_loader):
        with open(txt_path) as input_file:
            lines = input_file.readlines()
            self.img_name =A= [os.path.join(img_path, line.strip().split(' ')[0]) for line in lines]
            self.img_label =B= [int(line.strip('
').split(' ')[-1]) for line in lines]


        self.data_transforms = data_transforms
        self.dataset = dataset
        self.loader = loader

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

    def __getitem__(self, item):
        img_name = self.img_name[item]
        label = self.img_label[item]
        img = self.loader(img_name)

        if self.data_transforms is not None:
            try:
                img = self.data_transforms[self.dataset](img)
            except:
                print("Cannot transform image: {}".format(img_name))
        return img, label

def default_loader(path):
    return Image.open(path).convert('RGB')


class Train(Dataset):
    def __init__(self, txt, transform=True, target_transform=None, loader=default_loader):
        # mean = [0.42963392, 0.45779333, 0.45690465]
        # std = [0.27065045, 0.26811078, 0.29823384]

        mean = [0.4309895, 0.4576381, 0.4534026]
        std = [0.2699252, 0.26827288, 0.29846913]
        transform = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor(),transforms.Normalize(mean, std)])
        super(Train, self).__init__()
        fh = open(txt, 'r')
        imgs = []
        for line in fh:
            words = line.split()    ##   train/0.jpg 1
            words1 = 'SceneClassification/'+os.path.join(words[0])
            # print('img path:', words1, 'label:', words[1])
            imgs.append((words1, int(words[1])))
            self.imgs = imgs
            self.transforms =transform
            self.target_transform = target_transform
            self.loader = loader

    def __getitem__(self, index):
        # print('index:',index)
        img_path, label = self.imgs[index]
        img = self.loader(img_path)
        if self.transforms is not None:
            img = self.transforms(img)
            # 数据标签转换为Tensor
        return label, img

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

class Test(Dataset):
    def __init__(self, txt, transform=True, target_transform=None, loader=default_loader):

        # mean = [0.42963392, 0.45779333, 0.45690465]
        # std = [0.27065045, 0.26811078, 0.29823384]

        mean = [0.4309895, 0.4576381, 0.4534026]
        std = [0.2699252, 0.26827288, 0.29846913]
        transform = transforms.Compose([transforms.Resize((32, 32)), transforms.ToTensor(),transforms.Normalize(mean, std)])
        super(Test, self).__init__()
        fh = open(txt, 'r')

        imgs = []
        for line in fh:
            words = line.split()  ##   train/0.jpg 1
            words1 = 'SceneClassification/' + os.path.join(words[0])
            # print('img path:', words1, 'label:', words[1])
            imgs.append((words1, int(words[1])))
            self.imgs = imgs
            self.transforms = transform
            self.target_transform = target_transform
            self.loader = loader

    def __getitem__(self, index):
        # print('index:',index)
        img_path, label = self.imgs[index]
        img = self.loader(img_path)
        if self.transforms is not None:
            img = self.transforms(img)
            # 数据标签转换为Tensor
        return label, img

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

写了一个简单地分类任务（基于PyTorch）：

from __future__ import print_function, division

import torch
import torch.nn as nn
import torch.optim as optim
from torch.optim import lr_scheduler
from torch.autograd import Variable
from torchvision import  models, transforms

import time
import os
from torch.utils.data import Dataset

from PIL import Image

# use PIL Image to read image
def default_loader(path):
    try:
        img = Image.open(path)
        return img.convert('RGB')
    except:
        print("Cannot read image: {}".format(path))

# define your Dataset. Assume each line in your .txt file is [name/tab/label], for example:0001.jpg 1
class Data(Dataset):
    def __init__(self, img_path, txt_path, dataset = '', data_transforms=None, loader = default_loader):
        with open(txt_path) as input_file:
            lines = input_file.readlines()
            self.img_name =A= [os.path.join(img_path, line.strip().split(' ')[0]) for line in lines]
            self.img_label =B= [int(line.strip('
').split(' ')[-1]) for line in lines]


        self.data_transforms = data_transforms
        self.dataset = dataset
        self.loader = loader

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

    def __getitem__(self, item):
        img_name = self.img_name[item]
        label = self.img_label[item]
        img = self.loader(img_name)

        if self.data_transforms is not None:
            try:
                img = self.data_transforms[self.dataset](img)
            except:
                print("Cannot transform image: {}".format(img_name))
        return img, label

def train_model(model, criterion, optimizer, scheduler, num_epochs, use_gpu):
    since = time.time()

    best_model_wts = model.state_dict()
    best_acc = 0.0
    count_batch = 0

    for epoch in range(num_epochs):
        begin_time = time.time()
        print('Epoch {}/{}'.format(epoch, num_epochs - 1))
        print('-' * 10)

        # Each epoch has a training and validation phase
        for phase in ['train', 'test']:

            if phase == 'train':
                scheduler.step()
                model.train(True)  # Set model to training mode
            else:
                model.train(False)  # Set model to evaluate mode

            running_loss = 0.0
            running_corrects = 0.0

            # Iterate over data.
            for data in dataloders[phase]:
                count_batch += 1
                # get the inputs
                inputs, labels = data

                # wrap them in Variable
                if use_gpu:
                    inputs = Variable(inputs.cuda())
                    labels = Variable(labels.cuda())
                else:
                    inputs, labels = Variable(inputs), Variable(labels)

                # zero the parameter gradients
                optimizer.zero_grad()

                # forward
                outputs = model(inputs)
                _, preds = torch.max(outputs.data, 1)
                loss = criterion(outputs, labels)

                # backward + optimize only if in training phase
                if phase == 'train':
                    loss.backward()
                    optimizer.step()
                # statistics
                # running_loss += loss.data[0]
                running_loss += loss.item()
                running_corrects += torch.sum(preds == labels.data).to(torch.float32)

                # print result every 10 batch
                if count_batch%10 == 0:
                    batch_loss = running_loss / (batch_size*count_batch)
                    batch_acc = running_corrects / (batch_size*count_batch)
                    print('{} Epoch [{}] Batch [{}] Loss: {:.4f} Acc: {:.4f} Time: {:.4f}s'. 
                          format(phase, epoch, count_batch, batch_loss, batch_acc, time.time()-begin_time))
                    begin_time = time.time()

            epoch_loss = running_loss / dataset_sizes[phase]
            epoch_acc = running_corrects / dataset_sizes[phase]
            print('{} Loss: {:.4f} Acc: {:.4f}'.format(phase, epoch_loss, epoch_acc))

            # save model
            if phase == 'train':
                if not os.path.exists('output'):
                    os.makedirs('output')
                torch.save(model, 'output/resnet_epoch{}.pkl'.format(epoch))

            # deep copy the model
            if phase == 'test' and epoch_acc > best_acc:
                best_acc = epoch_acc
                best_model_wts = model.state_dict()

    time_elapsed = time.time() - since
    print('Training complete in {:.0f}m {:.0f}s'.format(
        time_elapsed // 60, time_elapsed % 60))
    print('Best test Acc: {:4f}'.format(best_acc))

    # load best model weights
    model.load_state_dict(best_model_wts)
    return model

if __name__ == '__main__':

    mean = [0.4309895, 0.4576381, 0.4534026]
    std = [0.2699252, 0.26827288, 0.29846913]

    data_transforms = {
        'train': transforms.Compose([
            transforms.Resize((150, 150)),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ]),
        'test': transforms.Compose([
            transforms.Resize((150, 150)),
            transforms.ToTensor(),
            transforms.Normalize(mean, std)
        ]),
    }

    use_gpu = torch.cuda.is_available()

    batch_size = 16
    num_class = 6

    image_datasets = {x: Data(img_path='SceneClassification',
                                    txt_path=('SceneClassification/' + x + '.txt'),
                                    data_transforms=data_transforms,
                                    dataset=x) for x in ['train', 'test']}

    # wrap your data and label into Tensor
    dataloders = {x: torch.utils.data.DataLoader(image_datasets[x],
                                                 batch_size=batch_size,
                                                 shuffle=True) for x in ['train', 'test']}

    dataset_sizes = {x: len(image_datasets[x]) for x in ['train', 'test']}

    # get model and replace the original fc layer with your fc layer
    model_ft = models.resnet50(pretrained=True)
    num_ftrs = model_ft.fc.in_features
    model_ft.fc = nn.Linear(num_ftrs, num_class)

    # if use gpu
    if use_gpu:
        model_ft = model_ft.cuda()

    # define cost function
    criterion = nn.CrossEntropyLoss()

    # Observe that all parameters are being optimized
    optimizer_ft = optim.SGD(model_ft.parameters(), lr=0.005, momentum=0.9)

    # Decay LR by a factor of 0.2 every 5 epochs
    exp_lr_scheduler = lr_scheduler.StepLR(optimizer_ft, step_size=5, gamma=0.2)

    # multi-GPU
    model_ft = torch.nn.DataParallel(model_ft, device_ids=[0])

    # train model
    model_ft = train_model(model=model_ft,
                           criterion=criterion,
                           optimizer=optimizer_ft,
                           scheduler=exp_lr_scheduler,
                           num_epochs=25,
                           use_gpu=use_gpu)

    # save best model
    torch.save(model_ft,"output/best_resnet.pkl")