Pytorch生成Data数据类

from torch.utils import data 

class MyData(data.Dataset)
    def __init__(self):


    def function(self):

    def __getitem__(self, index):

        return []
    def __len__(self):
        return len

def __init__(self): 这里初始化你所需要的用到的路径，各种配置

def function(self): 这里是定义你自己的内部函数：

def __getitem__(self, index): 这个函数很关键，为迭代过程中取数据

def __len__(self): 这个必须有，返回数据长度

这是一个CV任务的数据example:

class FFDataset(data.Dataset):
    def __init__(self, root, info_list):
        self.root = root
        self.train_list = self.collect_image(info_list, root)
        tfms = transforms.Compose([transforms.ToPILImage(),
                                   transforms.Resize((224, 224)),
                                   transforms.RandomHorizontalFlip(),
                                   transforms.RandomRotation(10),
                                   transforms.ToTensor(),
                                   transforms.Normalize(mean=(0.5, 0.5, 0.5), std=(0.5, 0.5, 0.5))
                                   ])
        self.transform = tfms

    def collect_image(self, info_list, root):
        image_path_list = []
        frame = pd.read_csv(info_list, delimiter=',', header=None)
        for ind in frame.index:
            video_name = str(frame.iloc[ind, 1])
            img_path = os.path.join(root, video_name)
            img_list = os.listdir(img_path)
            for img_name in img_list:
                image_path_list.append(path)
        return image_path_list

    def read_image(self, path):
        img = cv2.imread(path)
        face_scale = np.random.randint(12, 15)
        face_scale = face_scale/10.0
        face = self.crop_face(path, img, face_scale)
        return face

    def __getitem__(self, index):
        image_path = self.train_list[index]
        img = self.read_image(image_path)
        img = self.transform(img)
        img_type = self.train_list[index].split('/')[2]
        label = 1.0 if img_type == 'live_images' else 0.0
        mask = np.ones((1, 14, 14), dtype=np.float32) * label

        return [img, label, mask]

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