pytorch官方例程：
DATA LOADING AND PROCESSING TUTORIAL
torch.utils.data.Dataset 是dataset的抽象類，我們可以同過繼承Dataset來定義自己的dataset，然后重寫類里的兩個方法：

• len 返回數據集的長度
• getitem 根據索引對數據集采樣

class FaceLandmarksDataset(Dataset):"""Face Landmarks dataset."""def __init__(self, csv_file, root_dir, transform=None):"""Args:csv_file (string): Path to the csv file with annotations.root_dir (string): Directory with all the images.transform (callable, optional): Optional transform to be appliedon a sample."""self.landmarks_frame = pd.read_csv(csv_file)self.root_dir = root_dirself.transform = transformdef __len__(self):return len(self.landmarks_frame)def __getitem__(self, idx):img_name = os.path.join(self.root_dir,self.landmarks_frame.iloc[idx, 0])image = io.imread(img_name)landmarks = self.landmarks_frame.iloc[idx, 1:].as_matrix()landmarks = landmarks.astype('float').reshape(-1, 2)sample = {'image': image, 'landmarks': landmarks}if self.transform:sample = self.transform(sample)return sample

實列化數據集對象

face_dataset = FaceLandmarksDataset(csv_file='data/faces/face_landmarks.csv',root_dir='data/faces/') dataset_loader = torch.utils.data.DataLoader(face_dataset,batch_size=4, shuffle=True,num_workers=4)

總結

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