Dataset

class paddle.fluid.dataloader.dataset. Dataset [source]

An abstract class to encapsulate methods and behaviors of datasets.

All datasets in map-style(dataset samples can be get by a given key) should be a subclass of paddle.io.Dataset. All subclasses should implement following methods:

__getitem__: get sample from dataset with a given index. This method is required by reading dataset sample in paddle.io.DataLoader.

__len__: return dataset sample number. This method is required by some implements of paddle.io.BatchSampler

see paddle.io.DataLoader.

Examples

import numpy as np
from paddle.io import Dataset

# define a random dataset
class RandomDataset(Dataset):
    def __init__(self, num_samples):
        self.num_samples = num_samples

    def __getitem__(self, idx):
        image = np.random.random([784]).astype('float32')
        label = np.random.randint(0, 9, (1, )).astype('int64')
        return image, label

    def __len__(self):
        return self.num_samples

dataset = RandomDataset(10)
for i in range(len(dataset)):
    print(dataset[i])