DataLoader
- 方法
  - from_generator(feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False, use_multiprocess=False, drop_last=True)
  - from_dataset(dataset, places, drop_last=True)

DataLoader

class paddle.fluid.io.DataLoader[源代码]

方法

from_generator(feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False, use_multiprocess=False, drop_last=True)

注解

框架保证DataLoader的数据加载顺序与用户提供的数据源读取顺序一致。

创建一个DataLoader对象用于加载Python生成器产生的数据。数据会由Python线程预先读取，并异步送入一个队列中。

本方法创建的DataLoader对象提供了3个方法设置数据源，分别是 set_sample_generator , set_sample_list_generator 和 set_batch_generator 。请查阅下述示例代码了解它们的使用方法。

如果iterable = True，本方法创建的DataLoader对象时一个Python生成器，可以for-range的方法循环迭代。

如果iterable = False，本方法创建的DataLoader对象提供 start() 和 reset() 方法控制数据读取过程。此模式用于兼容 fluid.layers.py_reader 的使用方式。用户可使用iterable = False模式，方便地将 fluid.layers.py_reader 的代码迁移至 fluid.io.DataLoader 。

参数

feed_list (list(Variable)|tuple(Variable)) - feed变量列表，由 fluid.layers.data() 创建。
capacity (int) - DataLoader对象内部维护队列的容量大小。单位是batch数量。若reader读取速度较快，建议设置较大的capacity值。
use_double_buffer (bool) - 是否使用 double_buffer_reader 。若use_double_buffer=True，DataLoader会异步地预读取下一个batch的数据，可加速数据读取过程，但同时会占用少量的CPU/GPU存储，即一个batch输入数据的存储空间。
iterable (bool) - 所创建的DataLoader对象是否可迭代。
return_list (bool) - 每个设备上的数据是否以list形式返回。仅在iterable = True模式下有效。若return_list = False，每个设备上的返回数据均是str -> LoDTensor的映射表，其中映射表的key是每个输入变量的名称。若return_list = True，则每个设备上的返回数据均是list(LoDTensor)。推荐在静态图模式下使用return_list = False，在动态图模式下使用return_list = True。
use_multiprocess (bool) - 设置是否是用多进程加速动态图的数据载入过程。注意：该参数的设置仅在动态图模式下有效, 在静态图模式下，该参数设置与否均无任何影响。默认值为False。
drop_last (bool): 是否丢弃最后的不足CPU/GPU设备数的批次。默认值为True。在网络训练时，用户不能设置drop_last=False，此时所有CPU/GPU设备均应从DataLoader中读取到数据。在网络预测时，用户可以设置drop_last=False，此时最后不足CPU/GPU设备数的批次可以进行预测。

被创建的DataLoader对象

返回类型

loader (DataLoader)

代码示例 1

import paddle.fluid as fluid
import numpy as np
BATCH_NUM = 10
BATCH_SIZE = 16
EPOCH_NUM = 4
CLASS_NUM = 10
ITERABLE = True # whether the created DataLoader object is iterable
USE_GPU = False # whether to use GPU
DATA_FORMAT = 'batch_generator' # data format of data source user provides
def simple_net(image, label):
    fc_tmp = fluid.layers.fc(image, size=CLASS_NUM)
    cross_entropy = fluid.layers.softmax_with_cross_entropy(image, label)
    loss = fluid.layers.reduce_mean(cross_entropy)
    sgd = fluid.optimizer.SGD(learning_rate=1e-3)
    sgd.minimize(loss)
    return loss
def get_random_images_and_labels(image_shape, label_shape):
    image = np.random.random(size=image_shape).astype('float32')
    label = np.random.random(size=label_shape).astype('int64')
    return image, label
# If the data generator yields one sample each time,
# use DataLoader.set_sample_generator to set the data source.
def sample_generator_creator():
    def __reader__():
        for _ in range(BATCH_NUM * BATCH_SIZE):
            image, label = get_random_images_and_labels([784], [1])
            yield image, label
    return __reader__
# If the data generator yield list of samples each time,
# use DataLoader.set_sample_list_generator to set the data source.
def sample_list_generator_creator():
    def __reader__():
        for _ in range(BATCH_NUM):
            sample_list = []
            for _ in range(BATCH_SIZE):
                image, label = get_random_images_and_labels([784], [1])
                sample_list.append([image, label])
            yield sample_list
    return __reader__
# If the data generator yields a batch each time,
# use DataLoader.set_batch_generator to set the data source.
def batch_generator_creator():
    def __reader__():
        for _ in range(BATCH_NUM):
            batch_image, batch_label = get_random_images_and_labels([BATCH_SIZE, 784], [BATCH_SIZE, 1])
            yield batch_image, batch_label
    return __reader__
# If DataLoader is iterable, use for loop to train the network
def train_iterable(exe, prog, loss, loader):
    for _ in range(EPOCH_NUM):
        for data in loader():
            exe.run(prog, feed=data, fetch_list=[loss])
# If DataLoader is not iterable, use start() and reset() method to control the process
def train_non_iterable(exe, prog, loss, loader):
    for _ in range(EPOCH_NUM):
        loader.start() # call DataLoader.start() before each epoch starts
        try:
            while True:
                exe.run(prog, fetch_list=[loss])
        except fluid.core.EOFException:
            loader.reset() # call DataLoader.reset() after catching EOFException
def set_data_source(loader, places):
    if DATA_FORMAT == 'sample_generator':
        loader.set_sample_generator(sample_generator_creator(), batch_size=BATCH_SIZE, drop_last=True, places=places)
    elif DATA_FORMAT == 'sample_list_generator':
        loader.set_sample_list_generator(sample_list_generator_creator(), places=places)
    elif DATA_FORMAT == 'batch_generator':
        loader.set_batch_generator(batch_generator_creator(), places=places)
    else:
        raise ValueError('Unsupported data format')
image = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
# Define DataLoader
loader = fluid.io.DataLoader.from_generator(feed_list=[image, label], capacity=16, iterable=ITERABLE)
# Define network
loss = simple_net(image, label)
# Set data source of DataLoader
#
# If DataLoader is iterable, places must be given and the number of places must be the same with device number.
#  - If you are using GPU, call `fluid.cuda_places()` to get all GPU places.
#  - If you are using CPU, call `fluid.cpu_places()` to get all CPU places.
#
# If DataLoader is not iterable, places can be None.
places = fluid.cuda_places() if USE_GPU else fluid.cpu_places()
set_data_source(loader, places)
exe = fluid.Executor(places[0])
exe.run(fluid.default_startup_program())
prog = fluid.CompiledProgram(fluid.default_main_program()).with_data_parallel(loss_name=loss.name)
if loader.iterable:
    train_iterable(exe, prog, loss, loader)
else:
    train_non_iterable(exe, prog, loss, loader)
'''
Users can use return_list = True in dygraph mode.
'''
with fluid.dygraph.guard(places[0]):
    loader = fluid.io.DataLoader.from_generator(capacity=2, return_list=True)
    set_data_source(loader, places[0])
    for image, label in loader():
        relu = fluid.layers.relu(image)
        assert image.shape == [BATCH_SIZE, 784]
        assert label.shape == [BATCH_SIZE, 1]
        assert relu.shape == [BATCH_SIZE, 784]

代码示例 2

import paddle.fluid as fluid
import numpy as np
import os
# We use 2 CPU cores to run inference network
os.environ['CPU_NUM'] = '2'
# The data source has only 3 batches, which can not be
# divided evenly to each CPU core
def batch_generator():
    for i in range(3):
        yield np.array([i+1]).astype('float32'),
x = fluid.data(name='x', shape=[None], dtype='float32')
y = x * x
def run_inference(drop_last):
    loader = fluid.io.DataLoader.from_generator(feed_list=[x],
            capacity=8, drop_last=drop_last)
    loader.set_batch_generator(batch_generator, fluid.cpu_places())
    exe = fluid.Executor(fluid.CPUPlace())
    prog = fluid.CompiledProgram(fluid.default_main_program())
    prog = prog.with_data_parallel()
    result = []
    for data in loader():
        each_ret, = exe.run(prog, feed=data, fetch_list=[y])
        result.extend(each_ret)
    return result
# Set drop_last to True, so that the last batch whose
# number is less than CPU core number would be discarded.
print(run_inference(drop_last=True)) # [1.0, 4.0]
# Set drop_last to False, so that the last batch whose
# number is less than CPU core number can be tested.
print(run_inference(drop_last=False)) # [1.0, 4.0, 9.0]

from_dataset(dataset, places, drop_last=True)

创建一个DataLoader对象用于加载Dataset产生的数据。目前，Dataset仅支持Linux系统下使用。

参数

dataset (InMemoryDataset|QueueDataset) - Dataset对象。
places (list(CUDAPlace)|list(CPUPlace)) - DataLoader对象返回数据所在的place。
drop_last (bool) - 是否丢弃最后样本数量不足batch size的batch。若drop_last = True则丢弃，若drop_last = False则不丢弃。

被创建的DataLoader对象，可以for-range的方式循环迭代

返回类型

loader (DataLoader)

代码示例

import paddle.fluid as fluid
image = fluid.layers.data(name='image', shape=[784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
dataset = fluid.DatasetFactory().create_dataset("QueueDataset")
dataset.set_batch_size(32)
dataset.set_filelist(['a.txt', 'b.txt', 'c.txt'])
dataset.set_use_var([image, label])
dataset.set_pipe_command('cat')
loader = fluid.io.DataLoader.from_dataset(dataset, fluid.cpu_places())