PyReader¶

class paddle.fluid.io. PyReader ( feed_list=None, capacity=None, use_double_buffer=True, iterable=True, return_list=False ) [源代码] ¶

在python中为数据输入创建一个reader对象。将使用python线程预取数据，并将其异步插入队列。当调用Executor.run（…）时，将自动提取队列中的数据。

参数:

feed_list (list(Variable)|tuple(Variable)) - feed变量列表，由 fluid.layers.data() 创建。
capacity (int) - PyReader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，PyReader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。

返回: 被创建的reader对象

返回类型： reader (Reader)

代码示例

1.如果iterable=False，则创建的PyReader对象几乎与 fluid.layers.py_reader（） 相同。算子将被插入program中。用户应该在每个epoch之前调用 start（） ，并在epoch结束时捕获 Executor.run（） 抛出的 fluid.core.EOFException 。一旦捕获到异常，用户应该调用 reset（） 手动重置reader。

         import paddle
import paddle.fluid as fluid
import numpy as np

EPOCH_NUM = 3
ITER_NUM = 5
BATCH_SIZE = 3

def network(image, label):
    # 用户定义网络，此处以softmax回归为例
    predict = fluid.layers.fc(input=image, size=10, act='softmax')
    return fluid.layers.cross_entropy(input=predict, label=label)

def reader_creator_random_image_and_label(height, width):
    def reader():
        for i in range(ITER_NUM):
            fake_image = np.random.uniform(low=0,
                                           high=255,
                                           size=[height, width])
            fake_label = np.ones([1])
            yield fake_image, fake_label
    return reader

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')

reader = fluid.io.PyReader(feed_list=[image, label],
                           capacity=4,
                           iterable=False)

user_defined_reader = reader_creator_random_image_and_label(784, 784)
reader.decorate_sample_list_generator(
    paddle.batch(user_defined_reader, batch_size=BATCH_SIZE))

loss = network(image, label)
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())
for i in range(EPOCH_NUM):
    reader.start()
    while True:
        try:
            executor.run(feed=None)
        except fluid.core.EOFException:
            reader.reset()
            break

        

2.如果iterable=True，则创建的PyReader对象与程序分离。程序中不会插入任何算子。在本例中，创建的reader是一个python生成器，它是可迭代的。用户应将从PyReader对象生成的数据输入 Executor.run(feed=...) 。

         import paddle
import paddle.fluid as fluid
import numpy as np

EPOCH_NUM = 3
ITER_NUM = 5
BATCH_SIZE = 10

def network(image, label):
     # 用户定义网络，此处以softmax回归为例
     predict = fluid.layers.fc(input=image, size=10, act='softmax')
     return fluid.layers.cross_entropy(input=predict, label=label)

def reader_creator_random_image(height, width):
    def reader():
        for i in range(ITER_NUM):
            fake_image = np.random.uniform(low=0, high=255, size=[height, width]),
            fake_label = np.ones([1])
            yield fake_image, fake_label
    return reader

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True, return_list=False)

user_defined_reader = reader_creator_random_image(784, 784)
reader.decorate_sample_list_generator(
    paddle.batch(user_defined_reader, batch_size=BATCH_SIZE),
    fluid.core.CPUPlace())
loss = network(image, label)
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())

for _ in range(EPOCH_NUM):
    for data in reader():
        executor.run(feed=data, fetch_list=[loss])

        

return_list=True，返回值将用list表示而非dict，通常用于动态图模式中。

         import paddle
import paddle.fluid as fluid
import numpy as np

EPOCH_NUM = 3
ITER_NUM = 5
BATCH_SIZE = 10

def reader_creator_random_image(height, width):
    def reader():
        for i in range(ITER_NUM):
            yield np.random.uniform(low=0, high=255, size=[height, width]), \
                np.random.random_integers(low=0, high=9, size=[1])
    return reader

place = fluid.CPUPlace()
with fluid.dygraph.guard(place):
    py_reader = fluid.io.PyReader(capacity=2, return_list=True)
    user_defined_reader = reader_creator_random_image(784, 784)
    py_reader.decorate_sample_list_generator(
        paddle.batch(user_defined_reader, batch_size=BATCH_SIZE),
        place)
    for image, label in py_reader():
        relu = fluid.layers.relu(image)

        

start ( ) ¶

启动数据输入线程。只能在reader对象不可迭代时调用。

代码示例

         import paddle
import paddle.fluid as fluid
import numpy as np

BATCH_SIZE = 10

def generator():
  for i in range(5):
     yield np.random.uniform(low=0, high=255, size=[784, 784]),

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=False)
reader.decorate_sample_list_generator(
  paddle.batch(generator, batch_size=BATCH_SIZE))

executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())
for i in range(3):
  reader.start()
  while True:
      try:
          executor.run(feed=None)
      except fluid.core.EOFException:
          reader.reset()
          break

        

reset ( ) ¶

当 fluid.core.EOFException 抛出时重置reader对象。只能在reader对象不可迭代时调用。

代码示例

         import paddle
import paddle.fluid as fluid
import numpy as np

BATCH_SIZE = 10

def generator():
    for i in range(5):
        yield np.random.uniform(low=0, high=255, size=[784, 784]),

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=False)
reader.decorate_sample_list_generator(
    paddle.batch(generator, batch_size=BATCH_SIZE))

executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())
for i in range(3):
    reader.start()
    while True:
        try:
            executor.run(feed=None)
        except fluid.core.EOFException:
            reader.reset()
            break

        

decorate_sample_generator ( sample_generator, batch_size, drop_last=True, places=None ) ¶

设置PyReader对象的数据源。

提供的 sample_generator 应该是一个python生成器，它生成的数据类型应为list(numpy.ndarray)。

当PyReader对象可迭代时，必须设置 places 。

如果所有的输入都没有LOD，这个方法比 decorate_sample_list_generator(paddle.batch(sample_generator, ...)) 更快。

参数:

sample_generator (generator) – Python生成器，yield 类型为list(numpy.ndarray)
batch_size (int) – batch size，必须大于0
drop_last (bool) – 当样本数小于batch数量时，是否删除最后一个batch
places (None|list(CUDAPlace)|list(CPUPlace)) – 位置列表。当PyReader可迭代时必须被提供

代码示例

         import paddle.fluid as fluid
import numpy as np

EPOCH_NUM = 3
ITER_NUM = 15
BATCH_SIZE = 3

def network(image, label):
    # 用户定义网络，此处以softmax回归为例
    predict = fluid.layers.fc(input=image, size=10, act='softmax')
    return fluid.layers.cross_entropy(input=predict, label=label)

def random_image_and_label_generator(height, width):
    def generator():
        for i in range(ITER_NUM):
            fake_image = np.random.uniform(low=0,
                                           high=255,
                                           size=[height, width])
            fake_label = np.array([1])
            yield fake_image, fake_label
    return generator

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)

user_defined_generator = random_image_and_label_generator(784, 784)
reader.decorate_sample_generator(user_defined_generator,
                                 batch_size=BATCH_SIZE,
                                 places=[fluid.CPUPlace()])
loss = network(image, label)
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())

for _ in range(EPOCH_NUM):
    for data in reader():
        executor.run(feed=data, fetch_list=[loss])

        

decorate_sample_list_generator ( reader, places=None ) ¶

设置PyReader对象的数据源。

提供的 reader 应该是一个python生成器，它生成列表（numpy.ndarray）类型的批处理数据。

当PyReader对象不可迭代时，必须设置 places 。

参数:

reader (generator) – 返回列表（numpy.ndarray）类型的批处理数据的Python生成器
places (None|list(CUDAPlace)|list(CPUPlace)) – 位置列表。当PyReader可迭代时必须被提供

代码示例

         import paddle
import paddle.fluid as fluid
import numpy as np

EPOCH_NUM = 3
ITER_NUM = 15
BATCH_SIZE = 3

def network(image, label):
    # 用户定义网络，此处以softmax回归为例
    predict = fluid.layers.fc(input=image, size=10, act='softmax')
    return fluid.layers.cross_entropy(input=predict, label=label)

def random_image_and_label_generator(height, width):
    def generator():
        for i in range(ITER_NUM):
            fake_image = np.random.uniform(low=0,
                                           high=255,
                                           size=[height, width])
            fake_label = np.ones([1])
            yield fake_image, fake_label
    return generator

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)

user_defined_generator = random_image_and_label_generator(784, 784)
reader.decorate_sample_list_generator(
    paddle.batch(user_defined_generator, batch_size=BATCH_SIZE),
    fluid.core.CPUPlace())
loss = network(image, label)
executor = fluid.Executor(fluid.core.CPUPlace())
executor.run(fluid.default_startup_program())

for _ in range(EPOCH_NUM):
    for data in reader():
        executor.run(feed=data, fetch_list=[loss])

        

decorate_batch_generator ( reader, places=None ) ¶

设置PyReader对象的数据源。

提供的 reader 应该是一个python生成器，它生成列表（numpy.ndarray）类型或LoDTensor类型的批处理数据。

当PyReader对象不可迭代时，必须设置 places 。

参数:

reader (generator) – 返回LoDTensor类型的批处理数据的Python生成器
places (None|list(CUDAPlace)|list(CPUPlace)) – 位置列表。当PyReader可迭代时必须被提供

代码示例

         import paddle.fluid as fluid
import numpy as np

EPOCH_NUM = 3
ITER_NUM = 15
BATCH_SIZE = 3

def network(image, label):
    # 用户定义网络，此处以softmax回归为例
    predict = fluid.layers.fc(input=image, size=10, act='softmax')
    return fluid.layers.cross_entropy(input=predict, label=label)

def random_image_and_label_generator(height, width):
    def generator():
        for i in range(ITER_NUM):
            batch_image = np.random.uniform(low=0,
                                            high=255,
                                            size=[BATCH_SIZE, height, width])
            batch_label = np.ones([BATCH_SIZE, 1])
            batch_image = batch_image.astype('float32')
            batch_label = batch_label.astype('int64')
            yield batch_image, batch_label
    return generator

image = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
reader = fluid.io.PyReader(feed_list=[image, label], capacity=4, iterable=True)

user_defined_generator = random_image_and_label_generator(784, 784)
reader.decorate_batch_generator(user_defined_generator, fluid.CPUPlace())

loss = network(image, label)
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())

for _ in range(EPOCH_NUM):
    for data in reader():
        executor.run(feed=data, fetch_list=[loss])

        

next ( ) ¶

获取下一个数据。用户不应直接调用此方法。此方法用于PaddlePaddle框架内部实现Python 2.x的迭代器协议。