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e658762a
编写于
7月 24, 2018
作者:
S
sneaxiy
提交者:
GitHub
7月 24, 2018
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Merge pull request #12313 from sneaxiy/py_reader_doc
Modify PyReader doc in python/paddle/fluid/layers/io.py
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python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
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python/paddle/fluid/layers/io.py
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@@ -456,52 +456,124 @@ def py_reader(capacity,
name
=
None
,
use_double_buffer
=
True
):
"""
Create a
reader and blocking queue
for data feeding in Python
Create a
Python reader
for data feeding in Python
This layer returns a Reader Variable and a BlockingQueue.
The BlockingQueue provides `push()` method to push a `LoDTensorArray`
object into the queue in Python side. In C++ side, the Reader
Variable would invoke `pop()` method of the queue to retrieve the
feeding data. The process of feeding data in Python side and fetching
data in C++ side can run in parallel. The BlockingQueue should be closed
using `close()` method when unused.
This layer returns a Reader Variable.
The Reader provides :code:`decorate_paddle_reader()` and
:code:`decorate_tensor_provider()` to set a Python generator as the data
source in Python side. When :code:`Executor::Run()` is invoked in C++
side, the data from the generator would be read automatically. Unlike
:code:`DataFeeder.feed()`, the data reading process and
:code:`Executor::Run()` process can run in parallel using
:code:`py_reader`. The :code:`start()` method of the Reader should be
called when each pass begins, while the :code:`reset()` method should be
called when the pass ends and :code:`fluid.core.EOFException` raises.
Note that :code:`Program.clone()` method cannot clone :code:`py_reader`.
Args:
use_double_buffer(bool): Whether use double buffer or not.
capacity(int): The maximum capacity of the BlockingQueue.
capacity(int): The buffer capacity maintained by :code:`py_reader`.
shapes(list|tuple): List of tuples which declaring data shapes.
dtypes(list|tuple): List of strs which declaring data type.
lod_levels(list|tuple): List of ints which declaring data lod_level.
name(basestring): The prefix Python queue name and Reader name. None will
be generated automatically.
use_double_buffer(bool): Whether use double buffer or not.
Returns:
tuple(Variable, BlockingQueue):
A Reader Variable from which we can get feeding data.
A BlockingQueue object for data feeding.
Variable: A Reader from which we can get feeding data.
Examples:
.. code-block:: python
1. The basic usage of :code:`py_reader` is as follows:
reader, queue = fluid.layers.py_reader(
capacity=10,
shapes=[[-1,3,224,224], [-1,1]],
dtypes=['float32', 'int64'])
# Via the reader, we can use 'read_file' layer to get data:
image, label = fluid.layers.read_file(reader)
# Via the blocking queue, we can feed data using threads
def feed_data(queue, feed_images, feed_labels):
for feed_image, feed_label in zip(feed_images, feed_labels):
data = core.LoDTensorArray()
data.append(feed_image)
data.append(feed_label)
queue.push(data)
thread = threading.Thread(target=feed_data, args=(queue, feed_images, feed_labels))
thread.start()
>>> import paddle.v2
>>> import paddle.fluid as fluid
>>> import paddle.dataset.mnist as mnist
>>>
>>> reader = fluid.layers.py_reader(capacity=64,
>>> shapes=[(-1,3,224,224), (-1,1)],
>>> dtypes=['float32', 'int64'])
>>> reader.decorate_paddle_reader(
>>> paddle.v2.reader.shuffle(paddle.batch(mnist.train())
>>>
>>> img, label = fluid.layers.read_file(reader)
>>> loss = network(img, label) # some network definition
>>>
>>> fluid.Executor(fluid.CUDAPlace(0)).run(fluid.default_startup_program())
>>>
>>> exe = fluid.ParallelExecutor(use_cuda=True, loss_name=loss.name)
>>> for epoch_id in range(10):
>>> reader.start()
>>> try:
>>> while True:
>>> exe.run(fetch_list=[loss.name])
>>> except fluid.core.EOFException:
>>> reader.reset()
2. When training and testing are both performed, two different
:code:`py_reader` should be created with different names, e.g.:
>>> import paddle.v2
>>> import paddle.fluid as fluid
>>> import paddle.dataset.mnist as mnist
>>>
>>> def network(reader):
>>> img, label = fluid.layers.read_file(reader)
>>> # Here, we omitted the network definition
>>> return loss
>>>
>>> train_reader = fluid.layers.py_reader(capacity=64,
>>> shapes=[(-1,3,224,224), (-1,1)],
>>> dtypes=['float32', 'int64'],
>>> name='train_reader')
>>> train_reader.decorate_paddle_reader(
>>> paddle.v2.reader.shuffle(paddle.batch(mnist.train())
>>>
>>> test_reader = fluid.layers.py_reader(capacity=32,
>>> shapes=[(-1,3,224,224), (-1,1)],
>>> dtypes=['float32', 'int64'],
>>> name='test_reader')
>>> test_reader.decorate_paddle_reader(paddle.batch(mnist.test(), 512))
>>>
>>> # Create train_main_prog and train_startup_prog
>>> train_main_prog = fluid.Program()
>>> train_startup_prog = fluid.Program()
>>> with fluid.program_guard(train_main_prog, train_startup_prog):
>>> # Use fluid.unique_name.guard() to share parameters with test program
>>> with fluid.unique_name.guard():
>>> train_loss = network(train_reader) # some network definition
>>> adam = fluid.optimizer.Adam(learning_rate=0.01)
>>> adam.minimize(loss)
>>>
>>> # Create test_main_prog and test_startup_prog
>>> test_main_prog = fluid.Program()
>>> test_startup_prog = fluid.Program()
>>> with fluid.program_guard(test_main_prog, test_startup_prog):
>>> # Use fluid.unique_name.guard() to share parameters with train program
>>> with fluid.unique_name.guard():
>>> test_loss = network(test_reader)
>>>
>>> fluid.Executor(fluid.CUDAPlace(0)).run(train_startup_prog)
>>> fluid.Executor(fluid.CUDAPlace(0)).run(test_startup_prog)
>>>
>>> train_exe = fluid.ParallelExecutor(use_cuda=True,
>>> loss_name=train_loss.name, main_program=train_main_prog)
>>> test_exe = fluid.ParallelExecutor(use_cuda=True,
>>> loss_name=test_loss.name, main_program=test_main_prog)
>>> for epoch_id in range(10):
>>> train_reader.start()
>>> try:
>>> while True:
>>> train_exe.run(fetch_list=[train_loss.name])
>>> except fluid.core.EOFException:
>>> train_reader.reset()
>>>
>>> test_reader.start()
>>> try:
>>> while True:
>>> test_exe.run(fetch_list=[test_loss.name])
>>> except fluid.core.EOFException:
>>> test_reader.reset()
"""
dtypes
=
[
convert_np_dtype_to_dtype_
(
dt
)
for
dt
in
dtypes
]
shape_concat
=
[]
...
...
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