提交 bb166a1e 编写于 作者: S sneaxiy

fix API.spec

test=develop
上级 3a09693f
......@@ -47,7 +47,7 @@ paddle.fluid.AsyncExecutor.run (ArgSpec(args=['self', 'program', 'data_feed', 'f
paddle.fluid.AsyncExecutor.save_model (ArgSpec(args=['self', 'save_path'], varargs=None, keywords=None, defaults=None), ('document', 'c8ac0dfcb3b187aba25d03af7fea56b2'))
paddle.fluid.AsyncExecutor.stop (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '5f23d043607bb5d55e466ec3f578e093'))
paddle.fluid.CompiledProgram.__init__ (ArgSpec(args=['self', 'program_or_graph'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.CompiledProgram.with_data_parallel (ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from', 'places'], varargs=None, keywords=None, defaults=(None, None, None, None, None)), ('document', 'dbf542d1384741650a1238ddb05daa37'))
paddle.fluid.CompiledProgram.with_data_parallel (ArgSpec(args=['self', 'loss_name', 'build_strategy', 'exec_strategy', 'share_vars_from', 'places'], varargs=None, keywords=None, defaults=(None, None, None, None, None)), ('document', '5e8cca4619a5d7c3280fb3cae7021b14'))
paddle.fluid.CompiledProgram.with_inference_optimize (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=None), ('document', '9e5b009d850191a010e859189c127fd8'))
paddle.fluid.ExecutionStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.ExecutionStrategy) -> None
paddle.fluid.BuildStrategy.GradientScaleStrategy.__init__ __init__(self: paddle.fluid.core.ParallelExecutor.BuildStrategy.GradientScaleStrategy, arg0: int) -> None
......@@ -61,8 +61,8 @@ paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program'
paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '28df5bfe26ca7a077f91156abb0fe6d2'))
paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment'], varargs=None, keywords=None, defaults=(None, None, None, True)), ('document', '582d87b8df75a5a639a107db8ff86f9c'))
paddle.fluid.io.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '7a5255386075dac3c75b7058254fcdcb'))
paddle.fluid.io.PyReader.__init__ (ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable'], varargs=None, keywords=None, defaults=(True, False)), ('document', 'b3d72958b2568aae3f90f72abdcb7d1a'))
paddle.fluid.io.PyReader.decorate_batch_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', 'd10224fef1095247063b6976da793021'))
paddle.fluid.io.PyReader.__init__ (ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable'], varargs=None, keywords=None, defaults=(True, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.io.PyReader.decorate_batch_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a3fefec8bacd6ce83f49906a9d05e779'))
paddle.fluid.io.PyReader.decorate_sample_generator (ArgSpec(args=['self', 'sample_generator', 'batch_size', 'drop_last', 'places'], varargs=None, keywords=None, defaults=(True, None)), ('document', '7abd9cf7d695bab5bb6cf7ded5903cb2'))
paddle.fluid.io.PyReader.decorate_sample_list_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', 'faef298f73e91aedcfaf5d184f3109b7'))
paddle.fluid.io.PyReader.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'ff1cc1e2beb8824d453656c72c28ddfb'))
......@@ -521,7 +521,7 @@ paddle.fluid.unique_name.guard (ArgSpec(args=['new_generator'], varargs=None, ke
paddle.fluid.recordio_writer.convert_reader_to_recordio_file (ArgSpec(args=['filename', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', '65c7523e86f0c50bb729b01667f36310'))
paddle.fluid.recordio_writer.convert_reader_to_recordio_files (ArgSpec(args=['filename', 'batch_per_file', 'reader_creator', 'feeder', 'compressor', 'max_num_records', 'feed_order'], varargs=None, keywords=None, defaults=(Compressor.Snappy, 1000, None)), ('document', 'bc643f0f5f1b9db57ff0d8a57d379bd7'))
paddle.fluid.Scope Scope() -> paddle.fluid.core._Scope
paddle.reader.cache (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '83b94750674c6a04b5f96599d4bf3105'))
paddle.reader.cache (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', '1676886070eb607cb608f7ba47be0d3c'))
paddle.reader.map_readers (ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None), ('document', '77cbadb09df588e21e5cc0819b69c87d'))
paddle.reader.buffered (ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None), ('document', '0d6186f109feceb99f60ec50a0a624cb'))
paddle.reader.compose (ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None), ('document', '884291104e1c3f37f33aae44b7deeb0d'))
......
......@@ -123,7 +123,7 @@ class CompiledProgram(object):
will share variables from `share_vars_from`. `share_vars_from`
must be run by the executor before this CompiledProgram so that
vars are ready.
places(list(CUDAPlace)|list(CPUPlace)|None): If provide, only compile
places(list(CUDAPlace)|list(CPUPlace)|None): If provided, only compile
program in the given places. Otherwise, the places used when compiled
is determined by the Executor, and the places used are controlled
by environment variables: FLAGS_selected_gpus or CUDA_VISIBLE_DEVICES
......@@ -148,7 +148,7 @@ class CompiledProgram(object):
if places is not None:
if not isinstance(places, (list, tuple)):
places = [places]
self._places = [_place_obj(p) for p in places]
self._places = places
else:
self._places = None
self._build_strategy.is_distribution = _is_pserver_mode(self._program)
......@@ -195,14 +195,12 @@ class CompiledProgram(object):
self._exec_strategy.use_cuda = use_cuda
has_set_place = (self._places is not None)
if has_set_place:
desire_place = _place_obj(self._place)
for p in self._places:
assert p._type() == desire_place._type(), \
assert p._type() == self._place._type(), \
"Place type not match. You may set the wrong type of places"
else:
places = cuda_places(
self._places = cuda_places(
) if self._exec_strategy.use_cuda else cpu_places()
self._places = [_place_obj(p) for p in places]
assert self._places, "no place for execution"
if self._exec_strategy.num_threads == 0:
......
......@@ -40,6 +40,77 @@ def _convert_places(places):
class PyReader(object):
"""
Create a reader object for data feeding in Python.
Data would be prefetched using Python thread and be pushed
into a queue asynchronously. Data in the queue would be extracted
automatically when `Executor.run(...)` is called.
Args:
feed_list (list(Variable)|tuple(Variable)): feed variable list.
The variables should be created by :code:`fluid.layers.data()`.
capacity (int): capacity of the queue maintained in PyReader object.
use_double_buffer (bool): whether to use double_buffer_reader to
speed up data feeding.
iterable (bool): whether the created reader object is iterable.
Returns:
reader (Reader): the created reader object.
Examples:
1. If iterable = False, the created PyReader object is almost the
same as :code:`fluid.layers.py_reader()`. Operators would be
inserted into the program. User should call :code:`start()`
before each epoch and catch :code:`fluid.core.EOFException`
thrown by :code:`Executor.run()` when epoch ends. Once the
exception is caught, user should call :code:`reset()` to reset
the reader manually.
.. code-block:: python
image = fluid.layers.data(
name='image', shape=[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)
reader.decorate_sample_list_generator(user_defined_reader)
... # definition of network is omitted
executor.run(fluid.default_main_program())
for _ in range(EPOCH_NUM):
reader.start()
while True:
try:
executor.run(feed=None, ...)
except fluid.core.EOFException:
reader.reset()
break
2. If iterable=True, the created PyReader object is decoupled with
the program. No operator would be inserted into the program.
In this case, the created reader is a Python generator, which
is iterable. User should feed the data yielded from PyReader
object into :code:`Executor.run(feed=...)`.
.. code-block:: python
image = fluid.layers.data(
name='image', shape=[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)
reader.decorate_sample_list_generator(user_defined_reader,
places=fluid.cuda_places())
... # definition of network is omitted
executor.run(fluid.default_main_program())
for _ in range(EPOCH_NUM):
for data in reader():
executor.run(feed=data, ...)
"""
unique_name_generator = UniqueNameGenerator()
def __init__(self,
......@@ -47,76 +118,6 @@ class PyReader(object):
capacity,
use_double_buffer=True,
iterable=False):
"""
Create a reader object for data feeding in Python.
Data would be prefetched using Python thread and be pushed
into a queue asynchronously. Data in the queue would be extracted
automatically when `Executor.run(...)` is called.
Args:
feed_list (list(Variable)|tuple(Variable)): feed variable list.
The variables should be created by :code:`fluid.layers.data()`.
capacity (int): capacity of the queue maintained in PyReader object.
use_double_buffer (bool): whether to use double_buffer_reader to
speed up data feeding.
iterable (bool): whether the created reader object is iterable.
Returns:
reader (Reader): the created reader object.
Examples:
1. If iterable = False, the created PyReader object is almost the
same as :code:`fluid.layers.py_reader()`. Operators would be
inserted into the program. User should call :code:`start()`
before each epoch and catch :code:`fluid.core.EOFException`
thrown by :code:`Executor.run()` when epoch ends. Once the
exception is caught, user should call :code:`reset()` to reset
the reader manually.
.. code-block:: python
image = fluid.layers.data(
name='image', shape=[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)
reader.decorate_sample_list_generator(user_defined_reader)
... # definition of network is omitted
executor.run(fluid.default_main_program())
for _ in range(EPOCH_NUM):
reader.start()
while True:
try:
executor.run(feed=None, ...)
except fluid.core.EOFException:
reader.reset()
break
2. If iterable=True, the created PyReader object is decoupled with
the program. No operator would be inserted into the program.
In this case, the created reader is a Python generator, which
is iterable. User should feed the data yielded from PyReader
object into :code:`Executor.run(feed=...)`.
.. code-block:: python
image = fluid.layers.data(
name='image', shape=[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)
reader.decorate_sample_list_generator(user_defined_reader,
places=fluid.cuda_places())
... # definition of network is omitted
executor.run(fluid.default_main_program())
for _ in range(EPOCH_NUM):
for data in reader():
executor.run(feed=data, ...)
"""
self._tensor_reader = None
self._thread = None
self._iterable = iterable
......@@ -361,7 +362,7 @@ class PyReader(object):
Args:
reader (generator): Python generator that yields LoDTensor-typed
batched data.
places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
places (None|list(CUDAPlace)|list(CPUPlace)): place list. Must
be provided when PyReader is iterable.
'''
assert self._tensor_reader is None, \
......
......@@ -46,8 +46,7 @@ def cache(reader):
data each time.
Returns:
reader (generator): a decorated reader object
which yields data from cached memory.
generator: a decorated reader object which yields data from cached memory.
"""
all_data = tuple(reader())
......
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