提交 f8df9eb3 编写于 作者: Z Zeng Jinle 提交者: Cheerego

fix api doc (#16201)

test=develop
上级 13816dd4
......@@ -12,7 +12,7 @@ paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], va
paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '0ef753f5cec69fef9ae6ad8b867b33a2'))
paddle.fluid.Executor.__init__ (ArgSpec(args=['self', 'place'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.Executor.close (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'f5369953dd0c443961cf79f7a00e1a03'))
paddle.fluid.Executor.run (ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)), ('document', 'aba8093edebf2d5c869b735b92811e45'))
paddle.fluid.Executor.run (ArgSpec(args=['self', 'program', 'feed', 'fetch_list', 'feed_var_name', 'fetch_var_name', 'scope', 'return_numpy', 'use_program_cache'], varargs=None, keywords=None, defaults=(None, None, None, 'feed', 'fetch', None, True, False)), ('document', 'f482e93b38b4018796969a2e1dde479d'))
paddle.fluid.global_scope (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'e148d3ab1ed8edf3e928212a375959c0'))
paddle.fluid.scope_guard (ArgSpec(args=['scope'], varargs=None, keywords=None, defaults=None), ('document', 'b94d1f6bcc29c4fb58fc0058561250c2'))
paddle.fluid.DistributeTranspiler.__init__ (ArgSpec(args=['self', 'config'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......@@ -494,7 +494,7 @@ paddle.fluid.CUDAPinnedPlace.__init__ __init__(self: paddle.fluid.core.CUDAPinne
paddle.fluid.ParamAttr.__init__ (ArgSpec(args=['self', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.WeightNormParamAttr.__init__ (ArgSpec(args=['self', 'dim', 'name', 'initializer', 'learning_rate', 'regularizer', 'trainable', 'gradient_clip', 'do_model_average'], varargs=None, keywords=None, defaults=(None, None, None, 1.0, None, True, None, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.DataFeeder.__init__ (ArgSpec(args=['self', 'feed_list', 'place', 'program'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', '0eed2f198dc73c08a41b61edbc755753'))
paddle.fluid.DataFeeder.decorate_reader (ArgSpec(args=['self', 'reader', 'multi_devices', 'num_places', 'drop_last'], varargs=None, keywords=None, defaults=(None, True)), ('document', 'f8f3df23c5633c614db781a91b81fb62'))
paddle.fluid.DataFeeder.feed (ArgSpec(args=['self', 'iterable'], varargs=None, keywords=None, defaults=None), ('document', '459e316301279dfd82001b46f0b8ffca'))
paddle.fluid.DataFeeder.feed_parallel (ArgSpec(args=['self', 'iterable', 'num_places'], varargs=None, keywords=None, defaults=(None,)), ('document', '543863d1f9d4853758adb613b8659e85'))
paddle.fluid.clip.ErrorClipByValue.__init__ (ArgSpec(args=['self', 'max', 'min'], varargs=None, keywords=None, defaults=(None,)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
......@@ -518,11 +518,11 @@ paddle.reader.compose (ArgSpec(args=[], varargs='readers', keywords='kwargs', de
paddle.reader.chain (ArgSpec(args=[], varargs='readers', keywords=None, defaults=None), ('document', 'd22c34e379a53901ae67a6bca7f4def4'))
paddle.reader.shuffle (ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None), ('document', 'e42ea6fee23ce26b23cb142cd1d6522d'))
paddle.reader.firstn (ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None), ('document', 'c5bb8f7dd4f917f1569a368aab5b8aad'))
paddle.reader.xmap_readers (ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)), ('document', '283bc0b8a0e26ae186b8b9bee4aec560'))
paddle.reader.xmap_readers (ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)), ('document', '9c804a42f8a4dbaa76b3c98e0ab7f796'))
paddle.reader.PipeReader.__init__ (ArgSpec(args=['self', 'command', 'bufsize', 'file_type'], varargs=None, keywords=None, defaults=(8192, 'plain')), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.reader.PipeReader.get_line (ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n')), ('document', '5f80a7ed70052f01665e4c74acccfa69'))
paddle.reader.PipeReader.get_line (ArgSpec(args=['self', 'cut_lines', 'line_break'], varargs=None, keywords=None, defaults=(True, '\n')), ('document', '9621ae612e595b6c34eb3bb5f3eb1a45'))
paddle.reader.multiprocess_reader (ArgSpec(args=['readers', 'use_pipe', 'queue_size'], varargs=None, keywords=None, defaults=(True, 1000)), ('document', '7d8b3a96e592107c893d5d51ce968ba0'))
paddle.reader.Fake.__init__ (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.reader.creator.np_array (ArgSpec(args=['x'], varargs=None, keywords=None, defaults=None), ('document', '28d457fbc9a71efa4ac91a3be179cada'))
paddle.reader.creator.text_file (ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None), ('document', '44fe286ab6175a5464d3a961a68c266a'))
paddle.reader.creator.recordio (ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,)), ('document', '11b3704ea42cfd537953387a7e58dae8'))
paddle.reader.creator.text_file (ArgSpec(args=['path'], varargs=None, keywords=None, defaults=None), ('document', 'f45fcb7add066c8e042c6774fc7c3db2'))
paddle.reader.creator.recordio (ArgSpec(args=['paths', 'buf_size'], varargs=None, keywords=None, defaults=(100,)), ('document', 'b4a94ee0e2cefb495619275c2f8c61d2'))
......@@ -268,8 +268,8 @@ class DataFeeder(object):
Args:
reader(function): the reader is the function which can generate data.
multi_devices(bool): whether to use multiple devices or not.
num_places(int): if the multi_devices is True, you can specify the number
of GPU to use, if 'num_places' is None, the function will use all the
num_places(int): if multi_devices is True, you can specify the number
of GPU to use, if multi_devices is None, the function will use all the
GPU of the current machine. Default None.
drop_last(bool): whether to drop the last batch if the
size of the last batch is less than batch_size. Default True.
......@@ -278,7 +278,7 @@ class DataFeeder(object):
dict: the result of conversion.
Raises:
ValueError: If drop_last is False and the data batch which cannot fit for devices.
ValueError: If drop_last is False and the data batch cannot fit for devices.
"""
def __reader_creator__():
......
......@@ -470,12 +470,20 @@ class Executor(object):
program(Program|CompiledProgram): the program that need to run,
if not provided, then default_main_program (not compiled) will be used.
feed(dict): feed variable map, e.g. {"image": ImageData, "label": LabelData}
fetch_list(list): a list of variable or variable names that user want to get, run will return them according to this list.
feed_var_name(str): the name for the input variable of feed Operator.
fetch_var_name(str): the name for the output variable of fetch Operator.
scope(Scope): the scope used to run this program, you can switch it to different scope. default is global_scope
fetch_list(list): a list of variable or variable names that user
wants to get, this method will return them according to this list.
feed_var_name(str): the name for the input variable of
feed Operator.
fetch_var_name(str): the name for the output variable of
fetch Operator.
scope(Scope): the scope used to run this program, you can switch
it to different scope. default is global_scope
return_numpy(bool): if convert the fetched tensor to numpy
use_program_cache(bool): set use_program_cache to true if program not changed compare to the last step.
use_program_cache(bool): whether to use the cached program
settings across batches. Setting it be true would be faster
only when (1) the program is not compiled with data parallel,
and (2) program, feed variable names and fetch_list variable
names do not changed compared to the last step.
Returns:
......
......@@ -38,9 +38,8 @@ items. It can be any function with no parameter that creates a iterable
Element produced from the iterable should be a **single** entry of data,
**not** a mini batch. That entry of data could be a single item, or a tuple of
items.
Item should be of `supported type <http://www.paddlepaddle.org/doc/ui/data_provider
/pydataprovider2.html?highlight=dense_vector#input-types>`_ (e.g., numpy 1d
array of float32, int, list of int)
Item should be of supported type (e.g., numpy array or list/tuple of float
or int).
An example implementation for single item data reader creator:
......@@ -62,8 +61,6 @@ An example implementation for multiple item data reader creator:
yield numpy.random.uniform(-1, 1, size=width*height), label
return reader
TODO(yuyang18): Should we add whole design doc here?
"""
import paddle.reader.decorator
......
......@@ -44,8 +44,11 @@ def text_file(path):
Creates a data reader that outputs text line by line from given text file.
Trailing new line ('\\\\n') of each line will be removed.
:path: path of the text file.
:returns: data reader of text file
Args:
path (str): path of the text file.
Returns:
callable: data reader of text file.
"""
def reader():
......@@ -59,10 +62,15 @@ def text_file(path):
def recordio(paths, buf_size=100):
"""
Creates a data reader from given RecordIO file paths separated by ",",
glob pattern is supported.
:path: path of recordio files, can be a string or a string list.
:returns: data reader of recordio files.
Creates a data reader from given RecordIO file paths separated
by ",", glob pattern is supported.
Args:
paths (str|list(str)): path of recordio files.
buf_size (int): prefetched buffer size.
Returns:
callable: data reader of recordio files.
"""
import recordio as rec
......
......@@ -242,20 +242,18 @@ class XmapEndSignal():
def xmap_readers(mapper, reader, process_num, buffer_size, order=False):
"""
Use multiprocess to map samples from reader by a mapper defined by user.
And this function contains a buffered decorator.
:param mapper: a function to map sample.
:type mapper: callable
:param reader: the data reader to read from
:type reader: callable
:param process_num: process number to handle original sample
:type process_num: int
:param buffer_size: max buffer size
:type buffer_size: int
:param order: keep the order of reader
:type order: bool
:return: the decarated reader
:rtype: callable
Use multi-threads to map samples from reader by a mapper defined by user.
Args:
mapper (callable): a function to map the data from reader.
reader (callable): a data reader which yields the data.
process_num (int): thread number to handle original sample.
buffer_size (int): size of the queue to read data in.
order (bool): whether to keep the data order from original reader.
Default False.
Returns:
callable: a decorated reader with data mapping.
"""
end = XmapEndSignal()
......@@ -477,7 +475,7 @@ class PipeReader:
"""
:param cut_lines: cut buffer to lines
:type cut_lines: bool
:param line_break: line break of the file, like \n or \r
:param line_break: line break of the file, like '\\\\n' or '\\\\r'
:type line_break: string
:return: one line or a buffer of bytes
......
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