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提交 9b85f401 编写于 作者: G guofei 提交者： Huihuang Zheng
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Showing with 302 addition and 175 deletion
paddle/fluid/API.spec
浏览文件 @ 9b85f401
......@@ -9,7 +9,7 @@ paddle.fluid.Program.parse_from_string (ArgSpec(args=['binary_str'], varargs=Non
paddle.fluid.Program.to_string (ArgSpec(args=['self', 'throw_on_error', 'with_details'], varargs=None, keywords=None, defaults=(False,)), ('document', '7dde33f16b63aa50d474870a9cebb539'))
paddle.fluid.default_startup_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', 'f53890b2fb8c0642b6047e4fee2d6d58'))
paddle.fluid.default_main_program (ArgSpec(args=[], varargs=None, keywords=None, defaults=None), ('document', '082aa471d247bd8d7c87814105439e1a'))
paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', '78fb5c7f70ef76bcf4a1862c3f6b8191'))
paddle.fluid.program_guard (ArgSpec(args=['main_program', 'startup_program'], varargs=None, keywords=None, defaults=(None,)), ('document', 'eb4eabc13405a8c6dc2f14308ddf5ed8'))
paddle.fluid.name_scope (ArgSpec(args=['prefix'], varargs=None, keywords=None, defaults=(None,)), ('document', '907a5f877206079d8e67ae69b06bb3ba'))
paddle.fluid.cuda_places (ArgSpec(args=['device_ids'], varargs=None, keywords=None, defaults=(None,)), ('document', 'ab9bd2079536114aa7c1488a489ee87f'))
paddle.fluid.cpu_places (ArgSpec(args=['device_count'], varargs=None, keywords=None, defaults=(None,)), ('document', 'a7352a3dd39308fde4fbbf6421a4193d'))
......@@ -72,24 +72,24 @@ paddle.fluid.BuildStrategy.ReduceStrategy.__init__ __init__(self: paddle.fluid.c
paddle.fluid.BuildStrategy.__init__ __init__(self: paddle.fluid.core_avx.ParallelExecutor.BuildStrategy) -> None
paddle.fluid.gradients (ArgSpec(args=['targets', 'inputs', 'target_gradients', 'no_grad_set'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'e2097e1e0ed84ae44951437bfe269a1b'))
paddle.fluid.io.save_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '9ff7159eef501e9dfaf520073e681c10'))
paddle.fluid.io.save_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '046d7c43d67e08c2660bb3bd7e081015'))
paddle.fluid.io.save_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'ffcee38044975c29f2ab2fec0576f963'))
paddle.fluid.io.save_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'a03d0de7594d311103671b7275f1b464'))
paddle.fluid.io.save_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '3cd8facbe536a09310e95453914ca322'))
paddle.fluid.io.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '12dd2c3f29d63f7a920bb1e0a0e8caff'))
paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'f3f16db75ae076d46608c7e976650cfc'))
paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '1e039084ad3781eb43966581eed48688'))
paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment', 'program_only'], varargs=None, keywords=None, defaults=(None, None, None, True, False)), ('document', 'fc82bfd137a9b1ab8ebd1651bd35b6e5'))
paddle.fluid.io.save_inference_model (ArgSpec(args=['dirname', 'feeded_var_names', 'target_vars', 'executor', 'main_program', 'model_filename', 'params_filename', 'export_for_deployment', 'program_only'], varargs=None, keywords=None, defaults=(None, None, None, True, False)), ('document', '827797614e194d31ceb5a3a68c46efab'))
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', '7a863032bf7613dec1c8dd99efbd82e5'))
paddle.fluid.io.batch (ArgSpec(args=['reader', 'batch_size', 'drop_last'], varargs=None, keywords=None, defaults=(False,)), ('document', 'cf2869b408b39cadadd95206b4e03b39'))
paddle.fluid.io.batch (ArgSpec(args=['reader', 'batch_size', 'drop_last'], varargs=None, keywords=None, defaults=(False,)), ('document', '16acb4e1215d5fc4386add454e717440'))
paddle.fluid.io.save (ArgSpec(args=['program', 'model_path'], varargs=None, keywords=None, defaults=None), ('document', 'cef7d50c36b93c02b6d12bcea7d025ce'))
paddle.fluid.io.load (ArgSpec(args=['program', 'model_path'], varargs=None, keywords=None, defaults=None), ('document', '8d0f200c20f8a4581e1843967230ad45'))
paddle.fluid.io.PyReader ('paddle.fluid.reader.PyReader', ('document', 'b03399246f69cd6fc03b43e87af8bd4e'))
paddle.fluid.io.PyReader ('paddle.fluid.reader.PyReader', ('document', 'f5875acee86f9f4432933bab40873722'))
paddle.fluid.io.PyReader.__init__ (ArgSpec(args=['self', 'feed_list', 'capacity', 'use_double_buffer', 'iterable', 'return_list'], varargs=None, keywords=None, defaults=(None, None, True, True, False)), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.io.PyReader.decorate_batch_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', '4364e836e3cb8ab5e68e411b763c50c7'))
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', 'efa4c8b90fe6d99dcbda637b70351bb1'))
paddle.fluid.io.PyReader.decorate_sample_list_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', '6c11980092720de304863de98074a64a'))
paddle.fluid.io.PyReader.decorate_batch_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', '386f969058852594b916be7cc15b8066'))
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', 'c3ab4fd82a4560e369adcd23a36c2a7b'))
paddle.fluid.io.PyReader.decorate_sample_list_generator (ArgSpec(args=['self', 'reader', 'places'], varargs=None, keywords=None, defaults=(None,)), ('document', '182443165b1b6a607ca821052b1b9085'))
paddle.fluid.io.PyReader.next (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '08b2fd1463f3ea99d79d17303988349b'))
paddle.fluid.io.PyReader.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '7432197701fdaab1848063860dc0b97e'))
paddle.fluid.io.PyReader.start (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'a0983fb21a0a51e6a31716009fe9a9c1'))
paddle.fluid.io.PyReader.reset (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '259e025143f0bb7cfc6d7163bc333679'))
paddle.fluid.io.PyReader.start (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'c91176b919e19be11cbc11ab3d47318e'))
paddle.fluid.io.DataLoader ('paddle.fluid.reader.DataLoader', ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.io.DataLoader.__init__
paddle.fluid.io.DataLoader.from_dataset (ArgSpec(args=['dataset', 'places', 'drop_last'], varargs=None, keywords=None, defaults=(True,)), ('document', 'eb8b6d31e1c2ec2ca8ebbb62fcf46557'))
......@@ -310,10 +310,10 @@ paddle.fluid.layers.hard_swish (ArgSpec(args=['x', 'threshold', 'scale', 'offset
paddle.fluid.layers.gather_tree (ArgSpec(args=['ids', 'parents'], varargs=None, keywords=None, defaults=None), ('document', '201b54fa7512305078c70a6610beaead'))
paddle.fluid.layers.mse_loss (ArgSpec(args=['input', 'label'], varargs=None, keywords=None, defaults=None), ('document', '88b967ef5132567396062d5d654b3064'))
paddle.fluid.layers.uniform_random (ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=('float32', -1.0, 1.0, 0)), ('document', '34e7c1ff0263baf9551000b6bb3bc47e'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', '9d7806e31bdf727c1a23b8782a09b545'))
paddle.fluid.layers.data (ArgSpec(args=['name', 'shape', 'append_batch_size', 'dtype', 'lod_level', 'type', 'stop_gradient'], varargs=None, keywords=None, defaults=(True, 'float32', 0, VarType.LOD_TENSOR, True)), ('document', 'a43c597ac4e1cec20cf193c083d946be'))
paddle.fluid.layers.read_file (ArgSpec(args=['reader'], varargs=None, keywords=None, defaults=None), ('document', 'd5b41c7b2df1b064fbd42dcf435268cd'))
paddle.fluid.layers.double_buffer (ArgSpec(args=['reader', 'place', 'name'], varargs=None, keywords=None, defaults=(None, None)), ('document', '556fa82daf62cbb0fb393f4125daba77'))
paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', 'd78a1c7344955c5caed8dc13adb7beb6'))
paddle.fluid.layers.py_reader (ArgSpec(args=['capacity', 'shapes', 'dtypes', 'lod_levels', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, None, True)), ('document', 'c9c52b0a57e541d751e7a839ad26ee1a'))
paddle.fluid.layers.create_py_reader_by_data (ArgSpec(args=['capacity', 'feed_list', 'name', 'use_double_buffer'], varargs=None, keywords=None, defaults=(None, True)), ('document', '1321d4ce89d82f96fcfd5601f816b0f3'))
paddle.fluid.layers.load (ArgSpec(args=['out', 'file_path', 'load_as_fp16'], varargs=None, keywords=None, defaults=(None,)), ('document', '309f9e5249463e1b207a7347b2a91134'))
paddle.fluid.layers.create_tensor (ArgSpec(args=['dtype', 'name', 'persistable'], varargs=None, keywords=None, defaults=(None, False)), ('document', 'fdc2d964488e99fb0743887454c34e36'))
......
python/paddle/batch.py
浏览文件 @ 9b85f401
......@@ -17,16 +17,39 @@ __all__ = ['batch']
def batch(reader, batch_size, drop_last=False):
"""
Create a batched reader.
:param reader: the data reader to read from.
:type reader: callable
:param batch_size: size of each mini-batch
:type batch_size: int
:param drop_last: drop the last batch, if the size of last batch is not equal to batch_size.
:type drop_last: bool
:return: the batched reader.
:rtype: callable
This operator creates a batched reader which combines the data from the
input reader to batched data.
Args:
reader(generator): the data reader to read from.
batch_size(int): size of each mini-batch.
drop_last(bool, optional): If set to True, the last batch is dropped when
the size of last batch is not equal to batch_size, if set to False,
it will not. Default: False.
Returns:
The batched reader.
Return Type:
generator
Examples:
.. code-block:: python
import paddle.fluid as fluid
def reader():
for i in range(10):
yield i
batch_reader = fluid.io.batch(reader, batch_size=2)
for data in batch_reader():
print(data)
# Output is
# [0, 1]
# [2, 3]
# [4, 5]
# [6, 7]
# [8, 9]
"""
def batch_reader():
......
python/paddle/fluid/framework.py
浏览文件 @ 9b85f401
......@@ -4626,6 +4626,13 @@ def program_guard(main_program, startup_program=None):
Layer functions in the Python `"with"` block will append operators and
variables to the new main programs.
Args:
main_program(Program): New main program inside `"with"` statement.
startup_program(Program, optional): New startup program inside `"with"`
statement. :code:`None` means not changing startup program,
default_startup_program is still used.
Default: None.
Examples:
.. code-block:: python
......@@ -4634,7 +4641,7 @@ def program_guard(main_program, startup_program=None):
main_program = fluid.Program()
startup_program = fluid.Program()
with fluid.program_guard(main_program, startup_program):
data = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
hidden = fluid.layers.fc(input=data, size=10, act='relu')
Notes: The temporary :code:`Program` can be used if the user does not need
......@@ -4648,12 +4655,8 @@ def program_guard(main_program, startup_program=None):
main_program = fluid.Program()
# does not care about startup program. Just pass a temporary value.
with fluid.program_guard(main_program, fluid.Program()):
data = fluid.layers.data(name='image', shape=[784, 784], dtype='float32')
Args:
main_program(Program): New main program inside `"with"` statement.
startup_program(Program): New startup program inside `"with"` statement.
None means not changing startup program.
data = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
"""
if not isinstance(main_program, Program):
raise TypeError("main_program should be Program")
......
python/paddle/fluid/io.py
浏览文件 @ 9b85f401
......@@ -258,32 +258,40 @@ def save_vars(executor,
def save_params(executor, dirname, main_program=None, filename=None):
"""
This function filters out all parameters from the give `main_program`
and then save them to the folder `dirname` or the file `filename`.
This operator saves all parameters from the :code:`main_program` to
the folder :code:`dirname` or file :code:`filename`. You can refer to
:ref:`api_guide_model_save_reader_en` for more details.
Use the `dirname` to specify the saving folder. If you would like to
save parameters in separate files, set `filename` None; if you would
like to save all parameters in a single file, use `filename` to specify
Use the :code:`dirname` to specify the saving folder. If you would like to
save parameters in separate files, set :code:`filename` None; if you would
like to save all parameters in a single file, use :code:`filename` to specify
the file name.
NOTICE: Some variables are not Parameter while they are necessary for
training. So you can NOT save and continue your training just by
`save_params()` and `load_params()`. Please use `save_persistables()`
and `load_persistables()` instead. If you want to save your model for
the inference, please use the `save_inference_model` API. You can refer
to :ref:`api_guide_model_save_reader_en` for more details.
Note:
Some variables are not Parameter while they are necessary for
training, such as learning rate, global step, etc. So you can NOT save
and continue your training just by :ref:`api_fluid_io_save_params`
and :ref:`api_fluid_io_load_params`. Please use :ref:`api_fluid_io_save_persistables`
and :ref:`api_fluid_io_load_persistables` instead.
If you want to save your model for the inference, please use the
:ref:`api_fluid_io_save_inference_model`. You can refer to
:ref:`api_guide_model_save_reader_en` for more details.
Args:
executor(Executor): The executor to run for saving parameters.
executor(Executor): The executor to run for saving parameters, You can
refer to :ref:`api_guide_executor_en`.
dirname(str): The saving directory path.
main_program(Program|None): The program whose parameters will be
saved. If it is None, the default
main program will be used automatically.
Default: None
filename(str|None): The file to save all parameters. If you prefer
to save parameters in differnet files, set it
to None.
Default: None
main_program(Program, optional): The program whose parameters will be
saved. You can refer to
:ref:`api_guide_Program_en` for more
details. If it is None, the default main
program will be used.
Default: None
filename(str, optional): The file to save all parameters. If you prefer
to save parameters in different files, set it
to None.
Default: None
Returns:
None
......@@ -292,12 +300,21 @@ def save_params(executor, dirname, main_program=None, filename=None):
.. code-block:: python
import paddle.fluid as fluid
params_path = "./my_paddle_model"
image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace())
predict = fluid.layers.fc(input=image, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=predict, label=label)
avg_loss = fluid.layers.mean(loss)
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
fluid.io.save_params(executor=exe, dirname=param_path,
main_program=None)
exe.run(fluid.default_startup_program())
fluid.io.save_params(executor=exe, dirname=params_path)
# The parameters weights and bias of the fc layer in the network are going to
# be saved in different files in the path "./my_paddle_model"
"""
save_vars(
executor,
......@@ -491,25 +508,31 @@ def _save_distributed_persistables(executor, dirname, main_program):
def save_persistables(executor, dirname, main_program=None, filename=None):
"""
This function filters out all variables with `persistable==True` from the
give `main_program` and then saves these variables to the folder `dirname`
or file `filename`.
This operator saves all persistable variables from :code:`main_program` to
the folder :code:`dirname` or file :code:`filename`. You can refer to
:ref:`api_guide_model_save_reader_en` for more details. And then
saves these persistables variables to the folder :code:`dirname` or file
:code:`filename`.
The `dirname` is used to specify the folder where persistable variables
The :code:`dirname` is used to specify the folder where persistable variables
are going to be saved. If you would like to save variables in separate
files, set `filename` None; if you would like to save all variables in a
single file, use `filename` to specify the file name.
files, set :code:`filename` None; if you would like to save all variables in a
single file, use :code:`filename` to specify the file name.
Args:
executor(Executor): The executor to run for saving persistable variables.
dirname(str): The directory path.
main_program(Program|None): The program whose persistbale variables will
be saved. If it is None, the default main
program will be used automatically.
Default: None
filename(str|None): The file to saved all variables. If you prefer to
save variables in differnet files, set it to None.
Default: None
You can refer to :ref:`api_guide_executor_en` for
more details.
dirname(str): The saving directory path.
main_program(Program, optional): The program whose persistbale variables will
be saved. You can refer to
:ref:`api_guide_Program_en` for more details.
If it is None, the default main program will
be used.
Default: None.
filename(str, optional): The file to save all variables. If you prefer to
save variables in different files, set it to None.
Default: None.
Returns:
None
......@@ -518,13 +541,22 @@ def save_persistables(executor, dirname, main_program=None, filename=None):
.. code-block:: python
import paddle.fluid as fluid
dir_path = "./my_paddle_model"
file_name = "persistables"
image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace())
predict = fluid.layers.fc(input=image, size=10, act='softmax')
loss = fluid.layers.cross_entropy(input=predict, label=label)
avg_loss = fluid.layers.mean(loss)
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
# `prog` can be a program defined by the user
prog = fluid.default_main_program()
fluid.io.save_persistables(executor=exe, dirname=param_path,
main_program=prog)
exe.run(fluid.default_startup_program())
fluid.io.save_persistables(executor=exe, dirname=dir_path, filename=file_name)
# The persistables variables weights and bias in the fc layer of the network
# are going to be saved in the same file named "persistables" in the path
# "./my_paddle_model"
"""
if main_program and main_program._is_distributed:
_save_distributed_persistables(
......@@ -973,42 +1005,54 @@ def save_inference_model(dirname,
program_only=False):
"""
Prune the given `main_program` to build a new program especially for inference,
and then save it and all related parameters to given `dirname` by the `executor`.
and then save it and all related parameters to given `dirname` .
If you just want to save parameters of your trained model, please use the
`save_params` API. You can refer to :ref:`api_guide_model_save_reader_en` for
more details.
:ref:`api_fluid_io_save_params` . You can refer to :ref:`api_guide_model_save_reader_en`
for more details.
Note:
The :code:`dirname` is used to specify the folder where inference model
structure and parameters are going to be saved. If you would like to save params of
Program in separate files, set `params_filename` None; if you would like to save all
params of Program in a single file, use `params_filename` to specify the file name.
Args:
dirname(str): The directory path to save the inference model.
feeded_var_names(list[str]): Names of variables that need to be feeded data
during inference.
target_vars(list[Variable]): Variables from which we can get inference
results.
executor(Executor): The executor that saves the inference model.
main_program(Program|None): The original program, which will be pruned to
build the inference model. If is setted None,
the default main program will be used.
Default: None.
model_filename(str|None): The name of file to save the inference program
itself. If is setted None, a default filename
`__model__` will be used.
params_filename(str|None): The name of file to save all related parameters.
If it is setted None, parameters will be saved
in separate files .
feeded_var_names(list[str]): list of string. Names of variables that need to be feeded
data during inference.
target_vars(list[Variable]): list of Variable. Variables from which we can get
inference results.
executor(Executor): The executor that saves the inference model. You can refer
to :ref:`api_guide_executor_en` for more details.
main_program(Program, optional): The original program, which will be pruned to
build the inference model. If is setted None,
the global default :code:`_main_program_` will be used.
Default: None.
model_filename(str, optional): The name of file to save the inference program
itself. If is setted None, a default filename
:code:`__model__` will be used.
params_filename(str, optional): The name of file to save all related parameters.
If it is setted None, parameters will be saved
in separate files .
export_for_deployment(bool): If True, programs are modified to only support
direct inference deployment. Otherwise,
more information will be stored for flexible
optimization and re-training. Currently, only
True is supported.
program_only(bool): If True, It will save inference program only, and do not save params of Program.
Default: True.
program_only(bool, optional): If True, It will save inference program only, and do not
save params of Program.
Default: False.
Returns:
target_var_name_list(list): The fetch variables' name list
The fetch variables' name list
Return Type:
list
Raises:
ValueError: If `feed_var_names` is not a list of basestring.
ValueError: If `target_vars` is not a list of Variable.
ValueError: If `feed_var_names` is not a list of basestring, an exception is thrown.
ValueError: If `target_vars` is not a list of Variable, an exception is thrown.
Examples:
.. code-block:: python
......@@ -1018,8 +1062,8 @@ def save_inference_model(dirname,
path = "./infer_model"
# User defined network, here a softmax regresssion example
image = fluid.layers.data(name='img', shape=[1, 28, 28], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
image = fluid.data(name='img', shape=[None, 28, 28], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
feeder = fluid.DataFeeder(feed_list=[image, label], place=fluid.CPUPlace())
predict = fluid.layers.fc(input=image, size=10, act='softmax')
......@@ -1037,9 +1081,9 @@ def save_inference_model(dirname,
target_vars=[predict],
executor=exe)
# In this example, the function will prune the default main program
# to make it suitable for infering the `predict` var. The pruned
# inference program is going to be saved in the "./infer_model/__model__"
# In this example, the save_inference_mode inference will prune the default
# main program according to the network's input node (img) and output node(predict).
# The pruned inference program is going to be saved in the "./infer_model/__model__"
# and parameters are going to be saved in separate files under folder
# "./infer_model".
......
python/paddle/fluid/layers/io.py
浏览文件 @ 9b85f401
......@@ -47,25 +47,25 @@ def data(name,
"""
**Data Layer**
This function takes in the input and based on whether data has
to be returned back as a minibatch, it creates the global variable by using
the helper functions. The global variables can be accessed by all the
following operators in the graph.
This operator creates the global variable. The global variables can be
accessed by all the following operators in the graph.
All the input variables of this function are passed in as local variables
to the LayerHelper constructor.
Note:
:code:`paddle.fluid.layers.data` is deprecated as it will be removed in
a later version. Please use :code:`paddle.fluid.data` .
Notice that paddle would only use :code:`shape` to infer the shapes of
following variables in the network during compile-time. During run-time,
paddle would not check whether the shape of the feeded data matches the
:code:`shape` settings in this function.
The :code:`paddle.fluid.layers.data` set shape and dtype at compile time
but does NOT check the shape or the dtype of feeded data, this
:code:`paddle.fluid.data` checks the shape and the dtype of data feeded
by Executor or ParallelExecutor during run time.
Args:
name(str): The name/alias of the function
name(str): The name/alias of the variable, see :ref:`api_guide_Name`
for more details.
shape(list): Tuple declaring the shape. If :code:`append_batch_size` is
True and there is no -1 inside :code:`shape`, it should be
considered as the shape of the each sample. Otherwise, it
should be considered as the shape of the batched data.
True and there is no -1 inside :code:`shape`, it should be
considered as the shape of the each sample. Otherwise, it should
be considered as the shape of the batched data.
append_batch_size(bool):
1. If true, it prepends -1 to the shape.
For example if shape=[1], the resulting shape is [-1, 1]. This will
......@@ -74,13 +74,20 @@ def data(name,
append_batch_size will be enforced to be be False (ineffective)
because PaddlePaddle cannot set more than 1 unknown number on the
shape.
dtype(np.dtype|VarType|str): The type of data : float32, float16, int etc
type(VarType): The output type. By default it is LOD_TENSOR.
dtype(np.dtype|VarType|str): The type of the data. Supported dtype: bool,
float16, float32, float64, int8, int16, int32, int64, uint8.
type(VarType): The output type. Supported dtype: VarType.LOD_TENSOR,
VarType.SELECTED_ROWS, VarType.NCCL_ID. Default: VarType.LOD_TENSOR.
lod_level(int): The LoD Level. 0 means the input data is not a sequence.
Default: 0.
stop_gradient(bool): A boolean that mentions whether gradient should flow.
Default: True.
Returns:
Variable: The global variable that gives access to the data.
The global variable that gives access to the data.
Return Type:
Variable
Examples:
.. code-block:: python
......@@ -531,29 +538,43 @@ def py_reader(capacity,
"""
Create a Python reader for data feeding in Python
This layer returns a Reader Variable.
This operator 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. More details :ref:`user_guide_use_py_reader_en` . 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`.
source and feed the data from the data source to the Reader Variable.
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:
:code:`Program.clone()` method cannot clone :code:`py_reader`. You can
refer to :ref:`api_fluid_Program` for more details.
The :code:`read_file` call needs to be in the program block of :code:`py_reader`.
You can refer to :ref:`api_fluid_layers_read_file` for more details.
Args:
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.
shapes(list|tuple): List of tuples which declaring data shapes. shapes[i]
represents the i-th data shape.
dtypes(list|tuple): List of strings which declaring data type. Supported dtype:
bool, float16, float32, float64, int8, int16, int32, int64, uint8.
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.
name(basestring): The default value is None. Normally there is no
need for user to set this property. For more information, please
refer to :ref:`api_guide_Name`.
use_double_buffer(bool): Whether use double buffer or not. The double buffer is
for pre-reading the data of the next batch and copy the data asynchronously
from CPU to GPU. Default is True.
Returns:
Variable: A Reader from which we can get feeding data.
A Reader from which we can get feeding data.
Return Type:
Variable
Examples:
1. The basic usage of :code:`py_reader` is as follows:
......
python/paddle/fluid/reader.py
浏览文件 @ 9b85f401
......@@ -595,7 +595,10 @@ class PyReader(DataLoaderBase):
use return_list=True in dygraph mode.
Returns:
reader (Reader): the created reader object.
the created reader object.
Return type:
reader(Reader)
Examples:
1. If iterable = False, the created PyReader object is almost the
......@@ -615,6 +618,11 @@ class PyReader(DataLoaderBase):
EPOCH_NUM = 3
ITER_NUM = 5
BATCH_SIZE = 3
def network(image, label):
# User-defined network, here is an example of softmax regression.
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():
......@@ -626,8 +634,8 @@ class PyReader(DataLoaderBase):
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')
image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 1], dtype='int64')
reader = fluid.io.PyReader(feed_list=[image, label],
capacity=4,
......@@ -636,8 +644,8 @@ class PyReader(DataLoaderBase):
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))
# definition of network is omitted
executor = fluid.Executor(fluid.CUDAPlace(0))
loss = network(image, label)
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())
for i in range(EPOCH_NUM):
reader.start()
......@@ -665,26 +673,35 @@ class PyReader(DataLoaderBase):
ITER_NUM = 5
BATCH_SIZE = 10
def network(image, label):
# User-defined network, here is an example of softmax regression.
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):
yield np.random.uniform(low=0, high=255, size=[height, width]),
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')
reader = fluid.io.PyReader(feed_list=[image], capacity=4, iterable=True, return_list=False)
image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 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.CUDAPlace(0))
# definition of network is omitted
executor = fluid.Executor(fluid.CUDAPlace(0))
executor.run(fluid.default_main_program())
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)
executor.run(feed=data, fetch_list=[loss])
3. If return_list=True, the return values would be presented as list instead of dict.
......@@ -745,8 +762,8 @@ class PyReader(DataLoaderBase):
Start the data feeding thread.
Can only call when the reader object is not iterable.
Example:
.. code-block:: python
Example:
.. code-block:: python
import paddle
import paddle.fluid as fluid
......@@ -758,12 +775,12 @@ class PyReader(DataLoaderBase):
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')
image = fluid.data(name='image', shape=[None, 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.CUDAPlace(0))
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())
for i in range(3):
reader.start()
......@@ -795,12 +812,12 @@ class PyReader(DataLoaderBase):
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')
image = fluid.data(name='image', shape=[None, 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.CUDAPlace(0))
executor = fluid.Executor(fluid.CPUPlace())
executor.run(fluid.default_startup_program())
for i in range(3):
reader.start()
......@@ -848,6 +865,11 @@ class PyReader(DataLoaderBase):
EPOCH_NUM = 3
ITER_NUM = 15
BATCH_SIZE = 3
def network(image, label):
# User-defined network, here is an example of softmax regression.
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():
......@@ -859,21 +881,21 @@ class PyReader(DataLoaderBase):
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='int32')
image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 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.CUDAPlace(0)])
# definition of network is omitted
executor = fluid.Executor(fluid.CUDAPlace(0))
executor.run(fluid.default_main_program())
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)
executor.run(feed=data, fetch_list=[loss])
'''
self._loader.set_sample_generator(sample_generator, batch_size,
......@@ -905,6 +927,11 @@ class PyReader(DataLoaderBase):
ITER_NUM = 15
BATCH_SIZE = 3
def network(image, label):
# User-defined network, here is an example of softmax regression.
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):
......@@ -915,21 +942,22 @@ class PyReader(DataLoaderBase):
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='int32')
image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 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.CUDAPlace(0))
# definition of network is omitted
executor = fluid.Executor(fluid.core.CUDAPlace(0))
executor.run(fluid.default_main_program())
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)
executor.run(feed=data, fetch_list=[loss])
'''
self._loader.set_sample_list_generator(reader, places)
......@@ -958,6 +986,11 @@ class PyReader(DataLoaderBase):
EPOCH_NUM = 3
ITER_NUM = 15
BATCH_SIZE = 3
def network(image, label):
# User-defined network, here is an example of softmax regression.
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():
......@@ -966,22 +999,25 @@ class PyReader(DataLoaderBase):
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='int32')
image = fluid.data(name='image', shape=[None, 784, 784], dtype='float32')
label = fluid.data(name='label', shape=[None, 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.CUDAPlace(0))
# definition of network is omitted
executor = fluid.Executor(fluid.CUDAPlace(0))
executor.run(fluid.default_main_program())
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)
executor.run(feed=data, fetch_list=[loss])
'''
self._loader.set_batch_generator(reader, places)
......
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