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Fix docs of load_inference_model&load_params&load_persistables&chain. (#20274)

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* improve the docs of load_inference_model&load_params&load_persistables&chain.
revert-20712-fix_depthwise_conv
Zhen Wang 6 years ago committed by GitHub
parent 2c28e3283a
commit 2c71de243d
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3 changed files with 131 additions and 79 deletions
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8
paddle/fluid/API.spec
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@ -74,10 +74,10 @@ paddle.fluid.io.save_vars (ArgSpec(args=['executor', 'dirname', 'main_program',
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.load_vars (ArgSpec(args=['executor', 'dirname', 'main_program', 'vars', 'predicate', 'filename'], varargs=None, keywords=None, defaults=(None, None, None, None)), ('document', '1bb9454cf09d71f190bb51550c5a3ac9'))
paddle.fluid.io.load_params (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', '116a9ed169e7ff0226faccff3c29364c'))
paddle.fluid.io.load_persistables (ArgSpec(args=['executor', 'dirname', 'main_program', 'filename'], varargs=None, keywords=None, defaults=(None, None)), ('document', 'cfa84ef7c5435625bff4cc132cb8a0e3'))
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.load_inference_model (ArgSpec(args=['dirname', 'executor', 'model_filename', 'params_filename', 'pserver_endpoints'], varargs=None, keywords=None, defaults=(None, None, None)), ('document', '2f54d7c206b62f8c10f4f9d78c731cfd'))
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.PyReader ('paddle.fluid.reader.PyReader', ('document', 'b03399246f69cd6fc03b43e87af8bd4e'))
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'))
@ -95,7 +95,7 @@ paddle.fluid.io.cache (ArgSpec(args=['reader'], varargs=None, keywords=None, def
paddle.fluid.io.map_readers (ArgSpec(args=['func'], varargs='readers', keywords=None, defaults=None), ('document', '77cbadb09df588e21e5cc0819b69c87d'))
paddle.fluid.io.buffered (ArgSpec(args=['reader', 'size'], varargs=None, keywords=None, defaults=None), ('document', '0d6186f109feceb99f60ec50a0a624cb'))
paddle.fluid.io.compose (ArgSpec(args=[], varargs='readers', keywords='kwargs', defaults=None), ('document', '884291104e1c3f37f33aae44b7deeb0d'))
paddle.fluid.io.chain (ArgSpec(args=[], varargs='readers', keywords=None, defaults=None), ('document', 'd22c34e379a53901ae67a6bca7f4def4'))
paddle.fluid.io.chain (ArgSpec(args=[], varargs='readers', keywords=None, defaults=None), ('document', 'e0311508658a7e741fc39feea8be0ad2'))
paddle.fluid.io.shuffle (ArgSpec(args=['reader', 'buf_size'], varargs=None, keywords=None, defaults=None), ('document', 'e42ea6fee23ce26b23cb142cd1d6522d'))
paddle.fluid.io.firstn (ArgSpec(args=['reader', 'n'], varargs=None, keywords=None, defaults=None), ('document', 'c5bb8f7dd4f917f1569a368aab5b8aad'))
paddle.fluid.io.xmap_readers (ArgSpec(args=['mapper', 'reader', 'process_num', 'buffer_size', 'order'], varargs=None, keywords=None, defaults=(False,)), ('document', '9c804a42f8a4dbaa76b3c98e0ab7f796'))

150
python/paddle/fluid/io.py
Unescape View File

@ -706,33 +706,38 @@ def load_vars(executor,
def load_params(executor, dirname, main_program=None, filename=None):
"""
This function filters out all parameters from the give `main_program`
and then trys to load these parameters from the folder `dirname` or
the file `filename`.
Use the `dirname` to specify the folder where parameters were saved. If
parameters were saved in separate files in the folder `dirname`, set
`filename` None; if all parameters were saved in a single file, use
`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 load the pre-trained model structure and parameters
for the inference, please use the `load_inference_model` API. You can
refer to :ref:`api_guide_model_save_reader_en` for more details.
This API filters out all parameters from the give ``main_program``
and then tries to load these parameters from the directory ``dirname`` or
the file ``filename``.
Use the ``dirname`` to specify the directory where parameters were saved. If
parameters were saved in separate files under the directory `dirname`, set
``filename`` as None; if all parameters were saved in a single file, use
``filename`` to specify the file name.
**Note**:
Some variables are not Parameter while they are necessary for
training, such as learning rate, global step, etc. So you cannot save and
continue your training just by using :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 load the pre-trained model structure and parameters
for the inference, please use the :ref:`api_fluid_io_load_inference_model` API. You can
refer to :ref:`api_guide_model_save_reader_en` for more details.
Args:
executor(Executor): The executor to run for loading parameters.
executor(Executor): The executor used for loading parameters.
See :ref:`api_guide_executor_en` for more details about it.
dirname(str): The directory path.
main_program(Program|None): The program whose parameters will be
loaded. If it is None, the default
main program will be used automatically.
Default: None
filename(str|None): The file which saved all parameters. If parameters
were saved in differnet files, set it to None.
Default: None
main_program(Program, optional): The program whose parameters will be
loaded. If it is None, the ``default_main_program``
will be used automatically. See :ref:`api_guide_Program_en`
for more about ``Program``.
Default: None.
filename(str, optional): The file which saved all parameters. If parameters
were saved in separated files, set it to None.
Default: None.
Returns:
None
@ -741,6 +746,7 @@ def load_params(executor, dirname, main_program=None, filename=None):
.. code-block:: python
import paddle.fluid as fluid
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
@ -757,25 +763,27 @@ def load_params(executor, dirname, main_program=None, filename=None):
def load_persistables(executor, dirname, main_program=None, filename=None):
"""
This function filters out all variables with `persistable==True` from the
give `main_program` and then trys to load these variables from the folder
`dirname` or the file `filename`.
This API filters out all variables with ``persistable==True`` from the
given ``main_program`` and then tries to load these variables from the
directory ``dirnameme`` or the file ``filename``.
Use the `dirname` to specify the folder where persistable variables were
saved. If variables were saved in separate files, set `filename` None;
if all variables were saved in a single file, use `filename` to specify
the file name.
Use the ``dirname`` to specify the directory where persistable variables
(refer to :ref:`api_guide_model_save_reader_en`) were saved. If variables
were saved in separate files, set ``filename`` as None; if all variables
were saved in a single file, use ``filename`` to specify the file name.
Args:
executor(Executor): The executor to run for loading persistable variables.
executor(Executor): The executor used for loading persistable variables.
See :ref:`api_guide_executor_en` for more details about it.
dirname(str): The directory path.
main_program(Program|None): The program whose persistbale variables will
be loaded. If it is None, the default main
program will be used automatically.
Default: None
filename(str|None): The file which saved all variables. If variables were
saved in differnet files, set it to None.
Default: None
main_program(Program, optional): The program whose persistbale variables will
be loaded. If it is None, the ``default_main_program``
will be used automatically. See :ref:`api_guide_Program_en`
for more about ``Program``.
Default: None.
filename(str, optional): The file which saved all persistable variables. If variables
were saved in separated files, set it to None.
Default: None.
Returns:
None
@ -784,6 +792,7 @@ def load_persistables(executor, dirname, main_program=None, filename=None):
.. code-block:: python
import paddle.fluid as fluid
exe = fluid.Executor(fluid.CPUPlace())
param_path = "./my_paddle_model"
prog = fluid.default_main_program()
@ -1160,36 +1169,39 @@ def load_inference_model(dirname,
params_filename=None,
pserver_endpoints=None):
"""
Load inference model from a directory. By this API, you can get the model
structure(inference program) and model parameters. If you just want to load
parameters of the pre-trained model, please use the `load_params` API.
Load the inference model from a given directory. By this API, you can get the model
structure(Inference Program) and model parameters. If you just want to load
parameters of the pre-trained model, please use the :ref:`api_fluid_io_load_params` API.
You can refer to :ref:`api_guide_model_save_reader_en` for more details.
Args:
dirname(str): The directory path
dirname(str): The given directory path.
executor(Executor): The executor to run for loading inference model.
model_filename(str|None): The name of file to load inference program.
See :ref:`api_guide_executor_en` for more details about it.
model_filename(str, optional): The name of file to load the inference program.
If it is None, the default filename
'__model__' will be used.
Default: None
params_filename(str|None): The name of file to load all parameters.
``__model__`` will be used.
Default: ``None``.
params_filename(str, optional): The name of file to load all parameters.
It is only used for the case that all
parameters were saved in a single binary
file. If parameters were saved in separate
files, set it as 'None'.
pserver_endpoints(list|None): This only need by distributed inference.
When use distributed look up table in training,
We also need it in inference.The parameter is
files, set it as ``None``.
Default: ``None``.
pserver_endpoints(list, optional): It is only needed by the distributed inference.
If using a distributed look up table during the training,
this table is also needed by the inference process. Its value is
a list of pserver endpoints.
Returns:
tuple: The return of this function is a tuple with three elements:
list: The return of this API is a list with three elements:
(program, feed_target_names, fetch_targets). The `program` is a
Program, it's the program for inference. The `feed_target_names` is
a list of str, it contains Names of variables that need to feed
data in the inference program. The `fetch_targets` is a list of
Variable. It contains variables from which we can get inference
results.
``Program`` (refer to :ref:`api_guide_Program_en`), which is used for inference.
The `feed_target_names` is a list of ``str``, which contains names of variables
that need to feed data in the inference program. The `fetch_targets` is a list of
``Variable`` (refer to :ref:`api_guide_Program_en`). It contains variables from which
we can get inference results.
Raises:
ValueError: If `dirname` is not a existing directory.
@ -1199,6 +1211,8 @@ def load_inference_model(dirname,
import paddle.fluid as fluid
import numpy as np
# Build the model
main_prog = fluid.Program()
startup_prog = fluid.Program()
with fluid.program_guard(main_prog, startup_prog):
@ -1210,30 +1224,36 @@ def load_inference_model(dirname,
place = fluid.CPUPlace()
exe = fluid.Executor(place)
exe.run(startup_prog)
# Save the inference model
path = "./infer_model"
fluid.io.save_inference_model(dirname=path, feeded_var_names=['img'],
target_vars=[hidden_b], executor=exe, main_program=main_prog)
tensor_img = np.array(np.random.random((1, 64, 784)), dtype=np.float32)
# Demo one. Not need to set the distributed look up table, because the
# training doesn't use a distributed look up table.
[inference_program, feed_target_names, fetch_targets] = (
fluid.io.load_inference_model(dirname=path, executor=exe))
tensor_img = np.array(np.random.random((1, 64, 784)), dtype=np.float32)
results = exe.run(inference_program,
feed={feed_target_names[0]: tensor_img},
fetch_list=fetch_targets)
# endpoints is your pserver endpoints list, the above is just an example
# Demo two. If the training uses a distributed look up table, the pserver
# endpoints list should be supported when loading the inference model.
# The below is just an example.
endpoints = ["127.0.0.1:2023","127.0.0.1:2024"]
# if we need lookup table, we will use:
[dist_inference_program, dist_feed_target_names, dist_fetch_targets] = (
fluid.io.load_inference_model(dirname=path,
executor=exe,
pserver_endpoints=endpoints))
# In this example, the inference program was saved in the
# In this example, the inference program was saved in the file
# "./infer_model/__model__" and parameters were saved in
# separate files in "./infer_model".
# After getting inference program, feed target names and
# fetch targets, we can use an Executor to run the inference
# program to get the inference result.
# separate files under the directory "./infer_model".
# By the inference program, feed_target_names and
# fetch_targets, we can use an executor to run the inference
# program for getting the inference result.
"""
load_dirname = os.path.normpath(dirname)
if not os.path.isdir(load_dirname):

52
python/paddle/reader/decorator.py
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@ -117,19 +117,51 @@ def shuffle(reader, buf_size):
def chain(*readers):
"""
Creates a data reader whose output is the outputs of input data
readers chained together.
Use the input data readers to create a chained data reader. The new created reader
chains the outputs of input readers together as its output.
If input readers output following data entries:
[0, 0, 0]
[1, 1, 1]
[2, 2, 2]
**Note**:
``paddle.reader.chain`` is the alias of ``paddle.fluid.io.chain``, and
``paddle.fluid.io.chain`` is recommended to use.
For example, if three input readers' outputs are as follows:
[0, 0, 0],
[10, 10, 10],
[20, 20, 20].
The chained reader will output:
[0, 0, 0, 1, 1, 1, 2, 2, 2]
[[0, 0, 0], [10, 10, 10], [20, 20, 20]].
Args:
readers(list): input data readers.
Returns:
callable: the new chained data reader.
Examples:
.. code-block:: python
import paddle
def reader_creator_3(start):
def reader():
for i in range(start, start + 3):
yield [i, i, i]
return reader
c = paddle.reader.chain(reader_creator_3(0), reader_creator_3(10), reader_creator_3(20))
for e in c():
print(e)
# Output:
# [0, 0, 0]
# [1, 1, 1]
# [2, 2, 2]
# [10, 10, 10]
# [11, 11, 11]
# [12, 12, 12]
# [20, 20, 20]
# [21, 21, 21]
# [22, 22, 22]
:param readers: input readers.
:return: the new data reader.
:rtype: callable
"""
def reader():

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