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体验新版 GitCode,发现更多精彩内容 >>
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3f8d9b0a
编写于
6月 20, 2018
作者:
C
chengduo
提交者:
GitHub
6月 20, 2018
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差异文件
Merge pull request #11580 from chengduoZH/fix_doc_data_reader
Refine doc of data reader
上级
4b7ae145
10cee7ed
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
104 addition
and
3 deletion
+104
-3
python/paddle/fluid/data_feeder.py
python/paddle/fluid/data_feeder.py
+97
-0
python/paddle/fluid/executor.py
python/paddle/fluid/executor.py
+7
-3
未找到文件。
python/paddle/fluid/data_feeder.py
浏览文件 @
3f8d9b0a
...
...
@@ -79,6 +79,61 @@ class DataToLoDTensorConverter(object):
class
DataFeeder
(
object
):
"""
DataFeeder converts the data that returned by a reader into a data
structure that can feed into Executor and ParallelExecutor. The reader
usually returns a list of mini-batch data entries. Each data entry in
the list is one sample. Each sample is a list or a tuple with one
feature or multiple features.
The simple usage shows below:
.. code-block:: python
place = fluid.CPUPlace()
img = fluid.layers.data(name='image', shape=[1, 28, 28])
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
feeder = fluid.DataFeeder([img, label], fluid.CPUPlace())
result = feeder.feed([([0] * 784, [9]), ([1] * 784, [1])])
If you want to feed data into GPU side separately in advance when you
use multi-GPU to train a model, you can use `decorate_reader` function.
.. code-block:: python
place=fluid.CUDAPlace(0)
feeder = fluid.DataFeeder(place=place, feed_list=[data, label])
reader = feeder.decorate_reader(
paddle.batch(flowers.train(), batch_size=16))
Args:
feed_list(list): The Variables or Variables'name that will
feed into model.
place(Place): place indicates feed data into CPU or GPU, if you want to
feed data into GPU, please using `fluid.CUDAPlace(i)` (`i` represents
the GPU id), or if you want to feed data into CPU, please using
`fluid.CPUPlace()`.
program(Program): The Program that will feed data into, if program
is None, it will use default_main_program(). Default None.
Raises:
ValueError: If some Variable is not in this Program.
Examples:
.. code-block:: python
# ...
place = fluid.CPUPlace()
feed_list = [
main_program.global_block().var(var_name) for var_name in feed_vars_name
] # feed_vars_name is a list of variables' name.
feeder = fluid.DataFeeder(feed_list, place)
for data in reader():
outs = exe.run(program=main_program,
feed=feeder.feed(data))
"""
def
__init__
(
self
,
feed_list
,
place
,
program
=
None
):
self
.
feed_dtypes
=
[]
self
.
feed_names
=
[]
...
...
@@ -108,6 +163,16 @@ class DataFeeder(object):
self
.
place
=
place
def
feed
(
self
,
iterable
):
"""
According to feed_list and iterable, converters the input into
a data structure that can feed into Executor and ParallelExecutor.
Args:
iterable(list|tuple): the input data.
Returns:
dict: the result of conversion.
"""
converter
=
[]
for
lod_level
,
shape
,
dtype
in
six
.
zip
(
self
.
feed_lod_level
,
self
.
feed_shapes
,
self
.
feed_dtypes
):
...
...
@@ -130,6 +195,20 @@ class DataFeeder(object):
return
ret_dict
def
feed_parallel
(
self
,
iterable
,
num_places
=
None
):
"""
Takes multiple mini-batches. Each mini-batch will be feed on each
device in advance.
Args:
iterable(list|tuple): the input data.
num_places(int): the number of devices. Default None.
Returns:
dict: the result of conversion.
Notes:
The number of devices and number of mini-batches must be same.
"""
if
isinstance
(
self
.
place
,
core
.
CUDAPlace
):
places
=
[
core
.
CUDAPlace
(
i
)
...
...
@@ -168,6 +247,24 @@ class DataFeeder(object):
multi_devices
,
num_places
=
None
,
drop_last
=
True
):
"""
Converter the input data into a data that returned by reader into
multiple mini-batches. Each mini-batch will be feed on each device.
Args:
reader(fun): the input data.
multi_devices(bool): the number of places. Default None.
num_places(int): the number of places. Default None.
drop_last(bool): the number of places. Default None.
Returns:
dict: the result of conversion.
Raises:
ValueError: If drop_last is False and the data batch which cannot
fit for devices.
"""
def
__reader_creator__
():
if
not
multi_devices
:
for
item
in
reader
():
...
...
python/paddle/fluid/executor.py
浏览文件 @
3f8d9b0a
...
...
@@ -135,14 +135,18 @@ def has_fetch_operators(block, fetch_targets, fetch_holder_name):
def
fetch_var
(
name
,
scope
=
None
,
return_numpy
=
True
):
"""
Fetch the value of the variable with the given name from the given scope
Fetch the value of the variable with the given name from the
given scope.
Args:
name(str): name of the variable. Typically, only persistable variables
can be found in the scope used for running the program.
scope(core.Scope|None): scope object. It should be the scope where
you pass to Executor.run() when running your program.
If None, global_scope() will be used.
return_numpy(bool): whether convert the tensor to numpy.ndarray
If None, global_scope() will be used. Default None.
return_numpy(bool): whether convert the tensor to numpy.ndarray.
Default True.
Returns:
LodTensor|numpy.ndarray
"""
...
...
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