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体验新版 GitCode,发现更多精彩内容 >>
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488152a6
编写于
9月 30, 2020
作者:
W
Wilber
提交者:
GitHub
9月 30, 2020
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差异文件
[API 2.0]Update 2.0 api from fluid to paddle. (#27598)
上级
7f9b198d
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
58 addition
and
49 deletion
+58
-49
python/paddle/fluid/executor.py
python/paddle/fluid/executor.py
+5
-4
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+32
-28
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+6
-6
python/paddle/fluid/param_attr.py
python/paddle/fluid/param_attr.py
+10
-10
python/paddle/static/__init__.py
python/paddle/static/__init__.py
+3
-0
python/paddle/static/nn/__init__.py
python/paddle/static/nn/__init__.py
+2
-0
tools/wlist.json
tools/wlist.json
+0
-1
未找到文件。
python/paddle/fluid/executor.py
浏览文件 @
488152a6
...
...
@@ -94,12 +94,13 @@ def scope_guard(scope):
Examples:
.. code-block:: python
import paddle
.fluid as fluid
import paddle
import numpy
paddle.enable_static()
new_scope =
fluid
.Scope()
with
fluid
.scope_guard(new_scope):
fluid.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), fluid
.CPUPlace())
new_scope =
paddle.static
.Scope()
with
paddle.static
.scope_guard(new_scope):
paddle.static.global_scope().var("data").get_tensor().set(numpy.ones((2, 2)), paddle
.CPUPlace())
numpy.array(new_scope.find_var("data").get_tensor())
"""
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
488152a6
...
...
@@ -13546,15 +13546,15 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
"""
:api_attr: Static Graph
This OP is used to register customized Python OP to Paddle
Fluid
. The design
principe of py_func is that
Lod
Tensor and numpy array can be converted to each
This OP is used to register customized Python OP to Paddle. The design
principe of py_func is that Tensor and numpy array can be converted to each
other easily. So you can use Python and numpy API to register a python OP.
The forward function of the registered OP is ``func`` and the backward function
of that is ``backward_func``. Paddle will call ``func`` at forward runtime and
call ``backward_func`` at backward runtime(if ``backward_func`` is not None).
``x`` is the input of ``func``, whose type must be
LoD
Tensor; ``out`` is
the output of ``func``, whose type can be either
LoD
Tensor or numpy array.
``x`` is the input of ``func``, whose type must be Tensor; ``out`` is
the output of ``func``, whose type can be either Tensor or numpy array.
The input of the backward function ``backward_func`` is ``x``, ``out`` and
the gradient of ``out``. If some variables of ``out`` have no gradient, the
...
...
@@ -13572,14 +13572,14 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
func (callable): The forward function of the registered OP. When the network
is running, the forward output ``out`` will be calculated according to this
function and the forward input ``x``. In ``func`` , it's suggested that we
actively convert
LoD
Tensor into a numpy array, so that we can use Python and
actively convert Tensor into a numpy array, so that we can use Python and
numpy API arbitrarily. If not, some operations of numpy may not be compatible.
x (Variable|tuple(Variale)|list[Variale]): The input of the forward function ``func``.
It can be Variable|tuple(Variale)|list[Variale], where Variable is
LoD
Tensor or
It can be Variable|tuple(Variale)|list[Variale], where Variable is Tensor or
Tenosor. In addition, Multiple Variable should be passed in the form of tuple(Variale)
or list[Variale].
out (Variable|tuple(Variale)|list[Variale]): The output of the forward function ``func``,
it can be Variable|tuple(Variale)|list[Variale], where Variable can be either
LoD
Tensor
it can be Variable|tuple(Variale)|list[Variale], where Variable can be either Tensor
or numpy array. Since Paddle cannot automatically infer the shape and type of ``out``,
you must create ``out`` in advance.
backward_func (callable, optional): The backward function of the registered OP.
...
...
@@ -13600,16 +13600,18 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
.. code-block:: python
# example 1:
import paddle
.fluid as fluid
import paddle
import six
# Creates a forward function, LodTensor can be input directly without
paddle.enable_static()
# Creates a forward function, Tensor can be input directly without
# being converted into numpy array.
def tanh(x):
return np.tanh(x)
# Skip x in backward function and return the gradient of x
#
Lod
Tensor must be actively converted to numpy array, otherwise,
# Tensor must be actively converted to numpy array, otherwise,
# operations such as +/- can't be used.
def tanh_grad(y, dy):
return np.array(dy) * (1 - np.square(np.array(y)))
...
...
@@ -13619,36 +13621,38 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
print(x)
def create_tmp_var(name, dtype, shape):
return
fluid
.default_main_program().current_block().create_var(
return
paddle.static
.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape)
def simple_net(img, label):
hidden = img
for idx in six.moves.range(4):
hidden =
fluid.layers
.fc(hidden, size=200)
hidden =
paddle.static.nn
.fc(hidden, size=200)
new_hidden = create_tmp_var(name='hidden_{}'.format(idx),
dtype=hidden.dtype, shape=hidden.shape)
# User-defined forward and backward
hidden =
fluid.layers
.py_func(func=tanh, x=hidden,
hidden =
paddle.static.nn
.py_func(func=tanh, x=hidden,
out=new_hidden, backward_func=tanh_grad,
skip_vars_in_backward_input=hidden)
# User-defined debug functions that print out the input
Lod
Tensor
fluid.layers
.py_func(func=debug_func, x=hidden, out=None)
# User-defined debug functions that print out the input Tensor
paddle.static.nn
.py_func(func=debug_func, x=hidden, out=None)
prediction =
fluid.layers
.fc(hidden, size=10, act='softmax')
loss =
fluid.layers
.cross_entropy(input=prediction, label=label)
return
fluid.layers
.mean(loss)
prediction =
paddle.static.nn
.fc(hidden, size=10, act='softmax')
loss =
paddle.static.nn
.cross_entropy(input=prediction, label=label)
return
paddle
.mean(loss)
# example 2:
# This example shows how to turn
LoD
Tensor into numpy array and
# This example shows how to turn Tensor into numpy array and
# use numpy API to register an Python OP
import paddle
.fluid as fluid
import paddle
import numpy as np
paddle.enable_static()
def element_wise_add(x, y):
#
Lod
Tensor must be actively converted to numpy array, otherwise,
# Tensor must be actively converted to numpy array, otherwise,
# numpy.shape can't be used.
x = np.array(x)
y = np.array(y)
...
...
@@ -13664,24 +13668,24 @@ def py_func(func, x, out, backward_func=None, skip_vars_in_backward_input=None):
return result
def create_tmp_var(name, dtype, shape):
return
fluid
.default_main_program().current_block().create_var(
return
paddle.static
.default_main_program().current_block().create_var(
name=name, dtype=dtype, shape=shape)
def py_func_demo():
start_program =
fluid
.default_startup_program()
main_program =
fluid
.default_main_program()
start_program =
paddle.static
.default_startup_program()
main_program =
paddle.static
.default_main_program()
# Input of the forward function
x =
fluid
.data(name='x', shape=[2,3], dtype='int32')
y =
fluid
.data(name='y', shape=[2,3], dtype='int32')
x =
paddle.static
.data(name='x', shape=[2,3], dtype='int32')
y =
paddle.static
.data(name='y', shape=[2,3], dtype='int32')
# Output of the forward function, name/dtype/shape must be specified
output = create_tmp_var('output','int32', [3,1])
# Multiple Variable should be passed in the form of tuple(Variale) or list[Variale]
fluid.layers
.py_func(func=element_wise_add, x=[x,y], out=output)
paddle.static.nn
.py_func(func=element_wise_add, x=[x,y], out=output)
exe=
fluid.Executor(fluid
.CPUPlace())
exe=
paddle.static.Executor(paddle
.CPUPlace())
exe.run(start_program)
# Feed numpy array to main_program
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
488152a6
...
...
@@ -103,9 +103,9 @@ def create_parameter(shape,
Examples:
.. code-block:: python
import paddle
.fluid as fluid
import paddle.fluid.layers as layers
W =
layers
.create_parameter(shape=[784, 200], dtype='float32')
import paddle
paddle.enable_static()
W =
paddle.static
.create_parameter(shape=[784, 200], dtype='float32')
"""
check_type
(
shape
,
'shape'
,
(
list
,
tuple
,
numpy
.
ndarray
),
'create_parameter'
)
for
item
in
shape
:
...
...
@@ -161,9 +161,9 @@ def create_global_var(shape,
Examples:
.. code-block:: python
import paddle
.fluid as fluid
import paddle.fluid.layers as layers
var =
layers
.create_global_var(shape=[2,3], value=1.0, dtype='float32',
import paddle
paddle.enable_static()
var =
paddle.static
.create_global_var(shape=[2,3], value=1.0, dtype='float32',
persistable=True, force_cpu=True, name='new_var')
"""
check_type
(
shape
,
'shape'
,
(
list
,
tuple
,
numpy
.
ndarray
),
...
...
python/paddle/fluid/param_attr.py
浏览文件 @
488152a6
...
...
@@ -61,15 +61,15 @@ class ParamAttr(object):
Examples:
.. code-block:: python
import paddle
.fluid as fluid
w_param_attrs = fluid.ParamAttr(name="fc_weight",
learning_rate=0.5
,
regularizer=fluid.regularizer.L2Decay(1.0)
,
trainable=True)
print(w_param_attrs.name) # "fc_weight"
x = fluid.data(name='X', shape=[None, 1], dtype='float32')
y_predict = fluid.layers.fc(input=x, size=10, param_attr=w_param_attrs
)
import paddle
paddle.enable_static()
weight_attr = paddle.ParamAttr(name="weight"
,
learning_rate=0.5
,
regularizer=paddle.regularizer.L2Decay(1.0),
trainable=True)
print(weight_attr.name) # "weight"
paddle.nn.Linear(3, 4, weight_attr=weight_attr
)
"""
def
__init__
(
self
,
...
...
@@ -206,7 +206,7 @@ class ParamAttr(object):
class
WeightNormParamAttr
(
ParamAttr
):
"""
:api_attr: Static Graph
:api_attr: Static Graph
Note:
Please use 'paddle.nn.utils.weight_norm' in dygraph mode.
...
...
python/paddle/static/__init__.py
浏览文件 @
488152a6
...
...
@@ -23,6 +23,7 @@ __all__ = [
]
from
.
import
nn
from
..fluid
import
Scope
#DEFINE_ALIAS
from
.input
import
data
#DEFINE_ALIAS
from
.input
import
InputSpec
#DEFINE_ALIAS
from
..fluid.executor
import
Executor
#DEFINE_ALIAS
...
...
@@ -50,3 +51,5 @@ from ..fluid.io import save_inference_model #DEFINE_ALIAS
from
..fluid.io
import
load_inference_model
#DEFINE_ALIAS
from
..fluid.io
import
load_program_state
#DEFINE_ALIAS
from
..fluid.io
import
set_program_state
#DEFINE_ALIAS
from
..fluid.layers
import
create_parameter
#DEFINE_ALIAS
from
..fluid.layers
import
create_global_var
#DEFINE_ALIAS
python/paddle/static/nn/__init__.py
浏览文件 @
488152a6
...
...
@@ -33,6 +33,7 @@ __all__ = [
'multi_box_head'
,
'nce'
,
'prelu'
,
'py_func'
,
'row_conv'
,
'spectral_norm'
,
'switch_case'
,
...
...
@@ -57,6 +58,7 @@ from ...fluid.layers import layer_norm #DEFINE_ALIAS
from
...fluid.layers
import
multi_box_head
#DEFINE_ALIAS
from
...fluid.layers
import
nce
#DEFINE_ALIAS
from
...fluid.layers
import
prelu
#DEFINE_ALIAS
from
...fluid.layers
import
py_func
#DEFINE_ALIAS
from
...fluid.layers
import
row_conv
#DEFINE_ALIAS
from
...fluid.layers
import
spectral_norm
#DEFINE_ALIAS
from
...fluid.layers
import
switch_case
#DEFINE_ALIAS
...
...
tools/wlist.json
浏览文件 @
488152a6
...
...
@@ -279,7 +279,6 @@
"thresholded_relu"
,
"group_norm"
,
"random_crop"
,
"py_func"
,
"row_conv"
,
"hard_shrink"
,
"ssd_loss"
,
...
...
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