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[xdoctest][task 292] reformat example code with google style in python/paddle/nn/functional/norm.py #56825

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Aug 31, 2023
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[xdoctest][task 292] reformat example code with google style in python/paddle/nn/functional/norm.py #56825

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20c4ca3
[Doctest] fix No.292, test=docs_preview
jinyouzhi Aug 30, 2023
70c5c10
fix
jinyouzhi Aug 30, 2023
0adc028
fix format
jinyouzhi Aug 30, 2023
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142 changes: 81 additions & 61 deletions python/paddle/nn/functional/norm.py
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Original file line number Diff line number Diff line change
Expand Up @@ -53,28 +53,29 @@ def normalize(x, p=2, axis=1, epsilon=1e-12, name=None):

.. code-block:: python

import paddle
import paddle.nn.functional as F

paddle.disable_static()
x = paddle.arange(6, dtype="float32").reshape([2,3])
y = F.normalize(x)
print(y)
# Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[0. , 0.44721359, 0.89442718],
# [0.42426404, 0.56568539, 0.70710671]])

y = F.normalize(x, p=1.5)
print(y)
# Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[0. , 0.40862012, 0.81724024],
# [0.35684016, 0.47578689, 0.59473360]])

y = F.normalize(x, axis=0)
print(y)
# Tensor(shape=[2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[0. , 0.24253564, 0.37139067],
# [1. , 0.97014254, 0.92847669]])
>>> import paddle
>>> import paddle.nn.functional as F

>>> paddle.disable_static()
>>> x = paddle.arange(6, dtype="float32").reshape([2,3])
>>> y = F.normalize(x)
>>> print(y)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 0.44721359, 0.89442718],
[0.42426404, 0.56568539, 0.70710671]])

>>> y = F.normalize(x, p=1.5)
>>> print(y)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 0.40862012, 0.81724024],
[0.35684016, 0.47578689, 0.59473360]])

>>> y = F.normalize(x, axis=0)
>>> print(y)
Tensor(shape=[2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[0. , 0.24253564, 0.37139067],
[1. , 0.97014254, 0.92847669]])

"""

if in_dygraph_mode():
Expand Down Expand Up @@ -148,31 +149,29 @@ def batch_norm(
Examples:
.. code-block:: python

import paddle

x = paddle.arange(12, dtype="float32").reshape([2, 1, 2, 3])
print(x)
# Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[[0. , 1. , 2. ],
# [3. , 4. , 5. ]]],

# [[[6. , 7. , 8. ],
# [9. , 10., 11.]]]])

running_mean = paddle.to_tensor([0], dtype="float32")
running_variance = paddle.to_tensor([1], dtype="float32")
weight = paddle.to_tensor([2], dtype="float32")
bias = paddle.to_tensor([1], dtype="float32")

batch_norm_out = paddle.nn.functional.batch_norm(x, running_mean,
running_variance, weight, bias)
print(batch_norm_out)
# Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True,
# [[[[1. , 2.99998999 , 4.99997997 ],
# [6.99996948 , 8.99995995 , 10.99994946]]],
>>> import paddle

>>> x = paddle.arange(12, dtype="float32").reshape([2, 1, 2, 3])
>>> print(x)
Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[0. , 1. , 2. ],
[3. , 4. , 5. ]]],
[[[6. , 7. , 8. ],
[9. , 10., 11.]]]])
>>> running_mean = paddle.to_tensor([0], dtype="float32")
>>> running_variance = paddle.to_tensor([1], dtype="float32")
>>> weight = paddle.to_tensor([2], dtype="float32")
>>> bias = paddle.to_tensor([1], dtype="float32")

>>> batch_norm_out = paddle.nn.functional.batch_norm(x, running_mean,
... running_variance, weight, bias)
>>> print(batch_norm_out)
Tensor(shape=[2, 1, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[1. , 2.99998999 , 4.99997997 ],
[6.99996948 , 8.99995995 , 10.99994946]]],
[[[12.99993896, 14.99992943, 16.99991989],
[18.99990845, 20.99989891, 22.99988937]]]])

# [[[12.99993896, 14.99992943, 16.99991989],
# [18.99990845, 20.99989891, 22.99988937]]]])
"""
assert len(x.shape) >= 2, "input dim must be larger than 1"

Expand Down Expand Up @@ -300,11 +299,21 @@ def layer_norm(

.. code-block:: python

import paddle
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.rand((2, 2, 2, 3))
>>> layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:])
>>> print(layer_norm_out)
Tensor(shape=[2, 2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[ 0.87799639, -0.32706568, -1.23529339],
[ 1.01540327, -0.66222906, -0.72354043]],
[[ 1.24183702, 0.45458138, -0.33506915],
[ 0.41468468, 1.26852870, -1.98983312]]],
[[[ 0.02837803, 1.27684665, -0.90110683],
[-0.94709367, -0.15110941, -1.16546965]],
[[-0.82010198, 0.11218392, -0.86506516],
[ 1.09489357, 0.19107464, 2.14656854]]]])

x = paddle.rand((2, 2, 2, 3))
layer_norm_out = paddle.nn.functional.layer_norm(x, x.shape[1:])
print(layer_norm_out)
"""
input_shape = list(x.shape)
input_ndim = len(input_shape)
Expand Down Expand Up @@ -415,12 +424,21 @@ def instance_norm(

.. code-block:: python

import paddle

x = paddle.rand((2, 2, 2, 3))
instance_norm_out = paddle.nn.functional.instance_norm(x)

print(instance_norm_out)
>>> import paddle
>>> paddle.seed(2023)
>>> x = paddle.rand((2, 2, 2, 3))
>>> instance_norm_out = paddle.nn.functional.instance_norm(x)

>>> print(instance_norm_out)
Tensor(shape=[2, 2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True,
[[[[ 1.25768495, -0.18054862, -1.26451230],
[ 1.42167914, -0.58056390, -0.65373862]],
[[ 0.95882601, 0.25075224, -0.45947552],
[ 0.21486834, 0.98283297, -1.94780385]]],
[[[ 0.40697321, 1.90885782, -0.71117985],
[-0.76650119, 0.19105314, -1.02920341]],
[[-1.06926346, -0.18710862, -1.11180890],
[ 0.74275863, -0.11246002, 1.73788261]]]])

"""
if in_dygraph_mode():
Expand Down Expand Up @@ -510,13 +528,15 @@ def local_response_norm(

Examples:

.. code-block:: python
.. code-block:: python

>>> import paddle

import paddle
>>> x = paddle.rand(shape=(3, 3, 112, 112), dtype="float32")
>>> y = paddle.nn.functional.local_response_norm(x, size=5)
>>> print(y.shape)
[3, 3, 112, 112]

x = paddle.rand(shape=(3, 3, 112, 112), dtype="float32")
y = paddle.nn.functional.local_response_norm(x, size=5)
print(y.shape) # [3, 3, 112, 112]
"""
if not in_dynamic_mode():
check_variable_and_dtype(
Expand Down