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282c9e7c
编写于
10月 10, 2019
作者:
L
LielinJiang
提交者:
qingqing01
10月 10, 2019
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电子邮件补丁
差异文件
Polish english apis' doc (#20198)
* refine Normal Uniform documnet
上级
f4c56e9f
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
103 addition
and
72 deletion
+103
-72
paddle/fluid/API.spec
paddle/fluid/API.spec
+11
-11
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+21
-5
python/paddle/fluid/layers/distributions.py
python/paddle/fluid/layers/distributions.py
+42
-43
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+29
-13
未找到文件。
paddle/fluid/API.spec
浏览文件 @
282c9e7c
...
...
@@ -279,7 +279,7 @@ paddle.fluid.layers.maxout (ArgSpec(args=['x', 'groups', 'name'], varargs=None,
paddle.fluid.layers.space_to_depth (ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '26decdea9376b6b9a0d3432d82ca207b'))
paddle.fluid.layers.affine_grid (ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '315b50c1cbd9569375b098c56f1e91c9'))
paddle.fluid.layers.sequence_reverse (ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5b32ed21ab89140a8e758002923a0da3'))
paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '
9f303c67538e468a36c5904a0a3aa110
'))
paddle.fluid.layers.affine_channel (ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name', 'act'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None, None)), ('document', '
ecc4b1323028bde0518d666882d03515
'))
paddle.fluid.layers.similarity_focus (ArgSpec(args=['input', 'axis', 'indexes', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '18ec2e3afeb90e70c8b73d2b71c40fdb'))
paddle.fluid.layers.hash (ArgSpec(args=['input', 'hash_size', 'num_hash', 'name'], varargs=None, keywords=None, defaults=(1, None)), ('document', 'a0b73c21be618cec0281e7903039e5e3'))
paddle.fluid.layers.grid_sampler (ArgSpec(args=['x', 'grid', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '5d16663e096d7f04954c70ce1cc5e195'))
...
...
@@ -424,7 +424,7 @@ paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 't
paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random', 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', 'f2342042127b536a0a16390f149f1bba'))
paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', '5cba014b41610431f8949e2d7336f1cc'))
paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef'))
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '
72fca4a39ccf82d5c746ae62d1868a99
'))
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '
e24478fd1fcf1727d4947fe14356b3d4
'))
paddle.fluid.layers.box_coder (ArgSpec(args=['prior_box', 'prior_box_var', 'target_box', 'code_type', 'box_normalized', 'name', 'axis'], varargs=None, keywords=None, defaults=('encode_center_size', True, None, 0)), ('document', '511d7033c0cfce1a5b88c04ad6e7ed5b'))
paddle.fluid.layers.polygon_box_transform (ArgSpec(args=['input', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '2183f03c4f16712dcef6a474dbcefa24'))
paddle.fluid.layers.yolov3_loss (ArgSpec(args=['x', 'gt_box', 'gt_label', 'anchors', 'anchor_mask', 'class_num', 'ignore_thresh', 'downsample_ratio', 'gt_score', 'use_label_smooth', 'name'], varargs=None, keywords=None, defaults=(None, True, None)), ('document', '400403175718d5a632402cdae88b01b8'))
...
...
@@ -446,18 +446,18 @@ paddle.fluid.layers.piecewise_decay (ArgSpec(args=['boundaries', 'values'], vara
paddle.fluid.layers.noam_decay (ArgSpec(args=['d_model', 'warmup_steps'], varargs=None, keywords=None, defaults=None), ('document', 'fd57228fb76195e66bbcc8d8e42c494d'))
paddle.fluid.layers.cosine_decay (ArgSpec(args=['learning_rate', 'step_each_epoch', 'epochs'], varargs=None, keywords=None, defaults=None), ('document', '1062e487dd3b50a6e58b5703b4f594c9'))
paddle.fluid.layers.linear_lr_warmup (ArgSpec(args=['learning_rate', 'warmup_steps', 'start_lr', 'end_lr'], varargs=None, keywords=None, defaults=None), ('document', 'dc7292c456847ba41cfd318e9f7f4363'))
paddle.fluid.layers.Uniform ('paddle.fluid.layers.distributions.Uniform', ('document', '
af70e7003f437e7a8a9e28cded35c433
'))
paddle.fluid.layers.Uniform ('paddle.fluid.layers.distributions.Uniform', ('document', '
9b1a9ebdd8ae18bf562486611ed74e59
'))
paddle.fluid.layers.Uniform.__init__ (ArgSpec(args=['self', 'low', 'high'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.Uniform.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '
ba59f9ce77af3c93e2b4c8af1801a24e
'))
paddle.fluid.layers.Uniform.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '
cde9f1980a2be7939798b32ec8cd59e1
'))
paddle.fluid.layers.Uniform.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', '3baee52abbed82d47e9588d9dfe2f42f'))
paddle.fluid.layers.Uniform.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '
b79091014ceaffb6a7372a198a341c23
'))
paddle.fluid.layers.Uniform.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', '
adac334af13f6984e991b3ecf12b8cb7
'))
paddle.fluid.layers.Normal ('paddle.fluid.layers.distributions.Normal', ('document', '
3265262d0d8b3b32c6245979a5cdced
9'))
paddle.fluid.layers.Uniform.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '
ad4ed169f86c00923621504c782010b0
'))
paddle.fluid.layers.Uniform.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', '
9002ab4a80769211565b64298a770db5
'))
paddle.fluid.layers.Normal ('paddle.fluid.layers.distributions.Normal', ('document', '
948f3a95ca14c952401e6a2ec30a35f
9'))
paddle.fluid.layers.Normal.__init__ (ArgSpec(args=['self', 'loc', 'scale'], varargs=None, keywords=None, defaults=None), ('document', '6adf97f83acf6453d4a6a4b1070f3754'))
paddle.fluid.layers.Normal.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '
d2db47b1e62c037a2570fc526b93f518
'))
paddle.fluid.layers.Normal.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', '
2e8845cdf1129647e6fa6e816876cd3b
'))
paddle.fluid.layers.Normal.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '
b79091014ceaffb6a7372a198a341c23
'))
paddle.fluid.layers.Normal.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', '
adac334af13f6984e991b3ecf12b8cb7
'))
paddle.fluid.layers.Normal.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', '
254ff8081a9df3cb96db045411dfbcbd
'))
paddle.fluid.layers.Normal.kl_divergence (ArgSpec(args=['self', 'other'], varargs=None, keywords=None, defaults=None), ('document', '
9fc9bd26e5211e2c6ad703a7fba08e65
'))
paddle.fluid.layers.Normal.log_prob (ArgSpec(args=['self', 'value'], varargs=None, keywords=None, defaults=None), ('document', '
ad4ed169f86c00923621504c782010b0
'))
paddle.fluid.layers.Normal.sample (ArgSpec(args=['self', 'shape', 'seed'], varargs=None, keywords=None, defaults=(0,)), ('document', '
9002ab4a80769211565b64298a770db5
'))
paddle.fluid.layers.Categorical ('paddle.fluid.layers.distributions.Categorical', ('document', '865c9dac8af6190e05588486ba091ee8'))
paddle.fluid.layers.Categorical.__init__ (ArgSpec(args=['self', 'logits'], varargs=None, keywords=None, defaults=None), ('document', '933b96c9ebab8e2c1f6007a50287311e'))
paddle.fluid.layers.Categorical.entropy (ArgSpec(args=['self'], varargs=None, keywords=None, defaults=None), ('document', 'b360a2a7a4da07c2d268b329e09c82c1'))
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
282c9e7c
...
...
@@ -587,20 +587,36 @@ def iou_similarity(x, y, name=None):
${comment}
Args:
x
(${x_type}): ${x_comment}
y
(${y_type}): ${y_comment}
x
(Variable): ${x_comment}.The data type is float32 or float64.
y
(Variable): ${y_comment}.The data type is float32 or float64.
Returns:
out(${out_type}): ${out_comment}
Variable: ${out_comment}.The data type is same with x.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
x = fluid.layers.data(name='x', shape=[4], dtype='float32')
y = fluid.layers.data(name='y', shape=[4], dtype='float32')
use_gpu = False
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
x = fluid.data(name='x', shape=[None, 4], dtype='float32')
y = fluid.data(name='y', shape=[None, 4], dtype='float32')
iou = fluid.layers.iou_similarity(x=x, y=y)
exe.run(fluid.default_startup_program())
test_program = fluid.default_main_program().clone(for_test=True)
[out_iou] = exe.run(test_program,
fetch_list=iou,
feed={'x': np.array([[0.5, 0.5, 2.0, 2.0],
[0., 0., 1.0, 1.0]]).astype('float32'),
'y': np.array([[1.0, 1.0, 2.5, 2.5]]).astype('float32')})
# out_iou is [[0.2857143],
# [0. ]] with shape: [2, 1]
"""
helper
=
LayerHelper
(
"iou_similarity"
,
**
locals
())
if
name
is
None
:
...
...
python/paddle/fluid/layers/distributions.py
浏览文件 @
282c9e7c
...
...
@@ -135,12 +135,13 @@ class Uniform(Distribution):
broadcasting (e.g., `high - low` is a valid operation).
Args:
low(float|list|numpy.ndarray|Variable): The lower boundary of uniform distribution.
high(float|list|numpy.ndarray|Variable): The higher boundary of uniform distribution.
low(float|list|numpy.ndarray|Variable): The lower boundary of uniform distribution.
The data type is float32
high(float|list|numpy.ndarray|Variable): The higher boundary of uniform distribution.
The data type is float32
Examples:
.. code-block:: python
import numpy as np
from paddle.fluid import layers
from paddle.fluid.layers import Uniform
...
...
@@ -158,19 +159,19 @@ class Uniform(Distribution):
# With broadcasting:
u4 = Uniform(low=3.0, high=[5.0, 6.0, 7.0])
# Variable as input
dims = 3
low = layers.data(name='low', shape=[dims], dtype='float32')
high = layers.data(name='high', shape=[dims], dtype='float32')
values = layers.data(name='values', shape=[dims], dtype='float32')
# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)
uniform = Uniform(
low, high
)
uniform = Uniform(
[0.], [2.]
)
sample = uniform.sample([2, 3])
sample = uniform.sample([2])
# a random tensor created by uniform distribution with shape: [2, 1]
entropy = uniform.entropy()
lp = uniform.log_prob(values)
# [0.6931472] with shape: [1]
lp = uniform.log_prob(value_tensor)
# [-0.6931472] with shape: [1]
"""
def
__init__
(
self
,
low
,
high
):
...
...
@@ -193,7 +194,7 @@ class Uniform(Distribution):
seed (int): Python integer number.
Returns:
Variable: A tensor with prepended dimensions shape.
Variable: A tensor with prepended dimensions shape.
The data type is float32.
"""
batch_shape
=
list
((
self
.
low
+
self
.
high
).
shape
)
...
...
@@ -224,7 +225,7 @@ class Uniform(Distribution):
value (Variable): The input tensor.
Returns:
Variable: log probability.
Variable: log probability.
The data type is same with value.
"""
lb_bool
=
control_flow
.
less_than
(
self
.
low
,
value
)
...
...
@@ -237,7 +238,7 @@ class Uniform(Distribution):
"""Shannon entropy in nats.
Returns:
Variable: Shannon entropy of uniform distribution.
Variable: Shannon entropy of uniform distribution.
The data type is float32.
"""
return
nn
.
log
(
self
.
high
-
self
.
low
)
...
...
@@ -265,8 +266,8 @@ class Normal(Distribution):
* :math:`Z`: is the normalization constant.
Args:
loc(float|list|numpy.ndarray|Variable): The mean of normal distribution.
scale(float|list|numpy.ndarray|Variable): The std of normal distribution.
loc(float|list|numpy.ndarray|Variable): The mean of normal distribution.
The data type is float32.
scale(float|list|numpy.ndarray|Variable): The std of normal distribution.
The data type is float32.
Examples:
.. code-block:: python
...
...
@@ -278,36 +279,34 @@ class Normal(Distribution):
dist = Normal(loc=0., scale=3.)
# Define a batch of two scalar valued Normals.
# The first has mean 1 and standard deviation 11, the second 2 and 22.
dist = Normal(loc=[1
, 2.], scale=[11
, 22.])
dist = Normal(loc=[1
., 2.], scale=[11.
, 22.])
# Get 3 samples, returning a 3 x 2 tensor.
dist.sample([3])
# Define a batch of two scalar valued Normals.
# Both have mean 1, but different standard deviations.
dist = Normal(loc=1., scale=[11, 22.])
dist = Normal(loc=1., scale=[11
.
, 22.])
# Define a batch of two scalar valued Normals.
# Both have mean 1, but different standard deviations.
dist = Normal(loc=1., scale=[11, 22.])
# Variable as input
dims = 3
loc = layers.data(name='loc', shape=[dims], dtype='float32')
scale = layers.data(name='scale', shape=[dims], dtype='float32')
other_loc = layers.data(
name='other_loc', shape=[dims], dtype='float32')
other_scale = layers.data(
name='other_scale', shape=[dims], dtype='float32')
values = layers.data(name='values', shape=[dims], dtype='float32')
normal = Normal(loc, scale)
other_normal = Normal(other_loc, other_scale)
sample = normal.sample([2, 3])
entropy = normal.entropy()
lp = normal.log_prob(values)
kl = normal.kl_divergence(other_normal)
dist = Normal(loc=1., scale=[11., 22.])
# Complete example
value_npdata = np.array([0.8], dtype="float32")
value_tensor = layers.create_tensor(dtype="float32")
layers.assign(value_npdata, value_tensor)
normal_a = Normal([0.], [1.])
normal_b = Normal([0.5], [2.])
sample = normal_a.sample([2])
# a random tensor created by normal distribution with shape: [2, 1]
entropy = normal_a.entropy()
# [1.4189385] with shape: [1]
lp = normal_a.log_prob(value_tensor)
# [-1.2389386] with shape: [1]
kl = normal_a.kl_divergence(normal_b)
# [0.34939718] with shape: [1]
"""
def
__init__
(
self
,
loc
,
scale
):
...
...
@@ -330,7 +329,7 @@ class Normal(Distribution):
seed (int): Python integer number.
Returns:
Variable: A tensor with prepended dimensions shape.
Variable: A tensor with prepended dimensions shape.
The data type is float32.
"""
batch_shape
=
list
((
self
.
loc
+
self
.
scale
).
shape
)
...
...
@@ -356,7 +355,7 @@ class Normal(Distribution):
"""Shannon entropy in nats.
Returns:
Variable: Shannon entropy of normal distribution.
Variable: Shannon entropy of normal distribution.
The data type is float32.
"""
batch_shape
=
list
((
self
.
loc
+
self
.
scale
).
shape
)
...
...
@@ -372,7 +371,7 @@ class Normal(Distribution):
value (Variable): The input tensor.
Returns:
Variable: log probability.
Variable: log probability.
The data type is same with value.
"""
var
=
self
.
scale
*
self
.
scale
...
...
@@ -387,7 +386,7 @@ class Normal(Distribution):
other (Normal): instance of Normal.
Returns:
Variable: kl-divergence between two normal distributions.
Variable: kl-divergence between two normal distributions.
The data type is float32.
"""
assert
isinstance
(
other
,
Normal
),
"another distribution must be Normal"
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
282c9e7c
...
...
@@ -13780,32 +13780,48 @@ def affine_channel(x,
Args:
x (Variable): Feature map input can be a 4D tensor with order NCHW
or NHWC. It also can be a 2D tensor and the affine transformation
is applied in the second dimension.
is applied in the second dimension.
The data type is float32 or float64.
scale (Variable): 1D input of shape (C), the c-th element is the scale
factor of the affine transformation for the c-th channel of
the input.
the input.
The data type is float32 or float64.
bias (Variable): 1D input of shape (C), the c-th element is the bias
of the affine transformation for the c-th channel of the input.
data_layout (string, default NCHW): NCHW or NHWC. If input is 2D
The data type is float32 or float64.
data_layout (str, default NCHW): NCHW or NHWC. If input is 2D
tensor, you can ignore data_layout.
name (str, default None): The name of this layer.
name (str, default None): The name of this layer. For more information,
please refer to :ref:`api_guide_Name` .
act (str, default None): Activation to be applied to the output of this layer.
Returns:
out (Variable): A tensor of the same shape and data layout
with x.
Variable: A tensor which has the same shape, data layout and data type
with x.
Examples:
.. code-block:: python
import numpy as np
import paddle.fluid as fluid
data = fluid.layers.data(name='data', shape=[3, 32, 32],
dtype='float32')
input_scale = fluid.layers.create_parameter(shape=[3],
dtype="float32")
input_bias = fluid.layers.create_parameter(shape=[3],
dtype="float32")
use_gpu = False
place = fluid.CUDAPlace(0) if use_gpu else fluid.CPUPlace()
exe = fluid.Executor(place)
data = fluid.data(name='data', shape=[None, 1, 2, 2], dtype='float32')
input_scale = fluid.layers.create_parameter(shape=[1], dtype="float32",
default_initializer=fluid.initializer.Constant(2.0))
input_bias = fluid.layers.create_parameter(shape=[1],dtype="float32",
default_initializer=fluid.initializer.Constant(0.5))
out = fluid.layers.affine_channel(data,scale=input_scale,
bias=input_bias)
bias=input_bias)
exe.run(fluid.default_startup_program())
test_program = fluid.default_main_program().clone(for_test=True)
[out_array] = exe.run(test_program,
fetch_list=out,
feed={'data': np.ones([1,1,2,2]).astype('float32')})
# out_array is [[[[2.5, 2.5],
# [2.5, 2.5]]]] with shape: [1, 1, 2, 2]
"""
helper = LayerHelper("affine_channel", **locals())
...
...
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