Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
PaddlePaddle
PaddleDetection
提交
980499fa
P
PaddleDetection
项目概览
PaddlePaddle
/
PaddleDetection
12 个月 前同步成功
通知
692
Star
11112
Fork
2696
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
184
列表
看板
标记
里程碑
合并请求
40
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
PaddleDetection
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
184
Issue
184
列表
看板
标记
里程碑
合并请求
40
合并请求
40
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
980499fa
编写于
6月 14, 2018
作者:
F
fengjiayi
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix errors
上级
29bf727e
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
92 addition
and
34 deletion
+92
-34
paddle/fluid/operators/pool_op.cc
paddle/fluid/operators/pool_op.cc
+11
-4
python/paddle/fluid/layers/control_flow.py
python/paddle/fluid/layers/control_flow.py
+12
-11
python/paddle/fluid/layers/io.py
python/paddle/fluid/layers/io.py
+26
-0
python/paddle/fluid/layers/learning_rate_scheduler.py
python/paddle/fluid/layers/learning_rate_scheduler.py
+2
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+37
-14
python/paddle/fluid/layers/tensor.py
python/paddle/fluid/layers/tensor.py
+4
-3
未找到文件。
paddle/fluid/operators/pool_op.cc
浏览文件 @
980499fa
...
...
@@ -204,8 +204,6 @@ void Pool2dOpMaker::Make() {
// TODO(dzhwinter): need to registered layout transform function
AddComment
(
R"DOC(
Pool2d Operator.
The pooling2d operation calculates the output based on
the input, pooling_type and ksize, strides, paddings parameters.
Input(X) and output(Out) are in NCHW format, where N is batch size, C is the
...
...
@@ -215,18 +213,27 @@ These two elements represent height and width, respectively.
The input(X) size and output(Out) size may be different.
Example:
Input:
X shape: $(N, C, H_{in}, W_{in})$
Output:
Out shape: $(N, C, H_{out}, W_{out})$
For ceil_mode = false:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0])}{strides[0]} + 1
$$
$$
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1])}{strides[1]} + 1
$$
For ceil_mode = true:
$$
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1 \\
H_{out} = \frac{(H_{in} - ksize[0] + 2 * paddings[0] + strides[0] - 1)}{strides[0]} + 1
$$
$$
W_{out} = \frac{(W_{in} - ksize[1] + 2 * paddings[1] + strides[1] - 1)}{strides[1]} + 1
$$
...
...
python/paddle/fluid/layers/control_flow.py
浏览文件 @
980499fa
...
...
@@ -753,9 +753,9 @@ def lod_tensor_to_array(x, table):
This function split a LoDTesnor to a LoDTensorArray according to its LoD
information. LoDTensorArray is an alias of C++ std::vector<LoDTensor> in
Paddle. The generated LoDTensorArray of this function can be further read
or written by
'read_from_array()' and 'write_to_array()'
operators. However,
this function is generally an internal component of Paddle
'DynamicRNN'
.
Paddle
Paddle
. The generated LoDTensorArray of this function can be further read
or written by
`read_from_array()` and `write_to_array()`
operators. However,
this function is generally an internal component of Paddle
Paddle `DynamicRNN`
.
Users should not use it directly.
Args:
...
...
@@ -763,11 +763,10 @@ def lod_tensor_to_array(x, table):
table (ParamAttr|list): The variable that stores the level of lod
which is ordered by sequence length in
descending order. It is generally generated
by
'layers.lod_rank_table()'
API.
by
`layers.lod_rank_table()`
API.
Returns:
Variable: The LoDTensorArray that has been converted from the input
tensor.
Variable: The LoDTensorArray that has been converted from the input tensor.
Examples:
.. code-block:: python
...
...
@@ -1579,24 +1578,26 @@ def reorder_lod_tensor_by_rank(x, rank_table):
def
is_empty
(
x
,
cond
=
None
,
**
ignored
):
"""
Test whether a
n
Variable is empty.
Test whether a Variable is empty.
Args:
x (Variable): The Variable to be tested.
cond (Variable|None): Output parameter. Returns the test result
of given 'x'.
of given 'x'.
Default: None
Returns:
Variable:
The tensor variable storing the test result of 'x'
.
Variable:
A bool scalar. True if 'x' is an empty Variable
.
Raises:
TypeError: If input cond is not a variable, or cond's dtype is
not bool
not bool
.
Examples:
.. code-block:: python
less = fluid.layers.is_empty(x=input)
res = fluid.layers.is_empty(x=input)
# or:
fluid.layers.is_empty(x=input, cond=res)
"""
helper
=
LayerHelper
(
"is_empty"
,
**
locals
())
if
cond
is
None
:
...
...
python/paddle/fluid/layers/io.py
浏览文件 @
980499fa
...
...
@@ -572,6 +572,32 @@ def parallel(reader):
def
read_file
(
file_obj
):
"""
Read data from a file object.
A file object is also a Variable. It can be a raw file object generated by
`fluid.layers.open_files()` or a decorated one generated by
`fluid.layers.double_buffer()` and so on.
Args:
file_obj(Variable): The file object from where to read data.
Returns:
Tuple[Variable]: Data read from the given file object.
Examples:
.. code-block:: python
data_file = fluid.layers.open_files(
filenames=['mnist.recordio'],
shapes=[(-1, 748), (-1, 1)],
lod_levels=[0, 0],
dtypes=["float32", "int64"])
data_file = fluid.layers.double_buffer(
fluid.layers.batch(data_file, batch_size=64))
input, label = fluid.layers.read_file(data_file)
"""
helper
=
LayerHelper
(
'read_file'
)
out
=
[
helper
.
create_tmp_variable
(
...
...
python/paddle/fluid/layers/learning_rate_scheduler.py
浏览文件 @
980499fa
...
...
@@ -90,7 +90,7 @@ def exponential_decay(learning_rate, decay_steps, decay_rate, staircase=False):
Default: False
Returns:
The decayed learning rate
Variable:
The decayed learning rate
Examples:
.. code-block:: python
...
...
@@ -167,7 +167,7 @@ def inverse_time_decay(learning_rate, decay_steps, decay_rate, staircase=False):
Default: False
Returns:
The decayed learning rate
Variable:
The decayed learning rate
Examples:
.. code-block:: python
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
980499fa
...
...
@@ -151,7 +151,7 @@ def fc(input,
name (str, default None): The name of this layer.
Returns:
A tensor variable storing t
he transformation result.
Variable: T
he transformation result.
Raises:
ValueError: If rank of the input tensor is less than 2.
...
...
@@ -159,8 +159,7 @@ def fc(input,
Examples:
.. code-block:: python
data = fluid.layers.data(
name="data", shape=[32, 32], dtype="float32")
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
"""
...
...
@@ -1543,21 +1542,24 @@ def pool2d(input,
${comment}
Args:
input (Variable): ${input_comment}
input (Variable): The input tensor of pooling operator. The format of
input tensor is NCHW, where N is batch size, C is the number of
channels, H is the height of the feature, and W is the width of
the feature.
pool_size (int): The side length of pooling windows. All pooling
windows are squares with pool_size on a side.
pool_type
(str)
: ${pooling_type_comment}
pool_type: ${pooling_type_comment}
pool_stride (int): stride of the pooling layer.
pool_padding (int): padding size.
global_pooling
(bool)
: ${global_pooling_comment}
use_cudnn
(bool)
: ${use_cudnn_comment}
ceil_mode
(bool)
: ${ceil_mode_comment}
use_mkldnn
(bool)
: ${use_mkldnn_comment}
global_pooling: ${global_pooling_comment}
use_cudnn: ${use_cudnn_comment}
ceil_mode: ${ceil_mode_comment}
use_mkldnn: ${use_mkldnn_comment}
name (str|None): A name for this layer(optional). If set None, the
layer will be named automatically.
Returns:
Variable:
output of pool2d layer
.
Variable:
The pooling result
.
Raises:
ValueError: If 'pool_type' is not "max" nor "avg"
...
...
@@ -2764,6 +2766,27 @@ def topk(input, k, name=None):
If the input is a Tensor with higher rank, this operator computes the top k
entries along the last dimension.
For example:
.. code-block:: text
If:
input = [[5, 4, 2, 3],
[9, 7, 10, 25],
[6, 2, 10, 1]]
k = 2
Then:
The first output:
values = [[5, 4],
[10, 25],
[6, 10]]
The second output:
indices = [[0, 1],
[2, 3],
[0, 2]]
Args:
input(Variable): The input variable which can be a vector or Tensor with
higher rank.
...
...
@@ -2774,10 +2797,10 @@ def topk(input, k, name=None):
Default: None
Returns:
values(Variable): The k largest elements along each last dimensional
slice.
indices(Variable): The indices of values within the last dimension of
input.
Tuple[Variable]: A tuple with two elements. Each element is a Variable.
The first one is k largest elements along each last
dimensional slice. The second one is indices of values
within the last dimension of
input.
Raises:
ValueError: If k < 1 or k is not less than the last dimension of input
...
...
python/paddle/fluid/layers/tensor.py
浏览文件 @
980499fa
...
...
@@ -159,20 +159,21 @@ def concat(input, axis=0, name=None):
def
sums
(
input
,
out
=
None
):
"""This function performs the sum operation on the input and returns the
"""
This function performs the sum operation on the input and returns the
result as the output.
Args:
input (Variable|list): The input tensor that has the elements
that need to be summed up.
out (Variable|None): Output parameter.
Returns t
he sum result.
out (Variable|None): Output parameter.
T
he sum result.
Default: None
Returns:
Variable: the sum of input. The same as the argument 'out'
Examples:
.. code-block::python
.. code-block::
python
tmp = fluid.layers.zeros(shape=[10], dtype='int32')
i = fluid.layers.fill_constant(shape=[1], dtype='int64', value=10)
...
...
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录