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体验新版 GitCode,发现更多精彩内容 >>
提交
a3e952f4
编写于
11月 18, 2018
作者:
P
peizhilin
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add the jit back
fix compile error on windows
上级
1cc23ef6
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
241 addition
and
258 deletion
+241
-258
CMakeLists.txt
CMakeLists.txt
+5
-0
cmake/operators.cmake
cmake/operators.cmake
+2
-3
cmake/simd.cmake
cmake/simd.cmake
+14
-11
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+4
-5
paddle/fluid/operators/hierarchical_sigmoid_op.h
paddle/fluid/operators/hierarchical_sigmoid_op.h
+1
-1
paddle/fluid/operators/math/CMakeLists.txt
paddle/fluid/operators/math/CMakeLists.txt
+15
-20
paddle/fluid/operators/math/matrix_bit_code.h
paddle/fluid/operators/math/matrix_bit_code.h
+1
-2
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+178
-196
python/paddle/fluid/layers/ops.py
python/paddle/fluid/layers/ops.py
+21
-20
未找到文件。
CMakeLists.txt
浏览文件 @
a3e952f4
...
...
@@ -130,6 +130,11 @@ if (APPLE OR WIN32)
"Disable MKL for building on mac and windows"
FORCE
)
endif
()
if
(
WIN32
)
set
(
WITH_AVX OFF CACHE STRING
"Disable AVX when compiling for Windows"
FORCE
)
endif
()
set
(
THIRD_PARTY_PATH
"
${
CMAKE_BINARY_DIR
}
/third_party"
CACHE STRING
"A path setting third party libraries download & build directories."
)
...
...
cmake/operators.cmake
浏览文件 @
a3e952f4
...
...
@@ -84,9 +84,8 @@ function(op_library TARGET)
endif
()
if
(
WIN32
)
# remove windows unsupported op, because windows has no nccl, no warpctc such ops.
foreach
(
windows_unsupport_op
"nccl_op"
"gen_nccl_id_op"
"warpctc_op"
"hierarchical_sigmoid_op"
"crf_decoding_op"
"select_op"
"lstmp_op"
"gru_op"
"fusion_gru_op"
"lstm_op"
"fusion_lstm_op"
"cumsum_op"
"fusion_seqconv_eltadd_relu_op"
"channel_send_op"
"channel_create_op"
"channel_close_op"
"channel_recv_op"
)
foreach
(
windows_unsupport_op
"nccl_op"
"gen_nccl_id_op"
"warpctc_op"
"channel_send_op"
"channel_create_op"
"channel_close_op"
"channel_recv_op"
)
if
(
"
${
TARGET
}
"
STREQUAL
"
${
windows_unsupport_op
}
"
)
return
()
endif
()
...
...
cmake/simd.cmake
浏览文件 @
a3e952f4
...
...
@@ -70,17 +70,20 @@ int main()
return 0;
}"
AVX_FOUND
)
# Check AVX 2
set
(
CMAKE_REQUIRED_FLAGS
${
AVX2_FLAG
}
)
set
(
AVX2_FOUND_EXITCODE 1 CACHE STRING
"Result from TRY_RUN"
FORCE
)
CHECK_CXX_SOURCE_RUNS
(
"
#include <immintrin.h>
int main()
{
__m256i a = _mm256_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4);
__m256i result = _mm256_abs_epi32 (a);
return 0;
}"
AVX2_FOUND
)
# disable AVX2 by default on windows
if
(
NOT WIN32
)
# Check AVX 2
set
(
CMAKE_REQUIRED_FLAGS
${
AVX2_FLAG
}
)
set
(
AVX2_FOUND_EXITCODE 1 CACHE STRING
"Result from TRY_RUN"
FORCE
)
CHECK_CXX_SOURCE_RUNS
(
"
#include <immintrin.h>
int main()
{
__m256i a = _mm256_set_epi32 (-1, 2, -3, 4, -1, 2, -3, 4);
__m256i result = _mm256_abs_epi32 (a);
return 0;
}"
AVX2_FOUND
)
endif
(
NOT WIN32
)
# Check AVX512F
set
(
CMAKE_REQUIRED_FLAGS
${
AVX512F_FLAG
}
)
...
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
a3e952f4
...
...
@@ -22,9 +22,7 @@ if(WITH_DISTRIBUTE)
add_subdirectory
(
distributed_ops
)
endif
()
if
(
NOT WIN32
)
add_subdirectory
(
reader
)
endif
()
add_subdirectory
(
reader
)
if
(
NOT WIN32
)
add_subdirectory
(
nccl
)
...
...
@@ -49,9 +47,10 @@ endif()
set
(
COMMON_OP_DEPS
""
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
xxhash selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor dynload_warpctc sequence_padding sequence_scale cos_sim_functor memory jit_kernel concat_and_split cross_entropy softmax vol2col im2col sampler
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
xxhash selected_rows_functor selected_rows lod_tensor maxouting unpooling pooling lod_rank_table context_project sequence_pooling executor sequence_padding sequence_scale cos_sim_functor memory concat_and_split cross_entropy softmax vol2col im2col sampler
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
lstm_compute matrix_bit_code sequence2batch gru_compute activation_functions jit_kernel
)
if
(
NOT WIN32
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
sequence2batch lstm_compute matrix_bit_code gru_compute activation_functions
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
dynload_warpctc
)
endif
()
if
(
WITH_GPU
)
set
(
COMMON_OP_DEPS
${
COMMON_OP_DEPS
}
depthwise_conv cub
)
...
...
paddle/fluid/operators/hierarchical_sigmoid_op.h
浏览文件 @
a3e952f4
...
...
@@ -111,7 +111,7 @@ class HierarchicalSigmoidGradOpKernel : public framework::OpKernel<T> {
auto
pre_out_mat
=
EigenMatrix
<
T
>::
From
(
*
pre_out
);
auto
pre_out_grad_mat
=
EigenMatrix
<
T
>::
From
(
pre_out_grad
);
auto
out_grad_mat
=
EigenMatrix
<
T
>::
From
(
*
out_grad
);
Eigen
::
array
<
int
,
2
>
bcast
({{
1
,
static_cast
<
int
>
(
pre_out_grad
.
dims
()[
1
])}})
;
Eigen
::
array
<
int
,
2
>
bcast
{
1
,
static_cast
<
int
>
(
pre_out_grad
.
dims
()[
1
])}
;
// softrelu derivative
pre_out_grad_mat
.
device
(
place
)
=
...
...
paddle/fluid/operators/math/CMakeLists.txt
浏览文件 @
a3e952f4
if
(
NOT WIN32
)
add_subdirectory
(
detail
)
endif
(
NOT WIN32
)
add_subdirectory
(
detail
)
function
(
math_library TARGET
)
# math_library is a function to create math library.
...
...
@@ -43,10 +41,8 @@ math_library(depthwise_conv)
math_library
(
im2col
)
math_library
(
sampler
)
if
(
NOT WIN32
)
# windows do not support avx functions yet.
math_library
(
gru_compute DEPS activation_functions math_function
)
math_library
(
lstm_compute DEPS activation_functions
)
endif
(
NOT WIN32
)
math_library
(
gru_compute DEPS activation_functions math_function
)
math_library
(
lstm_compute DEPS activation_functions
)
cc_library
(
blas SRCS blas.cc DEPS cblas framework_proto device_context
)
math_library
(
math_function DEPS blas
)
...
...
@@ -58,9 +54,9 @@ math_library(sequence_padding)
math_library
(
sequence_pooling DEPS math_function
)
math_library
(
sequence_scale
)
math_library
(
softmax DEPS math_function
)
if
(
NOT WIN32
)
math_library
(
matrix_bit_code
)
endif
(
NOT WIN32
)
math_library
(
matrix_bit_code
)
math_library
(
unpooling
)
math_library
(
vol2col
)
...
...
@@ -76,13 +72,12 @@ if(WITH_GPU)
endif
()
cc_test
(
concat_test SRCS concat_test.cc DEPS concat_and_split
)
cc_test
(
cpu_vec_test SRCS cpu_vec_test.cc DEPS blas cpu_info
)
if
(
NOT WIN32
)
set
(
JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
)
set
(
JIT_KERNEL_DEPS cpu_info cblas gflags enforce
)
if
(
WITH_XBYAK
)
list
(
APPEND JIT_KERNEL_SRCS jit_gen.cc jit_code.cc
)
list
(
APPEND JIT_KERNEL_DEPS xbyak
)
endif
()
cc_library
(
jit_kernel SRCS
${
JIT_KERNEL_SRCS
}
DEPS
${
JIT_KERNEL_DEPS
}
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
endif
(
NOT WIN32
)
set
(
JIT_KERNEL_SRCS jit_kernel.cc jit_kernel_blas.cc jit_kernel_exp.cc jit_kernel_rnn.cc jit_kernel_crf_decode.cc
)
set
(
JIT_KERNEL_DEPS cpu_info cblas gflags enforce
)
if
(
WITH_XBYAK
)
list
(
APPEND JIT_KERNEL_SRCS jit_gen.cc jit_code.cc
)
list
(
APPEND JIT_KERNEL_DEPS xbyak
)
endif
()
cc_library
(
jit_kernel SRCS
${
JIT_KERNEL_SRCS
}
DEPS
${
JIT_KERNEL_DEPS
}
)
cc_test
(
jit_kernel_test SRCS jit_kernel_test.cc DEPS jit_kernel
)
paddle/fluid/operators/math/matrix_bit_code.h
浏览文件 @
a3e952f4
...
...
@@ -67,7 +67,7 @@ inline constexpr size_t FindLastSet(size_t x) {
:
(
std
::
is_same
<
size_t
,
unsigned
long
>::
value
// NOLINT
?
(
x
?
8
*
sizeof
(
x
)
-
__builtin_clzl
(
x
)
:
0
)
:
(
x
?
8
*
sizeof
(
x
)
-
__builtin_clzll
(
x
)
:
0
));
}
#else
// windows don't have built-in clz, ctz function
template
<
typename
T
>
...
...
@@ -92,7 +92,6 @@ inline int clz(const T& value) {
inline
size_t
FindLastSet
(
size_t
x
)
{
return
sizeof
(
size_t
)
*
8
-
clz
(
x
);
}
#endif // !_WIN32
}
struct
SimpleCode
{
SimpleCode
(
size_t
code
,
size_t
num_classes
)
:
c_
(
code
+
num_classes
)
{}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
a3e952f4
...
...
@@ -170,12 +170,6 @@ __all__ = [
'bilinear_tensor_product'
,
]
# To avoid the api checker complains
if
os
.
name
==
'nt'
:
__all__
.
remove
(
'dynamic_lstm'
)
__all__
.
remove
(
'crf_decoding'
)
__all__
.
remove
(
'roi_pool'
)
def
fc
(
input
,
size
,
...
...
@@ -349,128 +343,126 @@ def embedding(input,
return
tmp
if
os
.
name
!=
'nt'
:
@
templatedoc
(
op_type
=
"lstm"
)
def
dynamic_lstm
(
input
,
size
,
h_0
=
None
,
c_0
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
use_peepholes
=
True
,
is_reverse
=
False
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'tanh'
,
candidate_activation
=
'tanh'
,
dtype
=
'float32'
,
name
=
None
):
"""
${comment}
@
templatedoc
(
op_type
=
"lstm"
)
def
dynamic_lstm
(
input
,
size
,
h_0
=
None
,
c_0
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
use_peepholes
=
True
,
is_reverse
=
False
,
gate_activation
=
'sigmoid'
,
cell_activation
=
'tanh'
,
candidate_activation
=
'tanh'
,
dtype
=
'float32'
,
name
=
None
):
"""
${comment}
Args:
input (Variable): ${input_comment}
size (int): 4 * hidden size.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the hidden size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weights.
- Weights = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}
- The shape is (D x 4D), where D is the hidden
size.
If it is set to None or one attribute of ParamAttr,
dynamic_lstm will create ParamAttr as param_attr.
If the Initializer of the param_attr is not set, the
parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|None): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic},
\
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
If it is set to None or one attribute of ParamAttr,
dynamic_lstm will create ParamAttr as bias_attr.
If the Initializer of the bias_attr is not set,
the bias is initialized zero. Default: None.
use_peepholes (bool): ${use_peepholes_comment}
is_reverse (bool): ${is_reverse_comment}
gate_activation (str): ${gate_activation_comment}
cell_activation (str): ${cell_activation_comment}
candidate_activation (str): ${candidate_activation_comment}
dtype (str): Data type. Choices = ["float32", "float64"], default "float32".
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The hidden state, and cell state of LSTM. The shape of both
\
is (T x D), and lod is the same with the `input`.
Examples:
.. code-block:: python
hidden_dim = 512
forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
bias_attr=False)
forward, _ = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
"""
assert
bias_attr
is
not
False
,
"bias_attr should not be False in dynamic_lstmp."
helper
=
LayerHelper
(
'lstm'
,
**
locals
())
size
=
size
//
4
weight
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
[
size
,
4
*
size
],
dtype
=
dtype
)
bias_size
=
[
1
,
7
*
size
]
if
not
use_peepholes
:
bias_size
[
1
]
=
4
*
size
bias
=
helper
.
create_parameter
(
attr
=
helper
.
bias_attr
,
shape
=
bias_size
,
dtype
=
dtype
,
is_bias
=
True
)
Args:
input (Variable): ${input_comment}
size (int): 4 * hidden size.
h_0(Variable): The initial hidden state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size and D is the hidden size.
c_0(Variable): The initial cell state is an optional input, default is zero.
This is a tensor with shape (N x D), where N is the
batch size. `h_0` and `c_0` can be NULL but only at the same time.
param_attr(ParamAttr|None): The parameter attribute for the learnable
hidden-hidden weights.
hidden
=
helper
.
create_variable_for_type_inference
(
dtype
)
cell
=
helper
.
create_variable_for_type_inference
(
dtype
)
batch_gate
=
helper
.
create_variable_for_type_inference
(
dtype
)
batch_cell_pre_act
=
helper
.
create_variable_for_type_inference
(
dtype
)
inputs
=
{
'Input'
:
input
,
'Weight'
:
weight
,
'Bias'
:
bias
}
batch_size
=
input
.
shape
[
0
]
if
h_0
:
assert
h_0
.
shape
==
(
batch_size
,
size
),
\
'The shape of h0 should be (batch_size, %d)'
%
size
inputs
[
'H0'
]
=
h_0
if
c_0
:
assert
c_0
.
shape
==
(
batch_size
,
size
),
\
'The shape of c0 should be (batch_size, %d)'
%
size
inputs
[
'C0'
]
=
c_0
- Weights = {:math:`W_{ch}, W_{ih},
\
W_{fh}, W_{oh}`}
- The shape is (D x 4D), where D is the hidden
size.
helper
.
append_op
(
type
=
'lstm'
,
inputs
=
inputs
,
outputs
=
{
'Hidden'
:
hidden
,
'Cell'
:
cell
,
'BatchGate'
:
batch_gate
,
'BatchCellPreAct'
:
batch_cell_pre_act
},
attrs
=
{
'use_peepholes'
:
use_peepholes
,
'is_reverse'
:
is_reverse
,
'gate_activation'
:
gate_activation
,
'cell_activation'
:
cell_activation
,
'candidate_activation'
:
candidate_activation
})
return
hidden
,
cell
If it is set to None or one attribute of ParamAttr,
dynamic_lstm will create ParamAttr as param_attr.
If the Initializer of the param_attr is not set, the
parameter is initialized with Xavier. Default: None.
bias_attr (ParamAttr|None): The bias attribute for the learnable bias
weights, which contains two parts, input-hidden
bias weights and peephole connections weights if
setting `use_peepholes` to `True`.
1. `use_peepholes = False`
- Biases = {:math:`b_c, b_i, b_f, b_o`}.
- The shape is (1 x 4D).
2. `use_peepholes = True`
- Biases = { :math:`b_c, b_i, b_f, b_o, W_{ic},
\
W_{fc}, W_{oc}`}.
- The shape is (1 x 7D).
If it is set to None or one attribute of ParamAttr,
dynamic_lstm will create ParamAttr as bias_attr.
If the Initializer of the bias_attr is not set,
the bias is initialized zero. Default: None.
use_peepholes (bool): ${use_peepholes_comment}
is_reverse (bool): ${is_reverse_comment}
gate_activation (str): ${gate_activation_comment}
cell_activation (str): ${cell_activation_comment}
candidate_activation (str): ${candidate_activation_comment}
dtype (str): Data type. Choices = ["float32", "float64"], default "float32".
name (str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
tuple: The hidden state, and cell state of LSTM. The shape of both
\
is (T x D), and lod is the same with the `input`.
Examples:
.. code-block:: python
hidden_dim = 512
forward_proj = fluid.layers.fc(input=input_seq, size=hidden_dim * 4,
bias_attr=False)
forward, _ = fluid.layers.dynamic_lstm(
input=forward_proj, size=hidden_dim * 4, use_peepholes=False)
"""
assert
bias_attr
is
not
False
,
"bias_attr should not be False in dynamic_lstmp."
helper
=
LayerHelper
(
'lstm'
,
**
locals
())
size
=
size
//
4
weight
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
[
size
,
4
*
size
],
dtype
=
dtype
)
bias_size
=
[
1
,
7
*
size
]
if
not
use_peepholes
:
bias_size
[
1
]
=
4
*
size
bias
=
helper
.
create_parameter
(
attr
=
helper
.
bias_attr
,
shape
=
bias_size
,
dtype
=
dtype
,
is_bias
=
True
)
hidden
=
helper
.
create_variable_for_type_inference
(
dtype
)
cell
=
helper
.
create_variable_for_type_inference
(
dtype
)
batch_gate
=
helper
.
create_variable_for_type_inference
(
dtype
)
batch_cell_pre_act
=
helper
.
create_variable_for_type_inference
(
dtype
)
inputs
=
{
'Input'
:
input
,
'Weight'
:
weight
,
'Bias'
:
bias
}
batch_size
=
input
.
shape
[
0
]
if
h_0
:
assert
h_0
.
shape
==
(
batch_size
,
size
),
\
'The shape of h0 should be (batch_size, %d)'
%
size
inputs
[
'H0'
]
=
h_0
if
c_0
:
assert
c_0
.
shape
==
(
batch_size
,
size
),
\
'The shape of c0 should be (batch_size, %d)'
%
size
inputs
[
'C0'
]
=
c_0
helper
.
append_op
(
type
=
'lstm'
,
inputs
=
inputs
,
outputs
=
{
'Hidden'
:
hidden
,
'Cell'
:
cell
,
'BatchGate'
:
batch_gate
,
'BatchCellPreAct'
:
batch_cell_pre_act
},
attrs
=
{
'use_peepholes'
:
use_peepholes
,
'is_reverse'
:
is_reverse
,
'gate_activation'
:
gate_activation
,
'cell_activation'
:
cell_activation
,
'candidate_activation'
:
candidate_activation
})
return
hidden
,
cell
def
dynamic_lstmp
(
input
,
...
...
@@ -969,43 +961,39 @@ def linear_chain_crf(input, label, param_attr=None):
return
log_likelihood
if
os
.
name
!=
'nt'
:
@
templatedoc
()
def
crf_decoding
(
input
,
param_attr
,
label
=
None
):
"""
${comment}
@
templatedoc
()
def
crf_decoding
(
input
,
param_attr
,
label
=
None
):
"""
${comment}
Args:
input(${emission_type}): ${emission_comment}
Args:
input(${emission_type}): ${emission_comment}
param_attr(ParamAttr): The parameter attribute for training.
param_attr(ParamAttr): The parameter attribute for training.
label(${label_type}): ${label_comment}
label(${label_type}): ${label_comment}
Returns:
Variable: ${viterbi_path_comment}
Returns:
Variable: ${viterbi_path_comment}
Examples:
.. code-block:: python
Examples:
.. code-block:: python
crf_decode = layers.crf_decoding(
input=hidden, param_attr=ParamAttr(name="crfw"))
"""
helper
=
LayerHelper
(
'crf_decoding'
,
**
locals
())
transition
=
helper
.
get_parameter
(
param_attr
.
name
)
viterbi_path
=
helper
.
create_variable_for_type_inference
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'crf_decoding'
,
inputs
=
{
"Emission"
:
[
input
],
crf_decode = layers.crf_decoding(
input=hidden, param_attr=ParamAttr(name="crfw"))
"""
helper
=
LayerHelper
(
'crf_decoding'
,
**
locals
())
transition
=
helper
.
get_parameter
(
param_attr
.
name
)
viterbi_path
=
helper
.
create_variable_for_type_inference
(
dtype
=
helper
.
input_dtype
())
helper
.
append_op
(
type
=
'crf_decoding'
,
inputs
=
{
"Emission"
:
[
input
],
"Transition"
:
transition
,
"Label"
:
label
},
outputs
=
{
"ViterbiPath"
:
[
viterbi_path
]})
"Label"
:
label
},
outputs
=
{
"ViterbiPath"
:
[
viterbi_path
]})
return
viterbi_path
return
viterbi_path
@
templatedoc
()
...
...
@@ -5599,48 +5587,42 @@ def label_smooth(label,
return
smooth_label
if
os
.
name
!=
'nt'
:
@
templatedoc
()
def
roi_pool
(
input
,
rois
,
pooled_height
=
1
,
pooled_width
=
1
,
spatial_scale
=
1.0
):
"""
${comment}
Args:
input (Variable): ${x_comment}
rois (Variable): ROIs (Regions of Interest) to pool over.
pooled_height (integer): ${pooled_height_comment} Default: 1
pooled_width (integer): ${pooled_width_comment} Default: 1
spatial_scale (float): ${spatial_scale_comment} Default: 1.0
Returns:
Variable: ${out_comment}.
Examples:
.. code-block:: python
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
"""
helper
=
LayerHelper
(
'roi_pool'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
argmaxes
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
helper
.
append_op
(
type
=
"roi_pool"
,
inputs
=
{
"X"
:
input
,
"ROIs"
:
rois
},
outputs
=
{
"Out"
:
pool_out
,
"Argmax"
:
argmaxes
},
attrs
=
{
"pooled_height"
:
pooled_height
,
"pooled_width"
:
pooled_width
,
"spatial_scale"
:
spatial_scale
})
return
pool_out
@
templatedoc
()
def
roi_pool
(
input
,
rois
,
pooled_height
=
1
,
pooled_width
=
1
,
spatial_scale
=
1.0
):
"""
${comment}
Args:
input (Variable): ${x_comment}
rois (Variable): ROIs (Regions of Interest) to pool over.
pooled_height (integer): ${pooled_height_comment} Default: 1
pooled_width (integer): ${pooled_width_comment} Default: 1
spatial_scale (float): ${spatial_scale_comment} Default: 1.0
Returns:
Variable: ${out_comment}.
Examples:
.. code-block:: python
pool_out = fluid.layers.roi_pool(input=x, rois=rois, 7, 7, 1.0)
"""
helper
=
LayerHelper
(
'roi_pool'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
pool_out
=
helper
.
create_variable_for_type_inference
(
dtype
)
argmaxes
=
helper
.
create_variable_for_type_inference
(
dtype
=
'int32'
)
helper
.
append_op
(
type
=
"roi_pool"
,
inputs
=
{
"X"
:
input
,
"ROIs"
:
rois
},
outputs
=
{
"Out"
:
pool_out
,
"Argmax"
:
argmaxes
},
attrs
=
{
"pooled_height"
:
pooled_height
,
"pooled_width"
:
pooled_width
,
"spatial_scale"
:
spatial_scale
})
return
pool_out
@
templatedoc
()
...
...
python/paddle/fluid/layers/ops.py
浏览文件 @
a3e952f4
...
...
@@ -100,26 +100,27 @@ Examples:
>>> result = fluid.layers.hard_shrink(x=data, threshold=0.3)
"""
if
os
.
name
!=
'nt'
:
__all__
+=
[
'cumsum'
]
_cum_sum_
=
generate_layer_fn
(
'cumsum'
)
def
cumsum
(
x
,
axis
=
None
,
exclusive
=
None
,
reverse
=
None
):
locals_var
=
locals
().
keys
()
kwargs
=
dict
()
for
name
in
locals_var
:
val
=
locals
()[
name
]
if
val
is
not
None
:
kwargs
[
name
]
=
val
return
_cum_sum_
(
**
kwargs
)
cumsum
.
__doc__
=
_cum_sum_
.
__doc__
+
"""
Examples:
>>> data = fluid.layers.data(name="input", shape=[32, 784])
>>> result = fluid.layers.cumsum(data, axis=0)
"""
__all__
+=
[
'cumsum'
]
_cum_sum_
=
generate_layer_fn
(
'cumsum'
)
def
cumsum
(
x
,
axis
=
None
,
exclusive
=
None
,
reverse
=
None
):
locals_var
=
locals
().
keys
()
kwargs
=
dict
()
for
name
in
locals_var
:
val
=
locals
()[
name
]
if
val
is
not
None
:
kwargs
[
name
]
=
val
return
_cum_sum_
(
**
kwargs
)
cumsum
.
__doc__
=
_cum_sum_
.
__doc__
+
"""
Examples:
>>> data = fluid.layers.data(name="input", shape=[32, 784])
>>> result = fluid.layers.cumsum(data, axis=0)
"""
__all__
+=
[
'thresholded_relu'
]
...
...
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