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b8ea7a08
编写于
7月 11, 2018
作者:
C
chenweihang
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' of
https://github.com/PaddlePaddle/Paddle
into unsqueeze_op
上级
fbef49e7
f9202447
变更
18
隐藏空白更改
内联
并排
Showing
18 changed file
with
496 addition
and
133 deletion
+496
-133
paddle/contrib/inference/CMakeLists.txt
paddle/contrib/inference/CMakeLists.txt
+16
-1
paddle/contrib/inference/check_symbol.sh
paddle/contrib/inference/check_symbol.sh
+12
-0
paddle/contrib/inference/demo/CMakeLists.txt
paddle/contrib/inference/demo/CMakeLists.txt
+0
-2
paddle/contrib/inference/demo_ci/CMakeLists.txt
paddle/contrib/inference/demo_ci/CMakeLists.txt
+77
-0
paddle/contrib/inference/demo_ci/run.sh
paddle/contrib/inference/demo_ci/run.sh
+34
-0
paddle/contrib/inference/demo_ci/simple_on_word2vec.cc
paddle/contrib/inference/demo_ci/simple_on_word2vec.cc
+39
-23
paddle/contrib/inference/paddle_inference_api.map
paddle/contrib/inference/paddle_inference_api.map
+6
-0
paddle/contrib/inference/paddle_inference_api.sym
paddle/contrib/inference/paddle_inference_api.sym
+1
-0
paddle/fluid/inference/CMakeLists.txt
paddle/fluid/inference/CMakeLists.txt
+6
-0
paddle/fluid/inference/paddle_fluid.sym
paddle/fluid/inference/paddle_fluid.sym
+1
-0
paddle/fluid/operators/conv_mkldnn_op.cc
paddle/fluid/operators/conv_mkldnn_op.cc
+120
-52
paddle/fluid/operators/detection/prior_box_op.cc
paddle/fluid/operators/detection/prior_box_op.cc
+7
-0
paddle/fluid/operators/detection/prior_box_op.cu
paddle/fluid/operators/detection/prior_box_op.cu
+26
-10
paddle/fluid/operators/detection/prior_box_op.h
paddle/fluid/operators/detection/prior_box_op.h
+50
-15
paddle/fluid/platform/mkldnn_helper.h
paddle/fluid/platform/mkldnn_helper.h
+9
-8
paddle/scripts/paddle_build.sh
paddle/scripts/paddle_build.sh
+14
-1
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+18
-4
python/paddle/fluid/tests/unittests/test_prior_box_op.py
python/paddle/fluid/tests/unittests/test_prior_box_op.py
+60
-17
未找到文件。
paddle/contrib/inference/CMakeLists.txt
浏览文件 @
b8ea7a08
...
...
@@ -45,6 +45,10 @@ endfunction(inference_api_test)
cc_library
(
paddle_inference_api
SRCS paddle_inference_api.cc paddle_inference_api_impl.cc
DEPS
${
FLUID_CORE_MODULES
}
${
GLOB_OP_LIB
}
)
if
(
NOT APPLE
)
set
(
LINK_FLAGS
"-Wl,--retain-symbols-file
${
CMAKE_CURRENT_SOURCE_DIR
}
/paddle_inference_api.sym"
)
set_target_properties
(
paddle_inference_api PROPERTIES LINK_FLAGS
"
${
LINK_FLAGS
}
"
)
endif
()
# Here the shared library doesn't depend on other fluid libraries, or double free will occur.
cc_library
(
paddle_inference_api_shared SHARED
...
...
@@ -53,8 +57,19 @@ add_dependencies(paddle_inference_api_shared ${FLUID_CORE_MODULES} ${GLOB_OP_LIB
set_target_properties
(
paddle_inference_api_shared PROPERTIES OUTPUT_NAME paddle_inference_api
)
if
(
NOT APPLE
)
set
(
LINK_FLAGS
"-
fPIC -fvisibility=hidden
"
)
set
(
LINK_FLAGS
"-
Wl,--version-script
${
CMAKE_CURRENT_SOURCE_DIR
}
/paddle_inference_api.map
"
)
set_target_properties
(
paddle_inference_api_shared PROPERTIES LINK_FLAGS
"
${
LINK_FLAGS
}
"
)
FILE
(
WRITE
${
CMAKE_CURRENT_BINARY_DIR
}
/check_symbol.cmake
"execute_process(COMMAND bash -c
\"
${
CMAKE_CURRENT_SOURCE_DIR
}
/check_symbol.sh"
"
${
CMAKE_CURRENT_BINARY_DIR
}
/libpaddle_inference_api.so
\"
RESULT_VARIABLE symbol_res)
\n
"
"if(NOT
\"\$
{symbol_res}
\"
STREQUAL
\"
0
\"
)
\n
"
" message(FATAL_ERROR
\"
Check symbol failed.
\"
)
\n
"
"endif()
\n
"
)
add_custom_command
(
OUTPUT
"
${
CMAKE_CURRENT_BINARY_DIR
}
/.check_symbol"
COMMAND
${
CMAKE_COMMAND
}
-P
"
${
CMAKE_CURRENT_BINARY_DIR
}
/check_symbol.cmake"
DEPENDS paddle_inference_api_shared
)
add_custom_target
(
check_symbol ALL DEPENDS
"
${
CMAKE_CURRENT_BINARY_DIR
}
/.check_symbol"
)
endif
()
cc_test
(
test_paddle_inference_api
...
...
paddle/contrib/inference/check_symbol.sh
0 → 100755
浏览文件 @
b8ea7a08
#!/bin/bash
lib
=
$1
if
[
$#
-ne
1
]
;
then
echo
"No input library"
;
exit
-1
;
fi
num_paddle_syms
=
$(
nm
-D
--defined-only
${
lib
}
|
grep
paddle |
wc
-l
)
num_google_syms
=
$(
nm
-D
--defined-only
${
lib
}
|
grep
google |
wc
-l
)
if
[
$num_paddle_syms
-le
0
]
;
then
echo
"Have no paddle symbols"
;
exit
-1
;
fi
if
[
$num_google_syms
-ge
1
]
;
then
echo
"Have some google symbols"
;
exit
-1
;
fi
exit
0
paddle/contrib/inference/demo/CMakeLists.txt
浏览文件 @
b8ea7a08
...
...
@@ -13,8 +13,6 @@
# limitations under the License.
#
inference_api_test
(
simple_on_word2vec ARGS test_word2vec
)
option
(
WITH_INFERENCE_DEMO
"Compile with Inference demo"
OFF
)
if
(
NOT WITH_INFERENCE_DEMO
)
return
()
...
...
paddle/contrib/inference/demo_ci/CMakeLists.txt
0 → 100644
浏览文件 @
b8ea7a08
cmake_minimum_required
(
VERSION 3.0
)
project
(
cpp_inference_demo CXX C
)
set
(
CMAKE_CXX_FLAGS
"
${
CMAKE_CXX_FLAGS
}
-std=c++11"
)
if
(
NOT DEFINED PADDLE_LIB
)
message
(
FATAL_ERROR
"please set PADDLE_LIB with -DPADDLE_LIB=/path/paddle/lib"
)
endif
()
if
(
NOT DEFINED DEMO_NAME
)
message
(
FATAL_ERROR
"please set DEMO_NAME with -DDEMO_NAME=demo_name"
)
endif
()
option
(
WITH_MKL
"Compile demo with MKL/OpenBlas support, default use MKL."
ON
)
option
(
WITH_GPU
"Compile demo with GPU/CPU, default use CPU."
OFF
)
option
(
WITH_STATIC_LIB
"Compile demo with static/shared library, default use static."
ON
)
if
(
WITH_GPU
)
set
(
CUDA_LIB
"/usr/local/cuda/lib64/"
CACHE STRING
"CUDA Library"
)
endif
()
include_directories
(
"
${
PADDLE_LIB
}
"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/protobuf/include"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/glog/include"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/gflags/include"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/snappy/include"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/snappystream/include"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/zlib/include"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/boost"
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/eigen3"
)
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/snappy/lib"
)
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/snappystream/lib"
)
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/protobuf/lib"
)
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/glog/lib"
)
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/gflags/lib"
)
link_directories
(
"
${
PADDLE_LIB
}
/third_party/install/zlib/lib"
)
add_executable
(
${
DEMO_NAME
}
${
DEMO_NAME
}
.cc
)
if
(
WITH_MKL
)
include_directories
(
"
${
PADDLE_LIB
}
/third_party/install/mklml/include"
)
set
(
MATH_LIB
${
PADDLE_LIB
}
/third_party/install/mklml/lib/libmklml_intel.so
${
PADDLE_LIB
}
/third_party/install/mklml/lib/libiomp5.so
)
set
(
MKLDNN_PATH
"
${
PADDLE_LIB
}
/third_party/install/mkldnn"
)
if
(
EXISTS
${
MKLDNN_PATH
}
)
include_directories
(
"
${
MKLDNN_PATH
}
/include"
)
set
(
MKLDNN_LIB
${
MKLDNN_PATH
}
/lib/libmkldnn.so.0
)
endif
()
else
()
set
(
MATH_LIB
${
PADDLE_LIB
}
/third_party/install/openblas/lib/libopenblas.a
)
endif
()
if
(
WITH_STATIC_LIB
)
set
(
DEPS
"-Wl,--whole-archive"
${
PADDLE_LIB
}
/paddle/fluid/inference/libpaddle_fluid.a
"-Wl,--no-whole-archive"
${
PADDLE_LIB
}
/contrib/inference/libpaddle_inference_api.a
)
else
()
# Note: libpaddle_inference_api.so must put before libpaddle_fluid.so
set
(
DEPS
${
PADDLE_LIB
}
/contrib/inference/libpaddle_inference_api.so
${
PADDLE_LIB
}
/paddle/fluid/inference/libpaddle_fluid.so
)
endif
()
set
(
EXTERNAL_LIB
"-lrt -ldl -lpthread"
)
set
(
DEPS
${
DEPS
}
${
MATH_LIB
}
${
MKLDNN_LIB
}
glog gflags protobuf snappystream snappy z
${
EXTERNAL_LIB
}
)
if
(
WITH_GPU
)
set
(
DEPS
${
DEPS
}
${
CUDA_LIB
}
/libcudart.so
)
endif
()
target_link_libraries
(
${
DEMO_NAME
}
${
DEPS
}
)
paddle/contrib/inference/demo_ci/run.sh
0 → 100755
浏览文件 @
b8ea7a08
set
-x
PADDLE_ROOT
=
$1
WITH_MKL
=
$2
WITH_GPU
=
$3
if
[
$3
==
"ON"
]
;
then
use_gpu_list
=
'true false'
else
use_gpu_list
=
'false'
fi
mkdir
-p
build
cd
build
for
WITH_STATIC_LIB
in
false
;
do
rm
-rf
*
cmake ..
-DPADDLE_LIB
=
${
PADDLE_ROOT
}
/build/fluid_install_dir/
\
-DWITH_MKL
=
$WITH_MKL
\
-DDEMO_NAME
=
simple_on_word2vec
\
-DWITH_GPU
=
$WITH_GPU
\
-DWITH_STATIC_LIB
=
$WITH_STATIC_LIB
make
for
use_gpu
in
$use_gpu_list
;
do
./simple_on_word2vec
\
--dirname
=
${
PADDLE_ROOT
}
/build/python/paddle/fluid/tests/book/word2vec.inference.model
\
--use_gpu
=
$use_gpu
done
done
if
[
$?
-eq
0
]
;
then
exit
0
else
echo
"inference demo runs fail."
exit
1
fi
set
+x
paddle/contrib/inference/demo/simple_on_word2vec.cc
→
paddle/contrib/inference/demo
_ci
/simple_on_word2vec.cc
浏览文件 @
b8ea7a08
...
...
@@ -16,21 +16,27 @@ limitations under the License. */
* This file contains a simple demo for how to take a model for inference.
*/
#include <gflags/gflags.h>
#include <glog/logging.h>
#include <gtest/gtest.h>
#include <memory>
#include <thread>
#include "paddle/contrib/inference/paddle_inference_api.h"
#include "contrib/inference/paddle_inference_api.h"
#include "paddle/fluid/platform/enforce.h"
DEFINE_string
(
dirname
,
""
,
"Directory of the inference model."
);
DEFINE_bool
(
use_gpu
,
false
,
"Whether use gpu."
);
namespace
paddle
{
namespace
demo
{
DEFINE_string
(
dirname
,
""
,
"Directory of the inference model."
);
void
Main
(
bool
use_gpu
)
{
//# 1. Create PaddlePredictor with a config.
NativeConfig
config
;
config
.
model_dir
=
FLAGS_dirname
+
"word2vec.inference.model"
;
if
(
FLAGS_dirname
.
empty
())
{
LOG
(
INFO
)
<<
"Usage: ./simple_on_word2vec --dirname=path/to/your/model"
;
exit
(
1
);
}
config
.
model_dir
=
FLAGS_dirname
;
config
.
use_gpu
=
use_gpu
;
config
.
fraction_of_gpu_memory
=
0.15
;
config
.
device
=
0
;
...
...
@@ -54,12 +60,16 @@ void Main(bool use_gpu) {
CHECK
(
predictor
->
Run
(
slots
,
&
outputs
));
//# 4. Get output.
ASSERT_EQ
(
outputs
.
size
(),
1UL
);
LOG
(
INFO
)
<<
"output buffer size: "
<<
outputs
.
front
().
data
.
length
();
PADDLE_ENFORCE
(
outputs
.
size
(),
1UL
);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE
(
outputs
.
front
().
data
.
length
(),
33168UL
);
float
result
[
5
]
=
{
0.00129761
,
0.00151112
,
0.000423564
,
0.00108815
,
0.000932706
};
const
size_t
num_elements
=
outputs
.
front
().
data
.
length
()
/
sizeof
(
float
);
// The outputs' buffers are in CPU memory.
for
(
size_t
i
=
0
;
i
<
std
::
min
(
5UL
,
num_elements
);
i
++
)
{
LOG
(
INFO
)
<<
static_cast
<
float
*>
(
outputs
.
front
().
data
.
data
())[
i
];
PADDLE_ENFORCE
(
static_cast
<
float
*>
(
outputs
.
front
().
data
.
data
())[
i
],
result
[
i
]);
}
}
}
...
...
@@ -68,7 +78,7 @@ void MainThreads(int num_threads, bool use_gpu) {
// Multi-threads only support on CPU
// 0. Create PaddlePredictor with a config.
NativeConfig
config
;
config
.
model_dir
=
FLAGS_dirname
+
"word2vec.inference.model"
;
config
.
model_dir
=
FLAGS_dirname
;
config
.
use_gpu
=
use_gpu
;
config
.
fraction_of_gpu_memory
=
0.15
;
config
.
device
=
0
;
...
...
@@ -94,14 +104,17 @@ void MainThreads(int num_threads, bool use_gpu) {
CHECK
(
predictor
->
Run
(
inputs
,
&
outputs
));
// 4. Get output.
ASSERT_EQ
(
outputs
.
size
(),
1UL
);
LOG
(
INFO
)
<<
"TID: "
<<
tid
<<
", "
<<
"output buffer size: "
<<
outputs
.
front
().
data
.
length
();
PADDLE_ENFORCE
(
outputs
.
size
(),
1UL
);
// Check the output buffer size and result of each tid.
PADDLE_ENFORCE
(
outputs
.
front
().
data
.
length
(),
33168UL
);
float
result
[
5
]
=
{
0.00129761
,
0.00151112
,
0.000423564
,
0.00108815
,
0.000932706
};
const
size_t
num_elements
=
outputs
.
front
().
data
.
length
()
/
sizeof
(
float
);
// The outputs' buffers are in CPU memory.
for
(
size_t
i
=
0
;
i
<
std
::
min
(
5UL
,
num_elements
);
i
++
)
{
LOG
(
INFO
)
<<
static_cast
<
float
*>
(
outputs
.
front
().
data
.
data
())[
i
];
PADDLE_ENFORCE
(
static_cast
<
float
*>
(
outputs
.
front
().
data
.
data
())[
i
],
result
[
i
]);
}
}
});
...
...
@@ -111,15 +124,18 @@ void MainThreads(int num_threads, bool use_gpu) {
}
}
TEST
(
demo
,
word2vec_cpu
)
{
Main
(
false
/*use_gpu*/
);
}
TEST
(
demo_multi_threads
,
word2vec_cpu_1
)
{
MainThreads
(
1
,
false
/*use_gpu*/
);
}
TEST
(
demo_multi_threads
,
word2vec_cpu_4
)
{
MainThreads
(
4
,
false
/*use_gpu*/
);
}
#ifdef PADDLE_WITH_CUDA
TEST
(
demo
,
word2vec_gpu
)
{
Main
(
true
/*use_gpu*/
);
}
TEST
(
demo_multi_threads
,
word2vec_gpu_1
)
{
MainThreads
(
1
,
true
/*use_gpu*/
);
}
TEST
(
demo_multi_threads
,
word2vec_gpu_4
)
{
MainThreads
(
4
,
true
/*use_gpu*/
);
}
#endif
}
// namespace demo
}
// namespace paddle
int
main
(
int
argc
,
char
**
argv
)
{
google
::
ParseCommandLineFlags
(
&
argc
,
&
argv
,
true
);
paddle
::
demo
::
Main
(
false
/* use_gpu*/
);
paddle
::
demo
::
MainThreads
(
1
,
false
/* use_gpu*/
);
paddle
::
demo
::
MainThreads
(
4
,
false
/* use_gpu*/
);
if
(
FLAGS_use_gpu
)
{
paddle
::
demo
::
Main
(
true
/*use_gpu*/
);
paddle
::
demo
::
MainThreads
(
1
,
true
/*use_gpu*/
);
paddle
::
demo
::
MainThreads
(
4
,
true
/*use_gpu*/
);
}
return
0
;
}
paddle/contrib/inference/paddle_inference_api.map
0 → 100644
浏览文件 @
b8ea7a08
{
global:
*paddle*;
local:
*;
};
paddle/contrib/inference/paddle_inference_api.sym
0 → 100644
浏览文件 @
b8ea7a08
*paddle*
paddle/fluid/inference/CMakeLists.txt
浏览文件 @
b8ea7a08
...
...
@@ -13,6 +13,12 @@ endif()
# Create static library
cc_library
(
paddle_fluid DEPS
${
fluid_modules
}
paddle_fluid_api
)
if
(
NOT APPLE
)
# TODO(liuyiqu: Temporarily disable the link flag because it is not support on Mac.
set
(
LINK_FLAGS
"-Wl,--retain-symbols-file
${
CMAKE_CURRENT_SOURCE_DIR
}
/paddle_fluid.sym"
)
set_target_properties
(
paddle_fluid PROPERTIES LINK_FLAGS
"
${
LINK_FLAGS
}
"
)
endif
()
# Create shared library
cc_library
(
paddle_fluid_shared SHARED
SRCS io.cc
...
...
paddle/fluid/inference/paddle_fluid.sym
0 → 100644
浏览文件 @
b8ea7a08
*paddle*
paddle/fluid/operators/conv_mkldnn_op.cc
浏览文件 @
b8ea7a08
...
...
@@ -29,6 +29,79 @@ using mkldnn::stream;
using
platform
::
to_void_cast
;
using
platform
::
GetMKLDNNFormat
;
class
ConvMKLDNNHandler
:
public
platform
::
MKLDNNHandler
{
public:
ConvMKLDNNHandler
(
std
::
shared_ptr
<
mkldnn
::
convolution_forward
::
primitive_desc
>
conv_pd
,
const
platform
::
MKLDNNDeviceContext
&
dev_ctx
,
mkldnn
::
engine
engine
,
const
std
::
string
&
base_key
)
:
platform
::
MKLDNNHandler
(
dev_ctx
,
engine
,
base_key
)
{
conv_pd_
=
conv_pd
;
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireDstMemoryFromPrimitive
(
void
*
ptr
)
{
return
this
->
AcquireMemoryFromPrimitive
(
conv_pd_
->
dst_primitive_desc
(),
ptr
,
"@dst_mem_p"
);
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireSrcMemoryFromPrimitive
(
const
std
::
shared_ptr
<
mkldnn
::
memory
>
user_memory_p
,
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
)
{
auto
src_pd
=
conv_pd_
->
src_primitive_desc
();
auto
user_pd
=
user_memory_p
->
get_primitive_desc
();
return
this
->
AcquireMemory
(
src_pd
,
user_pd
,
user_memory_p
,
"@src_mem_p"
,
pipeline
);
}
std
::
shared_ptr
<
mkldnn
::
memory
>
AcquireWeightsMemoryFromPrimitive
(
const
std
::
shared_ptr
<
mkldnn
::
memory
>
user_weights_memory_p
,
std
::
vector
<
mkldnn
::
primitive
>&
pipeline
)
{
auto
user_weights_pd
=
user_weights_memory_p
->
get_primitive_desc
();
auto
weights_pd
=
conv_pd_
->
weights_primitive_desc
();
return
this
->
AcquireMemory
(
weights_pd
,
user_weights_pd
,
user_weights_memory_p
,
"@weights_mem_p"
,
pipeline
);
}
std
::
shared_ptr
<
mkldnn
::
convolution_forward
>
AcquireConvolution
(
std
::
shared_ptr
<
mkldnn
::
memory
>
src_memory_p
,
std
::
shared_ptr
<
mkldnn
::
memory
>
weights_memory_p
,
std
::
shared_ptr
<
mkldnn
::
memory
>
dst_memory_p
)
{
auto
prim_key
=
key_
+
"@conv_p"
;
auto
prim_desc_key
=
key_
+
"@conv_pd"
;
auto
conv_p
=
std
::
static_pointer_cast
<
mkldnn
::
convolution_forward
>
(
dev_ctx_
.
GetBlob
(
prim_key
));
PADDLE_ENFORCE
((
conv_p
!=
nullptr
)
||
(
is_reusing_
==
false
),
"Fail to find convolution primitive in device context"
);
if
(
conv_p
==
nullptr
)
{
conv_p
=
std
::
make_shared
<
mkldnn
::
convolution_forward
>
(
*
conv_pd_
,
*
(
src_memory_p
),
*
(
weights_memory_p
.
get
()),
*
(
dst_memory_p
.
get
()));
dev_ctx_
.
SetBlob
(
prim_key
,
conv_p
);
}
else
{
is_reusing_
=
true
;
}
return
conv_p
;
}
// Generate keys for storing/retriving primitives for this operator
// TODO(jczaja): Make hashing function more optimial
static
std
::
string
GetHash
(
memory
::
dims
&
input_dims
,
memory
::
dims
&
weights_dims
,
std
::
vector
<
int
>&
strides
,
std
::
vector
<
int
>&
paddings
,
std
::
vector
<
int
>&
dilations
,
int
groups
,
const
std
::
string
&
suffix
)
{
return
dims2str
(
input_dims
)
+
dims2str
(
weights_dims
)
+
dims2str
(
strides
)
+
dims2str
(
paddings
)
+
dims2str
(
dilations
)
+
std
::
to_string
(
groups
)
+
suffix
;
}
private:
std
::
shared_ptr
<
mkldnn
::
convolution_forward
::
primitive_desc
>
conv_pd_
;
};
template
<
typename
T
>
class
ConvMKLDNNOpKernel
:
public
paddle
::
framework
::
OpKernel
<
T
>
{
public:
...
...
@@ -36,10 +109,6 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
PADDLE_ENFORCE
(
paddle
::
platform
::
is_cpu_place
(
ctx
.
GetPlace
()),
"It must use CPUPlace."
);
// Get unique name for index
const
std
::
string
key
=
ctx
.
op
().
Output
(
"Output"
);
const
std
::
string
key_conv_pd
=
key
+
"@conv_pd"
;
auto
&
dev_ctx
=
ctx
.
template
device_context
<
paddle
::
platform
::
MKLDNNDeviceContext
>();
const
auto
&
mkldnn_engine
=
dev_ctx
.
GetEngine
();
...
...
@@ -80,68 +149,62 @@ class ConvMKLDNNOpKernel : public paddle::framework::OpKernel<T> {
paddle
::
framework
::
vectorize2int
(
filter
->
dims
());
std
::
vector
<
int
>
dst_tz
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
// create mkldnn memory from input tensors (data/weights)
auto
user_src_memory
=
memory
(
{{{
src_tz
},
memory
::
data_type
::
f32
,
input
->
format
()},
mkldnn_engine
},
to_void_cast
(
input_data
));
auto
user_weights_memory
=
memory
({{{
weights_tz
},
memory
::
data_type
::
f32
,
filter
->
format
()},
mkldnn_engine
},
to_void_cast
(
filter_data
));
// Get unique name for storing MKLDNN primitives
const
std
::
string
key
=
ConvMKLDNNHandler
::
GetHash
(
src_tz
,
weights_tz
,
strides
,
paddings
,
dilations
,
groups
,
ctx
.
op
().
Output
(
"Output"
));
const
std
::
string
key_conv_pd
=
key
+
"@conv_pd"
;
std
::
vector
<
primitive
>
pipeline
;
auto
user_src_md
=
platform
::
MKLDNNMemDesc
(
{
src_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
input
->
format
());
auto
user_weights_md
=
platform
::
MKLDNNMemDesc
(
{
weights_tz
},
platform
::
MKLDNNGetDataType
<
T
>
(),
filter
->
format
());
/* create memory descriptor for convolution without specified format
* ('any') which lets a primitive (convolution in this case) choose
* the memory format preferred for best performance
*/
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
memory
::
data_type
::
f32
,
memory
::
format
::
any
);
auto
src_md
=
platform
::
MKLDNNMemDesc
(
src_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
memory
::
format
::
any
);
auto
weights_md
=
platform
::
MKLDNNMemDesc
(
weights_tz
,
memory
::
data_type
::
f32
,
memory
::
format
::
any
);
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
memory
::
data_type
::
f32
,
memory
::
format
::
any
);
weights_tz
,
platform
::
MKLDNNGetDataType
<
T
>
()
,
memory
::
format
::
any
);
auto
dst_md
=
platform
::
MKLDNNMemDesc
(
dst_tz
,
platform
::
MKLDNNGetDataType
<
T
>
(),
memory
::
format
::
any
);
// create a conv primitive descriptor and save it for usage in backward
std
::
shared_ptr
<
conv_fwd
::
primitive_desc
>
conv_pd
=
ConvFwdPrimitiveDesc
(
src_md
,
weights_md
,
dst_md
,
strides
,
paddings
,
mkldnn_engine
);
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx
.
SetBlob
(
key_conv_pd
,
conv_pd
);
// create reorder primitive if the input format is not the preferred one
auto
src_memory
=
user_src_memory
;
primitive
reorder_src
;
bool
is_src_reordered
=
false
;
if
(
memory
::
primitive_desc
(
conv_pd
->
src_primitive_desc
())
!=
user_src_memory
.
get_primitive_desc
())
{
src_memory
=
memory
(
conv_pd
->
src_primitive_desc
());
reorder_src
=
reorder
(
user_src_memory
,
src_memory
);
is_src_reordered
=
true
;
}
auto
weights_memory
=
user_weights_memory
;
primitive
reorder_weights
;
bool
is_weights_reordered
=
false
;
if
(
memory
::
primitive_desc
(
conv_pd
->
weights_primitive_desc
())
!=
user_weights_memory
.
get_primitive_desc
())
{
weights_memory
=
memory
(
conv_pd
->
weights_primitive_desc
());
reorder_weights
=
reorder
(
user_weights_memory
,
weights_memory
);
is_weights_reordered
=
true
;
}
ConvMKLDNNHandler
handler
(
conv_pd
,
dev_ctx
,
mkldnn_engine
,
key
);
// create memory primitive for conv dst
auto
dst_memory
=
memory
(
conv_pd
->
dst_primitive_desc
(),
output_data
);
// create mkldnn memory from input tensors (data/weights)
auto
user_src_memory_p
=
handler
.
AcquireSrcMemory
(
user_src_md
,
to_void_cast
<
T
>
(
input_data
));
auto
user_weights_memory_p
=
handler
.
AcquireWeightsMemory
(
user_weights_md
,
to_void_cast
<
T
>
(
filter_data
));
// create reorder primitive if the input format is not the preferred one
auto
src_memory_p
=
handler
.
AcquireSrcMemoryFromPrimitive
(
user_src_memory_p
,
pipeline
);
auto
weights_memory_p
=
handler
.
AcquireWeightsMemoryFromPrimitive
(
user_weights_memory_p
,
pipeline
);
auto
dst_memory_p
=
handler
.
AcquireDstMemoryFromPrimitive
(
to_void_cast
<
T
>
(
output_data
));
// create convolution op primitive
auto
conv_prim
=
conv_fwd
(
*
conv_pd
,
src_memory
,
weights_memory
,
dst_memory
);
auto
conv_p
=
handler
.
AcquireConvolution
(
src_memory_p
,
weights_memory_p
,
dst_memory_p
);
// push primitive to stream and wait until it's executed
std
::
vector
<
primitive
>
pipeline
;
if
(
is_src_reordered
)
pipeline
.
push_back
(
reorder_src
);
if
(
is_weights_reordered
)
pipeline
.
push_back
(
reorder_weights
);
pipeline
.
push_back
(
conv_prim
);
pipeline
.
push_back
(
*
conv_p
);
stream
(
stream
::
kind
::
eager
).
submit
(
pipeline
).
wait
();
// Save conv_pd/src_memory/weights_memory for backward pass
dev_ctx
.
SetBlob
(
key_conv_pd
,
conv_pd
);
output
->
set_layout
(
DataLayout
::
kMKLDNN
);
output
->
set_format
(
GetMKLDNNFormat
(
dst_memory
));
output
->
set_format
(
GetMKLDNNFormat
(
*
dst_memory_p
));
}
private:
...
...
@@ -197,13 +260,10 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
if
(
!
input_grad
&&
!
filter_grad
)
return
;
// Get an unique name from "argument" name of "Output" variable
// This name will be used as key when saving info into device context
const
std
::
string
key
=
ctx
.
op
().
Input
(
"Output"
);
const
std
::
string
key_conv_pd
=
key
+
"@conv_pd"
;
std
::
vector
<
int
>
strides
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
const
T
*
input_data
=
input
->
data
<
T
>
();
const
T
*
filter_data
=
filter
->
data
<
T
>
();
...
...
@@ -223,6 +283,14 @@ class ConvMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> {
paddle
::
framework
::
vectorize2int
(
filter
->
dims
());
std
::
vector
<
int
>
dst_tz
=
paddle
::
framework
::
vectorize2int
(
output
->
dims
());
// Get an unique name from "argument" name of "Output" variable
// This name will be used as key when saving info into device context
const
std
::
string
key
=
ConvMKLDNNHandler
::
GetHash
(
src_tz
,
weights_tz
,
strides
,
paddings
,
dilations
,
groups
,
ctx
.
op
().
Input
(
"Output"
));
const
std
::
string
key_conv_pd
=
key
+
"@conv_pd"
;
// create mkldnn memory from input tensors (input/weights/output_grad)
auto
user_src_memory
=
memory
(
{{{
src_tz
},
memory
::
data_type
::
f32
,
input
->
format
()},
mkldnn_engine
},
...
...
paddle/fluid/operators/detection/prior_box_op.cc
浏览文件 @
b8ea7a08
...
...
@@ -149,6 +149,13 @@ class PriorBoxOpMaker : public framework::OpProtoAndCheckerMaker {
"(float) "
"Prior boxes center offset."
)
.
SetDefault
(
0.5
);
AddAttr
<
bool
>
(
"min_max_aspect_ratios_order"
,
"(bool) If set True, the output prior box is in order of"
"[min, max, aspect_ratios], which is consistent with Caffe."
"Please note, this order affects the weights order of convolution layer"
"followed by and does not affect the final detection results."
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
Prior box operator
Generate prior boxes for SSD(Single Shot MultiBox Detector) algorithm.
...
...
paddle/fluid/operators/detection/prior_box_op.cu
浏览文件 @
b8ea7a08
...
...
@@ -28,8 +28,8 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
const
int
im_width
,
const
int
as_num
,
const
T
offset
,
const
T
step_width
,
const
T
step_height
,
const
T
*
min_sizes
,
const
T
*
max_sizes
,
const
int
min_num
,
bool
is_clip
)
{
const
T
*
max_sizes
,
const
int
min_num
,
bool
is_clip
,
bool
min_max_aspect_ratios_order
)
{
int
num_priors
=
max_sizes
?
as_num
*
min_num
+
min_num
:
as_num
*
min_num
;
int
box_num
=
height
*
width
*
num_priors
;
for
(
int
i
=
blockIdx
.
x
*
blockDim
.
x
+
threadIdx
.
x
;
i
<
box_num
;
...
...
@@ -44,14 +44,28 @@ __global__ void GenPriorBox(T* out, const T* aspect_ratios, const int height,
T
min_size
=
min_sizes
[
m
];
if
(
max_sizes
)
{
int
s
=
p
%
(
as_num
+
1
);
if
(
s
<
as_num
)
{
T
ar
=
aspect_ratios
[
s
];
bw
=
min_size
*
sqrt
(
ar
)
/
2.
;
bh
=
min_size
/
sqrt
(
ar
)
/
2.
;
if
(
!
min_max_aspect_ratios_order
)
{
if
(
s
<
as_num
)
{
T
ar
=
aspect_ratios
[
s
];
bw
=
min_size
*
sqrt
(
ar
)
/
2.
;
bh
=
min_size
/
sqrt
(
ar
)
/
2.
;
}
else
{
T
max_size
=
max_sizes
[
m
];
bw
=
sqrt
(
min_size
*
max_size
)
/
2.
;
bh
=
bw
;
}
}
else
{
T
max_size
=
max_sizes
[
m
];
bw
=
sqrt
(
min_size
*
max_size
)
/
2.
;
bh
=
bw
;
if
(
s
==
0
)
{
bw
=
bh
=
min_size
/
2.
;
}
else
if
(
s
==
1
)
{
T
max_size
=
max_sizes
[
m
];
bw
=
sqrt
(
min_size
*
max_size
)
/
2.
;
bh
=
bw
;
}
else
{
T
ar
=
aspect_ratios
[
s
-
1
];
bw
=
min_size
*
sqrt
(
ar
)
/
2.
;
bh
=
min_size
/
sqrt
(
ar
)
/
2.
;
}
}
}
else
{
int
s
=
p
%
as_num
;
...
...
@@ -94,6 +108,8 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
auto
variances
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"variances"
);
auto
flip
=
ctx
.
Attr
<
bool
>
(
"flip"
);
auto
clip
=
ctx
.
Attr
<
bool
>
(
"clip"
);
auto
min_max_aspect_ratios_order
=
ctx
.
Attr
<
bool
>
(
"min_max_aspect_ratios_order"
);
std
::
vector
<
float
>
aspect_ratios
;
ExpandAspectRatios
(
input_aspect_ratio
,
flip
,
&
aspect_ratios
);
...
...
@@ -149,7 +165,7 @@ class PriorBoxOpCUDAKernel : public framework::OpKernel<T> {
GenPriorBox
<
T
><<<
grid
,
block
,
0
,
stream
>>>
(
boxes
->
data
<
T
>
(),
r
.
data
<
T
>
(),
height
,
width
,
im_height
,
im_width
,
aspect_ratios
.
size
(),
offset
,
step_width
,
step_height
,
min
.
data
<
T
>
(),
max_data
,
min_num
,
clip
);
max_data
,
min_num
,
clip
,
min_max_aspect_ratios_order
);
framework
::
Tensor
v
;
framework
::
TensorFromVector
(
variances
,
ctx
.
device_context
(),
&
v
);
...
...
paddle/fluid/operators/detection/prior_box_op.h
浏览文件 @
b8ea7a08
...
...
@@ -68,6 +68,8 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
auto
variances
=
ctx
.
Attr
<
std
::
vector
<
float
>>
(
"variances"
);
auto
flip
=
ctx
.
Attr
<
bool
>
(
"flip"
);
auto
clip
=
ctx
.
Attr
<
bool
>
(
"clip"
);
auto
min_max_aspect_ratios_order
=
ctx
.
Attr
<
bool
>
(
"min_max_aspect_ratios_order"
);
std
::
vector
<
float
>
aspect_ratios
;
ExpandAspectRatios
(
input_aspect_ratio
,
flip
,
&
aspect_ratios
);
...
...
@@ -108,26 +110,59 @@ class PriorBoxOpKernel : public framework::OpKernel<T> {
int
idx
=
0
;
for
(
size_t
s
=
0
;
s
<
min_sizes
.
size
();
++
s
)
{
auto
min_size
=
min_sizes
[
s
];
// priors with different aspect ratios
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
float
ar
=
aspect_ratios
[
r
];
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
// square prior with size sqrt(minSize * maxSize)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
if
(
min_max_aspect_ratios_order
)
{
box_width
=
box_height
=
min_size
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
// square prior with size sqrt(minSize * maxSize)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
// priors with different aspect ratios
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
float
ar
=
aspect_ratios
[
r
];
if
(
fabs
(
ar
-
1.
)
<
1e-6
)
{
continue
;
}
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
}
else
{
// priors with different aspect ratios
for
(
size_t
r
=
0
;
r
<
aspect_ratios
.
size
();
++
r
)
{
float
ar
=
aspect_ratios
[
r
];
box_width
=
min_size
*
sqrt
(
ar
)
/
2.
;
box_height
=
min_size
/
sqrt
(
ar
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
if
(
max_sizes
.
size
()
>
0
)
{
auto
max_size
=
max_sizes
[
s
];
// square prior with size sqrt(minSize * maxSize)
box_width
=
box_height
=
sqrt
(
min_size
*
max_size
)
/
2.
;
e_boxes
(
h
,
w
,
idx
,
0
)
=
(
center_x
-
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
1
)
=
(
center_y
-
box_height
)
/
img_height
;
e_boxes
(
h
,
w
,
idx
,
2
)
=
(
center_x
+
box_width
)
/
img_width
;
e_boxes
(
h
,
w
,
idx
,
3
)
=
(
center_y
+
box_height
)
/
img_height
;
idx
++
;
}
}
}
}
...
...
paddle/fluid/platform/mkldnn_helper.h
浏览文件 @
b8ea7a08
...
...
@@ -222,15 +222,16 @@ class MKLDNNHandler {
static
std
::
string
GetHash
(
mkldnn
::
memory
::
dims
&
operand_dims
,
// NOLINT
const
std
::
string
&
suffix
)
{
auto
dims2str
=
[](
const
mkldnn
::
memory
::
dims
&
operand_dims
)
{
std
::
string
dstr
=
""
;
for
(
size_t
i
=
0
;
i
<
operand_dims
.
size
();
++
i
)
{
dstr
+=
std
::
to_string
(
operand_dims
[
i
])
+
"-"
;
}
return
dstr
;
};
return
dims2str
(
operand_dims
)
+
suffix
;
};
protected:
static
std
::
string
dims2str
(
const
mkldnn
::
memory
::
dims
&
operand_dims
)
{
std
::
string
dstr
=
""
;
for
(
size_t
i
=
0
;
i
<
operand_dims
.
size
();
++
i
)
{
dstr
+=
std
::
to_string
(
operand_dims
[
i
])
+
"-"
;
}
return
dstr
;
}
protected:
...
...
paddle/scripts/paddle_build.sh
浏览文件 @
b8ea7a08
...
...
@@ -510,11 +510,23 @@ function gen_fluid_inference_lib() {
EOF
make
-j
`
nproc
`
inference_lib_dist
cd
${
PADDLE_ROOT
}
/build
mv
fluid_install_dir fluid
cp
-r
fluid_install_dir fluid
tar
-cf
fluid.tgz fluid
fi
}
function
test_fluid_inference_lib
()
{
if
[
${
WITH_C_API
:-
OFF
}
==
"OFF"
]
;
then
cat
<<
EOF
========================================
Testing fluid inference library ...
========================================
EOF
cd
${
PADDLE_ROOT
}
/paddle/contrib/inference/demo_ci
sh run.sh
${
PADDLE_ROOT
}
${
WITH_MKL
:-
ON
}
${
WITH_GPU
:-
OFF
}
fi
}
function
main
()
{
set
-e
local
CMD
=
$1
...
...
@@ -568,6 +580,7 @@ function main() {
run_test
gen_capi_package
gen_fluid_inference_lib
test_fluid_inference_lib
;;
*
)
print_usage
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
b8ea7a08
...
...
@@ -789,7 +789,8 @@ def prior_box(input,
clip
=
False
,
steps
=
[
0.0
,
0.0
],
offset
=
0.5
,
name
=
None
):
name
=
None
,
min_max_aspect_ratios_order
=
False
):
"""
**Prior Box Operator**
...
...
@@ -818,6 +819,11 @@ def prior_box(input,
Default: [0., 0.]
offset(float): Prior boxes center offset. Default: 0.5
name(str): Name of the prior box op. Default: None.
min_max_aspect_ratios_order(bool): If set True, the output prior box is
in order of [min, max, aspect_ratios], which is consistent with
Caffe. Please note, this order affects the weights order of
convolution layer followed by and does not affect the final
detection results. Default: False.
Returns:
tuple: A tuple with two Variable (boxes, variances)
...
...
@@ -871,7 +877,8 @@ def prior_box(input,
'clip'
:
clip
,
'step_w'
:
steps
[
0
],
'step_h'
:
steps
[
1
],
'offset'
:
offset
'offset'
:
offset
,
'min_max_aspect_ratios_order'
:
min_max_aspect_ratios_order
}
if
max_sizes
is
not
None
and
len
(
max_sizes
)
>
0
and
max_sizes
[
0
]
>
0
:
if
not
_is_list_or_tuple_
(
max_sizes
):
...
...
@@ -911,7 +918,8 @@ def multi_box_head(inputs,
kernel_size
=
1
,
pad
=
0
,
stride
=
1
,
name
=
None
):
name
=
None
,
min_max_aspect_ratios_order
=
False
):
"""
Generate prior boxes for SSD(Single Shot MultiBox Detector)
algorithm. The details of this algorithm, please refer the
...
...
@@ -954,6 +962,11 @@ def multi_box_head(inputs,
pad(int|list|tuple): The padding of conv2d. Default:0.
stride(int|list|tuple): The stride of conv2d. Default:1,
name(str): Name of the prior box layer. Default: None.
min_max_aspect_ratios_order(bool): If set True, the output prior box is
in order of [min, max, aspect_ratios], which is consistent with
Caffe. Please note, this order affects the weights order of
convolution layer followed by and does not affect the fininal
detection results. Default: False.
Returns:
tuple: A tuple with four Variables. (mbox_loc, mbox_conf, boxes, variances)
...
...
@@ -1068,7 +1081,8 @@ def multi_box_head(inputs,
step
=
[
step_w
[
i
]
if
step_w
else
0.0
,
step_h
[
i
]
if
step_w
else
0.0
]
box
,
var
=
prior_box
(
input
,
image
,
min_size
,
max_size
,
aspect_ratio
,
variance
,
flip
,
clip
,
step
,
offset
)
variance
,
flip
,
clip
,
step
,
offset
,
None
,
min_max_aspect_ratios_order
)
box_results
.
append
(
box
)
var_results
.
append
(
var
)
...
...
python/paddle/fluid/tests/unittests/test_prior_box_op.py
浏览文件 @
b8ea7a08
...
...
@@ -32,6 +32,7 @@ class TestPriorBoxOp(OpTest):
'variances'
:
self
.
variances
,
'flip'
:
self
.
flip
,
'clip'
:
self
.
clip
,
'min_max_aspect_ratios_order'
:
self
.
min_max_aspect_ratios_order
,
'step_w'
:
self
.
step_w
,
'step_h'
:
self
.
step_h
,
'offset'
:
self
.
offset
...
...
@@ -52,6 +53,9 @@ class TestPriorBoxOp(OpTest):
max_sizes
=
[
5
,
10
]
self
.
max_sizes
=
np
.
array
(
max_sizes
).
astype
(
'float32'
).
tolist
()
def
set_min_max_aspect_ratios_order
(
self
):
self
.
min_max_aspect_ratios_order
=
False
def
init_test_params
(
self
):
self
.
layer_w
=
32
self
.
layer_h
=
32
...
...
@@ -71,6 +75,7 @@ class TestPriorBoxOp(OpTest):
self
.
set_max_sizes
()
self
.
aspect_ratios
=
[
2.0
,
3.0
]
self
.
flip
=
True
self
.
set_min_max_aspect_ratios_order
()
self
.
real_aspect_ratios
=
[
1
,
2.0
,
1.0
/
2.0
,
3.0
,
1.0
/
3.0
]
self
.
aspect_ratios
=
np
.
array
(
self
.
aspect_ratios
,
dtype
=
np
.
float
).
flatten
()
...
...
@@ -78,7 +83,6 @@ class TestPriorBoxOp(OpTest):
self
.
variances
=
np
.
array
(
self
.
variances
,
dtype
=
np
.
float
).
flatten
()
self
.
clip
=
True
self
.
num_priors
=
len
(
self
.
real_aspect_ratios
)
*
len
(
self
.
min_sizes
)
if
len
(
self
.
max_sizes
)
>
0
:
self
.
num_priors
+=
len
(
self
.
max_sizes
)
...
...
@@ -106,26 +110,60 @@ class TestPriorBoxOp(OpTest):
idx
=
0
for
s
in
range
(
len
(
self
.
min_sizes
)):
min_size
=
self
.
min_sizes
[
s
]
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
if
not
self
.
min_max_aspect_ratios_order
:
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[
(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[
(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
else
:
c_w
=
c_h
=
min_size
/
2.
out_boxes
[
h
,
w
,
idx
,
:]
=
[(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
if
len
(
self
.
max_sizes
)
>
0
:
max_size
=
self
.
max_sizes
[
s
]
# second prior: aspect_ratio = 1,
c_w
=
c_h
=
math
.
sqrt
(
min_size
*
max_size
)
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[
(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
# rest of priors
for
r
in
range
(
len
(
self
.
real_aspect_ratios
)):
ar
=
self
.
real_aspect_ratios
[
r
]
if
abs
(
ar
-
1.
)
<
1e-6
:
continue
c_w
=
min_size
*
math
.
sqrt
(
ar
)
/
2
c_h
=
(
min_size
/
math
.
sqrt
(
ar
))
/
2
out_boxes
[
h
,
w
,
idx
,
:]
=
[
(
c_x
-
c_w
)
/
self
.
image_w
,
(
c_y
-
c_h
)
/
self
.
image_h
,
(
c_x
+
c_w
)
/
self
.
image_w
,
(
c_y
+
c_h
)
/
self
.
image_h
]
idx
+=
1
# clip the prior's coordidate such that it is within[0, 1]
if
self
.
clip
:
...
...
@@ -137,10 +175,15 @@ class TestPriorBoxOp(OpTest):
self
.
out_var
=
out_var
.
astype
(
'float32'
)
class
TestPriorBoxOpWithMaxSize
(
TestPriorBoxOp
):
class
TestPriorBoxOpWith
out
MaxSize
(
TestPriorBoxOp
):
def
set_max_sizes
(
self
):
self
.
max_sizes
=
[]
class
TestPriorBoxOpWithSpecifiedOutOrder
(
TestPriorBoxOp
):
def
set_min_max_aspect_ratios_order
(
self
):
self
.
min_max_aspect_ratios_order
=
True
if
__name__
==
'__main__'
:
unittest
.
main
()
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