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体验新版 GitCode,发现更多精彩内容 >>
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提交
672f2083
编写于
2月 27, 2018
作者:
W
Wang Hao
提交者:
GitHub
2月 27, 2018
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'develop' into python_map
上级
4161328e
1ac31d3d
变更
35
隐藏空白更改
内联
并排
Showing
35 changed file
with
774 addition
and
466 deletion
+774
-466
CMakeLists.txt
CMakeLists.txt
+1
-1
doc/howto/capi/workflow_of_capi_cn.md
doc/howto/capi/workflow_of_capi_cn.md
+1
-0
paddle/fluid/framework/executor.cc
paddle/fluid/framework/executor.cc
+3
-3
paddle/fluid/framework/framework.proto
paddle/fluid/framework/framework.proto
+4
-1
paddle/fluid/framework/lod_tensor.cc
paddle/fluid/framework/lod_tensor.cc
+7
-1
paddle/fluid/inference/io.cc
paddle/fluid/inference/io.cc
+7
-20
paddle/fluid/inference/tests/book/CMakeLists.txt
paddle/fluid/inference/tests/book/CMakeLists.txt
+1
-1
paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
...ference/tests/book/test_inference_label_semantic_roles.cc
+36
-10
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
...ference/tests/book/test_inference_understand_sentiment.cc
+6
-1
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
+5
-5
paddle/fluid/inference/tests/test_helper.h
paddle/fluid/inference/tests/test_helper.h
+2
-2
paddle/fluid/operators/CMakeLists.txt
paddle/fluid/operators/CMakeLists.txt
+23
-28
paddle/fluid/operators/bipartite_match_op.cc
paddle/fluid/operators/bipartite_match_op.cc
+55
-2
paddle/fluid/operators/concat_op.h
paddle/fluid/operators/concat_op.h
+41
-6
paddle/fluid/operators/detail/grpc_client.cc
paddle/fluid/operators/detail/grpc_client.cc
+2
-2
paddle/fluid/operators/listen_and_serv_op.cc
paddle/fluid/operators/listen_and_serv_op.cc
+2
-0
paddle/fluid/operators/nccl_op.cc
paddle/fluid/operators/nccl_op.cc
+1
-1
paddle/fluid/operators/send_op.cc
paddle/fluid/operators/send_op.cc
+19
-1
paddle/fluid/operators/send_recv_op_test.cc
paddle/fluid/operators/send_recv_op_test.cc
+16
-10
paddle/fluid/pybind/protobuf.cc
paddle/fluid/pybind/protobuf.cc
+1
-1
paddle/scripts/docker/build.sh
paddle/scripts/docker/build.sh
+2
-0
python/paddle/fluid/distribute_transpiler.py
python/paddle/fluid/distribute_transpiler.py
+1
-2
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+57
-41
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+16
-4
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+36
-50
python/paddle/fluid/layers/utils.py
python/paddle/fluid/layers/utils.py
+59
-0
python/paddle/fluid/tests/book/notest_rnn_encoder_decoer.py
python/paddle/fluid/tests/book/notest_rnn_encoder_decoer.py
+28
-26
python/paddle/fluid/tests/book/test_fit_a_line.py
python/paddle/fluid/tests/book/test_fit_a_line.py
+20
-17
python/paddle/fluid/tests/book/test_image_classification.py
python/paddle/fluid/tests/book/test_image_classification.py
+20
-16
python/paddle/fluid/tests/book/test_label_semantic_roles.py
python/paddle/fluid/tests/book/test_label_semantic_roles.py
+57
-47
python/paddle/fluid/tests/book/test_recognize_digits.py
python/paddle/fluid/tests/book/test_recognize_digits.py
+43
-29
python/paddle/fluid/tests/book/test_recommender_system.py
python/paddle/fluid/tests/book/test_recommender_system.py
+47
-45
python/paddle/fluid/tests/book/test_understand_sentiment.py
python/paddle/fluid/tests/book/test_understand_sentiment.py
+40
-29
python/paddle/fluid/tests/book/test_word2vec.py
python/paddle/fluid/tests/book/test_word2vec.py
+74
-61
python/paddle/fluid/tests/unittests/test_bipartite_match_op.py
...n/paddle/fluid/tests/unittests/test_bipartite_match_op.py
+41
-3
未找到文件。
CMakeLists.txt
浏览文件 @
672f2083
...
...
@@ -60,7 +60,7 @@ option(USE_NNPACK "Compile PaddlePaddle with NNPACK library" OFF)
option
(
WITH_DISTRIBUTE
"Compile with grpc distributed support"
OFF
)
option
(
USE_EIGEN_FOR_BLAS
"Use matrix multiplication in Eigen"
OFF
)
option
(
WITH_ARM_FP16
"Use half precision support on armv8.2-a cpu"
OFF
)
option
(
WITH_FAST_BUNDLE_TEST
"Bundle tests that can be run in a single process together to reduce launch overhead"
O
N
)
option
(
WITH_FAST_BUNDLE_TEST
"Bundle tests that can be run in a single process together to reduce launch overhead"
O
FF
)
# CMAKE_BUILD_TYPE
if
(
NOT CMAKE_BUILD_TYPE
)
...
...
doc/howto/capi/workflow_of_capi_cn.md
浏览文件 @
672f2083
...
...
@@ -65,6 +65,7 @@
output_file = "output.paddle.model"
merge_v2_model(net, param_file, output_file)
```
对[手写数字识别](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense)这个示例,可直接运行 `python` [merge_v2_model.py](https://github.com/PaddlePaddle/Paddle/tree/develop/paddle/capi/examples/model_inference/dense/merge_v2_model.py)。序列化结果会写入当前运行目录下的`output.paddle.model`文件中。使用这种方式,运行时C-API可以通过指定`output.paddle.model`文件的路径来加载预测模型。
#### 注意事项
...
...
paddle/fluid/framework/executor.cc
浏览文件 @
672f2083
...
...
@@ -58,13 +58,13 @@ static void CreateTensor(Variable* var, proto::VarType::Type var_type) {
var
->
GetMutable
<
ReaderHolder
>
();
}
else
if
(
var_type
==
proto
::
VarType
::
CHANNEL
)
{
var
->
GetMutable
<
ChannelHolder
>
();
}
else
if
(
var_type
==
proto
::
VarType
::
NCCL_COM
)
{
// GetMutable will be called in
ncclInit
}
else
if
(
var_type
==
proto
::
VarType
::
RAW
)
{
// GetMutable will be called in
operator
}
else
{
PADDLE_THROW
(
"Variable type %d is not in "
"[LOD_TENSOR, SELECTED_ROWS, FEED_MINIBATCH, FETCH_LIST, "
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL,
NCCL_COM
]"
,
"LOD_RANK_TABLE, PLACE_LIST, READER, CHANNEL,
RAW
]"
,
var_type
);
}
}
...
...
paddle/fluid/framework/framework.proto
浏览文件 @
672f2083
...
...
@@ -113,7 +113,10 @@ message VarType {
PLACE_LIST
=
14
;
READER
=
15
;
CHANNEL
=
16
;
NCCL_COM
=
17
;
// Any runtime decided variable type is raw
// raw variables should manage their own allocations
// in operators like nccl_op
RAW
=
17
;
}
required
Type
type
=
1
;
...
...
paddle/fluid/framework/lod_tensor.cc
浏览文件 @
672f2083
...
...
@@ -31,8 +31,14 @@ std::ostream &operator<<(std::ostream &os, const LoD &lod) {
os
<<
"{"
;
for
(
auto
&
v
:
lod
)
{
os
<<
"{"
;
bool
is_first
=
true
;
for
(
auto
&
i
:
v
)
{
os
<<
i
<<
","
;
if
(
is_first
)
{
os
<<
i
;
is_first
=
false
;
}
else
{
os
<<
", "
<<
i
;
}
}
os
<<
"}"
;
}
...
...
paddle/fluid/inference/io.cc
浏览文件 @
672f2083
...
...
@@ -32,23 +32,11 @@ void ReadBinaryFile(const std::string& filename, std::string& contents) {
inputfs
.
close
();
}
bool
IsParameter
(
const
framework
::
VarDesc
*
var
,
const
framework
::
ProgramDesc
&
main_program
)
{
if
(
var
->
Persistable
())
{
// There are many unreachable variables in the program
for
(
size_t
i
=
0
;
i
<
main_program
.
Size
();
++
i
)
{
const
framework
::
BlockDesc
&
block
=
main_program
.
Block
(
i
);
for
(
auto
*
op
:
block
.
AllOps
())
{
if
(
op
->
Type
()
==
framework
::
kFeedOpType
)
{
continue
;
}
for
(
auto
input_argument_name
:
op
->
InputArgumentNames
())
{
if
(
input_argument_name
==
var
->
Name
())
{
return
true
;
}
}
}
}
bool
IsPersistable
(
const
framework
::
VarDesc
*
var
)
{
if
(
var
->
Persistable
()
&&
var
->
GetType
()
!=
framework
::
proto
::
VarType
::
FEED_MINIBATCH
&&
var
->
GetType
()
!=
framework
::
proto
::
VarType
::
FETCH_LIST
)
{
return
true
;
}
return
false
;
}
...
...
@@ -65,8 +53,8 @@ void LoadPersistables(framework::Executor& executor,
std
::
vector
<
std
::
string
>
paramlist
;
for
(
auto
*
var
:
global_block
.
AllVars
())
{
if
(
IsP
arameter
(
var
,
main_program
))
{
VLOG
(
3
)
<<
"p
arameter
's name: "
<<
var
->
Name
();
if
(
IsP
ersistable
(
var
))
{
VLOG
(
3
)
<<
"p
ersistable variable
's name: "
<<
var
->
Name
();
framework
::
VarDesc
*
new_var
=
load_block
->
Var
(
var
->
Name
());
new_var
->
SetShape
(
var
->
GetShape
());
...
...
@@ -101,7 +89,6 @@ void LoadPersistables(framework::Executor& executor,
executor
.
Run
(
*
load_program
,
&
scope
,
0
,
true
,
true
);
VLOG
(
3
)
<<
"Ran loading successfully"
;
delete
load_program
;
}
...
...
paddle/fluid/inference/tests/book/CMakeLists.txt
浏览文件 @
672f2083
...
...
@@ -30,5 +30,5 @@ inference_test(label_semantic_roles)
inference_test
(
recognize_digits ARGS mlp conv
)
inference_test
(
recommender_system
)
#inference_test(rnn_encoder_decoder)
inference_test
(
understand_sentiment
)
inference_test
(
understand_sentiment
ARGS conv
)
inference_test
(
word2vec
)
paddle/fluid/inference/tests/book/test_inference_label_semantic_roles.cc
浏览文件 @
672f2083
...
...
@@ -32,16 +32,42 @@ TEST(inference, label_semantic_roles) {
paddle
::
framework
::
LoDTensor
word
,
predicate
,
ctx_n2
,
ctx_n1
,
ctx_0
,
ctx_p1
,
ctx_p2
,
mark
;
paddle
::
framework
::
LoD
lod
{{
0
,
4
,
10
}};
SetupLoDTensor
(
word
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
predicate
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
ctx_n2
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
ctx_n1
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
ctx_0
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
ctx_p1
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
ctx_p2
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
SetupLoDTensor
(
mark
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
1
));
int64_t
word_dict_len
=
44068
;
int64_t
predicate_dict_len
=
3162
;
int64_t
mark_dict_len
=
2
;
SetupLoDTensor
(
word
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
SetupLoDTensor
(
predicate
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
predicate_dict_len
-
1
));
SetupLoDTensor
(
ctx_n2
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
SetupLoDTensor
(
ctx_n1
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
SetupLoDTensor
(
ctx_0
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
SetupLoDTensor
(
ctx_p1
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
SetupLoDTensor
(
ctx_p2
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
SetupLoDTensor
(
mark
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
mark_dict_len
-
1
));
std
::
vector
<
paddle
::
framework
::
LoDTensor
*>
cpu_feeds
;
cpu_feeds
.
push_back
(
&
word
);
...
...
paddle/fluid/inference/tests/book/test_inference_understand_sentiment.cc
浏览文件 @
672f2083
...
...
@@ -31,7 +31,12 @@ TEST(inference, understand_sentiment) {
paddle
::
framework
::
LoDTensor
words
;
paddle
::
framework
::
LoD
lod
{{
0
,
4
,
10
}};
SetupLoDTensor
(
words
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
10
));
int64_t
word_dict_len
=
5147
;
SetupLoDTensor
(
words
,
lod
,
static_cast
<
int64_t
>
(
0
),
static_cast
<
int64_t
>
(
word_dict_len
-
1
));
std
::
vector
<
paddle
::
framework
::
LoDTensor
*>
cpu_feeds
;
cpu_feeds
.
push_back
(
&
words
);
...
...
paddle/fluid/inference/tests/book/test_inference_word2vec.cc
浏览文件 @
672f2083
...
...
@@ -31,12 +31,12 @@ TEST(inference, word2vec) {
paddle
::
framework
::
LoDTensor
first_word
,
second_word
,
third_word
,
fourth_word
;
paddle
::
framework
::
LoD
lod
{{
0
,
1
}};
int64_t
dict_size
=
207
2
;
// Hard-coding t
he size of dictionary
int64_t
dict_size
=
207
3
;
// T
he size of dictionary
SetupLoDTensor
(
first_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
);
SetupLoDTensor
(
second_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
);
SetupLoDTensor
(
third_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
);
SetupLoDTensor
(
fourth_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
);
SetupLoDTensor
(
first_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
-
1
);
SetupLoDTensor
(
second_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
-
1
);
SetupLoDTensor
(
third_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
-
1
);
SetupLoDTensor
(
fourth_word
,
lod
,
static_cast
<
int64_t
>
(
0
),
dict_size
-
1
);
std
::
vector
<
paddle
::
framework
::
LoDTensor
*>
cpu_feeds
;
cpu_feeds
.
push_back
(
&
first_word
);
...
...
paddle/fluid/inference/tests/test_helper.h
浏览文件 @
672f2083
...
...
@@ -101,8 +101,8 @@ void TestInference(const std::string& dirname,
if
(
IsCombined
)
{
// All parameters are saved in a single file.
// Hard-coding the file names of program and parameters in unittest.
//
Users are free to specify different filename
//
(provided: the filenames are changed in the python api as well: io.py)
//
The file names should be consistent with that used in Python API
//
`fluid.io.save_inference_model`.
std
::
string
prog_filename
=
"__model_combined__"
;
std
::
string
param_filename
=
"__params_combined__"
;
inference_program
=
paddle
::
inference
::
Load
(
executor
,
...
...
paddle/fluid/operators/CMakeLists.txt
浏览文件 @
672f2083
...
...
@@ -11,6 +11,8 @@ function(op_library TARGET)
set
(
cc_srcs
)
set
(
cu_srcs
)
set
(
cu_cc_srcs
)
set
(
cudnn_cu_cc_srcs
)
set
(
CUDNN_FILE
)
set
(
op_common_deps operator op_registry math_function
)
set
(
options
""
)
set
(
oneValueArgs
""
)
...
...
@@ -30,10 +32,16 @@ function(op_library TARGET)
if
(
EXISTS
${
CMAKE_CURRENT_SOURCE_DIR
}
/
${
TARGET
}
.cu
)
list
(
APPEND cu_srcs
${
TARGET
}
.cu
)
endif
()
string
(
REPLACE
"_op"
"_cudnn_op"
CUDNN_FILE
"
${
TARGET
}
"
)
if
(
EXISTS
${
CMAKE_CURRENT_SOURCE_DIR
}
/
${
CUDNN_FILE
}
.cu.cc
)
list
(
APPEND cudnn_cu_cc_srcs
${
CUDNN_FILE
}
.cu.cc
)
endif
()
else
()
foreach
(
src
${
op_library_SRCS
}
)
if
(
${
src
}
MATCHES
".*
\\
.cu$"
)
list
(
APPEND cu_srcs
${
src
}
)
elseif
(
${
src
}
MATCHES
".*_cudnn_op.cu.cc$"
)
list
(
APPEND cudnn_cu_cc_srcs
${
src
}
)
elseif
(
${
src
}
MATCHES
".*
\\
.cu.cc$"
)
list
(
APPEND cu_cc_srcs
${
src
}
)
elseif
(
${
src
}
MATCHES
".*
\\
.cc$"
)
...
...
@@ -54,7 +62,7 @@ function(op_library TARGET)
set
(
DEPS_OPS
${
TARGET
}
${
DEPS_OPS
}
PARENT_SCOPE
)
endif
()
if
(
WITH_GPU
)
nv_library
(
${
TARGET
}
SRCS
${
cc_srcs
}
${
cu_cc_srcs
}
${
cu_srcs
}
DEPS
${
op_library_DEPS
}
nv_library
(
${
TARGET
}
SRCS
${
cc_srcs
}
${
cu_cc_srcs
}
${
cu
dnn_cu_cc_srcs
}
${
cu
_srcs
}
DEPS
${
op_library_DEPS
}
${
op_common_deps
}
)
else
()
cc_library
(
${
TARGET
}
SRCS
${
cc_srcs
}
DEPS
${
op_library_DEPS
}
...
...
@@ -98,6 +106,12 @@ function(op_library TARGET)
set
(
pybind_flag 1
)
endif
()
# pybind USE_OP_DEVICE_KERNEL for CUDNN
list
(
LENGTH cudnn_cu_cc_srcs cudnn_cu_cc_srcs_len
)
if
(
WITH_GPU AND
${
cudnn_cu_cc_srcs_len
}
GREATER 0
)
file
(
APPEND
${
pybind_file
}
"USE_OP_DEVICE_KERNEL(
${
TARGET
}
, CUDNN);
\n
"
)
endif
()
# pybind USE_OP
if
(
${
pybind_flag
}
EQUAL 0
)
file
(
APPEND
${
pybind_file
}
"USE_OP(
${
TARGET
}
);
\n
"
)
...
...
@@ -152,43 +166,24 @@ op_library(lstm_op DEPS sequence2batch lstm_compute)
op_library
(
lstmp_op DEPS sequence2batch lstm_compute
)
op_library
(
gru_op DEPS sequence2batch gru_compute
)
op_library
(
recurrent_op DEPS executor
)
op_library
(
warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale
math_function
)
op_library
(
warpctc_op DEPS dynload_warpctc sequence_padding sequence_scale
)
op_library
(
cos_sim_op DEPS cos_sim_functor
)
op_library
(
parallel_do_op DEPS executor
)
op_library
(
create_reader_op DEPS reader
)
# Regist multiple Kernel to pybind
if
(
WITH_GPU
)
op_library
(
conv_op SRCS conv_op.cc conv_op.cu.cc conv_cudnn_op.cu.cc DEPS
vol2col depthwise_conv
)
op_library
(
edit_distance_op SRCS edit_distance_op.cc edit_distance_op.cu DEPS math_function
)
op_library
(
pool_op SRCS pool_op.cc pool_op.cu.cc pool_cudnn_op.cu.cc DEPS pooling
)
op_library
(
conv_transpose_op SRCS conv_transpose_op.cc conv_transpose_op.cu.cc
conv_transpose_cudnn_op.cu.cc DEPS vol2col
)
file
(
APPEND
${
pybind_file
}
"USE_OP_DEVICE_KERNEL(conv2d, CUDNN);
\n
"
)
file
(
APPEND
${
pybind_file
}
"USE_OP_DEVICE_KERNEL(pool2d, CUDNN);
\n
"
)
file
(
APPEND
${
pybind_file
}
"USE_OP_DEVICE_KERNEL(conv2d_transpose, CUDNN);
\n
"
)
op_library
(
conv_op DEPS vol2col depthwise_conv
)
else
()
op_library
(
conv_op SRCS conv_op.cc DEPS vol2col
)
op_library
(
pool_op SRCS pool_op.cc DEPS pooling
)
op_library
(
conv_transpose_op SRCS conv_transpose_op.cc DEPS vol2col
)
op_library
(
conv_op DEPS vol2col
)
endif
()
op_library
(
pool_op DEPS pooling
)
op_library
(
conv_transpose_op DEPS vol2col
)
cc_library
(
batch_size_like SRCS batch_size_like.cc DEPS op_registry
)
op_library
(
fill_constant_batch_size_like_op
SRCS fill_constant_batch_size_like_op.cc fill_constant_batch_size_like_op.cu.cc
DEPS batch_size_like
)
op_library
(
uniform_random_batch_size_like_op
SRCS uniform_random_batch_size_like_op.cc
DEPS batch_size_like uniform_random_op
)
op_library
(
gaussian_random_batch_size_like_op
SRCS gaussian_random_batch_size_like_op.cc
DEPS batch_size_like gaussian_random_op
)
op_library
(
fill_constant_batch_size_like_op DEPS batch_size_like
)
op_library
(
uniform_random_batch_size_like_op DEPS batch_size_like uniform_random_op
)
op_library
(
gaussian_random_batch_size_like_op DEPS batch_size_like gaussian_random_op
)
# FIXME(typhoonzero): save/load depends lodtensor serialization functions
op_library
(
save_op DEPS lod_tensor
)
...
...
paddle/fluid/operators/bipartite_match_op.cc
浏览文件 @
672f2083
...
...
@@ -94,6 +94,38 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
}
}
void
ArgMaxMatch
(
const
Tensor
&
dist
,
int
*
match_indices
,
T
*
match_dist
,
T
overlap_threshold
)
const
{
constexpr
T
kEPS
=
static_cast
<
T
>
(
1e-6
);
int64_t
row
=
dist
.
dims
()[
0
];
int64_t
col
=
dist
.
dims
()[
1
];
auto
*
dist_data
=
dist
.
data
<
T
>
();
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
if
(
match_indices
[
j
]
!=
-
1
)
{
// the j-th column has been matched to one entity.
continue
;
}
int
max_row_idx
=
-
1
;
T
max_dist
=
-
1
;
for
(
int
i
=
0
;
i
<
row
;
++
i
)
{
T
dist
=
dist_data
[
i
*
col
+
j
];
if
(
dist
<
kEPS
)
{
// distance is 0 between m-th row and j-th column
continue
;
}
if
(
dist
>=
overlap_threshold
&&
dist
>
max_dist
)
{
max_row_idx
=
i
;
max_dist
=
dist
;
}
}
if
(
max_row_idx
!=
-
1
)
{
PADDLE_ENFORCE_EQ
(
match_indices
[
j
],
-
1
);
match_indices
[
j
]
=
max_row_idx
;
match_dist
[
j
]
=
max_dist
;
}
}
}
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
dist_mat
=
context
.
Input
<
LoDTensor
>
(
"DistMat"
);
auto
*
match_indices
=
context
.
Output
<
Tensor
>
(
"ColToRowMatchIndices"
);
...
...
@@ -120,13 +152,21 @@ class BipartiteMatchKernel : public framework::OpKernel<T> {
int
*
indices
=
match_indices
->
data
<
int
>
();
T
*
dist
=
match_dist
->
data
<
T
>
();
auto
type
=
context
.
Attr
<
std
::
string
>
(
"match_type"
);
auto
threshold
=
context
.
Attr
<
float
>
(
"dist_threshold"
);
if
(
n
==
1
)
{
BipartiteMatch
(
*
dist_mat
,
indices
,
dist
);
if
(
type
==
"per_prediction"
)
{
ArgMaxMatch
(
*
dist_mat
,
indices
,
dist
,
threshold
);
}
}
else
{
auto
lod
=
dist_mat
->
lod
().
back
();
for
(
size_t
i
=
0
;
i
<
lod
.
size
()
-
1
;
++
i
)
{
Tensor
one_ins
=
dist_mat
->
Slice
(
lod
[
i
],
lod
[
i
+
1
]);
BipartiteMatch
(
one_ins
,
indices
+
i
*
col
,
dist
+
i
*
col
);
if
(
type
==
"per_prediction"
)
{
ArgMaxMatch
(
one_ins
,
indices
+
i
*
col
,
dist
+
i
*
col
,
threshold
);
}
}
}
}
...
...
@@ -147,6 +187,19 @@ class BipartiteMatchOpMaker : public framework::OpProtoAndCheckerMaker {
"This tensor can contain LoD information to represent a batch of "
"inputs. One instance of this batch can contain different numbers of "
"entities."
);
AddAttr
<
std
::
string
>
(
"match_type"
,
"(string, defalut: per_prediction) "
"The type of matching method, should be 'bipartite' or "
"'per_prediction', 'bipartite' by defalut."
)
.
SetDefault
(
"bipartite"
)
.
InEnum
({
"bipartite"
,
"per_prediction"
});
AddAttr
<
float
>
(
"dist_threshold"
,
"(float, defalut: 0.5) "
"If `match_type` is 'per_prediction', this threshold is to determine "
"the extra matching bboxes based on the maximum distance."
)
.
SetDefault
(
0.5
);
AddOutput
(
"ColToRowMatchIndices"
,
"(Tensor) A 2-D Tensor with shape [N, M] in int type. "
"N is the batch size. If ColToRowMatchIndices[i][j] is -1, it "
...
...
@@ -168,10 +221,10 @@ distance matrix. For input 2D matrix, the bipartite matching algorithm can
find the matched column for each row, also can find the matched row for
each column. And this operator only calculate matched indices from column
to row. For each instance, the number of matched indices is the number of
of columns of the input ditance matrix.
of columns of the input di
s
tance matrix.
There are two outputs to save matched indices and distance.
A simple description, this algo
thri
m matched the best (maximum distance)
A simple description, this algo
rith
m matched the best (maximum distance)
row entity to the column entity and the matched indices are not duplicated
in each row of ColToRowMatchIndices. If the column entity is not matched
any row entity, set -1 in ColToRowMatchIndices.
...
...
paddle/fluid/operators/concat_op.h
浏览文件 @
672f2083
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/strided_memcpy.h"
...
...
@@ -34,12 +35,46 @@ class ConcatKernel : public framework::OpKernel<T> {
auto
out_stride
=
framework
::
stride_numel
(
out
->
dims
());
size_t
output_offset
=
0
;
for
(
auto
*
in
:
ins
)
{
auto
in_stride
=
framework
::
stride_numel
(
in
->
dims
());
StridedNumelCopyWithAxis
<
T
>
(
ctx
.
device_context
(),
axis
,
out
->
data
<
T
>
()
+
output_offset
,
out_stride
,
in
->
data
<
T
>
(),
in_stride
,
in_stride
[
axis
]);
output_offset
+=
in_stride
[
axis
];
// If axis >=1, copy to out immediately need to call many times
// of cuda memcpy. Copy the input to cpu and do the stride copy,
// then copy to gpu output.
if
(
platform
::
is_gpu_place
(
place
)
&&
axis
>=
1
)
{
platform
::
CPUPlace
copy_place
;
auto
&
cpu_ctx
=
*
platform
::
DeviceContextPool
::
Instance
().
Get
(
copy_place
);
framework
::
Tensor
cpu_out
;
cpu_out
.
Resize
(
out
->
dims
());
cpu_out
.
mutable_data
<
T
>
(
copy_place
);
auto
&
dev_ctx
=
ctx
.
device_context
();
std
::
vector
<
std
::
unique_ptr
<
framework
::
Tensor
>>
cpu_ins
;
for
(
auto
*
in
:
ins
)
{
std
::
unique_ptr
<
framework
::
Tensor
>
cpu_in
(
new
framework
::
Tensor
);
framework
::
TensorCopy
(
*
in
,
copy_place
,
dev_ctx
,
cpu_in
.
get
());
cpu_ins
.
emplace_back
(
std
::
move
(
cpu_in
));
}
// TODO(dzhwinter): overlap copy and compute stream
// https://devblogs.nvidia.com/how-overlap-data-transfers-cuda-cc/
dev_ctx
.
Wait
();
for
(
auto
&
in
:
cpu_ins
)
{
auto
&
cpu_in
=
*
in
.
get
();
auto
in_stride
=
framework
::
stride_numel
(
cpu_in
.
dims
());
StridedNumelCopyWithAxis
<
T
>
(
cpu_ctx
,
axis
,
cpu_out
.
data
<
T
>
()
+
output_offset
,
out_stride
,
cpu_in
.
data
<
T
>
(),
in_stride
,
in_stride
[
axis
]);
output_offset
+=
in_stride
[
axis
];
}
framework
::
TensorCopy
(
cpu_out
,
place
,
dev_ctx
,
out
);
}
else
{
for
(
auto
*
in
:
ins
)
{
auto
in_stride
=
framework
::
stride_numel
(
in
->
dims
());
StridedNumelCopyWithAxis
<
T
>
(
ctx
.
device_context
(),
axis
,
out
->
data
<
T
>
()
+
output_offset
,
out_stride
,
in
->
data
<
T
>
(),
in_stride
,
in_stride
[
axis
]);
output_offset
+=
in_stride
[
axis
];
}
}
}
};
...
...
paddle/fluid/operators/detail/grpc_client.cc
浏览文件 @
672f2083
...
...
@@ -177,8 +177,8 @@ std::shared_ptr<grpc::Channel> RPCClient::GetChannel(const std::string& ep) {
args
.
SetMaxSendMessageSize
(
std
::
numeric_limits
<
int
>::
max
());
args
.
SetMaxReceiveMessageSize
(
std
::
numeric_limits
<
int
>::
max
());
auto
ch
=
std
::
shared_ptr
<
grpc
::
Channel
>
(
grpc
::
CreateCustomChannel
(
ep
,
grpc
::
InsecureChannelCredentials
(),
args
)
)
;
auto
ch
=
grpc
::
CreateCustomChannel
(
ep
,
grpc
::
InsecureChannelCredentials
(),
args
);
channels_
[
ep
]
=
ch
;
return
ch
;
...
...
paddle/fluid/operators/listen_and_serv_op.cc
浏览文件 @
672f2083
...
...
@@ -129,6 +129,8 @@ class ListenAndServOp : public framework::OperatorBase {
}
if
(
exit_flag
)
{
rpc_service_
->
ShutDown
();
rpc_service_
->
SetCond
(
1
);
break
;
}
try
{
executor
.
Run
(
*
program
,
&
recv_scope
,
block
->
ID
(),
/*global_block*/
...
...
paddle/fluid/operators/nccl_op.cc
浏览文件 @
672f2083
...
...
@@ -65,7 +65,7 @@ class NCCLInitOpVarTypeInference : public framework::VarTypeInference {
framework
::
BlockDesc
*
block
)
const
override
{
auto
out_var_name
=
op_desc
.
Output
(
"Communicator"
).
front
();
auto
&
out_var
=
block
->
FindRecursiveOrCreateVar
(
out_var_name
);
auto
var_type
=
framework
::
proto
::
VarType
::
NCCL_COM
;
auto
var_type
=
framework
::
proto
::
VarType
::
RAW
;
out_var
.
SetType
(
var_type
);
}
};
...
...
paddle/fluid/operators/send_op.cc
浏览文件 @
672f2083
...
...
@@ -121,9 +121,27 @@ This operator will send tensor to recv_op at the parameter server.
}
};
class
SendOpVarTypeInference
:
public
framework
::
VarTypeInference
{
public:
void
operator
()(
const
framework
::
OpDesc
&
op_desc
,
framework
::
BlockDesc
*
block
)
const
override
{
auto
out_var_name
=
op_desc
.
Output
(
"RPCClient"
).
front
();
auto
&
out_var
=
block
->
FindRecursiveOrCreateVar
(
out_var_name
);
auto
var_type
=
framework
::
proto
::
VarType
::
RAW
;
out_var
.
SetType
(
var_type
);
}
};
class
SendOpShapeInference
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
send
,
ops
::
SendOp
,
ops
::
SendOpMaker
);
REGISTER_OPERATOR
(
send
,
ops
::
SendOp
,
paddle
::
framework
::
EmptyGradOpMaker
,
ops
::
SendOpMaker
,
ops
::
SendOpVarTypeInference
,
ops
::
SendOpShapeInference
);
paddle/fluid/operators/send_recv_op_test.cc
浏览文件 @
672f2083
...
...
@@ -95,7 +95,7 @@ void AddOp(const std::string &type, const f::VariableNameMap &inputs,
for
(
auto
kv
:
outputs
)
{
for
(
auto
v
:
kv
.
second
)
{
auto
var
=
block
->
Var
(
v
);
var
->
SetDataType
(
f
::
proto
::
Data
Type
::
FP32
);
var
->
SetDataType
(
f
::
proto
::
Var
Type
::
FP32
);
}
}
...
...
@@ -122,33 +122,37 @@ void StartServerNet(bool is_sparse) {
// sub program run in listen_and_serv_op, for simple test we use sum
f
::
ProgramDesc
program
;
f
::
BlockDesc
*
block
=
program
.
MutableBlock
(
0
);
f
::
BlockDesc
*
optimize_
block
=
program
.
MutableBlock
(
0
);
// X for server side tensors, RX for received tensers, must be of same shape.
AddOp
(
"sum"
,
{{
"X"
,
{
"x0"
,
"x1"
}}},
{{
"Out"
,
{
"Out"
}}},
{},
block
);
AddOp
(
"sum"
,
{{
"X"
,
{
"x0"
,
"x1"
}}},
{{
"Out"
,
{
"Out"
}}},
{},
optimize_
block
);
f
::
AttributeMap
attrs
;
attrs
.
insert
({
"endpoint"
,
std
::
string
(
"127.0.0.1:6174"
)});
attrs
.
insert
({
"Fanin"
,
1
});
attrs
.
insert
({
"ParamList"
,
std
::
vector
<
std
::
string
>
({
"Out"
})});
attrs
.
insert
({
"GradList"
,
std
::
vector
<
std
::
string
>
({
"x1"
})});
attrs
.
insert
({
"OptimizeBlock"
,
block
});
attrs
.
insert
({
"OptimizeBlock"
,
optimize_
block
});
listen_and_serv_op
=
f
::
OpRegistry
::
CreateOp
(
"listen_and_serv"
,
{},
{},
attrs
);
f
::
OpRegistry
::
CreateOp
(
"listen_and_serv"
,
{
{
"X"
,
{
"x1"
}}
},
{},
attrs
);
listen_and_serv_op
->
Run
(
scope
,
place
);
}
TEST
(
SendRecvOp
,
CPUDense
)
{
std
::
thread
server_thread
(
StartServerNet
,
false
);
sleep
(
10
);
// wait server to start
sleep
(
5
);
// wait server to start
// local net
f
::
Scope
scope
;
p
::
CPUPlace
place
;
InitTensorsInScope
(
scope
,
place
);
// create rpc client var
scope
.
Var
(
"RPC_CLIENT_VAR"
);
f
::
AttributeMap
attrs
;
attrs
.
insert
({
"endpoints"
,
std
::
vector
<
std
::
string
>
({
"127.0.0.1:6174"
})});
attrs
.
insert
({
"epmap"
,
std
::
vector
<
std
::
string
>
({
"127.0.0.1:6174"
})});
auto
send_op
=
f
::
OpRegistry
::
CreateOp
(
"send"
,
{{
"X"
,
{
"x1"
}}},
{{
"Out"
,
{
"Out"
}}},
attrs
);
auto
send_op
=
f
::
OpRegistry
::
CreateOp
(
"send"
,
{{
"X"
,
{
"x1"
}}},
{{
"Out"
,
{
"Out"
}},
{
"RPCClient"
,
{
"RPC_CLIENT_VAR"
}}},
attrs
);
send_op
->
Run
(
scope
,
place
);
auto
in_var
=
scope
.
Var
(
"x1"
);
...
...
@@ -175,11 +179,13 @@ TEST(SendRecvOp, CPUSparse) {
p
::
CPUPlace
place
;
p
::
CPUDeviceContext
ctx
(
place
);
InitSelectedRowsInScope
(
scope
,
place
);
scope
.
Var
(
"RPC_CLIENT_VAR"
);
f
::
AttributeMap
attrs
;
attrs
.
insert
({
"endpoints"
,
std
::
vector
<
std
::
string
>
({
"127.0.0.1:6174"
})});
attrs
.
insert
({
"epmap"
,
std
::
vector
<
std
::
string
>
({
"127.0.0.1:6174"
})});
auto
send_op
=
f
::
OpRegistry
::
CreateOp
(
"send"
,
{{
"X"
,
{
"x1"
}}},
{{
"Out"
,
{
"Out"
}}},
attrs
);
auto
send_op
=
f
::
OpRegistry
::
CreateOp
(
"send"
,
{{
"X"
,
{
"x1"
}}},
{{
"Out"
,
{
"Out"
}},
{
"RPCClient"
,
{
"RPC_CLIENT_VAR"
}}},
attrs
);
send_op
->
Run
(
scope
,
place
);
auto
x0
=
scope
.
Var
(
"x0"
)
->
GetMutable
<
f
::
SelectedRows
>
();
...
...
paddle/fluid/pybind/protobuf.cc
浏览文件 @
672f2083
...
...
@@ -252,7 +252,7 @@ void BindVarDsec(py::module &m) {
.
value
(
"CHANNEL"
,
proto
::
VarType
::
CHANNEL
)
.
value
(
"PLACE_LIST"
,
proto
::
VarType
::
PLACE_LIST
)
.
value
(
"READER"
,
proto
::
VarType
::
READER
)
.
value
(
"
NCCL_COM"
,
proto
::
VarType
::
NCCL_COM
);
.
value
(
"
RAW"
,
proto
::
VarType
::
RAW
);
}
void
BindOpDesc
(
py
::
module
&
m
)
{
...
...
paddle/scripts/docker/build.sh
浏览文件 @
672f2083
...
...
@@ -49,6 +49,7 @@ function cmake_gen() {
-DCUDNN_ROOT=/usr/
-DWITH_STYLE_CHECK=
${
WITH_STYLE_CHECK
:-
ON
}
-DWITH_TESTING=
${
WITH_TESTING
:-
ON
}
-DWITH_FAST_BUNDLE_TEST=ON
-DCMAKE_EXPORT_COMPILE_COMMANDS=ON
========================================
EOF
...
...
@@ -72,6 +73,7 @@ EOF
-DCUDNN_ROOT
=
/usr/
\
-DWITH_STYLE_CHECK
=
${
WITH_STYLE_CHECK
:-
ON
}
\
-DWITH_TESTING
=
${
WITH_TESTING
:-
ON
}
\
-DWITH_FAST_BUNDLE_TEST
=
ON
\
-DCMAKE_EXPORT_COMPILE_COMMANDS
=
ON
}
...
...
python/paddle/fluid/distribute_transpiler.py
浏览文件 @
672f2083
...
...
@@ -226,8 +226,7 @@ class DistributeTranspiler:
rpc_client_var
=
program
.
global_block
().
create_var
(
name
=
"RPC_CLIENT_VAR"
,
persistable
=
True
,
dtype
=
'float32'
,
# dtype and shape is not used in fact
shape
=
[
0
])
type
=
core
.
VarDesc
.
VarType
.
RAW
)
# create send_op
program
.
global_block
().
append_op
(
...
...
python/paddle/fluid/io.py
浏览文件 @
672f2083
...
...
@@ -68,7 +68,7 @@ def save_vars(executor,
main_program
=
None
,
vars
=
None
,
predicate
=
None
,
save_file_
name
=
None
):
file
name
=
None
):
"""
Save variables to directory by executor.
...
...
@@ -80,8 +80,8 @@ def save_vars(executor,
as a bool. If it returns true, the corresponding input variable will be saved.
:param vars: variables need to be saved. If vars is specified, program & predicate
will be ignored
:param
save_file_name: The name of a single file that all vars are saved to.
If it is None, save variables to separate files.
:param
filename: The name of a single file that all vars are saved to.
If it is None, save variables to separate files.
:return: None
"""
...
...
@@ -95,7 +95,7 @@ def save_vars(executor,
executor
,
dirname
=
dirname
,
vars
=
filter
(
predicate
,
main_program
.
list_vars
()),
save_file_name
=
save_file_
name
)
filename
=
file
name
)
else
:
save_program
=
Program
()
save_block
=
save_program
.
global_block
()
...
...
@@ -103,7 +103,7 @@ def save_vars(executor,
save_var_map
=
{}
for
each_var
in
vars
:
new_var
=
_clone_var_in_block_
(
save_block
,
each_var
)
if
save_file_
name
is
None
:
if
file
name
is
None
:
save_block
.
append_op
(
type
=
'save'
,
inputs
=
{
'X'
:
[
new_var
]},
...
...
@@ -112,7 +112,7 @@ def save_vars(executor,
else
:
save_var_map
[
new_var
.
name
]
=
new_var
if
save_file_
name
is
not
None
:
if
file
name
is
not
None
:
save_var_list
=
[]
for
name
in
sorted
(
save_var_map
.
keys
()):
save_var_list
.
append
(
save_var_map
[
name
])
...
...
@@ -121,12 +121,12 @@ def save_vars(executor,
type
=
'save_combine'
,
inputs
=
{
'X'
:
save_var_list
},
outputs
=
{},
attrs
=
{
'file_path'
:
os
.
path
.
join
(
dirname
,
save_file_
name
)})
attrs
=
{
'file_path'
:
os
.
path
.
join
(
dirname
,
file
name
)})
executor
.
run
(
save_program
)
def
save_params
(
executor
,
dirname
,
main_program
=
None
,
save_file_
name
=
None
):
def
save_params
(
executor
,
dirname
,
main_program
=
None
,
file
name
=
None
):
"""
Save all parameters to directory with executor.
"""
...
...
@@ -136,11 +136,10 @@ def save_params(executor, dirname, main_program=None, save_file_name=None):
main_program
=
main_program
,
vars
=
None
,
predicate
=
is_parameter
,
save_file_name
=
save_file_
name
)
filename
=
file
name
)
def
save_persistables
(
executor
,
dirname
,
main_program
=
None
,
save_file_name
=
None
):
def
save_persistables
(
executor
,
dirname
,
main_program
=
None
,
filename
=
None
):
"""
Save all persistables to directory with executor.
"""
...
...
@@ -150,7 +149,7 @@ def save_persistables(executor, dirname, main_program=None,
main_program
=
main_program
,
vars
=
None
,
predicate
=
is_persistable
,
save_file_name
=
save_file_
name
)
filename
=
file
name
)
def
load_vars
(
executor
,
...
...
@@ -158,7 +157,7 @@ def load_vars(executor,
main_program
=
None
,
vars
=
None
,
predicate
=
None
,
load_file_
name
=
None
):
file
name
=
None
):
"""
Load variables from directory by executor.
...
...
@@ -170,8 +169,8 @@ def load_vars(executor,
as a bool. If it returns true, the corresponding input variable will be loaded.
:param vars: variables need to be loaded. If vars is specified, program &
predicate will be ignored
:param
load_file_name: The name of the single file that all vars are loaded from.
If it is None, load variables from separate files.
:param
filename: The name of the single file that all vars are loaded from.
If it is None, load variables from separate files.
:return: None
"""
...
...
@@ -185,7 +184,7 @@ def load_vars(executor,
executor
,
dirname
=
dirname
,
vars
=
filter
(
predicate
,
main_program
.
list_vars
()),
load_file_name
=
load_file_
name
)
filename
=
file
name
)
else
:
load_prog
=
Program
()
load_block
=
load_prog
.
global_block
()
...
...
@@ -194,7 +193,7 @@ def load_vars(executor,
for
each_var
in
vars
:
assert
isinstance
(
each_var
,
Variable
)
new_var
=
_clone_var_in_block_
(
load_block
,
each_var
)
if
load_file_
name
is
None
:
if
file
name
is
None
:
load_block
.
append_op
(
type
=
'load'
,
inputs
=
{},
...
...
@@ -203,7 +202,7 @@ def load_vars(executor,
else
:
load_var_map
[
new_var
.
name
]
=
new_var
if
load_file_
name
is
not
None
:
if
file
name
is
not
None
:
load_var_list
=
[]
for
name
in
sorted
(
load_var_map
.
keys
()):
load_var_list
.
append
(
load_var_map
[
name
])
...
...
@@ -212,12 +211,12 @@ def load_vars(executor,
type
=
'load_combine'
,
inputs
=
{},
outputs
=
{
"Out"
:
load_var_list
},
attrs
=
{
'file_path'
:
os
.
path
.
join
(
dirname
,
load_file_
name
)})
attrs
=
{
'file_path'
:
os
.
path
.
join
(
dirname
,
file
name
)})
executor
.
run
(
load_prog
)
def
load_params
(
executor
,
dirname
,
main_program
=
None
,
load_file_
name
=
None
):
def
load_params
(
executor
,
dirname
,
main_program
=
None
,
file
name
=
None
):
"""
load all parameters from directory by executor.
"""
...
...
@@ -226,11 +225,10 @@ def load_params(executor, dirname, main_program=None, load_file_name=None):
dirname
=
dirname
,
main_program
=
main_program
,
predicate
=
is_parameter
,
load_file_name
=
load_file_
name
)
filename
=
file
name
)
def
load_persistables
(
executor
,
dirname
,
main_program
=
None
,
load_file_name
=
None
):
def
load_persistables
(
executor
,
dirname
,
main_program
=
None
,
filename
=
None
):
"""
load all persistables from directory by executor.
"""
...
...
@@ -239,7 +237,7 @@ def load_persistables(executor, dirname, main_program=None,
dirname
=
dirname
,
main_program
=
main_program
,
predicate
=
is_persistable
,
load_file_name
=
load_file_
name
)
filename
=
file
name
)
def
get_inference_program
(
target_vars
,
main_program
=
None
):
...
...
@@ -299,7 +297,8 @@ def save_inference_model(dirname,
target_vars
,
executor
,
main_program
=
None
,
save_file_name
=
None
):
model_filename
=
None
,
params_filename
=
None
):
"""
Build a model especially for inference,
and save it to directory by the executor.
...
...
@@ -310,8 +309,11 @@ def save_inference_model(dirname,
:param executor: executor that save inference model
:param main_program: original program, which will be pruned to build the inference model.
Default default_main_program().
:param save_file_name: The name of a single file that all parameters are saved to.
If it is None, save parameters to separate files.
:param model_filename: The name of file to save inference program.
If not specified, default filename `__model__` will be used.
:param params_filename: The name of file to save parameters.
It is used for the case that all parameters are saved in a single binary file.
If not specified, parameters are considered saved in separate files.
:return: None
"""
...
...
@@ -342,15 +344,19 @@ def save_inference_model(dirname,
prepend_feed_ops
(
inference_program
,
feeded_var_names
)
append_fetch_ops
(
inference_program
,
fetch_var_names
)
if
save_file_name
==
None
:
model_file
_name
=
dirname
+
"/__model__"
if
model_filename
is
not
None
:
model_file
name
=
os
.
path
.
basename
(
model_filename
)
else
:
model_file_name
=
dirname
+
"/__model_combined__"
model_filename
=
"__model__"
model_filename
=
os
.
path
.
join
(
dirname
,
model_filename
)
with
open
(
model_file_name
,
"wb"
)
as
f
:
if
params_filename
is
not
None
:
params_filename
=
os
.
path
.
basename
(
params_filename
)
with
open
(
model_filename
,
"wb"
)
as
f
:
f
.
write
(
inference_program
.
desc
.
serialize_to_string
())
save_persistables
(
executor
,
dirname
,
inference_program
,
save_file_
name
)
save_persistables
(
executor
,
dirname
,
inference_program
,
params_file
name
)
def
get_feed_targets_names
(
program
):
...
...
@@ -371,15 +377,21 @@ def get_fetch_targets_names(program):
return
fetch_targets_names
def
load_inference_model
(
dirname
,
executor
,
load_file_name
=
None
):
def
load_inference_model
(
dirname
,
executor
,
model_filename
=
None
,
params_filename
=
None
):
"""
Load inference model from a directory
:param dirname: directory path
:param executor: executor that load inference model
:param load_file_name: The name of the single file that all parameters are loaded from.
If it is None, load parameters from separate files.
:param model_filename: The name of file to load inference program.
If not specified, default filename `__model__` will be used.
:param params_filename: The name of file to load parameters.
It is used for the case that all parameters are saved in a single binary file.
If not specified, parameters are considered saved in separate files.
:return: [program, feed_target_names, fetch_targets]
program: program especially for inference.
feed_target_names: Names of variables that need to feed data
...
...
@@ -388,16 +400,20 @@ def load_inference_model(dirname, executor, load_file_name=None):
if
not
os
.
path
.
isdir
(
dirname
):
raise
ValueError
(
"There is no directory named '%s'"
,
dirname
)
if
load_file_name
==
None
:
model_file
_name
=
dirname
+
"/__model__"
if
model_filename
is
not
None
:
model_file
name
=
os
.
path
.
basename
(
model_filename
)
else
:
model_file_name
=
dirname
+
"/__model_combined__"
model_filename
=
"__model__"
model_filename
=
os
.
path
.
join
(
dirname
,
model_filename
)
if
params_filename
is
not
None
:
params_filename
=
os
.
path
.
basename
(
params_filename
)
with
open
(
model_file
_
name
,
"rb"
)
as
f
:
with
open
(
model_filename
,
"rb"
)
as
f
:
program_desc_str
=
f
.
read
()
program
=
Program
.
parse_from_string
(
program_desc_str
)
load_persistables
(
executor
,
dirname
,
program
,
load_file_
name
)
load_persistables
(
executor
,
dirname
,
program
,
params_file
name
)
feed_target_names
=
get_feed_targets_names
(
program
)
fetch_target_names
=
get_fetch_targets_names
(
program
)
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
672f2083
...
...
@@ -133,7 +133,6 @@ def detection_output(scores,
})
return
nmsed_outs
@
autodoc
()
def
detection_map
(
detect_res
,
label
,
...
...
@@ -172,7 +171,10 @@ def detection_map(detect_res,
return
map_out
,
accum_pos_count_out
,
accum_true_pos_out
,
accum_false_pos_out
def
bipartite_match
(
dist_matrix
,
name
=
None
):
def
bipartite_match
(
dist_matrix
,
match_type
=
None
,
dist_threshold
=
None
,
name
=
None
):
"""
**Bipartite matchint operator**
...
...
@@ -204,6 +206,11 @@ def bipartite_match(dist_matrix, name=None):
This tensor can contain LoD information to represent a batch of
inputs. One instance of this batch can contain different numbers of
entities.
match_type(string|None): The type of matching method, should be
'bipartite' or 'per_prediction', 'bipartite' by defalut.
dist_threshold(float|None): If `match_type` is 'per_prediction',
this threshold is to determine the extra matching bboxes based
on the maximum distance, 0.5 by defalut.
Returns:
match_indices(Variable): A 2-D Tensor with shape [N, M] in int type.
N is the batch size. If match_indices[i][j] is -1, it
...
...
@@ -223,6 +230,10 @@ def bipartite_match(dist_matrix, name=None):
helper
.
append_op
(
type
=
'bipartite_match'
,
inputs
=
{
'DistMat'
:
dist_matrix
},
attrs
=
{
'match_type'
:
match_type
,
'dist_threshold'
:
dist_threshold
,
},
outputs
=
{
'ColToRowMatchIndices'
:
match_indices
,
'ColToRowMatchDist'
:
match_distance
...
...
@@ -373,7 +384,7 @@ def ssd_loss(location,
loc_loss_weight (float): Weight for localization loss, 1.0 by default.
conf_loss_weight (float): Weight for confidence loss, 1.0 by default.
match_type (str): The type of matching method during training, should
be 'bipartite' or 'per_prediction'.
be 'bipartite' or 'per_prediction'
, 'per_prediction' by defalut
.
mining_type (str): The hard example mining type, should be 'hard_example'
or 'max_negative', now only support `max_negative`.
...
...
@@ -421,7 +432,8 @@ def ssd_loss(location,
# 1.1 Compute IOU similarity between ground-truth boxes and prior boxes.
iou
=
iou_similarity
(
x
=
gt_box
,
y
=
prior_box
)
# 1.2 Compute matched boundding box by bipartite matching algorithm.
matched_indices
,
matched_dist
=
bipartite_match
(
iou
)
matched_indices
,
matched_dist
=
bipartite_match
(
iou
,
match_type
,
overlap_threshold
)
# 2. Compute confidence for mining hard examples
# 2.1. Get the target label based on matched indices
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
672f2083
...
...
@@ -21,6 +21,7 @@ from ..framework import Variable
from
..param_attr
import
ParamAttr
from
layer_function_generator
import
autodoc
from
tensor
import
concat
import
utils
__all__
=
[
'fc'
,
...
...
@@ -1138,8 +1139,8 @@ def sequence_conv(input,
def
conv2d
(
input
,
num_filters
,
filter_size
,
stride
=
None
,
padding
=
None
,
stride
=
1
,
padding
=
0
,
groups
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
...
...
@@ -1252,12 +1253,10 @@ def conv2d(input,
raise
ValueError
(
"num_channels must be divisible by groups."
)
num_filter_channels
=
num_channels
/
groups
if
isinstance
(
filter_size
,
int
):
filter_size
=
[
filter_size
,
filter_size
]
if
isinstance
(
stride
,
int
):
stride
=
[
stride
,
stride
]
if
isinstance
(
padding
,
int
):
padding
=
[
padding
,
padding
]
filter_size
=
utils
.
convert_to_list
(
filter_size
,
2
,
'filter_size'
)
stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
padding
=
utils
.
convert_to_list
(
padding
,
2
,
'padding'
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
...
...
@@ -1432,10 +1431,10 @@ def sequence_last_step(input):
def
pool2d
(
input
,
pool_size
,
pool_type
,
pool_stride
=
None
,
pool_padding
=
None
,
pool_size
=-
1
,
pool_type
=
"max"
,
pool_stride
=
1
,
pool_padding
=
0
,
global_pooling
=
False
,
use_cudnn
=
True
,
name
=
None
):
...
...
@@ -1443,20 +1442,20 @@ def pool2d(input,
This function adds the operator for pooling in 2 dimensions, using the
pooling configurations mentioned in input parameters.
"""
if
pool_padding
is
None
:
pool_padding
=
[
0
,
0
]
if
pool_stride
is
None
:
pool_stride
=
[
1
,
1
]
if
pool_type
not
in
[
"max"
,
"avg"
]:
raise
ValueError
(
"Unknown pool_type: '%s'. It can only be 'max' or 'avg'."
,
str
(
pool_type
))
if
isinstance
(
pool_size
,
int
):
pool_size
=
[
pool_size
,
pool_size
]
if
isinstance
(
pool_stride
,
int
):
pool_stride
=
[
pool_stride
,
pool_stride
]
if
isinstance
(
pool_padding
,
int
):
pool_padding
=
[
pool_padding
,
pool_padding
]
if
global_pooling
is
False
and
pool_size
==
-
1
:
raise
ValueError
(
"When the global_pooling is False, pool_size must be passed "
"and be a valid value. Received pool_size: "
+
str
(
pool_size
))
pool_size
=
utils
.
convert_to_list
(
pool_size
,
2
,
'pool_size'
)
pool_padding
=
utils
.
convert_to_list
(
pool_padding
,
2
,
'pool_padding'
)
pool_stride
=
utils
.
convert_to_list
(
pool_stride
,
2
,
'pool_stride'
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
...
...
@@ -1685,9 +1684,9 @@ def conv2d_transpose(input,
num_filters
,
output_size
=
None
,
filter_size
=
None
,
padding
=
None
,
stride
=
None
,
dilation
=
None
,
padding
=
0
,
stride
=
1
,
dilation
=
1
,
param_attr
=
None
,
use_cudnn
=
True
,
name
=
None
):
...
...
@@ -1783,26 +1782,12 @@ def conv2d_transpose(input,
raise
TypeError
(
"Input of conv2d_transpose must be Variable"
)
input_channel
=
input
.
shape
[
1
]
op_attr
=
dict
()
if
isinstance
(
padding
,
int
):
op_attr
[
'paddings'
]
=
[
padding
,
padding
]
elif
padding
is
not
None
:
op_attr
[
'paddings'
]
=
padding
if
isinstance
(
stride
,
int
):
op_attr
[
'strides'
]
=
[
stride
,
stride
]
elif
stride
is
not
None
:
op_attr
[
'strides'
]
=
stride
if
isinstance
(
dilation
,
int
):
op_attr
[
'dilations'
]
=
[
dilation
,
dilation
]
elif
dilation
is
not
None
:
op_attr
[
'dilations'
]
=
dilation
padding
=
utils
.
convert_to_list
(
padding
,
2
,
'padding'
)
stride
=
utils
.
convert_to_list
(
stride
,
2
,
'stride'
)
dilation
=
utils
.
convert_to_list
(
dilation
,
2
,
'dilation'
)
if
not
isinstance
(
use_cudnn
,
bool
):
raise
ValueError
(
"use_cudnn should be True or False"
)
op_attr
[
'use_cudnn'
]
=
use_cudnn
if
filter_size
is
None
:
if
output_size
is
None
:
...
...
@@ -1810,10 +1795,6 @@ def conv2d_transpose(input,
if
isinstance
(
output_size
,
int
):
output_size
=
[
output_size
,
output_size
]
padding
=
op_attr
.
get
(
'paddings'
,
[
0
,
0
])
stride
=
op_attr
.
get
(
'strides'
,
[
1
,
1
])
dilation
=
op_attr
.
get
(
'dilations'
,
[
1
,
1
])
h_in
=
input
.
shape
[
2
]
w_in
=
input
.
shape
[
3
]
...
...
@@ -1822,9 +1803,9 @@ def conv2d_transpose(input,
filter_size_w
=
(
output_size
[
1
]
-
(
w_in
-
1
)
*
stride
[
1
]
+
2
*
padding
[
1
]
-
1
)
/
dilation
[
1
]
+
1
filter_size
=
[
filter_size_h
,
filter_size_w
]
elif
isinstance
(
filter_size
,
int
):
filter_size
=
[
filter_size
,
filter_size
]
else
:
filter_size
=
utils
.
convert_to_list
(
filter_size
,
2
,
'conv2d_transpose.filter_size'
)
filter_shape
=
[
input_channel
,
num_filters
]
+
filter_size
img_filter
=
helper
.
create_parameter
(
...
...
@@ -1836,7 +1817,12 @@ def conv2d_transpose(input,
inputs
=
{
'Input'
:
[
input
],
'Filter'
:
[
img_filter
]},
outputs
=
{
'Output'
:
out
},
attrs
=
op_attr
)
attrs
=
{
'strides'
:
stride
,
'paddings'
:
padding
,
'dilations'
:
dilation
,
'use_cudnn'
:
use_cudnn
})
return
out
...
...
python/paddle/fluid/layers/utils.py
0 → 100644
浏览文件 @
672f2083
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
numpy
as
np
def
convert_to_list
(
value
,
n
,
name
,
dtype
=
np
.
int
):
"""
Converts a single numerical type or iterable of numerical
types into an numerical type list.
Arguments:
value: The value to validate and convert. Could an int, or any iterable
of ints.
n: The size of the list to be returned.
name: The name of the argument being validated, e.g. "stride" or
"filter_size". This is only used to format error messages.
dtype: the numerical type of the element of the list to be returned.
Returns:
A list of n dtypes.
Raises:
ValueError: If something else than an int/long or iterable thereof was
passed.
"""
if
isinstance
(
value
,
dtype
):
return
[
value
,
]
*
n
else
:
try
:
value_list
=
list
(
value
)
except
TypeError
:
raise
ValueError
(
"The "
+
name
+
"'s type must be list or tuple. Received: "
+
str
(
value
))
if
len
(
value_list
)
!=
n
:
raise
ValueError
(
"The "
+
name
+
"'s length must be "
+
str
(
n
)
+
". Received: "
+
str
(
value
))
for
single_value
in
value_list
:
try
:
dtype
(
single_value
)
except
(
ValueError
,
TypeError
):
raise
ValueError
(
"The "
+
name
+
"'s type must be a list or tuple of "
+
str
(
n
)
+
" "
+
str
(
dtype
)
+
" . Received: "
+
str
(
value
)
+
" "
"including element "
+
str
(
single_value
)
+
" of type"
+
" "
+
str
(
type
(
single_value
)))
return
value_list
python/paddle/fluid/tests/book/notest_rnn_encoder_decoer.py
浏览文件 @
672f2083
...
...
@@ -228,32 +228,34 @@ def infer(use_cuda, save_dirname=None):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
lod
=
[
0
,
4
,
10
]
word_data
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
trg_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert
feed_target_names
[
0
]
==
'source_sequence'
assert
feed_target_names
[
1
]
==
'target_sequence'
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
word_data
,
feed_target_names
[
1
]:
trg_word
,
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
lod
=
[
0
,
4
,
10
]
word_data
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
trg_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert
feed_target_names
[
0
]
==
'source_sequence'
assert
feed_target_names
[
1
]
==
'target_sequence'
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
word_data
,
feed_target_names
[
1
]:
trg_word
,
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_fit_a_line.py
浏览文件 @
672f2083
...
...
@@ -72,23 +72,26 @@ def infer(use_cuda, save_dirname=None):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size
=
10
tensor_x
=
numpy
.
random
.
uniform
(
0
,
10
,
[
batch_size
,
13
]).
astype
(
"float32"
)
assert
feed_target_names
[
0
]
==
'x'
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_x
},
fetch_list
=
fetch_targets
)
print
(
"infer shape: "
,
results
[
0
].
shape
)
print
(
"infer results: "
,
results
[
0
])
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# The input's dimension should be 2-D and the second dim is 13
# The input data should be >= 0
batch_size
=
10
tensor_x
=
numpy
.
random
.
uniform
(
0
,
10
,
[
batch_size
,
13
]).
astype
(
"float32"
)
assert
feed_target_names
[
0
]
==
'x'
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_x
},
fetch_list
=
fetch_targets
)
print
(
"infer shape: "
,
results
[
0
].
shape
)
print
(
"infer results: "
,
results
[
0
])
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_image_classification.py
浏览文件 @
672f2083
...
...
@@ -174,22 +174,26 @@ def infer(use_cuda, save_dirname=None):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# The input's dimension of conv should be 4-D or 5-D.
tensor_img
=
numpy
.
random
.
rand
(
1
,
3
,
32
,
32
).
astype
(
"float32"
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
print
(
"infer results: "
,
results
[
0
])
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [0, 1.0].
batch_size
=
1
tensor_img
=
numpy
.
random
.
rand
(
batch_size
,
3
,
32
,
32
).
astype
(
"float32"
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
print
(
"infer results: "
,
results
[
0
])
def
main
(
net_type
,
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_label_semantic_roles.py
浏览文件 @
672f2083
...
...
@@ -26,7 +26,7 @@ import unittest
word_dict
,
verb_dict
,
label_dict
=
conll05
.
get_dict
()
word_dict_len
=
len
(
word_dict
)
label_dict_len
=
len
(
label_dict
)
pred_len
=
len
(
verb_dict
)
pred_
dict_
len
=
len
(
verb_dict
)
mark_dict_len
=
2
word_dim
=
32
...
...
@@ -53,7 +53,7 @@ def db_lstm(word, predicate, ctx_n2, ctx_n1, ctx_0, ctx_p1, ctx_p2, mark,
# 8 features
predicate_embedding
=
fluid
.
layers
.
embedding
(
input
=
predicate
,
size
=
[
pred_len
,
word_dim
],
size
=
[
pred_
dict_
len
,
word_dim
],
dtype
=
'float32'
,
is_sparse
=
IS_SPARSE
,
param_attr
=
'vemb'
)
...
...
@@ -234,6 +234,7 @@ def train(use_cuda, save_dirname=None):
# Set the threshold low to speed up the CI test
if
float
(
pass_precision
)
>
0.05
:
if
save_dirname
is
not
None
:
# TODO(liuyiqun): Change the target to crf_decode
fluid
.
io
.
save_inference_model
(
save_dirname
,
[
'word_data'
,
'verb_data'
,
'ctx_n2_data'
,
'ctx_n1_data'
,
'ctx_0_data'
,
'ctx_p1_data'
,
...
...
@@ -251,51 +252,60 @@ def infer(use_cuda, save_dirname=None):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
lod
=
[
0
,
4
,
10
]
ts_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_pred
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_ctx_n2
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_ctx_n1
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_ctx_0
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_ctx_p1
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_ctx_p2
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
ts_mark
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
1
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert
feed_target_names
[
0
]
==
'word_data'
assert
feed_target_names
[
1
]
==
'verb_data'
assert
feed_target_names
[
2
]
==
'ctx_n2_data'
assert
feed_target_names
[
3
]
==
'ctx_n1_data'
assert
feed_target_names
[
4
]
==
'ctx_0_data'
assert
feed_target_names
[
5
]
==
'ctx_p1_data'
assert
feed_target_names
[
6
]
==
'ctx_p2_data'
assert
feed_target_names
[
7
]
==
'mark_data'
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
ts_word
,
feed_target_names
[
1
]:
ts_pred
,
feed_target_names
[
2
]:
ts_ctx_n2
,
feed_target_names
[
3
]:
ts_ctx_n1
,
feed_target_names
[
4
]:
ts_ctx_0
,
feed_target_names
[
5
]:
ts_ctx_p1
,
feed_target_names
[
6
]:
ts_ctx_p2
,
feed_target_names
[
7
]:
ts_mark
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference Shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
lod
=
[
0
,
4
,
10
]
word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
pred
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
pred_dict_len
-
1
)
ctx_n2
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
ctx_n1
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
ctx_0
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
ctx_p1
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
ctx_p2
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
mark
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
mark_dict_len
-
1
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert
feed_target_names
[
0
]
==
'word_data'
assert
feed_target_names
[
1
]
==
'verb_data'
assert
feed_target_names
[
2
]
==
'ctx_n2_data'
assert
feed_target_names
[
3
]
==
'ctx_n1_data'
assert
feed_target_names
[
4
]
==
'ctx_0_data'
assert
feed_target_names
[
5
]
==
'ctx_p1_data'
assert
feed_target_names
[
6
]
==
'ctx_p2_data'
assert
feed_target_names
[
7
]
==
'mark_data'
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
word
,
feed_target_names
[
1
]:
pred
,
feed_target_names
[
2
]:
ctx_n2
,
feed_target_names
[
3
]:
ctx_n1
,
feed_target_names
[
4
]:
ctx_0
,
feed_target_names
[
5
]:
ctx_p1
,
feed_target_names
[
6
]:
ctx_p2
,
feed_target_names
[
7
]:
mark
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference Shape: "
,
np_data
.
shape
)
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_recognize_digits.py
浏览文件 @
672f2083
...
...
@@ -78,7 +78,12 @@ def conv_net(img, label):
return
loss_net
(
conv_pool_2
,
label
)
def
train
(
nn_type
,
use_cuda
,
parallel
,
save_dirname
,
save_param_filename
):
def
train
(
nn_type
,
use_cuda
,
parallel
,
save_dirname
=
None
,
model_filename
=
None
,
params_filename
=
None
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
img
=
fluid
.
layers
.
data
(
name
=
'img'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
...
...
@@ -146,7 +151,8 @@ def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
fluid
.
io
.
save_inference_model
(
save_dirname
,
[
"img"
],
[
prediction
],
exe
,
save_file_name
=
save_param_filename
)
model_filename
=
model_filename
,
params_filename
=
params_filename
)
return
else
:
print
(
...
...
@@ -158,54 +164,62 @@ def train(nn_type, use_cuda, parallel, save_dirname, save_param_filename):
raise
AssertionError
(
"Loss of recognize digits is too large"
)
def
infer
(
use_cuda
,
save_dirname
=
None
,
param_filename
=
None
):
def
infer
(
use_cuda
,
save_dirname
=
None
,
model_filename
=
None
,
params_filename
=
None
):
if
save_dirname
is
None
:
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
,
param_filename
)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
batch_size
=
1
tensor_img
=
numpy
.
random
.
uniform
(
-
1.0
,
1.0
,
[
batch_size
,
1
,
28
,
28
]).
astype
(
"float32"
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
print
(
"infer results: "
,
results
[
0
])
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
,
model_filename
,
params_filename
)
# The input's dimension of conv should be 4-D or 5-D.
# Use normilized image pixels as input data, which should be in the range [-1.0, 1.0].
batch_size
=
1
tensor_img
=
numpy
.
random
.
uniform
(
-
1.0
,
1.0
,
[
batch_size
,
1
,
28
,
28
]).
astype
(
"float32"
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_img
},
fetch_list
=
fetch_targets
)
print
(
"infer results: "
,
results
[
0
])
def
main
(
use_cuda
,
parallel
,
nn_type
,
combine
):
save_dirname
=
None
model_filename
=
None
params_filename
=
None
if
not
use_cuda
and
not
parallel
:
save_dirname
=
"recognize_digits_"
+
nn_type
+
".inference.model"
save_filename
=
None
if
combine
==
True
:
save_filename
=
"__params_combined__"
else
:
save_dirname
=
None
save_filename
=
None
model_filename
=
"__model_combined__"
params_filename
=
"__params_combined__"
train
(
nn_type
=
nn_type
,
use_cuda
=
use_cuda
,
parallel
=
parallel
,
save_dirname
=
save_dirname
,
save_param_filename
=
save_filename
)
model_filename
=
model_filename
,
params_filename
=
params_filename
)
infer
(
use_cuda
=
use_cuda
,
save_dirname
=
save_dirname
,
param_filename
=
save_filename
)
model_filename
=
model_filename
,
params_filename
=
params_filename
)
class
TestRecognizeDigits
(
unittest
.
TestCase
):
...
...
python/paddle/fluid/tests/book/test_recommender_system.py
浏览文件 @
672f2083
...
...
@@ -251,13 +251,6 @@ def infer(use_cuda, save_dirname=None):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
def
create_lod_tensor
(
data
,
lod
=
None
):
tensor
=
fluid
.
LoDTensor
()
if
lod
is
None
:
...
...
@@ -275,44 +268,53 @@ def infer(use_cuda, save_dirname=None):
tensor
.
set
(
flattened_data
,
place
)
return
tensor
# Use the first data from paddle.dataset.movielens.test() as input
assert
feed_target_names
[
0
]
==
"user_id"
user_id
=
create_lod_tensor
([[
1
]])
assert
feed_target_names
[
1
]
==
"gender_id"
gender_id
=
create_lod_tensor
([[
1
]])
assert
feed_target_names
[
2
]
==
"age_id"
age_id
=
create_lod_tensor
([[
0
]])
assert
feed_target_names
[
3
]
==
"job_id"
job_id
=
create_lod_tensor
([[
10
]])
assert
feed_target_names
[
4
]
==
"movie_id"
movie_id
=
create_lod_tensor
([[
783
]])
assert
feed_target_names
[
5
]
==
"category_id"
category_id
=
create_lod_tensor
([[
10
],
[
8
],
[
9
]],
[[
0
,
3
]])
assert
feed_target_names
[
6
]
==
"movie_title"
movie_title
=
create_lod_tensor
([[
1069
],
[
4140
],
[
2923
],
[
710
],
[
988
]],
[[
0
,
5
]])
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
user_id
,
feed_target_names
[
1
]:
gender_id
,
feed_target_names
[
2
]:
age_id
,
feed_target_names
[
3
]:
job_id
,
feed_target_names
[
4
]:
movie_id
,
feed_target_names
[
5
]:
category_id
,
feed_target_names
[
6
]:
movie_title
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
"inferred score: "
,
np
.
array
(
results
[
0
]))
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
# Use the first data from paddle.dataset.movielens.test() as input
assert
feed_target_names
[
0
]
==
"user_id"
user_id
=
create_lod_tensor
([[
1
]])
assert
feed_target_names
[
1
]
==
"gender_id"
gender_id
=
create_lod_tensor
([[
1
]])
assert
feed_target_names
[
2
]
==
"age_id"
age_id
=
create_lod_tensor
([[
0
]])
assert
feed_target_names
[
3
]
==
"job_id"
job_id
=
create_lod_tensor
([[
10
]])
assert
feed_target_names
[
4
]
==
"movie_id"
movie_id
=
create_lod_tensor
([[
783
]])
assert
feed_target_names
[
5
]
==
"category_id"
category_id
=
create_lod_tensor
([[
10
],
[
8
],
[
9
]],
[[
0
,
3
]])
assert
feed_target_names
[
6
]
==
"movie_title"
movie_title
=
create_lod_tensor
([[
1069
],
[
4140
],
[
2923
],
[
710
],
[
988
]],
[[
0
,
5
]])
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
user_id
,
feed_target_names
[
1
]:
gender_id
,
feed_target_names
[
2
]:
age_id
,
feed_target_names
[
3
]:
job_id
,
feed_target_names
[
4
]:
movie_id
,
feed_target_names
[
5
]:
category_id
,
feed_target_names
[
6
]:
movie_title
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
"inferred score: "
,
np
.
array
(
results
[
0
]))
def
main
(
use_cuda
):
...
...
python/paddle/fluid/tests/book/test_understand_sentiment.py
浏览文件 @
672f2083
#
Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
...
...
@@ -193,36 +193,39 @@ def train(word_dict, net_method, use_cuda, parallel=False, save_dirname=None):
net_method
.
__name__
))
def
infer
(
use_cuda
,
save_dirname
=
None
):
def
infer
(
word_dict
,
use_cuda
,
save_dirname
=
None
):
if
save_dirname
is
None
:
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
lod
=
[
0
,
4
,
10
]
word_dict
=
paddle
.
dataset
.
imdb
.
word_dict
()
tensor_words
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
len
(
word_dict
)
-
1
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert
feed_target_names
[
0
]
==
"words"
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_words
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference Shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
word_dict_len
=
len
(
word_dict
)
lod
=
[
0
,
4
,
10
]
tensor_words
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
word_dict_len
-
1
)
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
assert
feed_target_names
[
0
]
==
"words"
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
tensor_words
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference Shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
def
main
(
word_dict
,
net_method
,
use_cuda
,
parallel
=
False
,
save_dirname
=
None
):
...
...
@@ -258,7 +261,7 @@ class TestUnderstandSentiment(unittest.TestCase):
self
.
word_dict
,
net_method
=
convolution_net
,
use_cuda
=
False
,
save_dirname
=
"understand_sentiment.inference.model"
)
save_dirname
=
"understand_sentiment
_conv
.inference.model"
)
def
test_conv_cpu_parallel
(
self
):
with
self
.
new_program_scope
():
...
...
@@ -271,7 +274,11 @@ class TestUnderstandSentiment(unittest.TestCase):
@
unittest
.
skip
(
reason
=
"make CI faster"
)
def
test_stacked_lstm_cpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
stacked_lstm_net
,
use_cuda
=
False
)
main
(
self
.
word_dict
,
net_method
=
stacked_lstm_net
,
use_cuda
=
False
,
save_dirname
=
"understand_sentiment_stacked_lstm.inference.model"
)
def
test_stacked_lstm_cpu_parallel
(
self
):
with
self
.
new_program_scope
():
...
...
@@ -287,7 +294,7 @@ class TestUnderstandSentiment(unittest.TestCase):
self
.
word_dict
,
net_method
=
convolution_net
,
use_cuda
=
True
,
save_dirname
=
"understand_sentiment.inference.model"
)
save_dirname
=
"understand_sentiment
_conv
.inference.model"
)
def
test_conv_gpu_parallel
(
self
):
with
self
.
new_program_scope
():
...
...
@@ -300,7 +307,11 @@ class TestUnderstandSentiment(unittest.TestCase):
@
unittest
.
skip
(
reason
=
"make CI faster"
)
def
test_stacked_lstm_gpu
(
self
):
with
self
.
new_program_scope
():
main
(
self
.
word_dict
,
net_method
=
stacked_lstm_net
,
use_cuda
=
True
)
main
(
self
.
word_dict
,
net_method
=
stacked_lstm_net
,
use_cuda
=
True
,
save_dirname
=
"understand_sentiment_stacked_lstm.inference.model"
)
def
test_stacked_lstm_gpu_parallel
(
self
):
with
self
.
new_program_scope
():
...
...
python/paddle/fluid/tests/book/test_word2vec.py
浏览文件 @
672f2083
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
# # Licensed under the Apache License, Version 2.0 (the "License");
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
...
...
@@ -21,6 +22,7 @@ import sys
def
create_random_lodtensor
(
lod
,
place
,
low
,
high
):
# The range of data elements is [low, high]
data
=
np
.
random
.
random_integers
(
low
,
high
,
[
lod
[
-
1
],
1
]).
astype
(
"int64"
)
res
=
fluid
.
LoDTensor
()
res
.
set
(
data
,
place
)
...
...
@@ -28,54 +30,7 @@ def create_random_lodtensor(lod, place, low, high):
return
res
def
infer
(
use_cuda
,
save_dirname
=
None
):
if
save_dirname
is
None
:
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
-
1
# Setup input, by creating 4 words, and setting up lod required for
# lookup_table_op
lod
=
[
0
,
1
]
first_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
)
second_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
)
third_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
)
fourth_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
)
assert
feed_target_names
[
0
]
==
'firstw'
assert
feed_target_names
[
1
]
==
'secondw'
assert
feed_target_names
[
2
]
==
'thirdw'
assert
feed_target_names
[
3
]
==
'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
first_word
,
feed_target_names
[
1
]:
second_word
,
feed_target_names
[
2
]:
third_word
,
feed_target_names
[
3
]:
fourth_word
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference Shape: "
,
np_data
.
shape
)
print
(
"Inference results: "
,
np_data
)
def
train
(
use_cuda
,
is_sparse
,
parallel
,
save_dirname
):
def
train
(
use_cuda
,
is_sparse
,
is_parallel
,
save_dirname
):
PASS_NUM
=
100
EMBED_SIZE
=
32
HIDDEN_SIZE
=
256
...
...
@@ -130,7 +85,7 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
forth_word
=
fluid
.
layers
.
data
(
name
=
'forthw'
,
shape
=
[
1
],
dtype
=
'int64'
)
next_word
=
fluid
.
layers
.
data
(
name
=
'nextw'
,
shape
=
[
1
],
dtype
=
'int64'
)
if
not
parallel
:
if
not
is_
parallel
:
avg_cost
,
predict_word
=
__network__
(
[
first_word
,
second_word
,
third_word
,
forth_word
,
next_word
])
else
:
...
...
@@ -176,11 +131,67 @@ def train(use_cuda, is_sparse, parallel, save_dirname):
raise
AssertionError
(
"Cost is too large {0:2.2}"
.
format
(
avg_cost_np
[
0
]))
def
main
(
use_cuda
,
is_sparse
,
parallel
):
def
infer
(
use_cuda
,
save_dirname
=
None
):
if
save_dirname
is
None
:
return
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
inference_scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
inference_scope
):
# Use fluid.io.load_inference_model to obtain the inference program desc,
# the feed_target_names (the names of variables that will be feeded
# data using feed operators), and the fetch_targets (variables that
# we want to obtain data from using fetch operators).
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
save_dirname
,
exe
)
word_dict
=
paddle
.
dataset
.
imikolov
.
build_dict
()
dict_size
=
len
(
word_dict
)
# Setup inputs, by creating 4 words, the lod of which should be [0, 1]
lod
=
[
0
,
1
]
first_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
second_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
third_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
fourth_word
=
create_random_lodtensor
(
lod
,
place
,
low
=
0
,
high
=
dict_size
-
1
)
assert
feed_target_names
[
0
]
==
'firstw'
assert
feed_target_names
[
1
]
==
'secondw'
assert
feed_target_names
[
2
]
==
'thirdw'
assert
feed_target_names
[
3
]
==
'forthw'
# Construct feed as a dictionary of {feed_target_name: feed_target_data}
# and results will contain a list of data corresponding to fetch_targets.
results
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
first_word
,
feed_target_names
[
1
]:
second_word
,
feed_target_names
[
2
]:
third_word
,
feed_target_names
[
3
]:
fourth_word
},
fetch_list
=
fetch_targets
,
return_numpy
=
False
)
print
(
results
[
0
].
lod
())
np_data
=
np
.
array
(
results
[
0
])
print
(
"Inference Shape: "
,
np_data
.
shape
)
def
main
(
use_cuda
,
is_sparse
,
is_parallel
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
save_dirname
=
"word2vec.inference.model"
train
(
use_cuda
,
is_sparse
,
parallel
,
save_dirname
)
if
not
is_parallel
:
save_dirname
=
"word2vec.inference.model"
else
:
save_dirname
=
None
train
(
use_cuda
,
is_sparse
,
is_parallel
,
save_dirname
)
infer
(
use_cuda
,
save_dirname
)
...
...
@@ -193,10 +204,10 @@ class W2VTest(unittest.TestCase):
pass
def
inject_test_method
(
use_cuda
,
is_sparse
,
parallel
):
def
inject_test_method
(
use_cuda
,
is_sparse
,
is_
parallel
):
fn_name
=
"test_{0}_{1}_{2}"
.
format
(
"cuda"
if
use_cuda
else
"cpu"
,
"sparse"
if
is_sparse
else
"dense"
,
"parallel"
if
parallel
else
"normal"
)
if
is_
parallel
else
"normal"
)
def
__impl__
(
*
args
,
**
kwargs
):
prog
=
fluid
.
Program
()
...
...
@@ -204,10 +215,12 @@ def inject_test_method(use_cuda, is_sparse, parallel):
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
main
(
use_cuda
=
use_cuda
,
is_sparse
=
is_sparse
,
parallel
=
parallel
)
main
(
use_cuda
=
use_cuda
,
is_sparse
=
is_sparse
,
is_parallel
=
is_parallel
)
# run only 2 cases: use_cuda is either True or False
if
is_sparse
==
False
and
parallel
==
False
:
if
use_cuda
and
is_sparse
:
fn
=
__impl__
else
:
# skip the other test when on CI server
...
...
@@ -219,8 +232,8 @@ def inject_test_method(use_cuda, is_sparse, parallel):
for
use_cuda
in
(
False
,
True
):
for
is_sparse
in
(
False
,
True
):
for
parallel
in
(
False
,
True
):
inject_test_method
(
use_cuda
,
is_sparse
,
parallel
)
for
is_
parallel
in
(
False
,
True
):
inject_test_method
(
use_cuda
,
is_sparse
,
is_
parallel
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_bipartite_match_op.py
浏览文件 @
672f2083
...
...
@@ -46,7 +46,20 @@ def bipartite_match(distance, match_indices, match_dist):
idx
+=
1
def
batch_bipartite_match
(
distance
,
lod
):
def
argmax_match
(
distance
,
match_indices
,
match_dist
,
threshold
):
r
,
c
=
distance
.
shape
for
j
in
xrange
(
c
):
if
match_indices
[
j
]
!=
-
1
:
continue
col_dist
=
distance
[:,
j
]
indices
=
np
.
argwhere
(
col_dist
>=
threshold
).
flatten
()
if
len
(
indices
)
<
1
:
continue
match_indices
[
j
]
=
indices
[
np
.
argmax
(
col_dist
[
indices
])]
match_dist
[
j
]
=
col_dist
[
match_indices
[
j
]]
def
batch_bipartite_match
(
distance
,
lod
,
match_type
=
None
,
dist_threshold
=
None
):
"""Bipartite Matching algorithm for batch input.
Arg:
distance (numpy.array) : The distance of two entries with shape [M, N].
...
...
@@ -59,6 +72,9 @@ def batch_bipartite_match(distance, lod):
for
i
in
range
(
len
(
lod
)
-
1
):
bipartite_match
(
distance
[
lod
[
i
]:
lod
[
i
+
1
],
:],
match_indices
[
i
,
:],
match_dist
[
i
,
:])
if
match_type
==
'per_prediction'
:
argmax_match
(
distance
[
lod
[
i
]:
lod
[
i
+
1
],
:],
match_indices
[
i
,
:],
match_dist
[
i
,
:],
dist_threshold
)
return
match_indices
,
match_dist
...
...
@@ -71,8 +87,8 @@ class TestBipartiteMatchOpWithLoD(OpTest):
self
.
inputs
=
{
'DistMat'
:
(
dist
,
lod
)}
self
.
outputs
=
{
'ColToRowMatchIndices'
:
(
match_indices
)
,
'ColToRowMatchDist'
:
(
match_dist
)
,
'ColToRowMatchIndices'
:
match_indices
,
'ColToRowMatchDist'
:
match_dist
,
}
def
test_check_output
(
self
):
...
...
@@ -96,5 +112,27 @@ class TestBipartiteMatchOpWithoutLoD(OpTest):
self
.
check_output
()
class
TestBipartiteMatchOpWithPerPredictionType
(
OpTest
):
def
setUp
(
self
):
self
.
op_type
=
'bipartite_match'
lod
=
[[
0
,
5
,
11
,
23
]]
dist
=
np
.
random
.
random
((
23
,
237
)).
astype
(
'float32'
)
match_indices
,
match_dist
=
batch_bipartite_match
(
dist
,
lod
[
0
],
'per_prediction'
,
0.5
)
self
.
inputs
=
{
'DistMat'
:
(
dist
,
lod
)}
self
.
outputs
=
{
'ColToRowMatchIndices'
:
match_indices
,
'ColToRowMatchDist'
:
match_dist
,
}
self
.
attrs
=
{
'match_type'
:
'per_prediction'
,
'dist_threshold'
:
0.5
,
}
def
test_check_output
(
self
):
self
.
check_output
()
if
__name__
==
'__main__'
:
unittest
.
main
()
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