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体验新版 GitCode,发现更多精彩内容 >>
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提交
2997b937
编写于
4月 15, 2020
作者:
M
MaxwellDing
提交者:
GitHub
4月 15, 2020
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refactor(*): reduce Wsign-compare warning (#3391)
refactor(*): reduce Wsign-compare warning
上级
3d2a99e7
变更
65
隐藏空白更改
内联
并排
Showing
65 changed file
with
164 addition
and
162 deletion
+164
-162
lite/api/light_api.cc
lite/api/light_api.cc
+4
-4
lite/api/light_api_test.cc
lite/api/light_api_test.cc
+2
-2
lite/api/lite_multithread_test.cc
lite/api/lite_multithread_test.cc
+2
-2
lite/api/model_test.cc
lite/api/model_test.cc
+1
-1
lite/api/model_test_classify.cc
lite/api/model_test_classify.cc
+1
-1
lite/api/model_test_detection.cc
lite/api/model_test_detection.cc
+2
-2
lite/api/paddle_api_test.cc
lite/api/paddle_api_test.cc
+2
-2
lite/api/test_googlenet_lite.cc
lite/api/test_googlenet_lite.cc
+2
-2
lite/api/test_inceptionv4_lite_x86.cc
lite/api/test_inceptionv4_lite_x86.cc
+4
-4
lite/api/test_mobilenetv1_lite_x86.cc
lite/api/test_mobilenetv1_lite_x86.cc
+4
-4
lite/api/test_mobilenetv2_lite_x86.cc
lite/api/test_mobilenetv2_lite_x86.cc
+4
-4
lite/api/test_resnet50_lite_x86.cc
lite/api/test_resnet50_lite_x86.cc
+4
-4
lite/api/transform_test.cc
lite/api/transform_test.cc
+2
-2
lite/backends/x86/jit/gen/matmul.cc
lite/backends/x86/jit/gen/matmul.cc
+1
-1
lite/backends/x86/math/beam_search.cc
lite/backends/x86/math/beam_search.cc
+1
-1
lite/backends/x86/math/blas.cc
lite/backends/x86/math/blas.cc
+1
-1
lite/backends/x86/math/sequence_pooling.cc
lite/backends/x86/math/sequence_pooling.cc
+7
-7
lite/core/arena/framework.cc
lite/core/arena/framework.cc
+2
-2
lite/core/arena/framework.h
lite/core/arena/framework.h
+1
-1
lite/core/device_info.cc
lite/core/device_info.cc
+1
-1
lite/core/kernel.cc
lite/core/kernel.cc
+1
-1
lite/core/mir/fusion/conv_bn_fuser.cc
lite/core/mir/fusion/conv_bn_fuser.cc
+10
-10
lite/core/mir/fusion/quant_dequant_op_fuser.cc
lite/core/mir/fusion/quant_dequant_op_fuser.cc
+1
-1
lite/core/mir/mlu_postprocess_pass.cc
lite/core/mir/mlu_postprocess_pass.cc
+2
-2
lite/core/mir/subgraph/subgraph_detector.cc
lite/core/mir/subgraph/subgraph_detector.cc
+4
-3
lite/core/mir/subgraph/subgraph_detector_test.cc
lite/core/mir/subgraph/subgraph_detector_test.cc
+5
-5
lite/core/mir/subgraph/subgraph_pass_test.cc
lite/core/mir/subgraph/subgraph_pass_test.cc
+3
-3
lite/core/op_lite.cc
lite/core/op_lite.cc
+4
-4
lite/core/program.cc
lite/core/program.cc
+1
-1
lite/core/tensor.cc
lite/core/tensor.cc
+1
-1
lite/kernels/mlu/bridges/act_op_test.cc
lite/kernels/mlu/bridges/act_op_test.cc
+8
-8
lite/kernels/mlu/bridges/concat_op_test.cc
lite/kernels/mlu/bridges/concat_op_test.cc
+3
-3
lite/kernels/mlu/bridges/conv_op.cc
lite/kernels/mlu/bridges/conv_op.cc
+6
-6
lite/kernels/mlu/bridges/conv_op_test.cc
lite/kernels/mlu/bridges/conv_op_test.cc
+2
-2
lite/kernels/mlu/bridges/fc_op_test.cc
lite/kernels/mlu/bridges/fc_op_test.cc
+2
-2
lite/kernels/mlu/bridges/interpolate_op.cc
lite/kernels/mlu/bridges/interpolate_op.cc
+1
-1
lite/kernels/mlu/bridges/interpolate_op_test.cc
lite/kernels/mlu/bridges/interpolate_op_test.cc
+1
-1
lite/kernels/mlu/bridges/utility.cc
lite/kernels/mlu/bridges/utility.cc
+3
-3
lite/kernels/npu/bridges/engine.cc
lite/kernels/npu/bridges/engine.cc
+3
-3
lite/kernels/x86/elementwise_op_function.h
lite/kernels/x86/elementwise_op_function.h
+7
-7
lite/kernels/x86/fill_constant_batch_size_like_compute_test.cc
...kernels/x86/fill_constant_batch_size_like_compute_test.cc
+1
-1
lite/kernels/x86/gather_compute.h
lite/kernels/x86/gather_compute.h
+1
-1
lite/kernels/x86/layer_norm_compute_test.cc
lite/kernels/x86/layer_norm_compute_test.cc
+1
-1
lite/kernels/x86/sequence_expand_as_compute.h
lite/kernels/x86/sequence_expand_as_compute.h
+2
-2
lite/kernels/x86/sequence_reverse_compute_test.cc
lite/kernels/x86/sequence_reverse_compute_test.cc
+1
-1
lite/kernels/x86/shape_compute.h
lite/kernels/x86/shape_compute.h
+1
-1
lite/kernels/x86/slice_compute.h
lite/kernels/x86/slice_compute.h
+1
-1
lite/kernels/x86/slice_compute_test.cc
lite/kernels/x86/slice_compute_test.cc
+6
-6
lite/kernels/x86/stack_compute.h
lite/kernels/x86/stack_compute.h
+1
-1
lite/kernels/x86/var_conv_2d_compute.h
lite/kernels/x86/var_conv_2d_compute.h
+1
-1
lite/model_parser/model_parser_test.cc
lite/model_parser/model_parser_test.cc
+1
-1
lite/operators/elementwise_ops.cc
lite/operators/elementwise_ops.cc
+7
-6
lite/operators/expand_op.cc
lite/operators/expand_op.cc
+1
-1
lite/operators/fill_constant_batch_size_like_op.cc
lite/operators/fill_constant_batch_size_like_op.cc
+1
-1
lite/operators/fill_constant_op.cc
lite/operators/fill_constant_op.cc
+1
-1
lite/operators/flatten_op.cc
lite/operators/flatten_op.cc
+1
-1
lite/operators/interpolate_op.cc
lite/operators/interpolate_op.cc
+2
-2
lite/operators/pool_op.h
lite/operators/pool_op.h
+1
-1
lite/operators/reduce_mean_op.cc
lite/operators/reduce_mean_op.cc
+3
-3
lite/operators/reshape_op.cc
lite/operators/reshape_op.cc
+2
-2
lite/operators/search_fc_op.cc
lite/operators/search_fc_op.cc
+3
-3
lite/operators/slice_op.cc
lite/operators/slice_op.cc
+5
-5
lite/operators/split_op.cc
lite/operators/split_op.cc
+1
-1
lite/operators/squeeze_op.cc
lite/operators/squeeze_op.cc
+2
-2
lite/operators/unsqueeze_op.cc
lite/operators/unsqueeze_op.cc
+1
-1
未找到文件。
lite/api/light_api.cc
浏览文件 @
2997b937
...
...
@@ -82,7 +82,7 @@ Tensor* LightPredictor::GetInputByName(const std::string& name) {
if
(
element
==
input_names_
.
end
())
{
LOG
(
ERROR
)
<<
"Model do not have input named with: ["
<<
name
<<
"], model's inputs include:"
;
for
(
in
t
i
=
0
;
i
<
input_names_
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
input_names_
.
size
();
i
++
)
{
LOG
(
ERROR
)
<<
"["
<<
input_names_
[
i
]
<<
"]"
;
}
return
nullptr
;
...
...
@@ -114,7 +114,7 @@ void LightPredictor::PrepareFeedFetch() {
auto
current_block
=
cpp_program_desc_
.
GetBlock
<
cpp
::
BlockDesc
>
(
0
);
std
::
vector
<
cpp
::
OpDesc
*>
feeds
;
std
::
vector
<
cpp
::
OpDesc
*>
fetchs
;
for
(
in
t
i
=
0
;
i
<
current_block
->
OpsSize
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
current_block
->
OpsSize
();
i
++
)
{
auto
op
=
current_block
->
GetOp
<
cpp
::
OpDesc
>
(
i
);
if
(
op
->
Type
()
==
"feed"
)
{
feeds
.
push_back
(
op
);
...
...
@@ -124,11 +124,11 @@ void LightPredictor::PrepareFeedFetch() {
}
input_names_
.
resize
(
feeds
.
size
());
output_names_
.
resize
(
fetchs
.
size
());
for
(
in
t
i
=
0
;
i
<
feeds
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
feeds
.
size
();
i
++
)
{
input_names_
[
feeds
[
i
]
->
GetAttr
<
int
>
(
"col"
)]
=
feeds
[
i
]
->
Output
(
"Out"
).
front
();
}
for
(
in
t
i
=
0
;
i
<
fetchs
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
fetchs
.
size
();
i
++
)
{
output_names_
[
fetchs
[
i
]
->
GetAttr
<
int
>
(
"col"
)]
=
fetchs
[
i
]
->
Input
(
"X"
).
front
();
}
...
...
lite/api/light_api_test.cc
浏览文件 @
2997b937
...
...
@@ -37,11 +37,11 @@ TEST(LightAPI, load) {
const
std
::
vector
<
std
::
string
>
inputs
=
predictor
.
GetInputNames
();
LOG
(
INFO
)
<<
"input size: "
<<
inputs
.
size
();
for
(
in
t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
LOG
(
INFO
)
<<
"inputnames: "
<<
inputs
[
i
];
}
const
std
::
vector
<
std
::
string
>
outputs
=
predictor
.
GetOutputNames
();
for
(
in
t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
LOG
(
INFO
)
<<
"outputnames: "
<<
outputs
[
i
];
}
...
...
lite/api/lite_multithread_test.cc
浏览文件 @
2997b937
...
...
@@ -293,13 +293,13 @@ int main(int argc, char** argv) {
std
::
vector
<
std
::
string
>
str_input_shapes
=
split_string
(
FLAGS_input_shape
);
std
::
vector
<
std
::
vector
<
int64_t
>>
input_shapes
;
for
(
in
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
input_shapes
.
push_back
(
get_shape
(
str_input_shapes
[
i
]));
}
std
::
vector
<
std
::
string
>
str_input_shapes_0
=
split_string
(
FLAGS_input_shape_0
);
std
::
vector
<
std
::
vector
<
int64_t
>>
input_shapes_0
;
for
(
in
t
i
=
0
;
i
<
str_input_shapes_0
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
str_input_shapes_0
.
size
();
++
i
)
{
input_shapes_0
.
push_back
(
get_shape
(
str_input_shapes_0
[
i
]));
}
...
...
lite/api/model_test.cc
浏览文件 @
2997b937
...
...
@@ -204,7 +204,7 @@ int main(int argc, char** argv) {
LOG
(
INFO
)
<<
"input shapes: "
<<
FLAGS_input_shape
;
std
::
vector
<
std
::
string
>
str_input_shapes
=
split_string
(
FLAGS_input_shape
);
std
::
vector
<
std
::
vector
<
int64_t
>>
input_shapes
;
for
(
in
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
LOG
(
INFO
)
<<
"input shape: "
<<
str_input_shapes
[
i
];
input_shapes
.
push_back
(
get_shape
(
str_input_shapes
[
i
]));
}
...
...
lite/api/model_test_classify.cc
浏览文件 @
2997b937
...
...
@@ -310,7 +310,7 @@ int main(int argc, char** argv) {
LOG
(
INFO
)
<<
"input shapes: "
<<
FLAGS_input_shape
;
std
::
vector
<
std
::
string
>
str_input_shapes
=
split_string
(
FLAGS_input_shape
);
std
::
vector
<
std
::
vector
<
int64_t
>>
input_shapes
;
for
(
in
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
LOG
(
INFO
)
<<
"input shape: "
<<
str_input_shapes
[
i
];
input_shapes
.
push_back
(
get_shape
(
str_input_shapes
[
i
]));
}
...
...
lite/api/model_test_detection.cc
浏览文件 @
2997b937
...
...
@@ -114,7 +114,7 @@ void detect_object(const float* dout,
}
std
::
string
name
=
FLAGS_out_txt
+
"_accu.txt"
;
FILE
*
fp
=
fopen
(
name
.
c_str
(),
"w"
);
for
(
in
t
i
=
0
;
i
<
objects
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
objects
.
size
();
++
i
)
{
Object
object
=
objects
.
at
(
i
);
if
(
object
.
prob
>
thresh
&&
object
.
x
>
0
&&
object
.
y
>
0
&&
object
.
width
>
0
&&
object
.
height
>
0
)
{
...
...
@@ -324,7 +324,7 @@ int main(int argc, char** argv) {
LOG
(
INFO
)
<<
"input shapes: "
<<
FLAGS_input_shape
;
std
::
vector
<
std
::
string
>
str_input_shapes
=
split_string
(
FLAGS_input_shape
);
std
::
vector
<
std
::
vector
<
int64_t
>>
input_shapes
;
for
(
in
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
str_input_shapes
.
size
();
++
i
)
{
LOG
(
INFO
)
<<
"input shape: "
<<
str_input_shapes
[
i
];
input_shapes
.
push_back
(
get_shape
(
str_input_shapes
[
i
]));
}
...
...
lite/api/paddle_api_test.cc
浏览文件 @
2997b937
...
...
@@ -36,11 +36,11 @@ TEST(CxxApi, run) {
auto
inputs
=
predictor
->
GetInputNames
();
LOG
(
INFO
)
<<
"input size: "
<<
inputs
.
size
();
for
(
in
t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
inputs
.
size
();
i
++
)
{
LOG
(
INFO
)
<<
"inputnames: "
<<
inputs
[
i
];
}
auto
outputs
=
predictor
->
GetOutputNames
();
for
(
in
t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
outputs
.
size
();
i
++
)
{
LOG
(
INFO
)
<<
"outputnames: "
<<
outputs
[
i
];
}
auto
input_tensor
=
predictor
->
GetInputByName
(
inputs
[
0
]);
...
...
lite/api/test_googlenet_lite.cc
浏览文件 @
2997b937
...
...
@@ -38,7 +38,7 @@ TEST(CXXApi, test_lite_googlenet) {
input_tensor
->
Resize
(
input_shape
);
auto
*
data
=
input_tensor
->
mutable_data
<
float
>
();
int
input_num
=
1
;
for
(
in
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
input_num
*=
input_shape
[
i
];
}
for
(
int
i
=
0
;
i
<
input_num
;
i
++
)
{
...
...
@@ -69,7 +69,7 @@ TEST(CXXApi, test_lite_googlenet) {
for
(
size_t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
EXPECT_NEAR
(
out
->
data
<
float
>
()[
i
*
51
],
results
[
i
],
1e-5
);
}
ASSERT_EQ
(
out
->
shape
().
size
(),
2
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
u
);
ASSERT_EQ
(
out
->
shape
()[
0
],
1
);
ASSERT_EQ
(
out
->
shape
()[
1
],
1000
);
}
...
...
lite/api/test_inceptionv4_lite_x86.cc
浏览文件 @
2997b937
...
...
@@ -38,7 +38,7 @@ TEST(InceptionV4, test_inceptionv4_lite_x86) {
input_tensor
->
Resize
(
input_shape
);
auto
*
data
=
input_tensor
->
mutable_data
<
float
>
();
int
input_num
=
1
;
for
(
in
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
input_num
*=
input_shape
[
i
];
}
for
(
int
i
=
0
;
i
<
input_num
;
i
++
)
{
...
...
@@ -69,13 +69,13 @@ TEST(InceptionV4, test_inceptionv4_lite_x86) {
0.0010612885
,
0.00089107914
,
0.0010112736
,
0.00097655767
}));
auto
out
=
predictor
->
GetOutput
(
0
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
u
);
ASSERT_EQ
(
out
->
shape
()[
0
],
1
);
ASSERT_EQ
(
out
->
shape
()[
1
],
1000
);
int
step
=
50
;
for
(
in
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
in
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
for
(
size_
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
size_
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
EXPECT_NEAR
(
out
->
data
<
float
>
()[
j
*
step
+
(
out
->
shape
()[
1
]
*
i
)],
results
[
i
][
j
],
1e-6
);
...
...
lite/api/test_mobilenetv1_lite_x86.cc
浏览文件 @
2997b937
...
...
@@ -38,7 +38,7 @@ TEST(Mobilenet_v1, test_mobilenetv1_lite_x86) {
input_tensor
->
Resize
(
input_shape
);
auto
*
data
=
input_tensor
->
mutable_data
<
float
>
();
int
input_num
=
1
;
for
(
in
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
input_num
*=
input_shape
[
i
];
}
for
(
int
i
=
0
;
i
<
input_num
;
i
++
)
{
...
...
@@ -68,13 +68,13 @@ TEST(Mobilenet_v1, test_mobilenetv1_lite_x86) {
0.0048292773
,
0.0013995157
,
0.0018453331
,
0.0002428986
,
0.00020211363
,
0.00013668182
,
0.0005855956
,
0.00025901722
}));
auto
out
=
predictor
->
GetOutput
(
0
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
u
);
ASSERT_EQ
(
out
->
shape
()[
0
],
1
);
ASSERT_EQ
(
out
->
shape
()[
1
],
1000
);
int
step
=
50
;
for
(
in
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
in
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
for
(
size_
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
size_
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
EXPECT_NEAR
(
out
->
data
<
float
>
()[
j
*
step
+
(
out
->
shape
()[
1
]
*
i
)],
results
[
i
][
j
],
1e-6
);
...
...
lite/api/test_mobilenetv2_lite_x86.cc
浏览文件 @
2997b937
...
...
@@ -39,7 +39,7 @@ TEST(Mobilenet_v2, test_mobilenetv2_lite_x86) {
input_tensor
->
Resize
(
input_shape
);
auto
*
data
=
input_tensor
->
mutable_data
<
float
>
();
int
input_num
=
1
;
for
(
in
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
input_num
*=
input_shape
[
i
];
}
for
(
int
i
=
0
;
i
<
input_num
;
i
++
)
{
...
...
@@ -69,13 +69,13 @@ TEST(Mobilenet_v2, test_mobilenetv2_lite_x86) {
0.0070957416
,
0.0016094646
,
0.0018807327
,
0.00010506048
,
6.823785e-05
,
0.00012269315
,
0.0007806194
,
0.00022354358
}));
auto
out
=
predictor
->
GetOutput
(
0
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
u
);
ASSERT_EQ
(
out
->
shape
()[
0
],
1
);
ASSERT_EQ
(
out
->
shape
()[
1
],
1000
);
int
step
=
50
;
for
(
in
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
in
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
for
(
size_
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
size_
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
EXPECT_NEAR
(
out
->
data
<
float
>
()[
j
*
step
+
(
out
->
shape
()[
1
]
*
i
)],
results
[
i
][
j
],
1e-6
);
...
...
lite/api/test_resnet50_lite_x86.cc
浏览文件 @
2997b937
...
...
@@ -38,7 +38,7 @@ TEST(Resnet50, test_resnet50_lite_x86) {
input_tensor
->
Resize
(
input_shape
);
auto
*
data
=
input_tensor
->
mutable_data
<
float
>
();
int
input_num
=
1
;
for
(
in
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
input_shape
.
size
();
++
i
)
{
input_num
*=
input_shape
[
i
];
}
for
(
int
i
=
0
;
i
<
input_num
;
i
++
)
{
...
...
@@ -69,13 +69,13 @@ TEST(Resnet50, test_resnet50_lite_x86) {
0.006387163
,
0.0037145028
,
0.0012812682
,
0.00045948103
,
0.00013535398
,
0.0002483765
,
0.00076759676
,
0.0002773295
}));
auto
out
=
predictor
->
GetOutput
(
0
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
);
ASSERT_EQ
(
out
->
shape
().
size
(),
2
u
);
ASSERT_EQ
(
out
->
shape
()[
0
],
1
);
ASSERT_EQ
(
out
->
shape
()[
1
],
1000
);
int
step
=
50
;
for
(
in
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
in
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
for
(
size_
t
i
=
0
;
i
<
results
.
size
();
++
i
)
{
for
(
size_
t
j
=
0
;
j
<
results
[
i
].
size
();
++
j
)
{
EXPECT_NEAR
(
out
->
data
<
float
>
()[
j
*
step
+
(
out
->
shape
()[
1
]
*
i
)],
results
[
i
][
j
],
1e-6
);
...
...
lite/api/transform_test.cc
浏览文件 @
2997b937
...
...
@@ -232,8 +232,8 @@ void TestModel(const std::vector<Place>& valid_places,
for
(
int
i
=
0
;
i
<
outs
->
numel
();
++
i
)
{
LOG
(
INFO
)
<<
o_data
[
i
];
}
for
(
in
t
i
=
0
;
i
<
lod
.
size
();
++
i
)
{
for
(
in
t
j
=
0
;
j
<
lod
[
i
].
size
();
++
j
)
{
for
(
size_
t
i
=
0
;
i
<
lod
.
size
();
++
i
)
{
for
(
size_
t
j
=
0
;
j
<
lod
[
i
].
size
();
++
j
)
{
LOG
(
INFO
)
<<
lod
[
i
][
j
];
}
}
...
...
lite/backends/x86/jit/gen/matmul.cc
浏览文件 @
2997b937
...
...
@@ -40,7 +40,7 @@ void MatMulJitCode::genCode() {
for
(
size_t
g
=
0
;
g
<
groups
.
size
();
++
g
)
{
size_t
x_offset
=
0
;
size_t
wgt_offset_tmp
=
0
;
for
(
in
t
i
=
0
;
i
<
g
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
g
;
++
i
)
{
wgt_offset_tmp
+=
groups
[
i
]
*
block_len
;
}
for
(
int
k
=
0
;
k
<
k_
;
++
k
)
{
...
...
lite/backends/x86/math/beam_search.cc
浏览文件 @
2997b937
...
...
@@ -265,7 +265,7 @@ class BeamSearchFunctor<TARGET(kX86), T> {
// size_t num_seqs = scores->NumElements(lod_level);
size_t
num_seqs
=
scores
->
lod
()[
lod_level
].
size
()
-
1
;
size_t
seq_width
=
1
;
for
(
in
t
i
=
1
;
i
<
scores
->
dims
().
size
();
i
++
)
{
for
(
size_
t
i
=
1
;
i
<
scores
->
dims
().
size
();
i
++
)
{
seq_width
*=
scores
->
dims
()[
i
];
}
...
...
lite/backends/x86/math/blas.cc
浏览文件 @
2997b937
...
...
@@ -23,7 +23,7 @@ namespace math {
MatDescriptor
CreateMatrixDescriptor
(
const
lite
::
DDimLite
&
tensor_dim
,
int
num_flatten_cols
,
bool
trans
)
{
PADDLE_ENFORCE_GT
(
tensor_dim
.
size
(),
1
);
PADDLE_ENFORCE_GT
(
tensor_dim
.
size
(),
1
u
);
MatDescriptor
retv
;
if
(
num_flatten_cols
>
1
)
{
auto
flatten_dim
=
tensor_dim
.
Flatten2D
(
num_flatten_cols
);
...
...
lite/backends/x86/math/sequence_pooling.cc
浏览文件 @
2997b937
...
...
@@ -46,9 +46,9 @@ class MaxSeqPoolFunctor {
auto
in_dims
=
input
.
dims
();
auto
out_dims
=
output
->
dims
();
auto
idx_dims
=
index
->
dims
();
PADDLE_ENFORCE_GT
(
in_dims
.
size
(),
1
);
PADDLE_ENFORCE_GT
(
out_dims
.
size
(),
1
);
for
(
int64
_t
i
=
1
;
i
<
in_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_GT
(
in_dims
.
size
(),
1
u
);
PADDLE_ENFORCE_GT
(
out_dims
.
size
(),
1
u
);
for
(
size
_t
i
=
1
;
i
<
in_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
in_dims
[
i
],
out_dims
[
i
]);
}
PADDLE_ENFORCE_EQ
(
idx_dims
,
out_dims
);
...
...
@@ -95,9 +95,9 @@ class MaxSeqPoolFunctor<T, true> {
lite
::
Tensor
*
index
)
{
auto
in_dims
=
input
.
dims
();
auto
out_dims
=
output
->
dims
();
PADDLE_ENFORCE_GT
(
in_dims
.
size
(),
1
);
PADDLE_ENFORCE_GT
(
out_dims
.
size
(),
1
);
for
(
int64
_t
i
=
1
;
i
<
in_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_GT
(
in_dims
.
size
(),
1
u
);
PADDLE_ENFORCE_GT
(
out_dims
.
size
(),
1
u
);
for
(
size
_t
i
=
1
;
i
<
in_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
in_dims
[
i
],
out_dims
[
i
]);
}
...
...
@@ -138,7 +138,7 @@ class MaxSeqPoolGradFunctor {
auto
idx_dims
=
index
.
dims
();
PADDLE_ENFORCE_GT
(
og_dims
.
size
(),
1
);
PADDLE_ENFORCE_GT
(
ig_dims
.
size
(),
1
);
for
(
int64
_t
i
=
1
;
i
<
og_dims
.
size
();
++
i
)
{
for
(
size
_t
i
=
1
;
i
<
og_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
og_dims
[
i
],
ig_dims
[
i
]);
}
PADDLE_ENFORCE_EQ
(
idx_dims
,
og_dims
);
...
...
lite/core/arena/framework.cc
浏览文件 @
2997b937
...
...
@@ -107,7 +107,7 @@ void TestCase::PrepareInputsForInstruction() {
CHECK
(
!
shared_tensor_array
->
empty
())
<<
"shared_tensor_array is empty yet"
;
target_tensor_array
->
resize
(
shared_tensor_array
->
size
());
for
(
in
t
i
=
0
;
i
<
shared_tensor_array
->
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
shared_tensor_array
->
size
();
i
++
)
{
target_tensor_array
->
at
(
i
).
Resize
(
shared_tensor_array
->
at
(
i
).
dims
());
TargetCopy
(
param_type
->
type
->
target
(),
...
...
@@ -219,7 +219,7 @@ bool TestCase::CheckPrecision(const std::string& var_name,
auto
b_tensor_array
=
base_scope_
->
FindVar
(
var_name
)
->
GetMutable
<
std
::
vector
<
Tensor
>>
();
CHECK_EQ
(
a_tensor_array
->
size
(),
b_tensor_array
->
size
());
for
(
in
t
i
=
0
;
i
<
a_tensor_array
->
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
a_tensor_array
->
size
();
i
++
)
{
Tensor
*
a_tensor
=
&
(
a_tensor_array
->
at
(
i
));
Tensor
*
b_tensor
=
&
(
b_tensor_array
->
at
(
i
));
if
(
a_tensor
->
dims
().
size
()
==
0
&&
b_tensor
->
dims
().
size
()
==
0
)
{
...
...
lite/core/arena/framework.h
浏览文件 @
2997b937
...
...
@@ -166,7 +166,7 @@ class TestCase {
// TODO(Superjomn) Move this method to utils or DDim?
bool
ShapeEquals
(
const
DDim
&
a
,
const
DDim
&
b
)
{
if
(
a
.
size
()
!=
b
.
size
())
return
false
;
for
(
in
t
i
=
0
;
i
<
a
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
a
.
size
();
i
++
)
{
if
(
a
[
i
]
!=
b
[
i
])
return
false
;
}
return
true
;
...
...
lite/core/device_info.cc
浏览文件 @
2997b937
...
...
@@ -947,7 +947,7 @@ void DeviceInfo::RequestPowerNoBindMode(int thread_num) {
active_ids_
=
core_ids_
;
}
else
{
active_ids_
.
resize
(
thread_num
);
for
(
in
t
i
=
0
;
i
<
thread_num
;
++
i
)
{
for
(
uint32_
t
i
=
0
;
i
<
thread_num
;
++
i
)
{
if
(
i
<
big_core_ids_
.
size
())
{
active_ids_
[
i
]
=
big_core_ids_
[
i
];
}
else
{
...
...
lite/core/kernel.cc
浏览文件 @
2997b937
...
...
@@ -57,7 +57,7 @@ void KernelBase::ParseKernelType(const std::string &kernel_type,
std
::
string
*
alias
,
Place
*
place
)
{
auto
parts
=
Split
(
kernel_type
,
"/"
);
CHECK_EQ
(
parts
.
size
(),
5
);
CHECK_EQ
(
parts
.
size
(),
5
u
);
*
op_type
=
parts
[
0
];
*
alias
=
parts
[
1
];
...
...
lite/core/mir/fusion/conv_bn_fuser.cc
浏览文件 @
2997b937
...
...
@@ -163,23 +163,23 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
int
c_size
=
conv_weight_t
->
dims
()[
1
]
*
conv_weight_t
->
dims
()[
2
]
*
conv_weight_t
->
dims
()[
3
];
int
hw
=
conv_weight_t
->
dims
()[
2
]
*
conv_weight_t
->
dims
()[
3
];
for
(
unsigned
int
k
=
0
;
k
<
conv_weight_t
->
dims
()[
0
];
++
k
)
{
for
(
unsigned
int
i
=
0
;
i
<
h
;
++
i
)
{
for
(
int
k
=
0
;
k
<
conv_weight_t
->
dims
()[
0
];
++
k
)
{
for
(
int
i
=
0
;
i
<
h
;
++
i
)
{
weight_scale
[
i
]
*=
fabsf
(
alpha_data
[
i
]);
if
(
alpha_data
[
i
]
<
0.
f
)
{
auto
ptr_row
=
conv_weight_d
+
k
*
c_size
+
i
*
hw
;
for
(
unsigned
int
j
=
0
;
j
<
hw
;
++
j
)
{
for
(
int
j
=
0
;
j
<
hw
;
++
j
)
{
ptr_row
[
j
]
*=
-
1
;
}
}
}
}
}
else
{
for
(
unsigned
int
i
=
0
;
i
<
h
;
++
i
)
{
for
(
int
i
=
0
;
i
<
h
;
++
i
)
{
weight_scale
[
i
]
*=
fabsf
(
alpha_data
[
i
]);
if
(
alpha_data
[
i
]
<
0.
f
)
{
auto
ptr_row
=
conv_weight_d
+
i
*
w
;
for
(
unsigned
int
j
=
0
;
j
<
w
;
++
j
)
{
for
(
int
j
=
0
;
j
<
w
;
++
j
)
{
ptr_row
[
j
]
*=
-
1
;
}
}
...
...
@@ -203,17 +203,17 @@ void ConvBNFuser::InsertNewNode(SSAGraph* graph, const key2nodes_t& matched) {
int
c_size
=
conv_weight_t
->
dims
()[
1
]
*
conv_weight_t
->
dims
()[
2
]
*
conv_weight_t
->
dims
()[
3
];
int
hw
=
conv_weight_t
->
dims
()[
2
]
*
conv_weight_t
->
dims
()[
3
];
for
(
unsigned
int
k
=
0
;
k
<
conv_weight_t
->
dims
()[
0
];
++
k
)
{
for
(
unsigned
int
i
=
0
;
i
<
h
;
++
i
)
{
for
(
int
k
=
0
;
k
<
conv_weight_t
->
dims
()[
0
];
++
k
)
{
for
(
int
i
=
0
;
i
<
h
;
++
i
)
{
auto
ptr_row
=
conv_weight_d
+
k
*
c_size
+
i
*
hw
;
for
(
unsigned
int
j
=
0
;
j
<
hw
;
++
j
)
{
for
(
int
j
=
0
;
j
<
hw
;
++
j
)
{
ptr_row
[
j
]
*=
alpha_data
[
i
];
}
}
}
}
else
{
for
(
unsigned
int
i
=
0
;
i
<
h
;
++
i
)
{
// n: conv2d output channels
for
(
unsigned
int
j
=
0
;
j
<
w
;
++
j
)
{
// w: conv2d input channels
for
(
int
i
=
0
;
i
<
h
;
++
i
)
{
// n: conv2d output channels
for
(
int
j
=
0
;
j
<
w
;
++
j
)
{
// w: conv2d input channels
conv_weight_d
[
i
*
w
+
j
]
*=
alpha_data
[
i
];
}
}
...
...
lite/core/mir/fusion/quant_dequant_op_fuser.cc
浏览文件 @
2997b937
...
...
@@ -260,7 +260,7 @@ void ChannelWiseDequantOpFuser::InsertNewNode(SSAGraph* graph,
auto
channel_scale_tensor
=
scope
->
FindVar
(
channel_scale_name
)
->
GetMutable
<
lite
::
Tensor
>
();
auto
*
channel_scale_data
=
channel_scale_tensor
->
data
<
float
>
();
for
(
in
t
i
=
0
;
i
<
channel_scale_tensor
->
data_size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
channel_scale_tensor
->
data_size
();
i
++
)
{
weight_scale
.
push_back
(
channel_scale_data
[
i
]
/
range
);
}
...
...
lite/core/mir/mlu_postprocess_pass.cc
浏览文件 @
2997b937
...
...
@@ -292,7 +292,7 @@ void MLUPostprocessPass::GetSubgraphOpArgType(Node* inst_node,
// get subgraph op's type info
size_t
kernel_size
=
inst_node
->
AsStmt
().
kernels
().
size
();
CHECK_GT
(
kernel_size
,
0
);
CHECK_GT
(
kernel_size
,
0
u
);
VLOG
(
4
)
<<
"subgraph kernel size: "
<<
kernel_size
;
for
(
size_t
i
=
0
;
i
<
kernel_size
;
++
i
)
{
...
...
@@ -450,7 +450,7 @@ bool MLUPostprocessPass::IsFirstConvInSubgraph(Node* arg_node, Node* inst) {
auto
*
block_desc
=
static_cast
<
operators
::
SubgraphOp
*>
(
inst
->
AsStmt
().
op
().
get
())
->
GetSubBlock
();
for
(
in
t
op_idx
=
0
;
op_idx
<
block_desc
->
OpsSize
();
op_idx
++
)
{
for
(
size_
t
op_idx
=
0
;
op_idx
<
block_desc
->
OpsSize
();
op_idx
++
)
{
auto
op_desc
=
block_desc
->
GetOp
<
cpp
::
OpDesc
>
(
op_idx
);
CHECK
(
op_desc
);
if
(
op_desc
->
Type
()
==
"conv2d"
)
{
...
...
lite/core/mir/subgraph/subgraph_detector.cc
浏览文件 @
2997b937
...
...
@@ -47,8 +47,8 @@ std::string SubgraphVisualizer::operator()() {
"turquoise4"
,
"snow3"
,
"sienna4"
,
"salmon2"
,
};
std
::
unordered_map
<
Node
*
,
int
>
subgraph_indices
;
for
(
in
t
i
=
0
;
i
<
subgraphs_
.
size
();
i
++
)
{
for
(
in
t
j
=
0
;
j
<
subgraphs_
[
i
].
size
();
j
++
)
{
for
(
size_
t
i
=
0
;
i
<
subgraphs_
.
size
();
i
++
)
{
for
(
size_
t
j
=
0
;
j
<
subgraphs_
[
i
].
size
();
j
++
)
{
subgraph_indices
[
subgraphs_
[
i
][
j
]]
=
i
;
}
}
...
...
@@ -538,7 +538,8 @@ void SubgraphFuser::ReplaceNodesWithSubgraphs(SSAGraph *graph,
std
::
vector
<
std
::
vector
<
Node
*>>
subgraphs
=
SubgraphDetector
(
graph
,
teller
)();
SubgraphVisualizer
(
graph
,
subgraphs
)();
for
(
int
subgraph_idx
=
0
;
subgraph_idx
<
subgraphs
.
size
();
subgraph_idx
++
)
{
for
(
size_t
subgraph_idx
=
0
;
subgraph_idx
<
subgraphs
.
size
();
subgraph_idx
++
)
{
if
(
subgraphs
[
subgraph_idx
].
size
()
>=
min_subgraph_size
)
{
InsertNewNode
(
graph
,
subgraph_idx
,
subgraphs
[
subgraph_idx
]);
}
...
...
lite/core/mir/subgraph/subgraph_detector_test.cc
浏览文件 @
2997b937
...
...
@@ -36,8 +36,8 @@ std::vector<std::string> AddFCDesc(
const
std
::
shared_ptr
<
Scope
>&
scope
,
const
std
::
vector
<
std
::
string
>&
input_var_names
,
const
std
::
vector
<
int64_t
>&
wshape
)
{
CHECK_EQ
(
input_var_names
.
size
(),
1
);
CHECK_EQ
(
wshape
.
size
(),
2
);
CHECK_EQ
(
input_var_names
.
size
(),
1
u
);
CHECK_EQ
(
wshape
.
size
(),
2
u
);
static
int
id
=
0
;
std
::
string
prefix
=
"fc_"
+
paddle
::
lite
::
to_string
(
id
);
auto
*
op_desc
=
block_desc
->
AddOp
<
cpp
::
OpDesc
>
();
...
...
@@ -169,8 +169,8 @@ TEST(Subgraph, detect_simple_model) {
};
std
::
vector
<
std
::
vector
<
mir
::
Node
*>>
subgraphs
=
mir
::
SubgraphDetector
(
graph
.
get
(),
teller
)();
ASSERT_EQ
(
subgraphs
.
size
(),
1
);
ASSERT_EQ
(
graph
->
nodes
().
size
(),
9
);
ASSERT_EQ
(
subgraphs
.
size
(),
1
u
);
ASSERT_EQ
(
graph
->
nodes
().
size
(),
9
u
);
mir
::
SubgraphVisualizer
(
graph
.
get
(),
subgraphs
)();
}
...
...
@@ -221,7 +221,7 @@ TEST(Subgraph, detect_custom_model) {
std
::
vector
<
std
::
vector
<
mir
::
Node
*>>
subgraphs
=
mir
::
SubgraphDetector
(
graph
.
get
(),
teller
)();
mir
::
SubgraphVisualizer
(
graph
.
get
(),
subgraphs
)();
ASSERT_EQ
(
subgraphs
.
size
(),
1
);
ASSERT_EQ
(
subgraphs
.
size
(),
1
u
);
}
}
// namespace lite
...
...
lite/core/mir/subgraph/subgraph_pass_test.cc
浏览文件 @
2997b937
...
...
@@ -39,7 +39,7 @@ std::vector<std::vector<int64_t>> ShapeParsing(std::string text) {
std
::
vector
<
std
::
vector
<
int64_t
>>
shapes
;
std
::
vector
<
std
::
string
>
shape_strings
=
Split
(
text
,
":"
);
shapes
.
resize
(
shape_strings
.
size
());
for
(
in
t
i
=
0
;
i
<
shape_strings
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
shape_strings
.
size
();
i
++
)
{
std
::
vector
<
std
::
string
>
shape_nums
=
Split
(
shape_strings
[
i
],
","
);
for
(
auto
shape_num
:
shape_nums
)
{
shapes
[
i
].
push_back
(
atoi
(
shape_num
.
c_str
()));
...
...
@@ -66,7 +66,7 @@ void FillInputTensors(
for (int j = 0; j < input_tensor_size; j++) { \
input_tensor_data[j] = static_cast<type>(value); \
}
for
(
in
t
i
=
0
;
i
<
input_tensor_shape
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
input_tensor_shape
.
size
();
i
++
)
{
auto
input_tensor
=
predictor
->
GetInput
(
i
);
input_tensor
->
Resize
(
input_tensor_shape
[
i
]);
auto
input_tensor_size
=
ShapeProduction
(
input_tensor
->
shape
());
...
...
@@ -95,7 +95,7 @@ void CheckOutputTensors(
<< " abs_diff: " << abs_diff << " rel_diff: " << rel_diff; \
EXPECT_LT(rel_diff, 0.1); \
}
for
(
in
t
i
=
0
;
i
<
output_tensor_type
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
output_tensor_type
.
size
();
i
++
)
{
auto
tar_output_tensor
=
tar_predictor
->
GetOutput
(
i
);
auto
ref_output_tensor
=
ref_predictor
->
GetOutput
(
i
);
auto
tar_output_tensor_size
=
ShapeProduction
(
tar_output_tensor
->
shape
());
...
...
lite/core/op_lite.cc
浏览文件 @
2997b937
...
...
@@ -41,7 +41,7 @@ bool OpLite::InferShapeWithCache() {
iter
++
)
{
// combined dims value into new_hash value.
auto
&
element_dims
=
(
*
iter
)
->
dims
();
for
(
in
t
i
=
0
;
i
<
element_dims
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
element_dims
.
size
();
i
++
)
{
new_hash
=
lite
::
hash_combine
(
new_hash
,
static_cast
<
int
>
(
element_dims
[
i
]));
}
...
...
@@ -49,7 +49,7 @@ bool OpLite::InferShapeWithCache() {
auto
&
emement_lods
=
(
*
iter
)
->
lod
();
for
(
auto
lod_iter
=
emement_lods
.
begin
();
lod_iter
!=
emement_lods
.
end
();
lod_iter
++
)
{
for
(
in
t
i
=
0
;
i
<
lod_iter
->
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
lod_iter
->
size
();
i
++
)
{
new_hash
=
lite
::
hash_combine
(
new_hash
,
static_cast
<
int
>
(
lod_iter
->
at
(
i
)));
}
...
...
@@ -60,7 +60,7 @@ bool OpLite::InferShapeWithCache() {
// if current hash value is consistent with io_shape_lod_hash_,
// previous outputs shape and lod are reused.
auto
*
current_outputs
=
param_
.
output_tensor_ptrs
();
for
(
in
t
i
=
0
;
i
<
current_outputs
->
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
current_outputs
->
size
();
i
++
)
{
current_outputs
->
at
(
i
)
->
Resize
(
last_output_shapes
[
i
]);
current_outputs
->
at
(
i
)
->
set_lod
(
last_output_lods
[
i
]);
}
...
...
@@ -69,7 +69,7 @@ bool OpLite::InferShapeWithCache() {
io_shape_lod_hash_
=
new_hash
;
this
->
InferShapeImpl
();
auto
*
current_outputs
=
param_
.
output_tensor_ptrs
();
for
(
in
t
i
=
0
;
i
<
current_outputs
->
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
current_outputs
->
size
();
i
++
)
{
last_output_shapes
[
i
]
=
current_outputs
->
at
(
i
)
->
dims
();
last_output_lods
[
i
]
=
current_outputs
->
at
(
i
)
->
lod
();
}
...
...
lite/core/program.cc
浏览文件 @
2997b937
...
...
@@ -72,7 +72,7 @@ void RuntimeProgram::UpdateVarsOfProgram(cpp::ProgramDesc* desc) {
std
::
unordered_map
<
std
::
string
,
cpp
::
VarDesc
>
origin_var_maps
;
auto
&
main_block
=
*
desc
->
GetBlock
<
cpp
::
BlockDesc
>
(
0
);
auto
var_size
=
main_block
.
VarsSize
();
for
(
in
t
i
=
0
;
i
<
var_size
;
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
var_size
;
i
++
)
{
auto
v
=
main_block
.
GetVar
<
cpp
::
VarDesc
>
(
i
);
auto
name
=
v
->
Name
();
origin_var_maps
.
emplace
(
name
,
*
v
);
...
...
lite/core/tensor.cc
浏览文件 @
2997b937
...
...
@@ -100,7 +100,7 @@ void *TensorLite::mutable_data(TargetType target, size_t memory_size) {
void
TensorLite
::
ResetBuffer
(
std
::
shared_ptr
<
Buffer
>
buffer
,
size_t
memory_size
)
{
CHECK_EQ
(
offset_
,
0
)
CHECK_EQ
(
offset_
,
0
u
)
<<
"Only the offset is supported to zero when the Buffer is reset."
;
if
(
buffer_
)
{
CHECK_LE
(
memory_size_
,
buffer
->
space
())
...
...
lite/kernels/mlu/bridges/act_op_test.cc
浏览文件 @
2997b937
...
...
@@ -44,40 +44,40 @@ void act_ref(const std::shared_ptr<operators::ActivationOp> op) {
// "sigmoid","relu","tanh","relu_clipped","leaky_relu","softsign","hard_sigmoid"
if
(
op_type
==
"sigmoid"
)
{
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
1.
f
/
(
1.
f
+
std
::
exp
(
-
x_data
[
i
]));
}
}
else
if
(
op_type
==
"relu"
)
{
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
std
::
max
(
0.
f
,
x_data
[
i
]);
}
}
else
if
(
op_type
==
"tanh"
)
{
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
(
std
::
exp
(
x_data
[
i
])
-
std
::
exp
(
-
x_data
[
i
]))
/
(
std
::
exp
(
x_data
[
i
])
+
std
::
exp
(
-
x_data
[
i
]));
}
}
else
if
(
op_type
==
"relu_clipped"
)
{
auto
relu_clipped_coef
=
op_info
->
GetAttr
<
float
>
(
"Relu_clipped_coef"
);
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
std
::
min
(
std
::
max
(
0.
f
,
x_data
[
i
]),
relu_clipped_coef
);
}
}
else
if
(
op_type
==
"relu6"
)
{
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
std
::
min
(
std
::
max
(
0.
f
,
x_data
[
i
]),
6.
f
);
}
}
else
if
(
op_type
==
"leaky_relu"
)
{
auto
alpha
=
op_info
->
GetAttr
<
float
>
(
"alpha"
);
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
std
::
max
(
x_data
[
i
],
x_data
[
i
]
*
alpha
);
}
}
else
if
(
op_type
==
"softsign"
)
{
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
x_data
[
i
]
/
(
1
+
std
::
abs
(
x_data
[
i
]));
}
}
else
if
(
op_type
==
"hard_sigmoid"
)
{
auto
slope
=
op_info
->
GetAttr
<
float
>
(
"slope"
);
auto
offset
=
op_info
->
GetAttr
<
float
>
(
"offset"
);
for
(
size_
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
for
(
in
t
i
=
0
;
i
<
out
->
numel
();
i
++
)
{
out_data
[
i
]
=
std
::
min
(
1.
f
,
slope
*
x_data
[
i
]
+
offset
);
out_data
[
i
]
=
std
::
max
(
0.
f
,
out_data
[
i
]);
}
...
...
lite/kernels/mlu/bridges/concat_op_test.cc
浏览文件 @
2997b937
...
...
@@ -37,7 +37,7 @@ void concat_ref(const std::shared_ptr<operators::ConcatOpLite> op) {
scope
->
FindVar
(
op_info
->
Output
(
"Out"
).
front
())
->
GetMutable
<
Tensor
>
();
int
axis
=
op_info
->
GetAttr
<
int
>
(
"axis"
);
std
::
vector
<
lite
::
Tensor
*>
inputs_concat
(
inputs
.
size
());
for
(
in
t
j
=
0
;
j
<
inputs
.
size
();
++
j
)
{
for
(
size_
t
j
=
0
;
j
<
inputs
.
size
();
++
j
)
{
inputs_concat
[
j
]
=
inputs
[
j
];
}
size_t
num
=
inputs
.
size
();
...
...
@@ -48,7 +48,7 @@ void concat_ref(const std::shared_ptr<operators::ConcatOpLite> op) {
}
int
out_rows
=
rows
,
out_cols
=
0
;
std
::
vector
<
int64_t
>
inputs_cols
(
inputs
.
size
());
for
(
in
t
i
=
0
;
i
<
num
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
num
;
++
i
)
{
int
t_cols
=
inputs
[
i
]
->
numel
()
/
rows
;
out_cols
+=
t_cols
;
inputs_cols
[
i
]
=
t_cols
;
...
...
@@ -56,7 +56,7 @@ void concat_ref(const std::shared_ptr<operators::ConcatOpLite> op) {
for
(
int
k
=
0
;
k
<
out_rows
;
++
k
)
{
float
*
dst_ptr
=
out
->
mutable_data
<
float
>
()
+
k
*
out_cols
;
int
col_idx
=
0
;
for
(
in
t
j
=
0
;
j
<
num
;
++
j
)
{
for
(
size_
t
j
=
0
;
j
<
num
;
++
j
)
{
int
col_len
=
inputs_cols
[
j
];
const
float
*
src_prt
=
inputs
[
j
]
->
data
<
float
>
()
+
k
*
col_len
;
std
::
memcpy
(
dst_ptr
+
col_idx
,
src_prt
,
sizeof
(
float
)
*
col_len
);
...
...
lite/kernels/mlu/bridges/conv_op.cc
浏览文件 @
2997b937
...
...
@@ -43,20 +43,20 @@ int ConvConverter(void* ctx, OpLite* op, KernelBase* kernel) {
const
auto
output_shape
=
output
->
dims
().
Vectorize
();
const
auto
bs
=
input_dims
[
0
];
const
auto
oc
=
filter_dims
[
0
];
CHECK_EQ
(
input_dims
.
size
(),
4
);
CHECK_EQ
(
filter_dims
.
size
(),
4
);
CHECK_EQ
(
input_dims
.
size
(),
4
u
);
CHECK_EQ
(
filter_dims
.
size
(),
4
u
);
const
auto
strides
=
op_info
->
GetAttr
<
std
::
vector
<
int
>>
(
"strides"
);
auto
dilations
=
op_info
->
GetAttr
<
std
::
vector
<
int
>>
(
"dilations"
);
auto
paddings
=
op_info
->
GetAttr
<
std
::
vector
<
int
>>
(
"paddings"
);
CHECK_EQ
(
strides
.
size
(),
2
L
);
CHECK_EQ
(
dilations
.
size
(),
2
L
);
if
(
paddings
.
size
()
==
2
L
)
{
CHECK_EQ
(
strides
.
size
(),
2
u
);
CHECK_EQ
(
dilations
.
size
(),
2
u
);
if
(
paddings
.
size
()
==
2
u
)
{
for
(
size_t
i
=
0
;
i
<
strides
.
size
();
++
i
)
{
int
copy_pad
=
*
(
paddings
.
begin
()
+
2
*
i
);
paddings
.
insert
(
paddings
.
begin
()
+
2
*
i
+
1
,
copy_pad
);
}
}
CHECK_EQ
(
paddings
.
size
(),
4
L
)
CHECK_EQ
(
paddings
.
size
(),
4
u
)
<<
"Paddings size should be the same or twice as the input size."
;
const
std
::
string
padding_algorithm
=
...
...
lite/kernels/mlu/bridges/conv_op_test.cc
浏览文件 @
2997b937
...
...
@@ -173,10 +173,10 @@ void test_conv(int bs,
Tensor
input_int
;
input_int
.
Resize
(
input_shape
);
FillTensor
<
int8_t
,
int8_t
>
(
&
input_int
,
-
127
,
127
);
for
(
in
t
i
=
0
;
i
<
input
->
data_size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
input
->
data_size
();
i
++
)
{
input
->
mutable_data
<
float
>
()[
i
]
=
input_int
.
data
<
int8_t
>
()[
i
]
*
input_scale
;
}
for
(
in
t
i
=
0
;
i
<
filter
->
data_size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
filter
->
data_size
();
i
++
)
{
filter
->
mutable_data
<
float
>
()[
i
]
=
filter_int
->
data
<
int8_t
>
()[
i
]
*
filter_scale
;
}
...
...
lite/kernels/mlu/bridges/fc_op_test.cc
浏览文件 @
2997b937
...
...
@@ -97,11 +97,11 @@ void test_fc(const std::vector<int64_t>& input_shape,
Tensor
input_int
;
input_int
.
Resize
(
input_shape
);
FillTensor
<
int8_t
,
int8_t
>
(
&
input_int
,
-
127
,
127
);
for
(
in
t
i
=
0
;
i
<
input
->
data_size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
input
->
data_size
();
i
++
)
{
input
->
mutable_data
<
float
>
()[
i
]
=
input_int
.
data
<
int8_t
>
()[
i
]
*
input_scale
;
}
for
(
in
t
i
=
0
;
i
<
w
->
data_size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
w
->
data_size
();
i
++
)
{
w
->
mutable_data
<
float
>
()[
i
]
=
w_int
->
data
<
int8_t
>
()[
i
]
*
w_scale
;
}
...
...
lite/kernels/mlu/bridges/interpolate_op.cc
浏览文件 @
2997b937
...
...
@@ -36,7 +36,7 @@ int InterpolateConverter(void* ctx, OpLite* op, KernelBase* kernel) {
auto
x
=
scope
->
FindVar
(
x_var_name
)
->
GetMutable
<
Tensor
>
();
auto
out
=
scope
->
FindVar
(
out_var_name
)
->
GetMutable
<
Tensor
>
();
auto
x_dims
=
x
->
dims
();
CHECK_EQ
(
x_dims
.
size
(),
4
);
CHECK_EQ
(
x_dims
.
size
(),
4
u
);
auto
scale
=
op_info
->
GetAttr
<
float
>
(
"scale"
);
auto
out_w
=
op_info
->
GetAttr
<
int
>
(
"out_w"
);
auto
out_h
=
op_info
->
GetAttr
<
int
>
(
"out_h"
);
...
...
lite/kernels/mlu/bridges/interpolate_op_test.cc
浏览文件 @
2997b937
...
...
@@ -85,7 +85,7 @@ void BilinearInterpRef(const lite::Tensor* x,
int
channel_size
=
x_dims
[
1
];
auto
x_h
=
x_dims
[
2
];
auto
x_w
=
x_dims
[
3
];
CHECK_EQ
(
x_dims
.
size
(),
4
);
CHECK_EQ
(
x_dims
.
size
(),
4
u
);
auto
out_dims
=
out
->
dims
();
int
out_h
=
out_dims
[
2
];
...
...
lite/kernels/mlu/bridges/utility.cc
浏览文件 @
2997b937
...
...
@@ -59,10 +59,10 @@ void dequant(float* dst,
size_t
size
,
size_t
size_in
,
std
::
vector
<
float
>
scales
)
{
for
(
in
t
out
=
0
;
out
<
size_o
;
++
out
)
{
for
(
in
t
s
=
0
;
s
<
size
;
++
s
)
{
for
(
size_
t
out
=
0
;
out
<
size_o
;
++
out
)
{
for
(
size_
t
s
=
0
;
s
<
size
;
++
s
)
{
auto
scale
=
scales
[
s
];
for
(
in
t
in
=
0
;
in
<
size_in
;
++
in
)
{
for
(
size_
t
in
=
0
;
in
<
size_in
;
++
in
)
{
int
idx
=
in
+
s
*
size_in
+
out
*
size_in
*
size
;
dst
[
idx
]
=
static_cast
<
float
>
(
src
[
idx
])
*
scale
;
}
...
...
lite/kernels/npu/bridges/engine.cc
浏览文件 @
2997b937
...
...
@@ -30,7 +30,7 @@ int Engine::BuildOriginProgram() {
// TODO(hong19860320) The block_desc need to be divided into subgraphs during
// the exection time. But only see them as a subgraph now.
origin_program_
.
clear
();
for
(
in
t
op_idx
=
0
;
op_idx
<
block_desc_
->
OpsSize
();
op_idx
++
)
{
for
(
size_
t
op_idx
=
0
;
op_idx
<
block_desc_
->
OpsSize
();
op_idx
++
)
{
auto
op_desc
=
block_desc_
->
GetOp
<
cpp
::
OpDesc
>
(
op_idx
);
CHECK
(
op_desc
);
std
::
string
op_type
=
op_desc
->
Type
();
...
...
@@ -46,7 +46,7 @@ int Engine::BuildOriginProgram() {
VLOG
(
3
)
<<
"Found the attr '"
<<
kKernelTypeAttr
<<
"': "
<<
kernel_type
<<
" for "
<<
op_type
;
auto
kernels
=
op
->
CreateKernels
({
place
});
CHECK_GT
(
kernels
.
size
(),
0
)
<<
"No kernels found for "
<<
op_type
;
CHECK_GT
(
kernels
.
size
(),
0
u
)
<<
"No kernels found for "
<<
op_type
;
auto
it
=
std
::
find_if
(
kernels
.
begin
(),
kernels
.
end
(),
[
&
](
std
::
unique_ptr
<
KernelBase
>&
it
)
{
return
it
->
alias
()
==
alias
;
...
...
@@ -96,7 +96,7 @@ int Engine::Build() {
}
bool
Engine
::
InputShapeChanged
()
{
for
(
in
t
i
=
0
;
i
<
origin_itensors_
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
origin_itensors_
.
size
();
i
++
)
{
if
(
origin_itensors_
[
i
]
->
dims
()
!=
origin_idims_
[
i
])
{
return
true
;
}
...
...
lite/kernels/x86/elementwise_op_function.h
浏览文件 @
2997b937
...
...
@@ -64,14 +64,14 @@ inline void get_mid_dims(const lite::DDim &x_dims,
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
{
(
*
pre
)
*=
x_dims
[
i
];
}
for
(
in
t
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
if
(
x_dims
[
i
+
axis
]
!=
y_dims
[
i
])
{
// only support single y_dims[i] = 1 now.
PADDLE_ENFORCE_EQ
(
*
mid_flag
,
0
,
"Broadcast support y_dims with single 1."
);
PADDLE_ENFORCE_EQ
(
y_dims
[
i
],
1
,
"Broadcast dimension mismatch."
);
// m*n*k m*1*k
for
(
in
t
j
=
0
;
j
<
i
;
++
j
)
{
for
(
size_
t
j
=
0
;
j
<
i
;
++
j
)
{
(
*
pre
)
*=
y_dims
[
j
];
}
*
n
=
std
::
max
(
x_dims
[
i
+
axis
],
y_dims
[
i
]);
...
...
@@ -82,11 +82,11 @@ inline void get_mid_dims(const lite::DDim &x_dims,
(
*
n
)
*=
y_dims
[
i
];
}
if
(
*
mid_flag
)
{
for
(
in
t
i
=
mid
+
1
;
i
<
x_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
mid
+
1
;
i
<
x_dims
.
size
();
++
i
)
{
(
*
post
)
*=
x_dims
[
i
];
}
}
else
{
for
(
in
t
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
(
*
post
)
*=
x_dims
[
i
];
}
}
...
...
@@ -95,13 +95,13 @@ inline void get_mid_dims(const lite::DDim &x_dims,
(
*
pre
)
*=
x_dims
[
i
];
}
for
(
in
t
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
y_dims
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
x_dims
[
i
+
axis
],
y_dims
[
i
],
"Broadcast dimension mismatch."
);
(
*
n
)
*=
y_dims
[
i
];
}
for
(
in
t
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
axis
+
y_dims
.
size
();
i
<
x_dims
.
size
();
++
i
)
{
(
*
post
)
*=
x_dims
[
i
];
}
}
...
...
@@ -116,7 +116,7 @@ inline lite::DDim trim_trailing_singular_dims(const lite::DDim &dims) {
std
::
vector
<
int64_t
>
trim_dims
;
trim_dims
.
resize
(
actual_dims_size
);
for
(
in
t
i
=
0
;
i
<
actual_dims_size
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
actual_dims_size
;
++
i
)
{
trim_dims
[
i
]
=
dims
[
i
];
}
if
(
trim_dims
.
size
()
==
0
)
{
...
...
lite/kernels/x86/fill_constant_batch_size_like_compute_test.cc
浏览文件 @
2997b937
...
...
@@ -71,7 +71,7 @@ TEST(fill_constant_batch_size_like_x86, run_test) {
std
::
vector
<
float
>
ref_results
{
3.5
,
3.5
,
3.5
,
3.5
,
3.5
,
3.5
,
3.5
,
3.5
,
3.5
,
3.5
};
for
(
in
t
i
=
0
;
i
<
ref_results
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
ref_results
.
size
();
i
++
)
{
EXPECT_NEAR
(
out_data
[
i
],
ref_results
[
i
],
1e-3
);
}
}
...
...
lite/kernels/x86/gather_compute.h
浏览文件 @
2997b937
...
...
@@ -56,7 +56,7 @@ void CPUGather(const lite::Tensor* src,
// slice size
int
slice_size
=
1
;
for
(
in
t
i
=
1
;
i
<
src_dims
.
size
();
++
i
)
slice_size
*=
src_dims
[
i
];
for
(
size_
t
i
=
1
;
i
<
src_dims
.
size
();
++
i
)
slice_size
*=
src_dims
[
i
];
const
size_t
slice_bytes
=
slice_size
*
sizeof
(
T
);
for
(
int64_t
i
=
0
;
i
<
index_size
;
++
i
)
{
...
...
lite/kernels/x86/layer_norm_compute_test.cc
浏览文件 @
2997b937
...
...
@@ -108,7 +108,7 @@ TEST(layer_norm_x86, run_test) {
for
(
int
i
=
0
;
i
<
begin_norm_axis
;
++
i
)
{
pre
*=
x_shape
[
i
];
}
for
(
in
t
i
=
begin_norm_axis
;
i
<
x_shape
.
size
();
++
i
)
{
for
(
size_
t
i
=
begin_norm_axis
;
i
<
x_shape
.
size
();
++
i
)
{
post
*=
x_shape
[
i
];
}
std
::
vector
<
int64_t
>
scale_shape
({
post
});
...
...
lite/kernels/x86/sequence_expand_as_compute.h
浏览文件 @
2997b937
...
...
@@ -66,8 +66,8 @@ class SequenceExpandAsCompute
auto
*
out
=
param
.
out
;
auto
&
y_lod
=
y
->
lod
();
CHECK_EQ
(
y_lod
.
size
(),
1
);
CHECK_GT
(
y_lod
[
0
].
size
(),
1
);
CHECK_EQ
(
y_lod
.
size
(),
1
u
);
CHECK_GT
(
y_lod
[
0
].
size
(),
1
u
);
out
->
template
mutable_data
<
T
,
T
>();
...
...
lite/kernels/x86/sequence_reverse_compute_test.cc
浏览文件 @
2997b937
...
...
@@ -30,7 +30,7 @@ static void sequence_reverse_ref(const lite::Tensor* x, lite::Tensor* y) {
auto
seq_offset
=
x
->
lod
()[
x
->
lod
().
size
()
-
1
];
int
width
=
x
->
numel
()
/
x
->
dims
()[
0
];
auto
*
y_data
=
y
->
mutable_data
<
float
>
();
for
(
in
t
i
=
0
;
i
<
seq_offset
.
size
()
-
1
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
seq_offset
.
size
()
-
1
;
++
i
)
{
auto
start_pos
=
seq_offset
[
i
];
auto
end_pos
=
seq_offset
[
i
+
1
];
for
(
auto
pos
=
start_pos
;
pos
<
end_pos
;
++
pos
)
{
...
...
lite/kernels/x86/shape_compute.h
浏览文件 @
2997b937
...
...
@@ -31,7 +31,7 @@ class ShapeCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
// auto& context = context_->As<X86Context>();
auto
out_data
=
param
.
Out
->
template
mutable_data
<
int32_t
>();
auto
in_dims
=
param
.
X
->
dims
();
for
(
in
t
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
out_data
[
i
]
=
in_dims
[
i
];
}
}
...
...
lite/kernels/x86/slice_compute.h
浏览文件 @
2997b937
...
...
@@ -118,7 +118,7 @@ void slice_compute(const lite::Tensor* in,
out_dims
[
decrease_axis
[
i
]]
=
0
;
}
for
(
in
t
i
=
0
;
i
<
out_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
out_dims
.
size
();
++
i
)
{
if
(
out_dims
[
i
]
!=
0
)
{
new_out_shape
.
push_back
(
out_dims
[
i
]);
}
...
...
lite/kernels/x86/slice_compute_test.cc
浏览文件 @
2997b937
...
...
@@ -34,10 +34,10 @@ static void slice_ref(const float* input,
std
::
vector
<
int
>
real_starts
(
in_dims
.
size
(),
0
);
std
::
vector
<
int
>
real_ends
(
in_dims
.
size
(),
0
);
std
::
vector
<
int
>
real_step
(
in_dims
.
size
(),
0
);
for
(
in
t
i
=
0
;
i
<
in_dims
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
in_dims
.
size
();
i
++
)
{
real_ends
[
i
]
=
in_dims
[
i
];
}
for
(
in
t
i
=
0
;
i
<
axes
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
axes
.
size
();
i
++
)
{
int
dim_value
=
in_dims
[
axes
[
i
]];
if
(
dim_value
>
0
)
{
int
start
=
starts
[
i
]
<
0
?
(
starts
[
i
]
+
dim_value
)
:
starts
[
i
];
...
...
@@ -52,11 +52,11 @@ static void slice_ref(const float* input,
}
const
int
LEN
=
in_dims
.
size
();
int
dst_step
[
LEN
];
for
(
in
t
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
dst_step
[
i
]
=
1
;
}
int
src_step
[
LEN
];
for
(
in
t
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
in_dims
.
size
();
++
i
)
{
src_step
[
i
]
=
1
;
}
int
out_num
=
out_dims
[
in_dims
.
size
()
-
1
];
...
...
@@ -69,7 +69,7 @@ static void slice_ref(const float* input,
for
(
int
dst_id
=
0
;
dst_id
<
out_num
;
dst_id
++
)
{
int
src_id
=
0
;
int
index_id
=
dst_id
;
for
(
in
t
j
=
0
;
j
<
out_dims
.
size
();
j
++
)
{
for
(
size_
t
j
=
0
;
j
<
out_dims
.
size
();
j
++
)
{
int
cur_id
=
index_id
/
dst_step
[
j
];
index_id
=
index_id
%
dst_step
[
j
];
src_id
+=
(
cur_id
+
real_starts
[
j
])
*
src_step
[
j
];
...
...
@@ -409,7 +409,7 @@ void test_tensor_case3(lite::Tensor x, lite::Tensor out) {
lite
::
Tensor
starts_tensor
,
ends_tensor
;
starts_tensor
.
Resize
(
DDim
({
3
}));
ends_tensor
.
Resize
(
DDim
({
3
}));
for
(
in
t
i
=
0
;
i
<
starts
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
starts
.
size
();
++
i
)
{
starts_tensor
.
mutable_data
<
int
>
()[
i
]
=
starts
[
i
];
ends_tensor
.
mutable_data
<
int
>
()[
i
]
=
ends
[
i
];
}
...
...
lite/kernels/x86/stack_compute.h
浏览文件 @
2997b937
...
...
@@ -47,7 +47,7 @@ class StackCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
int
pre
=
1
,
post
=
1
;
auto
dim
=
x
[
0
]
->
dims
();
for
(
int
i
=
0
;
i
<
axis
;
++
i
)
pre
*=
dim
[
i
];
for
(
in
t
i
=
axis
;
i
<
dim
.
size
();
++
i
)
post
*=
dim
[
i
];
for
(
size_
t
i
=
axis
;
i
<
dim
.
size
();
++
i
)
post
*=
dim
[
i
];
auto
x_data_arr
=
x_datas
.
data
();
...
...
lite/kernels/x86/var_conv_2d_compute.h
浏览文件 @
2997b937
...
...
@@ -44,7 +44,7 @@ class VarConv2DCompute : public KernelLite<TARGET(kX86), PRECISION(kFloat)> {
// 2-D lod info.
// const auto& offset_x = in_col->lod()[0];
// const auto& offset_y = in_row->lod()[0];
CHECK_EQ
(
param
.
X
->
lod
().
size
(),
3
)
<<
"input lod size should be 3!"
;
CHECK_EQ
(
param
.
X
->
lod
().
size
(),
3
u
)
<<
"input lod size should be 3!"
;
const
auto
&
offset_y
=
param
.
X
->
lod
()[
1
];
const
auto
&
offset_x
=
param
.
X
->
lod
()[
2
];
...
...
lite/model_parser/model_parser_test.cc
浏览文件 @
2997b937
...
...
@@ -107,7 +107,7 @@ TEST(ModelParser, LoadParamNaive) {
ASSERT_EQ
(
bg_lod
,
tensor
.
lod
());
ASSERT_EQ
(
tensor
.
data_size
(),
size
);
auto
*
data
=
tensor
.
data
<
float
>
();
for
(
in
t
i
=
0
;
i
<
size
;
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
size
;
++
i
)
{
EXPECT_NEAR
(
bg_data
[
i
],
data
[
i
],
1e-6
);
}
}
...
...
lite/operators/elementwise_ops.cc
浏览文件 @
2997b937
...
...
@@ -35,7 +35,8 @@ bool ElementwiseOp::InferShapeImpl() const {
auto
out_lod
=
param_
.
Out
->
mutable_lod
();
*
out_lod
=
param_
.
X
->
lod
();
}
else
{
int
max_dim
=
(
x_dim
.
size
()
>
y_dim
.
size
()
?
x_dim
.
size
()
:
y_dim
.
size
());
size_t
max_dim
=
(
x_dim
.
size
()
>
y_dim
.
size
()
?
x_dim
.
size
()
:
y_dim
.
size
());
int
axis
=
param_
.
axis
;
axis
=
(
axis
==
-
1
?
std
::
abs
(
static_cast
<
int
>
(
x_dim
.
size
()
-
y_dim
.
size
()))
:
axis
);
...
...
@@ -48,12 +49,12 @@ bool ElementwiseOp::InferShapeImpl() const {
y_dims_array
[
i
]
=
1
;
}
if
(
axis
+
y_dim
.
size
()
<
max_dim
)
{
for
(
in
t
i
=
axis
+
y_dim
.
size
();
i
<
max_dim
;
++
i
)
{
for
(
size_
t
i
=
axis
+
y_dim
.
size
();
i
<
max_dim
;
++
i
)
{
y_dims_array
[
i
]
=
1
;
}
}
x_dims_array
=
x_dim
.
Vectorize
();
for
(
in
t
i
=
0
;
i
<
y_dim
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
y_dim
.
size
();
++
i
)
{
y_dims_array
[
i
+
axis
]
=
y_dim
[
i
];
}
}
else
{
...
...
@@ -61,16 +62,16 @@ bool ElementwiseOp::InferShapeImpl() const {
x_dims_array
[
i
]
=
1
;
}
if
(
axis
+
x_dim
.
size
()
<
max_dim
)
{
for
(
in
t
i
=
axis
+
x_dim
.
size
();
i
<
max_dim
;
++
i
)
{
for
(
size_
t
i
=
axis
+
x_dim
.
size
();
i
<
max_dim
;
++
i
)
{
x_dims_array
[
i
]
=
1
;
}
}
y_dims_array
=
y_dim
.
Vectorize
();
for
(
in
t
i
=
0
;
i
<
x_dim
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
x_dim
.
size
();
++
i
)
{
x_dims_array
[
i
+
axis
]
=
x_dim
[
i
];
}
}
for
(
in
t
i
=
0
;
i
<
max_dim
;
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
max_dim
;
i
++
)
{
if
(
x_dims_array
[
i
]
==
-
1
||
y_dims_array
[
i
]
==
-
1
)
{
out_dims_array
[
i
]
=
-
1
;
}
else
{
...
...
lite/operators/expand_op.cc
浏览文件 @
2997b937
...
...
@@ -27,7 +27,7 @@ bool ExpandOpLite::CheckShape() const {
CHECK_EQ
(
expand_size
,
x_dims_size
)
<<
"The number of expand_times size must be qual to the rank of "
"Input(X)."
;
CHECK_LE
(
param_
.
X
->
dims
().
size
(),
6
)
CHECK_LE
(
param_
.
X
->
dims
().
size
(),
6
u
)
<<
"The rank of Input(X) must not be greater than 6."
;
return
true
;
}
...
...
lite/operators/fill_constant_batch_size_like_op.cc
浏览文件 @
2997b937
...
...
@@ -22,7 +22,7 @@ namespace operators {
bool
FillConstantBatchSizeLikeOp
::
CheckShape
()
const
{
CHECK
(
param_
.
out
);
CHECK
(
param_
.
input
);
CHECK_GT
(
param_
.
shape
.
size
(),
0
);
CHECK_GT
(
param_
.
shape
.
size
(),
0
u
);
CHECK_GE
(
param_
.
input_dim_idx
,
0
);
CHECK_GE
(
param_
.
output_dim_idx
,
0
);
return
true
;
...
...
lite/operators/fill_constant_op.cc
浏览文件 @
2997b937
...
...
@@ -34,7 +34,7 @@ bool FillConstantOp::InferShapeImpl() const {
out_shape
.
push_back
(
shape_tensor_data
[
i
]);
}
}
else
if
(
!
shape_tensor_list
.
empty
())
{
for
(
in
t
i
=
0
;
i
<
shape_tensor_list
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
shape_tensor_list
.
size
();
i
++
)
{
out_shape
.
push_back
(
shape_tensor_list
[
i
]
->
data
<
int
>
()[
0
]);
}
}
else
if
(
!
param_
.
shape
.
empty
())
{
...
...
lite/operators/flatten_op.cc
浏览文件 @
2997b937
...
...
@@ -32,7 +32,7 @@ bool FlattenOp::InferShapeImpl() const {
*
out_lod
=
param_
.
x
->
lod
();
int64_t
outer
=
1
,
inner
=
1
;
for
(
in
t
i
=
0
;
i
<
x_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
x_dims
.
size
();
++
i
)
{
if
(
i
<
axis_
)
{
outer
*=
x_dims
[
i
];
}
else
{
...
...
lite/operators/interpolate_op.cc
浏览文件 @
2997b937
...
...
@@ -48,14 +48,14 @@ bool InterpolateOp::InferShapeImpl() const {
auto
OutSize
=
param_
.
OutSize
;
auto
Scale
=
param_
.
Scale
;
if
(
!
SizeTensor
.
empty
())
{
CHECK_EQ
(
SizeTensor
.
size
(),
2
)
CHECK_EQ
(
SizeTensor
.
size
(),
2
u
)
<<
"Input(SizeTensor)'size of Op(interpolate) must be 2. "
"Attr(out_shape)'s length must be 2 for 4-D input tensor."
;
out_h
=
SizeTensor
[
0
]
->
data
<
int
>
()[
0
];
out_w
=
SizeTensor
[
1
]
->
data
<
int
>
()[
0
];
}
else
if
(
OutSize
)
{
auto
OutSize_dims
=
OutSize
->
dims
();
CHECK_EQ
(
OutSize_dims
.
size
(),
1
)
<<
"Input(OutSize)'s dims size must be 1"
;
CHECK_EQ
(
OutSize_dims
.
size
(),
1
u
)
<<
"Input(OutSize)'s dims size must be 1"
;
CHECK_EQ
(
OutSize_dims
[
0
],
2
)
<<
"OutSize's dim[0] must be 2"
;
auto
OutSize_data
=
OutSize
->
data
<
int
>
();
out_h
=
OutSize_data
[
0
];
...
...
lite/operators/pool_op.h
浏览文件 @
2997b937
...
...
@@ -105,7 +105,7 @@ inline void UpdatePadding(std::vector<int> *paddings,
const
std
::
vector
<
int
>
&
ksize
)
{
// when padding_algorithm is "VALID" or "SAME"
if
(
padding_algorithm
==
"SAME"
)
{
for
(
in
t
i
=
0
;
i
<
strides
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
strides
.
size
();
++
i
)
{
int
out_size
=
(
data_dims
[
i
+
2
]
+
strides
[
i
]
-
1
)
/
strides
[
i
];
int
pad_sum
=
std
::
max
((
out_size
-
1
)
*
strides
[
i
]
+
ksize
[
i
]
-
data_dims
[
i
+
2
],
...
...
lite/operators/reduce_mean_op.cc
浏览文件 @
2997b937
...
...
@@ -29,7 +29,7 @@ bool ReduceMeanOp::CheckShape() const {
auto
x_dims
=
param_
.
X
->
dims
();
int
x_rank
=
x_dims
.
size
();
if
(
dims
.
size
()
!=
0
)
{
for
(
in
t
i
=
0
;
i
<
dims
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
dims
.
size
();
i
++
)
{
if
(
dims
[
i
]
<
0
)
{
dims
[
i
]
=
x_rank
+
dims
[
i
];
}
...
...
@@ -46,7 +46,7 @@ bool ReduceMeanOp::InferShapeImpl() const {
bool
keep_dim
=
param_
.
keep_dim
;
auto
x_rank
=
x_dims
.
size
();
if
(
dims
.
size
()
!=
0
)
{
for
(
in
t
i
=
0
;
i
<
dims
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
dims
.
size
();
i
++
)
{
if
(
dims
[
i
]
<
0
)
{
dims
[
i
]
=
x_rank
+
dims
[
i
];
}
...
...
@@ -65,7 +65,7 @@ bool ReduceMeanOp::InferShapeImpl() const {
out_dims
.
push_back
(
1
);
}
}
else
{
for
(
in
t
i
=
0
;
i
<
x_dims
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
x_dims
.
size
();
i
++
)
{
out_dims
.
push_back
(
x_dims
[
i
]);
}
if
(
keep_dim
)
{
...
...
lite/operators/reshape_op.cc
浏览文件 @
2997b937
...
...
@@ -70,7 +70,7 @@ bool ReshapeOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
param_
.
shape_tensor_vct
.
push_back
(
var
->
GetMutable
<
lite
::
Tensor
>
());
}
}
CHECK_GT
(
param_
.
shape_tensor_vct
.
size
(),
0
)
CHECK_GT
(
param_
.
shape_tensor_vct
.
size
(),
0
u
)
<<
"ShapeError: When `shape` in ReshapeOp is a list or tuple "
"which contains Tensor, the shape's size can't be zero. "
"But received shape's size is "
...
...
@@ -145,7 +145,7 @@ std::vector<DDim::value_type> ValidateShape(const std::vector<int> &shape,
<<
"Only one input dimension of Attr(shape) can be unknown."
;
unk_dim_idx
=
i
;
}
else
if
(
shape
[
i
]
==
copy_dim_val
)
{
CHECK_LT
(
static_cast
<
int
>
(
i
)
,
input_dims
.
size
())
CHECK_LT
(
i
,
input_dims
.
size
())
<<
"The index of dimension to copy from input shape must be less "
"than the size of input shape."
;
}
else
{
...
...
lite/operators/search_fc_op.cc
浏览文件 @
2997b937
...
...
@@ -41,11 +41,11 @@ bool SearchFcOpLite::CheckShape() const {
CHECK_OR_FALSE
(
param_
.
Out
);
auto
x_dims
=
param_
.
X
->
dims
();
CHECK_EQ
(
x_dims
.
size
(),
2
)
<<
"The rank of X(Input) should be 2."
;
CHECK_EQ
(
x_dims
.
size
(),
2
u
)
<<
"The rank of X(Input) should be 2."
;
auto
w_dims
=
param_
.
W
->
dims
();
CHECK_EQ
(
w_dims
.
size
(),
2
)
<<
"W should be 2-D tensor."
;
CHECK_EQ
(
w_dims
.
size
(),
2
u
)
<<
"W should be 2-D tensor."
;
auto
b_dims
=
param_
.
b
->
dims
();
CHECK_EQ
(
b_dims
.
size
(),
1
)
<<
"b should be 1-D tensor."
;
CHECK_EQ
(
b_dims
.
size
(),
1
u
)
<<
"b should be 1-D tensor."
;
CHECK_EQ
(
w_dims
[
1
],
x_dims
[
1
])
<<
"wrong shape: w_dims[1] != x_dims[1]"
;
return
true
;
}
...
...
lite/operators/slice_op.cc
浏览文件 @
2997b937
...
...
@@ -22,7 +22,7 @@ namespace operators {
bool
SliceOp
::
CheckShape
()
const
{
CHECK_OR_FALSE
(
param_
.
X
);
CHECK_OR_FALSE
(
param_
.
Out
);
CHECK_LT
(
param_
.
X
->
dims
().
size
(),
7
)
CHECK_LT
(
param_
.
X
->
dims
().
size
(),
7
u
)
<<
"The rank of input X should be less than 7"
;
return
true
;
}
...
...
@@ -67,7 +67,7 @@ bool SliceOp::InferShapeImpl() const {
}
out_dims
[
decrease_axis
[
i
]]
=
0
;
}
for
(
in
t
i
=
0
;
i
<
out_dims
.
size
();
++
i
)
{
for
(
size_
t
i
=
0
;
i
<
out_dims
.
size
();
++
i
)
{
if
(
out_dims
[
i
]
!=
0
)
{
new_out_shape
.
push_back
(
out_dims
[
i
]);
}
...
...
@@ -108,7 +108,7 @@ bool SliceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
// The priority: StartsTensor > StartsTensorList > attr(starts).
// The priority: EndsTensor > EndsTensorList > attr(ends).
in
t
starts_size
,
ends_size
;
size_
t
starts_size
,
ends_size
;
if
(
opdesc
.
HasAttr
(
"starts"
))
{
param_
.
starts
=
opdesc
.
GetAttr
<
std
::
vector
<
int
>>
(
"starts"
);
}
...
...
@@ -129,7 +129,7 @@ bool SliceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
param_
.
StartsTensorList
.
push_back
(
scope
->
FindVar
(
var
)
->
GetMutable
<
lite
::
Tensor
>
());
}
CHECK_GT
(
param_
.
StartsTensorList
.
size
(),
0
)
CHECK_GT
(
param_
.
StartsTensorList
.
size
(),
0
u
)
<<
"StartsTensorList size can't be zero"
;
starts_size
=
param_
.
StartsTensorList
.
size
();
}
...
...
@@ -141,7 +141,7 @@ bool SliceOp::AttachImpl(const cpp::OpDesc &opdesc, lite::Scope *scope) {
param_
.
EndsTensorList
.
push_back
(
scope
->
FindVar
(
var
)
->
GetMutable
<
lite
::
Tensor
>
());
}
CHECK_GT
(
param_
.
EndsTensorList
.
size
(),
0
)
CHECK_GT
(
param_
.
EndsTensorList
.
size
(),
0
u
)
<<
"EndsTensorList size can't be zero"
;
ends_size
=
param_
.
EndsTensorList
.
size
();
}
...
...
lite/operators/split_op.cc
浏览文件 @
2997b937
...
...
@@ -67,7 +67,7 @@ bool SplitOp::InferShapeImpl() const {
axis
=
param_
.
axis_tensor
->
data
<
int
>
()[
0
];
}
for
(
in
t
j
=
0
;
j
<
outs_dims
.
size
();
++
j
)
{
for
(
size_
t
j
=
0
;
j
<
outs_dims
.
size
();
++
j
)
{
outs
[
j
]
->
Resize
(
outs_dims
[
j
]);
}
...
...
lite/operators/squeeze_op.cc
浏览文件 @
2997b937
...
...
@@ -28,7 +28,7 @@ static DDim GetOutputShape(const std::vector<int> &squeeze_dims,
// Determines number of dimensions of output tensor after squeeze.
// Mark and count the dimensions need to be squeezed
if
(
num_squeeze_dims
==
0
)
{
for
(
in
t
idx
=
0
;
idx
<
in_dims
.
size
();
++
idx
)
{
for
(
size_
t
idx
=
0
;
idx
<
in_dims
.
size
();
++
idx
)
{
if
(
in_dims
[
idx
]
==
1
)
{
should_squeeze
[
idx
]
=
true
;
++
cnt_squeezed_dims
;
...
...
@@ -57,7 +57,7 @@ static DDim GetOutputShape(const std::vector<int> &squeeze_dims,
// Make output dimensions
std
::
vector
<
int64_t
>
output_shape
(
in_dims
.
size
()
-
cnt_squeezed_dims
,
0
);
for
(
in
t
in_idx
=
0
,
out_idx
=
0
;
in_idx
<
in_dims
.
size
();
++
in_idx
)
{
for
(
size_
t
in_idx
=
0
,
out_idx
=
0
;
in_idx
<
in_dims
.
size
();
++
in_idx
)
{
if
(
!
should_squeeze
[
in_idx
])
{
output_shape
[
out_idx
++
]
=
in_dims
[
in_idx
];
}
...
...
lite/operators/unsqueeze_op.cc
浏览文件 @
2997b937
...
...
@@ -75,7 +75,7 @@ bool UnsqueezeOp::InferShapeImpl() const {
final_axes
=
std
::
vector
<
int
>
(
axes_tensor_data
,
axes_tensor_data
+
axes_tensor
->
numel
());
}
else
if
(
!
axes_tensor_vct
.
empty
())
{
for
(
in
t
i
=
0
;
i
<
axes_tensor_vct
.
size
();
i
++
)
{
for
(
size_
t
i
=
0
;
i
<
axes_tensor_vct
.
size
();
i
++
)
{
final_axes
.
push_back
(
axes_tensor_vct
[
i
]
->
data
<
int
>
()[
0
]);
}
}
else
{
...
...
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