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体验新版 GitCode,发现更多精彩内容 >>
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c061c082
编写于
11月 02, 2022
作者:
Z
Zhang Jun
提交者:
GitHub
11月 02, 2022
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电子邮件补丁
差异文件
[inference][trt] bilinear support OutSize input (#47495)
* add bilinear OutSize
上级
05a4be36
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
34 addition
and
9 deletion
+34
-9
paddle/fluid/inference/tensorrt/convert/bilinear_interp_v2_op.cc
...fluid/inference/tensorrt/convert/bilinear_interp_v2_op.cc
+30
-5
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+2
-2
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bilinear_interp_v2.py
...tests/ir/inference/test_trt_convert_bilinear_interp_v2.py
+2
-2
未找到文件。
paddle/fluid/inference/tensorrt/convert/bilinear_interp_v2_op.cc
浏览文件 @
c061c082
...
...
@@ -52,6 +52,7 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
auto
resize_inputs
=
op_desc
.
Inputs
();
auto
input_names
=
op_desc
.
Input
(
"X"
);
auto
out_h
=
PADDLE_GET_CONST
(
int
,
op_desc
.
GetAttr
(
"out_h"
));
auto
out_w
=
PADDLE_GET_CONST
(
int
,
op_desc
.
GetAttr
(
"out_w"
));
...
...
@@ -94,6 +95,15 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
out_w
=
static_cast
<
int
>
(
in_dim
.
d
[
w_axis
]
*
scale_w
);
}
// Priority: Input(OutSize) > attr(out_h/out_w) > attr(scale)
nvinfer1
::
ITensor
*
outsize_tensor
=
nullptr
;
if
(
engine_
->
with_dynamic_shape
()
&&
resize_inputs
.
find
(
"OutSize"
)
!=
resize_inputs
.
end
())
{
if
(
op_desc
.
Input
(
"OutSize"
).
size
()
>=
1
)
{
outsize_tensor
=
engine_
->
GetITensor
(
op_desc
.
Input
(
"OutSize"
)[
0
]);
}
}
if
(
out_h
>
0
&&
out_w
>
0
)
{
scale_h
=
static_cast
<
float
>
(
out_h
)
/
static_cast
<
float
>
(
in_dim
.
d
[
h_axis
]);
...
...
@@ -102,11 +112,9 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
}
std
::
vector
<
float
>
scales
;
if
(
engine_
->
with_dynamic_shape
())
{
scales
.
push_back
(
1.
f
);
}
if
(
data_layout
==
phi
::
DataLayout
::
kNCHW
)
{
scales
.
push_back
(
1.
f
);
scales
.
push_back
(
scale_h
);
...
...
@@ -115,12 +123,29 @@ class BilinearInterpolateV2OpConverter : public OpConverter {
scales
.
push_back
(
scale_h
);
scales
.
push_back
(
scale_w
);
scales
.
push_back
(
1.
f
);
}
if
(
engine_
->
with_dynamic_shape
())
{
if
(
outsize_tensor
!=
nullptr
)
{
std
::
vector
<
nvinfer1
::
ITensor
*>
outsize_itensors
;
auto
*
input_shape
=
Shape
(
input
);
outsize_itensors
.
push_back
(
GetEleTensorOfShape
(
input_shape
,
0
));
if
(
data_layout
==
phi
::
DataLayout
::
kNCHW
)
{
outsize_itensors
.
push_back
(
GetEleTensorOfShape
(
input_shape
,
1
));
outsize_itensors
.
push_back
(
outsize_tensor
);
}
else
if
(
data_layout
==
phi
::
DataLayout
::
kNHWC
)
{
outsize_itensors
.
push_back
(
outsize_tensor
);
outsize_itensors
.
push_back
(
GetEleTensorOfShape
(
input_shape
,
3
));
}
layer
->
setInput
(
1
,
*
Concat
(
outsize_itensors
));
}
else
{
layer
->
setScales
(
scales
.
data
(),
scales
.
size
());
}
}
else
{
PADDLE_THROW
(
platform
::
errors
::
InvalidArgument
(
"Data layout must be NCHW or NHWC."
));
layer
->
setScales
(
scales
.
data
(),
scales
.
size
());
}
layer
->
setScales
(
scales
.
data
(),
scales
.
size
());
RreplenishLayerAndOutput
(
layer
,
"bilinear_interp_v2"
,
{
output_name
},
test_mode
);
}
...
...
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
c061c082
...
...
@@ -803,8 +803,8 @@ struct SimpleOpTypeSetTeller : public Teller {
}
if
(
resize_inputs
.
find
(
"OutSize"
)
!=
resize_inputs
.
end
())
{
if
(
desc
.
Input
(
"OutSize"
).
size
()
>=
1
)
{
VLOG
(
3
)
<<
"
The Paddle-TRT does
n't support the OutSize for op_type "
if
(
!
with_dynamic_shape
)
{
VLOG
(
3
)
<<
"
Static shape do
n't support the OutSize for op_type "
<<
op_type
;
return
false
;
}
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_bilinear_interp_v2.py
浏览文件 @
c061c082
...
...
@@ -41,8 +41,8 @@ class TrtConvertBilinearInterpV2Test(TrtLayerAutoScanTest):
)
for
data_layout
in
[
"NCHW"
,
"NHWC"
]:
for
scale_y
in
[
2.0
,
-
1.0
,
0
.0
]:
for
scale_x
in
[
2.0
,
-
1.0
,
0
.0
]:
for
scale_y
in
[
2.0
,
1
.0
]:
for
scale_x
in
[
2.0
,
1
.0
]:
scale
=
[
scale_y
,
scale_x
]
for
out_h
in
[
32
,
64
,
128
,
192
]:
for
out_w
in
[
32
,
64
]:
...
...
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