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f68571b2
编写于
8月 17, 2022
作者:
littletomatodonkey
提交者:
GitHub
8月 17, 2022
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差异文件
rm dyg shape for trt (#7221)
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13f7b1c8
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1 changed file
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82 deletion
+3
-82
tools/infer/utility.py
tools/infer/utility.py
+3
-82
未找到文件。
tools/infer/utility.py
浏览文件 @
f68571b2
...
...
@@ -231,89 +231,10 @@ def create_predictor(args, mode, logger):
)
config
.
enable_tuned_tensorrt_dynamic_shape
(
args
.
shape_info_filename
,
True
)
use_dynamic_shape
=
True
if
mode
==
"det"
:
min_input_shape
=
{
"x"
:
[
1
,
3
,
50
,
50
],
"conv2d_92.tmp_0"
:
[
1
,
120
,
20
,
20
],
"conv2d_91.tmp_0"
:
[
1
,
24
,
10
,
10
],
"conv2d_59.tmp_0"
:
[
1
,
96
,
20
,
20
],
"nearest_interp_v2_1.tmp_0"
:
[
1
,
256
,
10
,
10
],
"nearest_interp_v2_2.tmp_0"
:
[
1
,
256
,
20
,
20
],
"conv2d_124.tmp_0"
:
[
1
,
256
,
20
,
20
],
"nearest_interp_v2_3.tmp_0"
:
[
1
,
64
,
20
,
20
],
"nearest_interp_v2_4.tmp_0"
:
[
1
,
64
,
20
,
20
],
"nearest_interp_v2_5.tmp_0"
:
[
1
,
64
,
20
,
20
],
"elementwise_add_7"
:
[
1
,
56
,
2
,
2
],
"nearest_interp_v2_0.tmp_0"
:
[
1
,
256
,
2
,
2
]
}
max_input_shape
=
{
"x"
:
[
1
,
3
,
1536
,
1536
],
"conv2d_92.tmp_0"
:
[
1
,
120
,
400
,
400
],
"conv2d_91.tmp_0"
:
[
1
,
24
,
200
,
200
],
"conv2d_59.tmp_0"
:
[
1
,
96
,
400
,
400
],
"nearest_interp_v2_1.tmp_0"
:
[
1
,
256
,
200
,
200
],
"conv2d_124.tmp_0"
:
[
1
,
256
,
400
,
400
],
"nearest_interp_v2_2.tmp_0"
:
[
1
,
256
,
400
,
400
],
"nearest_interp_v2_3.tmp_0"
:
[
1
,
64
,
400
,
400
],
"nearest_interp_v2_4.tmp_0"
:
[
1
,
64
,
400
,
400
],
"nearest_interp_v2_5.tmp_0"
:
[
1
,
64
,
400
,
400
],
"elementwise_add_7"
:
[
1
,
56
,
400
,
400
],
"nearest_interp_v2_0.tmp_0"
:
[
1
,
256
,
400
,
400
]
}
opt_input_shape
=
{
"x"
:
[
1
,
3
,
640
,
640
],
"conv2d_92.tmp_0"
:
[
1
,
120
,
160
,
160
],
"conv2d_91.tmp_0"
:
[
1
,
24
,
80
,
80
],
"conv2d_59.tmp_0"
:
[
1
,
96
,
160
,
160
],
"nearest_interp_v2_1.tmp_0"
:
[
1
,
256
,
80
,
80
],
"nearest_interp_v2_2.tmp_0"
:
[
1
,
256
,
160
,
160
],
"conv2d_124.tmp_0"
:
[
1
,
256
,
160
,
160
],
"nearest_interp_v2_3.tmp_0"
:
[
1
,
64
,
160
,
160
],
"nearest_interp_v2_4.tmp_0"
:
[
1
,
64
,
160
,
160
],
"nearest_interp_v2_5.tmp_0"
:
[
1
,
64
,
160
,
160
],
"elementwise_add_7"
:
[
1
,
56
,
40
,
40
],
"nearest_interp_v2_0.tmp_0"
:
[
1
,
256
,
40
,
40
]
}
min_pact_shape
=
{
"nearest_interp_v2_26.tmp_0"
:
[
1
,
256
,
20
,
20
],
"nearest_interp_v2_27.tmp_0"
:
[
1
,
64
,
20
,
20
],
"nearest_interp_v2_28.tmp_0"
:
[
1
,
64
,
20
,
20
],
"nearest_interp_v2_29.tmp_0"
:
[
1
,
64
,
20
,
20
]
}
max_pact_shape
=
{
"nearest_interp_v2_26.tmp_0"
:
[
1
,
256
,
400
,
400
],
"nearest_interp_v2_27.tmp_0"
:
[
1
,
64
,
400
,
400
],
"nearest_interp_v2_28.tmp_0"
:
[
1
,
64
,
400
,
400
],
"nearest_interp_v2_29.tmp_0"
:
[
1
,
64
,
400
,
400
]
}
opt_pact_shape
=
{
"nearest_interp_v2_26.tmp_0"
:
[
1
,
256
,
160
,
160
],
"nearest_interp_v2_27.tmp_0"
:
[
1
,
64
,
160
,
160
],
"nearest_interp_v2_28.tmp_0"
:
[
1
,
64
,
160
,
160
],
"nearest_interp_v2_29.tmp_0"
:
[
1
,
64
,
160
,
160
]
}
min_input_shape
.
update
(
min_pact_shape
)
max_input_shape
.
update
(
max_pact_shape
)
opt_input_shape
.
update
(
opt_pact_shape
)
elif
mode
==
"rec"
:
if
args
.
rec_algorithm
not
in
[
"CRNN"
,
"SVTR_LCNet"
]:
use_dynamic_shape
=
False
imgH
=
int
(
args
.
rec_image_shape
.
split
(
','
)[
-
2
])
min_input_shape
=
{
"x"
:
[
1
,
3
,
imgH
,
10
]}
max_input_shape
=
{
"x"
:
[
args
.
rec_batch_num
,
3
,
imgH
,
2304
]}
opt_input_shape
=
{
"x"
:
[
args
.
rec_batch_num
,
3
,
imgH
,
320
]}
config
.
exp_disable_tensorrt_ops
([
"transpose2"
])
elif
mode
==
"cls"
:
min_input_shape
=
{
"x"
:
[
1
,
3
,
48
,
10
]}
max_input_shape
=
{
"x"
:
[
args
.
rec_batch_num
,
3
,
48
,
1024
]}
opt_input_shape
=
{
"x"
:
[
args
.
rec_batch_num
,
3
,
48
,
320
]}
else
:
use_dynamic_shape
=
False
if
use_dynamic_shape
:
config
.
set_trt_dynamic_shape_info
(
min_input_shape
,
max_input_shape
,
opt_input_shape
)
logger
.
info
(
f
"when using tensorrt, dynamic shape is a suggested option, you can use '--shape_info_filename=shape.txt' for offline dygnamic shape tuning"
)
elif
args
.
use_xpu
:
config
.
enable_xpu
(
10
*
1024
*
1024
)
...
...
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