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体验新版 GitCode,发现更多精彩内容 >>
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42559f72
编写于
9月 13, 2021
作者:
B
baoachun
提交者:
GitHub
9月 13, 2021
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
add gather_nd trt converter test cases (#35464)
上级
666da145
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
518 addition
and
1 deletion
+518
-1
paddle/fluid/inference/tensorrt/op_teller.cc
paddle/fluid/inference/tensorrt/op_teller.cc
+8
-1
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
...ests/unittests/ir/inference/test_trt_convert_gather_nd.py
+510
-0
未找到文件。
paddle/fluid/inference/tensorrt/op_teller.cc
浏览文件 @
42559f72
...
...
@@ -328,6 +328,8 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
}
if
(
op_type
==
"gather_nd"
)
{
if
(
!
with_dynamic_shape
)
return
false
;
auto
*
block
=
desc
.
Block
();
auto
x_var_name
=
desc
.
Input
(
"X"
)[
0
];
auto
index_var_name
=
desc
.
Input
(
"Index"
)[
0
];
...
...
@@ -343,12 +345,17 @@ bool OpTeller::Tell(const framework::ir::Node* node, bool use_no_calib_int8,
const
auto
index_shape
=
index_var_desc
->
GetShape
();
const
auto
x_shape
=
x_var_desc
->
GetShape
();
if
(
x_shape
.
size
()
<=
2
)
{
VLOG
(
3
)
<<
"gather_nd op requires the input's dimension to be greater "
"than 2"
;
return
false
;
}
if
(
x_shape
.
size
()
!=
index_shape
.
size
())
{
VLOG
(
3
)
<<
"gather_nd op Index input dims size ["
<<
index_shape
.
size
()
<<
" ] not equal to x dims size ["
<<
x_shape
.
size
()
<<
"]"
;
return
false
;
}
if
(
!
with_dynamic_shape
)
return
false
;
}
if
(
op_type
==
"yolo_box"
)
{
...
...
python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_gather_nd.py
0 → 100644
浏览文件 @
42559f72
# Copyright (c) 2021 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.
from
trt_layer_auto_scan_test
import
TrtLayerAutoScanTest
,
SkipReasons
from
program_config
import
TensorConfig
,
ProgramConfig
import
numpy
as
np
import
paddle.inference
as
paddle_infer
from
functools
import
partial
from
typing
import
Optional
,
List
,
Callable
,
Dict
,
Any
,
Set
class
TrtConvertGatherNdTest_dim_4_1
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
():
return
np
.
random
.
random
([
2
,
32
,
64
,
64
]).
astype
(
np
.
float32
)
def
generate_input2
():
return
np
.
ones
([
1
]).
astype
(
np
.
int32
)
ops_config
=
[{
"op_type"
:
"gather_nd"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
{}
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
8
,
8
,
8
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
32
,
64
,
64
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
4
,
64
,
64
],
"index_data"
:
[
1
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
def
test
(
self
):
self
.
run_test
()
class
TrtConvertGatherNdTest_dim_4_1_2
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
():
return
np
.
random
.
random
([
2
,
32
,
64
,
64
]).
astype
(
np
.
float32
)
def
generate_input2
():
return
np
.
array
([
1
,
2
]).
astype
(
np
.
int32
)
ops_config
=
[{
"op_type"
:
"gather_nd"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
{}
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
8
,
8
,
8
],
"index_data"
:
[
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
32
,
64
,
64
],
"index_data"
:
[
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
4
,
64
,
64
],
"index_data"
:
[
2
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
def
test
(
self
):
self
.
run_test
()
class
TrtConvertGatherNdTest_dim_4_2
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
():
return
np
.
random
.
random
([
2
,
32
,
64
,
64
]).
astype
(
np
.
float32
)
def
generate_input2
():
return
np
.
ones
([
2
,
2
]).
astype
(
np
.
int32
)
ops_config
=
[{
"op_type"
:
"gather_nd"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
{}
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
8
,
8
,
8
],
"index_data"
:
[
1
,
2
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
32
,
64
,
64
],
"index_data"
:
[
4
,
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
4
,
64
,
64
],
"index_data"
:
[
2
,
2
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
def
test
(
self
):
self
.
run_test
()
class
TrtConvertGatherNdTest_dim_4_3
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
():
return
np
.
random
.
random
([
2
,
32
,
64
,
64
]).
astype
(
np
.
float32
)
def
generate_input2
():
return
np
.
ones
([
2
,
2
,
4
]).
astype
(
np
.
int32
)
ops_config
=
[{
"op_type"
:
"gather_nd"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
{}
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
8
,
8
,
8
],
"index_data"
:
[
1
,
2
,
2
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
32
,
64
,
64
],
"index_data"
:
[
4
,
4
,
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
4
,
64
,
64
],
"index_data"
:
[
2
,
2
,
2
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
def
test
(
self
):
self
.
run_test
()
class
TrtConvertGatherNdTest_dim_2_2
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
():
return
np
.
random
.
random
([
2
,
32
]).
astype
(
np
.
float32
)
def
generate_input2
():
return
np
.
ones
([
2
,
2
]).
astype
(
np
.
int32
)
ops_config
=
[{
"op_type"
:
"gather_nd"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
{}
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
4
],
"index_data"
:
[
1
,
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
],
"index_data"
:
[
4
,
2
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
8
],
"index_data"
:
[
2
,
2
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
def
add_skip_trt_case
(
self
):
def
teller
(
program_config
,
predictor_config
):
if
len
(
self
.
dynamic_shape
.
min_input_shape
)
!=
0
:
return
True
return
False
self
.
add_skip_case
(
teller
,
SkipReasons
.
TRT_NOT_SUPPORT
,
"Need to repair the case: the output of trt and GPU has diff when inputs' dim is 1 and 2."
)
def
test
(
self
):
self
.
add_skip_trt_case
()
self
.
run_test
()
class
TrtConvertGatherNdTest_dim_3_3
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
():
return
np
.
random
.
random
([
2
,
32
,
256
]).
astype
(
np
.
float32
)
def
generate_input2
():
return
np
.
ones
([
2
,
2
,
2
]).
astype
(
np
.
int32
)
ops_config
=
[{
"op_type"
:
"gather_nd"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
"Index"
:
[
"index_data"
]
},
"op_outputs"
:
{
"Out"
:
[
"output_data"
]
},
"op_attrs"
:
{}
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
)),
"index_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input2
)),
},
outputs
=
[
"output_data"
])
yield
program_config
def
sample_predictor_configs
(
self
,
program_config
)
->
(
paddle_infer
.
Config
,
List
[
int
],
float
):
def
generate_dynamic_shape
(
attrs
):
self
.
dynamic_shape
.
min_input_shape
=
{
"input_data"
:
[
1
,
4
,
4
],
"index_data"
:
[
1
,
1
,
1
]
}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
64
,
512
],
"index_data"
:
[
4
,
2
,
4
]
}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
2
,
8
,
64
],
"index_data"
:
[
2
,
2
,
2
]
}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
attrs
=
[
program_config
.
ops
[
i
].
attrs
for
i
in
range
(
len
(
program_config
.
ops
))
]
# for static_shape
clear_dynamic_shape
()
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
0
,
4
),
1e-5
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
(
1
,
3
),
1e-5
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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