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867f4fa0
编写于
9月 17, 2021
作者:
津
津
提交者:
GitHub
9月 17, 2021
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差异文件
leaky_relu test (#35318)
* add test * add test * add test * add test * add test
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python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_leaky_relu.py
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python/paddle/fluid/tests/unittests/ir/inference/test_trt_convert_leaky_relu.py
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# 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
import
unittest
class
TrtConvertLeakyReluTest
(
TrtLayerAutoScanTest
):
def
is_program_valid
(
self
,
program_config
:
ProgramConfig
)
->
bool
:
return
True
def
sample_program_configs
(
self
):
def
generate_input1
(
attrs
:
List
[
Dict
[
str
,
Any
]]):
return
np
.
ones
([
1
,
3
,
64
,
64
]).
astype
(
np
.
float32
)
for
alpha
in
[
0.02
,
1.0
,
100.0
,
-
1.0
,
0.0
]:
for
X_scale
in
[
1.0
,
100.0
,
0.01
,
-
0.1
,
0.0
]:
dics
=
[{
"alpha"
:
alpha
,
"use_mkldnn"
:
True
,
"enable_int8"
:
True
,
"X_scale"
:
X_scale
}]
ops_config
=
[{
"op_type"
:
"leaky_relu"
,
"op_inputs"
:
{
"X"
:
[
"input_data"
],
},
"op_outputs"
:
{
"Out"
:
[
"y_data"
],
},
"op_attrs"
:
dics
[
0
]
}]
ops
=
self
.
generate_op_config
(
ops_config
)
program_config
=
ProgramConfig
(
ops
=
ops
,
weights
=
{},
inputs
=
{
"input_data"
:
TensorConfig
(
data_gen
=
partial
(
generate_input1
,
dics
))
},
outputs
=
[
"y_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
,
3
,
32
,
32
]}
self
.
dynamic_shape
.
max_input_shape
=
{
"input_data"
:
[
4
,
3
,
64
,
64
]}
self
.
dynamic_shape
.
opt_input_shape
=
{
"input_data"
:
[
4
,
3
,
64
,
64
]}
def
clear_dynamic_shape
():
self
.
dynamic_shape
.
min_input_shape
=
{}
self
.
dynamic_shape
.
max_input_shape
=
{}
self
.
dynamic_shape
.
opt_input_shape
=
{}
def
generate_trt_nodes_num
(
attrs
,
dynamic_shape
):
return
1
,
2
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
(),
generate_trt_nodes_num
(
attrs
,
False
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
(
1e-5
,
1e-5
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Int8
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
False
),
(
1e-5
,
1e-5
)
# for dynamic_shape
generate_dynamic_shape
(
attrs
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Float32
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
True
),
1e-5
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Half
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
True
),
(
1e-5
,
1e-5
)
self
.
trt_param
.
precision
=
paddle_infer
.
PrecisionType
.
Int8
yield
self
.
create_inference_config
(),
generate_trt_nodes_num
(
attrs
,
True
),
(
1e-5
,
1e-5
)
def
test
(
self
):
self
.
run_test
()
if
__name__
==
"__main__"
:
unittest
.
main
()
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