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c524b930
编写于
3月 28, 2020
作者:
L
lidanqing
提交者:
GitHub
3月 28, 2020
浏览文件
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电子邮件补丁
差异文件
Update QAT INT8 related code (#23104)
上级
f836c8aa
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
393 addition
and
57 deletion
+393
-57
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
+38
-26
python/paddle/fluid/contrib/slim/tests/qat2_int8_image_classification_comparison.py
...b/slim/tests/qat2_int8_image_classification_comparison.py
+348
-0
python/paddle/fluid/contrib/slim/tests/qat2_int8_nlp_comparison.py
...ddle/fluid/contrib/slim/tests/qat2_int8_nlp_comparison.py
+2
-2
python/paddle/fluid/contrib/slim/tests/qat_int8_image_classification_comparison.py
...ib/slim/tests/qat_int8_image_classification_comparison.py
+5
-29
未找到文件。
python/paddle/fluid/contrib/slim/tests/CMakeLists.txt
浏览文件 @
c524b930
...
...
@@ -37,12 +37,18 @@ function(download_qat_model install_dir data_file)
endif
()
endfunction
()
function
(
inference_qat_int8_image_classification_test target model_dir dataset_path
)
function
(
download_qat_fp32_model install_dir data_file
)
if
(
NOT EXISTS
${
install_dir
}
/
${
data_file
}
)
inference_download_and_uncompress
(
${
install_dir
}
${
INFERENCE_URL
}
/int8/QAT_models/fp32
${
data_file
}
)
endif
()
endfunction
()
function
(
inference_qat_int8_image_classification_test target qat_model_dir dataset_path
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/qat_int8_image_classification_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
FLAGS_use_mkldnn=true
ARGS --qat_model
${
model_dir
}
/model
ARGS --qat_model
${
qat_model_dir
}
--infer_data
${
dataset_path
}
--batch_size 25
--batch_num 2
...
...
@@ -51,44 +57,45 @@ endfunction()
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 25
function
(
inference_qat2_int8_image_classification_test target
model_dir data
_path quantized_ops
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/qat_int8_image_classification_comparison.py"
function
(
inference_qat2_int8_image_classification_test target
qat_model_dir fp32_model_dir dataset
_path quantized_ops
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/qat
2
_int8_image_classification_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
FLAGS_use_mkldnn=true
ARGS --qat_model
${
model_dir
}
/float
--infer_data
${
data_path
}
ARGS --qat_model
${
qat_model_dir
}
--fp32_model
${
fp32_model_dir
}
--infer_data
${
dataset_path
}
--batch_size 10
--batch_num 2
--acc_diff_threshold 0.1
--quantized_ops
${
quantized_ops
}
--qat2
)
--quantized_ops
${
quantized_ops
}
)
endfunction
()
# set batch_size 10 for UT only (avoid OOM). For whole dataset, use batch_size 20
function
(
inference_qat2_int8_nlp_test target
model_dir data
_path labels_path quantized_ops
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/qat_int8_nlp_comparison.py"
function
(
inference_qat2_int8_nlp_test target
qat_model_dir fp32_model_dir dataset
_path labels_path quantized_ops
)
py_test
(
${
target
}
SRCS
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/qat
2
_int8_nlp_comparison.py"
ENVS FLAGS_OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
OMP_NUM_THREADS=
${
CPU_NUM_THREADS_ON_CI
}
FLAGS_use_mkldnn=true
ARGS --qat_model
${
model_dir
}
/float
--infer_data
${
data_path
}
ARGS --qat_model
${
qat_model_dir
}
--fp32_model
${
fp32_model_dir
}
--infer_data
${
dataset_path
}
--labels
${
labels_path
}
--batch_size 10
--batch_num 2
--quantized_ops
${
quantized_ops
}
--acc_diff_threshold 0.1
)
--acc_diff_threshold 0.1
--quantized_ops
${
quantized_ops
}
)
endfunction
()
function
(
download_qat_data install_dir data_file
)
if
(
NOT EXISTS
${
install_dir
}
/
${
data_file
}
)
inference_download_and_uncompress
(
${
install_dir
}
${
INFERENCE_URL
}
/int8
${
data_file
}
)
inference_download_and_uncompress
(
${
install_dir
}
${
INFERENCE_URL
}
/int8
${
data_file
}
)
endif
()
endfunction
()
function
(
download_qat_model install_dir data_file
)
if
(
NOT EXISTS
${
install_dir
}
/
${
data_file
}
)
inference_download_and_uncompress
(
${
install_dir
}
${
INFERENCE_URL
}
/int8/QAT_models
${
data_file
}
)
inference_download_and_uncompress
(
${
install_dir
}
${
INFERENCE_URL
}
/int8/QAT_models
${
data_file
}
)
endif
()
endfunction
()
...
...
@@ -163,43 +170,43 @@ if(LINUX AND WITH_MKLDNN)
set
(
QAT_RESNET50_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/ResNet50_QAT"
)
set
(
QAT_RESNET50_MODEL_ARCHIVE
"ResNet50_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_RESNET50_MODEL_DIR
}
${
QAT_RESNET50_MODEL_ARCHIVE
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_resnet50_mkldnn
${
QAT_RESNET50_MODEL_DIR
}
${
IMAGENET_DATA_PATH
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_resnet50_mkldnn
${
QAT_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# QAT ResNet101
set
(
QAT_RESNET101_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/ResNet101_QAT"
)
set
(
QAT_RESNET101_MODEL_ARCHIVE
"ResNet101_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_RESNET101_MODEL_DIR
}
${
QAT_RESNET101_MODEL_ARCHIVE
}
)
# inference_qat_int8_image_classification_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR} ${IMAGENET_DATA_PATH})
# inference_qat_int8_image_classification_test(test_qat_int8_resnet101_mkldnn ${QAT_RESNET101_MODEL_DIR}
/model
${IMAGENET_DATA_PATH})
# QAT GoogleNet
set
(
QAT_GOOGLENET_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/GoogleNet_QAT"
)
set
(
QAT_GOOGLENET_MODEL_ARCHIVE
"GoogleNet_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_GOOGLENET_MODEL_DIR
}
${
QAT_GOOGLENET_MODEL_ARCHIVE
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_googlenet_mkldnn
${
QAT_GOOGLENET_MODEL_DIR
}
${
IMAGENET_DATA_PATH
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_googlenet_mkldnn
${
QAT_GOOGLENET_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# QAT MobileNetV1
set
(
QAT_MOBILENETV1_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/MobileNetV1_QAT"
)
set
(
QAT_MOBILENETV1_MODEL_ARCHIVE
"MobileNetV1_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_MOBILENETV1_MODEL_DIR
}
${
QAT_MOBILENETV1_MODEL_ARCHIVE
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_mobilenetv1_mkldnn
${
QAT_MOBILENETV1_MODEL_DIR
}
${
IMAGENET_DATA_PATH
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_mobilenetv1_mkldnn
${
QAT_MOBILENETV1_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# QAT MobileNetV2
set
(
QAT_MOBILENETV2_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/MobileNetV2_QAT"
)
set
(
QAT_MOBILENETV2_MODEL_ARCHIVE
"MobileNetV2_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_MOBILENETV2_MODEL_DIR
}
${
QAT_MOBILENETV2_MODEL_ARCHIVE
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_mobilenetv2_mkldnn
${
QAT_MOBILENETV2_MODEL_DIR
}
${
IMAGENET_DATA_PATH
}
)
inference_qat_int8_image_classification_test
(
test_qat_int8_mobilenetv2_mkldnn
${
QAT_MOBILENETV2_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
)
# QAT VGG16
set
(
QAT_VGG16_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/VGG16_QAT"
)
set
(
QAT_VGG16_MODEL_ARCHIVE
"VGG16_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_VGG16_MODEL_DIR
}
${
QAT_VGG16_MODEL_ARCHIVE
}
)
# inference_qat_int8_image_classification_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR} ${IMAGENET_DATA_PATH})
# inference_qat_int8_image_classification_test(test_qat_int8_vgg16_mkldnn ${QAT_VGG16_MODEL_DIR}
/model
${IMAGENET_DATA_PATH})
# QAT VGG19
set
(
QAT_VGG19_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/VGG19_QAT"
)
set
(
QAT_VGG19_MODEL_ARCHIVE
"VGG19_qat_model.tar.gz"
)
download_qat_model
(
${
QAT_VGG19_MODEL_DIR
}
${
QAT_VGG19_MODEL_ARCHIVE
}
)
# inference_qat_int8_image_classification_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR} ${IMAGENET_DATA_PATH})
# inference_qat_int8_image_classification_test(test_qat_int8_vgg19_mkldnn ${QAT_VGG19_MODEL_DIR}
/model
${IMAGENET_DATA_PATH})
### QATv2 for image classification
...
...
@@ -207,15 +214,17 @@ if(LINUX AND WITH_MKLDNN)
# QAT2 ResNet50
set
(
QAT2_RESNET50_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/ResNet50_qat_perf"
)
set
(
FP32_RESNET50_MODEL_DIR
"
${
INT8_INSTALL_DIR
}
/resnet50"
)
set
(
QAT2_RESNET50_MODEL_ARCHIVE
"ResNet50_qat_perf.tar.gz"
)
download_qat_model
(
${
QAT2_RESNET50_MODEL_DIR
}
${
QAT2_RESNET50_MODEL_ARCHIVE
}
)
inference_qat2_int8_image_classification_test
(
test_qat2_int8_resnet50_mkldnn
${
QAT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf
${
IMAGENET_DATA_PATH
}
${
QAT2_IC_QUANTIZED_OPS
}
)
inference_qat2_int8_image_classification_test
(
test_qat2_int8_resnet50_mkldnn
${
QAT2_RESNET50_MODEL_DIR
}
/ResNet50_qat_perf
/float
${
FP32_RESNET50_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
${
QAT2_IC_QUANTIZED_OPS
}
)
# QAT2 MobileNetV1
set
(
QAT2_MOBILENETV1_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/MobileNet_qat_perf"
)
set
(
FP32_MOBILENETV1_MODEL_DIR
"
${
INT8_INSTALL_DIR
}
/mobilenetv1"
)
set
(
QAT2_MOBILENETV1_MODEL_ARCHIVE
"MobileNet_qat_perf.tar.gz"
)
download_qat_model
(
${
QAT2_MOBILENETV1_MODEL_DIR
}
${
QAT2_MOBILENETV1_MODEL_ARCHIVE
}
)
inference_qat2_int8_image_classification_test
(
test_qat2_int8_mobilenetv1_mkldnn
${
QAT2_MOBILENETV1_MODEL_DIR
}
/MobileNet_qat_perf
${
IMAGENET_DATA_PATH
}
${
QAT2_IC_QUANTIZED_OPS
}
)
inference_qat2_int8_image_classification_test
(
test_qat2_int8_mobilenetv1_mkldnn
${
QAT2_MOBILENETV1_MODEL_DIR
}
/MobileNet_qat_perf
/float
${
FP32_MOBILENETV1_MODEL_DIR
}
/model
${
IMAGENET_DATA_PATH
}
${
QAT2_IC_QUANTIZED_OPS
}
)
### QATv2 for NLP
...
...
@@ -231,7 +240,10 @@ if(LINUX AND WITH_MKLDNN)
set
(
QAT2_ERNIE_MODEL_ARCHIVE
"ernie_qat.tar.gz"
)
set
(
QAT2_ERNIE_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/Ernie_qat"
)
download_qat_model
(
${
QAT2_ERNIE_MODEL_DIR
}
${
QAT2_ERNIE_MODEL_ARCHIVE
}
)
inference_qat2_int8_nlp_test
(
test_qat2_int8_ernie_mkldnn
${
QAT2_ERNIE_MODEL_DIR
}
/Ernie_qat
${
NLP_DATA_PATH
}
${
NLP_LABLES_PATH
}
${
QAT2_NLP_QUANTIZED_OPS
}
)
set
(
FP32_ERNIE_MODEL_ARCHIVE
"ernie_fp32_model.tar.gz"
)
set
(
FP32_ERNIE_MODEL_DIR
"
${
QAT_INSTALL_DIR
}
/Ernie_float"
)
download_qat_fp32_model
(
${
FP32_ERNIE_MODEL_DIR
}
${
FP32_ERNIE_MODEL_ARCHIVE
}
)
inference_qat2_int8_nlp_test
(
test_qat2_int8_ernie_mkldnn
${
QAT2_ERNIE_MODEL_DIR
}
/Ernie_qat/float
${
FP32_ERNIE_MODEL_DIR
}
/ernie_fp32_model
${
NLP_DATA_PATH
}
${
NLP_LABLES_PATH
}
${
QAT2_NLP_QUANTIZED_OPS
}
)
### Save QAT2 FP32 model or QAT2 INT8 model
...
...
python/paddle/fluid/contrib/slim/tests/qat2_int8_image_classification_comparison.py
0 → 100644
浏览文件 @
c524b930
# copyright (c) 2019 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.
import
unittest
import
os
import
sys
import
argparse
import
logging
import
struct
import
six
import
numpy
as
np
import
time
import
paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid.contrib.slim.quantization
import
Qat2Int8MkldnnPass
from
paddle.fluid
import
core
logging
.
basicConfig
(
format
=
'%(asctime)s-%(levelname)s: %(message)s'
)
_logger
=
logging
.
getLogger
(
__name__
)
_logger
.
setLevel
(
logging
.
INFO
)
def
parse_args
():
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
'--batch_size'
,
type
=
int
,
default
=
1
,
help
=
'Batch size.'
)
parser
.
add_argument
(
'--skip_batch_num'
,
type
=
int
,
default
=
0
,
help
=
'Number of the first minibatches to skip in performance statistics.'
)
parser
.
add_argument
(
'--debug'
,
action
=
'store_true'
,
help
=
'If used, the graph of QAT model is drawn.'
)
parser
.
add_argument
(
'--qat_model'
,
type
=
str
,
default
=
''
,
help
=
'A path to a QAT model.'
)
parser
.
add_argument
(
'--fp32_model'
,
type
=
str
,
default
=
''
,
help
=
'A path to an FP32 model.'
)
parser
.
add_argument
(
'--infer_data'
,
type
=
str
,
default
=
''
,
help
=
'Data file.'
)
parser
.
add_argument
(
'--batch_num'
,
type
=
int
,
default
=
0
,
help
=
'Number of batches to process. 0 or less means whole dataset. Default: 0.'
)
parser
.
add_argument
(
'--acc_diff_threshold'
,
type
=
float
,
default
=
0.01
,
help
=
'Accepted accuracy difference threshold.'
)
parser
.
add_argument
(
'--quantized_ops'
,
type
=
str
,
default
=
''
,
help
=
'A comma separated list of quantized operators.'
)
test_args
,
args
=
parser
.
parse_known_args
(
namespace
=
unittest
)
return
test_args
,
sys
.
argv
[:
1
]
+
args
class
Qat2Int8ImageClassificationComparisonTest
(
unittest
.
TestCase
):
"""
Test for accuracy comparison of FP32 and QAT2 INT8 Image Classification inference.
"""
def
_reader_creator
(
self
,
data_file
=
'data.bin'
):
def
reader
():
with
open
(
data_file
,
'rb'
)
as
fp
:
num
=
fp
.
read
(
8
)
num
=
struct
.
unpack
(
'q'
,
num
)[
0
]
imgs_offset
=
8
img_ch
=
3
img_w
=
224
img_h
=
224
img_pixel_size
=
4
img_size
=
img_ch
*
img_h
*
img_w
*
img_pixel_size
label_size
=
8
labels_offset
=
imgs_offset
+
num
*
img_size
step
=
0
while
step
<
num
:
fp
.
seek
(
imgs_offset
+
img_size
*
step
)
img
=
fp
.
read
(
img_size
)
img
=
struct
.
unpack_from
(
'{}f'
.
format
(
img_ch
*
img_w
*
img_h
),
img
)
img
=
np
.
array
(
img
)
img
.
shape
=
(
img_ch
,
img_w
,
img_h
)
fp
.
seek
(
labels_offset
+
label_size
*
step
)
label
=
fp
.
read
(
label_size
)
label
=
struct
.
unpack
(
'q'
,
label
)[
0
]
yield
img
,
int
(
label
)
step
+=
1
return
reader
def
_get_batch_accuracy
(
self
,
batch_output
=
None
,
labels
=
None
):
total
=
0
correct
=
0
correct_5
=
0
for
n
,
result
in
enumerate
(
batch_output
):
index
=
result
.
argsort
()
top_1_index
=
index
[
-
1
]
top_5_index
=
index
[
-
5
:]
total
+=
1
if
top_1_index
==
labels
[
n
]:
correct
+=
1
if
labels
[
n
]
in
top_5_index
:
correct_5
+=
1
acc1
=
float
(
correct
)
/
float
(
total
)
acc5
=
float
(
correct_5
)
/
float
(
total
)
return
acc1
,
acc5
def
_prepare_for_fp32_mkldnn
(
self
,
graph
):
ops
=
graph
.
all_op_nodes
()
for
op_node
in
ops
:
name
=
op_node
.
name
()
if
name
in
[
'depthwise_conv2d'
]:
input_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
op_node
.
input
(
"Input"
)[
0
])
weight_var_node
=
graph
.
_find_node_by_name
(
op_node
.
inputs
,
op_node
.
input
(
"Filter"
)[
0
])
output_var_node
=
graph
.
_find_node_by_name
(
graph
.
all_var_nodes
(),
op_node
.
output
(
"Output"
)[
0
])
attrs
=
{
name
:
op_node
.
op
().
attr
(
name
)
for
name
in
op_node
.
op
().
attr_names
()
}
conv_op_node
=
graph
.
create_op_node
(
op_type
=
'conv2d'
,
attrs
=
attrs
,
inputs
=
{
'Input'
:
input_var_node
,
'Filter'
:
weight_var_node
},
outputs
=
{
'Output'
:
output_var_node
})
graph
.
link_to
(
input_var_node
,
conv_op_node
)
graph
.
link_to
(
weight_var_node
,
conv_op_node
)
graph
.
link_to
(
conv_op_node
,
output_var_node
)
graph
.
safe_remove_nodes
(
op_node
)
return
graph
def
_predict
(
self
,
test_reader
=
None
,
model_path
=
None
,
batch_size
=
1
,
batch_num
=
1
,
skip_batch_num
=
0
,
transform_to_int8
=
False
):
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
inference_scope
=
fluid
.
executor
.
global_scope
()
with
fluid
.
scope_guard
(
inference_scope
):
if
os
.
path
.
exists
(
os
.
path
.
join
(
model_path
,
'__model__'
)):
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
model_path
,
exe
)
else
:
[
inference_program
,
feed_target_names
,
fetch_targets
]
=
fluid
.
io
.
load_inference_model
(
model_path
,
exe
,
'model'
,
'params'
)
graph
=
IrGraph
(
core
.
Graph
(
inference_program
.
desc
),
for_test
=
True
)
if
(
self
.
_debug
):
graph
.
draw
(
'.'
,
'qat_orig'
,
graph
.
all_op_nodes
())
if
(
transform_to_int8
):
transform_to_mkldnn_int8_pass
=
Qat2Int8MkldnnPass
(
self
.
_quantized_ops
,
_scope
=
inference_scope
,
_place
=
place
,
_core
=
core
,
_debug
=
self
.
_debug
)
graph
=
transform_to_mkldnn_int8_pass
.
apply
(
graph
)
else
:
graph
=
self
.
_prepare_for_fp32_mkldnn
(
graph
)
inference_program
=
graph
.
to_program
()
dshape
=
[
3
,
224
,
224
]
outputs
=
[]
infer_accs1
=
[]
infer_accs5
=
[]
batch_acc1
=
0.0
batch_acc5
=
0.0
fpses
=
[]
batch_times
=
[]
batch_time
=
0.0
total_samples
=
0
iters
=
0
infer_start_time
=
time
.
time
()
for
data
in
test_reader
():
if
batch_num
>
0
and
iters
>=
batch_num
:
break
if
iters
==
skip_batch_num
:
total_samples
=
0
infer_start_time
=
time
.
time
()
if
six
.
PY2
:
images
=
map
(
lambda
x
:
x
[
0
].
reshape
(
dshape
),
data
)
if
six
.
PY3
:
images
=
list
(
map
(
lambda
x
:
x
[
0
].
reshape
(
dshape
),
data
))
images
=
np
.
array
(
images
).
astype
(
'float32'
)
labels
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
)
if
(
transform_to_int8
==
True
):
# QAT INT8 models do not have accuracy measuring layers
start
=
time
.
time
()
out
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
images
},
fetch_list
=
fetch_targets
)
batch_time
=
(
time
.
time
()
-
start
)
*
1000
# in miliseconds
outputs
.
append
(
out
[
0
])
# Calculate accuracy result
batch_acc1
,
batch_acc5
=
self
.
_get_batch_accuracy
(
out
[
0
],
labels
)
else
:
# FP32 models have accuracy measuring layers
labels
=
labels
.
reshape
([
-
1
,
1
])
start
=
time
.
time
()
out
=
exe
.
run
(
inference_program
,
feed
=
{
feed_target_names
[
0
]:
images
,
feed_target_names
[
1
]:
labels
},
fetch_list
=
fetch_targets
)
batch_time
=
(
time
.
time
()
-
start
)
*
1000
# in miliseconds
batch_acc1
,
batch_acc5
=
out
[
1
][
0
],
out
[
2
][
0
]
outputs
.
append
(
batch_acc1
)
infer_accs1
.
append
(
batch_acc1
)
infer_accs5
.
append
(
batch_acc5
)
samples
=
len
(
data
)
total_samples
+=
samples
batch_times
.
append
(
batch_time
)
fps
=
samples
/
batch_time
*
1000
fpses
.
append
(
fps
)
iters
+=
1
appx
=
' (warm-up)'
if
iters
<=
skip_batch_num
else
''
_logger
.
info
(
'batch {0}{5}, acc1: {1:.4f}, acc5: {2:.4f}, '
'latency: {3:.4f} ms, fps: {4:.2f}'
.
format
(
iters
,
batch_acc1
,
batch_acc5
,
batch_time
/
batch_size
,
fps
,
appx
))
# Postprocess benchmark data
batch_latencies
=
batch_times
[
skip_batch_num
:]
batch_latency_avg
=
np
.
average
(
batch_latencies
)
latency_avg
=
batch_latency_avg
/
batch_size
fpses
=
fpses
[
skip_batch_num
:]
fps_avg
=
np
.
average
(
fpses
)
infer_total_time
=
time
.
time
()
-
infer_start_time
acc1_avg
=
np
.
mean
(
infer_accs1
)
acc5_avg
=
np
.
mean
(
infer_accs5
)
_logger
.
info
(
'Total inference run time: {:.2f} s'
.
format
(
infer_total_time
))
return
outputs
,
acc1_avg
,
acc5_avg
,
fps_avg
,
latency_avg
def
_summarize_performance
(
self
,
fp32_fps
,
fp32_lat
,
int8_fps
,
int8_lat
):
_logger
.
info
(
'--- Performance summary ---'
)
_logger
.
info
(
'FP32: avg fps: {0:.2f}, avg latency: {1:.4f} ms'
.
format
(
fp32_fps
,
fp32_lat
))
_logger
.
info
(
'INT8: avg fps: {0:.2f}, avg latency: {1:.4f} ms'
.
format
(
int8_fps
,
int8_lat
))
def
_compare_accuracy
(
self
,
fp32_acc1
,
fp32_acc5
,
int8_acc1
,
int8_acc5
,
threshold
):
_logger
.
info
(
'--- Accuracy summary ---'
)
_logger
.
info
(
'Accepted top1 accuracy drop threshold: {0}. (condition: (FP32_top1_acc - IN8_top1_acc) <= threshold)'
.
format
(
threshold
))
_logger
.
info
(
'FP32: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'
.
format
(
fp32_acc1
,
fp32_acc5
))
_logger
.
info
(
'INT8: avg top1 accuracy: {0:.4f}, avg top5 accuracy: {1:.4f}'
.
format
(
int8_acc1
,
int8_acc5
))
assert
fp32_acc1
>
0.0
assert
int8_acc1
>
0.0
assert
fp32_acc1
-
int8_acc1
<=
threshold
def
test_graph_transformation
(
self
):
if
not
fluid
.
core
.
is_compiled_with_mkldnn
():
return
qat_model_path
=
test_case_args
.
qat_model
assert
qat_model_path
,
'The QAT model path cannot be empty. Please, use the --qat_model option.'
fp32_model_path
=
test_case_args
.
fp32_model
assert
fp32_model_path
,
'The FP32 model path cannot be empty. Please, use the --fp32_model option.'
data_path
=
test_case_args
.
infer_data
assert
data_path
,
'The dataset path cannot be empty. Please, use the --infer_data option.'
batch_size
=
test_case_args
.
batch_size
batch_num
=
test_case_args
.
batch_num
skip_batch_num
=
test_case_args
.
skip_batch_num
acc_diff_threshold
=
test_case_args
.
acc_diff_threshold
self
.
_debug
=
test_case_args
.
debug
self
.
_quantized_ops
=
set
(
test_case_args
.
quantized_ops
.
split
(
','
))
_logger
.
info
(
'FP32 & QAT INT8 prediction run.'
)
_logger
.
info
(
'QAT model: {0}'
.
format
(
qat_model_path
))
_logger
.
info
(
'FP32 model: {0}'
.
format
(
fp32_model_path
))
_logger
.
info
(
'Dataset: {0}'
.
format
(
data_path
))
_logger
.
info
(
'Batch size: {0}'
.
format
(
batch_size
))
_logger
.
info
(
'Batch number: {0}'
.
format
(
batch_num
))
_logger
.
info
(
'Accuracy drop threshold: {0}.'
.
format
(
acc_diff_threshold
))
_logger
.
info
(
'Quantized ops: {0}.'
.
format
(
self
.
_quantized_ops
))
_logger
.
info
(
'--- FP32 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
),
batch_size
=
batch_size
)
fp32_output
,
fp32_acc1
,
fp32_acc5
,
fp32_fps
,
fp32_lat
=
self
.
_predict
(
val_reader
,
fp32_model_path
,
batch_size
,
batch_num
,
skip_batch_num
,
transform_to_int8
=
False
)
_logger
.
info
(
'--- QAT INT8 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
),
batch_size
=
batch_size
)
int8_output
,
int8_acc1
,
int8_acc5
,
int8_fps
,
int8_lat
=
self
.
_predict
(
val_reader
,
qat_model_path
,
batch_size
,
batch_num
,
skip_batch_num
,
transform_to_int8
=
True
)
self
.
_summarize_performance
(
fp32_fps
,
fp32_lat
,
int8_fps
,
int8_lat
)
self
.
_compare_accuracy
(
fp32_acc1
,
fp32_acc5
,
int8_acc1
,
int8_acc5
,
acc_diff_threshold
)
if
__name__
==
'__main__'
:
global
test_case_args
test_case_args
,
remaining_args
=
parse_args
()
unittest
.
main
(
argv
=
remaining_args
)
python/paddle/fluid/contrib/slim/tests/qat_int8_nlp_comparison.py
→
python/paddle/fluid/contrib/slim/tests/qat
2
_int8_nlp_comparison.py
浏览文件 @
c524b930
...
...
@@ -254,7 +254,7 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
self
.
_debug
=
test_case_args
.
debug
self
.
_quantized_ops
=
set
(
test_case_args
.
quantized_ops
.
split
(
','
))
_logger
.
info
(
'
QAT FP32 &
INT8 prediction run.'
)
_logger
.
info
(
'
FP32 & QAT
INT8 prediction run.'
)
_logger
.
info
(
'QAT model: {0}'
.
format
(
qat_model_path
))
_logger
.
info
(
'FP32 model: {0}'
.
format
(
fp32_model_path
))
_logger
.
info
(
'Dataset: {0}'
.
format
(
data_path
))
...
...
@@ -264,7 +264,7 @@ class QatInt8NLPComparisonTest(unittest.TestCase):
_logger
.
info
(
'Accuracy drop threshold: {0}.'
.
format
(
acc_diff_threshold
))
_logger
.
info
(
'Quantized ops: {0}.'
.
format
(
self
.
_quantized_ops
))
_logger
.
info
(
'---
QAT
FP32 prediction start ---'
)
_logger
.
info
(
'--- FP32 prediction start ---'
)
val_reader
=
paddle
.
batch
(
self
.
_reader_creator
(
data_path
,
labels_path
),
batch_size
=
batch_size
)
fp32_acc
,
fp32_pps
,
fp32_lat
=
self
.
_predict
(
...
...
python/paddle/fluid/contrib/slim/tests/qat_int8_image_classification_comparison.py
浏览文件 @
c524b930
...
...
@@ -25,7 +25,6 @@ import paddle
import
paddle.fluid
as
fluid
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid.contrib.slim.quantization
import
QatInt8MkldnnPass
from
paddle.fluid.contrib.slim.quantization
import
Qat2Int8MkldnnPass
from
paddle.fluid
import
core
logging
.
basicConfig
(
format
=
'%(asctime)s-%(levelname)s: %(message)s'
)
...
...
@@ -48,11 +47,6 @@ def parse_args():
help
=
'If used, the graph of QAT model is drawn.'
)
parser
.
add_argument
(
'--qat_model'
,
type
=
str
,
default
=
''
,
help
=
'A path to a QAT model.'
)
parser
.
add_argument
(
'--qat2'
,
action
=
'store_true'
,
help
=
'If used, the QAT model is treated as a second generation model for performance optimization.'
)
parser
.
add_argument
(
'--infer_data'
,
type
=
str
,
default
=
''
,
help
=
'Data file.'
)
parser
.
add_argument
(
'--batch_num'
,
...
...
@@ -65,14 +59,8 @@ def parse_args():
type
=
float
,
default
=
0.01
,
help
=
'Accepted accuracy difference threshold.'
)
parser
.
add_argument
(
'--quantized_ops'
,
type
=
str
,
default
=
''
,
help
=
'A comma separated list of quantized operators.'
)
test_args
,
args
=
parser
.
parse_known_args
(
namespace
=
unittest
)
return
test_args
,
sys
.
argv
[:
1
]
+
args
...
...
@@ -183,19 +171,9 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
if
(
self
.
_debug
):
graph
.
draw
(
'.'
,
'qat_orig'
,
graph
.
all_op_nodes
())
if
(
transform_to_int8
):
if
(
test_case_args
.
qat2
):
transform_to_mkldnn_int8_pass
=
Qat2Int8MkldnnPass
(
self
.
_quantized_ops
,
_scope
=
inference_scope
,
_place
=
place
,
_core
=
core
,
_debug
=
self
.
_debug
)
graph
=
transform_to_mkldnn_int8_pass
.
apply
(
graph
)
else
:
mkldnn_int8_pass
=
QatInt8MkldnnPass
(
_scope
=
inference_scope
,
_place
=
place
)
graph
=
mkldnn_int8_pass
.
apply
(
graph
)
mkldnn_int8_pass
=
QatInt8MkldnnPass
(
_scope
=
inference_scope
,
_place
=
place
)
graph
=
mkldnn_int8_pass
.
apply
(
graph
)
else
:
graph
=
self
.
_prepare_for_fp32_mkldnn
(
graph
)
...
...
@@ -208,8 +186,6 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
fpses
=
[]
batch_times
=
[]
total_samples
=
0
top1
=
0.0
top5
=
0.0
iters
=
0
infer_start_time
=
time
.
time
()
for
data
in
test_reader
():
...
...
@@ -289,13 +265,14 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
return
qat_model_path
=
test_case_args
.
qat_model
assert
qat_model_path
,
'The QAT model path cannot be empty. Please, use the --qat_model option.'
data_path
=
test_case_args
.
infer_data
assert
data_path
,
'The dataset path cannot be empty. Please, use the --infer_data option.'
batch_size
=
test_case_args
.
batch_size
batch_num
=
test_case_args
.
batch_num
skip_batch_num
=
test_case_args
.
skip_batch_num
acc_diff_threshold
=
test_case_args
.
acc_diff_threshold
self
.
_debug
=
test_case_args
.
debug
self
.
_quantized_ops
=
set
(
test_case_args
.
quantized_ops
.
split
(
','
))
_logger
.
info
(
'QAT FP32 & INT8 prediction run.'
)
_logger
.
info
(
'QAT model: {0}'
.
format
(
qat_model_path
))
...
...
@@ -303,7 +280,6 @@ class QatInt8ImageClassificationComparisonTest(unittest.TestCase):
_logger
.
info
(
'Batch size: {0}'
.
format
(
batch_size
))
_logger
.
info
(
'Batch number: {0}'
.
format
(
batch_num
))
_logger
.
info
(
'Accuracy drop threshold: {0}.'
.
format
(
acc_diff_threshold
))
_logger
.
info
(
'Quantized ops: {0}.'
.
format
(
self
.
_quantized_ops
))
_logger
.
info
(
'--- QAT FP32 prediction start ---'
)
val_reader
=
paddle
.
batch
(
...
...
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