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体验新版 GitCode,发现更多精彩内容 >>
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ee3df4c2
编写于
7月 04, 2021
作者:
littletomatodonkey
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电子邮件补丁
差异文件
add support for quant distillation
上级
7608f163
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
44 addition
and
16 deletion
+44
-16
deploy/slim/quantization/export_model.py
deploy/slim/quantization/export_model.py
+34
-13
deploy/slim/quantization/quant.py
deploy/slim/quantization/quant.py
+10
-3
未找到文件。
deploy/slim/quantization/export_model.py
浏览文件 @
ee3df4c2
...
...
@@ -37,6 +37,17 @@ from paddleslim.dygraph.quant import QAT
from
ppocr.data
import
build_dataloader
def
export_single_model
(
quanter
,
model
,
infer_shape
,
save_path
,
logger
):
quanter
.
save_quantized_model
(
model
,
save_path
,
input_spec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
None
]
+
infer_shape
,
dtype
=
'float32'
)
])
logger
.
info
(
'inference QAT model is saved to {}'
.
format
(
save_path
))
def
main
():
############################################################################################################
# 1. quantization configs
...
...
@@ -76,7 +87,14 @@ def main():
# for rec algorithm
if
hasattr
(
post_process_class
,
'character'
):
char_num
=
len
(
getattr
(
post_process_class
,
'character'
))
config
[
'Architecture'
][
"Head"
][
'out_channels'
]
=
char_num
if
config
[
'Architecture'
][
"algorithm"
]
in
[
"Distillation"
,
]:
# distillation model
for
key
in
config
[
'Architecture'
][
"Models"
]:
config
[
'Architecture'
][
"Models"
][
key
][
"Head"
][
'out_channels'
]
=
char_num
else
:
# base rec model
config
[
'Architecture'
][
"Head"
][
'out_channels'
]
=
char_num
model
=
build_model
(
config
[
'Architecture'
])
# get QAT model
...
...
@@ -93,24 +111,27 @@ def main():
valid_dataloader
=
build_dataloader
(
config
,
'Eval'
,
device
,
logger
)
# start eval
metirc
=
program
.
eval
(
model
,
valid_dataloader
,
post_process_class
,
eval_class
)
model_type
=
config
[
'Architecture'
][
'model_type'
]
metric
=
program
.
eval
(
model
,
valid_dataloader
,
post_process_class
,
eval_class
,
model_type
)
logger
.
info
(
'metric eval ***************'
)
for
k
,
v
in
met
ir
c
.
items
():
for
k
,
v
in
met
ri
c
.
items
():
logger
.
info
(
'{}:{}'
.
format
(
k
,
v
))
save_path
=
'{}/inference'
.
format
(
config
[
'Global'
][
'save_inference_dir'
])
infer_shape
=
[
3
,
32
,
100
]
if
config
[
'Architecture'
][
'model_type'
]
!=
"det"
else
[
3
,
640
,
640
]
quanter
.
save_quantized_model
(
model
,
save_path
,
input_spec
=
[
paddle
.
static
.
InputSpec
(
shape
=
[
None
]
+
infer_shape
,
dtype
=
'float32'
)
])
logger
.
info
(
'inference QAT model is saved to {}'
.
format
(
save_path
))
save_path
=
config
[
"Global"
][
"save_inference_dir"
]
arch_config
=
config
[
"Architecture"
]
if
arch_config
[
"algorithm"
]
in
[
"Distillation"
,
]:
# distillation model
for
idx
,
name
in
enumerate
(
model
.
model_name_list
):
sub_model_save_path
=
os
.
path
.
join
(
save_path
,
name
,
"inference"
)
export_single_model
(
quanter
,
model
.
model_list
[
idx
],
infer_shape
,
sub_model_save_path
,
logger
)
else
:
save_path
=
os
.
path
.
join
(
save_path
,
"inference"
)
export_single_model
(
quanter
,
model
,
infer_shape
,
save_path
,
logger
)
if
__name__
==
"__main__"
:
...
...
deploy/slim/quantization/quant.py
浏览文件 @
ee3df4c2
...
...
@@ -109,9 +109,18 @@ def main(config, device, logger, vdl_writer):
# for rec algorithm
if
hasattr
(
post_process_class
,
'character'
):
char_num
=
len
(
getattr
(
post_process_class
,
'character'
))
config
[
'Architecture'
][
"Head"
][
'out_channels'
]
=
char_num
if
config
[
'Architecture'
][
"algorithm"
]
in
[
"Distillation"
,
]:
# distillation model
for
key
in
config
[
'Architecture'
][
"Models"
]:
config
[
'Architecture'
][
"Models"
][
key
][
"Head"
][
'out_channels'
]
=
char_num
else
:
# base rec model
config
[
'Architecture'
][
"Head"
][
'out_channels'
]
=
char_num
model
=
build_model
(
config
[
'Architecture'
])
quanter
=
QAT
(
config
=
quant_config
,
act_preprocess
=
PACT
)
quanter
.
quantize
(
model
)
if
config
[
'Global'
][
'distributed'
]:
model
=
paddle
.
DataParallel
(
model
)
...
...
@@ -132,8 +141,6 @@ def main(config, device, logger, vdl_writer):
logger
.
info
(
'train dataloader has {} iters, valid dataloader has {} iters'
.
format
(
len
(
train_dataloader
),
len
(
valid_dataloader
)))
quanter
=
QAT
(
config
=
quant_config
,
act_preprocess
=
PACT
)
quanter
.
quantize
(
model
)
# start train
program
.
train
(
config
,
train_dataloader
,
valid_dataloader
,
device
,
model
,
...
...
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