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fb8e883f
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体验新版 GitCode,发现更多精彩内容 >>
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fb8e883f
编写于
5月 12, 2021
作者:
L
LDOUBLEV
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
refine deploy slim
上级
c64e235a
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
37 addition
and
43 deletion
+37
-43
deploy/slim/prune/sensitivity_anal.py
deploy/slim/prune/sensitivity_anal.py
+34
-20
deploy/slim/quantization/quant.py
deploy/slim/quantization/quant.py
+3
-23
未找到文件。
deploy/slim/prune/sensitivity_anal.py
浏览文件 @
fb8e883f
...
...
@@ -24,6 +24,14 @@ sys.path.append(__dir__)
sys
.
path
.
append
(
os
.
path
.
join
(
__dir__
,
'..'
,
'..'
,
'..'
))
sys
.
path
.
append
(
os
.
path
.
join
(
__dir__
,
'..'
,
'..'
,
'..'
,
'tools'
))
import
json
import
cv2
import
paddle
from
paddle
import
fluid
import
paddleslim
as
slim
from
copy
import
deepcopy
from
tools
import
program
import
paddle
import
paddle.distributed
as
dist
from
ppocr.data
import
build_dataloader
...
...
@@ -38,14 +46,28 @@ import tools.program as program
dist
.
get_world_size
()
def
get_pruned_params
(
parameters
):
def
get_pruned_params
(
parameters
,
mode
=
"det"
):
if
mode
==
"det"
:
skip_prune_params
=
[
"conv2d_56.w_0"
,
"conv2d_54.w_0"
,
"conv2d_51.w_0"
,
"conv_last_weights"
,
"conv14_linear_weights"
,
"conv13_expand_weights"
,
"conv12_linear_weights"
,
"conv12_expand_weights"
,
"conv7_expand_weights"
,
"conv8_expand_weights"
,
"conv8_linear_weights"
,
"conv5_linear_weights"
,
"conv5_expand_weights"
,
"conv3_linear_weights"
]
skip_prune_params
=
skip_prune_params
+
[
'conv2d_53.w_0'
]
else
:
skip_prune_params
=
None
params
=
[]
for
param
in
parameters
:
if
len
(
param
.
shape
)
==
4
and
'depthwise'
not
in
param
.
name
and
'transpose'
not
in
param
.
name
and
"conv2d_57"
not
in
param
.
name
and
"conv2d_56"
not
in
param
.
name
:
params
.
append
(
param
.
name
)
if
param
.
name
not
in
skip_prune_params
:
params
.
append
(
param
.
name
)
return
params
...
...
@@ -75,7 +97,7 @@ def main(config, device, logger, vdl_writer):
model
=
build_model
(
config
[
'Architecture'
])
flops
=
paddle
.
flops
(
model
,
[
1
,
3
,
640
,
640
])
logger
.
info
(
f
"FLOPs before pruning:
{
flops
}
"
)
print
(
f
"FLOPs before pruning:
{
flops
}
"
)
from
paddleslim.dygraph
import
FPGMFilterPruner
model
.
train
()
...
...
@@ -96,11 +118,6 @@ def main(config, device, logger, vdl_writer):
# load pretrain model
pre_best_model_dict
=
init_model
(
config
,
model
,
logger
,
optimizer
)
logger
.
info
(
'train dataloader has {} iters, valid dataloader has {} iters'
.
format
(
len
(
train_dataloader
),
len
(
valid_dataloader
)))
# build metric
eval_class
=
build_metric
(
config
[
'Metric'
])
logger
.
info
(
'train dataloader has {} iters, valid dataloader has {} iters'
.
format
(
len
(
train_dataloader
),
len
(
valid_dataloader
)))
...
...
@@ -110,32 +127,29 @@ def main(config, device, logger, vdl_writer):
logger
.
info
(
f
"metric['hmean']:
{
metric
[
'hmean'
]
}
"
)
return
metric
[
'hmean'
]
p
arams_sensitive
=
p
runer
.
sensitive
(
pruner
.
sensitive
(
eval_func
=
eval_fn
,
sen_file
=
"./sen.pickle"
,
skip_vars
=
[
"conv2d_57.w_0"
,
"conv2d_transpose_2.w_0"
,
"conv2d_transpose_3.w_0"
])
logger
.
info
(
"The sensitivity analysis results of model parameters saved in sen.pickle"
)
# calculate pruned params's ratio
params_sensitive
=
pruner
.
_get_ratios_by_loss
(
params_sensitive
,
loss
=
0.02
)
for
key
in
params_sensitive
.
keys
():
logger
.
info
(
f
"
{
key
}
,
{
params_sensitive
[
key
]
}
"
)
params
=
get_pruned_params
(
model
.
parameters
())
ratios
=
{}
# set the prune ratio is 0.2
for
param
in
params
:
ratios
[
param
]
=
0.2
plan
=
pruner
.
prune_vars
(
params_sensitive
,
[
0
])
plan
=
pruner
.
prune_vars
(
ratios
,
[
0
])
for
param
in
model
.
parameters
():
if
(
"weights"
in
param
.
name
and
"conv"
in
param
.
name
)
or
(
"w_0"
in
param
.
name
and
"conv2d"
in
param
.
name
):
logger
.
info
(
f
"
{
param
.
name
}
:
{
param
.
shape
}
"
)
print
(
f
"
{
param
.
name
}
:
{
param
.
shape
}
"
)
flops
=
paddle
.
flops
(
model
,
[
1
,
3
,
640
,
640
])
logger
.
info
(
f
"FLOPs after pruning:
{
flops
}
"
)
print
(
f
"FLOPs after pruning:
{
flops
}
"
)
# start train
program
.
train
(
config
,
train_dataloader
,
valid_dataloader
,
device
,
model
,
loss_class
,
optimizer
,
lr_scheduler
,
post_process_class
,
eval_class
,
pre_best_model_dict
,
logger
,
vdl_writer
)
...
...
deploy/slim/quantization/quant.py
浏览文件 @
fb8e883f
...
...
@@ -112,10 +112,6 @@ def main(config, device, logger, vdl_writer):
config
[
'Architecture'
][
"Head"
][
'out_channels'
]
=
char_num
model
=
build_model
(
config
[
'Architecture'
])
# prepare to quant
quanter
=
QAT
(
config
=
quant_config
,
act_preprocess
=
PACT
)
quanter
.
quantize
(
model
)
if
config
[
'Global'
][
'distributed'
]:
model
=
paddle
.
DataParallel
(
model
)
...
...
@@ -136,31 +132,15 @@ 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
,
loss_class
,
optimizer
,
lr_scheduler
,
post_process_class
,
eval_class
,
pre_best_model_dict
,
logger
,
vdl_writer
)
def
test_reader
(
config
,
device
,
logger
):
loader
=
build_dataloader
(
config
,
'Train'
,
device
,
logger
)
import
time
starttime
=
time
.
time
()
count
=
0
try
:
for
data
in
loader
():
count
+=
1
if
count
%
1
==
0
:
batch_time
=
time
.
time
()
-
starttime
starttime
=
time
.
time
()
logger
.
info
(
"reader: {}, {}, {}"
.
format
(
count
,
len
(
data
[
0
]),
batch_time
))
except
Exception
as
e
:
logger
.
info
(
e
)
logger
.
info
(
"finish reader: {}, Success!"
.
format
(
count
))
if
__name__
==
'__main__'
:
config
,
device
,
logger
,
vdl_writer
=
program
.
preprocess
(
is_train
=
True
)
main
(
config
,
device
,
logger
,
vdl_writer
)
# test_reader(config, device, logger)
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