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1af11949
编写于
4月 02, 2019
作者:
Z
Zhen Wang
提交者:
GitHub
4月 02, 2019
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差异文件
Merge pull request #1965 from wzzju/add_checkoutpoint
Add checkpoint function for pass.
上级
3765db3d
e7be3bd9
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
102 addition
and
12 deletion
+102
-12
fluid/PaddleSlim/quant_low_level_api/quant.py
fluid/PaddleSlim/quant_low_level_api/quant.py
+16
-5
fluid/PaddleSlim/quant_low_level_api/run_quant.sh
fluid/PaddleSlim/quant_low_level_api/run_quant.sh
+7
-7
fluid/PaddleSlim/utility.py
fluid/PaddleSlim/utility.py
+79
-0
未找到文件。
fluid/PaddleSlim/quant_low_level_api/quant.py
浏览文件 @
1af11949
...
...
@@ -20,6 +20,7 @@ sys.path.append('..')
import
reader
import
models
from
utility
import
add_arguments
,
print_arguments
from
utility
import
save_persistable_nodes
,
load_persistable_nodes
parser
=
argparse
.
ArgumentParser
(
description
=
__doc__
)
add_arg
=
functools
.
partial
(
add_arguments
,
argparser
=
parser
)
...
...
@@ -31,7 +32,8 @@ add_arg('num_epochs', int, 120, "number of epochs.")
add_arg
(
'class_dim'
,
int
,
1000
,
"Class number."
)
add_arg
(
'image_shape'
,
str
,
"3,224,224"
,
"input image size"
)
add_arg
(
'model_save_dir'
,
str
,
"output"
,
"model save directory"
)
add_arg
(
'pretrained_model'
,
str
,
None
,
"Whether to use pretrained model."
)
add_arg
(
'pretrained_fp32_model'
,
str
,
None
,
"Whether to use the pretrained float32 model to initialize the weights."
)
add_arg
(
'checkpoint'
,
str
,
None
,
"Whether to resume the training process from the checkpoint."
)
add_arg
(
'lr'
,
float
,
0.1
,
"set learning rate."
)
add_arg
(
'lr_strategy'
,
str
,
"piecewise_decay"
,
"Set the learning rate decay strategy."
)
add_arg
(
'model'
,
str
,
"SE_ResNeXt50_32x4d"
,
"Set the network to use."
)
...
...
@@ -180,7 +182,8 @@ def build_program(is_train, main_prog, startup_prog, args):
def
train
(
args
):
# parameters from arguments
model_name
=
args
.
model
pretrained_model
=
args
.
pretrained_model
pretrained_fp32_model
=
args
.
pretrained_fp32_model
checkpoint
=
args
.
checkpoint
model_save_dir
=
args
.
model_save_dir
data_dir
=
args
.
data_dir
activation_quant_type
=
args
.
act_quant_type
...
...
@@ -210,11 +213,11 @@ def train(args):
main_graph
=
IrGraph
(
core
.
Graph
(
train_prog
.
desc
),
for_test
=
False
)
test_graph
=
IrGraph
(
core
.
Graph
(
test_prog
.
desc
),
for_test
=
True
)
if
pretrained_model
:
if
pretrained_
fp32_
model
:
def
if_exist
(
var
):
return
os
.
path
.
exists
(
os
.
path
.
join
(
pretrained_model
,
var
.
name
))
return
os
.
path
.
exists
(
os
.
path
.
join
(
pretrained_
fp32_
model
,
var
.
name
))
fluid
.
io
.
load_vars
(
exe
,
pretrained_model
,
main_program
=
train_prog
,
predicate
=
if_exist
)
exe
,
pretrained_
fp32_
model
,
main_program
=
train_prog
,
predicate
=
if_exist
)
if
args
.
use_gpu
:
visible_device
=
os
.
getenv
(
'CUDA_VISIBLE_DEVICES'
)
...
...
@@ -248,6 +251,9 @@ def train(args):
transform_pass
.
apply
(
main_graph
)
transform_pass
.
apply
(
test_graph
)
if
checkpoint
:
load_persistable_nodes
(
exe
,
checkpoint
,
main_graph
)
build_strategy
=
fluid
.
BuildStrategy
()
build_strategy
.
memory_optimize
=
False
build_strategy
.
enable_inplace
=
False
...
...
@@ -327,6 +333,11 @@ def train(args):
test_acc1
,
test_acc5
))
sys
.
stdout
.
flush
()
save_checkpoint_path
=
os
.
path
.
join
(
model_save_dir
,
model_name
,
str
(
pass_id
))
if
not
os
.
path
.
isdir
(
save_checkpoint_path
):
os
.
makedirs
(
save_checkpoint_path
)
save_persistable_nodes
(
exe
,
save_checkpoint_path
,
main_graph
)
model_path
=
os
.
path
.
join
(
model_save_dir
,
model_name
,
args
.
act_quant_type
)
float_path
=
os
.
path
.
join
(
model_path
,
'float'
)
int8_path
=
os
.
path
.
join
(
model_path
,
'int8'
)
...
...
fluid/PaddleSlim/quant_low_level_api/run_quant.sh
浏览文件 @
1af11949
#!/usr/bin/env bash
export
CUDA_VISIBLE_DEVICES
=
0
export
CUDA_VISIBLE_DEVICES
=
0
,1,2,3
#MobileNet v1:
python quant.py
\
--model
=
MobileNet
\
--pretrained_model
=
../data/pretrain/MobileNetV1_pretrained
\
--pretrained_
fp32_
model
=
../data/pretrain/MobileNetV1_pretrained
\
--use_gpu
=
True
\
--data_dir
=
../data/ILSVRC2012
\
--batch_size
=
64
\
--batch_size
=
256
\
--total_images
=
1281167
\
--class_dim
=
1000
\
--image_shape
=
3,224,224
\
--model_save_dir
=
output/
\
--lr_strategy
=
piecewise_decay
\
--num_epochs
=
1
0
\
--num_epochs
=
2
0
\
--lr
=
0.0001
\
--act_quant_type
=
abs_max
\
--wt_quant_type
=
abs_max
...
...
@@ -23,16 +23,16 @@ python quant.py \
#ResNet50:
#python quant.py \
# --model=ResNet50 \
# --pretrained_model=../data/pretrain/ResNet50_pretrained \
# --pretrained_
fp32_
model=../data/pretrain/ResNet50_pretrained \
# --use_gpu=True \
# --data_dir=../data/ILSVRC2012 \
# --batch_size=
32
\
# --batch_size=
128
\
# --total_images=1281167 \
# --class_dim=1000 \
# --image_shape=3,224,224 \
# --model_save_dir=output/ \
# --lr_strategy=piecewise_decay \
# --num_epochs=
1
0 \
# --num_epochs=
2
0 \
# --lr=0.0001 \
# --act_quant_type=abs_max \
# --wt_quant_type=abs_max
...
...
fluid/PaddleSlim/utility.py
浏览文件 @
1af11949
...
...
@@ -17,9 +17,13 @@ from __future__ import absolute_import
from
__future__
import
division
from
__future__
import
print_function
import
distutils.util
import
os
import
numpy
as
np
import
six
import
paddle.fluid
as
fluid
import
paddle.compat
as
cpt
from
paddle.fluid
import
core
from
paddle.fluid.framework
import
Program
def
print_arguments
(
args
):
...
...
@@ -61,3 +65,78 @@ def add_arguments(argname, type, default, help, argparser, **kwargs):
type
=
type
,
help
=
help
+
' Default: %(default)s.'
,
**
kwargs
)
def
save_persistable_nodes
(
executor
,
dirname
,
graph
):
"""
Save persistable nodes to the given directory by the executor.
Args:
executor(Executor): The executor to run for saving node values.
dirname(str): The directory path.
graph(IrGraph): All the required persistable nodes in the graph will be saved.
"""
persistable_node_names
=
set
()
persistable_nodes
=
[]
all_persistable_nodes
=
graph
.
all_persistable_nodes
()
for
node
in
all_persistable_nodes
:
name
=
cpt
.
to_text
(
node
.
name
())
if
name
not
in
persistable_node_names
:
persistable_node_names
.
add
(
name
)
persistable_nodes
.
append
(
node
)
program
=
Program
()
var_list
=
[]
for
node
in
persistable_nodes
:
var_desc
=
node
.
var
()
if
var_desc
.
type
()
==
core
.
VarDesc
.
VarType
.
RAW
or
\
var_desc
.
type
()
==
core
.
VarDesc
.
VarType
.
READER
:
continue
var
=
program
.
global_block
().
create_var
(
name
=
var_desc
.
name
(),
shape
=
var_desc
.
shape
(),
dtype
=
var_desc
.
dtype
(),
type
=
var_desc
.
type
(),
lod_level
=
var_desc
.
lod_level
(),
persistable
=
var_desc
.
persistable
())
var_list
.
append
(
var
)
fluid
.
io
.
save_vars
(
executor
=
executor
,
dirname
=
dirname
,
vars
=
var_list
)
def
load_persistable_nodes
(
executor
,
dirname
,
graph
):
"""
Load persistable node values from the given directory by the executor.
Args:
executor(Executor): The executor to run for loading node values.
dirname(str): The directory path.
graph(IrGraph): All the required persistable nodes in the graph will be loaded.
"""
persistable_node_names
=
set
()
persistable_nodes
=
[]
all_persistable_nodes
=
graph
.
all_persistable_nodes
()
for
node
in
all_persistable_nodes
:
name
=
cpt
.
to_text
(
node
.
name
())
if
name
not
in
persistable_node_names
:
persistable_node_names
.
add
(
name
)
persistable_nodes
.
append
(
node
)
program
=
Program
()
var_list
=
[]
def
_exist
(
var
):
return
os
.
path
.
exists
(
os
.
path
.
join
(
dirname
,
var
.
name
))
for
node
in
persistable_nodes
:
var_desc
=
node
.
var
()
if
var_desc
.
type
()
==
core
.
VarDesc
.
VarType
.
RAW
or
\
var_desc
.
type
()
==
core
.
VarDesc
.
VarType
.
READER
:
continue
var
=
program
.
global_block
().
create_var
(
name
=
var_desc
.
name
(),
shape
=
var_desc
.
shape
(),
dtype
=
var_desc
.
dtype
(),
type
=
var_desc
.
type
(),
lod_level
=
var_desc
.
lod_level
(),
persistable
=
var_desc
.
persistable
())
if
_exist
(
var
):
var_list
.
append
(
var
)
fluid
.
io
.
load_vars
(
executor
=
executor
,
dirname
=
dirname
,
vars
=
var_list
)
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