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PaddleDetection
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bf6b9d6d
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PaddleDetection
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bf6b9d6d
编写于
4月 22, 2019
作者:
Z
Zhen Wang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add checkpoint functions for graph. test=develop
上级
27cd3efd
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
116 addition
and
8 deletion
+116
-8
python/paddle/fluid/contrib/slim/tests/test_graph.py
python/paddle/fluid/contrib/slim/tests/test_graph.py
+33
-8
python/paddle/fluid/io.py
python/paddle/fluid/io.py
+83
-0
未找到文件。
python/paddle/fluid/contrib/slim/tests/test_graph.py
浏览文件 @
bf6b9d6d
...
@@ -15,6 +15,7 @@
...
@@ -15,6 +15,7 @@
from
__future__
import
print_function
from
__future__
import
print_function
import
os
import
os
import
six
import
six
import
numpy
as
np
import
unittest
import
unittest
import
paddle
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
...
@@ -53,10 +54,11 @@ class TestGraph(unittest.TestCase):
...
@@ -53,10 +54,11 @@ class TestGraph(unittest.TestCase):
def
graph_apis
(
self
,
use_cuda
=
False
,
for_ci
=
True
):
def
graph_apis
(
self
,
use_cuda
=
False
,
for_ci
=
True
):
main
=
fluid
.
Program
()
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
with
fluid
.
unique_name
.
guard
():
feeds
,
loss
=
conv_block
()
with
fluid
.
program_guard
(
main
,
startup
):
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
feeds
,
loss
=
conv_block
()
opt
.
minimize
(
loss
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
backup_graph
=
graph
.
clone
()
backup_graph
=
graph
.
clone
()
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
len
(
backup_graph
.
all_nodes
()))
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
len
(
backup_graph
.
all_nodes
()))
...
@@ -77,16 +79,39 @@ class TestGraph(unittest.TestCase):
...
@@ -77,16 +79,39 @@ class TestGraph(unittest.TestCase):
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
feeds
,
place
=
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
feeds
,
place
=
place
)
def
train
(
binary
):
def
_
train
(
binary
):
for
_
in
range
(
iters
):
for
_
in
range
(
iters
):
data
=
next
(
train_reader
())
data
=
next
(
train_reader
())
loss_v
=
exe
.
run
(
binary
,
loss_v
=
exe
.
run
(
binary
,
feed
=
feeder
.
feed
(
data
),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
loss
.
name
])
fetch_list
=
[
loss
.
name
])
print
(
'{}: {}'
.
format
(
'loss'
,
loss_v
))
if
not
for_ci
:
print
(
'{}: {}'
.
format
(
'loss'
,
loss_v
))
train
(
origin_binary
)
_train
(
origin_binary
)
train
(
backup_binary
)
_train
(
backup_binary
)
checkponit_dir
=
"checkpoint_gpu"
if
use_cuda
else
"checkpoint_cpu"
def
_set_zero
(
var_name
,
scope
,
place
):
var
=
scope
.
find_var
(
var_name
).
get_tensor
()
var_array
=
np
.
zeros
(
var
.
_get_dims
()).
astype
(
"float32"
)
var
.
set
(
var_array
,
place
)
sum_before
=
np
.
sum
(
np
.
array
(
fluid
.
global_scope
().
find_var
(
'conv2d_1.w_0'
).
get_tensor
(
)))
fluid
.
io
.
_save_persistable_nodes
(
exe
,
checkponit_dir
,
graph
)
_set_zero
(
'conv2d_1.w_0'
,
fluid
.
global_scope
(),
place
)
set_after
=
np
.
sum
(
np
.
array
(
fluid
.
global_scope
().
find_var
(
'conv2d_1.w_0'
).
get_tensor
(
)))
self
.
assertEqual
(
set_after
,
0
)
fluid
.
io
.
_load_persistable_nodes
(
exe
,
checkponit_dir
,
graph
)
sum_after
=
np
.
sum
(
np
.
array
(
fluid
.
global_scope
().
find_var
(
'conv2d_1.w_0'
).
get_tensor
(
)))
self
.
assertEqual
(
sum_before
,
sum_after
)
marked_nodes
=
set
()
marked_nodes
=
set
()
for
op
in
graph
.
all_op_nodes
():
for
op
in
graph
.
all_op_nodes
():
...
...
python/paddle/fluid/io.py
浏览文件 @
bf6b9d6d
...
@@ -20,6 +20,7 @@ import warnings
...
@@ -20,6 +20,7 @@ import warnings
import
time
import
time
import
shutil
import
shutil
import
six
import
six
import
logging
from
functools
import
reduce
from
functools
import
reduce
from
paddle.fluid
import
layers
from
paddle.fluid
import
layers
...
@@ -29,12 +30,17 @@ from paddle.fluid.framework import Program, Parameter, default_main_program, def
...
@@ -29,12 +30,17 @@ from paddle.fluid.framework import Program, Parameter, default_main_program, def
from
.
import
reader
from
.
import
reader
from
.reader
import
*
from
.reader
import
*
from
.
import
core
from
.
import
core
from
..
import
compat
as
cpt
__all__
=
[
__all__
=
[
'save_vars'
,
'save_params'
,
'save_persistables'
,
'load_vars'
,
'load_params'
,
'save_vars'
,
'save_params'
,
'save_persistables'
,
'load_vars'
,
'load_params'
,
'load_persistables'
,
'save_inference_model'
,
'load_inference_model'
'load_persistables'
,
'save_inference_model'
,
'load_inference_model'
]
+
reader
.
__all__
]
+
reader
.
__all__
logging
.
basicConfig
(
format
=
'%(asctime)s-%(levelname)s: %(message)s'
)
_logger
=
logging
.
getLogger
(
__name__
)
_logger
.
setLevel
(
logging
.
INFO
)
def
is_parameter
(
var
):
def
is_parameter
(
var
):
"""
"""
...
@@ -1181,3 +1187,80 @@ def get_parameter_value_by_name(name, executor, program=None):
...
@@ -1181,3 +1187,80 @@ def get_parameter_value_by_name(name, executor, program=None):
program
=
default_main_program
()
program
=
default_main_program
()
var
=
program
.
global_block
().
var
(
name
)
var
=
program
.
global_block
().
var
(
name
)
return
get_parameter_value
(
var
,
executor
)
return
get_parameter_value
(
var
,
executor
)
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
)
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
)
else
:
_logger
.
warn
(
"Cannot find the var %s!!!"
%
(
node
.
name
()))
load_vars
(
executor
=
executor
,
dirname
=
dirname
,
vars
=
var_list
)
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