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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
eebfb71d
编写于
2月 24, 2018
作者:
Y
Yu Yang
提交者:
GitHub
2月 24, 2018
浏览文件
操作
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差异文件
Merge pull request #8524 from reyoung/feature/unique_name_guard
Moving unique_name to python
上级
044fb0fb
63563b2f
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
173 addition
and
66 deletion
+173
-66
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+0
-6
python/paddle/v2/fluid/__init__.py
python/paddle/v2/fluid/__init__.py
+2
-0
python/paddle/v2/fluid/backward.py
python/paddle/v2/fluid/backward.py
+2
-1
python/paddle/v2/fluid/evaluator.py
python/paddle/v2/fluid/evaluator.py
+3
-2
python/paddle/v2/fluid/framework.py
python/paddle/v2/fluid/framework.py
+2
-21
python/paddle/v2/fluid/layer_helper.py
python/paddle/v2/fluid/layer_helper.py
+17
-12
python/paddle/v2/fluid/layers/control_flow.py
python/paddle/v2/fluid/layers/control_flow.py
+20
-16
python/paddle/v2/fluid/layers/device.py
python/paddle/v2/fluid/layers/device.py
+2
-1
python/paddle/v2/fluid/layers/math_op_patch.py
python/paddle/v2/fluid/layers/math_op_patch.py
+1
-1
python/paddle/v2/fluid/optimizer.py
python/paddle/v2/fluid/optimizer.py
+7
-6
python/paddle/v2/fluid/tests/unittests/test_unique_name.py
python/paddle/v2/fluid/tests/unittests/test_unique_name.py
+43
-0
python/paddle/v2/fluid/unique_name.py
python/paddle/v2/fluid/unique_name.py
+74
-0
未找到文件。
paddle/fluid/pybind/pybind.cc
浏览文件 @
eebfb71d
...
...
@@ -49,11 +49,6 @@ PYBIND11_MAKE_OPAQUE(paddle::framework::LoDTensorArray);
namespace
paddle
{
namespace
pybind
{
static
size_t
UniqueIntegerGenerator
(
const
std
::
string
&
prefix
)
{
static
std
::
unordered_map
<
std
::
string
,
std
::
atomic
<
size_t
>>
generators
;
return
generators
[
prefix
].
fetch_add
(
1
);
}
bool
IsCompiledWithCUDA
()
{
#ifndef PADDLE_WITH_CUDA
return
false
;
...
...
@@ -410,7 +405,6 @@ All parameter, weight, gradient are variables in Paddle.
(
void
(
Executor
::*
)(
const
ProgramDesc
&
,
Scope
*
,
int
,
bool
,
bool
))
&
Executor
::
Run
);
m
.
def
(
"unique_integer"
,
UniqueIntegerGenerator
);
m
.
def
(
"init_gflags"
,
framework
::
InitGflags
);
m
.
def
(
"init_glog"
,
framework
::
InitGLOG
);
m
.
def
(
"init_devices"
,
&
framework
::
InitDevices
);
...
...
python/paddle/v2/fluid/__init__.py
浏览文件 @
eebfb71d
...
...
@@ -39,6 +39,7 @@ from concurrency import (Go, make_channel, channel_send, channel_recv,
import
clip
from
memory_optimization_transpiler
import
memory_optimize
import
profiler
import
unique_name
Tensor
=
LoDTensor
...
...
@@ -63,6 +64,7 @@ __all__ = framework.__all__ + executor.__all__ + concurrency.__all__ + [
'DistributeTranspiler'
,
'memory_optimize'
,
'profiler'
,
'unique_name'
,
]
...
...
python/paddle/v2/fluid/backward.py
浏览文件 @
eebfb71d
...
...
@@ -16,6 +16,7 @@ from paddle.v2.fluid import framework as framework
from
.
import
core
import
collections
import
copy
import
unique_name
__all__
=
[
'append_backward'
,
...
...
@@ -391,7 +392,7 @@ def _rename_grad_(block, start_op_idx, grad_to_var, target_grad_map):
for
name
in
op_desc
.
output_arg_names
():
if
block
.
desc
.
find_var
(
name
.
encode
(
"ascii"
)):
new_name
=
"%s_%s"
%
(
name
,
core
.
unique_integer
(
name
)
)
new_name
=
unique_name
.
generate
(
name
)
op_desc
.
rename_output
(
name
,
new_name
)
var_map
[
name
]
=
new_name
...
...
python/paddle/v2/fluid/evaluator.py
浏览文件 @
eebfb71d
...
...
@@ -15,7 +15,8 @@
import
numpy
as
np
import
layers
from
framework
import
Program
,
unique_name
,
Variable
,
program_guard
from
framework
import
Program
,
Variable
,
program_guard
import
unique_name
from
layer_helper
import
LayerHelper
__all__
=
[
...
...
@@ -96,7 +97,7 @@ class Evaluator(object):
"""
state
=
self
.
helper
.
create_variable
(
name
=
"_"
.
join
([
unique_name
(
self
.
helper
.
name
),
suffix
]),
name
=
"_"
.
join
([
unique_name
.
generate
(
self
.
helper
.
name
),
suffix
]),
persistable
=
True
,
dtype
=
dtype
,
shape
=
shape
)
...
...
python/paddle/v2/fluid/framework.py
浏览文件 @
eebfb71d
...
...
@@ -20,6 +20,7 @@ import numpy as np
import
proto.framework_pb2
as
framework_pb2
from
.
import
core
import
unique_name
__all__
=
[
'Block'
,
...
...
@@ -47,20 +48,6 @@ def grad_var_name(var_name):
return
var_name
+
GRAD_VAR_SUFFIX
def
unique_name
(
prefix
):
"""
Generate unique names with prefix
Args:
prefix(str): The prefix of return string
Returns(str): A unique string with the prefix
"""
uid
=
core
.
unique_integer
(
prefix
)
# unique during whole process.
return
"_"
.
join
([
prefix
,
str
(
uid
)])
def
convert_np_dtype_to_dtype_
(
np_dtype
):
"""
Convert the data type in numpy to the data type in Paddle
...
...
@@ -175,7 +162,7 @@ class Variable(object):
self
.
error_clip
=
error_clip
if
name
is
None
:
name
=
Variable
.
_unique_var_name_
(
)
name
=
unique_name
.
generate
(
'_generated_var'
)
is_new_var
=
False
self
.
desc
=
self
.
block
.
desc
.
find_var
(
name
)
...
...
@@ -307,12 +294,6 @@ class Variable(object):
def
type
(
self
):
return
self
.
desc
.
type
()
@
staticmethod
def
_unique_var_name_
():
prefix
=
"_generated_var"
uid
=
core
.
unique_integer
(
prefix
)
# unique during whole process.
return
"_"
.
join
([
prefix
,
str
(
uid
)])
def
set_error_clip
(
self
,
error_clip
):
self
.
error_clip
=
error_clip
...
...
python/paddle/v2/fluid/layer_helper.py
浏览文件 @
eebfb71d
...
...
@@ -15,8 +15,8 @@
import
copy
import
itertools
from
framework
import
Variable
,
Parameter
,
default_main_program
,
default_startup_program
,
\
unique_name
,
dtype_is_floating
from
framework
import
Variable
,
Parameter
,
default_main_program
,
default_startup_program
,
dtype_is_floating
import
unique_name
from
paddle.v2.fluid.initializer
import
Constant
,
Xavier
from
param_attr
import
ParamAttr
,
WeightNormParamAttr
...
...
@@ -27,7 +27,7 @@ class LayerHelper(object):
self
.
layer_type
=
layer_type
name
=
self
.
kwargs
.
get
(
'name'
,
None
)
if
name
is
None
:
self
.
kwargs
[
'name'
]
=
unique_name
(
self
.
layer_type
)
self
.
kwargs
[
'name'
]
=
unique_name
.
generate
(
self
.
layer_type
)
@
property
def
name
(
self
):
...
...
@@ -117,17 +117,20 @@ class LayerHelper(object):
block
=
self
.
startup_program
.
global_block
()):
if
out
is
None
:
out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
([
self
.
name
,
'weight_norm_norm'
])),
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_norm'
])),
dtype
=
dtype
,
persistable
=
False
)
abs_out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
([
self
.
name
,
'weight_norm_abs'
])),
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_abs'
])),
dtype
=
dtype
,
persistable
=
False
)
block
.
append_op
(
type
=
'abs'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
abs_out
})
pow_out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
([
self
.
name
,
'weight_norm_pow'
])),
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_pow'
])),
dtype
=
dtype
,
persistable
=
False
)
block
.
append_op
(
...
...
@@ -136,7 +139,8 @@ class LayerHelper(object):
outputs
=
{
'Out'
:
pow_out
},
attrs
=
{
'factor'
:
float
(
p
)})
sum_out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
([
self
.
name
,
'weight_norm_sum'
])),
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_sum'
])),
dtype
=
dtype
,
persistable
=
False
)
block
.
append_op
(
...
...
@@ -161,7 +165,7 @@ class LayerHelper(object):
block
=
self
.
startup_program
.
global_block
()):
if
out
is
None
:
out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
(
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_reshape'
])),
dtype
=
dtype
,
persistable
=
False
)
...
...
@@ -178,7 +182,7 @@ class LayerHelper(object):
block
=
self
.
startup_program
.
global_block
()):
if
out
is
None
:
out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
(
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_transpose'
])),
dtype
=
dtype
,
persistable
=
False
)
...
...
@@ -196,7 +200,8 @@ class LayerHelper(object):
"""Computes the norm over all dimensions except dim"""
if
out
is
None
:
out
=
block
.
create_var
(
name
=
unique_name
(
"."
.
join
([
self
.
name
,
'weight_norm_norm'
])),
name
=
unique_name
.
generate
(
"."
.
join
(
[
self
.
name
,
'weight_norm_norm'
])),
dtype
=
dtype
,
persistable
=
False
)
if
dim
is
None
:
...
...
@@ -286,7 +291,7 @@ class LayerHelper(object):
assert
isinstance
(
attr
,
ParamAttr
)
suffix
=
'b'
if
is_bias
else
'w'
if
attr
.
name
is
None
:
attr
.
name
=
unique_name
(
"."
.
join
([
self
.
name
,
suffix
]))
attr
.
name
=
unique_name
.
generate
(
"."
.
join
([
self
.
name
,
suffix
]))
if
default_initializer
is
None
and
attr
.
initializer
is
None
:
if
is_bias
:
...
...
@@ -316,7 +321,7 @@ class LayerHelper(object):
def
create_tmp_variable
(
self
,
dtype
,
stop_gradient
=
False
):
return
self
.
main_program
.
current_block
().
create_var
(
name
=
unique_name
(
"."
.
join
([
self
.
name
,
'tmp'
])),
name
=
unique_name
.
generate
(
"."
.
join
([
self
.
name
,
'tmp'
])),
dtype
=
dtype
,
persistable
=
False
,
stop_gradient
=
stop_gradient
)
...
...
python/paddle/v2/fluid/layers/control_flow.py
浏览文件 @
eebfb71d
...
...
@@ -428,7 +428,8 @@ class StaticRNN(object):
raise
ValueError
(
"if init is None, memory at least need shape and batch_ref"
)
parent_block
=
self
.
parent_block
()
var_name
=
unique_name
(
"@"
.
join
([
self
.
helper
.
name
,
"memory_boot"
]))
var_name
=
unique_name
.
generate
(
"@"
.
join
(
[
self
.
helper
.
name
,
"memory_boot"
]))
boot_var
=
parent_block
.
create_var
(
name
=
var_name
,
shape
=
shape
,
...
...
@@ -450,7 +451,7 @@ class StaticRNN(object):
return
self
.
memory
(
init
=
boot_var
)
else
:
pre_mem
=
self
.
helper
.
create_variable
(
name
=
unique_name
(
"@"
.
join
([
self
.
helper
.
name
,
"mem"
])),
name
=
unique_name
.
generate
(
"@"
.
join
([
self
.
helper
.
name
,
"mem"
])),
dtype
=
init
.
dtype
,
shape
=
init
.
shape
)
self
.
memories
[
pre_mem
.
name
]
=
StaticRNNMemoryLink
(
...
...
@@ -710,7 +711,7 @@ def lod_rank_table(x, level=0):
helper
=
LayerHelper
(
"lod_rank_table"
,
**
locals
())
table
=
helper
.
create_variable
(
type
=
core
.
VarDesc
.
VarType
.
LOD_RANK_TABLE
,
name
=
unique_name
(
"lod_rank_table"
))
name
=
unique_name
.
generate
(
"lod_rank_table"
))
helper
.
append_op
(
type
=
'lod_rank_table'
,
inputs
=
{
'X'
:
x
},
...
...
@@ -808,7 +809,7 @@ def lod_tensor_to_array(x, table):
"""
helper
=
LayerHelper
(
"lod_tensor_to_array"
,
**
locals
())
array
=
helper
.
create_variable
(
name
=
unique_name
(
"lod_tensor_to_array"
),
name
=
unique_name
.
generate
(
"lod_tensor_to_array"
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
,
dtype
=
x
.
dtype
)
helper
.
append_op
(
...
...
@@ -1265,11 +1266,11 @@ class IfElse(object):
if
id
(
x
)
not
in
self
.
input_table
:
parent_block
=
self
.
parent_block
()
out_true
=
parent_block
.
create_var
(
name
=
unique_name
(
'ifelse_input'
+
self
.
helper
.
name
),
name
=
unique_name
.
generate
(
'ifelse_input'
+
self
.
helper
.
name
),
dtype
=
x
.
dtype
)
out_false
=
parent_block
.
create_var
(
name
=
unique_name
(
'ifelse_input'
+
self
.
helper
.
name
),
name
=
unique_name
.
generate
(
'ifelse_input'
+
self
.
helper
.
name
),
dtype
=
x
.
dtype
)
parent_block
.
append_op
(
type
=
'split_lod_tensor'
,
...
...
@@ -1311,7 +1312,8 @@ class IfElse(object):
raise
TypeError
(
"Each output should be a variable"
)
# create outside tensor
outside_out
=
parent_block
.
create_var
(
name
=
unique_name
(
"_"
.
join
([
self
.
helper
.
name
,
'output'
])),
name
=
unique_name
.
generate
(
"_"
.
join
(
[
self
.
helper
.
name
,
'output'
])),
dtype
=
each_out
.
dtype
)
out_table
.
append
(
outside_out
)
...
...
@@ -1374,7 +1376,7 @@ class DynamicRNN(object):
parent_block
=
self
.
_parent_block_
()
if
self
.
lod_rank_table
is
None
:
self
.
lod_rank_table
=
parent_block
.
create_var
(
name
=
unique_name
(
'lod_rank_table'
),
name
=
unique_name
.
generate
(
'lod_rank_table'
),
type
=
core
.
VarDesc
.
VarType
.
LOD_RANK_TABLE
)
self
.
lod_rank_table
.
stop_gradient
=
True
parent_block
.
append_op
(
...
...
@@ -1382,7 +1384,8 @@ class DynamicRNN(object):
inputs
=
{
"X"
:
x
},
outputs
=
{
"Out"
:
self
.
lod_rank_table
})
self
.
max_seq_len
=
parent_block
.
create_var
(
name
=
unique_name
(
'dynamic_rnn_max_seq_len'
),
dtype
=
'int64'
)
name
=
unique_name
.
generate
(
'dynamic_rnn_max_seq_len'
),
dtype
=
'int64'
)
self
.
max_seq_len
.
stop_gradient
=
False
parent_block
.
append_op
(
type
=
'max_sequence_len'
,
...
...
@@ -1396,7 +1399,7 @@ class DynamicRNN(object):
outputs
=
{
'Out'
:
self
.
cond
})
input_array
=
parent_block
.
create_var
(
name
=
unique_name
(
'dynamic_rnn_input_array'
),
name
=
unique_name
.
generate
(
'dynamic_rnn_input_array'
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
,
dtype
=
x
.
dtype
)
self
.
input_array
.
append
((
input_array
,
x
.
dtype
))
...
...
@@ -1417,7 +1420,7 @@ class DynamicRNN(object):
"static_input() must be called after step_input()."
)
parent_block
=
self
.
_parent_block_
()
x_reordered
=
parent_block
.
create_var
(
name
=
unique_name
(
"dynamic_rnn_static_input_reordered"
),
name
=
unique_name
.
generate
(
"dynamic_rnn_static_input_reordered"
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
dtype
=
x
.
dtype
)
parent_block
.
append_op
(
...
...
@@ -1479,7 +1482,7 @@ class DynamicRNN(object):
'invoked before '
'memory(init=init, need_reordered=True, ...).'
)
init_reordered
=
parent_block
.
create_var
(
name
=
unique_name
(
'dynamic_rnn_mem_init_reordered'
),
name
=
unique_name
.
generate
(
'dynamic_rnn_mem_init_reordered'
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
dtype
=
init
.
dtype
)
parent_block
.
append_op
(
...
...
@@ -1491,7 +1494,7 @@ class DynamicRNN(object):
outputs
=
{
'Out'
:
[
init_reordered
]})
init_tensor
=
init_reordered
mem_array
=
parent_block
.
create_var
(
name
=
unique_name
(
'dynamic_rnn_mem_array'
),
name
=
unique_name
.
generate
(
'dynamic_rnn_mem_array'
),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
,
dtype
=
init
.
dtype
)
parent_block
.
append_op
(
...
...
@@ -1511,9 +1514,10 @@ class DynamicRNN(object):
)
parent_block
=
self
.
_parent_block_
()
init
=
parent_block
.
create_var
(
name
=
unique_name
(
'mem_init'
),
dtype
=
dtype
)
name
=
unique_name
.
generate
(
'mem_init'
),
dtype
=
dtype
)
arr
,
dtype
=
self
.
input_array
[
0
]
in0
=
parent_block
.
create_var
(
name
=
unique_name
(
'in0'
),
dtype
=
dtype
)
in0
=
parent_block
.
create_var
(
name
=
unique_name
.
generate
(
'in0'
),
dtype
=
dtype
)
parent_block
.
append_op
(
type
=
'read_from_array'
,
inputs
=
{
'X'
:
[
arr
],
...
...
@@ -1552,7 +1556,7 @@ class DynamicRNN(object):
parent_block
=
self
.
_parent_block_
()
for
each
in
outputs
:
outside_array
=
parent_block
.
create_var
(
name
=
unique_name
(
"_"
.
join
(
name
=
unique_name
.
generate
(
"_"
.
join
(
[
self
.
helper
.
name
,
"output_array"
,
each
.
name
])),
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR_ARRAY
,
dtype
=
each
.
dtype
)
...
...
python/paddle/v2/fluid/layers/device.py
浏览文件 @
eebfb71d
...
...
@@ -25,7 +25,8 @@ __all__ = ['get_places']
@
autodoc
()
def
get_places
(
device_count
=
None
,
device_type
=
None
):
helper
=
LayerHelper
(
'get_places'
,
**
locals
())
out_places
=
helper
.
create_variable
(
name
=
unique_name
(
helper
.
name
+
".out"
))
out_places
=
helper
.
create_variable
(
name
=
unique_name
.
generate
(
helper
.
name
+
".out"
))
attrs
=
dict
()
if
device_count
is
not
None
:
attrs
[
'device_count'
]
=
int
(
device_count
)
...
...
python/paddle/v2/fluid/layers/math_op_patch.py
浏览文件 @
eebfb71d
...
...
@@ -21,7 +21,7 @@ __all__ = ['monkey_patch_variable']
def
monkey_patch_variable
():
def
unique_tmp_name
():
return
unique_name
(
"tmp"
)
return
unique_name
.
generate
(
"tmp"
)
def
safe_get_dtype
(
var
):
try
:
...
...
python/paddle/v2/fluid/optimizer.py
浏览文件 @
eebfb71d
...
...
@@ -17,7 +17,8 @@ from collections import defaultdict
import
framework
import
layers
from
backward
import
append_backward
from
framework
import
unique_name
,
program_guard
from
framework
import
program_guard
import
unique_name
from
initializer
import
Constant
from
layer_helper
import
LayerHelper
from
regularizer
import
append_regularization_ops
...
...
@@ -49,7 +50,7 @@ class Optimizer(object):
def
_create_global_learning_rate
(
self
):
if
isinstance
(
self
.
_global_learning_rate
,
float
):
self
.
_global_learning_rate
=
layers
.
create_global_var
(
name
=
unique_name
(
"learning_rate"
),
name
=
unique_name
.
generate
(
"learning_rate"
),
shape
=
[
1
],
value
=
float
(
self
.
_global_learning_rate
),
dtype
=
'float32'
,
...
...
@@ -118,7 +119,7 @@ class Optimizer(object):
assert
isinstance
(
self
.
helper
,
LayerHelper
)
var
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
name
),
name
=
unique_name
.
generate
(
name
),
persistable
=
True
,
dtype
=
dtype
or
param
.
dtype
,
type
=
param
.
type
,
...
...
@@ -379,7 +380,7 @@ class AdamOptimizer(Optimizer):
# Create beta1 and beta2 power tensors
beta_shape
=
[
1
]
self
.
_beta1_pow_acc
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
'beta1_pow_acc'
),
name
=
unique_name
.
generate
(
'beta1_pow_acc'
),
dtype
=
'float32'
,
shape
=
beta_shape
,
lod_level
=
0
,
...
...
@@ -388,7 +389,7 @@ class AdamOptimizer(Optimizer):
self
.
_beta1_pow_acc
,
initializer
=
Constant
(
self
.
_beta1
))
self
.
_beta2_pow_acc
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
'beta2_pow_acc'
),
name
=
unique_name
.
generate
(
'beta2_pow_acc'
),
dtype
=
'float32'
,
shape
=
beta_shape
,
lod_level
=
0
,
...
...
@@ -481,7 +482,7 @@ class AdamaxOptimizer(Optimizer):
# Create beta1 power accumulator tensor
beta_shape
=
[
1
]
self
.
_beta1_pow_acc
=
self
.
helper
.
create_global_variable
(
name
=
unique_name
(
'beta1_pow_acc'
),
name
=
unique_name
.
generate
(
'beta1_pow_acc'
),
dtype
=
'float32'
,
shape
=
beta_shape
,
lod_level
=
0
,
...
...
python/paddle/v2/fluid/tests/unittests/test_unique_name.py
0 → 100644
浏览文件 @
eebfb71d
# Copyright (c) 2018 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
paddle.v2.fluid
as
fluid
class
TestUniqueName
(
unittest
.
TestCase
):
def
test_guard
(
self
):
with
fluid
.
unique_name
.
guard
():
name_1
=
fluid
.
unique_name
.
generate
(
''
)
with
fluid
.
unique_name
.
guard
():
name_2
=
fluid
.
unique_name
.
generate
(
''
)
self
.
assertEqual
(
name_1
,
name_2
)
with
fluid
.
unique_name
.
guard
(
"A"
):
name_1
=
fluid
.
unique_name
.
generate
(
''
)
with
fluid
.
unique_name
.
guard
(
'B'
):
name_2
=
fluid
.
unique_name
.
generate
(
''
)
self
.
assertNotEqual
(
name_1
,
name_2
)
def
test_generate
(
self
):
with
fluid
.
unique_name
.
guard
():
name1
=
fluid
.
unique_name
.
generate
(
'fc'
)
name2
=
fluid
.
unique_name
.
generate
(
'fc'
)
name3
=
fluid
.
unique_name
.
generate
(
'tmp'
)
self
.
assertNotEqual
(
name1
,
name2
)
self
.
assertEqual
(
name1
[
-
2
:],
name3
[
-
2
:])
python/paddle/v2/fluid/unique_name.py
0 → 100644
浏览文件 @
eebfb71d
# Copyright (c) 2018 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
collections
import
contextlib
import
sys
__all__
=
[
'generate'
,
'switch'
,
'guard'
,
'UniqueNameGenerator'
]
class
UniqueNameGenerator
(
object
):
"""
Generate unique name with prefix.
Args:
prefix(str): The generated name prefix. All generated name will be
started with this prefix.
"""
def
__init__
(
self
,
prefix
=
None
):
self
.
ids
=
collections
.
defaultdict
(
int
)
if
prefix
is
None
:
prefix
=
""
self
.
prefix
=
prefix
def
__call__
(
self
,
key
):
"""
Generate unique names with prefix
Args:
key(str): The key of return string.
Returns(str): A unique string with the prefix
"""
tmp
=
self
.
ids
[
key
]
self
.
ids
[
key
]
+=
1
return
self
.
prefix
+
"_"
.
join
([
key
,
str
(
tmp
)])
generator
=
UniqueNameGenerator
()
def
generate
(
key
):
return
generator
(
key
)
def
switch
(
new_generator
=
None
):
global
generator
old
=
generator
if
new_generator
is
None
:
generator
=
UniqueNameGenerator
()
else
:
generator
=
new_generator
return
old
@
contextlib
.
contextmanager
def
guard
(
new_generator
=
None
):
if
isinstance
(
new_generator
,
basestring
):
new_generator
=
UniqueNameGenerator
(
new_generator
)
old
=
switch
(
new_generator
)
yield
switch
(
old
)
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