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3cd10a7c
编写于
12月 20, 2018
作者:
M
minqiyang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add Conv2D forward
test=develop
上级
8d88c5a8
变更
14
隐藏空白更改
内联
并排
Showing
14 changed file
with
198 addition
and
234 deletion
+198
-234
paddle/fluid/imperative/layer.cc
paddle/fluid/imperative/layer.cc
+3
-0
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+2
-1
paddle/fluid/imperative/tracer.h
paddle/fluid/imperative/tracer.h
+11
-32
paddle/fluid/pybind/imperative.cc
paddle/fluid/pybind/imperative.cc
+2
-3
paddle/fluid/pybind/pybind.cc
paddle/fluid/pybind/pybind.cc
+6
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+11
-4
python/paddle/fluid/imperative/__init__.py
python/paddle/fluid/imperative/__init__.py
+4
-0
python/paddle/fluid/imperative/base.py
python/paddle/fluid/imperative/base.py
+2
-3
python/paddle/fluid/imperative/layers.py
python/paddle/fluid/imperative/layers.py
+5
-8
python/paddle/fluid/initializer.py
python/paddle/fluid/initializer.py
+16
-8
python/paddle/fluid/layer_helper.py
python/paddle/fluid/layer_helper.py
+1
-1
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+6
-51
python/paddle/fluid/tests/unittests/test_imperative.py
python/paddle/fluid/tests/unittests/test_imperative.py
+0
-123
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
+129
-0
未找到文件。
paddle/fluid/imperative/layer.cc
浏览文件 @
3cd10a7c
...
@@ -144,6 +144,9 @@ void VarBase::ApplyGrad(framework::Scope* scope, Variable* grad) {
...
@@ -144,6 +144,9 @@ void VarBase::ApplyGrad(framework::Scope* scope, Variable* grad) {
std
::
vector
<
Variable
*>
OpBase
::
ApplyGrad
(
framework
::
Scope
*
scope
)
{
std
::
vector
<
Variable
*>
OpBase
::
ApplyGrad
(
framework
::
Scope
*
scope
)
{
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc_
->
Type
();
VLOG
(
3
)
<<
"op grad "
<<
grad_op_desc_
->
Type
();
if
(
!
grad_to_var_
)
{
return
{};
}
for
(
const
std
::
string
&
grad_invar
:
grad_op_desc_
->
InputArgumentNames
())
{
for
(
const
std
::
string
&
grad_invar
:
grad_op_desc_
->
InputArgumentNames
())
{
if
(
grad_to_var_
->
find
(
grad_invar
)
==
grad_to_var_
->
end
())
{
if
(
grad_to_var_
->
find
(
grad_invar
)
==
grad_to_var_
->
end
())
{
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
3cd10a7c
...
@@ -60,7 +60,8 @@ class OpBase {
...
@@ -60,7 +60,8 @@ class OpBase {
pre_ops_
(
new
std
::
vector
<
OpBase
*>
()),
pre_ops_
(
new
std
::
vector
<
OpBase
*>
()),
pre_ops_out_idx_
(
new
std
::
vector
<
int
>
()),
pre_ops_out_idx_
(
new
std
::
vector
<
int
>
()),
op_desc_
(
nullptr
),
op_desc_
(
nullptr
),
grad_op_desc_
(
nullptr
)
{}
grad_op_desc_
(
nullptr
),
grad_to_var_
(
nullptr
)
{}
virtual
~
OpBase
()
{
virtual
~
OpBase
()
{
delete
input_vars_
;
delete
input_vars_
;
...
...
paddle/fluid/imperative/tracer.h
浏览文件 @
3cd10a7c
...
@@ -43,20 +43,14 @@ void CreateGradOp(const framework::OpDesc& op_desc,
...
@@ -43,20 +43,14 @@ void CreateGradOp(const framework::OpDesc& op_desc,
class
Tracer
{
class
Tracer
{
public:
public:
explicit
Tracer
(
framework
::
BlockDesc
*
root_block
,
explicit
Tracer
(
framework
::
BlockDesc
*
root_block
)
framework
::
BlockDesc
*
startup_block
)
:
root_scope_
(
new
framework
::
Scope
())
{}
:
root_block_
(
root_block
),
startup_block_
(
startup_block
)
{
root_scope_
=
new
framework
::
Scope
();
scopes_
[
root_block_
]
=
root_scope_
;
scopes_
[
startup_block_
]
=
root_scope_
;
}
virtual
~
Tracer
()
{
delete
root_scope_
;
}
virtual
~
Tracer
()
{}
void
Trace
(
OpBase
*
op
,
const
std
::
vector
<
VarBase
*>&
inputs
,
void
Trace
(
OpBase
*
op
,
const
std
::
vector
<
VarBase
*>&
inputs
,
const
std
::
vector
<
VarBase
*>&
outputs
,
const
std
::
vector
<
VarBase
*>&
outputs
,
framework
::
BlockDesc
*
block
,
framework
::
BlockDesc
*
block
)
{
const
bool
stop_gradient
)
{
framework
::
Scope
*
scope
=
GetScope
(
block
);
framework
::
OpDesc
*
op_desc
=
op
->
op_desc_
;
framework
::
OpDesc
*
op_desc
=
op
->
op_desc_
;
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
();
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
();
op_desc
->
InferShape
(
*
block
);
op_desc
->
InferShape
(
*
block
);
...
@@ -67,7 +61,7 @@ class Tracer {
...
@@ -67,7 +61,7 @@ class Tracer {
*
op
->
input_vars_
=
inputs
;
*
op
->
input_vars_
=
inputs
;
for
(
VarBase
*
input
:
inputs
)
{
for
(
VarBase
*
input
:
inputs
)
{
const
std
::
string
vname
=
input
->
var_desc_
->
Name
();
const
std
::
string
vname
=
input
->
var_desc_
->
Name
();
framework
::
Variable
*
var
=
scope
->
Var
(
vname
);
framework
::
Variable
*
var
=
root_scope_
->
Var
(
vname
);
input
->
var_
=
var
;
input
->
var_
=
var
;
if
(
!
var
->
IsInitialized
())
{
if
(
!
var
->
IsInitialized
())
{
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
vname
);
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
vname
);
...
@@ -90,7 +84,7 @@ class Tracer {
...
@@ -90,7 +84,7 @@ class Tracer {
*
op
->
output_vars_
=
outputs
;
*
op
->
output_vars_
=
outputs
;
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
for
(
size_t
i
=
0
;
i
<
outputs
.
size
();
++
i
)
{
const
std
::
string
vname
=
outputs
[
i
]
->
var_desc_
->
Name
();
const
std
::
string
vname
=
outputs
[
i
]
->
var_desc_
->
Name
();
framework
::
Variable
*
var
=
scope
->
Var
(
vname
);
framework
::
Variable
*
var
=
root_scope_
->
Var
(
vname
);
if
(
!
var
->
IsInitialized
())
{
if
(
!
var
->
IsInitialized
())
{
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
vname
);
framework
::
VarDesc
*
var_desc
=
block
->
FindVar
(
vname
);
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
if
(
var_desc
->
GetType
()
==
framework
::
proto
::
VarType
::
LOD_TENSOR
)
{
...
@@ -105,11 +99,8 @@ class Tracer {
...
@@ -105,11 +99,8 @@ class Tracer {
}
}
VLOG
(
3
)
<<
"tracer running "
<<
op_desc
->
Type
();
VLOG
(
3
)
<<
"tracer running "
<<
op_desc
->
Type
();
op_base
->
Run
(
*
scope
,
platform
::
CPUPlace
());
op_base
->
Run
(
*
root_scope_
,
platform
::
CPUPlace
());
if
(
block
==
startup_block_
)
{
if
(
!
stop_gradient
)
{
op
->
grad_op_desc_
=
nullptr
;
op
->
grad_to_var_
=
nullptr
;
}
else
{
framework
::
OpDesc
*
grad_op_desc
;
framework
::
OpDesc
*
grad_op_desc
;
auto
grad_to_var
=
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
();
auto
grad_to_var
=
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
();
CreateGradOp
(
*
op_desc
,
{},
{
block
},
&
grad_op_desc
,
grad_to_var
);
CreateGradOp
(
*
op_desc
,
{},
{
block
},
&
grad_op_desc
,
grad_to_var
);
...
@@ -119,22 +110,10 @@ class Tracer {
...
@@ -119,22 +110,10 @@ class Tracer {
op
->
block_
=
block
;
op
->
block_
=
block
;
}
}
framework
::
Scope
*
GetScope
(
framework
::
BlockDesc
*
block
)
{
framework
::
Scope
*
GetScope
()
{
return
root_scope_
.
get
();
}
if
(
scopes_
.
find
(
block
)
!=
scopes_
.
end
())
{
return
scopes_
.
at
(
block
);
}
framework
::
BlockDesc
*
parent_block
=
block
->
ParentBlock
();
PADDLE_ENFORCE
(
scopes_
.
find
(
parent_block
)
!=
scopes_
.
end
());
framework
::
Scope
*
scope
=
&
scopes_
[
parent_block
]
->
NewScope
();
scopes_
[
block
]
=
scope
;
return
scope
;
}
private:
private:
std
::
map
<
framework
::
BlockDesc
*
,
framework
::
Scope
*>
scopes_
;
std
::
unique_ptr
<
framework
::
Scope
>
root_scope_
;
framework
::
BlockDesc
*
root_block_
;
framework
::
BlockDesc
*
startup_block_
;
framework
::
Scope
*
root_scope_
;
};
};
}
// namespace imperative
}
// namespace imperative
...
...
paddle/fluid/pybind/imperative.cc
浏览文件 @
3cd10a7c
...
@@ -24,9 +24,8 @@ namespace pybind {
...
@@ -24,9 +24,8 @@ namespace pybind {
void
BindTracer
(
pybind11
::
module
*
m
)
{
void
BindTracer
(
pybind11
::
module
*
m
)
{
pybind11
::
class_
<
imperative
::
Tracer
>
(
*
m
,
"Tracer"
,
""
)
pybind11
::
class_
<
imperative
::
Tracer
>
(
*
m
,
"Tracer"
,
""
)
.
def
(
"__init__"
,
.
def
(
"__init__"
,
[](
imperative
::
Tracer
&
self
,
framework
::
BlockDesc
*
root_block
,
[](
imperative
::
Tracer
&
self
,
framework
::
BlockDesc
*
root_block
)
{
framework
::
BlockDesc
*
startup_block
)
{
new
(
&
self
)
imperative
::
Tracer
(
root_block
);
new
(
&
self
)
imperative
::
Tracer
(
root_block
,
startup_block
);
})
})
.
def
(
"trace"
,
&
imperative
::
Tracer
::
Trace
)
.
def
(
"trace"
,
&
imperative
::
Tracer
::
Trace
)
.
def
(
"get_scope"
,
&
imperative
::
Tracer
::
GetScope
,
.
def
(
"get_scope"
,
&
imperative
::
Tracer
::
GetScope
,
...
...
paddle/fluid/pybind/pybind.cc
浏览文件 @
3cd10a7c
...
@@ -117,6 +117,12 @@ PYBIND11_MODULE(core, m) {
...
@@ -117,6 +117,12 @@ PYBIND11_MODULE(core, m) {
self
.
RunBackward
(
scope
);
self
.
RunBackward
(
scope
);
})
})
.
def
(
"_grad"
,
&
imperative
::
VarBase
::
Grad
)
.
def
(
"_grad"
,
&
imperative
::
VarBase
::
Grad
)
.
def_property
(
"value"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_
;
},
[](
imperative
::
VarBase
&
self
,
framework
::
Variable
*
var
)
{
self
.
var_
=
var
;
},
py
::
return_value_policy
::
reference
)
.
def_property
(
.
def_property
(
"desc"
,
"desc"
,
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_desc_
;
},
[](
const
imperative
::
VarBase
&
self
)
{
return
self
.
var_desc_
;
},
...
...
python/paddle/fluid/framework.py
浏览文件 @
3cd10a7c
...
@@ -361,7 +361,7 @@ class Variable(object):
...
@@ -361,7 +361,7 @@ class Variable(object):
self
.
_ivar
.
desc
=
self
.
desc
self
.
_ivar
.
desc
=
self
.
desc
def
_numpy
(
self
):
def
_numpy
(
self
):
scope
=
_imperative_tracer
().
get_scope
(
self
.
block
.
desc
)
scope
=
_imperative_tracer
().
get_scope
()
tensor
=
core
.
get_variable_tensor
(
scope
,
self
.
desc
.
name
())
tensor
=
core
.
get_variable_tensor
(
scope
,
self
.
desc
.
name
())
return
np
.
array
(
tensor
)
return
np
.
array
(
tensor
)
...
@@ -573,7 +573,8 @@ class Operator(object):
...
@@ -573,7 +573,8 @@ class Operator(object):
type
=
None
,
type
=
None
,
inputs
=
None
,
inputs
=
None
,
outputs
=
None
,
outputs
=
None
,
attrs
=
None
):
attrs
=
None
,
stop_gradient
=
False
):
self
.
block
=
block
self
.
block
=
block
self
.
desc
=
desc
self
.
desc
=
desc
# note: not add self.attrs here:
# note: not add self.attrs here:
...
@@ -1264,9 +1265,12 @@ class Block(object):
...
@@ -1264,9 +1265,12 @@ class Block(object):
"""
"""
op_desc
=
self
.
desc
.
append_op
()
op_desc
=
self
.
desc
.
append_op
()
op
=
Operator
(
block
=
self
,
desc
=
op_desc
,
*
args
,
**
kwargs
)
op
=
Operator
(
block
=
self
,
desc
=
op_desc
,
*
args
,
**
kwargs
)
print
(
"append_op"
,
kwargs
.
get
(
"type"
),
kwargs
.
get
(
"stop_gradient"
,
False
))
if
_in_imperative_mode
():
if
_in_imperative_mode
():
_imperative_tracer
().
trace
(
op
.
iop
,
[
v
.
_ivar
for
v
in
op
.
inputs
],
_imperative_tracer
().
trace
(
op
.
iop
,
[
v
.
_ivar
for
v
in
op
.
inputs
],
[
v
.
_ivar
for
v
in
op
.
outputs
],
self
.
desc
)
[
v
.
_ivar
for
v
in
op
.
outputs
],
self
.
desc
,
kwargs
.
get
(
"stop_gradient"
,
False
))
self
.
ops
.
append
(
op
)
self
.
ops
.
append
(
op
)
return
op
return
op
...
@@ -1316,9 +1320,12 @@ class Block(object):
...
@@ -1316,9 +1320,12 @@ class Block(object):
def
_prepend_op
(
self
,
*
args
,
**
kwargs
):
def
_prepend_op
(
self
,
*
args
,
**
kwargs
):
op_desc
=
self
.
desc
.
_prepend_op
()
op_desc
=
self
.
desc
.
_prepend_op
()
op
=
Operator
(
self
,
op_desc
,
*
args
,
**
kwargs
)
op
=
Operator
(
self
,
op_desc
,
*
args
,
**
kwargs
)
print
(
"prepend_op"
,
kwargs
.
get
(
"type"
),
kwargs
.
get
(
"stop_gradient"
,
False
))
if
_in_imperative_mode
():
if
_in_imperative_mode
():
_imperative_tracer
().
trace
(
op
.
iop
,
[
v
.
_ivar
for
v
in
op
.
inputs
],
_imperative_tracer
().
trace
(
op
.
iop
,
[
v
.
_ivar
for
v
in
op
.
inputs
],
[
v
.
_ivar
for
v
in
op
.
outputs
],
self
.
desc
)
[
v
.
_ivar
for
v
in
op
.
outputs
],
self
.
desc
,
kwargs
.
get
(
"stop_gradient"
,
False
))
self
.
ops
.
insert
(
0
,
op
)
self
.
ops
.
insert
(
0
,
op
)
return
op
return
op
...
...
python/paddle/fluid/imperative/__init__.py
浏览文件 @
3cd10a7c
...
@@ -20,6 +20,10 @@ from .base import *
...
@@ -20,6 +20,10 @@ from .base import *
from
.
import
layers
from
.
import
layers
from
.layers
import
*
from
.layers
import
*
from
.
import
nn
from
.nn
import
*
__all__
=
[]
__all__
=
[]
__all__
+=
layers
.
__all__
__all__
+=
layers
.
__all__
__all__
+=
base
.
__all__
__all__
+=
base
.
__all__
__all__
+=
nn
.
__all__
python/paddle/fluid/imperative/base.py
浏览文件 @
3cd10a7c
...
@@ -28,8 +28,7 @@ def enabled():
...
@@ -28,8 +28,7 @@ def enabled():
def
guard
():
def
guard
():
train
=
framework
.
Program
()
train
=
framework
.
Program
()
startup
=
framework
.
Program
()
startup
=
framework
.
Program
()
tracer
=
core
.
Tracer
(
train
.
current_block
().
desc
,
tracer
=
core
.
Tracer
(
train
.
current_block
().
desc
)
startup
.
current_block
().
desc
)
with
framework
.
program_guard
(
train
,
startup
):
with
framework
.
program_guard
(
train
,
startup
):
with
framework
.
unique_name
.
guard
():
with
framework
.
unique_name
.
guard
():
with
framework
.
_imperative_guard
(
tracer
):
with
framework
.
_imperative_guard
(
tracer
):
...
@@ -46,7 +45,7 @@ def to_variable(value, block=None):
...
@@ -46,7 +45,7 @@ def to_variable(value, block=None):
name
=
None
,
name
=
None
,
shape
=
value
.
shape
,
shape
=
value
.
shape
,
dtype
=
value
.
dtype
)
dtype
=
value
.
dtype
)
scope
=
framework
.
_imperative_tracer
().
get_scope
(
block
.
desc
)
scope
=
framework
.
_imperative_tracer
().
get_scope
()
var
=
scope
.
var
(
py_var
.
name
)
var
=
scope
.
var
(
py_var
.
name
)
tensor
=
var
.
get_tensor
()
tensor
=
var
.
get_tensor
()
tensor
.
set
(
value
,
core
.
CPUPlace
())
tensor
.
set
(
value
,
core
.
CPUPlace
())
...
...
python/paddle/fluid/imperative/layers.py
浏览文件 @
3cd10a7c
...
@@ -24,8 +24,10 @@ __all__ = ['PyLayer']
...
@@ -24,8 +24,10 @@ __all__ = ['PyLayer']
class
PyLayer
(
core
.
Layer
):
class
PyLayer
(
core
.
Layer
):
def
__init__
(
self
):
def
__init__
(
self
,
*
args
,
**
kwargs
):
self
.
_built
=
False
from
..layer_helper
import
LayerHelper
self
.
_helper
=
LayerHelper
(
type
(
self
).
__name__
,
**
kwargs
)
self
.
_dtype
=
kwargs
.
get
(
"dtype"
,
core
.
VarDesc
.
VarType
.
FP32
)
def
__call__
(
self
,
inputs
):
def
__call__
(
self
,
inputs
):
if
not
isinstance
(
inputs
,
list
)
and
not
isinstance
(
inputs
,
tuple
):
if
not
isinstance
(
inputs
,
list
)
and
not
isinstance
(
inputs
,
tuple
):
...
@@ -35,15 +37,10 @@ class PyLayer(core.Layer):
...
@@ -35,15 +37,10 @@ class PyLayer(core.Layer):
for
x
in
inputs
:
for
x
in
inputs
:
py_var
=
base
.
to_variable
(
x
)
py_var
=
base
.
to_variable
(
x
)
var_inputs
.
append
(
py_var
)
var_inputs
.
append
(
py_var
)
if
not
self
.
_built
:
self
.
_build_once
(
inputs
)
self
.
_built
=
True
outputs
=
self
.
forward
(
var_inputs
)
outputs
=
self
.
forward
(
var_inputs
)
return
outputs
def
_build_once
(
self
,
inputs
):
return
outputs
pass
def
forward
(
self
,
inputs
):
def
forward
(
self
,
inputs
):
return
[]
return
[]
python/paddle/fluid/initializer.py
浏览文件 @
3cd10a7c
...
@@ -161,7 +161,8 @@ class ConstantInitializer(Initializer):
...
@@ -161,7 +161,8 @@ class ConstantInitializer(Initializer):
"dtype"
:
int
(
var
.
dtype
),
"dtype"
:
int
(
var
.
dtype
),
"value"
:
float
(
self
.
_value
),
"value"
:
float
(
self
.
_value
),
'force_cpu'
:
self
.
_force_cpu
or
force_init_on_cpu
()
'force_cpu'
:
self
.
_force_cpu
or
force_init_on_cpu
()
})
},
stop_gradient
=
True
)
var
.
op
=
op
var
.
op
=
op
return
op
return
op
...
@@ -216,7 +217,8 @@ class UniformInitializer(Initializer):
...
@@ -216,7 +217,8 @@ class UniformInitializer(Initializer):
"min"
:
self
.
_low
,
"min"
:
self
.
_low
,
"max"
:
self
.
_high
,
"max"
:
self
.
_high
,
"seed"
:
self
.
_seed
"seed"
:
self
.
_seed
})
},
stop_gradient
=
True
)
var
.
op
=
op
var
.
op
=
op
return
op
return
op
...
@@ -271,7 +273,8 @@ class NormalInitializer(Initializer):
...
@@ -271,7 +273,8 @@ class NormalInitializer(Initializer):
"std"
:
self
.
_std_dev
,
"std"
:
self
.
_std_dev
,
"seed"
:
self
.
_seed
,
"seed"
:
self
.
_seed
,
"use_mkldnn"
:
False
"use_mkldnn"
:
False
})
},
stop_gradient
=
True
)
var
.
op
=
op
var
.
op
=
op
return
op
return
op
...
@@ -325,7 +328,8 @@ class TruncatedNormalInitializer(Initializer):
...
@@ -325,7 +328,8 @@ class TruncatedNormalInitializer(Initializer):
"mean"
:
self
.
_mean
,
"mean"
:
self
.
_mean
,
"std"
:
self
.
_std_dev
,
"std"
:
self
.
_std_dev
,
"seed"
:
self
.
_seed
"seed"
:
self
.
_seed
})
},
stop_gradient
=
True
)
var
.
op
=
op
var
.
op
=
op
return
op
return
op
...
@@ -415,7 +419,8 @@ class XavierInitializer(Initializer):
...
@@ -415,7 +419,8 @@ class XavierInitializer(Initializer):
"min"
:
-
limit
,
"min"
:
-
limit
,
"max"
:
limit
,
"max"
:
limit
,
"seed"
:
self
.
_seed
"seed"
:
self
.
_seed
})
},
stop_gradient
=
True
)
else
:
else
:
std
=
np
.
sqrt
(
2.0
/
float
(
fan_in
+
fan_out
))
std
=
np
.
sqrt
(
2.0
/
float
(
fan_in
+
fan_out
))
...
@@ -428,7 +433,8 @@ class XavierInitializer(Initializer):
...
@@ -428,7 +433,8 @@ class XavierInitializer(Initializer):
"mean"
:
0.0
,
"mean"
:
0.0
,
"std"
:
std
,
"std"
:
std
,
"seed"
:
self
.
_seed
"seed"
:
self
.
_seed
})
},
stop_gradient
=
True
)
var
.
op
=
op
var
.
op
=
op
return
op
return
op
...
@@ -513,7 +519,8 @@ class MSRAInitializer(Initializer):
...
@@ -513,7 +519,8 @@ class MSRAInitializer(Initializer):
"min"
:
-
limit
,
"min"
:
-
limit
,
"max"
:
limit
,
"max"
:
limit
,
"seed"
:
self
.
_seed
"seed"
:
self
.
_seed
})
},
stop_gradient
=
True
)
else
:
else
:
std
=
np
.
sqrt
(
2.0
/
float
(
fan_in
))
std
=
np
.
sqrt
(
2.0
/
float
(
fan_in
))
...
@@ -526,7 +533,8 @@ class MSRAInitializer(Initializer):
...
@@ -526,7 +533,8 @@ class MSRAInitializer(Initializer):
"mean"
:
0.0
,
"mean"
:
0.0
,
"std"
:
std
,
"std"
:
std
,
"seed"
:
self
.
_seed
"seed"
:
self
.
_seed
})
},
stop_gradient
=
True
)
var
.
op
=
op
var
.
op
=
op
return
op
return
op
...
...
python/paddle/fluid/layer_helper.py
浏览文件 @
3cd10a7c
...
@@ -22,8 +22,8 @@ import numpy as np
...
@@ -22,8 +22,8 @@ import numpy as np
from
.framework
import
Variable
,
Parameter
,
default_main_program
,
default_startup_program
,
dtype_is_floating
from
.framework
import
Variable
,
Parameter
,
default_main_program
,
default_startup_program
,
dtype_is_floating
from
.
import
unique_name
from
.
import
unique_name
from
paddle.fluid.imperative.base
import
to_variable
from
paddle.fluid.initializer
import
Constant
,
Xavier
from
paddle.fluid.initializer
import
Constant
,
Xavier
from
paddle.fluid.imperative
import
base
from
.param_attr
import
ParamAttr
,
WeightNormParamAttr
from
.param_attr
import
ParamAttr
,
WeightNormParamAttr
from
.
import
core
from
.
import
core
from
six.moves
import
zip
from
six.moves
import
zip
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
3cd10a7c
...
@@ -29,7 +29,6 @@ from . import utils
...
@@ -29,7 +29,6 @@ from . import utils
from
..
import
unique_name
from
..
import
unique_name
from
functools
import
reduce
from
functools
import
reduce
from
..
import
core
from
..
import
core
from
..imperative
import
layers
__all__
=
[
__all__
=
[
'fc'
,
'fc'
,
...
@@ -2537,12 +2536,12 @@ def adaptive_pool2d(input,
...
@@ -2537,12 +2536,12 @@ def adaptive_pool2d(input,
Examples:
Examples:
.. code-block:: python
.. code-block:: python
# suppose input data in shape of [N, C, H, W], `pool_size` is [m, n],
# suppose input data in shape of [N, C, H, W], `pool_size` is [m, n],
# output shape is [N, C, m, n], adaptive pool divide H and W dimentions
# output shape is [N, C, m, n], adaptive pool divide H and W dimentions
# of input data into m * n grids averagely and performs poolings in each
# of input data into m * n grids averagely and performs poolings in each
# grid to get output.
# grid to get output.
# adaptive average pool performs calculations as follow:
# adaptive average pool performs calculations as follow:
#
#
# for i in range(m):
# for i in range(m):
# for j in range(n):
# for j in range(n):
# hstart = floor(i * H / m)
# hstart = floor(i * H / m)
...
@@ -2636,10 +2635,10 @@ def adaptive_pool3d(input,
...
@@ -2636,10 +2635,10 @@ def adaptive_pool3d(input,
# suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n],
# suppose input data in shape of [N, C, D, H, W], `pool_size` is [l, m, n],
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions
# output shape is [N, C, l, m, n], adaptive pool divide D, H and W dimentions
# of input data into l * m * n grids averagely and performs poolings in each
# of input data into l * m * n grids averagely and performs poolings in each
# grid to get output.
# grid to get output.
# adaptive average pool performs calculations as follow:
# adaptive average pool performs calculations as follow:
#
#
# for i in range(l):
# for i in range(l):
# for j in range(m):
# for j in range(m):
# for k in range(n):
# for k in range(n):
...
@@ -2649,7 +2648,7 @@ def adaptive_pool3d(input,
...
@@ -2649,7 +2648,7 @@ def adaptive_pool3d(input,
# hend = ceil((j + 1) * H / m)
# hend = ceil((j + 1) * H / m)
# wstart = floor(k * W / n)
# wstart = floor(k * W / n)
# wend = ceil((k + 1) * W / n)
# wend = ceil((k + 1) * W / n)
# output[:, :, i, j, k] =
# output[:, :, i, j, k] =
# avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
# avg(input[:, :, dstart:dend, hstart: hend, wstart: wend])
#
#
data = fluid.layers.data(
data = fluid.layers.data(
...
@@ -9427,47 +9426,3 @@ def huber_loss(input, label, delta):
...
@@ -9427,47 +9426,3 @@ def huber_loss(input, label, delta):
'Residual'
:
residual
},
'Residual'
:
residual
},
attrs
=
{
'delta'
:
delta
})
attrs
=
{
'delta'
:
delta
})
return
out
return
out
class
FC
(
layers
.
PyLayer
):
def
__init__
(
self
,
size
,
param_attr
=
None
,
num_flatten_dims
=
1
,
dtype
=
core
.
VarDesc
.
VarType
.
FP32
):
super
(
FC
,
self
).
__init__
()
self
.
_size
=
size
self
.
_num_flatten_dims
=
num_flatten_dims
self
.
_dtype
=
dtype
self
.
_helper
=
LayerHelper
(
'FC'
,
param_attr
=
param_attr
)
def
_build_once
(
self
,
inputs
):
input_shape
=
inputs
[
0
].
shape
param_shape
=
[
reduce
(
lambda
a
,
b
:
a
*
b
,
input_shape
[
self
.
_num_flatten_dims
:],
1
)
]
+
[
self
.
_size
]
self
.
_w
=
self
.
_helper
.
create_parameter
(
attr
=
self
.
_helper
.
param_attr
,
shape
=
param_shape
,
dtype
=
self
.
_dtype
,
is_bias
=
False
)
def
forward
(
self
,
inputs
):
tmp
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
self
.
_helper
.
append_op
(
type
=
"mul"
,
inputs
=
{
"X"
:
inputs
[
0
],
"Y"
:
self
.
_w
},
outputs
=
{
"Out"
:
tmp
},
attrs
=
{
"x_num_col_dims"
:
self
.
_num_flatten_dims
,
"y_num_col_dims"
:
1
})
out
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
self
.
_helper
.
append_op
(
type
=
"sum"
,
inputs
=
{
"X"
:
[
tmp
]},
outputs
=
{
"Out"
:
out
},
attrs
=
{
"use_mkldnn"
:
False
})
return
out
python/paddle/fluid/tests/unittests/test_imperative.py
已删除
100644 → 0
浏览文件 @
8d88c5a8
# 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
contextlib
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.layers.nn
import
FC
@
contextlib
.
contextmanager
def
new_program_scope
():
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
yield
class
MyLayer
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
MyLayer
,
self
).
__init__
()
def
forward
(
self
,
inputs
):
x
=
fluid
.
layers
.
relu
(
inputs
[
0
])
self
.
_x_for_debug
=
x
return
[
fluid
.
layers
.
elementwise_mul
(
x
,
x
)]
class
MLP
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
MLP
,
self
).
__init__
()
self
.
_fc1
=
FC
(
3
,
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.1
)))
self
.
_fc2
=
FC
(
4
,
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
Constant
(
value
=
0.1
)))
def
forward
(
self
,
inputs
):
x
=
self
.
_fc1
(
inputs
[
0
])
x
=
self
.
_fc2
(
x
)
x
=
fluid
.
layers
.
reduce_sum
(
x
)
return
x
class
TestImperative
(
unittest
.
TestCase
):
def
test_layer
(
self
):
with
fluid
.
imperative
.
guard
():
cl
=
core
.
Layer
()
cl
.
forward
([])
l
=
fluid
.
imperative
.
PyLayer
()
l
.
forward
([])
def
test_layer_in_out
(
self
):
np_inp
=
np
.
array
([
1.0
,
2.0
,
-
1.0
],
dtype
=
np
.
float32
)
with
fluid
.
imperative
.
guard
():
l
=
MyLayer
()
x
=
l
(
np_inp
)[
0
]
self
.
assertIsNotNone
(
x
)
dy_out
=
x
.
_numpy
()
x
.
_backward
()
dy_grad
=
l
.
_x_for_debug
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
3
],
append_batch_size
=
False
)
l
=
MyLayer
()
x
=
l
(
inp
)[
0
]
param_grads
=
fluid
.
backward
.
append_backward
(
x
,
parameter_list
=
[
l
.
_x_for_debug
.
name
])[
0
]
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
x
.
name
,
param_grads
[
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
def
test_mlp
(
self
):
np_inp
=
np
.
array
([[
1.0
,
2.0
],
[
3.0
,
4.0
]],
dtype
=
np
.
float32
)
with
fluid
.
imperative
.
guard
():
mlp
=
MLP
()
out
=
mlp
(
np_inp
)
dy_out
=
out
.
_numpy
()
out
.
_backward
()
dy_grad
=
mlp
.
_fc1
.
_w
.
_gradient
()
with
new_program_scope
():
inp
=
fluid
.
layers
.
data
(
name
=
"inp"
,
shape
=
[
2
,
2
],
append_batch_size
=
False
)
mlp
=
MLP
()
out
=
mlp
(
inp
)
param_grads
=
fluid
.
backward
.
append_backward
(
out
,
parameter_list
=
[
mlp
.
_fc1
.
_w
.
name
])[
0
]
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
())
exe
.
run
(
fluid
.
default_startup_program
())
static_out
,
static_grad
=
exe
.
run
(
feed
=
{
inp
.
name
:
np_inp
},
fetch_list
=
[
out
.
name
,
param_grads
[
1
].
name
])
self
.
assertTrue
(
np
.
allclose
(
dy_out
,
static_out
))
self
.
assertTrue
(
np
.
allclose
(
dy_grad
,
static_grad
))
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/test_imperative_mnist.py
0 → 100644
浏览文件 @
3cd10a7c
# 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
contextlib
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
from
paddle.fluid
import
core
from
paddle.fluid.imperative.nn
import
Conv2D
@
contextlib
.
contextmanager
def
new_program_scope
():
prog
=
fluid
.
Program
()
startup_prog
=
fluid
.
Program
()
scope
=
fluid
.
core
.
Scope
()
with
fluid
.
scope_guard
(
scope
):
with
fluid
.
program_guard
(
prog
,
startup_prog
):
yield
class
MNIST
(
fluid
.
imperative
.
PyLayer
):
def
__init__
(
self
):
super
(
MNIST
,
self
).
__init__
()
groups
=
1
dilation
=
[
1
,
1
]
pad
=
[
0
,
0
]
stride
=
[
1
,
1
]
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
assert
np
.
mod
(
input_size
[
1
],
groups
)
==
0
f_c
=
input_size
[
1
]
//
groups
filter_size
=
[
6
,
f_c
,
3
,
3
]
self
.
_conv2d
=
Conv2D
(
num_channels
=
3
,
num_filters
=
20
,
filter_size
=
3
,
stride
=
stride
,
padding
=
pad
,
dilation
=
dilation
,
groups
=
groups
,
use_cudnn
=
False
)
def
forward
(
self
,
inputs
):
x
=
self
.
_conv2d
(
inputs
)
return
x
class
TestImperativeMnist
(
unittest
.
TestCase
):
# def test_layer(self):
# with fluid.imperative.guard():
# cl = core.Layer()
# cl.forward([])
# l = fluid.imperative.PyLayer()
# l.forward([])
# def test_layer_in_out(self):
# np_inp = np.array([1.0, 2.0, -1.0], dtype=np.float32)
# with fluid.imperative.guard():
# l = MyLayer()
# x = l(np_inp)[0]
# self.assertIsNotNone(x)
# dy_out = x._numpy()
# x._backward()
# dy_grad = l._x_for_debug._gradient()
# with new_program_scope():
# inp = fluid.layers.data(
# name="inp", shape=[3], append_batch_size=False)
# l = MyLayer()
# x = l(inp)[0]
# param_grads = fluid.backward.append_backward(
# x, parameter_list=[l._x_for_debug.name])[0]
# exe = fluid.Executor(fluid.CPUPlace())
# static_out, static_grad = exe.run(
# feed={inp.name: np_inp},
# fetch_list=[x.name, param_grads[1].name])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad, static_grad))
def
test_mnist_cpu_float32
(
self
):
with
fluid
.
imperative
.
guard
():
mnist
=
MNIST
()
data
=
np
.
random
.
rand
(
2
,
3
,
5
,
5
).
astype
(
'float32'
)
mnist
(
data
)
# np_inp = np.array([[1.0, 2.0], [3.0, 4.0]], dtype=np.float32)
# with fluid.imperative.guard():
# mlp = MLP()
# out = mlp(np_inp)
# dy_out = out._numpy()
# out._backward()
# dy_grad = mlp._fc1._w._gradient()
# with new_program_scope():
# inp = fluid.layers.data(
# name="inp", shape=[2, 2], append_batch_size=False)
# mlp = MLP()
# out = mlp(inp)
# param_grads = fluid.backward.append_backward(
# out, parameter_list=[mlp._fc1._w.name])[0]
# exe = fluid.Executor(fluid.CPUPlace())
# exe.run(fluid.default_startup_program())
# static_out, static_grad = exe.run(
# feed={inp.name: np_inp},
# fetch_list=[out.name, param_grads[1].name])
# self.assertTrue(np.allclose(dy_out, static_out))
# self.assertTrue(np.allclose(dy_grad, static_grad))
if
__name__
==
'__main__'
:
unittest
.
main
()
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