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体验新版 GitCode,发现更多精彩内容 >>
未验证
提交
4bd28b30
编写于
2月 25, 2019
作者:
Q
Qiyang Min
提交者:
GitHub
2月 25, 2019
浏览文件
操作
浏览文件
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差异文件
Merge pull request #15831 from velconia/imperative_engine
Imperative training network to the end
上级
a6e3cd5e
e9fdf909
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
163 addition
and
102 deletion
+163
-102
paddle/fluid/framework/block_desc.cc
paddle/fluid/framework/block_desc.cc
+14
-0
paddle/fluid/framework/block_desc.h
paddle/fluid/framework/block_desc.h
+2
-0
paddle/fluid/imperative/layer.h
paddle/fluid/imperative/layer.h
+15
-13
paddle/fluid/imperative/tracer.cc
paddle/fluid/imperative/tracer.cc
+14
-6
paddle/fluid/imperative/tracer.h
paddle/fluid/imperative/tracer.h
+6
-4
paddle/fluid/pybind/imperative.cc
paddle/fluid/pybind/imperative.cc
+4
-4
paddle/fluid/pybind/protobuf.cc
paddle/fluid/pybind/protobuf.cc
+2
-0
python/paddle/fluid/framework.py
python/paddle/fluid/framework.py
+42
-14
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
...paddle/fluid/tests/unittests/test_imperative_optimizer.py
+59
-56
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
...on/paddle/fluid/tests/unittests/test_imperative_resnet.py
+5
-5
未找到文件。
paddle/fluid/framework/block_desc.cc
浏览文件 @
4bd28b30
...
...
@@ -163,6 +163,20 @@ std::vector<OpDesc *> BlockDesc::AllOps() const {
return
res
;
}
void
BlockDesc
::
Clear
()
{
// clear all ops
ops_
.
clear
();
// clear all vars which are not persistable
for
(
auto
it
=
vars_
.
begin
();
it
!=
vars_
.
end
();)
{
if
(
it
->
second
->
Persistable
())
{
++
it
;
}
else
{
vars_
.
erase
(
it
++
);
}
}
}
void
BlockDesc
::
Flush
()
{
for
(
auto
&
op_desc
:
ops_
)
{
op_desc
->
Flush
();
...
...
paddle/fluid/framework/block_desc.h
浏览文件 @
4bd28b30
...
...
@@ -97,6 +97,8 @@ class BlockDesc {
std
::
vector
<
OpDesc
*>
AllOps
()
const
;
void
Clear
();
size_t
OpSize
()
const
{
return
ops_
.
size
();
}
OpDesc
*
Op
(
int
idx
)
const
{
return
ops_
.
at
(
idx
).
get
();
}
...
...
paddle/fluid/imperative/layer.h
浏览文件 @
4bd28b30
...
...
@@ -114,23 +114,23 @@ class VarBase {
public:
VarBase
()
:
VarBase
(
new
framework
::
Variable
(),
new
VarBase
(
true
))
{}
// Owns `var` and `grad`
explicit
VarBase
(
bool
stop_gradient
)
:
VarBase
(
new
framework
::
Variable
(),
stop_gradient
?
nullptr
:
new
VarBase
(
true
),
stop_gradient
)
{}
VarBase
(
framework
::
Variable
*
var
,
VarBase
*
grad
)
:
VarBase
(
var
,
grad
,
false
)
{}
private:
VarBase
(
framework
::
Variable
*
var
,
VarBase
*
grad
,
bool
stop_gradient
)
:
var_desc_
(
nullptr
),
var_
(
var
),
grads_
(
grad
),
stop_gradient_
(
false
),
pre_op_
(
nullptr
),
pre_op_out_idx_
(
-
1
)
{}
explicit
VarBase
(
bool
stop_gradient
)
:
var_desc_
(
nullptr
),
var_
(
new
framework
::
Variable
()),
grads_
(
stop_gradient
?
nullptr
:
new
VarBase
(
true
)),
stop_gradient_
(
stop_gradient
),
pre_op_
(
nullptr
),
pre_op_out_idx_
(
-
1
)
{}
public:
virtual
~
VarBase
()
{
if
(
var_
)
{
delete
var_
;
...
...
@@ -141,11 +141,13 @@ class VarBase {
}
}
OpBase
*
PreOp
()
const
{
return
pre_op_
;
}
int
PreOpOutIdx
()
const
{
return
pre_op_out_idx_
;
}
inline
OpBase
*
PreOp
()
const
{
return
pre_op_
;
}
in
line
in
t
PreOpOutIdx
()
const
{
return
pre_op_out_idx_
;
}
void
SetStopGradient
(
bool
stop_gradient
)
{
stop_gradient_
=
stop_gradient
;
}
bool
IsStopGradient
()
const
{
return
stop_gradient_
;
}
inline
void
SetStopGradient
(
bool
stop_gradient
)
{
stop_gradient_
=
stop_gradient
;
}
inline
bool
IsStopGradient
()
const
{
return
stop_gradient_
;
}
void
RunBackward
();
...
...
paddle/fluid/imperative/tracer.cc
浏览文件 @
4bd28b30
...
...
@@ -14,6 +14,8 @@
#include "paddle/fluid/imperative/tracer.h"
#include <set>
#include "paddle/fluid/operators/math/math_function.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/enforce.h"
...
...
@@ -66,16 +68,18 @@ platform::Place GetExpectedPlace(platform::Place place, VarBasePtrMap inputs) {
return
result
;
}
void
Tracer
::
Trace
(
OpBase
*
op
,
const
VarBasePtrMap
&
inputs
,
const
VarBasePtrMap
&
outputs
,
framework
::
BlockDesc
*
block
,
const
platform
::
Place
expected_place
,
const
bool
stop_gradient
)
{
std
::
set
<
std
::
string
>
Tracer
::
Trace
(
OpBase
*
op
,
const
VarBasePtrMap
&
inputs
,
const
VarBasePtrMap
&
outputs
,
framework
::
BlockDesc
*
block
,
const
platform
::
Place
expected_place
,
const
bool
stop_gradient
)
{
std
::
map
<
std
::
string
,
VarBase
*>
vars
;
framework
::
OpDesc
*
op_desc
=
op
->
op_desc_
;
VLOG
(
3
)
<<
"tracer tracing "
<<
op_desc
->
Type
();
op_desc
->
InferShape
(
*
block
);
op_desc
->
InferVarType
(
block
);
std
::
unique_ptr
<
framework
::
OperatorBase
>
op_base
=
framework
::
OpRegistry
::
CreateOp
(
*
op_desc
);
...
...
@@ -92,7 +96,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
invars
.
emplace_back
(
inp
->
var_
);
vars
[
inp
->
var_desc_
->
Name
()]
=
inp
;
if
(
inp
->
PreOp
())
{
if
(
inp
->
PreOp
()
&&
!
inp
->
IsStopGradient
()
)
{
op
->
pre_ops_
[
it
.
first
].
push_back
(
inp
->
PreOp
());
op
->
pre_ops_out_idx_
[
it
.
first
].
push_back
(
inp
->
PreOpOutIdx
());
}
else
{
...
...
@@ -142,6 +146,8 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
framework
::
ExecutionContext
(
prepared_op
.
op
,
scope
,
*
prepared_op
.
dev_ctx
,
prepared_op
.
ctx
,
prepared_op
.
kernel_configs
));
std
::
set
<
std
::
string
>
vars_saved_for_backward
;
if
(
!
stop_gradient
)
{
std
::
unique_ptr
<
std
::
unordered_map
<
std
::
string
,
std
::
string
>>
grad_to_var
(
new
std
::
unordered_map
<
std
::
string
,
std
::
string
>
());
...
...
@@ -161,6 +167,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
PADDLE_ENFORCE
(
fwd_var_it
!=
vars
.
end
());
// Forward inputs or outputs.
grad_in_vars
.
push_back
(
fwd_var_it
->
second
->
var_
);
vars_saved_for_backward
.
insert
(
it
.
first
);
}
else
{
VarBase
*
var
=
vars
[
var_it
->
second
];
if
(
!
var
->
grads_
->
var_
->
IsInitialized
())
{
...
...
@@ -194,6 +201,7 @@ void Tracer::Trace(OpBase* op, const VarBasePtrMap& inputs,
}
op
->
block_
=
block
;
return
vars_saved_for_backward
;
}
std
::
vector
<
VarBase
*>
Tracer
::
PyTrace
(
OpBase
*
op
,
...
...
@@ -203,7 +211,7 @@ std::vector<VarBase*> Tracer::PyTrace(OpBase* op,
op
->
input_vars_
[
PyLayer
::
kFwdInp
]
=
inputs
;
op
->
output_vars_
[
PyLayer
::
kFwdOut
]
=
PyLayer
::
Apply
(
op
->
forward_id_
,
inputs
);
for
(
VarBase
*
inp
:
inputs
)
{
if
(
inp
->
PreOp
())
{
if
(
inp
->
PreOp
()
&&
!
inp
->
IsStopGradient
()
)
{
op
->
pre_ops_
[
PyLayer
::
kFwdInp
].
push_back
(
inp
->
PreOp
());
op
->
pre_ops_out_idx_
[
PyLayer
::
kFwdInp
].
push_back
(
inp
->
PreOpOutIdx
());
}
else
{
...
...
paddle/fluid/imperative/tracer.h
浏览文件 @
4bd28b30
...
...
@@ -15,6 +15,7 @@
#pragma once
#include <map>
#include <set>
#include <string>
#include <vector>
...
...
@@ -43,10 +44,11 @@ class Tracer {
virtual
~
Tracer
()
{}
void
Trace
(
OpBase
*
op
,
const
VarBasePtrMap
&
inputs
,
const
VarBasePtrMap
&
outputs
,
framework
::
BlockDesc
*
block
,
const
platform
::
Place
expected_place
,
const
bool
stop_gradient
=
false
);
std
::
set
<
std
::
string
>
Trace
(
OpBase
*
op
,
const
VarBasePtrMap
&
inputs
,
const
VarBasePtrMap
&
outputs
,
framework
::
BlockDesc
*
block
,
const
platform
::
Place
expected_place
,
const
bool
stop_gradient
=
false
);
std
::
vector
<
VarBase
*>
PyTrace
(
OpBase
*
op
,
const
std
::
vector
<
VarBase
*>&
inputs
,
bool
stop_gradient
=
false
);
...
...
paddle/fluid/pybind/imperative.cc
浏览文件 @
4bd28b30
...
...
@@ -34,8 +34,8 @@ void BindTracer(pybind11::module* m) {
framework
::
BlockDesc
*
block
,
const
platform
::
CPUPlace
expected_place
,
const
bool
stop_gradient
=
false
)
{
self
.
Trace
(
op
,
inputs
,
outputs
,
block
,
expected_place
,
stop_gradient
);
return
self
.
Trace
(
op
,
inputs
,
outputs
,
block
,
expected_place
,
stop_gradient
);
})
.
def
(
"trace"
,
[](
imperative
::
Tracer
&
self
,
imperative
::
OpBase
*
op
,
...
...
@@ -44,8 +44,8 @@ void BindTracer(pybind11::module* m) {
framework
::
BlockDesc
*
block
,
const
platform
::
CUDAPlace
expected_place
,
const
bool
stop_gradient
=
false
)
{
self
.
Trace
(
op
,
inputs
,
outputs
,
block
,
expected_place
,
stop_gradient
);
return
self
.
Trace
(
op
,
inputs
,
outputs
,
block
,
expected_place
,
stop_gradient
);
})
.
def
(
"py_trace"
,
&
imperative
::
Tracer
::
PyTrace
,
pybind11
::
return_value_policy
::
take_ownership
);
...
...
paddle/fluid/pybind/protobuf.cc
浏览文件 @
4bd28b30
...
...
@@ -189,6 +189,8 @@ void BindBlockDesc(pybind11::module *m) {
return
self
.
HasVar
(
name
);
},
pybind11
::
return_value_policy
::
reference
)
.
def
(
"_clear_block"
,
[](
pd
::
BlockDesc
&
self
)
{
return
self
.
Clear
();
},
pybind11
::
return_value_policy
::
reference
)
.
def
(
"_rename_var"
,
[](
pd
::
BlockDesc
&
self
,
const
pybind11
::
bytes
&
byte_name
,
const
pybind11
::
bytes
&
byte_name_new
)
{
...
...
python/paddle/fluid/framework.py
浏览文件 @
4bd28b30
...
...
@@ -387,16 +387,19 @@ class Variable(object):
# get_capacity is implemented
pass
self
.
block
.
vars
[
name
]
=
self
self
.
op
=
None
self
.
stop_gradient
=
stop_gradient
self
.
is_data
=
is_data
if
_in_imperative_mode
():
# record vars in tracer rather than blocks
self
.
_ivar
=
kwargs
.
get
(
"ivar"
,
None
)
if
not
self
.
_ivar
:
self
.
_ivar
=
core
.
VarBase
()
self
.
_ivar
=
core
.
VarBase
(
stop_gradient
)
self
.
_ivar
.
desc
=
self
.
desc
self
.
_ivar
.
stop_gradient
=
stop_gradient
if
persistable
:
self
.
block
.
vars
[
name
]
=
self
else
:
self
.
block
.
vars
[
name
]
=
self
self
.
op
=
None
self
.
stop_gradient
=
stop_gradient
self
.
is_data
=
is_data
def
_numpy
(
self
):
new_ivar
=
self
.
_ivar
.
_copy_to
(
core
.
CPUPlace
(),
True
)
...
...
@@ -739,6 +742,7 @@ class Operator(object):
if
_in_imperative_mode
():
self
.
iop
=
core
.
OpBase
()
self
.
iop
.
desc
=
self
.
desc
self
.
inputs
=
defaultdict
(
list
)
if
inputs
is
not
None
:
for
k
,
v
in
six
.
iteritems
(
inputs
):
...
...
@@ -746,6 +750,7 @@ class Operator(object):
self
.
inputs
[
k
].
append
(
v
.
_ivar
)
elif
isinstance
(
v
,
list
)
or
isinstance
(
v
,
tuple
):
self
.
inputs
[
k
].
extend
([
var
.
_ivar
for
var
in
v
])
self
.
outputs
=
defaultdict
(
list
)
if
outputs
is
not
None
:
for
k
,
v
in
six
.
iteritems
(
outputs
):
...
...
@@ -1195,6 +1200,15 @@ class Block(object):
else
:
raise
ValueError
(
"Var {0} is not found recursively"
.
format
(
name
))
def
_clear_block
(
self
):
# TODO(minqiyang): move this to backward_hooks
self
.
desc
.
_clear_block
()
for
name
in
self
.
vars
.
keys
():
assert
self
.
vars
[
name
].
persistable
del
self
.
ops
[:]
def
all_parameters
(
self
):
return
list
(
self
.
iter_parameters
())
...
...
@@ -1325,18 +1339,31 @@ class Block(object):
inputs
=
kwargs
.
get
(
"inputs"
,
None
),
outputs
=
kwargs
.
get
(
"outputs"
,
None
),
attrs
=
kwargs
.
get
(
"attrs"
,
None
))
if
_in_imperative_mode
():
# record ops in tracer rather than blocks
#
# TODO(minqiyang): add op stop_gradient support in static mode too.
# currently, we only support stop_gradient in imperative mode.
self
.
_trace_op
(
op
,
kwargs
.
get
(
"stop_gradient"
,
False
))
self
.
ops
.
append
(
op
)
# TODO(minqiyang): add stop_gradient support in static mode too.
# currently, we only support stop_gradient in imperative mode.
self
.
_trace_op
(
op
,
kwargs
.
get
(
"stop_gradient"
,
False
))
return
op
def
_trace_op
(
self
,
op
,
stop_gradient
=
False
):
if
_in_imperative_mode
():
_imperative_tracer
().
trace
(
op
.
iop
,
op
.
inputs
,
op
.
outputs
,
self
.
desc
,
_imperative_current_expected_place_
,
stop_gradient
)
backward_refs
=
_imperative_tracer
().
trace
(
op
.
iop
,
op
.
inputs
,
op
.
outputs
,
self
.
desc
,
_imperative_current_expected_place_
,
stop_gradient
)
# TODO(minqiyang): support backward_hooks to eager remove backward_refs
op
.
backward_refs
=
defaultdict
(
list
)
for
k
,
v
in
six
.
iteritems
(
op
.
inputs
):
if
k
in
backward_refs
:
op
.
backward_refs
[
k
]
=
op
.
inputs
[
k
]
for
k
,
v
in
six
.
iteritems
(
op
.
outputs
):
if
k
in
backward_refs
:
op
.
backward_refs
[
k
]
=
op
.
outputs
[
k
]
def
_insert_op
(
self
,
index
,
*
args
,
**
kwargs
):
"""
...
...
@@ -1391,7 +1418,8 @@ class Block(object):
outputs
=
kwargs
.
get
(
"outputs"
,
None
),
attrs
=
kwargs
.
get
(
"attrs"
,
None
))
self
.
ops
.
insert
(
0
,
op
)
self
.
_trace_op
(
op
,
kwargs
.
get
(
"stop_gradient"
,
False
))
if
_in_imperative_mode
():
self
.
_trace_op
(
op
,
kwargs
.
get
(
"stop_gradient"
,
False
))
return
op
def
_sync_with_cpp
(
self
):
...
...
python/paddle/fluid/tests/unittests/test_imperative_optimizer.py
浏览文件 @
4bd28b30
...
...
@@ -105,7 +105,7 @@ class MNIST(fluid.imperative.Layer):
class
TestImperativeMnist
(
unittest
.
TestCase
):
def
test_mnist_float32
(
self
):
seed
=
90
batch_num
=
2
epoch_num
=
1
with
fluid
.
imperative
.
guard
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
...
...
@@ -113,39 +113,40 @@ class TestImperativeMnist(unittest.TestCase):
mnist
=
MNIST
(
"mnist"
)
sgd
=
SGDOptimizer
(
learning_rate
=
1e-3
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
)
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
,
drop_last
=
True
)
dy_param_init_value
=
{}
for
batch_id
,
data
in
enumerate
(
train_reader
()):
if
batch_id
>=
batch_num
:
break
dy_x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
128
,
1
)
img
=
to_variable
(
dy_x_data
)
label
=
to_variable
(
y_data
)
label
.
_stop_gradient
=
True
cost
=
mnist
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
cost
,
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
dy_out
=
avg_loss
.
_numpy
()
if
batch_id
==
0
:
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
dy_param_init_value
[
param
.
name
]
=
param
.
_numpy
()
avg_loss
.
_backward
()
sgd
.
minimize
(
avg_loss
)
mnist
.
clear_gradients
()
dy_param_value
=
{}
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
dy_param_value
[
param
.
name
]
=
param
.
_numpy
()
for
epoch
in
range
(
epoch_num
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
dy_x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
(
[
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
128
,
1
)
img
=
to_variable
(
dy_x_data
)
label
=
to_variable
(
y_data
)
label
.
_stop_gradient
=
True
cost
=
mnist
(
img
)
loss
=
fluid
.
layers
.
cross_entropy
(
cost
,
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
dy_out
=
avg_loss
.
_numpy
()
if
epoch
==
0
and
batch_id
==
0
:
for
param
in
mnist
.
parameters
():
dy_param_init_value
[
param
.
name
]
=
param
.
_numpy
()
avg_loss
.
_backward
()
sgd
.
minimize
(
avg_loss
)
mnist
.
clear_gradients
()
fluid
.
default_main_program
().
global_block
().
_clear_block
()
dy_param_value
=
{}
for
param
in
mnist
.
parameters
():
dy_param_value
[
param
.
name
]
=
param
.
_numpy
()
with
new_program_scope
():
fluid
.
default_startup_program
().
random_seed
=
seed
...
...
@@ -157,7 +158,7 @@ class TestImperativeMnist(unittest.TestCase):
mnist
=
MNIST
(
"mnist"
)
sgd
=
SGDOptimizer
(
learning_rate
=
1e-3
)
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
)
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
128
,
drop_last
=
True
)
img
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
...
...
@@ -170,8 +171,7 @@ class TestImperativeMnist(unittest.TestCase):
# initialize params and fetch them
static_param_init_value
=
{}
static_param_name_list
=
[]
for
param
in
fluid
.
default_startup_program
().
global_block
(
).
all_parameters
():
for
param
in
mnist
.
parameters
():
static_param_name_list
.
append
(
param
.
name
)
out
=
exe
.
run
(
fluid
.
default_startup_program
(),
...
...
@@ -180,26 +180,29 @@ class TestImperativeMnist(unittest.TestCase):
for
i
in
range
(
len
(
static_param_name_list
)):
static_param_init_value
[
static_param_name_list
[
i
]]
=
out
[
i
]
for
batch_id
,
data
in
enumerate
(
train_reader
()):
if
batch_id
>=
batch_num
:
break
static_x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
([
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
(
[
128
,
1
])
fetch_list
=
[
avg_loss
.
name
]
fetch_list
.
extend
(
static_param_name_list
)
out
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"pixel"
:
static_x_data
,
"label"
:
y_data
},
fetch_list
=
fetch_list
)
static_param_value
=
{}
static_out
=
out
[
0
]
for
i
in
range
(
1
,
len
(
out
)):
static_param_value
[
static_param_name_list
[
i
-
1
]]
=
out
[
i
]
for
epoch
in
range
(
epoch_num
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
static_x_data
=
np
.
array
(
[
x
[
0
].
reshape
(
1
,
28
,
28
)
for
x
in
data
]).
astype
(
'float32'
)
y_data
=
np
.
array
(
[
x
[
1
]
for
x
in
data
]).
astype
(
'int64'
).
reshape
([
128
,
1
])
fetch_list
=
[
avg_loss
.
name
]
fetch_list
.
extend
(
static_param_name_list
)
out
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"pixel"
:
static_x_data
,
"label"
:
y_data
},
fetch_list
=
fetch_list
)
static_param_value
=
{}
static_out
=
out
[
0
]
for
i
in
range
(
1
,
len
(
out
)):
static_param_value
[
static_param_name_list
[
i
-
1
]]
=
out
[
i
]
self
.
assertTrue
(
np
.
allclose
(
dy_x_data
.
all
(),
static_x_data
.
all
()))
for
key
,
value
in
six
.
iteritems
(
static_param_init_value
):
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_init_value
[
key
]))
...
...
@@ -207,7 +210,7 @@ class TestImperativeMnist(unittest.TestCase):
self
.
assertTrue
(
np
.
allclose
(
static_out
,
dy_out
))
for
key
,
value
in
six
.
iteritems
(
static_param_value
):
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_value
[
key
]))
self
.
assertTrue
(
np
.
allclose
(
value
,
dy_param_value
[
key
]
,
atol
=
1e-5
))
if
__name__
==
'__main__'
:
...
...
python/paddle/fluid/tests/unittests/test_imperative_resnet.py
浏览文件 @
4bd28b30
...
...
@@ -231,7 +231,7 @@ class TestImperativeResnet(unittest.TestCase):
seed
=
90
batch_size
=
train_parameters
[
"batch_size"
]
batch_num
=
1
batch_num
=
2
with
fluid
.
imperative
.
guard
():
fluid
.
default_startup_program
().
random_seed
=
seed
fluid
.
default_main_program
().
random_seed
=
seed
...
...
@@ -286,6 +286,8 @@ class TestImperativeResnet(unittest.TestCase):
optimizer
.
minimize
(
avg_loss
)
resnet
.
clear_gradients
()
fluid
.
default_main_program
().
global_block
().
_clear_block
()
dy_param_value
=
{}
for
param
in
resnet
.
parameters
():
dy_param_value
[
param
.
name
]
=
param
.
_numpy
()
...
...
@@ -319,11 +321,9 @@ class TestImperativeResnet(unittest.TestCase):
static_param_init_value
=
{}
static_param_name_list
=
[]
static_grad_name_list
=
[]
for
param
in
fluid
.
default_startup_program
().
global_block
(
).
all_parameters
():
for
param
in
resnet
.
parameters
():
static_param_name_list
.
append
(
param
.
name
)
for
param
in
fluid
.
default_main_program
().
global_block
(
).
all_parameters
():
for
param
in
resnet
.
parameters
():
if
not
param
.
stop_gradient
:
static_grad_name_list
.
append
(
param
.
name
+
core
.
grad_var_suffix
())
...
...
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