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PaddleDetection
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08b22cf1
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08b22cf1
编写于
9月 21, 2018
作者:
G
gongweibao
浏览文件
操作
浏览文件
下载
差异文件
Merge branch 'generator2' of
https://github.com/gongweibao/Paddle
into generator2
上级
a3019474
bee213e5
变更
6
显示空白变更内容
内联
并排
Showing
6 changed file
with
274 addition
and
51 deletion
+274
-51
paddle/fluid/API.spec
paddle/fluid/API.spec
+6
-6
paddle/fluid/framework/details/reference_count_op_handle.h
paddle/fluid/framework/details/reference_count_op_handle.h
+28
-13
paddle/fluid/framework/details/reference_count_pass.cc
paddle/fluid/framework/details/reference_count_pass.cc
+64
-11
paddle/fluid/operators/adam_op.h
paddle/fluid/operators/adam_op.h
+31
-13
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+145
-2
python/paddle/fluid/layers/ops.py
python/paddle/fluid/layers/ops.py
+0
-6
未找到文件。
paddle/fluid/API.spec
浏览文件 @
08b22cf1
...
...
@@ -160,6 +160,12 @@ paddle.fluid.layers.relu ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.log ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.crop ArgSpec(args=['x', 'shape', 'offsets', 'name'], varargs=None, keywords=None, defaults=(None, None, None))
paddle.fluid.layers.rank_loss ArgSpec(args=['label', 'left', 'right', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.elu ArgSpec(args=['x', 'alpha', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.relu6 ArgSpec(args=['x', 'threshold', 'name'], varargs=None, keywords=None, defaults=(6.0, None))
paddle.fluid.layers.pow ArgSpec(args=['x', 'factor', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.stanh ArgSpec(args=['x', 'scale_a', 'scale_b', 'name'], varargs=None, keywords=None, defaults=(0.6666666666666666, 1.7159, None))
paddle.fluid.layers.hard_sigmoid ArgSpec(args=['x', 'slope', 'offset', 'name'], varargs=None, keywords=None, defaults=(0.2, 0.5, None))
paddle.fluid.layers.swish ArgSpec(args=['x', 'beta', 'name'], varargs=None, keywords=None, defaults=(1.0, None))
paddle.fluid.layers.prelu ArgSpec(args=['x', 'mode', 'param_attr', 'name'], varargs=None, keywords=None, defaults=(None, None))
paddle.fluid.layers.flatten ArgSpec(args=['x', 'axis', 'name'], varargs=None, keywords=None, defaults=(1, None))
paddle.fluid.layers.sequence_mask ArgSpec(args=['x', 'maxlen', 'dtype', 'name'], varargs=None, keywords=None, defaults=(None, 'int64', None))
...
...
@@ -276,12 +282,6 @@ paddle.fluid.layers.softsign ArgSpec(args=[], varargs='args', keywords='kwargs',
paddle.fluid.layers.brelu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.leaky_relu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.soft_relu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.elu ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.relu6 ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.pow ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.stanh ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.hard_sigmoid ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.swish ArgSpec(args=[], varargs='args', keywords='kwargs', defaults=None)
paddle.fluid.layers.uniform_random ArgSpec(args=['shape', 'dtype', 'min', 'max', 'seed'], varargs=None, keywords=None, defaults=(None, None, None, None))
paddle.fluid.layers.hard_shrink ArgSpec(args=['x', 'threshold'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.cumsum ArgSpec(args=['x', 'axis', 'exclusive', 'reverse'], varargs=None, keywords=None, defaults=(None, None, None))
...
...
paddle/fluid/framework/details/reference_count_op_handle.h
浏览文件 @
08b22cf1
...
...
@@ -22,6 +22,7 @@
#include "paddle/fluid/framework/details/op_handle_base.h"
#include "paddle/fluid/framework/garbage_collector.h"
#include "paddle/fluid/framework/scope.h"
#include "paddle/fluid/framework/selected_rows.h"
#include "paddle/fluid/framework/tensor.h"
namespace
paddle
{
...
...
@@ -46,17 +47,15 @@ class ReferenceCountOpHandle : public OpHandleBase {
const
std
::
vector
<
std
::
string
>
&
var_names
,
GarbageCollector
<
Tensor
>
*
gc
,
AtomicReferenceCountMap
*
ref_cnts
)
:
OpHandleBase
(
node
),
scope_
(
scope
),
var_names_
(
var_names
),
gc_
(
gc
),
ref_cnts_
(
ref_cnts
)
{
:
OpHandleBase
(
node
),
scope_
(
scope
),
gc_
(
gc
),
ref_cnts_
(
ref_cnts
)
{
dev_ctx_
=
static_cast
<
platform
::
CUDADeviceContext
*>
(
platform
::
DeviceContextPool
::
Instance
().
Get
(
place
));
if
(
IsStreamGarabageCollector
())
{
PADDLE_ENFORCE
(
cudaSetDevice
(
place
.
device
));
PADDLE_ENFORCE
(
cudaEventCreateWithFlags
(
&
event_
,
cudaEventDisableTiming
));
}
for
(
auto
&
name
:
var_names
)
AddVar
(
name
);
}
~
ReferenceCountOpHandle
()
{
...
...
@@ -69,20 +68,36 @@ class ReferenceCountOpHandle : public OpHandleBase {
std
::
string
Name
()
const
override
{
return
"reference_count"
;
}
void
AddVar
(
const
std
::
string
&
name
)
{
auto
it
=
var_names_
.
find
(
name
);
if
(
it
!=
var_names_
.
end
())
++
(
it
->
second
);
else
var_names_
[
name
]
=
1
;
}
protected:
void
RunImpl
()
override
{
auto
*
exec_scope
=
scope_
->
FindVar
(
kLocalExecScopeName
)
->
Get
<
Scope
*>
();
std
::
vector
<
LoDTensor
*>
tensors
;
for
(
auto
&
name
:
var_names_
)
{
std
::
vector
<
Tensor
*>
tensors
;
for
(
auto
&
pair
:
var_names_
)
{
auto
&
name
=
pair
.
first
;
auto
it
=
ref_cnts_
->
find
(
name
);
if
(
it
==
ref_cnts_
->
end
())
continue
;
auto
*
var
=
exec_scope
->
FindVar
(
name
);
if
(
var
==
nullptr
||
!
var
->
IsType
<
LoDTensor
>
()
)
continue
;
if
(
var
==
nullptr
)
continue
;
if
(
it
->
second
.
fetch_sub
(
1
)
<=
1
)
{
if
(
var
->
IsType
<
LoDTensor
>
())
{
if
(
it
->
second
.
fetch_sub
(
pair
.
second
)
<=
pair
.
second
)
{
tensors
.
emplace_back
(
var
->
GetMutable
<
LoDTensor
>
());
}
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
if
(
it
->
second
.
fetch_sub
(
pair
.
second
)
<=
pair
.
second
)
{
tensors
.
emplace_back
(
var
->
GetMutable
<
SelectedRows
>
()
->
mutable_value
());
}
}
}
if
(
!
tensors
.
empty
())
{
...
...
@@ -91,7 +106,7 @@ class ReferenceCountOpHandle : public OpHandleBase {
}
private:
void
ClearTensors
(
const
std
::
vector
<
LoD
Tensor
*>
&
tensors
)
{
void
ClearTensors
(
const
std
::
vector
<
Tensor
*>
&
tensors
)
{
auto
*
gc
=
dynamic_cast
<
StreamGarbageCollector
<
Tensor
>
*>
(
gc_
);
if
(
gc
!=
nullptr
)
{
auto
compute_stream
=
dev_ctx_
->
stream
();
...
...
@@ -112,7 +127,7 @@ class ReferenceCountOpHandle : public OpHandleBase {
const
Scope
*
scope_
;
platform
::
CUDADeviceContext
*
dev_ctx_
;
std
::
vector
<
std
::
string
>
var_names_
;
std
::
unordered_map
<
std
::
string
,
int
>
var_names_
;
GarbageCollector
<
Tensor
>
*
gc_
;
// not own
AtomicReferenceCountMap
*
ref_cnts_
;
// not own
cudaEvent_t
event_
;
...
...
paddle/fluid/framework/details/reference_count_pass.cc
浏览文件 @
08b22cf1
...
...
@@ -12,6 +12,7 @@
// See the License for the specific language governing permissions and
// limitations under the License.
#include <queue>
#include <string>
#include <vector>
...
...
@@ -23,6 +24,25 @@ namespace paddle {
namespace
framework
{
namespace
details
{
static
ComputationOpHandle
*
FindNextComputationOpHandle
(
VarHandle
*
var_in
)
{
std
::
queue
<
VarHandleBase
*>
queue
;
queue
.
push
(
var_in
);
do
{
auto
*
var
=
queue
.
front
();
queue
.
pop
();
for
(
auto
*
op
:
var
->
PendingOps
())
{
auto
*
compute_op
=
dynamic_cast
<
ComputationOpHandle
*>
(
op
);
if
(
compute_op
!=
nullptr
&&
compute_op
->
GetPlace
()
==
var_in
->
place_
)
{
return
compute_op
;
}
for
(
auto
*
out_var
:
op
->
Outputs
())
{
queue
.
push
(
out_var
);
}
}
}
while
(
!
queue
.
empty
());
return
nullptr
;
}
std
::
unique_ptr
<
ir
::
Graph
>
ReferenceCountPass
::
ApplyImpl
(
std
::
unique_ptr
<
ir
::
Graph
>
graph
)
const
{
auto
&
ref_cnts
=
Get
<
DeviceReferenceCountMap
>
(
kGlobalReferenceCount
);
...
...
@@ -34,6 +54,9 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
// Step 2: Find all variables in non-computation ops which refers to variables
// in computation ops
std
::
unordered_set
<
std
::
string
>
names
;
std
::
unordered_map
<
OpHandleBase
*
,
std
::
unique_ptr
<
ReferenceCountOpHandle
>>
compute_ref_cnt_map
;
auto
get_ref_cnts_from_compute_op
=
[
&
](
const
std
::
unique_ptr
<
OpHandleBase
>
&
op
,
const
std
::
vector
<
VarHandleBase
*>
&
vars
)
{
...
...
@@ -54,16 +77,19 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
VarDesc
*
var_desc
=
var_handle
->
Node
()
->
Var
();
auto
var_name
=
var_handle
->
Node
()
->
Name
();
// This is w
ie
rd but there is really some variables without var_desc
// This is w
ei
rd but there is really some variables without var_desc
// in computation_op
if
(
var_desc
==
nullptr
)
{
if
(
compute_op
->
Node
()
->
Op
()
->
Block
()
->
FindVar
(
var_name
)
==
nullptr
)
continue
;
}
else
{
if
(
var_desc
->
Persistable
()
||
var_desc
->
Proto
()
->
type
().
type
()
!=
proto
::
VarType
::
LOD_TENSOR
)
if
(
var_desc
->
Persistable
())
continue
;
auto
var_type
=
var_desc
->
Proto
()
->
type
().
type
();
if
(
var_type
!=
proto
::
VarType
::
LOD_TENSOR
&&
var_type
!=
proto
::
VarType
::
SELECTED_ROWS
)
{
continue
;
}
}
// compute op only runs in one device
if
(
ref_cnts
[
place
.
device
]
->
count
(
var_name
))
...
...
@@ -93,12 +119,33 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
if
(
ref_cnts
.
count
(
place
.
device
)
&&
ref_cnts
[
place
.
device
]
->
count
(
var_name
))
{
++
(
*
ref_cnts
[
place
.
device
])[
var_name
];
auto
*
next_compute_op
=
FindNextComputationOpHandle
(
var_handle
);
if
(
next_compute_op
!=
nullptr
)
{
if
(
compute_ref_cnt_map
.
count
(
next_compute_op
))
{
compute_ref_cnt_map
[
next_compute_op
]
->
AddVar
(
var_name
);
VLOG
(
5
)
<<
"Add reference count of "
<<
var_name
<<
" to Operator "
<<
next_compute_op
->
Name
();
}
else
{
// Create new reference_count_op_handle
ir
::
Node
*
ref_cnt_node
=
graph
->
CreateEmptyNode
(
"reference_count"
,
ir
::
Node
::
Type
::
kOperation
);
auto
*
ref_cnt_handle
=
new
ReferenceCountOpHandle
(
ref_cnt_node
,
next_compute_op
->
GetScope
(),
place
,
{
var_name
},
gcs
[
place
.
device
].
get
(),
cur_ref_cnts
[
place
.
device
].
get
());
if
(
next_compute_op
->
Outputs
().
empty
())
{
auto
*
dep_var
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
next_compute_op
->
AddOutput
(
dep_var
);
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dep_var
);
}
ref_cnt_handle
->
AddInput
(
next_compute_op
->
Outputs
().
front
());
compute_ref_cnt_map
[
next_compute_op
].
reset
(
ref_cnt_handle
);
}
}
}
}
};
std
::
unordered_map
<
OpHandleBase
*
,
ReferenceCountOpHandle
*>
compute_ref_cnt_map
;
auto
&
all_ops
=
graph
->
Get
<
GraphOps
>
(
kGraphOps
);
for
(
auto
&
op
:
all_ops
)
{
auto
in_var_names
=
get_ref_cnts_from_compute_op
(
op
,
op
->
Inputs
());
...
...
@@ -113,11 +160,13 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
auto
*
ref_cnt_handle
=
new
ReferenceCountOpHandle
(
ref_cnt_node
,
compute_op
->
GetScope
(),
place
,
in_var_names
,
gcs
[
place
.
device
].
get
(),
cur_ref_cnts
[
place
.
device
].
get
());
if
(
compute_op
->
Outputs
().
empty
())
{
auto
*
dep_var
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
compute_op
->
AddOutput
(
dep_var
);
ref_cnt_handle
->
AddInput
(
dep_var
);
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dep_var
);
compute_ref_cnt_map
[
compute_op
]
=
ref_cnt_handle
;
}
ref_cnt_handle
->
AddInput
(
compute_op
->
Outputs
().
front
());
compute_ref_cnt_map
[
compute_op
].
reset
(
ref_cnt_handle
);
}
for
(
auto
&
op
:
all_ops
)
{
...
...
@@ -131,7 +180,11 @@ std::unique_ptr<ir::Graph> ReferenceCountPass::ApplyImpl(
new_all_ops
.
emplace_back
(
std
::
move
(
op
));
auto
it
=
compute_ref_cnt_map
.
find
(
new_all_ops
.
back
().
get
());
if
(
it
!=
compute_ref_cnt_map
.
end
())
{
new_all_ops
.
emplace_back
(
it
->
second
);
// Add LeafNode to ReferenceCountOpHandle
auto
*
dummy_leaf
=
new
DummyVarHandle
(
graph
->
CreateControlDepVar
());
graph
->
Get
<
GraphDepVars
>
(
kGraphDepVars
).
emplace
(
dummy_leaf
);
it
->
second
->
AddOutput
(
dummy_leaf
);
new_all_ops
.
emplace_back
(
std
::
move
(
it
->
second
));
}
}
...
...
paddle/fluid/operators/adam_op.h
浏览文件 @
08b22cf1
...
...
@@ -15,6 +15,7 @@ limitations under the License. */
#pragma once
#include <math.h> // for sqrt in CPU and CUDA
#include <Eigen/Dense>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/selected_rows_functor.h"
...
...
@@ -306,26 +307,43 @@ class AdamOpKernel : public framework::OpKernel<T> {
VLOG
(
3
)
<<
"grad row size is 0!!"
;
return
;
}
std
::
vector
<
int64_t
>
cpu_rows
(
grad
.
rows
().
begin
(),
grad
.
rows
().
end
());
bool
is_strict_sorted
=
true
;
for
(
size_t
i
=
1
;
i
<
cpu_rows
.
size
();
++
i
)
{
if
(
cpu_rows
[
i
-
1
]
>=
cpu_rows
[
i
])
{
is_strict_sorted
=
false
;
break
;
}
}
const
framework
::
SelectedRows
*
grad_merge_ptr
;
if
(
is_strict_sorted
)
{
grad_merge_ptr
=
&
grad
;
}
else
{
// merge duplicated rows if any.
// The rows of grad_merge have been sorted inside MergeAdd functor
scatter
::
MergeAdd
<
DeviceContext
,
T
>
merge_func
;
auto
&
grad_merge
=
*
(
ctx
.
scope
()
.
NewScope
()
.
Var
(
"sparse_adam_grad_merge"
)
->
GetMutable
<
framework
::
SelectedRows
>
());
auto
*
grad_merge_var
=
const_cast
<
framework
::
Scope
&>
(
ctx
.
scope
())
.
Var
()
->
GetMutable
<
framework
::
SelectedRows
>
();
merge_func
(
ctx
.
template
device_context
<
DeviceContext
>(),
grad
,
&
grad_merge
);
grad_merge_var
);
grad_merge_ptr
=
grad_merge_var
;
}
auto
&
grad_merge
=
*
grad_merge_ptr
;
auto
&
grad_tensor
=
grad_merge
.
value
();
const
T
*
grad_data
=
grad_tensor
.
template
data
<
T
>();
int64_t
*
rows
=
nullptr
;
// When compiled without CUDA, the CUDA
Mutable
Data() interface should not be
const
int64_t
*
rows
=
nullptr
;
// When compiled without CUDA, the CUDAData() interface should not be
// provided.
#if defined(PADDLE_WITH_CUDA)
if
(
platform
::
is_gpu_place
(
ctx
.
GetPlace
()))
{
rows
=
grad_merge
.
mutable_rows
()
->
CUDAMutable
Data
(
ctx
.
GetPlace
());
rows
=
grad_merge
.
rows
().
CUDA
Data
(
ctx
.
GetPlace
());
}
else
{
#endif
rows
=
grad_merge
.
mutable_rows
()
->
data
();
rows
=
grad_merge
.
rows
().
data
();
#if defined(PADDLE_WITH_CUDA)
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
08b22cf1
...
...
@@ -45,8 +45,9 @@ __all__ = [
'lod_reset'
,
'lrn'
,
'pad'
,
'pad_constant_like'
,
'label_smooth'
,
'roi_pool'
,
'dice_loss'
,
'image_resize'
,
'image_resize_short'
,
'resize_bilinear'
,
'gather'
,
'scatter'
,
'sequence_scatter'
,
'random_crop'
,
'mean_iou'
,
'relu'
,
'log'
,
'crop'
,
'rank_loss'
,
'prelu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'log'
,
'crop'
,
'rank_loss'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
'prelu'
,
'flatten'
,
'sequence_mask'
,
'stack'
,
'pad2d'
,
'unstack'
,
'sequence_enumerate'
,
'expand'
,
'sequence_concat'
,
'uniform_random_batch_size_like'
,
'gaussian_random'
,
'sampling_id'
,
'gaussian_random_batch_size_like'
,
'sum'
,
'slice'
,
'shape'
]
...
...
@@ -5828,6 +5829,148 @@ def pad2d(input,
return
out
@
templatedoc
()
def
elu
(
x
,
alpha
=
1.0
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
alpha(${alpha_type}|1.0): ${alpha_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
'elu'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'elu'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'alpha'
:
alpha
})
return
out
@
templatedoc
()
def
relu6
(
x
,
threshold
=
6.0
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
threshold(${threshold_type}|6.0): ${threshold_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
'relu6'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'relu6'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'threshold'
:
threshold
})
return
out
@
templatedoc
()
def
pow
(
x
,
factor
=
1.0
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
factor(${factor_type}|1.0): ${factor_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
'pow'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'pow'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'factor'
:
factor
})
return
out
@
templatedoc
()
def
stanh
(
x
,
scale_a
=
2.0
/
3.0
,
scale_b
=
1.7159
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
scale_a(${scale_a_type}|2.0 / 3.0): ${scale_a_comment}
scale_b(${scale_b_type}|1.7159): ${scale_b_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
'stanh'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'stanh'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'scale_a'
:
scale_a
,
'scale_b'
:
scale_b
})
return
out
@
templatedoc
()
def
hard_sigmoid
(
x
,
slope
=
0.2
,
offset
=
0.5
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
slope(${slope_type}|0.2): ${slope_comment}
offset(${offset_type}|0.5): ${offset_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
'hard_sigmoid'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'hard_sigmoid'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'slope'
:
slope
,
'offset'
:
offset
})
return
out
@
templatedoc
()
def
swish
(
x
,
beta
=
1.0
,
name
=
None
):
"""
${comment}
Args:
x(${x_type}): ${x_comment}
beta(${beta_type}|1.0): ${beta_comment}
name(str|None): A name for this layer(optional). If set None, the layer
will be named automatically.
Returns:
output(${out_type}): ${out_comment}
"""
helper
=
LayerHelper
(
'swish'
,
**
locals
())
out
=
helper
.
create_tmp_variable
(
dtype
=
x
.
dtype
)
helper
.
append_op
(
type
=
'swish'
,
inputs
=
{
'X'
:
x
},
outputs
=
{
'Out'
:
out
},
attrs
=
{
'slope'
:
beta
})
return
out
def
prelu
(
x
,
mode
,
param_attr
=
None
,
name
=
None
):
"""
Equation:
...
...
python/paddle/fluid/layers/ops.py
浏览文件 @
08b22cf1
...
...
@@ -36,12 +36,6 @@ __activations__ = [
'brelu'
,
'leaky_relu'
,
'soft_relu'
,
'elu'
,
'relu6'
,
'pow'
,
'stanh'
,
'hard_sigmoid'
,
'swish'
,
]
__all__
=
[
...
...
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