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CaffeOnACL/src/caffe/layers/acl_pooling_layer.cpp at master · 2php/CaffeOnACL · GitHub
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CaffeOnACL
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CaffeOnACL
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src
/
caffe
/
layers
/
acl_pooling_layer.cpp
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CaffeOnACL
/
src
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caffe
/
layers
/
acl_pooling_layer.cpp
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#
ifdef
USE_ACL
#
include
<
vector
>
#
include
"
caffe/layers/acl_pooling_layer.hpp
"
namespace
caffe
{
template
<
typename
Dtype>
void
ACLPoolingLayer<Dtype>::LayerSetUp(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
PoolingLayer<Dtype>::
LayerSetUp
(bottom, top);
this
->
force_bypass_acl_path_
= bypass_acl_class_layer &
FLAGS_ENABLE_ACL_POOLING
;
}
template
<
typename
Dtype>
void
ACLPoolingLayer<Dtype>::SetupACLLayer(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top){
TensorShape
in_shape
((
unsigned
int
)
this
->
width_
, (
unsigned
int
)
this
->
height_
,(
unsigned
int
)
this
->
channels_
);
TensorShape
out_shape
((
unsigned
int
)
this
->
pooled_width_
, (
unsigned
int
)
this
->
pooled_height_
,(
unsigned
int
)
this
->
channels_
);
checkreshape
(in_shape,
Caffe::arm_gpu_mode
());
if
(!
this
->
init_layer_
)
return
;
this
->
init_layer_
=
false
;
//
Initialize ACL.
if
(
Caffe::arm_gpu_mode
()) {
new_gpulayer
();
}
else
{
new_cpulayer
();
}
this
->
force_bypass_acl_path_
=
false
;
PoolingLayerInfo *pool_info;
if
(
this
->
layer_param_
.
pooling_param
().
pool
()==PoolingParameter_PoolMethod_MAX)
pool_info=
new
PoolingLayerInfo
(PoolingType::
MAX
,
this
->
kernel_w_
,
PadStrideInfo
(
this
->
stride_w_
,
this
->
stride_h_
,
this
->
pad_w_
,
this
->
pad_h_
,DimensionRoundingType::
CEIL
));
else
pool_info=
new
PoolingLayerInfo
(PoolingType::
AVG
,
this
->
kernel_w_
,
PadStrideInfo
(
this
->
stride_w_
,
this
->
stride_h_
,
this
->
pad_w_
,
this
->
pad_h_
,DimensionRoundingType::
CEIL
));
if
(
Caffe::arm_gpu_mode
()) {
Dtype *top_data = top[
0
]->
mutable_gpu_data
();
const
Dtype* bottom_data = bottom[
0
]->
gpu_data
();
new_tensor
(
this
->
gpu
().
input
,in_shape,(
void
*)bottom_data);
new_tensor
(
this
->
gpu
().
output
,out_shape,(
void
*)top_data);
#
ifdef
USE_PROFILING
logtime_util
log_time
(
ACL_CONFIG_INFO
);
#
endif
//
USE_PROFILING
this
->
gpu
().
layer
->
configure
(
this
->
gpu
().
input
,
this
->
gpu
().
output
,*pool_info);
}
else
{
Dtype *top_data = top[
0
]->
mutable_cpu_data
();
const
Dtype* bottom_data = bottom[
0
]->
cpu_data
();
new_tensor
(
this
->
cpu
().
input
,in_shape,(
void
*)bottom_data);
new_tensor
(
this
->
cpu
().
output
,out_shape,(
void
*)top_data);
#
ifdef
USE_PROFILING
logtime_util
log_time
(
ACL_CONFIG_INFO
);
#
endif
//
USE_PROFILING
this
->
cpu
().
layer
->
configure
(
this
->
cpu
().
input
,
this
->
cpu
().
output
,*pool_info);
}
delete
pool_info;
}
template
<
typename
Dtype>
void
ACLPoolingLayer<Dtype>::Reshape(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
PoolingLayer<Dtype>::
Reshape
(bottom, top);
}
template
<
typename
Dtype>
void
ACLPoolingLayer<Dtype>::Forward_cpu(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
if
(
Caffe::arm_gpu_mode
()){
Forward_gpu
(bottom, top);
return
;
}
#
ifdef
USE_PROFILING
logtime_util
log_time
(
ACL_POOLING_INFO
);
#
endif
//
USE_PROFILING
if
(
this
->
force_bypass_acl_path_
||
this
->
layer_param_
.
pooling_param
().
global_pooling
()) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
const
Dtype* bottom_data = bottom[
0
]->
cpu_data
();
Dtype* top_data = top[
0
]->
mutable_cpu_data
();
if
(
this
->
layer_param_
.
pooling_param
().
pool
()!=PoolingParameter_PoolMethod_MAX &&
this
->
layer_param_
.
pooling_param
().
pool
()!=PoolingParameter_PoolMethod_AVE) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
if
(
this
->
kernel_h_
!=
this
->
kernel_w_
|| top.
size
()>
1
) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
if
(
this
->
kernel_h_
!=
2
&&
this
->
kernel_h_
!=
3
) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
SetupACLLayer
(bottom,top);
for
(
int
n =
0
; n < bottom[
0
]->
num
(); ++n) {
tensor_mem
(
this
->
cpu
().
input
,(
void
*)(bottom_data));
cpu_run
();
tensor_mem
((
void
*)(top_data),
this
->
cpu
().
output
);
bottom_data += bottom[
0
]->
offset
(
1
);
top_data += top[
0
]->
offset
(
1
);
}
}
template
<
typename
Dtype>
void
ACLPoolingLayer<Dtype>::Forward_gpu(
const
vector<Blob<Dtype>*>& bottom,
const
vector<Blob<Dtype>*>& top) {
#
ifdef
USE_PROFILING
logtime_util
log_time
(
ACL_POOLING_INFO
);
#
endif
//
USE_PROFILING
if
(
this
->
force_bypass_acl_path_
||
this
->
layer_param_
.
pooling_param
().
global_pooling
()) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
const
Dtype* bottom_data = bottom[
0
]->
gpu_data
();
Dtype* top_data = top[
0
]->
mutable_gpu_data
();
if
(
this
->
layer_param_
.
pooling_param
().
pool
()!=PoolingParameter_PoolMethod_MAX &&
this
->
layer_param_
.
pooling_param
().
pool
()!=PoolingParameter_PoolMethod_AVE) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
if
(
this
->
kernel_h_
!=
this
->
kernel_w_
) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
if
(
this
->
kernel_h_
!=
2
&&
this
->
kernel_h_
!=
3
) {
PoolingLayer<Dtype>::
Forward_cpu
(bottom,top);
return
;
}
SetupACLLayer
(bottom,top);
for
(
int
n =
0
; n < bottom[
0
]->
num
(); ++n) {
tensor_mem
(
this
->
gpu
().
input
,(
void
*)(bottom_data));
gpu_run
();
tensor_mem
((
void
*)(top_data),
this
->
gpu
().
output
);
bottom_data += bottom[
0
]->
offset
(
1
);
top_data += top[
0
]->
offset
(
1
);
}
}
template
<
typename
Dtype>
ACLPoolingLayer<Dtype>::
~ACLPoolingLayer
() {
}
INSTANTIATE_CLASS
(ACLPoolingLayer);
}
//
namespace caffe
#
endif
//
USE_ACL
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