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ec186c12
编写于
12月 04, 2018
作者:
Y
yangfei963158659
提交者:
GitHub
12月 04, 2018
浏览文件
操作
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差异文件
Merge pull request #1329 from yangfei963158659/develop
imp resnet and squeezenet
上级
2c088e20
20a4473c
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
758 addition
and
107 deletion
+758
-107
src/framework/cl/cl_image.h
src/framework/cl/cl_image.h
+1
-0
src/operators/fusion_conv_bn_add_relu_op.cpp
src/operators/fusion_conv_bn_add_relu_op.cpp
+3
-0
src/operators/kernel/cl/batchnorm_kernel.cpp
src/operators/kernel/cl/batchnorm_kernel.cpp
+19
-9
src/operators/kernel/cl/cl_kernel/conv_bn_add_relu_kernel.cl
src/operators/kernel/cl/cl_kernel/conv_bn_add_relu_kernel.cl
+20
-0
src/operators/kernel/cl/cl_kernel/conv_kernel.inc.cl
src/operators/kernel/cl/cl_kernel/conv_kernel.inc.cl
+381
-0
src/operators/kernel/cl/conv_bn_add_relu_kernel.cpp
src/operators/kernel/cl/conv_bn_add_relu_kernel.cpp
+233
-0
src/operators/kernel/cl/depthwise_conv_kernel.cpp
src/operators/kernel/cl/depthwise_conv_kernel.cpp
+96
-97
src/operators/kernel/cl/elementwise_add_kernel.cpp
src/operators/kernel/cl/elementwise_add_kernel.cpp
+5
-1
未找到文件。
src/framework/cl/cl_image.h
浏览文件 @
ec186c12
...
...
@@ -56,6 +56,7 @@ class CLImage {
tensor_dims_
=
dim
;
}
bool
isInit
()
{
return
initialized_
;
}
/*
* need call SetTensorData first
*
...
...
src/operators/fusion_conv_bn_add_relu_op.cpp
浏览文件 @
ec186c12
...
...
@@ -55,6 +55,9 @@ REGISTER_FUSION_MATCHER(fusion_conv_bn_add_relu,
#ifdef PADDLE_MOBILE_CPU
REGISTER_OPERATOR_CPU
(
fusion_conv_bn_add_relu
,
ops
::
FusionConvBNAddReluOp
);
#endif
#ifdef PADDLE_MOBILE_CL
REGISTER_OPERATOR_CL
(
fusion_conv_bn_add_relu
,
ops
::
FusionConvBNAddReluOp
);
#endif
#ifdef PADDLE_MOBILE_FPGA
REGISTER_OPERATOR_FPGA
(
fusion_conv_bn_add_relu
,
ops
::
FusionConvBNAddReluOp
);
#endif
...
...
src/operators/kernel/cl/batchnorm_kernel.cpp
浏览文件 @
ec186c12
...
...
@@ -77,15 +77,25 @@ void BatchNormKernel<GPU_CL, float>::Compute(
auto
new_scale
=
param
.
NewScale
()
->
GetCLImage
();
auto
new_bias
=
param
.
NewBias
()
->
GetCLImage
();
const
int
out_width
=
default_work_size
[
1
];
clSetKernelArg
(
kernel
,
1
,
sizeof
(
int
),
&
out_width
);
clSetKernelArg
(
kernel
,
2
,
sizeof
(
cl_mem
),
&
input
);
clSetKernelArg
(
kernel
,
3
,
sizeof
(
cl_mem
),
&
new_scale
);
clSetKernelArg
(
kernel
,
4
,
sizeof
(
cl_mem
),
&
new_bias
);
clSetKernelArg
(
kernel
,
5
,
sizeof
(
cl_mem
),
&
out
);
// cl_event out_event = param.OutputY()->GetClEvent();
// cl_event wait_event = param.InputX()->GetClEvent();
DLOG
<<
*
param
.
InputX
();
DLOG
<<
*
param
.
NewBias
();
DLOG
<<
*
param
.
NewScale
();
DLOG
<<
default_work_size
[
0
];
DLOG
<<
default_work_size
[
1
];
DLOG
<<
default_work_size
[
2
];
DLOG
<<
out_width
;
DLOG
<<
*
param
.
OutputY
();
cl_int
status
;
clSetKernelArg
(
kernel
,
0
,
sizeof
(
cl_int
),
&
out_width
);
CL_CHECK_ERRORS
(
status
);
clSetKernelArg
(
kernel
,
1
,
sizeof
(
cl_mem
),
&
input
);
CL_CHECK_ERRORS
(
status
);
clSetKernelArg
(
kernel
,
2
,
sizeof
(
cl_mem
),
&
new_scale
);
CL_CHECK_ERRORS
(
status
);
clSetKernelArg
(
kernel
,
3
,
sizeof
(
cl_mem
),
&
new_bias
);
CL_CHECK_ERRORS
(
status
);
clSetKernelArg
(
kernel
,
4
,
sizeof
(
cl_mem
),
&
out
);
CL_CHECK_ERRORS
(
status
);
clEnqueueNDRangeKernel
(
this
->
cl_helper_
.
CLCommandQueue
(),
kernel
,
3
,
NULL
,
default_work_size
.
data
(),
NULL
,
0
,
NULL
,
NULL
);
}
...
...
src/operators/kernel/cl/cl_kernel/conv_bn_add_relu_kernel.cl
0 → 100644
浏览文件 @
ec186c12
/*
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.
*/
#
define
BATCH_NORM
#
define
BIASE
#
define
RELU
#
include
"conv_kernel.inc.cl"
src/operators/kernel/cl/cl_kernel/conv_kernel.inc.cl
浏览文件 @
ec186c12
...
...
@@ -924,6 +924,387 @@ __kernel void conv_5x5(__private const int global_size_dim0,
write_imageh(output_image, (int2)(out_c * global_size_dim1 + out_w, out_nh), output);
}
__kernel void convBNAdd_3x3(__private const int global_size_dim0,
__private const int global_size_dim1,
__private const int global_size_dim2,
__read_only image2d_t input_image,
__read_only image2d_t filter,
#ifdef BIASE
__read_only image2d_t bias,
#endif
#ifdef BATCH_NORM
__read_only image2d_t new_scale,
__read_only image2d_t new_biase,
#endif
__write_only image2d_t output_image,
__private const int stride,
__private const int offset,
__private const int input_c,
__private const int dilation,
__private const int input_width,/* of one block */
__private const int input_height,/* of one block */
__private const int output_width,
__private const int output_height) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
if (out_c >= global_size_dim0 ||
out_w >= global_size_dim1 ||
out_nh >= global_size_dim2) {
return;
}
int2 stride_xy;
stride_xy.x = stride;
stride_xy.y = stride;
int2 ouput_pos_in_one_block;
ouput_pos_in_one_block.x = out_w;
ouput_pos_in_one_block.y = out_nh;
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP
|
CLK_FILTER_NEAREST;
int2 in_pos_in_one_block;
in_pos_in_one_block.x = ouput_pos_in_one_block.x * stride + offset;
in_pos_in_one_block.y = ouput_pos_in_one_block.y * stride + offset;
half4 output = (half4)0.0f;
half4 input[9];
for (int i = 0; i < input_c; ++i) {
int2 pos_in = (int2)(i * input_width + in_pos_in_one_block.x, in_pos_in_one_block.y);
input[0] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height) << 15));
input[1] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y - dilation >= input_height) << 15));
input[2] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y - dilation)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y - dilation < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y - dilation >= input_height) << 15));
input[3] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y >= input_height) << 15));
input[4] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y >= input_height) << 15));
input[5] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y >= input_height) << 15));
input[6] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x - dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x - dilation < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x - dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height) << 15));
input[7] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x >= input_width || in_pos_in_one_block.y + dilation >= input_height) << 15));
input[8] = select(read_imageh(input_image, sampler,
(int2)(pos_in.x + dilation, pos_in.y + dilation)),
(half4)(0.0f),
(ushort4)((in_pos_in_one_block.x + dilation < 0 || in_pos_in_one_block.y + dilation < 0 || in_pos_in_one_block.x + dilation >= input_width || in_pos_in_one_block.y + dilation >= input_height) << 15));
/*
for (int j = 0; j < 9; ++j) {
int2 pos_of_weight;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
half4 weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
half4 weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
half4 weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
half4 weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
}
*/
int j = 0;
int2 pos_of_weight;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
half4 weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
half4 weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
half4 weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
half4 weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 1;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 2;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 3;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 4;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 5;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 6;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 7;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
j = 8;
pos_of_weight.x = i * 3 + j % 3;
pos_of_weight.y = out_c * 4 * 3 + 0 * 3 + j / 3;
weight_x = read_imageh(filter, sampler, pos_of_weight);
output.x += dot(input[j], weight_x);
pos_of_weight.y = out_c * 4 * 3 + 1 * 3 + j / 3;
weight_y = read_imageh(filter, sampler, pos_of_weight);
output.y += dot(input[j], weight_y);
pos_of_weight.y = out_c * 4 * 3 + 2 * 3 + j / 3;
weight_z = read_imageh(filter, sampler, pos_of_weight);
output.z += dot(input[j], weight_z);
pos_of_weight.y = out_c * 4 * 3 + 3 * 3 + j / 3;
weight_w = read_imageh(filter, sampler, pos_of_weight);
output.w += dot(input[j], weight_w);
}
#ifdef BATCH_NORM
output = output * read_imageh(new_scale, sampler, (int2)(out_c, 0)) + read_imageh(new_biase, sampler, (int2)(out_c, 0));
#endif
#ifdef BIASE
output += read_imageh(bias, sampler, (int2)(out_c * global_size_dim1 + out_w, out_nh));
#endif
#ifdef RELU
output = activation(output);
#endif
write_imageh(output_image, (int2)(out_c * global_size_dim1 + out_w, out_nh), output);
}
__kernel void convBNAdd_1x1(__private const int global_size_dim0,
__private const int global_size_dim1,
__private const int global_size_dim2,
__read_only image2d_t input_image,
__read_only image2d_t filter,
#ifdef BIASE
__read_only image2d_t bias,
#endif
#ifdef BATCH_NORM
__read_only image2d_t new_scale,
__read_only image2d_t new_biase,
#endif
__write_only image2d_t output_image,
__private const int stride,
__private const int offset,
__private const int input_c,
__private const int dilation,
__private const int input_width,/* of one block */
__private const int input_height,/* of one block */
__private const int output_width,
__private const int output_height) {
const int out_c = get_global_id(0);
const int out_w = get_global_id(1);
const int out_nh = get_global_id(2);
const sampler_t sampler = CLK_NORMALIZED_COORDS_TRUE |
CLK_ADDRESS_CLAMP
|
CLK_FILTER_NEAREST
;
const
uint
kernelHXW
=
1
;
int2
stride_xy
=
(
int2
)(
stride,
stride
)
;
int2
ouput_pos_in_one_block
=
(
int2
)(
out_w,
out_nh
)
;
int2
in_pos_in_one_block
=
ouput_pos_in_one_block
*
stride_xy
+
(
int2
)(
offset,
offset
)
;
half4
output
=
0.0f
;
for
(
int
i
=
0
; i < input_c; ++i) {
int2
pos_in
=
(
int2
)(
i
*
input_width
+
in_pos_in_one_block.x,
in_pos_in_one_block.y
)
;
half4
input
=
read_imageh
(
input_image,
sampler,
pos_in
)
;
half4
weight0
=
read_imageh
(
filter,
sampler,
(
int2
)(
out_c,
i
*
4
+
0
))
;
half4
weight1
=
read_imageh
(
filter,
sampler,
(
int2
)(
out_c,
i
*
4
+
1
))
;
half4
weight2
=
read_imageh
(
filter,
sampler,
(
int2
)(
out_c,
i
*
4
+
2
))
;
half4
weight3
=
read_imageh
(
filter,
sampler,
(
int2
)(
out_c,
i
*
4
+
3
))
;
/*
output.x
=
dot
(
input,
weight0
)
;
output.y
=
dot
(
input,
weight1
)
;
output.z
=
dot
(
input,
weight2
)
;
output.w
=
dot
(
input,
weight3
)
;
*/
output
=
mad
(
input.x,
weight0,
output
)
;
output
=
mad
(
input.y,
weight1,
output
)
;
output
=
mad
(
input.z,
weight2,
output
)
;
output
=
mad
(
input.w,
weight3,
output
)
;
}
#
ifdef
BATCH_NORM
output
=
output
*
read_imageh
(
new_scale,
sampler,
(
int2
)(
out_c,
0
))
+
read_imageh
(
new_biase,
sampler,
(
int2
)(
out_c,
0
))
;
#
endif
#
ifdef
BIASE
output
+=
read_imageh
(
bias,
sampler,
(
int2
)(
out_c
*
global_size_dim1
+
out_w,
out_nh
))
;
#
endif
#
ifdef
RELU
output
=
activation
(
output
)
;
#
endif
int2
output_pos
=
(
int2
)(
out_c
*
global_size_dim1
+
out_w,
out_nh
)
;
write_imageh
(
output_image,
output_pos,
output
)
;
}
...
...
src/operators/kernel/cl/conv_bn_add_relu_kernel.cpp
0 → 100644
浏览文件 @
ec186c12
/* 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. */
#ifdef FUSION_CONVBNADDRELU_OP
#include "operators/kernel/conv_bn_add_relu_kernel.h"
namespace
paddle_mobile
{
namespace
operators
{
template
<
>
bool
ConvBNAddReluKernel
<
GPU_CL
,
float
>::
Init
(
FusionConvBNAddReluParam
<
GPU_CL
>
*
param
)
{
PADDLE_MOBILE_ENFORCE
(
param
->
Filter
()
->
dims
()[
2
]
==
param
->
Filter
()
->
dims
()[
3
]
&&
param
->
Paddings
()[
0
]
==
param
->
Paddings
()[
1
],
"need equal"
);
const
framework
::
CLImage
*
mean
=
param
->
InputMean
();
const
framework
::
CLImage
*
variance
=
param
->
InputVariance
();
const
framework
::
CLImage
*
scale
=
param
->
InputScale
();
const
framework
::
CLImage
*
bias
=
param
->
InputBias
();
const
float
epsilon
=
param
->
Epsilon
();
const
int
C
=
mean
->
numel
();
auto
mean_ptr
=
mean
->
data
<
float
>
();
auto
variance_ptr
=
variance
->
data
<
float
>
();
auto
scale_ptr
=
scale
->
data
<
float
>
();
auto
bias_ptr
=
bias
->
data
<
float
>
();
float
inv_std_ptr
[
C
];
for
(
int
i
=
0
;
i
<
C
;
i
++
)
{
inv_std_ptr
[
i
]
=
1
/
static_cast
<
float
>
(
pow
((
variance_ptr
[
i
]
+
epsilon
),
0.5
));
}
float
*
new_scale_ptr
=
new
float
[
C
];
float
*
new_bias_ptr
=
new
float
[
C
];
for
(
int
i
=
0
;
i
<
C
;
i
++
)
{
new_scale_ptr
[
i
]
=
inv_std_ptr
[
i
]
*
scale_ptr
[
i
];
new_bias_ptr
[
i
]
=
bias_ptr
[
i
]
-
mean_ptr
[
i
]
*
inv_std_ptr
[
i
]
*
scale_ptr
[
i
];
}
framework
::
CLImage
*
new_scale
=
new
framework
::
CLImage
();
// for (int j = 0; j < C; ++j) {
// DLOG << " new scale - " << j << new_scale_ptr[j];
// }
//
// for (int j = 0; j < C; ++j) {
// DLOG << " new bias - " << j << new_bias_ptr[j];
// }
new_scale
->
SetTensorData
(
new_scale_ptr
,
variance
->
dims
());
new_scale
->
InitCLImage
(
this
->
cl_helper_
.
CLContext
(),
cl_helper_
.
CLCommandQueue
());
// DLOG << " climage - y bias: " << *(param->Bias());
//
// DLOG << " climage - new scale: " << *new_scale;
framework
::
CLImage
*
new_bias
=
new
framework
::
CLImage
();
new_bias
->
SetTensorData
(
new_bias_ptr
,
variance
->
dims
());
new_bias
->
InitCLImage
(
this
->
cl_helper_
.
CLContext
(),
cl_helper_
.
CLCommandQueue
());
// DLOG << " climage - new bias: " << *new_bias;
//
// DLOG << " climage - filter: " << *(param->Filter());
param
->
SetNewScale
(
new_scale
);
param
->
SetNewBias
(
new_bias
);
delete
[](
new_scale_ptr
);
delete
[](
new_bias_ptr
);
PADDLE_MOBILE_ENFORCE
(
param
->
Filter
()
->
dims
()[
2
]
==
param
->
Filter
()
->
dims
()[
3
]
&&
param
->
Paddings
()[
0
]
==
param
->
Paddings
()[
1
],
"need equal"
);
int
offset
=
static_cast
<
int
>
(
param
->
Filter
()
->
dims
()[
2
])
/
2
-
static_cast
<
int
>
(
param
->
Paddings
()[
1
]);
param
->
SetOffset
(
offset
);
if
(
param
->
Filter
()
->
dims
()[
2
]
==
1
&&
param
->
Filter
()
->
dims
()[
3
]
==
1
)
{
param
->
Filter
()
->
InitNImage
(
cl_helper_
.
CLContext
(),
cl_helper_
.
CLCommandQueue
());
this
->
cl_helper_
.
AddKernel
(
"convBNAdd_1x1"
,
"conv_bn_add_relu_kernel.cl"
);
DLOG
<<
" conv bn add relu conv 1x1"
;
}
else
if
(
param
->
Filter
()
->
dims
()[
1
]
==
1
&&
param
->
Input
()
->
dims
()[
1
]
==
param
->
Output
()
->
dims
()[
1
]
&&
param
->
Filter
()
->
dims
()[
2
]
==
3
)
{
param
->
Filter
()
->
InitDWImage
(
cl_helper_
.
CLContext
(),
cl_helper_
.
CLCommandQueue
());
this
->
cl_helper_
.
AddKernel
(
"depth_convBNAdd_3x3"
,
"conv_bn_add_relu_kernel.cl"
);
DLOG
<<
" conv bn add relu depth_conv_3x3"
;
}
else
if
(
param
->
Filter
()
->
dims
()[
2
]
==
3
&&
param
->
Filter
()
->
dims
()[
3
]
==
3
)
{
param
->
Filter
()
->
InitCLImage
(
cl_helper_
.
CLContext
(),
cl_helper_
.
CLCommandQueue
());
this
->
cl_helper_
.
AddKernel
(
"convBNAdd_3x3"
,
"conv_bn_add_relu_kernel.cl"
);
DLOG
<<
" conv bn add relu conv_3x3"
;
}
else
{
PADDLE_MOBILE_THROW_EXCEPTION
(
" not support "
);
}
return
true
;
}
template
<
>
void
ConvBNAddReluKernel
<
GPU_CL
,
float
>::
Compute
(
const
FusionConvBNAddReluParam
<
GPU_CL
>
&
param
)
{
auto
kernel
=
this
->
cl_helper_
.
KernelAt
(
0
);
auto
default_work_size
=
this
->
cl_helper_
.
DefaultWorkSize
(
*
param
.
Output
());
int
c_block
=
default_work_size
[
0
];
int
w
=
default_work_size
[
1
];
int
nh
=
default_work_size
[
2
];
auto
input
=
param
.
Input
()
->
GetCLImage
();
auto
filter
=
param
.
Filter
()
->
GetCLImage
();
auto
biase
=
param
.
Bias
()
->
GetCLImage
();
auto
new_scale
=
param
.
NewScale
()
->
GetCLImage
();
auto
new_bias
=
param
.
NewBias
()
->
GetCLImage
();
auto
output
=
param
.
Output
()
->
GetCLImage
();
int
stride
=
param
.
Strides
()[
0
];
int
offset
=
param
.
Offset
();
int
input_c
=
reinterpret_cast
<
framework
::
CLImageConverterFolder
*>
(
param
.
Input
()
->
Converter
())
->
GetCBlock
();
int
dilation
=
param
.
Dilations
()[
0
];
int
input_width
=
param
.
Input
()
->
dims
()[
3
];
int
input_height
=
param
.
Input
()
->
dims
()[
2
];
int
output_width
=
param
.
Output
()
->
dims
()[
3
];
int
output_height
=
param
.
Output
()
->
dims
()[
2
];
// DLOG << " c block " << c_block;
// DLOG << " w " << w;
// DLOG << " nh " << nh;
// DLOG << " stride " << stride;
// DLOG << " offset " << offset;
// DLOG << " input_c " << input_c;
// DLOG << " dilation " << dilation;
// DLOG << " input width " << input_width;
// DLOG << " input height " << input_height;
// DLOG << " output width " << output_width;
// DLOG << " output height " << output_height;
// DLOG << " input dim " << *param.Input();
// DLOG << " output dim " <<* param.Output();
// DLOG << " filter dim " << *param.Filter();
// DLOG<<*param.Bias();
cl_int
status
;
status
=
clSetKernelArg
(
kernel
,
0
,
sizeof
(
int
),
&
c_block
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
1
,
sizeof
(
int
),
&
w
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
2
,
sizeof
(
int
),
&
nh
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
3
,
sizeof
(
cl_mem
),
&
input
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
4
,
sizeof
(
cl_mem
),
&
filter
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
5
,
sizeof
(
cl_mem
),
&
biase
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
6
,
sizeof
(
cl_mem
),
&
new_scale
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
7
,
sizeof
(
cl_mem
),
&
new_bias
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
8
,
sizeof
(
cl_mem
),
&
output
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
9
,
sizeof
(
int
),
&
stride
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
10
,
sizeof
(
int
),
&
offset
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
11
,
sizeof
(
int
),
&
input_c
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
12
,
sizeof
(
int
),
&
dilation
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
13
,
sizeof
(
int
),
&
input_width
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
14
,
sizeof
(
int
),
&
input_height
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
15
,
sizeof
(
int
),
&
output_width
);
CL_CHECK_ERRORS
(
status
);
status
=
clSetKernelArg
(
kernel
,
16
,
sizeof
(
int
),
&
output_height
);
CL_CHECK_ERRORS
(
status
);
status
=
clEnqueueNDRangeKernel
(
this
->
cl_helper_
.
CLCommandQueue
(),
kernel
,
default_work_size
.
size
(),
NULL
,
default_work_size
.
data
(),
NULL
,
0
,
NULL
,
NULL
);
CL_CHECK_ERRORS
(
status
);
}
template
class
ConvBNAddReluKernel
<
GPU_CL
,
float
>;
}
// namespace operators
}
// namespace paddle_mobile
#endif
src/operators/kernel/cl/depthwise_conv_kernel.cpp
浏览文件 @
ec186c12
/* 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. */
#ifdef DEPTHWISECONV_OP
#include "operators/kernel/depthwise_conv_kernel.h"
#include "operators/kernel/central-arm-func/depthwise_conv_arm_func.h"
namespace
paddle_mobile
{
namespace
operators
{
template
<
>
bool
DepthwiseConvKernel
<
GPU_CL
,
float
>::
Init
(
ConvParam
<
GPU_CL
>
*
param
)
{
DLOG
<<
" depthwise conv kernel init begin "
;
PADDLE_MOBILE_ENFORCE
(
param
->
Filter
()
->
dims
()[
2
]
==
param
->
Filter
()
->
dims
()[
3
]
&&
param
->
Paddings
()[
0
]
==
param
->
Paddings
()[
1
],
"need equal"
);
param
->
Filter
()
->
InitCLImage
(
cl_helper_
.
CLContext
(),
this
->
cl_helper_
.
CLCommandQueue
());
int
offset
=
static_cast
<
int
>
(
param
->
Filter
()
->
dims
()[
2
])
/
2
-
static_cast
<
int
>
(
param
->
Paddings
()[
1
]);
param
->
SetOffset
(
offset
);
this
->
cl_helper_
.
AddKernel
(
"depth_conv_3x3"
,
"conv_add_bn_relu_kernel.cl"
);
DLOG
<<
" depthwise conv kernel init end "
;
return
true
;
}
template
<
>
void
DepthwiseConvKernel
<
GPU_CL
,
float
>::
Compute
(
const
ConvParam
<
GPU_CL
>
&
param
)
{
auto
kernel
=
this
->
cl_helper_
.
KernelAt
(
0
);
auto
default_work_size
=
this
->
cl_helper_
.
DefaultWorkSize
(
*
param
.
Output
());
int
c_block
=
default_work_size
[
0
];
int
w
=
default_work_size
[
1
];
int
nh
=
default_work_size
[
2
];
auto
input
=
param
.
Input
()
->
GetCLImage
();
auto
filter
=
param
.
Filter
()
->
GetCLImage
();
auto
output
=
param
.
Output
()
->
GetCLImage
();
int
stride
=
param
.
Strides
()[
0
];
int
offset
=
param
.
Offset
();
int
input_c
=
reinterpret_cast
<
framework
::
CLImageConverterFolder
*>
(
param
.
Input
()
->
Converter
())
->
GetCBlock
();
int
dilation
=
param
.
Dilations
()[
0
];
int
input_width
=
param
.
Input
()
->
dims
()[
3
];
int
input_height
=
param
.
Input
()
->
dims
()[
2
];
int
output_width
=
param
.
Output
()
->
dims
()[
3
];
int
output_height
=
param
.
Output
()
->
dims
()[
2
];
cl_int
status
;
status
=
clSetKernelArg
(
kernel
,
0
,
sizeof
(
int
),
&
c_block
);
status
=
clSetKernelArg
(
kernel
,
1
,
sizeof
(
int
),
&
w
);
status
=
clSetKernelArg
(
kernel
,
2
,
sizeof
(
int
),
&
nh
);
status
=
clSetKernelArg
(
kernel
,
3
,
sizeof
(
cl_mem
),
&
input
);
status
=
clSetKernelArg
(
kernel
,
4
,
sizeof
(
cl_mem
),
&
filter
);
status
=
clSetKernelArg
(
kernel
,
5
,
sizeof
(
cl_mem
),
&
output
);
status
=
clSetKernelArg
(
kernel
,
6
,
sizeof
(
int
),
&
stride
);
status
=
clSetKernelArg
(
kernel
,
7
,
sizeof
(
int
),
&
offset
);
status
=
clSetKernelArg
(
kernel
,
8
,
sizeof
(
int
),
&
input_c
);
status
=
clSetKernelArg
(
kernel
,
9
,
sizeof
(
int
),
&
dilation
);
status
=
clSetKernelArg
(
kernel
,
10
,
sizeof
(
int
),
&
input_width
);
status
=
clSetKernelArg
(
kernel
,
11
,
sizeof
(
int
),
&
input_height
);
status
=
clSetKernelArg
(
kernel
,
12
,
sizeof
(
int
),
&
output_width
);
status
=
clSetKernelArg
(
kernel
,
13
,
sizeof
(
int
),
&
output_height
);
CL_CHECK_ERRORS
(
status
);
// cl_event out_event = param.Output()->GetClEvent();
// cl_event wait_event = param.Input()->GetClEvent();
status
=
clEnqueueNDRangeKernel
(
this
->
cl_helper_
.
CLCommandQueue
(),
kernel
,
default_work_size
.
size
(),
NULL
,
default_work_size
.
data
(),
NULL
,
0
,
NULL
,
NULL
);
CL_CHECK_ERRORS
(
status
);
}
template
class
DepthwiseConvKernel
<
GPU_CL
,
float
>;
}
// namespace operators
}
// namespace paddle_mobile
#endif
///* 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. */
//
//#ifdef DEQUANT_OP
//
//#include "operators/kernel/dequantize_kernel.h"
//
// namespace paddle_mobile {
// namespace operators {
//
// template <>
// bool DequantizeKernel<GPU_CL, float>::Init(DequantizeParam<GPU_CL> *param) {
// DLOG << " depthwise conv kernel init begin ";
// PADDLE_MOBILE_ENFORCE(
// param->Filter()->dims()[2] == param->Filter()->dims()[3] &&
// param->Paddings()[0] == param->Paddings()[1],
// "need equal");
// param->Filter()->InitCLImage(cl_helper_.CLContext(),
// this->cl_helper_.CLCommandQueue());
// int offset = static_cast<int>(param->Filter()->dims()[2]) / 2 -
// static_cast<int>(param->Paddings()[1]);
// param->SetOffset(offset);
// this->cl_helper_.AddKernel("depth_conv_3x3", "conv_add_bn_relu_kernel.cl");
// DLOG << " depthwise conv kernel init end ";
// return true;
//}
//
// template <>
// void DequantizeKernel<GPU_CL, float>::Compute(
// const DequantizeParam<GPU_CL> ¶m) {
// auto kernel = this->cl_helper_.KernelAt(0);
// auto default_work_size = this->cl_helper_.DefaultWorkSize(*param.Output());
// int c_block = default_work_size[0];
// int w = default_work_size[1];
// int nh = default_work_size[2];
// auto input = param.Input()->GetCLImage();
// auto filter = param.Filter()->GetCLImage();
// auto output = param.Output()->GetCLImage();
// int stride = param.Strides()[0];
// int offset = param.Offset();
// int input_c = reinterpret_cast<framework::CLImageConverterFolder *>(
// param.Input()->Converter())
// ->GetCBlock();
// int dilation = param.Dilations()[0];
//
// int input_width = param.Input()->dims()[3];
// int input_height = param.Input()->dims()[2];
// int output_width = param.Output()->dims()[3];
// int output_height = param.Output()->dims()[2];
//
// cl_int status;
//
// status = clSetKernelArg(kernel, 0, sizeof(int), &c_block);
// status = clSetKernelArg(kernel, 1, sizeof(int), &w);
// status = clSetKernelArg(kernel, 2, sizeof(int), &nh);
// status = clSetKernelArg(kernel, 3, sizeof(cl_mem), &input);
// status = clSetKernelArg(kernel, 4, sizeof(cl_mem), &filter);
// status = clSetKernelArg(kernel, 5, sizeof(cl_mem), &output);
// status = clSetKernelArg(kernel, 6, sizeof(int), &stride);
// status = clSetKernelArg(kernel, 7, sizeof(int), &offset);
// status = clSetKernelArg(kernel, 8, sizeof(int), &input_c);
// status = clSetKernelArg(kernel, 9, sizeof(int), &dilation);
// status = clSetKernelArg(kernel, 10, sizeof(int), &input_width);
// status = clSetKernelArg(kernel, 11, sizeof(int), &input_height);
// status = clSetKernelArg(kernel, 12, sizeof(int), &output_width);
// status = clSetKernelArg(kernel, 13, sizeof(int), &output_height);
//
// CL_CHECK_ERRORS(status);
//
// // cl_event out_event = param.Output()->GetClEvent();
// // cl_event wait_event = param.Input()->GetClEvent();
//
// status = clEnqueueNDRangeKernel(
// this->cl_helper_.CLCommandQueue(), kernel, default_work_size.size(),
// NULL, default_work_size.data(), NULL, 0, NULL, NULL);
//
// CL_CHECK_ERRORS(status);
//}
//
// template class DepthwiseConvKernel<GPU_CL, float>;
//
//} // namespace operators
//} // namespace paddle_mobile
//
//#endif
src/operators/kernel/cl/elementwise_add_kernel.cpp
浏览文件 @
ec186c12
...
...
@@ -24,7 +24,11 @@ bool ElementwiseAddKernel<GPU_CL, float>::Init(
ElementwiseAddParam
<
GPU_CL
>
*
param
)
{
DLOG
<<
"-----init add-----"
;
CLImage
*
bias
=
(
CLImage
*
)(
param
->
InputY
());
bias
->
InitCLImage
(
cl_helper_
.
CLContext
(),
this
->
cl_helper_
.
CLCommandQueue
());
if
(
!
bias
->
isInit
())
{
bias
->
InitCLImage
(
cl_helper_
.
CLContext
(),
this
->
cl_helper_
.
CLCommandQueue
());
}
DLOG
<<
" bias: "
<<
*
bias
;
if
(
bias
->
dims
().
size
()
==
4
)
{
this
->
cl_helper_
.
AddKernel
(
"elementwise_add"
,
"elementwise_add_kernel.cl"
);
...
...
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