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b1397592
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体验新版 GitCode,发现更多精彩内容 >>
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b1397592
编写于
12月 12, 2017
作者:
Y
yejianwu
浏览文件
操作
浏览文件
下载
差异文件
fix conflix
上级
79d940af
ecef3596
变更
15
展开全部
隐藏空白更改
内联
并排
Showing
15 changed file
with
626 addition
and
357 deletion
+626
-357
mace/kernels/opencl/addn.cc
mace/kernels/opencl/addn.cc
+42
-8
mace/kernels/opencl/batch_norm_opencl.cc
mace/kernels/opencl/batch_norm_opencl.cc
+6
-1
mace/kernels/opencl/concat.cc
mace/kernels/opencl/concat.cc
+50
-13
mace/kernels/opencl/conv_2d_opencl_1x1.cc
mace/kernels/opencl/conv_2d_opencl_1x1.cc
+6
-1
mace/kernels/opencl/conv_2d_opencl_3x3.cc
mace/kernels/opencl/conv_2d_opencl_3x3.cc
+6
-1
mace/kernels/opencl/conv_2d_opencl_general.cc
mace/kernels/opencl/conv_2d_opencl_general.cc
+6
-1
mace/kernels/opencl/pooling_opencl.cc
mace/kernels/opencl/pooling_opencl.cc
+55
-18
mace/kernels/opencl/relu_opencl.cc
mace/kernels/opencl/relu_opencl.cc
+6
-1
mace/kernels/opencl/resize_bilinear_opencl.cc
mace/kernels/opencl/resize_bilinear_opencl.cc
+46
-12
mace/python/tools/BUILD
mace/python/tools/BUILD
+1
-0
mace/python/tools/memory_optimizer.py
mace/python/tools/memory_optimizer.py
+4
-17
mace/python/tools/tf_converter_lib.py
mace/python/tools/tf_converter_lib.py
+379
-268
mace/python/tools/tf_dsp_converter_lib.py
mace/python/tools/tf_dsp_converter_lib.py
+1
-0
tools/validate.py
tools/validate.py
+6
-2
tools/validate_gcn.sh
tools/validate_gcn.sh
+12
-14
未找到文件。
mace/kernels/opencl/addn.cc
浏览文件 @
b1397592
...
@@ -6,6 +6,7 @@
...
@@ -6,6 +6,7 @@
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/utils.h"
#include "mace/utils/utils.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
mace
{
namespace
kernels
{
namespace
kernels
{
...
@@ -33,8 +34,6 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
...
@@ -33,8 +34,6 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
built_options
.
emplace
(
"-DINPUT_NUM="
+
ToString
(
input_tensors
.
size
()));
built_options
.
emplace
(
"-DINPUT_NUM="
+
ToString
(
input_tensors
.
size
()));
auto
addn_kernel
=
runtime
->
BuildKernel
(
"addn"
,
"addn"
,
built_options
);
auto
addn_kernel
=
runtime
->
BuildKernel
(
"addn"
,
"addn"
,
built_options
);
const
uint32_t
lws
=
runtime
->
GetKernelMaxWorkGroupSize
(
addn_kernel
);
uint32_t
idx
=
0
;
uint32_t
idx
=
0
;
for
(
auto
input
:
input_tensors
)
{
for
(
auto
input
:
input_tensors
)
{
addn_kernel
.
setArg
(
idx
++
,
addn_kernel
.
setArg
(
idx
++
,
...
@@ -42,12 +41,47 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
...
@@ -42,12 +41,47 @@ static void AddN(const std::vector<const Tensor *> &input_tensors,
}
}
addn_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
addn_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
const
uint32_t
gws
[
2
]
=
{
addn_kernel
,
cl
::
NullRange
,
static_cast
<
uint32_t
>
(
width_pixels
),
cl
::
NDRange
(
width_pixels
,
batch_height_pixels
),
static_cast
<
uint32_t
>
(
batch_height_pixels
)
cl
::
NDRange
(
64
,
16
),
// TODO fix this
};
nullptr
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
addn_kernel
);
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"error code: "
<<
error
;
std
::
vector
<
uint32_t
>
lws
=
{
64
,
16
};
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
uint32_t
local_ws
[
2
];
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
width_pixels
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
batch_height_pixels
,
kwg_size
/
local_ws
[
0
]);
return
{{
local_ws
[
0
],
local_ws
[
1
]},
{
kwg_size
/
16
,
16
},
{
kwg_size
/
32
,
32
},
{
kwg_size
/
64
,
64
},
{
kwg_size
/
128
,
128
},
{
kwg_size
/
256
,
256
},
{
kwg_size
,
1
},
{
1
,
kwg_size
}
};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
addn_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
};
std
::
stringstream
ss
;
ss
<<
"addn_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(),
lws
,
params_generator
,
func
);
}
}
template
<
typename
T
>
template
<
typename
T
>
...
...
mace/kernels/opencl/batch_norm_opencl.cc
浏览文件 @
b1397592
...
@@ -48,8 +48,13 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(
...
@@ -48,8 +48,13 @@ void BatchNormFunctor<DeviceType::OPENCL, T>::operator()(
static_cast
<
uint32_t
>
(
height
*
batch
)};
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
bm_kernel
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
bm_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
8
,
128
,
1
},
//SNPE size
return
{{
8
,
128
,
1
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/concat.cc
浏览文件 @
b1397592
...
@@ -6,6 +6,7 @@
...
@@ -6,6 +6,7 @@
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/utils.h"
#include "mace/utils/utils.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
mace
{
namespace
kernels
{
namespace
kernels
{
...
@@ -41,21 +42,57 @@ static void Concat2(const Tensor *input0,
...
@@ -41,21 +42,57 @@ static void Concat2(const Tensor *input0,
concat_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
input0
->
dim
(
3
)));
concat_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
input0
->
dim
(
3
)));
concat_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
concat_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blk
),
static_cast
<
uint32_t
>
(
width
),
static_cast
<
uint32_t
>
(
batch
*
height
),
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
concat_kernel
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
concat_kernel
);
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blk
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
64
,
8
,
8
},
{
kwg_size
/
64
,
16
,
4
},
{
kwg_size
/
128
,
8
,
16
},
{
kwg_size
/
128
,
16
,
8
},
{
kwg_size
/
128
,
32
,
4
},
{
1
,
kwg_size
/
32
,
32
},
{
1
,
kwg_size
/
64
,
64
},
{
1
,
kwg_size
/
128
,
128
},
{
3
,
15
,
9
},
{
7
,
15
,
9
},
{
9
,
7
,
15
},
{
15
,
7
,
9
},
{
1
,
kwg_size
,
1
}};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
concat_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
],
params
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
uint32_t
lws
[
3
]
=
{
8
,
16
,
8
}
;
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
// lws[0] = std::min<uint32_t>(channel_blk, kwg_size)
;
return
error
;
// lws[1] = std::min<uint32_t>(width, kwg_size / lws[0])
;
}
;
// lws[2] = std::min<uint32_t>(height * batch, kwg_size / (lws[0] * lws[1]))
;
std
::
stringstream
ss
;
ss
<<
"concat_opencl_kernel_"
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
<<
output
->
dim
(
0
)
<<
"_"
concat_kernel
,
cl
::
NullRange
,
<<
output
->
dim
(
1
)
<<
"_"
cl
::
NDRange
(
static_cast
<
uint32_t
>
(
channel_blk
),
<<
output
->
dim
(
2
)
<<
"_"
static_cast
<
uint32_t
>
(
width
),
<<
output
->
dim
(
3
);
static_cast
<
uint32_t
>
(
height
*
batch
)
),
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(
),
cl
::
NDRange
(
lws
[
0
],
lws
[
1
],
lws
[
2
])
,
lws
,
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
params_generator
,
MACE_CHECK
(
error
==
CL_SUCCESS
);
func
);
}
}
template
<
typename
T
>
template
<
typename
T
>
...
...
mace/kernels/opencl/conv_2d_opencl_1x1.cc
浏览文件 @
b1397592
...
@@ -68,8 +68,13 @@ void Conv1x1(const Tensor *input,
...
@@ -68,8 +68,13 @@ void Conv1x1(const Tensor *input,
static_cast
<
uint32_t
>
(
height
*
batch
)};
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
15
,
8
};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
15
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width_blocks
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/conv_2d_opencl_3x3.cc
浏览文件 @
b1397592
...
@@ -60,8 +60,13 @@ static void Conv2d3x3S12(const Tensor *input, const Tensor *filter,
...
@@ -60,8 +60,13 @@ static void Conv2d3x3S12(const Tensor *input, const Tensor *filter,
static_cast
<
uint32_t
>
(
height
*
batch
)};
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
4
,
15
,
8
};
const
std
::
vector
<
uint32_t
>
lws
=
{
4
,
15
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width_blocks
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/conv_2d_opencl_general.cc
浏览文件 @
b1397592
...
@@ -62,8 +62,13 @@ void Conv2dOpencl(const Tensor *input, const Tensor *filter,
...
@@ -62,8 +62,13 @@ void Conv2dOpencl(const Tensor *input, const Tensor *filter,
static_cast
<
uint32_t
>
(
height
*
batch
)};
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
conv_2d_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width_blocks
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/pooling_opencl.cc
浏览文件 @
b1397592
...
@@ -6,6 +6,7 @@
...
@@ -6,6 +6,7 @@
#include "mace/core/runtime/opencl/cl2_header.h"
#include "mace/core/runtime/opencl/cl2_header.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/core/runtime/opencl/opencl_runtime.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
mace
{
namespace
kernels
{
namespace
kernels
{
...
@@ -23,11 +24,6 @@ static void Pooling(const Tensor *input,
...
@@ -23,11 +24,6 @@ static void Pooling(const Tensor *input,
index_t
channels
=
output
->
dim
(
3
);
index_t
channels
=
output
->
dim
(
3
);
index_t
channel_blocks
=
(
channels
+
3
)
/
4
;
index_t
channel_blocks
=
(
channels
+
3
)
/
4
;
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blocks
),
static_cast
<
uint32_t
>
(
out_width
),
static_cast
<
uint32_t
>
(
batch
*
out_height
),
};
auto
runtime
=
OpenCLRuntime
::
Get
();
auto
runtime
=
OpenCLRuntime
::
Get
();
std
::
set
<
std
::
string
>
built_options
;
std
::
set
<
std
::
string
>
built_options
;
...
@@ -44,13 +40,6 @@ static void Pooling(const Tensor *input,
...
@@ -44,13 +40,6 @@ static void Pooling(const Tensor *input,
}
}
auto
pooling_kernel
=
runtime
->
BuildKernel
(
"pooling"
,
"pooling"
,
built_options
);
auto
pooling_kernel
=
runtime
->
BuildKernel
(
"pooling"
,
"pooling"
,
built_options
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
pooling_kernel
);
uint32_t
lws
[
3
];
lws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
lws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
lws
[
0
]);
lws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
lws
[
0
]
*
lws
[
1
]));
uint32_t
idx
=
0
;
uint32_t
idx
=
0
;
pooling_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
const
cl
::
Image2D
*>
(
input
->
buffer
())));
pooling_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
const
cl
::
Image2D
*>
(
input
->
buffer
())));
pooling_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
input
->
dim
(
1
)));
pooling_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
input
->
dim
(
1
)));
...
@@ -62,12 +51,60 @@ static void Pooling(const Tensor *input,
...
@@ -62,12 +51,60 @@ static void Pooling(const Tensor *input,
pooling_kernel
.
setArg
(
idx
++
,
pooling_size
);
pooling_kernel
.
setArg
(
idx
++
,
pooling_size
);
pooling_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
pooling_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
const
uint32_t
gws
[
3
]
=
{
pooling_kernel
,
cl
::
NullRange
,
static_cast
<
uint32_t
>
(
channel_blocks
),
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
static_cast
<
uint32_t
>
(
out_width
),
cl
::
NDRange
(
lws
[
0
],
lws
[
1
],
lws
[
2
]),
static_cast
<
uint32_t
>
(
batch
*
out_height
),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
};
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
error
;
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
pooling_kernel
);
std
::
vector
<
uint32_t
>
lws
(
3
,
0
);
lws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
lws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
lws
[
0
]);
lws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
lws
[
0
]
*
lws
[
1
]));
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
64
,
8
,
8
},
{
kwg_size
/
64
,
16
,
4
},
{
kwg_size
/
128
,
8
,
16
},
{
kwg_size
/
128
,
16
,
8
},
{
kwg_size
/
128
,
32
,
4
},
{
1
,
kwg_size
/
32
,
32
},
{
1
,
kwg_size
/
64
,
64
},
{
1
,
kwg_size
/
128
,
128
},
{
3
,
15
,
9
},
{
7
,
15
,
9
},
{
9
,
7
,
15
},
{
15
,
7
,
9
},
{
1
,
kwg_size
,
1
}};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
pooling_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
],
params
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
};
std
::
stringstream
ss
;
ss
<<
"pooling_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(),
lws
,
params_generator
,
func
);
}
}
template
<
typename
T
>
template
<
typename
T
>
...
...
mace/kernels/opencl/relu_opencl.cc
浏览文件 @
b1397592
...
@@ -50,8 +50,13 @@ void ReluFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
...
@@ -50,8 +50,13 @@ void ReluFunctor<DeviceType::OPENCL, T>::operator()(const Tensor *input,
static_cast
<
uint32_t
>
(
height
*
batch
)};
static_cast
<
uint32_t
>
(
height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
relu_kernel
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
relu_kernel
);
auto
params_generator
=
[
&
kwg_size
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
32
,
8
,
4
},
...
...
mace/kernels/opencl/resize_bilinear_opencl.cc
浏览文件 @
b1397592
...
@@ -7,6 +7,7 @@
...
@@ -7,6 +7,7 @@
#include "mace/kernels/resize_bilinear.h"
#include "mace/kernels/resize_bilinear.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/kernels/opencl/helper.h"
#include "mace/utils/utils.h"
#include "mace/utils/utils.h"
#include "mace/utils/tuner.h"
namespace
mace
{
namespace
mace
{
namespace
kernels
{
namespace
kernels
{
...
@@ -44,8 +45,6 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
...
@@ -44,8 +45,6 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
built_options
.
emplace
(
"-DCMD_DATA_TYPE="
+
DtToUpstreamCLCMDDt
(
dt
));
built_options
.
emplace
(
"-DCMD_DATA_TYPE="
+
DtToUpstreamCLCMDDt
(
dt
));
auto
rb_kernel
=
runtime
->
BuildKernel
(
"resize_bilinear"
,
"resize_bilinear_nocache"
,
built_options
);
auto
rb_kernel
=
runtime
->
BuildKernel
(
"resize_bilinear"
,
"resize_bilinear_nocache"
,
built_options
);
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
rb_kernel
);
uint32_t
idx
=
0
;
uint32_t
idx
=
0
;
rb_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
const
cl
::
Image2D
*>
(
input
->
buffer
())));
rb_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
const
cl
::
Image2D
*>
(
input
->
buffer
())));
rb_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
rb_kernel
.
setArg
(
idx
++
,
*
(
static_cast
<
cl
::
Image2D
*>
(
output
->
buffer
())));
...
@@ -55,17 +54,52 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
...
@@ -55,17 +54,52 @@ void ResizeBilinearFunctor<DeviceType::OPENCL, T>::operator()(
rb_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
in_width
));
rb_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
in_width
));
rb_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
out_height
));
rb_kernel
.
setArg
(
idx
++
,
static_cast
<
int32_t
>
(
out_height
));
auto
command_queue
=
runtime
->
command_queue
();
const
uint32_t
gws
[
3
]
=
{
static_cast
<
uint32_t
>
(
channel_blocks
),
static_cast
<
uint32_t
>
(
out_width
),
static_cast
<
uint32_t
>
(
out_height
*
batch
)};
const
std
::
vector
<
uint32_t
>
lws
=
{
8
,
16
,
8
};
const
uint32_t
kwg_size
=
runtime
->
GetKernelMaxWorkGroupSize
(
rb_kernel
);
auto
params_generator
=
[
&
]()
->
std
::
vector
<
std
::
vector
<
uint32_t
>>
{
std
::
vector
<
uint32_t
>
local_ws
(
3
,
0
);
local_ws
[
0
]
=
std
::
min
<
uint32_t
>
(
channel_blocks
,
kwg_size
);
local_ws
[
1
]
=
std
::
min
<
uint32_t
>
(
out_width
,
kwg_size
/
local_ws
[
0
]);
local_ws
[
2
]
=
std
::
min
<
uint32_t
>
(
out_height
*
batch
,
kwg_size
/
(
local_ws
[
0
]
*
local_ws
[
1
]));
return
{{
4
,
15
,
8
},
//SNPE size
{
local_ws
[
0
],
local_ws
[
1
],
local_ws
[
2
]},
{
kwg_size
/
16
,
4
,
4
},
{
kwg_size
/
32
,
4
,
8
},
{
kwg_size
/
32
,
8
,
4
},
{
kwg_size
/
64
,
8
,
8
},
{
kwg_size
/
64
,
16
,
4
},
{
kwg_size
/
128
,
8
,
16
},
{
kwg_size
/
128
,
16
,
8
},
{
kwg_size
/
128
,
32
,
4
},
{
1
,
kwg_size
/
32
,
32
},
{
1
,
kwg_size
/
64
,
64
},
{
1
,
kwg_size
/
128
,
128
},
{
1
,
kwg_size
,
1
}};
};
auto
func
=
[
&
](
const
std
::
vector
<
uint32_t
>
&
params
)
->
cl_int
{
cl_int
error
=
runtime
->
command_queue
().
enqueueNDRangeKernel
(
rb_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
gws
[
0
],
gws
[
1
],
gws
[
2
]),
cl
::
NDRange
(
params
[
0
],
params
[
1
],
params
[
2
]),
NULL
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
)
<<
"Error code: "
<<
error
;
return
error
;
};
std
::
stringstream
ss
;
ss
<<
"resize_bilinear_opencl_kernel_"
<<
output
->
dim
(
0
)
<<
"_"
<<
output
->
dim
(
1
)
<<
"_"
<<
output
->
dim
(
2
)
<<
"_"
<<
output
->
dim
(
3
);
Tuner
<
uint32_t
>::
Get
()
->
template
TuneOrRun
<
cl_int
>(
ss
.
str
(),
lws
,
params_generator
,
func
);
cl_int
error
=
command_queue
.
enqueueNDRangeKernel
(
rb_kernel
,
cl
::
NullRange
,
cl
::
NDRange
(
static_cast
<
int32_t
>
(
channel_blocks
),
static_cast
<
int32_t
>
(
out_width
),
static_cast
<
int32_t
>
(
out_height
*
batch
)),
// TODO tuning
cl
::
NDRange
(
1
,
static_cast
<
int32_t
>
(
out_width
>
kwg_size
?
kwg_size
:
out_width
),
1
),
nullptr
,
OpenCLRuntime
::
Get
()
->
GetDefaultEvent
());
MACE_CHECK
(
error
==
CL_SUCCESS
,
error
);
}
}
template
struct
ResizeBilinearFunctor
<
DeviceType
::
OPENCL
,
float
>;
template
struct
ResizeBilinearFunctor
<
DeviceType
::
OPENCL
,
float
>;
...
...
mace/python/tools/BUILD
浏览文件 @
b1397592
...
@@ -8,6 +8,7 @@ py_library(
...
@@ -8,6 +8,7 @@ py_library(
],
],
srcs_version
=
"PY2AND3"
,
srcs_version
=
"PY2AND3"
,
deps
=
[
deps
=
[
":memory_optimizer"
,
"//mace/proto:mace_py"
,
"//mace/proto:mace_py"
,
],
],
)
)
...
...
mace/python/tools/memory_optimizer.py
浏览文件 @
b1397592
...
@@ -65,7 +65,7 @@ class MemoryOptimizer(object):
...
@@ -65,7 +65,7 @@ class MemoryOptimizer(object):
raise
Exception
(
'ref count is less than 0'
)
raise
Exception
(
'ref count is less than 0'
)
for
mem
in
self
.
mem_block
:
for
mem
in
self
.
mem_block
:
arena
=
net_def
.
mem_arena
arena
=
self
.
net_def
.
mem_arena
block
=
arena
.
mem_block
.
add
()
block
=
arena
.
mem_block
.
add
()
block
.
mem_id
=
mem
block
.
mem_id
=
mem
block
.
x
=
self
.
mem_block
[
mem
][
0
]
block
.
x
=
self
.
mem_block
[
mem
][
0
]
...
@@ -83,20 +83,7 @@ class MemoryOptimizer(object):
...
@@ -83,20 +83,7 @@ class MemoryOptimizer(object):
print
(
'origin mem: %d, optimized mem: %d'
,
origin_mem_size
,
optimized_mem_size
)
print
(
'origin mem: %d, optimized mem: %d'
,
origin_mem_size
,
optimized_mem_size
)
if
__name__
==
'__main__'
:
model_file
=
sys
.
argv
[
1
]
opt_model_file
=
sys
.
argv
[
2
]
with
open
(
model_file
,
"rb"
)
as
f
:
net_def
=
mace_pb2
.
NetDef
()
net_def
.
ParseFromString
(
f
.
read
())
optimizer
=
MemoryOptimizer
(
net_def
)
optimizer
.
optimize
()
with
open
(
opt_model_file
,
"wb"
)
as
f
:
f
.
write
(
net_def
.
SerializeToString
())
with
open
(
opt_model_file
+
'_txt'
,
"wb"
)
as
f
:
net_def
.
ClearField
(
'tensors'
)
f
.
write
(
str
(
net_def
))
def
optimize_memory
(
net_def
):
mem_optimizer
=
MemoryOptimizer
(
net_def
)
mem_optimizer
.
optimize
()
\ No newline at end of file
mace/python/tools/tf_converter_lib.py
浏览文件 @
b1397592
此差异已折叠。
点击以展开。
mace/python/tools/tf_dsp_converter_lib.py
浏览文件 @
b1397592
...
@@ -149,6 +149,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
...
@@ -149,6 +149,7 @@ def convert_ops(unresolved_ops, resolved_ops, net_def, output_node, dsp_ops):
elif
is_node_flatten_reshape
(
first_op
):
elif
is_node_flatten_reshape
(
first_op
):
op_def
.
type
=
'Flatten'
op_def
.
type
=
'Flatten'
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
op_def
.
out_max_byte_size
.
extend
([
max_elem_size
(
out
)
for
out
in
first_op
.
outputs
])
convert_op_outputs
(
op_def
,
first_op
)
convert_op_outputs
(
op_def
,
first_op
)
elif
dsp_ops
.
has_op
(
first_op
.
type
):
elif
dsp_ops
.
has_op
(
first_op
.
type
):
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
op_def
.
input
.
extend
([
t
.
name
for
t
in
first_op
.
inputs
])
...
...
tools/validate.py
浏览文件 @
b1397592
...
@@ -4,6 +4,7 @@ import os
...
@@ -4,6 +4,7 @@ import os
import
os.path
import
os.path
import
tensorflow
as
tf
import
tensorflow
as
tf
import
numpy
as
np
import
numpy
as
np
from
scipy
import
spatial
from
tensorflow
import
gfile
from
tensorflow
import
gfile
...
@@ -34,9 +35,12 @@ def load_data(file):
...
@@ -34,9 +35,12 @@ def load_data(file):
def
valid_output
(
out_shape
,
mace_out_file
,
tf_out_value
):
def
valid_output
(
out_shape
,
mace_out_file
,
tf_out_value
):
mace_out_value
=
load_data
(
mace_out_file
)
mace_out_value
=
load_data
(
mace_out_file
)
if
mace_out_value
.
size
!=
0
:
if
mace_out_value
.
size
!=
0
:
similarity
=
(
1
-
spatial
.
distance
.
cosine
(
tf_out_value
.
flat
,
mace_out_value
))
print
'MACE VS TF similarity: '
,
similarity
if
similarity
>
0.999
:
print
'=======================Passed! Haha======================'
mace_out_value
=
mace_out_value
.
reshape
(
out_shape
)
mace_out_value
=
mace_out_value
.
reshape
(
out_shape
)
np
.
testing
.
assert_allclose
(
mace_out_value
,
tf_out_value
,
rtol
=
0.05
)
np
.
testing
.
assert_allclose
(
mace_out_value
,
tf_out_value
,
rtol
=
0.05
)
print
'=======================Passed! Haha======================'
else
:
else
:
print
'=======================Skip empty node==================='
print
'=======================Skip empty node==================='
...
@@ -62,7 +66,7 @@ def run_model(input_shape):
...
@@ -62,7 +66,7 @@ def run_model(input_shape):
input_value
=
input_value
.
reshape
(
input_shape
)
input_value
=
input_value
.
reshape
(
input_shape
)
output_value
=
session
.
run
(
output_node
,
feed_dict
=
{
input_node
:
[
input_value
]})
output_value
=
session
.
run
(
output_node
,
feed_dict
=
{
input_node
:
[
input_value
]})
# output_value.astype(np.float32).tofile( os.path.dirname(FLAGS.input_file) + '/tf_weigh
t')
output_value
.
astype
(
np
.
float32
).
tofile
(
os
.
path
.
dirname
(
FLAGS
.
input_file
)
+
'/tf_ou
t'
)
return
output_value
return
output_value
def
main
(
unused_args
):
def
main
(
unused_args
):
...
...
tools/validate_gcn.sh
浏览文件 @
b1397592
...
@@ -2,10 +2,10 @@
...
@@ -2,10 +2,10 @@
# Must run at root dir of mace project.
# Must run at root dir of mace project.
set
+x
set
+x
Usage
()
{
Usage
()
{
echo
'Usage: bash tools/validate_gcn.sh tf_model_
fil
e'
echo
'Usage: bash tools/validate_gcn.sh tf_model_
path image_siz
e'
}
}
if
[
$#
!=
1
]
;
then
if
[
$#
!=
2
]
;
then
Usage
Usage
exit
-1
exit
-1
fi
fi
...
@@ -13,18 +13,18 @@ fi
...
@@ -13,18 +13,18 @@ fi
TF_MODEL_FILE_PATH
=
$1
TF_MODEL_FILE_PATH
=
$1
MODEL_DIR
=
$(
dirname
${
TF_MODEL_FILE_PATH
}
)
MODEL_DIR
=
$(
dirname
${
TF_MODEL_FILE_PATH
}
)
MACE_MODEL_NAME
=
'mace_model.pb'
MACE_MODEL_NAME
=
'mace_model.pb'
MACE_OPT_MODEL_NAME
=
'mace_opt_model.pb'
INPUT_FILE_NAME
=
'model_input'
INPUT_FILE_NAME
=
'model_input'
OUTPUT_FILE_NAME
=
'gcn.out'
OUTPUT_FILE_NAME
=
'gcn.out'
OUTPUT_LIST_FILE
=
'gcn.list'
OUTPUT_LIST_FILE
=
'gcn.list'
PHONE_DATA_DIR
=
"/data/local/tmp/
${
MACE_MODEL_NAME
}
"
PHONE_DATA_DIR
=
"/data/local/tmp/
${
MACE_MODEL_NAME
}
"
KERNEL_DIR
=
"
${
PHONE_DATA_DIR
}
/cl/"
KERNEL_DIR
=
"
${
PHONE_DATA_DIR
}
/cl/"
IMAGE_SIZE
=
$2
# Step 1: Generate input data
# Step 1: Generate input data
echo
"Step 1: Generate input data"
echo
"Step 1: Generate input data"
python tools/validate.py
--generate_data
true
--random_seed
1
\
python tools/validate.py
--generate_data
true
--random_seed
1
\
--input_file
=
${
MODEL_DIR
}
/
${
INPUT_FILE_NAME
}
\
--input_file
=
${
MODEL_DIR
}
/
${
INPUT_FILE_NAME
}
\
--input_shape
=
512,512,3
--input_shape
=
"
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,3"
# Step 2: convert tf model to mace model
# Step 2: convert tf model to mace model
echo
"Step 2: convert tf model to mace model and optimize memory"
echo
"Step 2: convert tf model to mace model and optimize memory"
...
@@ -35,10 +35,6 @@ bazel-bin/mace/python/tools/tf_converter --input=${TF_MODEL_FILE_PATH} \
...
@@ -35,10 +35,6 @@ bazel-bin/mace/python/tools/tf_converter --input=${TF_MODEL_FILE_PATH} \
--output_node
=
GCN/br_result_2/fcn_br
\
--output_node
=
GCN/br_result_2/fcn_br
\
--data_type
=
DT_HALF
\
--data_type
=
DT_HALF
\
--runtime
=
gpu
--runtime
=
gpu
bazel build mace/python/tools:memory_optimizer
bazel-bin/mace/python/tools/memory_optimizer
${
MODEL_DIR
}
/
${
MACE_MODEL_NAME
}
\
${
MODEL_DIR
}
/
${
MACE_OPT_MODEL_NAME
}
# Step 3: Run model on the phone
# Step 3: Run model on the phone
echo
"Step 3: Run model on the phone"
echo
"Step 3: Run model on the phone"
...
@@ -49,21 +45,22 @@ bazel build -c opt --strip always mace/examples:mace_run \
...
@@ -49,21 +45,22 @@ bazel build -c opt --strip always mace/examples:mace_run \
adb shell
"mkdir -p
${
PHONE_DATA_DIR
}
"
adb shell
"mkdir -p
${
PHONE_DATA_DIR
}
"
adb shell
"mkdir -p
${
KERNEL_DIR
}
"
adb shell
"mkdir -p
${
KERNEL_DIR
}
"
adb push mace/kernels/opencl/cl/
${
KERNEL_DIR
}
adb push mace/kernels/opencl/cl/
*
${
KERNEL_DIR
}
adb push
${
MODEL_DIR
}
/
${
MACE_
OPT_
MODEL_NAME
}
${
PHONE_DATA_DIR
}
adb push
${
MODEL_DIR
}
/
${
MACE_MODEL_NAME
}
${
PHONE_DATA_DIR
}
adb push
${
MODEL_DIR
}
/
${
INPUT_FILE_NAME
}
${
PHONE_DATA_DIR
}
adb push
${
MODEL_DIR
}
/
${
INPUT_FILE_NAME
}
${
PHONE_DATA_DIR
}
adb push bazel-bin/mace/examples/mace_run
${
PHONE_DATA_DIR
}
adb push bazel-bin/mace/examples/mace_run
${
PHONE_DATA_DIR
}
num_threads
=
${
1
:-
4
}
num_threads
=
${
1
:-
4
}
adb </dev/null shell
MACE_RUN_PARAMETER_PATH
=
${
PHONE_DATA_DIR
}
/mace_run.config
\
adb </dev/null shell
MACE_CPP_MIN_VLOG_LEVEL
=
0
\
MACE_RUN_PARAMETER_PATH
=
${
PHONE_DATA_DIR
}
/mace_run.config
\
MACE_KERNEL_PATH
=
$KERNEL_DIR
\
MACE_KERNEL_PATH
=
$KERNEL_DIR
\
OMP_NUM_THREADS
=
$num_threads
\
OMP_NUM_THREADS
=
$num_threads
\
${
PHONE_DATA_DIR
}
/mace_run
\
${
PHONE_DATA_DIR
}
/mace_run
\
--model
=
${
PHONE_DATA_DIR
}
/
${
MACE_
OPT_
MODEL_NAME
}
\
--model
=
${
PHONE_DATA_DIR
}
/
${
MACE_MODEL_NAME
}
\
--input
=
mace_input_node
\
--input
=
mace_input_node
\
--output
=
mace_output_node
\
--output
=
mace_output_node
\
--input_shape
=
1,512,512,3
\
--input_shape
=
"1,
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,3"
\
--input_file
=
${
PHONE_DATA_DIR
}
/
${
INPUT_FILE_NAME
}
\
--input_file
=
${
PHONE_DATA_DIR
}
/
${
INPUT_FILE_NAME
}
\
--output_file
=
${
PHONE_DATA_DIR
}
/
${
OUTPUT_FILE_NAME
}
\
--output_file
=
${
PHONE_DATA_DIR
}
/
${
OUTPUT_FILE_NAME
}
\
--device
=
OPENCL
\
--device
=
OPENCL
\
...
@@ -81,4 +78,5 @@ python tools/validate.py --model_file ${TF_MODEL_FILE_PATH} \
...
@@ -81,4 +78,5 @@ python tools/validate.py --model_file ${TF_MODEL_FILE_PATH} \
--mace_out_file
${
MODEL_DIR
}
/
${
OUTPUT_FILE_NAME
}
\
--mace_out_file
${
MODEL_DIR
}
/
${
OUTPUT_FILE_NAME
}
\
--input_node
input
\
--input_node
input
\
--output_node
GCN/br_result_2/fcn_br
\
--output_node
GCN/br_result_2/fcn_br
\
--output_shape
1,512,512,2
--input_shape
"
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,3"
\
--output_shape
"1,
${
IMAGE_SIZE
}
,
${
IMAGE_SIZE
}
,2"
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