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钟尚武
dlib
Commits
87dff094
Commit
87dff094
authored
Jun 11, 2016
by
Davis King
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Plain Diff
Updated tests now that the resizable_tensor has an assignment mode that
automatically sizes the tensor.
parent
6008ad21
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1 changed file
with
24 additions
and
20 deletions
+24
-20
dnn.cpp
dlib/test/dnn.cpp
+24
-20
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dlib/test/dnn.cpp
View file @
87dff094
...
...
@@ -43,7 +43,7 @@ namespace
{
using
namespace
dlib
::
tt
;
print_spinner
();
resizable_tensor
src
(
5
,
5
),
dest
(
5
,
5
),
gradient_input
(
5
,
5
)
;
resizable_tensor
src
,
dest
,
gradient_input
;
src
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
0
));
dest
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
1
));
gradient_input
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
2
));
...
...
@@ -79,7 +79,7 @@ namespace
{
using
namespace
dlib
::
tt
;
print_spinner
();
resizable_tensor
src
(
5
,
5
),
dest
(
5
,
5
),
gradient_input
(
5
,
5
)
;
resizable_tensor
src
,
dest
,
gradient_input
;
src
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
0
));
dest
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
1
));
gradient_input
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
2
));
...
...
@@ -118,8 +118,10 @@ namespace
const
long
nr
=
3
;
const
long
nc
=
3
;
resizable_tensor
src
(
5
,
5
,
nr
,
nr
),
dest
(
5
,
5
,
nr
,
nc
),
gradient_input
(
5
,
5
,
nr
,
nc
);
src
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
*
nr
*
nc
,
0
));
dest
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
*
nr
*
nc
,
1
));
tt
::
tensor_rand
rnd
;
rnd
.
fill_uniform
(
src
);
rnd
.
fill_uniform
(
dest
);
// fill like this as a test of the assignment operator.
gradient_input
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
*
nr
*
nc
,
2
));
...
...
@@ -153,7 +155,7 @@ namespace
{
using
namespace
dlib
::
tt
;
print_spinner
();
resizable_tensor
src
(
5
,
5
),
gamma
(
1
,
5
),
beta
(
1
,
5
),
dest
,
dest2
,
dest3
,
means
,
vars
,
gradient_input
(
5
,
5
)
;
resizable_tensor
src
,
gamma
,
beta
,
dest
,
dest2
,
dest3
,
means
,
vars
,
gradient_input
;
src
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
,
0
));
gamma
=
matrix_cast
<
float
>
(
gaussian_randm
(
1
,
5
,
1
));
beta
=
matrix_cast
<
float
>
(
gaussian_randm
(
1
,
5
,
2
));
...
...
@@ -238,11 +240,12 @@ namespace
{
using
namespace
dlib
::
tt
;
print_spinner
();
resizable_tensor
src
(
5
,
5
,
4
,
4
),
gamma
(
1
,
5
),
beta
(
1
,
5
),
dest
,
dest2
,
dest3
,
means
,
vars
,
gradient_input
(
5
,
5
,
4
,
4
);
src
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
*
4
*
4
,
0
));
resizable_tensor
src
(
5
,
5
,
4
,
4
),
gamma
,
beta
,
dest
,
dest2
,
dest3
,
means
,
vars
,
gradient_input
(
5
,
5
,
4
,
4
);
tt
::
tensor_rand
rnd
;
rnd
.
fill_gaussian
(
src
);
rnd
.
fill_gaussian
(
gradient_input
);
gamma
=
matrix_cast
<
float
>
(
gaussian_randm
(
1
,
5
,
1
));
beta
=
matrix_cast
<
float
>
(
gaussian_randm
(
1
,
5
,
2
));
gradient_input
=
matrix_cast
<
float
>
(
gaussian_randm
(
5
,
5
*
4
*
4
,
3
));
gamma
=
1
;
beta
=
0
;
...
...
@@ -351,8 +354,6 @@ namespace
dlog
<<
LINFO
<<
mat
(
dest
);
DLIB_TEST
(
max
(
abs
(
truth2
-
mat
(
dest
)))
<
1e-5
);
A
.
set_size
(
3
,
4
);
B
.
set_size
(
3
,
4
);
A
=
matrix_cast
<
float
>
(
gaussian_randm
(
3
,
4
,
1
));
B
=
matrix_cast
<
float
>
(
gaussian_randm
(
3
,
4
,
2
));
affine_transform
(
dest
,
src
,
A
,
B
);
...
...
@@ -1418,10 +1419,11 @@ namespace
resizable_tensor
src1
(
10
,
3
,
7
,
15
);
resizable_tensor
src2
(
10
,
3
,
7
,
15
);
resizable_tensor
src3
(
10
,
9
,
7
,
15
);
dest
=
matrix_cast
<
float
>
(
gaussian_randm
(
dest
.
num_samples
(),
dest
.
k
()
*
dest
.
nr
()
*
dest
.
nc
(),
1
));
src1
=
matrix_cast
<
float
>
(
gaussian_randm
(
src1
.
num_samples
(),
src1
.
k
()
*
src1
.
nr
()
*
src1
.
nc
(),
0
));
src2
=
matrix_cast
<
float
>
(
gaussian_randm
(
src1
.
num_samples
(),
src2
.
k
()
*
src2
.
nr
()
*
src2
.
nc
(),
0
));
src3
=
matrix_cast
<
float
>
(
gaussian_randm
(
src1
.
num_samples
(),
src3
.
k
()
*
src3
.
nr
()
*
src3
.
nc
(),
0
));
tt
::
tensor_rand
rnd
;
rnd
.
fill_gaussian
(
dest
);
rnd
.
fill_gaussian
(
src1
);
rnd
.
fill_gaussian
(
src2
);
rnd
.
fill_gaussian
(
src3
);
cpu
::
copy_tensor
(
dest
,
0
,
src1
,
0
,
src1
.
k
());
//full copy src1->dest
cpu
::
copy_tensor
(
dest
,
src1
.
k
(),
src2
,
0
,
src2
.
k
());
//full copy src2->dest with offset of src1
...
...
@@ -1469,10 +1471,11 @@ namespace
resizable_tensor
src1
(
10
,
3
,
7
,
15
);
resizable_tensor
src2
(
10
,
3
,
7
,
15
);
resizable_tensor
src3
(
10
,
9
,
7
,
15
);
dest
=
matrix_cast
<
float
>
(
gaussian_randm
(
dest
.
num_samples
(),
dest
.
k
()
*
dest
.
nr
()
*
dest
.
nc
(),
1
));
src1
=
matrix_cast
<
float
>
(
gaussian_randm
(
src1
.
num_samples
(),
src1
.
k
()
*
src1
.
nr
()
*
src1
.
nc
(),
0
));
src2
=
matrix_cast
<
float
>
(
gaussian_randm
(
src1
.
num_samples
(),
src2
.
k
()
*
src2
.
nr
()
*
src2
.
nc
(),
0
));
src3
=
matrix_cast
<
float
>
(
gaussian_randm
(
src1
.
num_samples
(),
src3
.
k
()
*
src3
.
nr
()
*
src3
.
nc
(),
0
));
tt
::
tensor_rand
rnd
;
rnd
.
fill_gaussian
(
dest
);
rnd
.
fill_gaussian
(
src1
);
rnd
.
fill_gaussian
(
src2
);
rnd
.
fill_gaussian
(
src3
);
cuda
::
copy_tensor
(
dest
,
0
,
src1
,
0
,
src1
.
k
());
//full copy src1->dest
cuda
::
copy_tensor
(
dest
,
src1
.
k
(),
src2
,
0
,
src2
.
k
());
//full copy src2->dest with offset of src1
cuda
::
copy_tensor
(
dest
,
src1
.
k
()
+
src2
.
k
(),
src3
,
3
,
3
);
//partial copy src3 into the rest place of dest
...
...
@@ -1525,7 +1528,8 @@ namespace
using
net_type
=
concat_incept
<
input
<
matrix
<
float
>>>
;
resizable_tensor
data
(
10
,
1
,
111
,
222
);
data
=
matrix_cast
<
float
>
(
gaussian_randm
(
data
.
num_samples
(),
data
.
k
()
*
data
.
nr
()
*
data
.
nc
(),
1
));
tt
::
tensor_rand
rnd
;
rnd
.
fill_gaussian
(
data
);
net_type
net
;
...
...
@@ -1546,7 +1550,7 @@ namespace
DLIB_TEST
(
error
==
0
);
resizable_tensor
gr
(
10
,
14
,
111
,
222
);
gr
=
matrix_cast
<
float
>
(
gaussian_randm
(
gr
.
num_samples
(),
gr
.
k
()
*
gr
.
nr
()
*
gr
.
nc
(),
1
)
);
rnd
.
fill_gaussian
(
gr
);
resizable_tensor
params
;
net
.
layer_details
().
backward
(
gr
,
net
,
params
);
...
...
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