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c2fbf8c5
编写于
10月 12, 2017
作者:
C
chengduoZH
浏览文件
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差异文件
Add unit test
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1 changed file
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python/paddle/v2/framework/tests/test_conv3d_op.py
python/paddle/v2/framework/tests/test_conv3d_op.py
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未找到文件。
python/paddle/v2/framework/tests/test_conv3d_op.py
0 → 100644
浏览文件 @
c2fbf8c5
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
class
TestConv3dOp
(
OpTest
):
def
setUp
(
self
):
self
.
init_groups
()
self
.
op_type
=
"conv3d"
batch_size
=
2
input_channels
=
3
input_depth
=
5
input_height
=
5
input_width
=
5
output_channels
=
6
filter_depth
=
3
filter_height
=
3
filter_width
=
3
stride
=
1
padding
=
0
output_depth
=
(
input_depth
-
filter_depth
+
2
*
padding
)
/
stride
+
1
output_height
=
(
input_height
-
filter_height
+
2
*
padding
)
/
stride
+
1
output_width
=
(
input_width
-
filter_width
+
2
*
padding
)
/
stride
+
1
input
=
np
.
random
.
random
((
batch_size
,
input_channels
,
input_depth
,
input_height
,
input_width
)).
astype
(
"float32"
)
filter
=
np
.
random
.
random
(
(
output_channels
,
input_channels
/
self
.
groups
,
filter_depth
,
filter_height
,
filter_width
)).
astype
(
"float32"
)
output
=
np
.
ndarray
((
batch_size
,
output_channels
,
output_depth
,
output_height
,
output_width
))
self
.
inputs
=
{
'Input'
:
input
,
'Filter'
:
filter
}
self
.
attrs
=
{
'strides'
:
[
1
,
1
,
1
],
'paddings'
:
[
0
,
0
,
0
],
'groups'
:
self
.
groups
}
output_group_channels
=
output_channels
/
self
.
groups
input_group_channels
=
input_channels
/
self
.
groups
for
batchid
in
xrange
(
batch_size
):
for
group
in
xrange
(
self
.
groups
):
for
outchannelid
in
range
(
group
*
output_group_channels
,
(
group
+
1
)
*
output_group_channels
):
for
deepid
in
xrange
(
output_depth
):
for
rowid
in
xrange
(
output_height
):
for
colid
in
xrange
(
output_width
):
start_d
=
(
deepid
*
stride
)
-
padding
start_h
=
(
rowid
*
stride
)
-
padding
start_w
=
(
colid
*
stride
)
-
padding
output_value
=
0.0
for
inchannelid
in
range
(
group
*
input_group_channels
,
(
group
+
1
)
*
input_group_channels
):
for
fdeepid
in
xrange
(
filter_depth
):
for
frowid
in
xrange
(
filter_height
):
for
fcolid
in
xrange
(
filter_width
):
input_value
=
0.0
indeepid
=
start_d
+
fdeepid
inrowid
=
start_h
+
frowid
incolid
=
start_w
+
fcolid
if
((
indeepid
>=
0
and
indeepid
<
input_depth
)
and
(
inrowid
>=
0
and
inrowid
<
input_height
)
and
(
incolid
>=
0
and
incolid
<
input_width
)):
input_value
=
input
[
batchid
][
inchannelid
][
indeepid
][
inrowid
][
incolid
]
filter_value
=
filter
[
outchannelid
][
inchannelid
%
input_group_channels
][
fdeepid
][
frowid
][
fcolid
]
output_value
+=
input_value
*
filter_value
output
[
batchid
][
outchannelid
][
deepid
][
rowid
][
colid
]
=
output_value
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
self
.
check_output
()
def
test_check_grad
(
self
):
self
.
check_grad
(
set
([
'Input'
,
'Filter'
]),
'Output'
,
max_relative_error
=
0.05
)
def
test_check_grad_no_filter
(
self
):
self
.
check_grad
(
[
'Input'
],
'Output'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad_no_input
(
self
):
self
.
check_grad
(
[
'Filter'
],
'Output'
,
max_relative_error
=
0.05
,
no_grad_set
=
set
([
'Input'
]))
def
init_groups
(
self
):
self
.
groups
=
1
class
TestWithGroup
(
TestConv3dOp
):
def
init_groups
(
self
):
self
.
groups
=
3
if
__name__
==
'__main__'
:
unittest
.
main
()
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