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7eeaae16
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7eeaae16
编写于
10月 19, 2017
作者:
Z
zchen0211
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
deconv
上级
c33575a5
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
109 addition
and
7 deletion
+109
-7
paddle/operators/deconv2d_op.h
paddle/operators/deconv2d_op.h
+8
-7
python/paddle/v2/framework/tests/test_deconv_op.py
python/paddle/v2/framework/tests/test_deconv_op.py
+101
-0
未找到文件。
paddle/operators/deconv2d_op.h
浏览文件 @
7eeaae16
...
...
@@ -14,6 +14,7 @@ limitations under the License. */
#pragma once
#include "glog/logging.h"
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/im2col.h"
...
...
@@ -117,8 +118,7 @@ class GemmDeconv2DKernel : public framework::OpKernel<T> {
// of shape (C * K_H * K_W, H * W)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
true
,
input_batch
,
false
,
T
(
1.0
),
&
col_matrix
,
T
(
0.0
));
col2im
(
context
.
device_context
(),
output_batch
,
col_matrix
,
strides
[
0
],
col2im
(
context
.
device_context
(),
output_batch
,
col
,
strides
[
0
],
strides
[
1
],
0
,
0
);
}
}
...
...
@@ -203,8 +203,8 @@ class GemmDeconvGrad2DKernel : public framework::OpKernel<T> {
input_grad
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// im2col: dy from (C, O_H, O_W) -> (C * K_H * K_W, H * W)
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
_matrix
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
1
]);
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
]
,
strides
[
1
],
paddings
[
0
],
paddings
[
1
]);
// gemm: dx = filter * dy
// (M, C * K_H * K_W) * (C * K_H * K_W, H * W) -> (M, C, H)
...
...
@@ -234,13 +234,14 @@ class GemmDeconvGrad2DKernel : public framework::OpKernel<T> {
Tensor
in_batch
=
input
->
Slice
<
T
>
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// im2col: (C * H * W, K_H * K_W)
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
_matrix_f
,
strides
[
0
],
strides
[
1
],
paddings
[
0
],
paddings
[
1
]);
im2col
(
context
.
device_context
(),
output_grad_batch
,
col
,
strides
[
0
]
,
strides
[
1
],
paddings
[
0
],
paddings
[
1
]);
// gemm: d_filter = x * y_grad^T
// (M, C * H * W) * (K_H * K_W, C * H * W) -> (M, C, H)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
in_batch
,
false
,
col_matrix
,
true
,
T
(
1.0
),
&
filter_grad_
,
T
(
1.0
));
col_matrix_f
,
true
,
T
(
1.0
),
&
filter_grad_
,
T
(
1.0
));
}
}
}
...
...
python/paddle/v2/framework/tests/test_deconv_op.py
0 → 100644
浏览文件 @
7eeaae16
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
deconv2d_forward_naive
(
input_
,
filter_
,
deconv_param
):
# [2, 3, 5, 5]
in_n
,
in_c
,
in_h
,
in_w
=
input_
.
shape
# [3, 6, 3, 3]
f_c
,
out_c
,
f_h
,
f_w
=
filter_
.
shape
assert
in_c
==
f_c
stride
,
pad
=
deconv_param
[
'stride'
],
deconv_param
[
'pad'
]
out_h
=
(
in_h
-
1
)
*
stride
[
0
]
+
f_h
out_w
=
(
in_w
-
1
)
*
stride
[
1
]
+
f_w
out
=
np
.
zeros
((
in_n
,
out_c
,
out_h
,
out_w
))
for
n
in
range
(
in_n
):
for
i
in
range
(
in_h
):
for
j
in
range
(
in_w
):
input_masked
=
input_
[
n
,
:,
i
,
j
]
# (c)
input_masked
=
np
.
reshape
(
input_masked
,
(
in_c
,
1
,
1
))
input_masked
=
np
.
tile
(
input_masked
,
(
1
,
f_h
,
f_w
))
for
k
in
range
(
out_c
):
tmp_out
=
np
.
sum
(
input_masked
*
filter_
[:,
k
,
:,
:],
axis
=
0
)
i1
,
i2
=
i
*
stride
[
0
],
i
*
stride
[
0
]
+
f_h
j1
,
j2
=
j
*
stride
[
0
],
j
*
stride
[
0
]
+
f_w
out
[
n
,
k
,
i1
:
i2
,
j1
:
j2
]
+=
tmp_out
return
out
class
TestDeconv2dOp
(
OpTest
):
def
setUp
(
self
):
# init as deconv
self
.
init_op_type
()
# [2, 3, 5, 5] -> kernel [3, 6, 3, 3] -> output [2, 6, 7, 7]
self
.
init_test_case
()
deconv2d_param
=
{
'stride'
:
self
.
stride
,
'pad'
:
self
.
pad
}
input_
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
"float32"
)
filter_
=
np
.
random
.
random
(
self
.
filter_size
).
astype
(
"float32"
)
output
=
deconv2d_forward_naive
(
input_
,
filter_
,
deconv2d_param
)
# print 'deconv output py', output, output.shape
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
# 'dilations': self.dilations
}
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
print
'check output here'
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_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
def
init_op_type
(
self
):
self
.
op_type
=
"deconv2d"
"""
class TestCudnn(TestConv2dOp):
def init_group(self):
self.groups = 1
def init_op_type(self):
self.op_type = "conv_cudnn"
"""
if
__name__
==
'__main__'
:
unittest
.
main
()
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