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体验新版 GitCode,发现更多精彩内容 >>
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f605f167
编写于
12月 21, 2017
作者:
C
chengduo
提交者:
GitHub
12月 21, 2017
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差异文件
Merge pull request #6279 from chengduoZH/feature/add_dilation_for_conv_trans
Add dilation for conv_trans_op
上级
09189732
dcf5e948
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
172 addition
and
37 deletion
+172
-37
paddle/operators/conv_transpose_cudnn_op.cc
paddle/operators/conv_transpose_cudnn_op.cc
+0
-4
paddle/operators/conv_transpose_op.cc
paddle/operators/conv_transpose_op.cc
+21
-3
paddle/operators/conv_transpose_op.h
paddle/operators/conv_transpose_op.h
+2
-2
python/paddle/v2/fluid/layers/nn.py
python/paddle/v2/fluid/layers/nn.py
+17
-5
python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
+63
-10
python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
+69
-13
未找到文件。
paddle/operators/conv_transpose_cudnn_op.cc
浏览文件 @
f605f167
...
...
@@ -21,8 +21,6 @@ class CudnnConv2DTransposeOpMaker : public Conv2DTransposeOpMaker {
public:
CudnnConv2DTransposeOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
Conv2DTransposeOpMaker
(
proto
,
op_checker
)
{
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"dilations of convolution operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
int
>
(
"workspace_size_MB"
,
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
...
...
@@ -37,8 +35,6 @@ class CudnnConv3DTransposeOpMaker : public Conv3DTransposeOpMaker {
public:
CudnnConv3DTransposeOpMaker
(
OpProto
*
proto
,
OpAttrChecker
*
op_checker
)
:
Conv3DTransposeOpMaker
(
proto
,
op_checker
)
{
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"dilations of convolution operator."
)
.
SetDefault
({
1
,
1
,
1
});
AddAttr
<
int
>
(
"workspace_size_MB"
,
"workspace size for cudnn, in MB, "
"workspace is a section of GPU memory which will be "
...
...
paddle/operators/conv_transpose_op.cc
浏览文件 @
f605f167
...
...
@@ -29,6 +29,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
std
::
vector
<
int
>
strides
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"dilations"
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
"ConvTransposeOp intput should be 4-D or 5-D tensor."
);
...
...
@@ -41,14 +42,18 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
PADDLE_ENFORCE_EQ
(
paddings
.
size
(),
strides
.
size
(),
"ConvTransposeOp paddings dimension and strides "
"dimension should be the same."
);
PADDLE_ENFORCE_EQ
(
paddings
.
size
(),
dilations
.
size
(),
"ConvTransposeOp paddings dimension and dilations "
"dimension should be the same."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
"In ConvTransposeOp, The input channel should be the same "
"as the number of filters."
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]});
for
(
size_t
i
=
0
;
i
<
strides
.
size
();
++
i
)
{
auto
filter_extent
=
dilations
[
i
]
*
(
filter_dims
[
i
+
2
]
-
1
)
+
1
;
output_shape
.
push_back
((
in_dims
[
i
+
2
]
-
1
)
*
strides
[
i
]
-
2
*
paddings
[
i
]
+
filter_
dims
[
i
+
2
]
);
filter_
extent
);
}
ctx
->
SetOutputDim
(
"Output"
,
framework
::
make_ddim
(
output_shape
));
}
...
...
@@ -73,6 +78,12 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(OpProto* proto,
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of convolution "
"transpose operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> default:{1, 1}), the strides(h_stride, w_stride) of "
...
...
@@ -87,7 +98,7 @@ Conv2DTransposeOpMaker::Conv2DTransposeOpMaker(OpProto* proto,
Convolution2D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and
dilations,
strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input) and output(Output) are in NCHW format. Where N is batchsize, C is the
number of channels, H is the height of the feature, and W is the width of the feature.
...
...
@@ -136,6 +147,13 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(OpProto* proto,
"Where N is batch size, C is "
"the number of channels, D is the depth of the feature, H is the "
"height of the feature, and W is the width of the feature."
);
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"(vector<int> default:{1, 1, 1}), the "
"dilations(d_dilation,h_dilation, w_dilation) of convolution "
"transpose operator."
)
.
SetDefault
({
1
,
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"(vector<int> default:{1, 1, 1}), the "
"strides{d_stride, h_stride, w_stride} of "
...
...
@@ -149,7 +167,7 @@ Conv3DTransposeOpMaker::Conv3DTransposeOpMaker(OpProto* proto,
Convolution3D Transpose Operator.
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
and
dilations,
strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
Input(Input) and output(Output) are in NCDHW format. Where N is batch size, C is the
number of channels, D is the depth of the feature, H is the height of the feature,
...
...
paddle/operators/conv_transpose_op.h
浏览文件 @
f605f167
...
...
@@ -61,6 +61,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
// groups will alway be disabled in conv2dtranspose.
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
...
@@ -113,7 +114,6 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
DeviceContext
,
T
>
col2im
;
math
::
Col2VolFunctor
<
DeviceContext
,
T
>
col2vol
;
std
::
vector
<
int
>
dilations
({
1
,
1
,
1
});
// convolution transpose: gemm + col2im or col2vol (similar to conv-backward
// on input)
...
...
@@ -165,6 +165,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
std
::
vector
<
int
>
dilations
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
...
@@ -219,7 +220,6 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
math
::
Im2ColFunctor
<
math
::
ColFormat
::
kCFO
,
DeviceContext
,
T
>
im2col
;
math
::
Vol2ColFunctor
<
DeviceContext
,
T
>
vol2col
;
std
::
vector
<
int
>
dilations
({
1
,
1
,
1
});
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
...
...
python/paddle/v2/fluid/layers/nn.py
浏览文件 @
f605f167
...
...
@@ -704,6 +704,7 @@ def conv2d_transpose(input,
filter_size
=
None
,
padding
=
None
,
stride
=
None
,
dilation
=
None
,
param_attr
=
None
):
"""
The transpose of conv2d layer.
...
...
@@ -727,6 +728,9 @@ def conv2d_transpose(input,
stride(int|tuple): The stride size. If stride is a tuple, it must
contain two integers, (stride_H, stride_W). Otherwise, the
stride_H = stride_W = stride.
dilation(int|tuple): The dilation size. If dilation is a tuple, it must
contain two integers, (dilation_H, dilation_W). Otherwise, the
dilation_H = dilation_W = dilation.
param_attr: Parameter Attribute.
main_program(Program): the main program
startup_program(Program): the startup program
...
...
@@ -747,10 +751,15 @@ def conv2d_transpose(input,
op_attr
[
'paddings'
]
=
padding
if
isinstance
(
stride
,
int
):
op_attr
[
'strides'
]
=
stride
op_attr
[
'strides'
]
=
[
stride
,
stride
]
elif
stride
is
not
None
:
op_attr
[
'strides'
]
=
stride
if
isinstance
(
dilation
,
int
):
op_attr
[
'dilations'
]
=
[
dilation
,
dilation
]
elif
dilation
is
not
None
:
op_attr
[
'dilations'
]
=
dilation
if
filter_size
is
None
:
if
output_size
is
None
:
raise
ValueError
(
"output_size must be set when filter_size is None"
)
...
...
@@ -759,14 +768,17 @@ def conv2d_transpose(input,
padding
=
op_attr
.
get
(
'paddings'
,
[
0
,
0
])
stride
=
op_attr
.
get
(
'strides'
,
[
1
,
1
])
dilation
=
op_attr
.
get
(
'dilations'
,
[
1
,
1
])
h_in
=
input
.
shape
[
2
]
w_in
=
input
.
shape
[
3
]
filter_size_h
=
output_size
[
0
]
-
\
(
h_in
-
1
)
*
stride
[
0
]
+
2
*
padding
[
0
]
filter_size_w
=
output_size
[
1
]
-
\
(
w_in
-
1
)
*
stride
[
1
]
+
2
*
padding
[
1
]
filter_size_h
=
(
output_size
[
0
]
-
(
h_in
-
1
)
*
stride
[
0
]
+
2
*
padding
[
0
]
-
1
)
/
dilation
[
0
]
+
1
filter_size_w
=
(
output_size
[
1
]
-
(
w_in
-
1
)
*
stride
[
1
]
+
2
*
padding
[
1
]
-
1
)
/
dilation
[
1
]
+
1
filter_size
=
[
filter_size_h
,
filter_size_w
]
elif
isinstance
(
filter_size
,
int
):
filter_size
=
[
filter_size
,
filter_size
]
...
...
python/paddle/v2/fluid/tests/test_conv2d_transpose_op.py
浏览文件 @
f605f167
...
...
@@ -3,14 +3,17 @@ import numpy as np
from
op_test
import
OpTest
def
conv2dtranspose_forward_naive
(
input_
,
filter_
,
conv2dtranspose_param
):
def
conv2dtranspose_forward_naive
(
input_
,
filter_
,
attrs
):
in_n
,
in_c
,
in_h
,
in_w
=
input_
.
shape
f_c
,
out_c
,
f_h
,
f_w
=
filter_
.
shape
assert
in_c
==
f_c
stride
,
pad
=
conv2dtranspose_param
[
'stride'
],
conv2dtranspose_param
[
'pad'
]
out_h
=
(
in_h
-
1
)
*
stride
[
0
]
+
f_h
out_w
=
(
in_w
-
1
)
*
stride
[
1
]
+
f_w
stride
,
pad
,
dilations
=
attrs
[
'strides'
],
attrs
[
'paddings'
],
attrs
[
'dilations'
]
d_bolck_h
=
dilations
[
0
]
*
(
f_h
-
1
)
+
1
d_bolck_w
=
dilations
[
1
]
*
(
f_w
-
1
)
+
1
out_h
=
(
in_h
-
1
)
*
stride
[
0
]
+
d_bolck_h
out_w
=
(
in_w
-
1
)
*
stride
[
1
]
+
d_bolck_w
out
=
np
.
zeros
((
in_n
,
out_c
,
out_h
,
out_w
))
...
...
@@ -23,9 +26,9 @@ def conv2dtranspose_forward_naive(input_, filter_, conv2dtranspose_param):
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
i1
,
i2
=
i
*
stride
[
0
],
i
*
stride
[
0
]
+
d_bolck
_h
j1
,
j2
=
j
*
stride
[
0
],
j
*
stride
[
0
]
+
d_bolck_h
out
[
n
,
k
,
i1
:
i2
:
dilations
[
0
],
j1
:
j2
:
dilations
[
1
]
]
+=
tmp_out
out
=
out
[:,
:,
pad
[
0
]:
out_h
-
pad
[
0
],
pad
[
1
]:
out_w
-
pad
[
1
]]
return
out
...
...
@@ -37,11 +40,8 @@ class TestConv2dTransposeOp(OpTest):
self
.
init_op_type
()
self
.
init_test_case
()
conv2dtranspose_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
=
conv2dtranspose_forward_naive
(
input_
,
filter_
,
conv2dtranspose_param
).
astype
(
'float32'
)
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
self
.
attrs
=
{
...
...
@@ -49,6 +49,10 @@ class TestConv2dTransposeOp(OpTest):
'paddings'
:
self
.
pad
,
'dilations'
:
self
.
dilations
}
output
=
conv2dtranspose_forward_naive
(
input_
,
filter_
,
self
.
attrs
).
astype
(
'float32'
)
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
...
...
@@ -104,11 +108,60 @@ class TestWithStride(TestConv2dTransposeOp):
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithDilation
(
TestConv2dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
2
,
2
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
# ------------ test_cudnn ------------
class
TestCudnn
(
TestConv2dTransposeOp
):
def
init_op_type
(
self
):
self
.
op_type
=
"conv2d_transpose_cudnn"
class
TestCudnnWithPad
(
TestWithPad
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
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
=
"conv2d_transpose_cudnn"
class
TestCudnnWithStride
(
TestWithStride
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
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
=
"conv2d_transpose_cudnn"
# #cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_test_case(self):
# self.pad = [1, 1]
# self.stride = [2, 2]
# self.dilations = [2, 2]
# 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 = "conv2d_transpose_cudnn"
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/v2/fluid/tests/test_conv3d_transpose_op.py
浏览文件 @
f605f167
...
...
@@ -3,15 +3,20 @@ import numpy as np
from
op_test
import
OpTest
def
conv3dtranspose_forward_naive
(
input_
,
filter_
,
conv3dtranspose_param
):
def
conv3dtranspose_forward_naive
(
input_
,
filter_
,
attrs
):
in_n
,
in_c
,
in_d
,
in_h
,
in_w
=
input_
.
shape
f_c
,
out_c
,
f_d
,
f_h
,
f_w
=
filter_
.
shape
assert
in_c
==
f_c
stride
,
pad
=
conv3dtranspose_param
[
'stride'
],
conv3dtranspose_param
[
'pad'
]
out_d
=
(
in_d
-
1
)
*
stride
[
0
]
+
f_d
out_h
=
(
in_h
-
1
)
*
stride
[
1
]
+
f_h
out_w
=
(
in_w
-
1
)
*
stride
[
2
]
+
f_w
stride
,
pad
,
dilations
=
attrs
[
'strides'
],
attrs
[
'paddings'
],
attrs
[
'dilations'
]
d_bolck_d
=
dilations
[
0
]
*
(
f_d
-
1
)
+
1
d_bolck_h
=
dilations
[
1
]
*
(
f_h
-
1
)
+
1
d_bolck_w
=
dilations
[
2
]
*
(
f_w
-
1
)
+
1
out_d
=
(
in_d
-
1
)
*
stride
[
0
]
+
d_bolck_d
out_h
=
(
in_h
-
1
)
*
stride
[
1
]
+
d_bolck_h
out_w
=
(
in_w
-
1
)
*
stride
[
2
]
+
d_bolck_w
out
=
np
.
zeros
((
in_n
,
out_c
,
out_d
,
out_h
,
out_w
))
for
n
in
range
(
in_n
):
...
...
@@ -25,10 +30,11 @@ def conv3dtranspose_forward_naive(input_, filter_, conv3dtranspose_param):
for
k
in
range
(
out_c
):
tmp_out
=
np
.
sum
(
input_masked
*
filter_
[:,
k
,
:,
:,
:],
axis
=
0
)
d1
,
d2
=
d
*
stride
[
0
],
d
*
stride
[
0
]
+
f_d
i1
,
i2
=
i
*
stride
[
1
],
i
*
stride
[
1
]
+
f_h
j1
,
j2
=
j
*
stride
[
2
],
j
*
stride
[
2
]
+
f_w
out
[
n
,
k
,
d1
:
d2
,
i1
:
i2
,
j1
:
j2
]
+=
tmp_out
d1
,
d2
=
d
*
stride
[
0
],
d
*
stride
[
0
]
+
d_bolck_d
i1
,
i2
=
i
*
stride
[
1
],
i
*
stride
[
1
]
+
d_bolck_h
j1
,
j2
=
j
*
stride
[
2
],
j
*
stride
[
2
]
+
d_bolck_w
out
[
n
,
k
,
d1
:
d2
:
dilations
[
0
],
i1
:
i2
:
dilations
[
1
],
j1
:
j2
:
dilations
[
2
]]
+=
tmp_out
out
=
out
[:,
:,
pad
[
0
]:
out_d
-
pad
[
0
],
pad
[
1
]:
out_h
-
pad
[
1
],
pad
[
2
]:
out_w
-
pad
[
2
]]
...
...
@@ -41,18 +47,19 @@ class TestConv3dTransposeOp(OpTest):
self
.
init_op_type
()
self
.
init_test_case
()
conv3dtranspose_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
=
conv3dtranspose_forward_naive
(
input_
,
filter_
,
conv3dtranspose_param
).
astype
(
"float32"
)
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
#
'dilations': self.dilations
'dilations'
:
self
.
dilations
}
output
=
conv3dtranspose_forward_naive
(
input_
,
filter_
,
self
.
attrs
).
astype
(
"float32"
)
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
...
...
@@ -108,11 +115,60 @@ class TestWithStride(TestConv3dTransposeOp):
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
,
3
]
class
TestWithDilation
(
TestConv3dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
2
,
2
,
2
]
self
.
input_size
=
[
2
,
3
,
5
,
5
,
5
]
# NCDHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
,
3
]
# ------------ test_cudnn ------------
class
TestCudnn
(
TestConv3dTransposeOp
):
def
init_op_type
(
self
):
self
.
op_type
=
"conv3d_transpose_cudnn"
class
TestCudnnWithPad
(
TestWithPad
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
,
5
]
# NCDHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
,
3
]
def
init_op_type
(
self
):
self
.
op_type
=
"conv3d_transpose_cudnn"
class
TestCudnnWithStride
(
TestWithStride
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
2
,
2
,
2
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
,
5
]
# NCDHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
,
3
]
def
init_op_type
(
self
):
self
.
op_type
=
"conv3d_transpose_cudnn"
# #cudnn v5 does not support dilation conv.
# class TestCudnnWithDilation(TestWithDilation):
# def init_test_case(self):
# self.pad = [1, 1, 1]
# self.stride = [2, 2, 2]
# self.dilations = [2, 2, 2]
# self.input_size = [2, 3, 5, 5, 5] # NCDHW
# f_c = self.input_size[1]
# self.filter_size = [f_c, 6, 3, 3, 3]
#
# def init_op_type(self):
# self.op_type = "conv3d_transpose_cudnn"
if
__name__
==
'__main__'
:
unittest
.
main
()
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