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体验新版 GitCode,发现更多精彩内容 >>
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提交
20bdc3e1
编写于
5月 23, 2018
作者:
Y
Yibing Liu
提交者:
GitHub
5月 23, 2018
浏览文件
操作
浏览文件
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差异文件
Merge pull request #10846 from kuke/deconv_group
Add groups for conv transpose ops
上级
55d3951b
4bafbf41
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
224 addition
and
75 deletion
+224
-75
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
+39
-19
paddle/fluid/operators/conv_transpose_op.cc
paddle/fluid/operators/conv_transpose_op.cc
+12
-4
paddle/fluid/operators/conv_transpose_op.h
paddle/fluid/operators/conv_transpose_op.h
+58
-28
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+9
-1
python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
.../paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
+52
-10
python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py
.../paddle/fluid/tests/unittests/test_conv3d_transpose_op.py
+54
-13
未找到文件。
paddle/fluid/operators/conv_transpose_cudnn_op.cu.cc
浏览文件 @
20bdc3e1
...
...
@@ -44,6 +44,7 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
// cudnn v5 does not support dilations
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
int
user_workspace_size
=
ctx
.
Attr
<
int
>
(
"workspace_size_MB"
);
const
T
*
input_data
=
input
->
data
<
T
>
();
...
...
@@ -64,13 +65,13 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
// (N, M, H, W) or (N, M, D, H, W)
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
input
->
dims
()));
layout
,
framework
::
vectorize2int
(
input
->
dims
())
,
groups
);
// (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
output
->
dims
()));
layout
,
framework
::
vectorize2int
(
output
->
dims
())
,
groups
);
// (M, C, K_h, K_w) or (M, C, K_d, K_h, K_w)
cudnnFilterDescriptor_t
cudnn_filter_desc
=
filter_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
filter
->
dims
()));
layout
,
framework
::
vectorize2int
(
filter
->
dims
())
,
groups
);
cudnnConvolutionDescriptor_t
cudnn_conv_desc
=
conv_desc
.
descriptor
<
T
>
(
paddings
,
strides
,
dilations
);
...
...
@@ -104,11 +105,17 @@ class CUDNNConvTransposeOpKernel : public framework::OpKernel<T> {
cudnn_workspace
=
paddle
::
memory
::
Alloc
(
gpu
,
workspace_size_in_bytes
);
// ------------------- cudnn conv transpose forward ---------------------
int
input_offset
=
input
->
numel
()
/
input
->
dims
()[
0
]
/
groups
;
int
output_offset
=
output
->
numel
()
/
output
->
dims
()[
0
]
/
groups
;
int
filter_offset
=
filter
->
numel
()
/
groups
;
T
alpha
=
1.0
f
,
beta
=
0.0
f
;
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionBackwardData
(
handle
,
&
alpha
,
cudnn_filter_desc
,
filter_data
,
cudnn_input_desc
,
input_data
,
cudnn_conv_desc
,
algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_output_desc
,
output_data
));
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionBackwardData
(
handle
,
&
alpha
,
cudnn_filter_desc
,
filter_data
+
filter_offset
*
g
,
cudnn_input_desc
,
input_data
+
input_offset
*
g
,
cudnn_conv_desc
,
algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_output_desc
,
output_data
+
output_offset
*
g
));
}
// Release the cudnn workspace
paddle
::
memory
::
Free
(
gpu
,
cudnn_workspace
);
...
...
@@ -134,6 +141,7 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
std
::
vector
<
int
>
paddings
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
// cudnn v5 does not support dilations
std
::
vector
<
int
>
dilations
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"dilations"
);
int
groups
=
ctx
.
Attr
<
int
>
(
"groups"
);
int
user_workspace_size
=
ctx
.
Attr
<
int
>
(
"workspace_size_MB"
);
// ------------------- cudnn descriptors ---------------------
...
...
@@ -145,13 +153,13 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
// Input: (N, M, H, W) or (N, M, D, H, W)
cudnnTensorDescriptor_t
cudnn_input_desc
=
input_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
input
->
dims
()));
layout
,
framework
::
vectorize2int
(
input
->
dims
())
,
groups
);
// Output: (N, C, O_h, O_w) or (N, C, O_d, O_h, O_w)
cudnnTensorDescriptor_t
cudnn_output_desc
=
output_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
output_grad
->
dims
()));
layout
,
framework
::
vectorize2int
(
output_grad
->
dims
())
,
groups
);
// Filter (M, C, K_h, K_w) or (M, C, K_d K_h, K_w)
cudnnFilterDescriptor_t
cudnn_filter_desc
=
filter_desc
.
descriptor
<
T
>
(
layout
,
framework
::
vectorize2int
(
filter
->
dims
()));
layout
,
framework
::
vectorize2int
(
filter
->
dims
())
,
groups
);
cudnnConvolutionDescriptor_t
cudnn_conv_desc
=
conv_desc
.
descriptor
<
T
>
(
paddings
,
strides
,
dilations
);
...
...
@@ -205,15 +213,22 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
cudnn_workspace
=
paddle
::
memory
::
Alloc
(
gpu
,
workspace_size_in_bytes
);
// ------------------- cudnn conv backward data ---------------------
// FIXME(typhoonzero): template type T may not be the same as cudnn call.
int
input_offset
=
input
->
numel
()
/
input
->
dims
()[
0
]
/
groups
;
int
output_grad_offset
=
output_grad
->
numel
()
/
output_grad
->
dims
()[
0
]
/
groups
;
int
filter_offset
=
filter
->
numel
()
/
groups
;
T
alpha
=
1.0
f
,
beta
=
0.0
f
;
if
(
input_grad
)
{
T
*
input_grad_data
=
input_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// Because beta is zero, it is unnecessary to reset input_grad.
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionForward
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
,
cudnn_filter_desc
,
filter_data
,
cudnn_conv_desc
,
data_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_input_desc
,
input_grad_data
));
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionForward
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
+
output_grad_offset
*
g
,
cudnn_filter_desc
,
filter_data
+
filter_offset
*
g
,
cudnn_conv_desc
,
data_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_input_desc
,
input_grad_data
+
input_offset
*
g
));
}
}
// ------------------- cudnn conv backward filter ---------------------
...
...
@@ -221,11 +236,16 @@ class CUDNNConvTransposeGradOpKernel : public framework::OpKernel<T> {
T
*
filter_grad_data
=
filter_grad
->
mutable_data
<
T
>
(
ctx
.
GetPlace
());
// Because beta is zero, it is unnecessary to reset filter_grad.
// Gradient with respect to the filter
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionBackwardFilter
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
,
cudnn_input_desc
,
input_data
,
cudnn_conv_desc
,
filter_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_filter_desc
,
filter_grad_data
));
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
PADDLE_ENFORCE
(
platform
::
dynload
::
cudnnConvolutionBackwardFilter
(
handle
,
&
alpha
,
cudnn_output_desc
,
output_grad_data
+
output_grad_offset
*
g
,
cudnn_input_desc
,
input_data
+
input_offset
*
g
,
cudnn_conv_desc
,
filter_algo
,
cudnn_workspace
,
workspace_size_in_bytes
,
&
beta
,
cudnn_filter_desc
,
filter_grad_data
+
filter_offset
*
g
));
}
}
// Release the cudnn workspace
paddle
::
memory
::
Free
(
gpu
,
cudnn_workspace
);
}
...
...
paddle/fluid/operators/conv_transpose_op.cc
浏览文件 @
20bdc3e1
...
...
@@ -32,6 +32,7 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
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"
);
int
groups
=
ctx
->
Attrs
().
Get
<
int
>
(
"groups"
);
PADDLE_ENFORCE
(
in_dims
.
size
()
==
4
||
in_dims
.
size
()
==
5
,
"ConvTransposeOp intput should be 4-D or 5-D tensor."
);
...
...
@@ -48,10 +49,10 @@ void ConvTransposeOp::InferShape(framework::InferShapeContext* ctx) const {
"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 filter
s."
);
"In ConvTransposeOp, The
number of input channels should
"
"
be equal to the number of filter's channel
s."
);
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]});
std
::
vector
<
int64_t
>
output_shape
({
in_dims
[
0
],
filter_dims
[
1
]
*
groups
});
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
]
+
...
...
@@ -102,7 +103,10 @@ void Conv2DTransposeOpMaker::Make() {
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator. "
"The format of output tensor is also NCHW."
);
AddAttr
<
int
>
(
"groups"
,
"(int default:1), the groups number of the convolution "
"transpose operator. "
)
.
SetDefault
(
1
);
AddAttr
<
std
::
vector
<
int
>>
(
"dilations"
,
"(vector<int> default:{1, 1}), the "
"dilations(h_dilation, w_dilation) of convolution "
...
...
@@ -204,6 +208,10 @@ void Conv3DTransposeOpMaker::Make() {
"(vector<int> default:{0, 0, 0}), paddings(d_pad, "
"h_pad, w_pad) of convolution transpose operator."
)
.
SetDefault
({
0
,
0
,
0
});
AddAttr
<
int
>
(
"groups"
,
"(int default:1), the groups number of the convolution3d "
"transpose operator. "
)
.
SetDefault
(
1
);
AddAttr
<
bool
>
(
"use_cudnn"
,
"(bool, default false) Only used in cudnn kernel, need install cudnn"
)
...
...
paddle/fluid/operators/conv_transpose_op.h
浏览文件 @
20bdc3e1
...
...
@@ -70,7 +70,7 @@ 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.
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
...
@@ -81,10 +81,10 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// use col_shape in the im2col and col2im (or vol2col and col2vol)
// calculation
// col_shape_vec: {c
, k_h, k_w, h, w} or {c
, k_d, k_h, k_w, d, h, w}
// col_shape_vec: {c
/g, k_h, k_w, h, w} or {c/g
, k_d, k_h, k_w, d, h, w}
size_t
data_dim
=
filter_shape_vec
.
size
()
-
2
;
std
::
vector
<
int64_t
>
col_shape_vec
(
1
+
2
*
data_dim
);
col_shape_vec
[
0
]
=
output
->
dims
()[
1
];
col_shape_vec
[
0
]
=
output
->
dims
()[
1
]
/
groups
;
for
(
size_t
j
=
0
;
j
<
data_dim
;
++
j
)
{
col_shape_vec
[
j
+
1
]
=
filter_shape_vec
[
j
+
2
];
col_shape_vec
[
j
+
1
+
data_dim
]
=
input_shape_vec
[
j
+
2
];
...
...
@@ -92,7 +92,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
DDim
col_shape
(
framework
::
make_ddim
(
col_shape_vec
));
// use col_matrix_shape in the gemm calculation
// size: (c
* k_h * k_w, h * w) or (c
* k_d * k_h * k_w, d * h * w)
// size: (c
/g * k_h * k_w, h * w) or (c/g
* k_d * k_h * k_w, d * h * w)
DDim
col_matrix_shape
=
framework
::
flatten_to_2d
(
col_shape
,
data_dim
+
1
);
Tensor
col
;
...
...
@@ -111,7 +111,7 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// input matrix size: (m, h * w) or (m, d * h * w)
DDim
input_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
1
]};
// filter size: (m, c
* k_h * k_w) or (m, c
* k_d * k_h * k_w)
// filter size: (m, c
/g * k_h * k_w) or (m, c/g
* k_d * k_h * k_w)
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]};
filter
.
Resize
(
filter_matrix_shape
);
...
...
@@ -121,6 +121,8 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
auto
blas
=
math
::
GetBlas
<
DeviceContext
,
T
>
(
dev_ctx
);
set_zero
(
dev_ctx
,
output
,
static_cast
<
T
>
(
0
));
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
out_step
=
static_cast
<
int
>
(
output
->
dims
()[
1
])
/
groups
;
math
::
Col2ImFunctor
<
math
::
ColFormat
::
kCFO
,
DeviceContext
,
T
>
col2im
;
math
::
Col2VolFunctor
<
DeviceContext
,
T
>
col2vol
;
...
...
@@ -133,22 +135,29 @@ class GemmConvTransposeKernel : public framework::OpKernel<T> {
// output size: (c, o_h, o_w) or (c, o_d, o_h, o_w)
Tensor
output_batch
=
output
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// col_matrix = filter * input_batch
// of shape (c * k_h * k_w, h * w) or (c * k_d * k_h * k_w, d * h * w)
blas
.
MatMul
(
filter
,
true
,
input_batch
,
false
,
static_cast
<
T
>
(
1.0
),
&
col_matrix
,
static_cast
<
T
>
(
0.0
));
if
(
data_dim
==
2U
)
{
// col2im: col_matrix -> dy
// from (c * k_h * k_w, h * w) to (c, o_h, o_w)
col2im
(
dev_ctx
,
col
,
dilations
,
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
output_batch
);
}
else
if
(
data_dim
==
3U
)
{
// col2vol: col_matrix -> dy
// from (c * k_d * k_h * k_w, d * h * w) to (c, o_d, o_h, o_w)
col2vol
(
dev_ctx
,
col
,
dilations
,
strides
,
paddings
,
&
output_batch
);
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
Tensor
in_slice
=
input_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
out_slice
=
output_batch
.
Slice
(
g
*
out_step
,
(
g
+
1
)
*
out_step
);
// col_matrix = filter_slice * input_slice
// of shape (c/g * k_h * k_w, h * w)
// or (c/g * k_d * k_h * k_w, d * h * w)
blas
.
MatMul
(
filter_slice
,
true
,
in_slice
,
false
,
static_cast
<
T
>
(
1.0
),
&
col_matrix
,
static_cast
<
T
>
(
0.0
));
if
(
data_dim
==
2U
)
{
// col2im: col_matrix -> dy
// from (c/g * k_h * k_w, h * w) to (c/g, o_h, o_w)
col2im
(
dev_ctx
,
col
,
dilations
,
strides
,
std
::
vector
<
int
>
{
paddings
[
0
],
paddings
[
1
],
paddings
[
0
],
paddings
[
1
]},
&
out_slice
);
}
else
if
(
data_dim
==
3U
)
{
// col2vol: col_matrix -> dy
// from (c/g * k_d * k_h * k_w, d * h * w) to (c/g, o_d, o_h, o_w)
col2vol
(
dev_ctx
,
col
,
dilations
,
strides
,
paddings
,
&
out_slice
);
}
}
}
}
...
...
@@ -174,6 +183,7 @@ 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"
);
int
groups
=
context
.
Attr
<
int
>
(
"groups"
);
const
int
batch_size
=
static_cast
<
int
>
(
input
->
dims
()[
0
]);
...
...
@@ -205,9 +215,11 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// input matrix size: (m, h * w) or (m, d * h * w)
DDim
input_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
1
]};
// filter size: (m, c
* k_h * k_w) or (m, c
* k_d * k_h * k_w)
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]};
// filter size: (m, c
/g * k_h * k_w) or (m, c/g
* k_d * k_h * k_w)
DDim
filter_matrix_shape
=
{
input
->
dims
()[
1
],
col_matrix_shape
[
0
]
/
groups
};
filter
.
Resize
(
filter_matrix_shape
);
int
in_step
=
static_cast
<
int
>
(
input
->
dims
()[
1
])
/
groups
;
int
col_step
=
static_cast
<
int
>
(
col_matrix_shape
[
0
])
/
groups
;
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
...
...
@@ -233,7 +245,7 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
if
(
input_grad
)
{
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
}
if
(
filter_grad
)
{
// filter size (m, c, k_h, k_w)
if
(
filter_grad
)
{
// filter size (m, c
/g
, k_h, k_w)
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
set_zero
(
dev_ctx
,
filter_grad
,
static_cast
<
T
>
(
0
));
filter_grad_
=
*
filter_grad
;
...
...
@@ -268,8 +280,17 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, c * k_d * k_h * k_w) * (c * k_d * k_h * k_w, d * h * w) -> (m,
// d, h, w)
blas
.
MatMul
(
filter
,
false
,
col_matrix
,
false
,
static_cast
<
T
>
(
1.0
),
&
input_grad_batch
,
static_cast
<
T
>
(
0.0
));
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
Tensor
input_grad_slice
=
input_grad_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
filter_slice
=
filter
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
col_matrix_slice
=
col_matrix
.
Slice
(
g
*
col_step
,
(
g
+
1
)
*
col_step
);
blas
.
MatMul
(
filter_slice
,
false
,
col_matrix_slice
,
false
,
static_cast
<
T
>
(
1.0
),
&
input_grad_slice
,
static_cast
<
T
>
(
0.0
));
}
}
if
(
filter_grad
)
{
// input batch
...
...
@@ -279,8 +300,17 @@ class GemmConvTransposeGradKernel : public framework::OpKernel<T> {
// or
// (m, d * h * w) * (d * h * w, c * k_d * k_h * k_w) -> (m, c * k_d *
// k_h * k_w)
blas
.
MatMul
(
in_batch
,
false
,
col_matrix
,
true
,
static_cast
<
T
>
(
1.0
),
&
filter_grad_
,
static_cast
<
T
>
(
1.0
));
for
(
int
g
=
0
;
g
<
groups
;
g
++
)
{
Tensor
in_batch_slice
=
in_batch
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
filter_grad_slice
=
filter_grad_
.
Slice
(
g
*
in_step
,
(
g
+
1
)
*
in_step
);
Tensor
col_matrix_slice
=
col_matrix
.
Slice
(
g
*
col_step
,
(
g
+
1
)
*
col_step
);
blas
.
MatMul
(
in_batch_slice
,
false
,
col_matrix_slice
,
true
,
static_cast
<
T
>
(
1.0
),
&
filter_grad_slice
,
static_cast
<
T
>
(
1.0
));
}
}
}
}
...
...
python/paddle/fluid/layers/nn.py
浏览文件 @
20bdc3e1
...
...
@@ -1708,6 +1708,7 @@ def conv2d_transpose(input,
padding
=
0
,
stride
=
1
,
dilation
=
1
,
groups
=
None
,
param_attr
=
None
,
bias_attr
=
None
,
use_cudnn
=
True
,
...
...
@@ -1778,6 +1779,12 @@ def conv2d_transpose(input,
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. Default: dilation = 1.
groups(int): The groups number of the Conv2d transpose layer. Inspired by
grouped convolution in Alex Krizhevsky's Deep CNN paper, in which
when group=2, the first half of the filters is only connected to the
first half of the input channels, while the second half of the
filters is only connected to the second half of the input channels.
Default: groups=1
param_attr(ParamAttr): The parameters to the Conv2d_transpose Layer.
Default: None
bias_attr(ParamAttr): Bias parameter for the Conv2d layer. Default: None
...
...
@@ -1832,7 +1839,8 @@ def conv2d_transpose(input,
filter_size
=
utils
.
convert_to_list
(
filter_size
,
2
,
'conv2d_transpose.filter_size'
)
filter_shape
=
[
input_channel
,
num_filters
]
+
filter_size
groups
=
1
if
groups
is
None
else
groups
filter_shape
=
[
input_channel
,
num_filters
/
groups
]
+
filter_size
img_filter
=
helper
.
create_parameter
(
dtype
=
input
.
dtype
,
shape
=
filter_shape
,
attr
=
helper
.
param_attr
)
...
...
python/paddle/fluid/tests/unittests/test_conv2d_transpose_op.py
浏览文件 @
20bdc3e1
...
...
@@ -21,8 +21,11 @@ from op_test import OpTest
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
f_c
,
f_out_c
,
f_h
,
f_w
=
filter_
.
shape
groups
=
attrs
[
'groups'
]
assert
in_c
==
f_c
out_c
=
f_out_c
*
groups
sub_in_c
=
in_c
/
groups
stride
,
pad
,
dilations
=
attrs
[
'strides'
],
attrs
[
'paddings'
],
attrs
[
'dilations'
]
...
...
@@ -36,15 +39,21 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
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
]
+
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
for
g
in
range
(
groups
):
input_masked
=
input_
[
n
,
g
*
sub_in_c
:(
g
+
1
)
*
sub_in_c
,
i
,
j
]
# (c)
input_masked
=
np
.
reshape
(
input_masked
,
(
sub_in_c
,
1
,
1
))
input_masked
=
np
.
tile
(
input_masked
,
(
1
,
f_h
,
f_w
))
for
k
in
range
(
f_out_c
):
tmp_out
=
np
.
sum
(
input_masked
*
filter_
[
g
*
sub_in_c
:(
g
+
1
)
*
sub_in_c
,
k
,
:,
:],
axis
=
0
)
i1
,
i2
=
i
*
stride
[
0
],
i
*
stride
[
0
]
+
d_bolck_h
j1
,
j2
=
j
*
stride
[
0
],
j
*
stride
[
0
]
+
d_bolck_h
out
[
n
,
g
*
f_out_c
+
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
...
...
@@ -64,6 +73,7 @@ class TestConv2dTransposeOp(OpTest):
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
'groups'
:
self
.
groups
,
'dilations'
:
self
.
dilations
,
'use_cudnn'
:
self
.
use_cudnn
,
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
...
...
@@ -127,6 +137,7 @@ class TestConv2dTransposeOp(OpTest):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
...
...
@@ -140,16 +151,29 @@ class TestWithPad(TestConv2dTransposeOp):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithGroups
(
TestConv2dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
]
class
TestWithStride
(
TestConv2dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
...
...
@@ -159,6 +183,7 @@ class TestWithDilation(TestConv2dTransposeOp):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
groups
=
1
self
.
dilations
=
[
2
,
2
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
...
...
@@ -176,6 +201,7 @@ class TestCUDNNWithPad(TestWithPad):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
groups
=
1
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
...
...
@@ -190,6 +216,7 @@ class TestCUDNNWithStride(TestWithStride):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
groups
=
1
self
.
dilations
=
[
1
,
1
]
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
...
...
@@ -200,6 +227,21 @@ class TestCUDNNWithStride(TestWithStride):
self
.
op_type
=
"conv2d_transpose"
class
TestCUDNNWithGroups
(
TestWithGroups
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
]
def
init_op_type
(
self
):
self
.
use_cudnn
=
True
self
.
op_type
=
"conv2d_transpose"
# Please Don't remove the following code.
# Currently, CI use cudnn V5.0 which not support dilation conv.
# class TestCUDNNWithDilation(TestWithDilation):
...
...
python/paddle/fluid/tests/unittests/test_conv3d_transpose_op.py
浏览文件 @
20bdc3e1
...
...
@@ -21,8 +21,11 @@ from op_test import OpTest
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
f_c
,
f_out_c
,
f_d
,
f_h
,
f_w
=
filter_
.
shape
groups
=
attrs
[
'groups'
]
assert
in_c
==
f_c
out_c
=
f_out_c
*
groups
sub_in_c
=
in_c
/
groups
stride
,
pad
,
dilations
=
attrs
[
'strides'
],
attrs
[
'paddings'
],
attrs
[
'dilations'
]
...
...
@@ -39,18 +42,23 @@ def conv3dtranspose_forward_naive(input_, filter_, attrs):
for
d
in
range
(
in_d
):
for
i
in
range
(
in_h
):
for
j
in
range
(
in_w
):
input_masked
=
input_
[
n
,
:,
d
,
i
,
j
]
# (c)
input_masked
=
np
.
reshape
(
input_masked
,
(
in_c
,
1
,
1
,
1
))
input_masked
=
np
.
tile
(
input_masked
,
(
1
,
f_d
,
f_h
,
f_w
))
for
k
in
range
(
out_c
):
tmp_out
=
np
.
sum
(
input_masked
*
filter_
[:,
k
,
:,
:,
:],
axis
=
0
)
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
for
g
in
range
(
groups
):
input_masked
=
input_
[
n
,
g
*
sub_in_c
:(
g
+
1
)
*
sub_in_c
,
d
,
i
,
j
]
# (c)
input_masked
=
np
.
reshape
(
input_masked
,
(
sub_in_c
,
1
,
1
,
1
))
input_masked
=
np
.
tile
(
input_masked
,
(
1
,
f_d
,
f_h
,
f_w
))
for
k
in
range
(
f_out_c
):
tmp_out
=
np
.
sum
(
input_masked
*
filter_
[
g
*
sub_in_c
:(
g
+
1
)
*
sub_in_c
,
k
,
:,
:,
:],
axis
=
0
)
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
,
g
*
f_out_c
+
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
]]
...
...
@@ -72,6 +80,7 @@ class TestConv3dTransposeOp(OpTest):
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
'dilations'
:
self
.
dilations
,
'groups'
:
self
.
groups
,
'use_cudnn'
:
self
.
use_cudnn
,
'data_format'
:
'AnyLayout'
# TODO(dzhwinter) : should be fix latter
}
...
...
@@ -134,6 +143,7 @@ class TestConv3dTransposeOp(OpTest):
self
.
pad
=
[
0
,
0
,
0
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
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
]
...
...
@@ -147,16 +157,29 @@ class TestWithPad(TestConv3dTransposeOp):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
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
]
class
TestWithGroups
(
TestConv3dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
,
3
]
class
TestWithStride
(
TestConv3dTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
2
,
2
,
2
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
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
]
...
...
@@ -167,6 +190,7 @@ class TestWithDilation(TestConv3dTransposeOp):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
2
,
2
,
2
]
self
.
groups
=
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
]
...
...
@@ -184,6 +208,7 @@ class TestCUDNNWithPad(TestWithPad):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
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
]
...
...
@@ -198,6 +223,7 @@ class TestCUDNNWithStride(TestWithStride):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
2
,
2
,
2
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
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
]
...
...
@@ -207,6 +233,21 @@ class TestCUDNNWithStride(TestWithStride):
self
.
op_type
=
"conv3d_transpose"
class
TestCUDNNWithGroups
(
TestWithGroups
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
,
1
]
self
.
stride
=
[
1
,
1
,
1
]
self
.
dilations
=
[
1
,
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
,
3
]
def
init_op_type
(
self
):
self
.
use_cudnn
=
True
self
.
op_type
=
"conv3d_transpose"
# Please Don't remove the following code.
# Currently, CI use cudnn V5.0 which not support dilation conv.
# class TestCUDNNWithDilation(TestWithDilation):
...
...
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