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b03a44e0
编写于
6年前
作者:
X
Xin Pan
提交者:
GitHub
6年前
浏览文件
操作
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差异文件
Merge pull request #14026 from JiabinYang/add_reorg_op
Add reorg op
上级
ff6c809b
9f65b616
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
498 addition
and
0 deletion
+498
-0
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-0
paddle/fluid/operators/space_to_depth_op.cc
paddle/fluid/operators/space_to_depth_op.cc
+131
-0
paddle/fluid/operators/space_to_depth_op.cu
paddle/fluid/operators/space_to_depth_op.cu
+30
-0
paddle/fluid/operators/space_to_depth_op.h
paddle/fluid/operators/space_to_depth_op.h
+127
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+61
-0
python/paddle/fluid/op.py
python/paddle/fluid/op.py
+2
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+11
-0
python/paddle/fluid/tests/unittests/test_space_to_depth_op.py
...on/paddle/fluid/tests/unittests/test_space_to_depth_op.py
+135
-0
未找到文件。
paddle/fluid/API.spec
浏览文件 @
b03a44e0
...
...
@@ -174,6 +174,7 @@ paddle.fluid.layers.mean ArgSpec(args=['x', 'name'], varargs=None, keywords=None
paddle.fluid.layers.mul ArgSpec(args=['x', 'y', 'x_num_col_dims', 'y_num_col_dims', 'name'], varargs=None, keywords=None, defaults=(1, 1, None))
paddle.fluid.layers.sigmoid_cross_entropy_with_logits ArgSpec(args=['x', 'label', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.maxout ArgSpec(args=['x', 'groups', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.space_to_depth ArgSpec(args=['x', 'blocksize', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_grid ArgSpec(args=['theta', 'out_shape', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.sequence_reverse ArgSpec(args=['x', 'name'], varargs=None, keywords=None, defaults=(None,))
paddle.fluid.layers.affine_channel ArgSpec(args=['x', 'scale', 'bias', 'data_layout', 'name'], varargs=None, keywords=None, defaults=(None, None, 'NCHW', None))
...
...
This diff is collapsed.
Click to expand it.
paddle/fluid/operators/space_to_depth_op.cc
0 → 100644
浏览文件 @
b03a44e0
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#include "paddle/fluid/operators/space_to_depth_op.h"
#include <string>
#include <vector>
namespace
paddle
{
namespace
operators
{
class
SpaceToDepthOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) of SpaceToDepthOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Out"
),
"Output(Out) of SpaceToDepthOp should not be null."
);
auto
x_dims
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
4
,
"input should be a 4D tensor"
);
auto
blocksize
=
ctx
->
Attrs
().
Get
<
int64_t
>
(
"blocksize"
);
PADDLE_ENFORCE_GT
(
blocksize
,
1
,
"The blocksize should be Greater than 1"
);
PADDLE_ENFORCE_GT
(
x_dims
[
1
],
0
,
"input channel should be Greater than 0"
);
PADDLE_ENFORCE_GT
(
x_dims
[
2
],
0
,
"input Height should be Greater than 0"
);
PADDLE_ENFORCE_GT
(
x_dims
[
3
],
0
,
"input Width should be Greater than 0"
);
PADDLE_ENFORCE_EQ
(
x_dims
[
1
]
%
(
blocksize
*
blocksize
),
0
,
"input channel should be divisible of the square of "
"SpaceToDepthOp blocksize"
);
PADDLE_ENFORCE_EQ
(
x_dims
[
2
]
%
(
blocksize
),
0
,
"input Height should be divisible of the square of "
"SpaceToDepthOp blocksize"
);
PADDLE_ENFORCE_EQ
(
x_dims
[
3
]
%
(
blocksize
),
0
,
"input Width should be divisible of the square of "
"SpaceToDepthOp blocksize"
);
VLOG
(
3
)
<<
"SpaceToDepthOp operator x.shape="
<<
x_dims
<<
"Attribute blocksize"
<<
blocksize
<<
std
::
endl
;
std
::
vector
<
int64_t
>
output_shape
(
4
,
0
);
// [B,C,H,W]
output_shape
[
0
]
=
x_dims
[
0
];
output_shape
[
1
]
=
x_dims
[
1
]
*
blocksize
*
blocksize
;
output_shape
[
2
]
=
x_dims
[
2
]
/
blocksize
;
output_shape
[
3
]
=
x_dims
[
3
]
/
blocksize
;
auto
out_dims
=
framework
::
make_ddim
(
output_shape
);
ctx
->
SetOutputDim
(
"Out"
,
out_dims
);
if
(
x_dims
[
0
]
==
out_dims
[
0
])
{
// Only pass LoD when the first dimension of output and Input(X)
// are the same.
ctx
->
ShareLoD
(
"X"
,
/*->*/
"Out"
);
}
}
};
class
SpaceToDepthOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"(Tensor). The input should be a 4D tensor B * C * W * H of "
"SpaceToDepthOp "
"operator."
);
AddOutput
(
"Out"
,
"(Tensor), The output should be a 4D tensor B * C2 * W2 * H2 of "
"SpaceToDepthOp operator."
);
AddAttr
<
int64_t
>
(
"blocksize"
,
"(int64_t, default 2) blocksize used to do change Space To Depth."
)
.
SetDefault
(
2
)
.
GreaterThan
(
1
);
AddComment
(
R"DOC(
reorg operator used in Yolo v2.
The equation is: C2 = C1/blocksize * blocksize, W2 = W1 ∗ blocksize + offset % blocksize, H2 = H1 ∗ blocksize + offset / blocksize,
Reshape Input(X) into the shape according to Attr(blocksize). The
data in Input(X) are unchanged.
Examples:
1. Given a 4-D tensor Input(X) with a shape [128, 2048, 26, 26], and the blocksize is 2, the reorg operator will transform Input(X)
into a 4-D tensor with shape [128, 2048, 13, 13] and leaving Input(X)'s data unchanged.
)DOC"
);
}
};
class
SpaceToDepthGradOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"X"
),
"Input(X) shouldn't be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
framework
::
GradVarName
(
"Out"
)),
"Input(Out@GRAD) shouldn't be null."
);
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"X"
),
ctx
->
GetInputDim
(
"X"
));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OPERATOR
(
space_to_depth
,
ops
::
SpaceToDepthOp
,
ops
::
SpaceToDepthOpMaker
,
paddle
::
framework
::
DefaultGradOpDescMaker
<
true
>
);
REGISTER_OPERATOR
(
space_to_depth_grad
,
ops
::
SpaceToDepthGradOp
);
REGISTER_OP_CPU_KERNEL
(
space_to_depth
,
ops
::
SpaceToDepthKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SpaceToDepthKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SpaceToDepthKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
REGISTER_OP_CPU_KERNEL
(
space_to_depth_grad
,
ops
::
SpaceToDepthGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
float
>
,
ops
::
SpaceToDepthGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
double
>
,
ops
::
SpaceToDepthGradKernel
<
paddle
::
platform
::
CPUDeviceContext
,
int64_t
>
);
This diff is collapsed.
Click to expand it.
paddle/fluid/operators/space_to_depth_op.cu
0 → 100644
浏览文件 @
b03a44e0
// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
//
// Licensed under the Apache License, Version 2.0 (the "License");
// you may not use this file except in compliance with the License.
// You may obtain a copy of the License at
//
// http://www.apache.org/licenses/LICENSE-2.0
//
// Unless required by applicable law or agreed to in writing, software
// distributed under the License is distributed on an "AS IS" BASIS,
// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
// See the License for the specific language governing permissions and
// limitations under the License.
#include "paddle/fluid/operators/space_to_depth_op.h"
namespace
plat
=
paddle
::
platform
;
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_CUDA_KERNEL
(
space_to_depth
,
ops
::
SpaceToDepthKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SpaceToDepthKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SpaceToDepthKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
REGISTER_OP_CUDA_KERNEL
(
space_to_depth_grad
,
ops
::
SpaceToDepthGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
float
>
,
ops
::
SpaceToDepthGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
double
>
,
ops
::
SpaceToDepthGradKernel
<
paddle
::
platform
::
CUDADeviceContext
,
int64_t
>
);
This diff is collapsed.
Click to expand it.
paddle/fluid/operators/space_to_depth_op.h
0 → 100644
浏览文件 @
b03a44e0
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at
http://www.apache.org/licenses/LICENSE-2.0
Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License. */
#ifndef PADDLE_FLUID_OPERATORS_SPACE_TO_DEPTH_OP_H_
#define PADDLE_FLUID_OPERATORS_SPACE_TO_DEPTH_OP_H_
#endif // PADDLE_FLUID_OPERATORS_SPACE_TO_DEPTH_OP_H_
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/platform/for_range.h"
namespace
paddle
{
namespace
operators
{
template
<
typename
T
>
class
space_to_depth_compute
{
public:
HOSTDEVICE
space_to_depth_compute
(
const
T
*
x
,
int64_t
w
,
int64_t
h
,
int64_t
c
,
int64_t
batch
,
int64_t
blocksize
,
int64_t
forward
,
T
*
out
)
:
x_
(
x
),
w_
(
w
),
h_
(
h
),
c_
(
c
),
batch_
(
batch
),
blocksize_
(
blocksize
),
forward_
(
forward
),
out_
(
out
)
{}
HOSTDEVICE
void
operator
()(
int64_t
in_index
)
{
int64_t
out_c
=
c_
/
(
blocksize_
*
blocksize_
);
// calculate each dim position with index of tensor
int64_t
b
=
in_index
/
(
c_
*
h_
*
w_
);
int64_t
k
=
(
in_index
%
(
c_
*
h_
*
w_
))
/
(
h_
*
w_
);
int64_t
j
=
((
in_index
%
(
c_
*
h_
*
w_
))
%
(
h_
*
w_
))
/
w_
;
int64_t
i
=
((
in_index
%
(
c_
*
h_
*
w_
))
%
(
h_
*
w_
))
%
w_
;
int64_t
c2
=
k
%
out_c
;
int64_t
offset
=
k
/
out_c
;
int64_t
w2
=
i
*
blocksize_
+
offset
%
blocksize_
;
int64_t
h2
=
j
*
blocksize_
+
offset
/
blocksize_
;
int64_t
out_index
=
w2
+
w_
*
blocksize_
*
(
h2
+
h_
*
blocksize_
*
(
c2
+
out_c
*
b
));
if
(
forward_
)
out_
[
out_index
]
=
x_
[
in_index
];
else
out_
[
in_index
]
=
x_
[
out_index
];
}
private:
const
T
*
x_
;
int64_t
w_
,
h_
,
c_
,
batch_
,
blocksize_
,
forward_
;
T
*
out_
;
};
template
<
typename
DeviceContext
,
typename
T
>
class
SpaceToDepthKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
out
=
context
.
Output
<
framework
::
LoDTensor
>
(
"Out"
);
auto
*
x
=
context
.
Input
<
framework
::
LoDTensor
>
(
"X"
);
auto
blocksize
=
context
.
Attr
<
int64_t
>
(
"blocksize"
);
auto
in_dims
=
x
->
dims
();
out
->
mutable_data
(
context
.
GetPlace
(),
x
->
type
());
auto
out_dims
=
out
->
dims
();
auto
B
=
in_dims
[
0
];
auto
C
=
in_dims
[
1
];
auto
H
=
in_dims
[
2
];
auto
W
=
in_dims
[
3
];
platform
::
ForRange
<
DeviceContext
>
for_range
(
context
.
template
device_context
<
DeviceContext
>(),
static_cast
<
size_t
>
(
x
->
numel
()));
auto
*
x_data
=
x
->
data
<
T
>
();
auto
*
out_data
=
out
->
data
<
T
>
();
paddle
::
operators
::
space_to_depth_compute
<
T
>
computer
(
x_data
,
W
,
H
,
C
,
B
,
blocksize
,
1
,
out_data
);
for_range
(
computer
);
out
->
Resize
(
out_dims
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
SpaceToDepthGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
auto
*
d_out
=
context
.
Input
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"Out"
));
auto
*
d_x
=
context
.
Output
<
framework
::
LoDTensor
>
(
framework
::
GradVarName
(
"X"
));
auto
blocksize
=
context
.
Attr
<
int64_t
>
(
"blocksize"
);
auto
in_dims
=
d_x
->
dims
();
d_x
->
mutable_data
(
context
.
GetPlace
(),
d_out
->
type
());
auto
B
=
in_dims
[
0
];
auto
C
=
in_dims
[
1
];
auto
H
=
in_dims
[
2
];
auto
W
=
in_dims
[
3
];
platform
::
ForRange
<
DeviceContext
>
for_range
(
context
.
template
device_context
<
DeviceContext
>(),
static_cast
<
size_t
>
(
d_x
->
numel
()));
auto
*
dx_data
=
d_x
->
data
<
T
>
();
auto
*
dout_data
=
d_out
->
data
<
T
>
();
paddle
::
operators
::
space_to_depth_compute
<
T
>
computer
(
dout_data
,
W
,
H
,
C
,
B
,
blocksize
,
0
,
dx_data
);
for_range
(
computer
);
d_x
->
Resize
(
in_dims
);
}
};
}
// namespace operators
}
// namespace paddle
This diff is collapsed.
Click to expand it.
python/paddle/fluid/layers/nn.py
浏览文件 @
b03a44e0
...
...
@@ -154,6 +154,7 @@ __all__ = [
'mul'
,
'sigmoid_cross_entropy_with_logits'
,
'maxout'
,
'space_to_depth'
,
'affine_grid'
,
'sequence_reverse'
,
'affine_channel'
,
...
...
@@ -7674,6 +7675,66 @@ def maxout(x, groups, name=None):
return
out
def
space_to_depth
(
x
,
blocksize
,
name
=
None
):
"""
Gives a blocksize to space_to_depth the input LoDtensor with Layout: [batch, channel, height, width]
This op rearranges blocks of spatial data, into depth. More specifically, this op outputs a copy of the
input LoDtensor where values from the height and width dimensions are moved to the channel dimension.
The attr blocksize indicates the input block size.
space_to_depth will reorgnize the elements of input with shape[batch, channel, height, width] according
to blocksize to construct output with shape [batch, channel * blocksize * blocksize, height/blocksize, width/blocksize]:
space_to_depth is used to This operation is useful for resizing the activations between convolutions
(but keeping all data)
- Non-overlapping blocks of size block_size x block size are rearranged into depth at each location.
- The depth of the output tensor is block_size * block_size * input channel
- The Y, X coordinates within each block of the input become the high order component of the output channel index
- channel should be divisible by square of blocksize
- height, width should be divsible by blocksize
Args:
x(variable): The input LoDtensor.
blocksize(variable): The blocksize to select the element on each feature map should be > 2
Returns:
Variable: The output LoDtensor.
Raises:
TypeError: blocksize type must be a long.
Examples:
.. code-block:: python
data = fluid.layers.data(
name='data', shape=[1, 4, 2, 2], dtype='float32')
space_to_depthed = fluid.layers.space_to_depth(
x=data, blocksize=2)
"""
helper
=
LayerHelper
(
"space_to_depth"
,
**
locals
())
if
not
(
isinstance
(
blocksize
,
int
)):
raise
ValueError
(
"blocksize must be a python Int"
)
if
name
is
None
:
out
=
helper
.
create_variable_for_type_inference
(
dtype
=
x
.
dtype
)
#fix create
else
:
out
=
helper
.
create_variable
(
name
=
name
,
dtype
=
x
.
dtype
,
persistable
=
False
)
helper
.
append_op
(
type
=
"space_to_depth"
,
inputs
=
{
"X"
:
x
},
attrs
=
{
"blocksize"
:
blocksize
},
outputs
=
{
"Out"
:
out
})
return
out
@
templatedoc
()
def
sequence_reverse
(
x
,
name
=
None
):
"""
...
...
This diff is collapsed.
Click to expand it.
python/paddle/fluid/op.py
浏览文件 @
b03a44e0
...
...
@@ -108,6 +108,8 @@ class OpDescCreationMethod(object):
new_attr
.
i
=
user_defined_attr
elif
attr
.
type
==
framework_pb2
.
FLOAT
:
new_attr
.
f
=
user_defined_attr
elif
attr
.
type
==
framework_pb2
.
LONG
:
new_attr
.
l
=
user_defined_attr
elif
attr
.
type
==
framework_pb2
.
STRING
:
new_attr
.
s
=
user_defined_attr
elif
attr
.
type
==
framework_pb2
.
BOOLEAN
:
...
...
This diff is collapsed.
Click to expand it.
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
b03a44e0
...
...
@@ -248,6 +248,17 @@ class TestBook(unittest.TestCase):
self
.
assertIsNotNone
(
layers
.
softmax
(
hid
))
print
(
str
(
program
))
def
test_space_to_depth
(
self
):
program
=
Program
()
with
program_guard
(
program
):
data
=
layers
.
data
(
name
=
'data'
,
shape
=
[
32
,
9
,
6
,
6
],
append_batch_size
=
False
,
dtype
=
'float32'
)
self
.
assertIsNotNone
(
layers
.
space_to_depth
(
data
,
3
))
print
(
str
(
program
))
def
test_sequence_unsqueeze
(
self
):
program
=
Program
()
with
program_guard
(
program
):
...
...
This diff is collapsed.
Click to expand it.
python/paddle/fluid/tests/unittests/test_space_to_depth_op.py
0 → 100644
浏览文件 @
b03a44e0
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
paddle.fluid
as
fluid
from
op_test
import
OpTest
class
TestSpaceToDepthOp
(
OpTest
):
@
staticmethod
def
helper
(
in_
,
width
,
height
,
channel
,
batch
,
blocksize
,
forward
,
out_
):
channel_out
=
channel
//
(
blocksize
*
blocksize
)
for
b
in
range
(
batch
):
for
k
in
range
(
channel
):
for
j
in
range
(
height
):
for
i
in
range
(
width
):
in_index
=
i
+
width
*
(
j
+
height
*
(
k
+
channel
*
b
))
channel2
=
k
%
channel_out
offset
=
k
//
channel_out
width2
=
i
*
blocksize
+
offset
%
blocksize
height2
=
j
*
blocksize
+
offset
//
blocksize
out_index
=
width2
+
width
*
blocksize
*
(
height2
+
height
*
blocksize
*
(
channel2
+
channel_out
*
b
))
if
forward
:
out_
[
out_index
]
=
in_
[
in_index
]
else
:
out_
[
in_index
]
=
in_
[
out_index
]
def
setUp
(
self
):
self
.
init_data
()
self
.
op_type
=
"space_to_depth"
self
.
inputs
=
{
"X"
:
self
.
x
}
self
.
helper
(
self
.
x_1d
,
self
.
x
.
shape
[
3
],
self
.
x
.
shape
[
2
],
self
.
x
.
shape
[
1
],
self
.
x
.
shape
[
0
],
self
.
blocksize
,
self
.
forward
,
self
.
out_1d
)
self
.
out
=
np
.
reshape
(
self
.
out_1d
,
self
.
infered_shape
)
self
.
attrs
=
{
"blocksize"
:
self
.
blocksize
}
self
.
outputs
=
{
"Out"
:
self
.
out
}
def
init_data
(
self
):
self
.
ori_shape
=
(
32
,
12
,
6
,
6
)
self
.
infered_shape
=
(
32
,
48
,
3
,
3
)
self
.
one_d_len
=
32
*
48
*
3
*
3
self
.
blocksize
=
2
self
.
x
=
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
'float32'
)
self
.
x_1d
=
np
.
reshape
(
self
.
x
,
self
.
one_d_len
)
self
.
out
=
np
.
zeros
(
self
.
infered_shape
).
astype
(
'float32'
)
self
.
out_1d
=
np
.
reshape
(
self
.
out
,
self
.
one_d_len
)
self
.
forward
=
1
def
test_check_output
(
self
):
place
=
fluid
.
core
.
CUDAPlace
(
0
)
if
fluid
.
core
.
is_compiled_with_cuda
(
)
else
fluid
.
core
.
CPUPlace
()
self
.
check_output_with_place
(
place
,
1e-5
,
None
,
False
)
def
test_check_grad
(
self
):
place
=
fluid
.
core
.
CUDAPlace
(
0
)
if
fluid
.
core
.
is_compiled_with_cuda
(
)
else
fluid
.
core
.
CPUPlace
()
self
.
check_grad_with_place
(
place
,
[
'X'
],
'Out'
)
class
TestSpaceToDepthOpBasic
(
TestSpaceToDepthOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
32
,
8
,
6
,
6
)
self
.
infered_shape
=
(
32
,
32
,
3
,
3
)
self
.
one_d_len
=
32
*
32
*
3
*
3
self
.
blocksize
=
2
self
.
x
=
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
'float32'
)
self
.
x_1d
=
np
.
reshape
(
self
.
x
,
self
.
one_d_len
)
self
.
out
=
np
.
zeros
(
self
.
infered_shape
).
astype
(
'float32'
)
self
.
out_1d
=
np
.
reshape
(
self
.
out
,
self
.
one_d_len
)
self
.
forward
=
1
class
TestSpaceToDepthOpDoubleBasic
(
TestSpaceToDepthOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
32
,
8
,
6
,
6
)
self
.
infered_shape
=
(
32
,
32
,
3
,
3
)
self
.
one_d_len
=
32
*
32
*
3
*
3
self
.
blocksize
=
2
self
.
x
=
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
'float64'
)
self
.
x_1d
=
np
.
reshape
(
self
.
x
,
self
.
one_d_len
)
self
.
out
=
np
.
zeros
(
self
.
infered_shape
).
astype
(
'float64'
)
self
.
out_1d
=
np
.
reshape
(
self
.
out
,
self
.
one_d_len
)
self
.
forward
=
1
class
TestSpaceToDepthOpWithStride3
(
TestSpaceToDepthOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
32
,
9
,
6
,
6
)
self
.
infered_shape
=
(
32
,
81
,
2
,
2
)
self
.
one_d_len
=
32
*
81
*
2
*
2
self
.
blocksize
=
3
self
.
x
=
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
'float32'
)
self
.
x_1d
=
np
.
reshape
(
self
.
x
,
self
.
one_d_len
)
self
.
out
=
np
.
zeros
(
self
.
infered_shape
).
astype
(
'float32'
)
self
.
out_1d
=
np
.
reshape
(
self
.
out
,
self
.
one_d_len
)
self
.
forward
=
1
class
TestSpaceToDepthOpWithNotSquare
(
TestSpaceToDepthOp
):
def
init_data
(
self
):
self
.
ori_shape
=
(
32
,
9
,
9
,
6
)
self
.
infered_shape
=
(
32
,
81
,
3
,
2
)
self
.
one_d_len
=
32
*
81
*
3
*
2
self
.
blocksize
=
3
self
.
x
=
np
.
random
.
random
(
self
.
ori_shape
).
astype
(
'float32'
)
self
.
x_1d
=
np
.
reshape
(
self
.
x
,
self
.
one_d_len
)
self
.
out
=
np
.
zeros
(
self
.
infered_shape
).
astype
(
'float32'
)
self
.
out_1d
=
np
.
reshape
(
self
.
out
,
self
.
one_d_len
)
self
.
forward
=
1
if
__name__
==
'__main__'
:
unittest
.
main
()
This diff is collapsed.
Click to expand it.
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