Skip to content
体验新版
项目
组织
正在加载...
登录
切换导航
打开侧边栏
机器未来
Paddle
提交
8fdc315a
P
Paddle
项目概览
机器未来
/
Paddle
与 Fork 源项目一致
Fork自
PaddlePaddle / Paddle
通知
1
Star
1
Fork
0
代码
文件
提交
分支
Tags
贡献者
分支图
Diff
Issue
1
列表
看板
标记
里程碑
合并请求
0
Wiki
0
Wiki
分析
仓库
DevOps
项目成员
Pages
P
Paddle
项目概览
项目概览
详情
发布
仓库
仓库
文件
提交
分支
标签
贡献者
分支图
比较
Issue
1
Issue
1
列表
看板
标记
里程碑
合并请求
0
合并请求
0
Pages
分析
分析
仓库分析
DevOps
Wiki
0
Wiki
成员
成员
收起侧边栏
关闭侧边栏
动态
分支图
创建新Issue
提交
Issue看板
体验新版 GitCode,发现更多精彩内容 >>
提交
8fdc315a
编写于
10月 23, 2017
作者:
Z
Zhuoyuan
提交者:
GitHub
10月 23, 2017
浏览文件
操作
浏览文件
下载
差异文件
Merge pull request #4739 from zchen0211/develop
deconv op implementing ...
上级
94e741d6
cc5e118b
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
487 addition
and
0 deletion
+487
-0
paddle/operators/conv2dtranspose_op.cc
paddle/operators/conv2dtranspose_op.cc
+107
-0
paddle/operators/conv2dtranspose_op.cu
paddle/operators/conv2dtranspose_op.cu
+24
-0
paddle/operators/conv2dtranspose_op.h
paddle/operators/conv2dtranspose_op.h
+254
-0
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
+102
-0
未找到文件。
paddle/operators/conv2dtranspose_op.cc
0 → 100644
浏览文件 @
8fdc315a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/conv2dtranspose_op.h"
namespace
paddle
{
namespace
operators
{
void
Conv2DTransposeOp
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Input"
),
"Input(Input) of Conv2DTransposeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasInput
(
"Filter"
),
"Input(Filter) of Conv2DTransposeOp should not be null."
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"Output"
),
"Output(Output) of Conv2DTransposeOp should not be null."
);
auto
in_dims
=
ctx
->
GetInputDim
(
"Input"
);
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"
);
for
(
size_t
i
=
0
;
i
<
paddings
.
size
();
++
i
)
{
PADDLE_ENFORCE_EQ
(
paddings
[
i
],
0
,
"No Padding allowed in conv transpose op."
);
}
PADDLE_ENFORCE_EQ
(
in_dims
.
size
(),
4
,
"Conv2DTransposeOp input should be 4-D tensor."
);
PADDLE_ENFORCE_EQ
(
filter_dims
.
size
(),
4
,
"Conv2DTransposeOp filter should be 4-D tensor."
);
PADDLE_ENFORCE_EQ
(
in_dims
[
1
],
filter_dims
[
0
],
"input and kernel input dimension should be equal."
);
auto
output_height
=
(
in_dims
[
2
]
-
1
)
*
strides
[
0
]
+
filter_dims
[
2
];
auto
output_width
=
(
in_dims
[
3
]
-
1
)
*
strides
[
1
]
+
filter_dims
[
3
];
ctx
->
SetOutputDim
(
"Output"
,
{
in_dims
[
0
],
filter_dims
[
1
],
output_height
,
output_width
});
}
Conv2DTransposeOpMaker
::
Conv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
)
:
OpProtoAndCheckerMaker
(
proto
,
op_checker
)
{
AddInput
(
"Input"
,
"(Tensor) The input tensor of convolution transpose operator. "
"The format of input tensor is NCHW. Where N is batch size, C is the "
"number of input channels, H and W is the height and width of image."
);
AddInput
(
"Filter"
,
"(Tensor) The filter tensor of convolution transpose operator."
"The format of the filter tensor is CMHW, where C is the number of "
"output image channels, M is the number of input image channels, "
"H and W is height and width of filter. "
"We enforce groups number == 1 and padding == 0 in "
"convolution transpose Scenario."
);
AddOutput
(
"Output"
,
"(Tensor) The output tensor of convolution transpose operator."
"The format of output tensor is also NCHW."
);
AddAttr
<
std
::
vector
<
int
>>
(
"strides"
,
"strides of convolution transpose operator."
)
.
SetDefault
({
1
,
1
});
AddAttr
<
std
::
vector
<
int
>>
(
"paddings"
,
"paddings of convolution transpose operator."
)
.
SetDefault
({
0
,
0
});
AddComment
(
R"DOC(
The convolution transpose operation calculates the output based on the input, filter
and strides, paddings, groups parameters. The size of each dimension of the
parameters is checked in the infer-shape.
)DOC"
);
}
void
Conv2DTransposeOpGrad
::
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
{
auto
in_dims
=
ctx
->
GetInputDim
(
"Input"
);
auto
filter_dims
=
ctx
->
GetInputDim
(
"Filter"
);
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Input"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Input"
),
in_dims
);
}
if
(
ctx
->
HasOutput
(
framework
::
GradVarName
(
"Filter"
)))
{
ctx
->
SetOutputDim
(
framework
::
GradVarName
(
"Filter"
),
filter_dims
);
}
}
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
REGISTER_OP
(
conv2dtranspose
,
ops
::
Conv2DTransposeOp
,
ops
::
Conv2DTransposeOpMaker
,
conv2dtranspose_grad
,
ops
::
Conv2DTransposeOpGrad
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
REGISTER_OP_CPU_KERNEL
(
conv2dtranspose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
CPUPlace
,
float
>
);
paddle/operators/conv2dtranspose_op.cu
0 → 100644
浏览文件 @
8fdc315a
/* Copyright (c) 2016 PaddlePaddle Authors All Rights Reserve.
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/operators/conv2dtranspose_op.h"
namespace
ops
=
paddle
::
operators
;
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose
,
ops
::
GemmConv2DTransposeKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
REGISTER_OP_GPU_KERNEL
(
conv2dtranspose_grad
,
ops
::
GemmConv2DTransposeGradKernel
<
paddle
::
platform
::
GPUPlace
,
float
>
);
paddle/operators/conv2dtranspose_op.h
0 → 100644
浏览文件 @
8fdc315a
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#pragma once
#include "paddle/framework/eigen.h"
#include "paddle/framework/op_registry.h"
#include "paddle/operators/math/im2col.h"
#include "paddle/operators/math/math_function.h"
namespace
paddle
{
namespace
operators
{
using
Tensor
=
framework
::
Tensor
;
using
DDim
=
framework
::
DDim
;
// Define Op classes in .h file so that other conv transpose
// operator implementations can reuse the code.
class
Conv2DTransposeOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
Conv2DTransposeOpMaker
(
framework
::
OpProto
*
proto
,
framework
::
OpAttrChecker
*
op_checker
);
};
class
Conv2DTransposeOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
class
Conv2DTransposeOpGrad
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
void
InferShape
(
framework
::
InferShapeContext
*
ctx
)
const
override
;
};
template
<
typename
Place
,
typename
T
>
class
GemmConv2DTransposeKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
// The filter will be reshaped, so it should not be constant pointer
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
output
=
context
.
Output
<
Tensor
>
(
"Output"
);
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// TODO(Zhuoyuan): Paddings can be added in future.
// groups will alway be disabled in conv2dtranspose.
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output
->
dims
()[
1
];
// output channels
const
int
o_h
=
output
->
dims
()[
2
];
const
int
o_w
=
output
->
dims
()[
3
];
paddle
::
operators
::
math
::
Col2ImFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
col2im
;
// use col_shape in the im2col and col2im calculation
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
w
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
col_matrix
.
Resize
(
col_matrix_shape
);
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose: gemm + col2im (similar to conv-backward on input)
output
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
output
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (M, h * w)
Tensor
input_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// filter size: (M, c * k_h * k_w)
// output size: (c, 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)
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
,
strides
[
0
],
strides
[
1
],
0
,
0
);
}
}
};
template
<
typename
Place
,
typename
T
>
class
GemmConv2DTransposeGradKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
void
Compute
(
const
framework
::
ExecutionContext
&
context
)
const
override
{
const
Tensor
*
input
=
context
.
Input
<
Tensor
>
(
"Input"
);
const
Tensor
*
output_grad
=
context
.
Input
<
Tensor
>
(
framework
::
GradVarName
(
"Output"
));
// For filter, we do not use const pointer b/c we will do reshape,
// but we should avoid modifying its value.
Tensor
filter
=
*
context
.
Input
<
Tensor
>
(
"Filter"
);
Tensor
*
input_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Input"
));
Tensor
*
filter_grad
=
context
.
Output
<
Tensor
>
(
framework
::
GradVarName
(
"Filter"
));
std
::
vector
<
int
>
strides
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"strides"
);
// Actually, no paddings and groups allowed in conv transpose.
std
::
vector
<
int
>
paddings
=
context
.
Attr
<
std
::
vector
<
int
>>
(
"paddings"
);
const
int
batch_size
=
input
->
dims
()[
0
];
const
int
m
=
input
->
dims
()[
1
];
const
int
h
=
input
->
dims
()[
2
];
const
int
w
=
input
->
dims
()[
3
];
const
int
k_h
=
filter
.
dims
()[
2
];
const
int
k_w
=
filter
.
dims
()[
3
];
const
int
c
=
output_grad
->
dims
()[
1
];
// output channels
const
int
o_h
=
output_grad
->
dims
()[
2
];
const
int
o_w
=
output_grad
->
dims
()[
3
];
// Only im2col functor required for bp to get to the right shape
paddle
::
operators
::
math
::
Im2ColFunctor
<
paddle
::
operators
::
math
::
ColFormat
::
kCFO
,
Place
,
T
>
im2col
;
// use col_shape in the im2col and col2im calculation
DDim
col_shape
=
{
c
,
k_h
,
k_w
,
h
,
w
};
// use col_matrix_shape in the gemm calculation
DDim
col_matrix_shape_f
=
{
c
*
h
*
w
,
k_h
*
k_w
};
Tensor
col
;
col
.
mutable_data
<
T
>
(
col_shape
,
context
.
GetPlace
());
// col_matrix shares the same piece of data with col,
// but will be reshaped into a two-dimensional matrix shape
// to call the matrix multiplication interface.
DDim
output_shape
=
{
c
,
o_h
,
o_w
};
DDim
input_matrix_shape
=
{
m
,
h
*
w
};
DDim
filter_matrix_shape
=
{
m
,
c
*
k_h
*
k_w
};
filter
.
Resize
(
filter_matrix_shape
);
// convolution transpose grad on input:
// im2col + gemm (similar to conv-forward)
// input need to compute gradient
if
(
input_grad
)
{
Tensor
col_matrix
;
col_matrix
.
ShareDataWith
(
col
);
DDim
col_matrix_shape
=
{
c
*
k_h
*
k_w
,
h
*
w
};
col_matrix
.
Resize
(
col_matrix_shape
);
input_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
*
input_grad
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
i
++
)
{
// batch with size (c, o_h * o_w)
Tensor
output_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// filter of size (m, c * k_h * k_w)
// batch with size (m, h, w)
Tensor
input_grad_batch
=
input_grad
->
Slice
(
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
,
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)
math
::
matmul
<
Place
,
T
>
(
context
.
device_context
(),
filter
,
false
,
col_matrix
,
false
,
T
(
1.0
),
&
input_grad_batch
,
T
(
0.0
));
}
}
// filter gradient required
if
(
filter_grad
)
{
Tensor
col_matrix_f
;
col_matrix_f
.
ShareDataWith
(
col
);
DDim
col_matrix_shape_f
=
{
c
*
h
*
w
,
k_h
*
k_w
};
col_matrix_f
.
Resize
(
col_matrix_shape_f
);
filter_grad
->
mutable_data
<
T
>
(
context
.
GetPlace
());
Tensor
filter_grad_
=
*
filter_grad
;
filter_grad_
.
Resize
(
filter_matrix_shape
);
auto
t
=
framework
::
EigenVector
<
T
>::
Flatten
(
filter_grad_
);
t
.
device
(
context
.
GetEigenDevice
<
Place
>
())
=
t
.
constant
(
static_cast
<
T
>
(
0
));
for
(
int
i
=
0
;
i
<
batch_size
;
++
i
)
{
// batch with size (c, o_h, o_w)
Tensor
output_grad_batch
=
output_grad
->
Slice
(
i
,
i
+
1
).
Resize
(
output_shape
);
// input batch
Tensor
in_batch
=
input
->
Slice
(
i
,
i
+
1
).
Resize
(
input_matrix_shape
);
// im2col: (c * h * w, k_h * k_w)
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_f
,
true
,
T
(
1.0
),
&
filter_grad_
,
T
(
1.0
));
}
}
}
};
}
// namespace operators
}
// namespace paddle
python/paddle/v2/framework/tests/test_conv2dtranspose_op.py
0 → 100644
浏览文件 @
8fdc315a
import
unittest
import
numpy
as
np
from
op_test
import
OpTest
def
conv2dtranspose_forward_naive
(
input_
,
filter_
,
conv2dtranspose_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
=
conv2dtranspose_param
[
'stride'
],
conv2dtranspose_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
TestConv2dTransposeOp
(
OpTest
):
def
setUp
(
self
):
# init as conv transpose
self
.
init_op_type
()
# [2, 3, 5, 5] -> kernel [3, 6, 3, 3] -> output [2, 6, 7, 7]
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
)
# 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
=
"conv2dtranspose"
"""
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
()
编辑
预览
Markdown
is supported
0%
请重试
或
添加新附件
.
添加附件
取消
You are about to add
0
people
to the discussion. Proceed with caution.
先完成此消息的编辑!
取消
想要评论请
注册
或
登录