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体验新版 GitCode,发现更多精彩内容 >>
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14905516
编写于
5月 30, 2018
作者:
Y
Yu Yang
提交者:
GitHub
5月 30, 2018
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差异文件
Merge pull request #10970 from JiayiFeng/dev_add_random_crop_op
Add random crop op
上级
654f5d3c
d2c1fac1
变更
7
隐藏空白更改
内联
并排
Showing
7 changed file
with
381 addition
and
12 deletion
+381
-12
paddle/fluid/framework/operator.cc
paddle/fluid/framework/operator.cc
+1
-0
paddle/fluid/operators/random_crop_op.cc
paddle/fluid/operators/random_crop_op.cc
+81
-0
paddle/fluid/operators/random_crop_op.cu
paddle/fluid/operators/random_crop_op.cu
+21
-0
paddle/fluid/operators/random_crop_op.h
paddle/fluid/operators/random_crop_op.h
+181
-0
python/paddle/fluid/layers/nn.py
python/paddle/fluid/layers/nn.py
+51
-12
python/paddle/fluid/tests/unittests/test_random_crop_op.py
python/paddle/fluid/tests/unittests/test_random_crop_op.py
+46
-0
tools/codestyle/docstring_checker.pyc
tools/codestyle/docstring_checker.pyc
+0
-0
未找到文件。
paddle/fluid/framework/operator.cc
浏览文件 @
14905516
...
...
@@ -469,6 +469,7 @@ class RuntimeInferShapeContext : public InferShapeContext {
protected:
DDim
GetDim
(
const
std
::
string
&
name
)
const
override
{
Variable
*
var
=
scope_
.
FindVar
(
name
);
PADDLE_ENFORCE_NOT_NULL
(
var
);
if
(
var
->
IsType
<
LoDTensor
>
())
{
return
var
->
Get
<
LoDTensor
>
().
dims
();
}
else
if
(
var
->
IsType
<
SelectedRows
>
())
{
...
...
paddle/fluid/operators/random_crop_op.cc
0 → 100644
浏览文件 @
14905516
// 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/random_crop_op.h"
namespace
paddle
{
namespace
operators
{
class
RandomCropOp
:
public
framework
::
OperatorWithKernel
{
public:
using
framework
::
OperatorWithKernel
::
OperatorWithKernel
;
protected:
framework
::
OpKernelType
GetExpectedKernelType
(
const
framework
::
ExecutionContext
&
ctx
)
const
override
{
return
framework
::
OpKernelType
(
framework
::
ToDataType
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
)
->
type
()),
ctx
.
device_context
());
}
};
class
RandomCropOpMaker
:
public
framework
::
OpProtoAndCheckerMaker
{
public:
void
Make
()
override
{
AddInput
(
"X"
,
"A batch of instances to random crop."
);
AddInput
(
"Seed"
,
"The random seed."
);
AddOutput
(
"Out"
,
"The cropped instance batch."
);
AddOutput
(
"SeedOut"
,
"The random seed after random cropping."
)
.
AsDispensable
();
AddAttr
<
std
::
vector
<
int
>>
(
"shape"
,
"The shape of a cropped instance."
);
AddComment
(
R"DOC(
This operator takes a batch of instance, and do random cropping on each instance.
It means that cropping positions differs on each instance, which is determined
by an uniform random generator. All cropped instances have the same shape, which
is determined by the operator's attribute 'shape'.
)DOC"
);
}
};
class
RandomCropOpInferShape
:
public
framework
::
InferShapeBase
{
public:
void
operator
()(
framework
::
InferShapeContext
*
ctx
)
const
override
{
auto
seed_dim
=
ctx
->
GetInputDim
(
"Seed"
);
PADDLE_ENFORCE
(
seed_dim
.
size
()
==
1
&&
seed_dim
[
0
]
==
1
);
auto
shape
=
ctx
->
Attrs
().
Get
<
std
::
vector
<
int
>>
(
"shape"
);
auto
x_dim
=
ctx
->
GetInputDim
(
"X"
);
PADDLE_ENFORCE_GT
(
x_dim
.
size
(),
static_cast
<
int64_t
>
(
shape
.
size
()));
auto
out_dim
=
framework
::
vectorize2int
(
x_dim
);
for
(
size_t
i
=
1
;
i
<=
shape
.
size
();
++
i
)
{
size_t
x_i
=
x_dim
.
size
()
-
i
;
size_t
shape_i
=
shape
.
size
()
-
i
;
PADDLE_ENFORCE_GE
(
x_dim
[
x_i
],
shape
[
shape_i
]);
out_dim
[
x_i
]
=
shape
[
shape_i
];
}
ctx
->
SetOutputDim
(
"Out"
,
framework
::
make_ddim
(
out_dim
));
ctx
->
SetOutputDim
(
"SeedOut"
,
framework
::
make_ddim
({
1
}));
}
};
}
// namespace operators
}
// namespace paddle
namespace
ops
=
paddle
::
operators
;
namespace
f
=
paddle
::
framework
;
REGISTER_OPERATOR
(
random_crop
,
ops
::
RandomCropOp
,
ops
::
RandomCropOpMaker
,
ops
::
RandomCropOpInferShape
,
f
::
EmptyGradOpMaker
);
template
<
typename
T
>
using
Kernel
=
ops
::
RandomCropKernel
<
paddle
::
platform
::
CPUDeviceContext
,
T
>
;
REGISTER_OP_CPU_KERNEL
(
random_crop
,
Kernel
<
float
>
,
Kernel
<
int
>
,
Kernel
<
double
>
,
Kernel
<
uint8_t
>
,
Kernel
<
int16_t
>
);
paddle/fluid/operators/random_crop_op.cu
0 → 100644
浏览文件 @
14905516
// 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/random_crop_op.h"
namespace
ops
=
paddle
::
operators
;
template
<
typename
T
>
using
Kernel
=
ops
::
RandomCropKernel
<
paddle
::
platform
::
CUDADeviceContext
,
T
>
;
REGISTER_OP_CUDA_KERNEL
(
random_crop
,
Kernel
<
float
>
,
Kernel
<
int
>
,
Kernel
<
double
>
,
Kernel
<
uint8_t
>
,
Kernel
<
int16_t
>
);
paddle/fluid/operators/random_crop_op.h
0 → 100644
浏览文件 @
14905516
// 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.
#pragma once
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/platform/device_context.h"
#include "paddle/fluid/platform/for_range.h"
#ifdef PADDLE_WITH_CUDA
#include <thrust/random.h>
#endif
namespace
paddle
{
namespace
operators
{
template
<
typename
DeviceContext
>
struct
Random
;
template
<
>
struct
Random
<
platform
::
CPUDeviceContext
>
{
using
Engine
=
std
::
minstd_rand
;
template
<
typename
T
>
using
UniformIntDist
=
std
::
uniform_int_distribution
<
T
>
;
};
#ifdef PADDLE_WITH_CUDA
template
<
>
struct
Random
<
platform
::
CUDADeviceContext
>
{
using
Engine
=
thrust
::
minstd_rand
;
template
<
typename
T
>
using
UniformIntDist
=
thrust
::
uniform_int_distribution
<
T
>
;
};
#endif
template
<
typename
T
>
HOSTDEVICE
inline
void
StridedMemcpy
(
const
T
*
x
,
const
size_t
*
x_dims
,
T
*
out
,
const
size_t
*
out_dims
,
int
i
,
int
rank
,
size_t
prod_x_remain
,
size_t
prod_out_remain
,
const
size_t
*
offsets
)
{
size_t
x_dim_i
=
x_dims
[
i
];
size_t
out_dim_i
=
out_dims
[
i
];
size_t
x_stride
=
prod_x_remain
/
x_dim_i
;
size_t
out_stride
=
prod_out_remain
/
out_dim_i
;
size_t
offset_i
=
offsets
[
i
];
if
(
i
==
rank
-
1
)
{
PADDLE_ASSERT
(
x_stride
==
1
&&
out_stride
==
1
);
x
+=
offset_i
;
for
(
size_t
j
=
0
;
j
<
out_dim_i
;
++
j
)
{
*
out
++
=
*
x
++
;
}
}
else
{
x
+=
offset_i
*
x_stride
;
for
(
size_t
j
=
0
;
j
<
out_dim_i
;
++
j
)
{
StridedMemcpy
<
T
>
(
x
,
x_dims
,
out
,
out_dims
,
i
+
1
,
rank
,
x_stride
,
out_stride
,
offsets
);
x
+=
x_stride
;
out
+=
out_stride
;
}
}
}
template
<
typename
DeviceContext
,
typename
T
>
struct
RandomCropFunctor
{
const
T
*
x_
;
T
*
out_
;
size_t
x_dims_
[
9
];
size_t
out_dims_
[
9
];
int
num_batchsize_dims_
;
int
rank_
;
int64_t
seed_
;
size_t
prod_batchsize_dims_
;
size_t
prod_x_ins_dims_
;
size_t
prod_out_ins_dims_
;
RandomCropFunctor
(
const
T
*
x
,
T
*
out
,
const
framework
::
DDim
&
x_dims
,
const
framework
::
DDim
&
out_dims
,
int
num_batchsize_dims
,
int64_t
seed
)
:
x_
(
x
),
out_
(
out
),
num_batchsize_dims_
(
num_batchsize_dims
),
rank_
(
x_dims
.
size
()),
seed_
(
seed
)
{
PADDLE_ENFORCE_EQ
(
x_dims
.
size
(),
out_dims
.
size
());
PADDLE_ENFORCE_GT
(
rank_
,
num_batchsize_dims_
);
prod_batchsize_dims_
=
1
;
prod_x_ins_dims_
=
1
;
prod_out_ins_dims_
=
1
;
for
(
size_t
i
=
0
;
i
<
static_cast
<
size_t
>
(
rank_
);
++
i
)
{
size_t
x_dim_i
=
x_dims
[
i
];
size_t
out_dim_i
=
out_dims
[
i
];
x_dims_
[
i
]
=
x_dim_i
;
out_dims_
[
i
]
=
out_dim_i
;
if
(
i
<
static_cast
<
size_t
>
(
num_batchsize_dims_
))
{
PADDLE_ENFORCE_EQ
(
x_dim_i
,
out_dim_i
);
prod_batchsize_dims_
*=
x_dim_i
;
}
else
{
prod_x_ins_dims_
*=
x_dim_i
;
prod_out_ins_dims_
*=
out_dim_i
;
}
}
}
HOSTDEVICE
void
operator
()(
size_t
ins_idx
)
{
typename
Random
<
DeviceContext
>::
Engine
engine
(
seed_
);
engine
.
discard
(
ins_idx
*
(
rank_
-
num_batchsize_dims_
));
size_t
offsets
[
9
];
for
(
int
i
=
num_batchsize_dims_
;
i
<
rank_
;
++
i
)
{
typename
Random
<
DeviceContext
>::
template
UniformIntDist
<
size_t
>
dist
(
0
,
x_dims_
[
i
]
-
out_dims_
[
i
]);
offsets
[
i
-
num_batchsize_dims_
]
=
dist
(
engine
);
}
const
T
*
x
=
x_
+
ins_idx
*
prod_x_ins_dims_
;
T
*
out
=
out_
+
ins_idx
*
prod_out_ins_dims_
;
StridedMemcpy
<
T
>
(
x
,
x_dims_
+
num_batchsize_dims_
,
out
,
out_dims_
+
num_batchsize_dims_
,
0
,
rank_
-
num_batchsize_dims_
,
prod_x_ins_dims_
,
prod_out_ins_dims_
,
offsets
);
}
};
template
<
typename
DeviceContext
,
typename
T
>
class
RandomCropKernel
:
public
framework
::
OpKernel
<
T
>
{
public:
virtual
void
Compute
(
const
framework
::
ExecutionContext
&
ctx
)
const
{
auto
&
seed_tensor
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"Seed"
));
int64_t
seed
=
0
;
if
(
platform
::
is_cpu_place
(
seed_tensor
.
place
()))
{
seed
=
*
seed_tensor
.
data
<
int64_t
>
();
}
else
{
LOG
(
WARNING
)
<<
"It is slow to place seed in GPU memory. Please verify "
"your program"
;
framework
::
LoDTensor
cpu_seed
;
framework
::
TensorCopySync
(
seed_tensor
,
platform
::
CPUPlace
(),
&
cpu_seed
);
seed
=
*
cpu_seed
.
data
<
int64_t
>
();
}
auto
shape
=
ctx
.
Attr
<
std
::
vector
<
int
>>
(
"shape"
);
auto
&
x
=
detail
::
Ref
(
ctx
.
Input
<
framework
::
LoDTensor
>
(
"X"
));
auto
&
out
=
detail
::
Ref
(
ctx
.
Output
<
framework
::
LoDTensor
>
(
"Out"
));
int
num_batchsize_dims
=
x
.
dims
().
size
()
-
shape
.
size
();
RandomCropFunctor
<
DeviceContext
,
T
>
functor
(
x
.
data
<
T
>
(),
out
.
mutable_data
<
T
>
(
ctx
.
GetPlace
()),
x
.
dims
(),
out
.
dims
(),
num_batchsize_dims
,
seed
);
platform
::
ForRange
<
DeviceContext
>
for_range
(
ctx
.
template
device_context
<
DeviceContext
>(),
functor
.
prod_batchsize_dims_
);
for_range
(
functor
);
Random
<
platform
::
CPUDeviceContext
>::
Engine
engine
(
seed
);
engine
.
discard
(
functor
.
prod_batchsize_dims_
*
(
functor
.
rank_
-
functor
.
num_batchsize_dims_
));
*
ctx
.
Output
<
framework
::
LoDTensor
>
(
"SeedOut"
)
->
mutable_data
<
int64_t
>
(
platform
::
CPUPlace
())
=
engine
();
}
};
// TODO(fengjiayi): Backward of random crop op
}
// namespace operators
}
// namespace paddle
python/paddle/fluid/layers/nn.py
浏览文件 @
14905516
...
...
@@ -82,6 +82,7 @@ __all__ = [
'roi_pool'
,
'dice_loss'
,
'upsampling_bilinear2d'
,
'random_crop'
,
]
...
...
@@ -154,7 +155,8 @@ def fc(input,
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[32, 32], dtype="float32")
data = fluid.layers.data(
name="data", shape=[32, 32], dtype="float32")
fc = fluid.layers.fc(input=data, size=1000, act="tanh")
"""
...
...
@@ -349,7 +351,8 @@ def dynamic_lstm(input,
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"],
Choices = ["sigmoid", "tanh",
"relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
...
...
@@ -516,10 +519,12 @@ def dynamic_lstmp(input,
cell_activation(str): The activation for cell output. Choices = ["sigmoid",
"tanh", "relu", "identity"], default "tanh".
candidate_activation(str): The activation for candidate hidden state.
Choices = ["sigmoid", "tanh", "relu", "identity"],
Choices = ["sigmoid", "tanh",
"relu", "identity"],
default "tanh".
proj_activation(str): The activation for projection output.
Choices = ["sigmoid", "tanh", "relu", "identity"],
Choices = ["sigmoid", "tanh",
"relu", "identity"],
default "tanh".
dtype(str): Data type. Choices = ["float32", "float64"], default "float32".
name(str|None): A name for this layer(optional). If set None, the layer
...
...
@@ -2174,7 +2179,8 @@ def reduce_mean(input, dim=None, keep_dim=False, name=None):
fluid.layers.reduce_mean(x) # [0.4375]
fluid.layers.reduce_mean(x, dim=0) # [0.15, 0.25, 0.55, 0.8]
fluid.layers.reduce_mean(x, dim=-1) # [0.475, 0.4]
fluid.layers.reduce_mean(x, dim=1, keep_dim=True) # [[0.475], [0.4]]
fluid.layers.reduce_mean(
x, dim=1, keep_dim=True) # [[0.475], [0.4]]
# x is a Tensor variable with shape [2, 2, 2] and elements as below:
# [[[1.0, 2.0], [3.0, 4.0]],
...
...
@@ -2393,7 +2399,8 @@ def split(input, num_or_sections, dim=-1, name=None):
x0.shape # [3, 3, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 3, 5]
x0, x1, x2 = fluid.layers.split(x, num_or_sections=[2, 3, 4], dim=1)
x0, x1, x2 = fluid.layers.split(
x, num_or_sections=[2, 3, 4], dim=1)
x0.shape # [3, 2, 5]
x1.shape # [3, 3, 5]
x2.shape # [3, 4, 5]
...
...
@@ -3305,7 +3312,8 @@ def softmax_with_cross_entropy(logits, label, soft_label=False):
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[1], dtype='int64')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.softmax_with_cross_entropy(logits=fc, label=label)
out = fluid.layers.softmax_with_cross_entropy(
logits=fc, label=label)
"""
helper
=
LayerHelper
(
'softmax_with_cross_entropy'
,
**
locals
())
softmax
=
helper
.
create_tmp_variable
(
dtype
=
logits
.
dtype
)
...
...
@@ -3352,7 +3360,8 @@ def smooth_l1(x, y, inside_weight=None, outside_weight=None, sigma=None):
.. code-block:: python
data = fluid.layers.data(name='data', shape=[128], dtype='float32')
label = fluid.layers.data(name='label', shape=[100], dtype='float32')
label = fluid.layers.data(
name='label', shape=[100], dtype='float32')
fc = fluid.layers.fc(input=data, size=100)
out = fluid.layers.smooth_l1(x=fc, y=label)
"""
...
...
@@ -3674,7 +3683,8 @@ def lrn(input, n=5, k=1.0, alpha=1e-4, beta=0.75, name=None):
Examples:
.. code-block:: python
data = fluid.layers.data(name="data", shape=[3, 112, 112], dtype="float32")
data = fluid.layers.data(
name="data", shape=[3, 112, 112], dtype="float32")
lrn = fluid.layers.lrn(input=data)
"""
helper
=
LayerHelper
(
'lrn'
,
**
locals
())
...
...
@@ -3929,10 +3939,10 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
Bilinear interpolation is an extension of linear interpolation for
interpolating functions of two variables (e.g. H-direction and
W-direction in this layer) on a rectilinear 2D grid.
For details, please refer to Wikipedia:
https://en.wikipedia.org/wiki/Bilinear_interpolation
Args:
input (Variable): The input tensor of bilinear interpolation,
This is a 4-D tensor of the shape
...
...
@@ -3950,7 +3960,7 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
Returns:
out (Variable): The output is a 4-D tensor of the shape
(num_batches, channls, out_h, out_w).
Examples:
.. code-block:: python
...
...
@@ -3983,3 +3993,32 @@ def upsampling_bilinear2d(input, out_shape=None, scale=None, name=None):
attrs
=
{
"out_h"
:
out_h
,
"out_w"
:
out_w
})
return
out
def
random_crop
(
input
,
shape
,
seed
=
1
):
helper
=
LayerHelper
(
"random_crop"
,
**
locals
())
dtype
=
helper
.
input_dtype
()
out
=
helper
.
create_tmp_variable
(
dtype
)
if
isinstance
(
seed
,
int
):
seed_value
=
seed
seed
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"fill_constant"
,
inputs
=
{},
outputs
=
{
"Out"
:
seed
},
attrs
=
{
"dtype"
:
seed
.
dtype
,
"shape"
:
[
1
],
"value"
:
float
(
seed_value
)
})
elif
not
isinstance
(
seed
,
Variable
):
raise
ValueError
(
"'seed' must be a Variable or an int."
)
seed_out
=
helper
.
create_tmp_variable
(
dtype
=
"int64"
)
helper
.
append_op
(
type
=
"random_crop"
,
inputs
=
{
"X"
:
input
,
"Seed"
:
seed
},
outputs
=
{
"Out"
:
out
,
"SeedOut"
:
seed_out
},
attrs
=
{
"shape"
:
shape
})
return
out
python/paddle/fluid/tests/unittests/test_random_crop_op.py
0 → 100644
浏览文件 @
14905516
# 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.
import
unittest
import
numpy
as
np
import
paddle.fluid.core
as
core
from
op_test
import
OpTest
class
TestRandomCropOp
(
OpTest
):
def
setUp
(
self
):
to_crop
=
np
.
array
([[[
1
,
2
,
3
,
4
],
[
5
,
6
,
7
,
8
],
[
9
,
10
,
11
,
12
]]]
*
5
).
astype
(
"float32"
)
self
.
possible_res
=
[
np
.
array
([[
1
,
2
,
3
],
[
5
,
6
,
7
]]),
np
.
array
([[
2
,
3
,
4
],
[
6
,
7
,
8
]]),
np
.
array
([[
5
,
6
,
7
],
[
9
,
10
,
11
]]),
np
.
array
([[
6
,
7
,
8
],
[
10
,
11
,
12
]])
]
self
.
op_type
=
"random_crop"
self
.
inputs
=
{
'X'
:
to_crop
,
'Seed'
:
np
.
array
([
10
])}
self
.
outputs
=
{
'Out'
:
np
.
array
([]),
'SeedOut'
:
np
.
array
([])}
self
.
attrs
=
{
'shape'
:
[
2
,
3
]}
def
test_check_output
(
self
):
self
.
check_output_customized
(
self
.
verify_output
)
def
verify_output
(
self
,
outs
):
out
=
np
.
array
(
outs
[
1
])
for
ins
in
out
[:]:
is_equal
=
[(
ins
==
res
).
all
()
for
res
in
self
.
possible_res
]
self
.
assertIn
(
True
,
is_equal
)
if
__name__
==
"__main__"
:
unittest
.
main
()
tools/codestyle/docstring_checker.pyc
0 → 100644
浏览文件 @
14905516
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