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3e7819c2
编写于
7月 19, 2017
作者:
W
wanghaoshuang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
1. Reading image shape from input data instead of image_config
2. Add crop layer unitest 3. Fix bugs
上级
de5ded6b
变更
9
隐藏空白更改
内联
并排
Showing
9 changed file
with
113 addition
and
99 deletion
+113
-99
CMakeLists.txt
CMakeLists.txt
+1
-1
paddle/function/CropOp.cpp
paddle/function/CropOp.cpp
+20
-14
paddle/function/CropOp.h
paddle/function/CropOp.h
+2
-0
paddle/function/CropOpGpu.cu
paddle/function/CropOpGpu.cu
+12
-12
paddle/gserver/layers/CropLayer.cpp
paddle/gserver/layers/CropLayer.cpp
+50
-39
paddle/gserver/layers/CropLayer.h
paddle/gserver/layers/CropLayer.h
+2
-3
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+0
-23
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+5
-7
python/paddle/trainer_config_helpers/tests/configs/test_crop.py
.../paddle/trainer_config_helpers/tests/configs/test_crop.py
+21
-0
未找到文件。
CMakeLists.txt
浏览文件 @
3e7819c2
...
...
@@ -13,7 +13,7 @@
# limitations under the License
cmake_minimum_required
(
VERSION 3.0
)
SET
(
CMAKE_CXX_FLAGS
"
${
CMAKE_CXX_FLAGS
}
-ldl -lpthread"
)
set
(
CMAKE_MODULE_PATH
${
CMAKE_MODULE_PATH
}
"
${
CMAKE_CURRENT_SOURCE_DIR
}
/cmake"
)
set
(
PROJ_ROOT
${
CMAKE_CURRENT_SOURCE_DIR
}
)
set
(
PROJ_BINARY_ROOT
${
CMAKE_CURRENT_BINARY_DIR
}
)
...
...
paddle/function/CropOp.cpp
浏览文件 @
3e7819c2
...
...
@@ -22,11 +22,10 @@ template <>
void
Crop
<
DEVICE_TYPE_CPU
>
(
real
*
outputs
,
const
real
*
inputs
,
const
TensorShape
inShape
,
const
TensorShape
outShape
,
const
FuncConfig
&
conf
)
{
std
::
vector
<
uint32_t
>
crop_corner
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_corner"
);
std
::
vector
<
uint32_t
>
crop_shape
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_shape"
);
int
cCrop
=
crop_corner
[
1
];
int
hCrop
=
crop_corner
[
2
];
int
wCrop
=
crop_corner
[
3
];
...
...
@@ -36,9 +35,9 @@ void Crop<DEVICE_TYPE_CPU>(real* outputs,
int
inH
=
inShape
[
2
];
int
inW
=
inShape
[
3
];
int
outC
=
crop_s
hape
[
1
];
int
outH
=
crop_s
hape
[
2
];
int
outW
=
crop_s
hape
[
3
];
int
outC
=
outS
hape
[
1
];
int
outH
=
outS
hape
[
2
];
int
outW
=
outS
hape
[
3
];
for
(
int
n
=
0
;
n
<
num
;
n
++
)
{
for
(
int
c
=
0
;
c
<
outC
;
c
++
)
{
...
...
@@ -54,12 +53,11 @@ void Crop<DEVICE_TYPE_CPU>(real* outputs,
template
<
>
void
CropGrad
<
DEVICE_TYPE_CPU
>
(
const
real
*
inGrad
,
real
*
outGrad
,
const
TensorShape
inShape
,
const
TensorShape
outShape
,
const
FuncConfig
&
conf
)
{
std
::
vector
<
uint32_t
>
crop_corner
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_corner"
);
std
::
vector
<
uint32_t
>
crop_shape
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_shape"
);
int
cCrop
=
crop_corner
[
1
];
int
hCrop
=
crop_corner
[
2
];
int
wCrop
=
crop_corner
[
3
];
...
...
@@ -69,9 +67,9 @@ void CropGrad<DEVICE_TYPE_CPU>(const real* inGrad,
int
outH
=
outShape
[
2
];
int
outW
=
outShape
[
3
];
int
inC
=
crop_s
hape
[
1
];
int
inH
=
crop_s
hape
[
2
];
int
inW
=
crop_s
hape
[
3
];
int
inC
=
inS
hape
[
1
];
int
inH
=
inS
hape
[
2
];
int
inW
=
inS
hape
[
3
];
for
(
int
n
=
0
;
n
<
num
;
n
++
)
{
for
(
int
c
=
0
;
c
<
inC
;
c
++
)
{
...
...
@@ -123,9 +121,13 @@ public:
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ASSIGN_TO
);
TensorShape
inShape
=
inputs
[
0
].
shape
();
TensorShape
outShape
=
outputs
[
0
].
shape
();
Crop
<
Device
>
(
outputs
[
0
].
data
<
real
>
(),
inputs
[
0
].
data
<
real
>
(),
inShape
,
conf_
);
Crop
<
Device
>
(
outputs
[
0
].
data
<
real
>
(),
inputs
[
0
].
data
<
real
>
(),
inShape
,
outShape
,
conf_
);
}
private:
...
...
@@ -152,9 +154,13 @@ public:
CHECK_EQ
(
outputs
[
0
].
getArgType
(),
ADD_TO
);
TensorShape
outShape
=
outputs
[
0
].
shape
();
TensorShape
inShape
=
inputs
[
0
].
shape
();
CropGrad
<
Device
>
(
inputs
[
0
].
data
<
real
>
(),
outputs
[
0
].
data
<
real
>
(),
outShape
,
conf_
);
CropGrad
<
Device
>
(
inputs
[
0
].
data
<
real
>
(),
outputs
[
0
].
data
<
real
>
(),
inShape
,
outShape
,
conf_
);
}
private:
...
...
paddle/function/CropOp.h
浏览文件 @
3e7819c2
...
...
@@ -31,6 +31,7 @@ template <DeviceType Device>
void
Crop
(
real
*
outputs
,
const
real
*
inputs
,
const
TensorShape
inShape
,
const
TensorShape
outShape
,
const
FuncConfig
&
conf
);
/**
...
...
@@ -45,5 +46,6 @@ template <DeviceType Device>
void
CropGrad
(
const
real
*
inGrad
,
real
*
outGrad
,
const
TensorShape
inShape
,
const
TensorShape
outShape
,
const
FuncConfig
&
conf
);
}
// namespace paddle
paddle/function/CropOpGpu.cu
浏览文件 @
3e7819c2
...
...
@@ -37,9 +37,9 @@ template <>
void
Crop
<
DEVICE_TYPE_GPU
>
(
real
*
outputs
,
const
real
*
inputs
,
const
TensorShape
inShape
,
const
TensorShape
outShape
,
const
FuncConfig
&
conf
)
{
std
::
vector
<
uint32_t
>
crop_corner
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_corner"
);
std
::
vector
<
uint32_t
>
crop_shape
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_shape"
);
int
cropC
=
crop_corner
[
1
];
int
cropH
=
crop_corner
[
2
];
int
cropW
=
crop_corner
[
3
];
...
...
@@ -49,14 +49,14 @@ void Crop<DEVICE_TYPE_GPU>(real* outputs,
int
inH
=
inShape
[
2
];
int
inW
=
inShape
[
3
];
int
outC
=
crop_s
hape
[
1
];
int
outH
=
crop_s
hape
[
2
];
int
outW
=
crop_s
hape
[
3
];
int
outC
=
outS
hape
[
1
];
int
outH
=
outS
hape
[
2
];
int
outW
=
outS
hape
[
3
];
size_t
nth
=
num
*
outC
*
outH
*
outW
;
int
blockSize
=
1024
;
int
gridSize
=
(
nth
+
blockSize
-
1
)
/
blockSize
;
KeCrop
<<<
gridSize
,
blockSize
,
0
,
STREAM_DEFAULT
>>>
(
outputs
,
inputs
,
inC
,
inH
,
inW
,
cropC
,
cropH
,
cropW
,
outC
,
outH
,
outW
,
nth
);
...
...
@@ -75,7 +75,7 @@ __global__ void KeCropDiff(const real* inGrad, real* outGrad,
const
int
n
=
idx
/
inW
/
inH
/
inC
;
const
int
off
=
((
n
*
outC
+
c
+
cropC
)
*
outH
+
h
+
cropH
)
*
outW
+
cropW
+
w
;
outGrad
[
off
]
+=
inGrad
[
idx
];
}
}
...
...
@@ -83,10 +83,10 @@ __global__ void KeCropDiff(const real* inGrad, real* outGrad,
template
<
>
void
CropGrad
<
DEVICE_TYPE_GPU
>
(
const
real
*
inGrad
,
real
*
outGrad
,
const
TensorShape
inShape
,
const
TensorShape
outShape
,
const
FuncConfig
&
conf
)
{
std
::
vector
<
uint32_t
>
crop_corner
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_corner"
);
std
::
vector
<
uint32_t
>
crop_shape
=
conf
.
get
<
std
::
vector
<
uint32_t
>>
(
"crop_shape"
);
int
cropC
=
crop_corner
[
1
];
int
cropH
=
crop_corner
[
2
];
int
cropW
=
crop_corner
[
3
];
...
...
@@ -96,10 +96,10 @@ void CropGrad<DEVICE_TYPE_GPU>(const real* inGrad,
int
outH
=
outShape
[
2
];
int
outW
=
outShape
[
3
];
int
inC
=
crop_s
hape
[
1
];
int
inH
=
crop_s
hape
[
2
];
int
inW
=
crop_s
hape
[
3
];
int
inC
=
inS
hape
[
1
];
int
inH
=
inS
hape
[
2
];
int
inW
=
inS
hape
[
3
];
size_t
nth
=
num
*
inC
*
inH
*
inW
;
int
blockSize
=
1024
;
int
gridSize
=
(
nth
+
blockSize
-
1
)
/
blockSize
;
...
...
paddle/gserver/layers/CropLayer.cpp
浏览文件 @
3e7819c2
...
...
@@ -22,7 +22,8 @@ bool CropLayer::init(const LayerMap& layerMap,
const
ParameterMap
&
parameterMap
)
{
/* Initialize the basic parent class */
Layer
::
init
(
layerMap
,
parameterMap
);
CHECK_LE
(
static_cast
<
int
>
(
inputLayers_
.
size
()),
2
);
CHECK_GE
(
static_cast
<
int
>
(
inputLayers_
.
size
()),
1
);
crop_axis_
=
config_
.
axis
();
for
(
int
i
=
0
;
i
<
config_
.
offset_size
();
i
++
)
{
crop_offsets_
.
push_back
(
config_
.
offset
(
i
));
...
...
@@ -36,8 +37,14 @@ bool CropLayer::init(const LayerMap& layerMap,
?
input0_img_conf
.
img_size_y
()
:
input0_img_conf
.
img_size
(),
input0_img_conf
.
img_size
()});
// 2. get output shape from input_1 or crop shap conf
if
(
config_
.
inputs_size
()
==
2
)
{
// 2. get target dims from config
if
(
config_
.
inputs_size
()
==
1
)
{
targetDims_
=
TensorShape
({
config_
.
shape
(
0
),
config_
.
shape
(
1
),
config_
.
shape
(
2
),
config_
.
shape
(
3
)});
}
else
{
// 2. get input_1 shape
auto
&
input1_img_conf
=
config_
.
inputs
(
1
).
image_conf
();
targetDims_
=
TensorShape
({
0
,
input1_img_conf
.
channels
(),
...
...
@@ -45,24 +52,10 @@ bool CropLayer::init(const LayerMap& layerMap,
?
input1_img_conf
.
img_size_y
()
:
input1_img_conf
.
img_size
(),
input1_img_conf
.
img_size
()});
}
else
{
targetDims_
=
TensorShape
({
config_
.
shape
(
0
),
config_
.
shape
(
1
),
config_
.
shape
(
2
),
config_
.
shape
(
3
)});
}
// 3. get final crop
shape
// 3. get final crop
corner
int
dimSize
=
4
;
for
(
int
i
=
0
;
i
<
dimSize
;
i
++
)
{
if
(
i
>=
crop_axis_
)
{
crop_shape_
.
push_back
(
targetDims_
[
i
]);
}
else
{
crop_shape_
.
push_back
(
inDims_
[
i
]);
}
}
// 4. get final crop corner
crop_corner_
=
{
0
,
0
,
0
,
0
};
for
(
int
i
=
0
;
i
<
dimSize
;
i
++
)
{
if
(
i
>=
crop_axis_
)
{
...
...
@@ -75,43 +68,61 @@ bool CropLayer::init(const LayerMap& layerMap,
}
outDims_
=
TensorShape
(
4
);
setOutDims
(
0
);
createFunction
(
forward_
,
"Crop"
,
FuncConfig
()
.
set
(
"crop_corner"
,
crop_corner_
)
.
set
(
"crop_shape"
,
crop_shape_
));
createFunction
(
backward_
,
"CropGrad"
,
FuncConfig
()
.
set
(
"crop_corner"
,
crop_corner_
)
.
set
(
"crop_shape"
,
crop_shape_
));
createFunction
(
forward_
,
"Crop"
,
FuncConfig
().
set
(
"crop_corner"
,
crop_corner_
));
createFunction
(
backward_
,
"CropGrad"
,
FuncConfig
().
set
(
"crop_corner"
,
crop_corner_
));
return
true
;
}
void
CropLayer
::
setOutDims
(
const
size_t
batchSize
)
{
outDims_
.
reshape
({
batchSize
,
crop_shape_
[
1
],
crop_shape_
[
2
],
crop_shape_
[
3
]});
void
CropLayer
::
setOutDims
()
{
MatrixPtr
input
=
inputLayers_
[
1
]
->
getOutputValue
();
size_t
batchSize
=
input
->
getHeight
();
// get target dims from input_1
if
(
config_
.
inputs_size
()
==
2
)
{
targetDims_
.
setDim
(
0
,
batchSize
);
int
ch
=
config_
.
inputs
(
0
).
image_conf
().
channels
();
if
(
ch
!=
0
)
targetDims_
.
setDim
(
1
,
ch
);
int
h
=
inputLayers_
[
1
]
->
getOutput
().
getFrameHeight
();
if
(
h
!=
0
)
targetDims_
.
setDim
(
2
,
h
);
int
w
=
inputLayers_
[
1
]
->
getOutput
().
getFrameWidth
();
if
(
w
!=
0
)
targetDims_
.
setDim
(
3
,
w
);
}
// get final crop shape from target dims and crop axis
std
::
vector
<
uint32_t
>
crop_shape
;
int
dimSize
=
4
;
for
(
int
i
=
0
;
i
<
dimSize
;
i
++
)
{
if
(
i
>=
crop_axis_
)
{
crop_shape
.
push_back
(
targetDims_
[
i
]);
}
else
{
crop_shape
.
push_back
(
inDims_
[
i
]);
}
}
outDims_
.
reshape
(
{
crop_shape
[
0
],
crop_shape
[
1
],
crop_shape
[
2
],
crop_shape
[
3
]});
output_
.
setFrameHeight
(
crop_shape
[
2
]);
output_
.
setFrameWidth
(
crop_shape
[
3
]);
}
void
CropLayer
::
setTensorDim
(
const
size_t
batchSize
)
{
CHECK_EQ
(
static_cast
<
int
>
(
inputLayers_
.
size
()),
2
);
void
CropLayer
::
setInDims
()
{
MatrixPtr
input
=
inputLayers_
[
0
]
->
getOutputValue
();
size_t
batchSize
=
input
->
getHeight
();
inDims_
.
setDim
(
0
,
batchSize
);
int
h
=
inputLayers_
[
0
]
->
getOutput
().
getFrameHeight
();
if
(
h
!=
0
)
inDims_
.
setDim
(
2
,
h
);
int
w
=
inputLayers_
[
0
]
->
getOutput
().
getFrameWidth
();
if
(
w
!=
0
)
inDims_
.
setDim
(
3
,
w
);
setOutDims
(
batchSize
);
}
void
CropLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
MatrixPtr
input
=
inputLayers_
[
0
]
->
getOutputValue
();
size_t
batchSize
=
input
->
getHeight
();
setTensorDim
(
batchSize
);
setInDims
();
setOutDims
();
int
size
=
outDims_
[
1
]
*
outDims_
[
2
]
*
outDims_
[
3
];
resetOutput
(
batchSize
,
size
);
resetOutput
(
outDims_
[
0
]
,
size
);
MatrixPtr
outV
=
getOutputValue
();
REGISTER_TIMER_INFO
(
"CropForward"
,
getName
().
c_str
());
...
...
paddle/gserver/layers/CropLayer.h
浏览文件 @
3e7819c2
...
...
@@ -39,13 +39,12 @@ public:
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
protected:
void
setOutDims
(
const
size_t
batchSize
);
void
set
TensorDim
(
const
size_t
batchSize
);
void
setOutDims
();
void
set
InDims
(
);
int32_t
crop_axis_
;
std
::
vector
<
uint32_t
>
crop_offsets_
;
std
::
vector
<
uint32_t
>
crop_corner_
;
std
::
vector
<
uint32_t
>
crop_shape_
;
TensorShape
inDims_
;
TensorShape
targetDims_
;
TensorShape
outDims_
;
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
3e7819c2
...
...
@@ -2005,29 +2005,6 @@ class CropLayer(LayerBase):
image_conf
.
img_size_y
=
input_layer
.
height
image_conf
.
channels
=
input_layer
.
size
/
(
input_layer
.
width
*
input_layer
.
height
)
out_ch
=
image_conf
.
channels
out_h
=
image_conf
.
img_size
out_w
=
image_conf
.
img_size_y
if
len
(
self
.
inputs
)
==
2
:
# get channels, width and height from input_1 layer
input_layer
=
self
.
get_input_layer
(
1
)
image_conf
=
self
.
config
.
inputs
[
1
].
image_conf
image_conf
.
img_size
=
input_layer
.
width
image_conf
.
img_size_y
=
input_layer
.
height
image_conf
.
channels
=
input_layer
.
size
/
(
input_layer
.
width
*
input_layer
.
height
)
out_ch
=
image_conf
.
channels
out_h
=
image_conf
.
img_size_y
out_w
=
image_conf
.
img_size
else
:
# set channels, width and heigth of current layer
if
len
(
shape
)
>
2
:
out_ch
=
shape
[
-
3
]
if
len
(
shape
)
>
1
:
out_h
=
shape
[
-
2
]
if
len
(
shape
)
>
0
:
out_w
=
shape
[
-
1
]
self
.
set_cnn_layer
(
name
,
out_h
,
out_w
,
out_ch
)
@
config_layer
(
'batch_norm'
)
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
3e7819c2
...
...
@@ -5881,9 +5881,9 @@ def prelu_layer(input,
@
wrap_name_default
()
@
layer_support
()
def
crop_layer
(
input
,
axis
,
offset
,
shape
=
None
,
name
=
None
,
layer_attr
=
None
):
def
crop_layer
(
input
,
offset
,
axis
=
2
,
shape
=
None
,
name
=
None
,
layer_attr
=
None
):
"""
The crop layer crop images by offset and shape. User can set crop shape by
The crop layer crop
s
images by offset and shape. User can set crop shape by
args 'shape' explicitly or by reference input layer.
...
...
@@ -5896,16 +5896,16 @@ def crop_layer(input, axis, offset, shape=None, name=None, layer_attr=None):
:param input: The input layer.If two inputs were setted,
the second input will be regarded as reference input
:type input: LayerOutput or Sequence
:param offset: The crop offset
:type offset: Sequence
:param axis: start axis to be cropped. To image input layer:
- 0: batch size
- 1: channels
- 2: height
- 3: width
:type partial_sum: int
:param offset: The crop offset
:type offset: Sequence
:param shape: The shape to be cropped. Default is None.
:type shape: S
q
quence | None
:type shape: S
e
quence | None
:param name: Name of this layer.
:type name: basestring
:return: LayerOutput object.
...
...
@@ -5913,8 +5913,6 @@ def crop_layer(input, axis, offset, shape=None, name=None, layer_attr=None):
"""
if
isinstance
(
input
,
LayerOutput
):
input
=
[
input
]
elif
isinstance
(
input
,
Projection
):
input
=
[
input
]
else
:
assert
isinstance
(
input
,
collections
.
Sequence
)
l
=
Layer
(
...
...
python/paddle/trainer_config_helpers/tests/configs/test_crop.py
0 → 100644
浏览文件 @
3e7819c2
from
paddle.trainer_config_helpers
import
*
settings
(
batch_size
=
1000
,
learning_rate
=
1e-5
)
data
=
data_layer
(
name
=
'data'
,
size
=
2016
,
height
=
48
,
width
=
42
)
refernce_data
=
data_layer
(
name
=
'data'
,
size
=
768
,
height
=
16
,
width
=
16
)
conv
=
img_conv_layer
(
input
=
data
,
filter_size
=
3
,
num_channels
=
1
,
num_filters
=
16
,
padding
=
1
,
act
=
LinearActivation
(),
bias_attr
=
True
)
pool
=
img_pool_layer
(
input
=
conv
,
pool_size
=
2
,
stride
=
2
,
pool_type
=
MaxPooling
())
crop
=
crop_layer
(
input
=
[
pool
,
refernce_data
],
axis
=
2
)
outputs
(
pad
)
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