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dcc66c6d
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PaddleDetection
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dcc66c6d
编写于
11月 10, 2017
作者:
Q
qingqing01
提交者:
GitHub
11月 10, 2017
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差异文件
Merge pull request #2982 from guoshengCS/add-ROIPooling
add ROIPooling for Fast(er) R-CNN
上级
80de144b
79e0a26a
变更
10
隐藏空白更改
内联
并排
Showing
10 changed file
with
507 addition
and
1 deletion
+507
-1
doc/api/v2/config/layer.rst
doc/api/v2/config/layer.rst
+5
-0
paddle/gserver/layers/ROIPoolLayer.cpp
paddle/gserver/layers/ROIPoolLayer.cpp
+220
-0
paddle/gserver/layers/ROIPoolLayer.h
paddle/gserver/layers/ROIPoolLayer.h
+56
-0
paddle/gserver/tests/test_LayerGrad.cpp
paddle/gserver/tests/test_LayerGrad.cpp
+37
-0
proto/ModelConfig.proto
proto/ModelConfig.proto
+9
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+12
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+46
-0
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
.../paddle/trainer_config_helpers/tests/configs/file_list.sh
+1
-1
python/paddle/trainer_config_helpers/tests/configs/protostr/test_roi_pool_layer.protostr
...lpers/tests/configs/protostr/test_roi_pool_layer.protostr
+98
-0
python/paddle/trainer_config_helpers/tests/configs/test_roi_pool_layer.py
...ainer_config_helpers/tests/configs/test_roi_pool_layer.py
+23
-0
未找到文件。
doc/api/v2/config/layer.rst
浏览文件 @
dcc66c6d
...
...
@@ -82,6 +82,11 @@ maxout
.. autoclass:: paddle.v2.layer.maxout
:noindex:
roi_pool
--------
.. autoclass:: paddle.v2.layer.roi_pool
:noindex:
Norm Layer
==========
...
...
paddle/gserver/layers/ROIPoolLayer.cpp
0 → 100644
浏览文件 @
dcc66c6d
/* 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 "ROIPoolLayer.h"
namespace
paddle
{
REGISTER_LAYER
(
roi_pool
,
ROIPoolLayer
);
bool
ROIPoolLayer
::
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
{
Layer
::
init
(
layerMap
,
parameterMap
);
const
ROIPoolConfig
&
layerConf
=
config_
.
inputs
(
0
).
roi_pool_conf
();
pooledWidth_
=
layerConf
.
pooled_width
();
pooledHeight_
=
layerConf
.
pooled_height
();
spatialScale_
=
layerConf
.
spatial_scale
();
return
true
;
}
void
ROIPoolLayer
::
forward
(
PassType
passType
)
{
Layer
::
forward
(
passType
);
const
ROIPoolConfig
&
layerConf
=
config_
.
inputs
(
0
).
roi_pool_conf
();
height_
=
getInput
(
0
).
getFrameHeight
();
if
(
!
height_
)
height_
=
layerConf
.
height
();
width_
=
getInput
(
0
).
getFrameWidth
();
if
(
!
width_
)
width_
=
layerConf
.
width
();
channels_
=
getInputValue
(
0
)
->
getWidth
()
/
width_
/
height_
;
size_t
batchSize
=
getInput
(
0
).
getBatchSize
();
size_t
numROIs
=
getInput
(
1
).
getBatchSize
();
MatrixPtr
dataValue
=
getInputValue
(
0
);
MatrixPtr
roiValue
=
getInputValue
(
1
);
resetOutput
(
numROIs
,
channels_
*
pooledHeight_
*
pooledWidth_
);
MatrixPtr
outputValue
=
getOutputValue
();
if
(
useGpu_
)
{
// TODO(guosheng): implement on GPU later
MatrixPtr
dataCpuBuffer
;
Matrix
::
resizeOrCreate
(
dataCpuBuffer
,
dataValue
->
getHeight
(),
dataValue
->
getWidth
(),
false
,
false
);
MatrixPtr
roiCpuBuffer
;
Matrix
::
resizeOrCreate
(
roiCpuBuffer
,
roiValue
->
getHeight
(),
roiValue
->
getWidth
(),
false
,
false
);
dataCpuBuffer
->
copyFrom
(
*
dataValue
);
roiCpuBuffer
->
copyFrom
(
*
roiValue
);
dataValue
=
dataCpuBuffer
;
roiValue
=
roiCpuBuffer
;
MatrixPtr
outputCpuBuffer
;
Matrix
::
resizeOrCreate
(
outputCpuBuffer
,
outputValue
->
getHeight
(),
outputValue
->
getWidth
(),
false
,
false
);
outputCpuBuffer
->
copyFrom
(
*
outputValue
);
outputValue
=
outputCpuBuffer
;
}
real
*
bottomData
=
dataValue
->
getData
();
size_t
batchOffset
=
dataValue
->
getWidth
();
size_t
channelOffset
=
height_
*
width_
;
real
*
bottomROIs
=
roiValue
->
getData
();
size_t
roiOffset
=
roiValue
->
getWidth
();
size_t
poolChannelOffset
=
pooledHeight_
*
pooledWidth_
;
real
*
outputData
=
outputValue
->
getData
();
Matrix
::
resizeOrCreate
(
maxIdxs_
,
numROIs
,
channels_
*
pooledHeight_
*
pooledWidth_
,
false
,
false
);
real
*
argmaxData
=
maxIdxs_
->
getData
();
for
(
size_t
n
=
0
;
n
<
numROIs
;
++
n
)
{
// the first five elememts of each RoI should be:
// batch_idx, roi_x_start, roi_y_start, roi_x_end, roi_y_end
size_t
roiBatchIdx
=
bottomROIs
[
0
];
size_t
roiStartW
=
round
(
bottomROIs
[
1
]
*
spatialScale_
);
size_t
roiStartH
=
round
(
bottomROIs
[
2
]
*
spatialScale_
);
size_t
roiEndW
=
round
(
bottomROIs
[
3
]
*
spatialScale_
);
size_t
roiEndH
=
round
(
bottomROIs
[
4
]
*
spatialScale_
);
CHECK_GE
(
roiBatchIdx
,
0
);
CHECK_LT
(
roiBatchIdx
,
batchSize
);
size_t
roiHeight
=
std
::
max
(
roiEndH
-
roiStartH
+
1
,
1UL
);
size_t
roiWidth
=
std
::
max
(
roiEndW
-
roiStartW
+
1
,
1UL
);
real
binSizeH
=
static_cast
<
real
>
(
roiHeight
)
/
static_cast
<
real
>
(
pooledHeight_
);
real
binSizeW
=
static_cast
<
real
>
(
roiWidth
)
/
static_cast
<
real
>
(
pooledWidth_
);
real
*
batchData
=
bottomData
+
batchOffset
*
roiBatchIdx
;
for
(
size_t
c
=
0
;
c
<
channels_
;
++
c
)
{
for
(
size_t
ph
=
0
;
ph
<
pooledHeight_
;
++
ph
)
{
for
(
size_t
pw
=
0
;
pw
<
pooledWidth_
;
++
pw
)
{
size_t
hstart
=
static_cast
<
size_t
>
(
std
::
floor
(
ph
*
binSizeH
));
size_t
wstart
=
static_cast
<
size_t
>
(
std
::
floor
(
pw
*
binSizeW
));
size_t
hend
=
static_cast
<
size_t
>
(
std
::
ceil
((
ph
+
1
)
*
binSizeH
));
size_t
wend
=
static_cast
<
size_t
>
(
std
::
ceil
((
pw
+
1
)
*
binSizeW
));
hstart
=
std
::
min
(
std
::
max
(
hstart
+
roiStartH
,
0UL
),
height_
);
wstart
=
std
::
min
(
std
::
max
(
wstart
+
roiStartW
,
0UL
),
width_
);
hend
=
std
::
min
(
std
::
max
(
hend
+
roiStartH
,
0UL
),
height_
);
wend
=
std
::
min
(
std
::
max
(
wend
+
roiStartW
,
0UL
),
width_
);
bool
isEmpty
=
(
hend
<=
hstart
)
||
(
wend
<=
wstart
);
size_t
poolIndex
=
ph
*
pooledWidth_
+
pw
;
if
(
isEmpty
)
{
outputData
[
poolIndex
]
=
0
;
argmaxData
[
poolIndex
]
=
-
1
;
}
for
(
size_t
h
=
hstart
;
h
<
hend
;
++
h
)
{
for
(
size_t
w
=
wstart
;
w
<
wend
;
++
w
)
{
size_t
index
=
h
*
width_
+
w
;
if
(
batchData
[
index
]
>
outputData
[
poolIndex
])
{
outputData
[
poolIndex
]
=
batchData
[
index
];
argmaxData
[
poolIndex
]
=
index
;
}
}
}
}
}
batchData
+=
channelOffset
;
outputData
+=
poolChannelOffset
;
argmaxData
+=
poolChannelOffset
;
}
bottomROIs
+=
roiOffset
;
}
if
(
useGpu_
)
{
getOutputValue
()
->
copyFrom
(
*
outputValue
);
}
}
void
ROIPoolLayer
::
backward
(
const
UpdateCallback
&
callback
)
{
MatrixPtr
inGradValue
=
getInputGrad
(
0
);
MatrixPtr
outGradValue
=
getOutputGrad
();
MatrixPtr
roiValue
=
getInputValue
(
1
);
if
(
useGpu_
)
{
MatrixPtr
inGradCpuBuffer
;
Matrix
::
resizeOrCreate
(
inGradCpuBuffer
,
inGradValue
->
getHeight
(),
inGradValue
->
getWidth
(),
false
,
false
);
MatrixPtr
outGradCpuBuffer
;
Matrix
::
resizeOrCreate
(
outGradCpuBuffer
,
outGradValue
->
getHeight
(),
outGradValue
->
getWidth
(),
false
,
false
);
MatrixPtr
roiCpuBuffer
;
Matrix
::
resizeOrCreate
(
roiCpuBuffer
,
roiValue
->
getHeight
(),
roiValue
->
getWidth
(),
false
,
false
);
inGradCpuBuffer
->
copyFrom
(
*
inGradValue
);
outGradCpuBuffer
->
copyFrom
(
*
outGradValue
);
roiCpuBuffer
->
copyFrom
(
*
roiValue
);
inGradValue
=
inGradCpuBuffer
;
outGradValue
=
outGradCpuBuffer
;
roiValue
=
roiCpuBuffer
;
}
real
*
bottomROIs
=
roiValue
->
getData
();
size_t
numROIs
=
getInput
(
1
).
getBatchSize
();
size_t
roiOffset
=
getInputValue
(
1
)
->
getWidth
();
real
*
inDiffData
=
inGradValue
->
getData
();
size_t
batchOffset
=
getInputValue
(
0
)
->
getWidth
();
size_t
channelOffset
=
height_
*
width_
;
real
*
outDiffData
=
outGradValue
->
getData
();
size_t
poolChannelOffset
=
pooledHeight_
*
pooledWidth_
;
real
*
argmaxData
=
maxIdxs_
->
getData
();
for
(
size_t
n
=
0
;
n
<
numROIs
;
++
n
)
{
size_t
roiBatchIdx
=
bottomROIs
[
0
];
real
*
batchDiffData
=
inDiffData
+
batchOffset
*
roiBatchIdx
;
for
(
size_t
c
=
0
;
c
<
channels_
;
++
c
)
{
for
(
size_t
ph
=
0
;
ph
<
pooledHeight_
;
++
ph
)
{
for
(
size_t
pw
=
0
;
pw
<
pooledWidth_
;
++
pw
)
{
size_t
poolIndex
=
ph
*
pooledWidth_
+
pw
;
if
(
argmaxData
[
poolIndex
]
>
0
)
{
size_t
index
=
static_cast
<
size_t
>
(
argmaxData
[
poolIndex
]);
batchDiffData
[
index
]
+=
outDiffData
[
poolIndex
];
}
}
}
batchDiffData
+=
channelOffset
;
outDiffData
+=
poolChannelOffset
;
argmaxData
+=
poolChannelOffset
;
}
bottomROIs
+=
roiOffset
;
}
if
(
useGpu_
)
{
getInputGrad
(
0
)
->
copyFrom
(
*
inGradValue
);
}
}
}
// namespace paddle
paddle/gserver/layers/ROIPoolLayer.h
0 → 100644
浏览文件 @
dcc66c6d
/* 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 "Layer.h"
namespace
paddle
{
/**
* A layer used by Fast R-CNN to extract feature maps of ROIs from the last
* feature map.
* - Input: This layer needs two input layers: The first input layer is a
* convolution layer; The second input layer contains the ROI data
* which is the output of ProposalLayer in Faster R-CNN. layers for
* generating bbox location offset and the classification confidence.
* - Output: The ROIs' feature map.
* Reference:
* Shaoqing Ren, Kaiming He, Ross Girshick, and Jian Sun.
* Faster R-CNN: Towards Real-Time Object Detection with Region Proposal
* Networks
*/
class
ROIPoolLayer
:
public
Layer
{
protected:
size_t
channels_
;
size_t
width_
;
size_t
height_
;
size_t
pooledWidth_
;
size_t
pooledHeight_
;
real
spatialScale_
;
// Since there is no int matrix, use real maxtrix instead.
MatrixPtr
maxIdxs_
;
public:
explicit
ROIPoolLayer
(
const
LayerConfig
&
config
)
:
Layer
(
config
)
{}
bool
init
(
const
LayerMap
&
layerMap
,
const
ParameterMap
&
parameterMap
)
override
;
void
forward
(
PassType
passType
)
override
;
void
backward
(
const
UpdateCallback
&
callback
=
nullptr
)
override
;
};
}
// namespace paddle
paddle/gserver/tests/test_LayerGrad.cpp
浏览文件 @
dcc66c6d
...
...
@@ -2056,6 +2056,43 @@ TEST(Layer, CropLayer) {
}
}
TEST
(
Layer
,
roi_pool
)
{
TestConfig
config
;
config
.
layerConfig
.
set_type
(
"roi_pool"
);
config
.
biasSize
=
0
;
LayerInputConfig
*
input
=
config
.
layerConfig
.
add_inputs
();
ROIPoolConfig
*
roiPoolConf
=
input
->
mutable_roi_pool_conf
();
roiPoolConf
->
set_pooled_width
(
7
);
roiPoolConf
->
set_pooled_height
(
7
);
roiPoolConf
->
set_spatial_scale
(
1.
/
16
);
roiPoolConf
->
set_width
(
14
);
roiPoolConf
->
set_height
(
14
);
const
size_t
roiNum
=
10
;
const
size_t
roiDim
=
10
;
const
size_t
batchSize
=
5
;
MatrixPtr
roiValue
=
Matrix
::
create
(
roiNum
,
roiDim
,
false
,
false
);
roiValue
->
zeroMem
();
real
*
roiData
=
roiValue
->
getData
();
for
(
size_t
i
=
0
;
i
<
roiNum
;
++
i
)
{
roiData
[
i
*
roiDim
+
0
]
=
std
::
rand
()
%
batchSize
;
roiData
[
i
*
roiDim
+
1
]
=
std
::
rand
()
%
224
;
// xMin
roiData
[
i
*
roiDim
+
2
]
=
std
::
rand
()
%
224
;
// yMin
size_t
xMin
=
static_cast
<
size_t
>
(
roiData
[
i
*
roiDim
+
1
]);
size_t
yMin
=
static_cast
<
size_t
>
(
roiData
[
i
*
roiDim
+
2
]);
roiData
[
i
*
roiDim
+
3
]
=
xMin
+
std
::
rand
()
%
(
224
-
xMin
);
// xMax
roiData
[
i
*
roiDim
+
4
]
=
yMin
+
std
::
rand
()
%
(
224
-
yMin
);
// yMax
}
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"input"
,
3
*
14
*
14
,
{}});
config
.
inputDefs
.
push_back
({
INPUT_SELF_DEFINE_DATA
,
"rois"
,
roiValue
,
{}});
config
.
layerConfig
.
add_inputs
();
for
(
auto
useGpu
:
{
false
,
true
})
{
testLayerGrad
(
config
,
"roi_pool"
,
batchSize
,
false
,
useGpu
,
false
);
}
}
TEST
(
Layer
,
SwitchOrderLayer
)
{
TestConfig
config
;
// config input_0
...
...
proto/ModelConfig.proto
浏览文件 @
dcc66c6d
...
...
@@ -321,6 +321,14 @@ message ClipConfig {
required
double
max
=
2
;
}
message
ROIPoolConfig
{
required
uint32
pooled_width
=
1
;
required
uint32
pooled_height
=
2
;
required
float
spatial_scale
=
3
;
optional
uint32
height
=
4
[
default
=
1
];
optional
uint32
width
=
5
[
default
=
1
];
}
message
ScaleSubRegionConfig
{
required
ImageConfig
image_conf
=
1
;
required
float
value
=
2
;
...
...
@@ -348,6 +356,7 @@ message LayerInputConfig {
optional
DetectionOutputConfig
detection_output_conf
=
17
;
optional
ClipConfig
clip_conf
=
18
;
optional
ScaleSubRegionConfig
scale_sub_region_conf
=
19
;
optional
ROIPoolConfig
roi_pool_conf
=
20
;
}
message
LayerConfig
{
...
...
python/paddle/trainer/config_parser.py
浏览文件 @
dcc66c6d
...
...
@@ -1969,6 +1969,18 @@ class DetectionOutputLayer(LayerBase):
self
.
config
.
size
=
size
@
config_layer
(
'roi_pool'
)
class
ROIPoolLayer
(
LayerBase
):
def
__init__
(
self
,
name
,
inputs
,
pooled_width
,
pooled_height
,
spatial_scale
,
num_channels
,
**
xargs
):
super
(
ROIPoolLayer
,
self
).
__init__
(
name
,
'roi_pool'
,
0
,
inputs
)
config_assert
(
len
(
inputs
)
==
2
,
'ROIPoolLayer must have 2 inputs'
)
self
.
config
.
inputs
[
0
].
roi_pool_conf
.
pooled_width
=
pooled_width
self
.
config
.
inputs
[
0
].
roi_pool_conf
.
pooled_height
=
pooled_height
self
.
config
.
inputs
[
0
].
roi_pool_conf
.
spatial_scale
=
spatial_scale
self
.
set_cnn_layer
(
name
,
pooled_height
,
pooled_width
,
num_channels
)
@
config_layer
(
'data'
)
class
DataLayer
(
LayerBase
):
def
__init__
(
self
,
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
dcc66c6d
...
...
@@ -122,6 +122,7 @@ __all__ = [
'cross_channel_norm_layer'
,
'multibox_loss_layer'
,
'detection_output_layer'
,
'roi_pool_layer'
,
'spp_layer'
,
'pad_layer'
,
'eos_layer'
,
...
...
@@ -221,6 +222,7 @@ class LayerType(object):
PRIORBOX_LAYER
=
'priorbox'
MULTIBOX_LOSS_LAYER
=
'multibox_loss'
DETECTION_OUTPUT_LAYER
=
'detection_output'
ROI_POOL_LAYER
=
'roi_pool'
CTC_LAYER
=
'ctc'
WARP_CTC_LAYER
=
'warp_ctc'
...
...
@@ -1305,6 +1307,50 @@ def detection_output_layer(input_loc,
name
,
LayerType
.
DETECTION_OUTPUT_LAYER
,
parents
=
parents
,
size
=
size
)
@
wrap_name_default
(
"roi_pool"
)
def
roi_pool_layer
(
input
,
rois
,
pooled_width
,
pooled_height
,
spatial_scale
,
num_channels
=
None
,
name
=
None
):
"""
A layer used by Fast R-CNN to extract feature maps of ROIs from the last
feature map.
:param name: The Layer Name.
:type name: basestring
:param input: The input layer.
:type input: LayerOutput.
:param rois: The input ROIs' data.
:type rois: LayerOutput.
:param pooled_width: The width after pooling.
:type pooled_width: int
:param pooled_height: The height after pooling.
:type pooled_height: int
:param spatial_scale: The spatial scale between the image and feature map.
:type spatial_scale: float
:param num_channels: number of input channel.
:type num_channels: int
:return: LayerOutput
"""
if
num_channels
is
None
:
assert
input
.
num_filters
is
not
None
num_channels
=
input
.
num_filters
size
=
num_channels
*
pooled_width
*
pooled_height
Layer
(
name
=
name
,
type
=
LayerType
.
ROI_POOL_LAYER
,
inputs
=
[
input
.
name
,
rois
.
name
],
pooled_width
=
pooled_width
,
pooled_height
=
pooled_height
,
spatial_scale
=
spatial_scale
,
num_channels
=
num_channels
)
return
LayerOutput
(
name
,
LayerType
.
ROI_POOL_LAYER
,
parents
=
[
input
,
rois
],
size
=
size
)
@
wrap_name_default
(
"cross_channel_norm"
)
def
cross_channel_norm_layer
(
input
,
name
=
None
,
param_attr
=
None
):
"""
...
...
python/paddle/trainer_config_helpers/tests/configs/file_list.sh
浏览文件 @
dcc66c6d
...
...
@@ -9,7 +9,7 @@ test_seq_concat_reshape test_pad test_smooth_l1 test_multiplex_layer
test_prelu_layer test_row_conv test_detection_output_layer test_multibox_loss_layer
test_recursive_topology test_gated_unit_layer test_clip_layer test_row_l2_norm_layer
test_kmax_seq_socre_layer test_sub_nested_seq_select_layer test_scale_shift_layer
test_seq_slice_layer test_cross_entropy_over_beam test_pooling3D_layer
test_seq_slice_layer test_cross_entropy_over_beam test_
roi_pool_layer test_
pooling3D_layer
test_conv3d_layer test_deconv3d_layer test_BatchNorm3D test_resize_layer test_scale_sub_region_layer
)
export
whole_configs
=(
test_split_datasource
)
python/paddle/trainer_config_helpers/tests/configs/protostr/test_roi_pool_layer.protostr
0 → 100644
浏览文件 @
dcc66c6d
type: "nn"
layers {
name: "data"
type: "data"
size: 588
active_type: ""
height: 14
width: 14
}
layers {
name: "rois"
type: "data"
size: 10
active_type: ""
}
layers {
name: "__conv_0__"
type: "exconv"
size: 3136
active_type: ""
inputs {
input_layer_name: "data"
input_parameter_name: "___conv_0__.w0"
conv_conf {
filter_size: 3
channels: 3
stride: 1
padding: 1
groups: 1
filter_channels: 3
output_x: 14
img_size: 14
caffe_mode: true
filter_size_y: 3
padding_y: 1
stride_y: 1
output_y: 14
img_size_y: 14
}
}
bias_parameter_name: "___conv_0__.wbias"
num_filters: 16
shared_biases: true
height: 14
width: 14
}
layers {
name: "__roi_pool_0__"
type: "roi_pool"
size: 784
active_type: ""
inputs {
input_layer_name: "__conv_0__"
roi_pool_conf {
pooled_width: 7
pooled_height: 7
spatial_scale: 0.0625
}
}
inputs {
input_layer_name: "rois"
}
height: 7
width: 7
}
parameters {
name: "___conv_0__.w0"
size: 432
initial_mean: 0.0
initial_std: 0.272165526976
initial_strategy: 0
initial_smart: false
}
parameters {
name: "___conv_0__.wbias"
size: 16
initial_mean: 0.0
initial_std: 0.0
dims: 16
dims: 1
initial_strategy: 0
initial_smart: false
}
input_layer_names: "data"
input_layer_names: "rois"
output_layer_names: "__roi_pool_0__"
sub_models {
name: "root"
layer_names: "data"
layer_names: "rois"
layer_names: "__conv_0__"
layer_names: "__roi_pool_0__"
input_layer_names: "data"
input_layer_names: "rois"
output_layer_names: "__roi_pool_0__"
is_recurrent_layer_group: false
}
python/paddle/trainer_config_helpers/tests/configs/test_roi_pool_layer.py
0 → 100644
浏览文件 @
dcc66c6d
from
paddle.trainer_config_helpers
import
*
data
=
data_layer
(
name
=
'data'
,
size
=
3
*
14
*
14
,
height
=
14
,
width
=
14
)
rois
=
data_layer
(
name
=
'rois'
,
size
=
10
)
conv
=
img_conv_layer
(
input
=
data
,
filter_size
=
3
,
num_channels
=
3
,
num_filters
=
16
,
padding
=
1
,
act
=
LinearActivation
(),
bias_attr
=
True
)
roi_pool
=
roi_pool_layer
(
input
=
conv
,
rois
=
rois
,
pooled_width
=
7
,
pooled_height
=
7
,
spatial_scale
=
1.
/
16
)
outputs
(
roi_pool
)
编辑
预览
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