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70e44732
编写于
10月 25, 2016
作者:
W
wangyang59
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电子邮件补丁
差异文件
added convTrans test and python components
上级
5c88f072
变更
5
隐藏空白更改
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并排
Showing
5 changed file
with
367 addition
and
0 deletion
+367
-0
.gitignore
.gitignore
+2
-0
paddle/gserver/tests/CMakeLists.txt
paddle/gserver/tests/CMakeLists.txt
+8
-0
paddle/gserver/tests/test_ConvTrans.cpp
paddle/gserver/tests/test_ConvTrans.cpp
+139
-0
python/paddle/trainer/config_parser.py
python/paddle/trainer/config_parser.py
+95
-0
python/paddle/trainer_config_helpers/layers.py
python/paddle/trainer_config_helpers/layers.py
+123
-0
未找到文件。
.gitignore
浏览文件 @
70e44732
...
...
@@ -5,4 +5,6 @@ build/
.vscode
.idea
.project
.cproject
.pydevproject
Makefile
paddle/gserver/tests/CMakeLists.txt
浏览文件 @
70e44732
...
...
@@ -26,6 +26,14 @@ add_unittest_without_exec(test_ActivationGrad
TestUtil.cpp
)
add_test
(
NAME test_ActivationGrad
COMMAND test_ActivationGrad
)
################# test_ConvTrans #######################
add_unittest_without_exec
(
test_ConvTrans
test_ConvTrans.cpp
LayerGradUtil.cpp
TestUtil.cpp
)
add_test
(
NAME test_ConvTrans
COMMAND test_ConvTrans
)
################## test_Evaluator #######################
add_unittest
(
test_Evaluator
...
...
paddle/gserver/tests/test_ConvTrans.cpp
0 → 100644
浏览文件 @
70e44732
/* Copyright (c) 2016 Baidu, Inc. 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 <gtest/gtest.h>
#include <vector>
#include <string>
#include "paddle/gserver/layers/DataLayer.h"
#include "ModelConfig.pb.h"
#include "paddle/trainer/Trainer.h"
#include "paddle/utils/GlobalConstants.h"
#include "paddle/gserver/layers/ExpandConvTransLayer.h"
#include "TestUtil.h"
#include "LayerGradUtil.h"
using
namespace
paddle
;
// NOLINT
using
namespace
std
;
// NOLINT
P_DECLARE_bool
(
use_gpu
);
P_DECLARE_int32
(
gpu_id
);
P_DECLARE_double
(
checkgrad_eps
);
P_DECLARE_bool
(
thread_local_rand_use_global_seed
);
P_DECLARE_bool
(
prev_batch_state
);
TEST
(
Layer
,
convTransLayerFwd
)
{
TestConfig
configt
;
configt
.
biasSize
=
3
;
configt
.
layerConfig
.
set_type
(
"exconvt"
);
configt
.
layerConfig
.
set_num_filters
(
3
);
configt
.
layerConfig
.
set_partial_sum
(
1
);
configt
.
layerConfig
.
set_shared_biases
(
true
);
configt
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_0"
,
1024
,
288
});
LayerInputConfig
*
input
=
configt
.
layerConfig
.
add_inputs
();
ConvConfig
*
conv
=
input
->
mutable_conv_conf
();
conv
->
set_filter_size
(
2
);
conv
->
set_filter_size_y
(
3
);
conv
->
set_channels
(
16
);
conv
->
set_padding
(
0
);
conv
->
set_padding_y
(
1
);
conv
->
set_stride
(
2
);
conv
->
set_stride_y
(
2
);
conv
->
set_groups
(
1
);
conv
->
set_filter_channels
(
3
/
conv
->
groups
());
conv
->
set_img_size
(
16
);
conv
->
set_output_x
(
(
2
*
conv
->
padding
()
+
conv
->
img_size
()
-
conv
->
filter_size
())
/
((
float
)
conv
->
stride
())
+
1.5
);
configt
.
layerConfig
.
set_size
(
conv
->
img_size
()
*
conv
->
img_size
()
*
configt
.
layerConfig
.
num_filters
());
configt
.
layerConfig
.
set_name
(
"convTrans"
);
// data layer initialize
std
::
vector
<
DataLayerPtr
>
dataLayers
;
LayerMap
layerMap
;
vector
<
Argument
>
datas
;
initDataLayer
(
configt
,
&
dataLayers
,
&
datas
,
&
layerMap
,
"convTrans"
,
100
,
false
,
useGpu
);
// test layer initialize
std
::
vector
<
ParameterPtr
>
parameters
;
LayerPtr
convtLayer
;
initTestLayer
(
configt
,
&
layerMap
,
&
parameters
,
&
convtLayer
);
convtLayer
->
getBiasParameter
()
->
zeroMem
();
convtLayer
->
forward
(
PASS_GC
);
TestConfig
config
;
config
.
biasSize
=
16
;
config
.
layerConfig
.
set_type
(
"exconv"
);
config
.
layerConfig
.
set_num_filters
(
16
);
config
.
layerConfig
.
set_partial_sum
(
1
);
config
.
layerConfig
.
set_shared_biases
(
true
);
config
.
inputDefs
.
push_back
({
INPUT_DATA
,
"layer_1"
,
768
,
288
});
input
=
config
.
layerConfig
.
add_inputs
();
conv
=
input
->
mutable_conv_conf
();
conv
->
set_filter_size
(
2
);
conv
->
set_filter_size_y
(
3
);
conv
->
set_channels
(
3
);
conv
->
set_padding
(
0
);
conv
->
set_padding_y
(
1
);
conv
->
set_stride
(
2
);
conv
->
set_stride_y
(
2
);
conv
->
set_groups
(
1
);
conv
->
set_filter_channels
(
conv
->
channels
()
/
conv
->
groups
());
conv
->
set_img_size
(
16
);
conv
->
set_output_x
(
(
2
*
conv
->
padding
()
+
conv
->
img_size
()
-
conv
->
filter_size
())
/
((
float
)
conv
->
stride
())
+
1.5
);
config
.
layerConfig
.
set_size
(
conv
->
output_x
()
*
conv
->
output_x
()
*
config
.
layerConfig
.
num_filters
());
config
.
layerConfig
.
set_name
(
"conv"
);
// data layer initialize
std
::
vector
<
DataLayerPtr
>
dataLayers2
;
LayerMap
layerMap2
;
vector
<
Argument
>
datas2
;
initDataLayer
(
config
,
&
dataLayers2
,
&
datas2
,
&
layerMap2
,
"conv"
,
100
,
false
,
useGpu
);
// test layer initialize
std
::
vector
<
ParameterPtr
>
parameters2
;
LayerPtr
convLayer
;
initTestLayer
(
config
,
&
layerMap2
,
&
parameters2
,
&
convLayer
);
convLayer
->
getBiasParameter
()
->
zeroMem
();
convLayer
->
getParameters
()[
0
]
->
getBuf
(
PARAMETER_VALUE
)
->
copyFrom
(
*
(
convtLayer
->
getParameters
()[
0
]
->
getBuf
(
PARAMETER_VALUE
)));
convLayer
->
forward
(
PASS_GC
);
convLayer
->
getOutput
().
grad
->
copyFrom
(
*
(
dataLayers
[
0
]
->
getOutputValue
()));
vector
<
int
>
callbackFlags
(
parameters2
.
size
(),
0
);
auto
callback
=
[
&
](
Parameter
*
para
)
{
++
callbackFlags
[
para
->
getID
()];
};
convLayer
->
backward
(
callback
);
checkMatrixEqual
(
convtLayer
->
getOutputValue
(),
dataLayers2
[
0
]
->
getOutputGrad
());
}
int
main
(
int
argc
,
char
**
argv
)
{
testing
::
InitGoogleTest
(
&
argc
,
argv
);
initMain
(
argc
,
argv
);
FLAGS_thread_local_rand_use_global_seed
=
true
;
srand
(
1
);
return
RUN_ALL_TESTS
();
}
python/paddle/trainer/config_parser.py
浏览文件 @
70e44732
...
...
@@ -1106,6 +1106,37 @@ def parse_conv(conv, input_layer_name, conv_conf):
conv_conf
.
padding
,
conv_conf
.
stride
,
conv_conf
.
caffe_mode
)
def
parse_convt
(
conv
,
input_layer_name
,
conv_conf
):
conv_conf
.
filter_size
=
conv
.
filter_size
conv_conf
.
filter_size_y
=
conv
.
filter_size_y
conv_conf
.
channels
=
conv
.
channels
conv_conf
.
padding
=
conv
.
padding
conv_conf
.
padding_y
=
conv
.
padding_y
conv_conf
.
stride
=
conv
.
stride
conv_conf
.
stride_y
=
conv
.
stride_y
conv_conf
.
groups
=
conv
.
groups
conv_conf
.
filter_channels
=
conv
.
channels
/
conv
.
groups
conv_conf
.
caffe_mode
=
conv
.
caffe_mode
outputSize
=
g_layer_map
[
input_layer_name
].
size
/
conv
.
channels
print
(
'channels=%d size=%d'
%
(
conv
.
channels
,
g_layer_map
[
input_layer_name
].
size
))
conv_conf
.
output_x
=
int
(
outputSize
**
0.5
)
config_assert
((
conv_conf
.
output_x
**
2
)
==
outputSize
,
(
"Input layer %s: Incorrect input image size %d for input "
+
"image pixels %d"
)
%
(
input_layer_name
,
conv_conf
.
img_size
,
img_pixels
))
if
conv
.
caffe_mode
:
conv_conf
.
img_size
=
\
(
conv_conf
.
output_x
-
1
)
*
conv
.
stride
\
+
conv
.
filter_size
-
2
*
conv
.
padding
else
:
conv_conf
.
img_size
=
\
(
conv_conf
.
output_x
-
1
)
*
conv
.
stride
\
+
conv
.
filter_size
-
2
*
conv
.
padding
+
1
def
parse_block_expand
(
block_expand
,
input_layer_name
,
block_expand_conf
):
block_expand_conf
.
channels
=
block_expand
.
channels
block_expand_conf
.
stride_x
=
block_expand
.
stride_x
...
...
@@ -1612,6 +1643,70 @@ class ConvLayer(ConvLayerBase):
class
ConvLayer
(
ConvLayerBase
):
layer_type
=
'cudnn_conv'
@
config_layer
(
'convt'
)
class
ConvTransLayerBase
(
LayerBase
):
layer_type
=
'convt'
def
__init__
(
self
,
name
,
inputs
=
[],
bias
=
True
,
num_filters
=
None
,
shared_biases
=
False
,
**
xargs
):
super
(
ConvLayerBase
,
self
).
__init__
(
name
,
self
.
layer_type
,
0
,
inputs
=
inputs
,
**
xargs
)
if
num_filters
is
not
None
:
self
.
config
.
num_filters
=
num_filters
use_gpu
=
int
(
g_command_config_args
.
get
(
"use_gpu"
,
0
))
parallel_nn
=
int
(
g_command_config_args
.
get
(
"parallel_nn"
,
0
))
# Automatically select cudnn_type for GPU and exconv for CPU
# if set type=conv, but still reserve the way user specify
# exconv or cudnn_conv manually.
if
self
.
layer_type
==
"cudnn_convt"
:
config_assert
(
use_gpu
,
"cudnn_convt only support GPU"
)
if
(
use_gpu
==
1
and
self
.
layer_type
!=
"exconvt"
and
(
parallel_nn
==
0
or
self
.
config
.
device
>
-
1
)):
self
.
layer_type
=
"cudnn_convt"
else
:
self
.
layer_type
=
"exconvt"
# need to specify layer in config
self
.
config
.
type
=
self
.
layer_type
if
shared_biases
is
not
None
:
self
.
config
.
shared_biases
=
shared_biases
for
input_index
in
xrange
(
len
(
self
.
inputs
)):
input_layer
=
self
.
get_input_layer
(
input_index
)
parse_convt
(
self
.
inputs
[
input_index
].
conv
,
input_layer
.
name
,
self
.
config
.
inputs
[
input_index
].
conv_conf
)
conv_conf
=
self
.
config
.
inputs
[
input_index
].
conv_conf
psize
=
self
.
calc_parameter_size
(
conv_conf
)
print
(
"output size for %s is %d "
%
(
name
,
conv_conf
.
output_x
))
self
.
create_input_parameter
(
input_index
,
psize
)
self
.
set_layer_size
(
(
conv_conf
.
img_size
**
2
)
*
self
.
config
.
num_filters
)
psize
=
self
.
config
.
size
if
shared_biases
:
psize
=
self
.
config
.
num_filters
self
.
create_bias_parameter
(
bias
,
psize
,
[
psize
,
1
])
def
calc_parameter_size
(
self
,
conv_conf
):
return
conv_conf
.
channels
()
*
conv_conf
.
filter_channels
\
*
(
conv_conf
.
filter_size
*
conv_conf
.
filter_size_y
)
@
config_layer
(
'exconvt'
)
class
ConvTransLayer
(
ConvTransLayerBase
):
layer_type
=
'exconvt'
@
config_layer
(
'norm'
)
class
NormLayer
(
LayerBase
):
def
__init__
(
...
...
python/paddle/trainer_config_helpers/layers.py
浏览文件 @
70e44732
...
...
@@ -78,6 +78,7 @@ class LayerType(object):
COSINE_SIM
=
'cos'
HSIGMOID
=
'hsigmoid'
CONV_LAYER
=
"conv"
CONVTRANS_LAYER
=
"convt"
POOL_LAYER
=
"pool"
BATCH_NORM_LAYER
=
'batch_norm'
NORM_LAYER
=
'norm'
...
...
@@ -1625,6 +1626,128 @@ def img_conv_layer(input, filter_size, num_filters,
return
LayerOutput
(
name
,
LayerType
.
CONV_LAYER
,
parents
=
[
input
],
activation
=
act
,
num_filters
=
num_filters
)
@
wrap_name_default
(
"convt"
)
@
wrap_param_attr_default
()
@
wrap_bias_attr_default
()
@
wrap_act_default
(
act
=
ReluActivation
())
@
layer_support
(
DROPOUT
)
def
img_convTrans_layer
(
input
,
filter_size
,
num_filters
,
name
=
None
,
num_channels
=
None
,
act
=
None
,
groups
=
1
,
stride
=
1
,
padding
=
0
,
bias_attr
=
None
,
param_attr
=
None
,
shared_biases
=
True
,
layer_attr
=
None
,
filter_size_y
=
None
,
stride_y
=
None
,
padding_y
=
None
):
"""
Convolution Transpose (deconv) layer for image. Paddle only support square
input currently and thus input image's width equals height.
The details of convolution transpose layer,
please refer to the following explanation and references therein
<http://datascience.stackexchange.com/questions/6107/
what-are-deconvolutional-layers/>`_ .
The num_channel means input image's channel number. It may be 1 or 3 when
input is raw pixels of image(mono or RGB), or it may be the previous layer's
num_filters * num_group.
There are several group of filter in PaddlePaddle implementation.
Each group will process some channel of the inputs. For example, if an input
num_channel = 256, group = 4, num_filter=32, the PaddlePaddle will create
32*4 = 128 filters to process inputs. The channels will be split into 4
pieces. First 256/4 = 64 channels will process by first 32 filters. The
rest channels will be processed by rest group of filters.
:param name: Layer name.
:type name: basestring
:param input: Layer Input.
:type input: LayerOutput
:param filter_size: The x dimension of a filter kernel. Or input a tuple for
two image dimension.
:type filter_size: int|tuple|list
:param filter_size_y: The y dimension of a filter kernel. Since PaddlePaddle
currently supports rectangular filters, the filter's
shape will be (filter_size, filter_size_y).
:type filter_size_y: int|None
:param num_filters: Each filter group's number of filter
:param act: Activation type. Default is tanh
:type act: BaseActivation
:param groups: Group size of filters.
:type groups: int
:param stride: The x dimension of the stride. Or input a tuple for two image
dimension.
:type stride: int|tuple|list
:param stride_y: The y dimension of the stride.
:type stride_y: int
:param padding: The x dimension of the padding. Or input a tuple for two
image dimension
:type padding: int|tuple|list
:param padding_y: The y dimension of the padding.
:type padding_y: int
:param bias_attr: Convolution bias attribute. None means default bias.
False means no bias.
:type bias_attr: ParameterAttribute|False
:param num_channels: number of input channels. If None will be set
automatically from previous output.
:type num_channels: int
:param param_attr: Convolution param attribute. None means default attribute
:type param_attr: ParameterAttribute
:param shared_biases: Is biases will be shared between filters or not.
:type shared_biases: bool
:param layer_attr: Layer Extra Attribute.
:type layer_attr: ExtraLayerAttribute
:return: LayerOutput object.
:rtype: LayerOutput
"""
if
num_channels
is
None
:
assert
input
.
num_filters
is
not
None
num_channels
=
input
.
num_filters
if
filter_size_y
is
None
:
if
isinstance
(
filter_size
,
collections
.
Sequence
):
assert
len
(
filter_size
)
==
2
filter_size
,
filter_size_y
=
filter_size
else
:
filter_size_y
=
filter_size
if
stride_y
is
None
:
if
isinstance
(
stride
,
collections
.
Sequence
):
assert
len
(
stride
)
==
2
stride
,
stride_y
=
stride
else
:
stride_y
=
stride
if
padding_y
is
None
:
if
isinstance
(
padding
,
collections
.
Sequence
):
assert
len
(
padding
)
==
2
padding
,
padding_y
=
padding
else
:
padding_y
=
padding
if
param_attr
.
attr
.
get
(
'initial_smart'
):
# special initial for conv layers.
init_w
=
(
2.0
/
(
filter_size
**
2
*
num_channels
))
**
0.5
param_attr
.
attr
[
"initial_mean"
]
=
0.0
param_attr
.
attr
[
"initial_std"
]
=
init_w
param_attr
.
attr
[
"initial_strategy"
]
=
0
param_attr
.
attr
[
"initial_smart"
]
=
False
Layer
(
name
=
name
,
inputs
=
Input
(
input
.
name
,
conv
=
Conv
(
filter_size
=
filter_size
,
padding
=
padding
,
stride
=
stride
,
channels
=
num_channels
,
groups
=
groups
,
filter_size_y
=
filter_size_y
,
padding_y
=
padding_y
,
stride_y
=
stride_y
),
**
param_attr
.
attr
),
active_type
=
act
.
name
,
num_filters
=
num_filters
,
bias
=
ParamAttr
.
to_bias
(
bias_attr
),
shared_biases
=
shared_biases
,
type
=
LayerType
.
CONVTRANS_LAYER
,
**
ExtraLayerAttribute
.
to_kwargs
(
layer_attr
)
)
return
LayerOutput
(
name
,
LayerType
.
CONVTRANS_LAYER
,
parents
=
[
input
],
activation
=
act
,
num_filters
=
num_filters
)
@
wrap_name_default
(
"pool"
)
@
layer_support
()
...
...
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