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体验新版 GitCode,发现更多精彩内容 >>
提交
e95c2283
编写于
3月 02, 2017
作者:
G
gangliao
提交者:
GitHub
3月 02, 2017
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差异文件
Merge pull request #1485 from gangliao/vgg_demo
Add vgg training via api v2
上级
a2a5f4af
69ac20c2
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
242 addition
and
2 deletion
+242
-2
demo/image_classification/api_v2_resnet.py
demo/image_classification/api_v2_resnet.py
+74
-0
demo/image_classification/api_v2_train.py
demo/image_classification/api_v2_train.py
+91
-0
demo/image_classification/api_v2_vgg.py
demo/image_classification/api_v2_vgg.py
+47
-0
paddle/api/GradientMachine.cpp
paddle/api/GradientMachine.cpp
+14
-0
paddle/api/PaddleAPI.h
paddle/api/PaddleAPI.h
+3
-0
paddle/py_paddle/util.py
paddle/py_paddle/util.py
+6
-0
python/paddle/v2/dataset/__init__.py
python/paddle/v2/dataset/__init__.py
+5
-1
python/paddle/v2/trainer.py
python/paddle/v2/trainer.py
+2
-1
未找到文件。
demo/image_classification/api_v2_resnet.py
0 → 100644
浏览文件 @
e95c2283
# Copyright (c) 2016 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
paddle.v2
as
paddle
__all__
=
[
'resnet_cifar10'
]
def
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
active_type
=
paddle
.
activation
.
Relu
(),
ch_in
=
None
):
tmp
=
paddle
.
layer
.
img_conv
(
input
=
input
,
filter_size
=
filter_size
,
num_channels
=
ch_in
,
num_filters
=
ch_out
,
stride
=
stride
,
padding
=
padding
,
act
=
paddle
.
activation
.
Linear
(),
bias_attr
=
False
)
return
paddle
.
layer
.
batch_norm
(
input
=
tmp
,
act
=
active_type
)
def
shortcut
(
ipt
,
n_in
,
n_out
,
stride
):
if
n_in
!=
n_out
:
return
conv_bn_layer
(
ipt
,
n_out
,
1
,
stride
,
0
,
paddle
.
activation
.
Linear
())
else
:
return
ipt
def
basicblock
(
ipt
,
ch_out
,
stride
):
ch_in
=
ch_out
*
2
tmp
=
conv_bn_layer
(
ipt
,
ch_out
,
3
,
stride
,
1
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
,
3
,
1
,
1
,
paddle
.
activation
.
Linear
())
short
=
shortcut
(
ipt
,
ch_in
,
ch_out
,
stride
)
return
paddle
.
layer
.
addto
(
input
=
[
tmp
,
short
],
act
=
paddle
.
activation
.
Relu
())
def
layer_warp
(
block_func
,
ipt
,
features
,
count
,
stride
):
tmp
=
block_func
(
ipt
,
features
,
stride
)
for
i
in
range
(
1
,
count
):
tmp
=
block_func
(
tmp
,
features
,
1
)
return
tmp
def
resnet_cifar10
(
ipt
,
depth
=
32
):
# depth should be one of 20, 32, 44, 56, 110, 1202
assert
(
depth
-
2
)
%
6
==
0
n
=
(
depth
-
2
)
/
6
nStages
=
{
16
,
64
,
128
}
conv1
=
conv_bn_layer
(
ipt
,
ch_in
=
3
,
ch_out
=
16
,
filter_size
=
3
,
stride
=
1
,
padding
=
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
64
,
n
,
2
)
pool
=
paddle
.
layer
.
img_pool
(
input
=
res3
,
pool_size
=
8
,
stride
=
1
,
pool_type
=
paddle
.
pooling
.
Avg
())
return
pool
demo/image_classification/api_v2_train.py
0 → 100644
浏览文件 @
e95c2283
# Copyright (c) 2016 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
sys
import
paddle.v2
as
paddle
from
api_v2_vgg
import
vgg_bn_drop
from
api_v2_resnet
import
resnet_cifar10
def
main
():
datadim
=
3
*
32
*
32
classdim
=
10
# PaddlePaddle init
paddle
.
init
(
use_gpu
=
True
,
trainer_count
=
1
)
image
=
paddle
.
layer
.
data
(
name
=
"image"
,
type
=
paddle
.
data_type
.
dense_vector
(
datadim
))
# Add neural network config
# option 1. resnet
net
=
resnet_cifar10
(
image
,
depth
=
32
)
# option 2. vgg
# net = vgg_bn_drop(image)
out
=
paddle
.
layer
.
fc
(
input
=
net
,
size
=
classdim
,
act
=
paddle
.
activation
.
Softmax
())
lbl
=
paddle
.
layer
.
data
(
name
=
"label"
,
type
=
paddle
.
data_type
.
integer_value
(
classdim
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
out
,
label
=
lbl
)
# Create parameters
parameters
=
paddle
.
parameters
.
create
(
cost
)
# Create optimizer
momentum_optimizer
=
paddle
.
optimizer
.
Momentum
(
momentum
=
0.9
,
regularization
=
paddle
.
optimizer
.
L2Regularization
(
rate
=
0.0002
*
128
),
learning_rate
=
0.1
/
128.0
,
learning_rate_decay_a
=
0.1
,
learning_rate_decay_b
=
50000
*
100
,
learning_rate_schedule
=
'discexp'
,
batch_size
=
128
)
# End batch and end pass event handler
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
100
==
0
:
print
"
\n
Pass %d, Batch %d, Cost %f, %s"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
,
event
.
metrics
)
else
:
sys
.
stdout
.
write
(
'.'
)
sys
.
stdout
.
flush
()
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
result
=
trainer
.
test
(
reader
=
paddle
.
reader
.
batched
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
128
),
reader_dict
=
{
'image'
:
0
,
'label'
:
1
})
print
"
\n
Test with Pass %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
# Create trainer
trainer
=
paddle
.
trainer
.
SGD
(
cost
=
cost
,
parameters
=
parameters
,
update_equation
=
momentum_optimizer
)
trainer
.
train
(
reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
50000
),
batch_size
=
128
),
num_passes
=
5
,
event_handler
=
event_handler
,
reader_dict
=
{
'image'
:
0
,
'label'
:
1
})
if
__name__
==
'__main__'
:
main
()
demo/image_classification/api_v2_vgg.py
0 → 100644
浏览文件 @
e95c2283
# Copyright (c) 2016 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
paddle.v2
as
paddle
__all__
=
[
'vgg_bn_drop'
]
def
vgg_bn_drop
(
input
):
def
conv_block
(
ipt
,
num_filter
,
groups
,
dropouts
,
num_channels
=
None
):
return
paddle
.
networks
.
img_conv_group
(
input
=
ipt
,
num_channels
=
num_channels
,
pool_size
=
2
,
pool_stride
=
2
,
conv_num_filter
=
[
num_filter
]
*
groups
,
conv_filter_size
=
3
,
conv_act
=
paddle
.
activation
.
Relu
(),
conv_with_batchnorm
=
True
,
conv_batchnorm_drop_rate
=
dropouts
,
pool_type
=
paddle
.
pooling
.
Max
())
conv1
=
conv_block
(
input
,
64
,
2
,
[
0.3
,
0
],
3
)
conv2
=
conv_block
(
conv1
,
128
,
2
,
[
0.4
,
0
])
conv3
=
conv_block
(
conv2
,
256
,
3
,
[
0.4
,
0.4
,
0
])
conv4
=
conv_block
(
conv3
,
512
,
3
,
[
0.4
,
0.4
,
0
])
conv5
=
conv_block
(
conv4
,
512
,
3
,
[
0.4
,
0.4
,
0
])
drop
=
paddle
.
layer
.
dropout
(
input
=
conv5
,
dropout_rate
=
0.5
)
fc1
=
paddle
.
layer
.
fc
(
input
=
drop
,
size
=
512
,
act
=
paddle
.
activation
.
Linear
())
bn
=
paddle
.
layer
.
batch_norm
(
input
=
fc1
,
act
=
paddle
.
activation
.
Relu
(),
layer_attr
=
paddle
.
attr
.
Extra
(
drop_rate
=
0.5
))
fc2
=
paddle
.
layer
.
fc
(
input
=
bn
,
size
=
512
,
act
=
paddle
.
activation
.
Linear
())
return
fc2
paddle/api/GradientMachine.cpp
浏览文件 @
e95c2283
...
@@ -142,6 +142,20 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
...
@@ -142,6 +142,20 @@ Parameter* GradientMachine::getParameter(size_t i) throw(RangeError) {
}
}
}
}
size_t
GradientMachine
::
getNonStaticParameterSize
()
const
{
return
m
->
machine
->
getNonStaticParameters
().
size
();
}
Parameter
*
GradientMachine
::
getNonStaticParameter
(
size_t
i
)
throw
(
RangeError
)
{
auto
params
=
m
->
machine
->
getNonStaticParameters
();
if
(
i
<
params
.
size
())
{
return
Parameter
::
createFromSharedPtr
(
&
m
->
machine
->
getNonStaticParameters
()[
i
]);
}
else
{
throw
RangeError
();
}
}
void
GradientMachine
::
randParameters
()
{
m
->
machine
->
randParameters
();
}
void
GradientMachine
::
randParameters
()
{
m
->
machine
->
randParameters
();
}
Arguments
*
GradientMachine
::
getLayerOutput
(
const
std
::
string
&
layerName
)
const
Arguments
*
GradientMachine
::
getLayerOutput
(
const
std
::
string
&
layerName
)
const
...
...
paddle/api/PaddleAPI.h
浏览文件 @
e95c2283
...
@@ -771,6 +771,9 @@ public:
...
@@ -771,6 +771,9 @@ public:
size_t
getParameterSize
()
const
;
size_t
getParameterSize
()
const
;
Parameter
*
getParameter
(
size_t
i
)
throw
(
RangeError
);
Parameter
*
getParameter
(
size_t
i
)
throw
(
RangeError
);
size_t
getNonStaticParameterSize
()
const
;
Parameter
*
getNonStaticParameter
(
size_t
i
)
throw
(
RangeError
);
void
randParameters
();
void
randParameters
();
Arguments
*
getLayerOutput
(
const
std
::
string
&
layerName
)
const
Arguments
*
getLayerOutput
(
const
std
::
string
&
layerName
)
const
...
...
paddle/py_paddle/util.py
浏览文件 @
e95c2283
...
@@ -195,6 +195,12 @@ def __monkeypatch_gradient_machine__():
...
@@ -195,6 +195,12 @@ def __monkeypatch_gradient_machine__():
swig_paddle
.
GradientMachine
.
getParameters
=
getParameters
swig_paddle
.
GradientMachine
.
getParameters
=
getParameters
def
getNonStaticParameters
(
self
):
return
(
self
.
getNonStaticParameter
(
i
)
for
i
in
xrange
(
self
.
getNonStaticParameterSize
()))
swig_paddle
.
GradientMachine
.
getNonStaticParameters
=
getNonStaticParameters
def
getLayerOutputs
(
self
,
layerNames
):
def
getLayerOutputs
(
self
,
layerNames
):
"""
"""
getLayerOutputs. get outputs of layers and return a numpy matrix dict.
getLayerOutputs. get outputs of layers and return a numpy matrix dict.
...
...
python/paddle/v2/dataset/__init__.py
浏览文件 @
e95c2283
import
mnist
import
mnist
import
imikolov
import
imdb
import
cifar
import
movielens
__all__
=
[
'mnist'
]
__all__
=
[
'mnist'
,
'imikolov'
,
'imdb'
,
'cifar'
,
'movielens'
]
python/paddle/v2/trainer.py
浏览文件 @
e95c2283
...
@@ -120,7 +120,8 @@ class SGD(ITrainer):
...
@@ -120,7 +120,8 @@ class SGD(ITrainer):
feeder
(
data_batch
),
out_args
,
pass_type
)
feeder
(
data_batch
),
out_args
,
pass_type
)
self
.
__gradient_machine__
.
eval
(
pass_evaluator
)
self
.
__gradient_machine__
.
eval
(
pass_evaluator
)
self
.
__gradient_machine__
.
eval
(
batch_evaluator
)
self
.
__gradient_machine__
.
eval
(
batch_evaluator
)
for
each_param
in
self
.
__gradient_machine__
.
getParameters
():
for
each_param
in
self
.
__gradient_machine__
.
getNonStaticParameters
(
):
updater
.
update
(
each_param
)
updater
.
update
(
each_param
)
# Get cost. We use numpy to calculate total cost for this batch.
# Get cost. We use numpy to calculate total cost for this batch.
cost_vec
=
out_args
.
getSlotValue
(
0
)
cost_vec
=
out_args
.
getSlotValue
(
0
)
...
...
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