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ad44a3eb
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ad44a3eb
编写于
3月 01, 2017
作者:
L
liaogang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Update vgg and resnet via api v2
上级
d227f447
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
139 addition
and
41 deletion
+139
-41
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
+18
-41
demo/image_classification/api_v2_vgg.py
demo/image_classification/api_v2_vgg.py
+47
-0
未找到文件。
demo/image_classification/
train
_v2_resnet.py
→
demo/image_classification/
api
_v2_resnet.py
浏览文件 @
ad44a3eb
...
...
@@ -14,12 +14,7 @@
import
paddle.v2
as
paddle
def
event_handler
(
event
):
if
isinstance
(
event
,
paddle
.
event
.
EndIteration
):
if
event
.
batch_id
%
100
==
0
:
print
"Pass %d, Batch %d, Cost %f"
%
(
event
.
pass_id
,
event
.
batch_id
,
event
.
cost
)
__all__
=
[
'resnet_cifar10'
]
def
conv_bn_layer
(
input
,
...
...
@@ -43,7 +38,6 @@ def conv_bn_layer(input,
def
shortcut
(
ipt
,
n_in
,
n_out
,
stride
):
if
n_in
!=
n_out
:
print
(
"n_in != n_out"
)
return
conv_bn_layer
(
ipt
,
n_out
,
1
,
stride
,
0
,
paddle
.
activation
.
Linear
())
else
:
...
...
@@ -51,22 +45,13 @@ def shortcut(ipt, n_in, n_out, stride):
def
basicblock
(
ipt
,
ch_out
,
stride
):
ch_in
=
ipt
.
num_filters
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
bottleneck
(
ipt
,
ch_out
,
stride
):
ch_in
=
ipt
.
num_filter
tmp
=
conv_bn_layer
(
ipt
,
ch_out
,
1
,
stride
,
0
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
,
3
,
1
,
1
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
*
4
,
1
,
1
,
0
,
paddle
.
activation
.
Linear
())
short
=
shortcut
(
ipt
,
ch_in
,
ch_out
*
4
,
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
):
...
...
@@ -74,29 +59,6 @@ def layer_warp(block_func, ipt, features, count, stride):
return
tmp
def
resnet_imagenet
(
ipt
,
depth
=
50
):
cfg
=
{
18
:
([
2
,
2
,
2
,
1
],
basicblock
),
34
:
([
3
,
4
,
6
,
3
],
basicblock
),
50
:
([
3
,
4
,
6
,
3
],
bottleneck
),
101
:
([
3
,
4
,
23
,
3
],
bottleneck
),
152
:
([
3
,
8
,
36
,
3
],
bottleneck
)
}
stages
,
block_func
=
cfg
[
depth
]
tmp
=
conv_bn_layer
(
ipt
,
ch_in
=
3
,
ch_out
=
64
,
filter_size
=
7
,
stride
=
2
,
padding
=
3
)
tmp
=
paddle
.
layer
.
img_pool
(
input
=
tmp
,
pool_size
=
3
,
stride
=
2
)
tmp
=
layer_warp
(
block_func
,
tmp
,
64
,
stages
[
0
],
1
)
tmp
=
layer_warp
(
block_func
,
tmp
,
128
,
stages
[
1
],
2
)
tmp
=
layer_warp
(
block_func
,
tmp
,
256
,
stages
[
2
],
2
)
tmp
=
layer_warp
(
block_func
,
tmp
,
512
,
stages
[
3
],
2
)
tmp
=
paddle
.
layer
.
img_pool
(
input
=
tmp
,
pool_size
=
7
,
stride
=
1
,
pool_type
=
paddle
.
pooling
.
Avg
())
tmp
=
paddle
.
layer
.
fc
(
input
=
tmp
,
size
=
1000
,
act
=
paddle
.
activation
.
Softmax
())
return
tmp
def
resnet_cifar10
(
ipt
,
depth
=
32
):
# depth should be one of 20, 32, 44, 56, 110, 1202
assert
(
depth
-
2
)
%
6
==
0
...
...
@@ -110,49 +72,3 @@ def resnet_cifar10(ipt, depth=32):
pool
=
paddle
.
layer
.
img_pool
(
input
=
res3
,
pool_size
=
8
,
stride
=
1
,
pool_type
=
paddle
.
pooling
.
Avg
())
return
pool
def
main
():
datadim
=
3
*
32
*
32
classdim
=
10
paddle
.
init
(
use_gpu
=
False
,
trainer_count
=
1
)
image
=
paddle
.
layer
.
data
(
name
=
"image"
,
type
=
paddle
.
data_type
.
dense_vector
(
datadim
))
net
=
resnet_cifar10
(
image
,
depth
=
32
)
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
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
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
)
trainer
=
paddle
.
trainer
.
SGD
(
update_equation
=
momentum_optimizer
)
trainer
.
train
(
reader
=
paddle
.
reader
.
batched
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
3072
),
batch_size
=
128
),
cost
=
cost
,
num_passes
=
1
,
parameters
=
parameters
,
event_handler
=
event_handler
,
reader_dict
=
{
'image'
:
0
,
'label'
:
1
},
)
if
__name__
==
'__main__'
:
main
()
demo/image_classification/
train_v2_vgg
.py
→
demo/image_classification/
api_v2_train
.py
浏览文件 @
ad44a3eb
...
...
@@ -10,9 +10,10 @@
# 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
.
# limitations under the License
import
paddle.v2
as
paddle
from
api_v2_vgg
import
resnet_cifar10
from
api_v2_resnet
import
vgg_bn_drop
def
event_handler
(
event
):
...
...
@@ -22,46 +23,21 @@ def event_handler(event):
event
.
cost
)
def
vgg_bn_drop
(
input
):
def
conv_block
(
ipt
,
num_filter
,
groups
,
dropouts
,
num_channels
=
None
):
return
paddle
.
layer
.
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
=
ExtraAttr
(
drop_rate
=
0.5
))
fc2
=
paddle
.
layer
.
fc
(
input
=
bn
,
size
=
512
,
act
=
paddle
.
activation
.
Linear
())
return
fc2
def
main
():
datadim
=
3
*
32
*
32
classdim
=
10
paddle
.
init
(
use_gpu
=
Fals
e
,
trainer_count
=
1
)
paddle
.
init
(
use_gpu
=
Tru
e
,
trainer_count
=
1
)
image
=
paddle
.
layer
.
data
(
name
=
"image"
,
type
=
paddle
.
data_type
.
dense_vector
(
datadim
))
# option 1. resnet
net
=
resnet_cifar10
(
image
,
depth
=
32
)
# option 2. vgg
# net = vgg_bn_drop(image)
out
=
paddle
.
layer
.
fc
(
input
=
image
,
out
=
paddle
.
layer
.
fc
(
input
=
net
,
size
=
classdim
,
act
=
paddle
.
activation
.
Softmax
())
...
...
@@ -70,27 +46,28 @@ def main():
cost
=
paddle
.
layer
.
classification_cost
(
input
=
out
,
label
=
lbl
)
parameters
=
paddle
.
parameters
.
create
(
cost
)
momentum_optimizer
=
paddle
.
optimizer
.
Momentum
(
momentum
=
0.9
,
regularization
=
paddle
.
optimizer
.
L2Regularization
(
rate
=
0.000
5
*
128
),
regularization
=
paddle
.
optimizer
.
L2Regularization
(
rate
=
0.000
2
*
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
)
trainer
=
paddle
.
trainer
.
SGD
(
update_equation
=
momentum_optimizer
)
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
=
3072
),
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
50000
),
batch_size
=
128
),
cost
=
cost
,
num_passes
=
1
,
parameters
=
parameters
,
num_passes
=
5
,
event_handler
=
event_handler
,
reader_dict
=
{
'image'
:
0
,
'label'
:
1
}
,
)
'label'
:
1
})
if
__name__
==
'__main__'
:
...
...
demo/image_classification/api_v2_vgg.py
0 → 100644
浏览文件 @
ad44a3eb
# 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
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