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65c962df
编写于
2月 09, 2018
作者:
Z
Zhaolong Xing
提交者:
GitHub
2月 09, 2018
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Merge pull request #634 from NHZlX/add_mobilenet_fluid
add fluid mobilenet config
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c9b41eef
1979f939
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fluid/image_classification/mobilenet.py
fluid/image_classification/mobilenet.py
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fluid/image_classification/mobilenet.py
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浏览文件 @
65c962df
import
os
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
from
paddle.v2.fluid.initializer
import
MSRA
from
paddle.v2.fluid.param_attr
import
ParamAttr
parameter_attr
=
ParamAttr
(
initializer
=
MSRA
())
def
conv_bn_layer
(
input
,
filter_size
,
num_filters
,
stride
,
padding
,
channels
=
None
,
num_groups
=
1
,
act
=
'relu'
,
use_cudnn
=
True
):
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
stride
=
stride
,
padding
=
padding
,
groups
=
num_groups
,
act
=
None
,
use_cudnn
=
use_cudnn
,
param_attr
=
parameter_attr
,
bias_attr
=
False
)
return
fluid
.
layers
.
batch_norm
(
input
=
conv
,
act
=
act
)
def
depthwise_separable
(
input
,
num_filters1
,
num_filters2
,
num_groups
,
stride
,
scale
):
"""
"""
depthwise_conv
=
conv_bn_layer
(
input
=
input
,
filter_size
=
3
,
num_filters
=
int
(
num_filters1
*
scale
),
stride
=
stride
,
padding
=
1
,
num_groups
=
int
(
num_groups
*
scale
),
use_cudnn
=
False
)
pointwise_conv
=
conv_bn_layer
(
input
=
depthwise_conv
,
filter_size
=
1
,
num_filters
=
int
(
num_filters2
*
scale
),
stride
=
1
,
padding
=
0
)
return
pointwise_conv
def
mobile_net
(
img
,
class_dim
,
scale
=
1.0
):
# conv1: 112x112
tmp
=
conv_bn_layer
(
img
,
filter_size
=
3
,
channels
=
3
,
num_filters
=
int
(
32
*
scale
),
stride
=
2
,
padding
=
1
)
# 56x56
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
32
,
num_filters2
=
64
,
num_groups
=
32
,
stride
=
1
,
scale
=
scale
)
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
64
,
num_filters2
=
128
,
num_groups
=
64
,
stride
=
2
,
scale
=
scale
)
# 28x28
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
128
,
num_filters2
=
128
,
num_groups
=
128
,
stride
=
1
,
scale
=
scale
)
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
128
,
num_filters2
=
256
,
num_groups
=
128
,
stride
=
2
,
scale
=
scale
)
# 14x14
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
256
,
num_filters2
=
256
,
num_groups
=
256
,
stride
=
1
,
scale
=
scale
)
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
256
,
num_filters2
=
512
,
num_groups
=
256
,
stride
=
2
,
scale
=
scale
)
# 14x14
for
i
in
range
(
5
):
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
512
,
num_filters2
=
512
,
num_groups
=
512
,
stride
=
1
,
scale
=
scale
)
# 7x7
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
512
,
num_filters2
=
1024
,
num_groups
=
512
,
stride
=
2
,
scale
=
scale
)
tmp
=
depthwise_separable
(
tmp
,
num_filters1
=
1024
,
num_filters2
=
1024
,
num_groups
=
1024
,
stride
=
1
,
scale
=
scale
)
tmp
=
fluid
.
layers
.
pool2d
(
input
=
tmp
,
pool_size
=
0
,
pool_stride
=
1
,
pool_type
=
'avg'
,
global_pooling
=
True
)
tmp
=
fluid
.
layers
.
fc
(
input
=
tmp
,
size
=
class_dim
,
act
=
'softmax'
,
param_attr
=
parameter_attr
)
return
tmp
def
train
(
learning_rate
,
batch_size
,
num_passes
,
model_save_dir
=
'model'
):
class_dim
=
102
image_shape
=
[
3
,
224
,
224
]
image
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
image_shape
,
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
out
=
mobile_net
(
image
,
class_dim
=
class_dim
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
out
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
learning_rate
,
momentum
=
0.9
,
regularization
=
fluid
.
regularizer
.
L2Decay
(
5
*
1e-5
))
opts
=
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
out
,
label
=
label
)
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
test_accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
out
,
label
=
label
)
test_target
=
[
avg_cost
]
+
test_accuracy
.
metrics
+
test_accuracy
.
states
inference_program
=
fluid
.
io
.
get_inference_program
(
test_target
)
place
=
fluid
.
CUDAPlace
(
0
)
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
train
(),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
flowers
.
test
(),
batch_size
=
batch_size
)
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
image
,
label
])
for
pass_id
in
range
(
num_passes
):
accuracy
.
reset
(
exe
)
for
batch_id
,
data
in
enumerate
(
train_reader
()):
loss
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
print
(
"Pass {0}, batch {1}, loss {2}, acc {3}"
.
format
(
pass_id
,
batch_id
,
loss
[
0
],
acc
[
0
]))
pass_acc
=
accuracy
.
eval
(
exe
)
test_accuracy
.
reset
(
exe
)
for
data
in
test_reader
():
loss
,
acc
=
exe
.
run
(
inference_program
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
test_accuracy
.
metrics
)
test_pass_acc
=
test_accuracy
.
eval
(
exe
)
print
(
"End pass {0}, train_acc {1}, test_acc {2}"
.
format
(
pass_id
,
pass_acc
,
test_pass_acc
))
if
pass_id
%
10
==
0
:
model_path
=
os
.
path
.
join
(
model_save_dir
,
str
(
pass_id
))
print
'save models to %s'
%
(
model_path
)
fluid
.
io
.
save_inference_model
(
model_path
,
[
'image'
],
[
out
],
exe
)
if
__name__
==
'__main__'
:
train
(
learning_rate
=
0.005
,
batch_size
=
40
,
num_passes
=
300
)
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