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体验新版 GitCode,发现更多精彩内容 >>
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提交
e675734c
编写于
6月 05, 2018
作者:
J
Jeff Wang
提交者:
GitHub
6月 05, 2018
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差异文件
Merge pull request #533 from jetfuel/image_classification_new_api
[High-Level-API] Image classification train.py update
上级
36fe7383
78d4347d
变更
3
隐藏空白更改
内联
并排
Showing
3 changed file
with
119 addition
and
114 deletion
+119
-114
03.image_classification/resnet.py
03.image_classification/resnet.py
+22
-24
03.image_classification/train.py
03.image_classification/train.py
+83
-75
03.image_classification/vgg.py
03.image_classification/vgg.py
+14
-15
未找到文件。
03.image_classification/resnet.py
浏览文件 @
e675734c
...
...
@@ -12,7 +12,7 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
paddle.
v2
as
paddle
import
paddle.
fluid
as
fluid
__all__
=
[
'resnet_cifar10'
]
...
...
@@ -22,37 +22,35 @@ def conv_bn_layer(input,
filter_size
,
stride
,
padding
,
act
ive_type
=
paddle
.
activation
.
Relu
()
,
ch_in
=
Non
e
):
tmp
=
paddle
.
layer
.
img_conv
(
act
=
'relu'
,
bias_attr
=
Fals
e
):
tmp
=
fluid
.
layers
.
conv2d
(
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
)
act
=
None
,
bias_attr
=
bias_attr
)
return
fluid
.
layers
.
batch_norm
(
input
=
tmp
,
act
=
act
)
def
shortcut
(
i
p
t
,
ch_in
,
ch_out
,
stride
):
def
shortcut
(
i
npu
t
,
ch_in
,
ch_out
,
stride
):
if
ch_in
!=
ch_out
:
return
conv_bn_layer
(
ipt
,
ch_out
,
1
,
stride
,
0
,
paddle
.
activation
.
Linear
())
return
conv_bn_layer
(
input
,
ch_out
,
1
,
stride
,
0
,
None
)
else
:
return
i
p
t
return
i
npu
t
def
basicblock
(
i
p
t
,
ch_in
,
ch_out
,
stride
):
tmp
=
conv_bn_layer
(
i
p
t
,
ch_out
,
3
,
stride
,
1
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
,
3
,
1
,
1
,
paddle
.
activation
.
Linear
()
)
short
=
shortcut
(
i
p
t
,
ch_in
,
ch_out
,
stride
)
return
paddle
.
layer
.
addto
(
input
=
[
tmp
,
short
],
act
=
paddle
.
activation
.
Relu
()
)
def
basicblock
(
i
npu
t
,
ch_in
,
ch_out
,
stride
):
tmp
=
conv_bn_layer
(
i
npu
t
,
ch_out
,
3
,
stride
,
1
)
tmp
=
conv_bn_layer
(
tmp
,
ch_out
,
3
,
1
,
1
,
act
=
None
,
bias_attr
=
True
)
short
=
shortcut
(
i
npu
t
,
ch_in
,
ch_out
,
stride
)
return
fluid
.
layers
.
elementwise_add
(
x
=
tmp
,
y
=
short
,
act
=
'relu'
)
def
layer_warp
(
block_func
,
i
p
t
,
ch_in
,
ch_out
,
count
,
stride
):
tmp
=
block_func
(
i
p
t
,
ch_in
,
ch_out
,
stride
)
def
layer_warp
(
block_func
,
i
npu
t
,
ch_in
,
ch_out
,
count
,
stride
):
tmp
=
block_func
(
i
npu
t
,
ch_in
,
ch_out
,
stride
)
for
i
in
range
(
1
,
count
):
tmp
=
block_func
(
tmp
,
ch_out
,
ch_out
,
1
)
return
tmp
...
...
@@ -63,11 +61,11 @@ def resnet_cifar10(ipt, depth=32):
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
)
conv1
=
conv_bn_layer
(
ipt
,
ch_out
=
16
,
filter_size
=
3
,
stride
=
1
,
padding
=
1
)
res1
=
layer_warp
(
basicblock
,
conv1
,
16
,
16
,
n
,
1
)
res2
=
layer_warp
(
basicblock
,
res1
,
16
,
32
,
n
,
2
)
res3
=
layer_warp
(
basicblock
,
res2
,
32
,
64
,
n
,
2
)
pool
=
paddle
.
layer
.
img_pool
(
input
=
res3
,
pool_size
=
8
,
stride
=
1
,
pool_type
=
paddle
.
pooling
.
Avg
())
return
pool
pool
=
fluid
.
layers
.
pool2d
(
input
=
res3
,
pool_size
=
8
,
pool_type
=
'avg'
,
pool_stride
=
1
)
predict
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
10
,
act
=
'softmax'
)
return
predict
03.image_classification/train.py
浏览文件 @
e675734c
...
...
@@ -12,92 +12,84 @@
# See the License for the specific language governing permissions and
# limitations under the License
import
sys
,
os
from
__future__
import
print_function
import
paddle.v2
as
paddle
import
paddle
import
paddle.fluid
as
fluid
import
numpy
import
sys
from
vgg
import
vgg_bn_drop
from
resnet
import
resnet_cifar10
with_gpu
=
os
.
getenv
(
'WITH_GPU'
,
'0'
)
!=
'0'
def
inference_network
():
# The image is 32 * 32 with RGB representation.
data_shape
=
[
3
,
32
,
32
]
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
data_shape
,
dtype
=
'float32'
)
def
main
():
datadim
=
3
*
32
*
32
classdim
=
10
predict
=
resnet_cifar10
(
images
,
32
)
# predict = vgg_bn_drop(images) # un-comment to use vgg net
return
predict
# PaddlePaddle init
paddle
.
init
(
use_gpu
=
with_gpu
,
trainer_count
=
1
)
image
=
paddle
.
layer
.
data
(
name
=
"image"
,
type
=
paddle
.
data_type
.
dense_vector
(
datadim
))
def
train_network
():
predict
=
inference_network
()
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
cost
)
accuracy
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
return
[
avg_cost
,
accuracy
]
# 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
())
def
train
(
use_cuda
,
train_program
,
params_dirname
):
BATCH_SIZE
=
128
EPOCH_NUM
=
2
lbl
=
paddle
.
layer
.
data
(
name
=
"label"
,
type
=
paddle
.
data_type
.
integer_value
(
classdim
))
cost
=
paddle
.
layer
.
classification_cost
(
input
=
out
,
label
=
lbl
)
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
50000
),
batch_size
=
BATCH_SIZE
)
# Create parameters
parameters
=
paddle
.
parameters
.
create
(
cost
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
BATCH_SIZE
)
# 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'
)
# Create trainer
trainer
=
paddle
.
trainer
.
SGD
(
cost
=
cost
,
parameters
=
parameters
,
update_equation
=
momentum_optimizer
)
# 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
)
if
isinstance
(
event
,
fluid
.
EndStepEvent
):
if
event
.
step
%
100
==
0
:
print
(
"Pass %d, Batch %d, Cost %f, Acc %f"
%
(
event
.
step
,
event
.
epoch
,
event
.
metrics
[
0
],
event
.
metrics
[
1
]))
else
:
sys
.
stdout
.
write
(
'.'
)
sys
.
stdout
.
flush
()
if
isinstance
(
event
,
paddle
.
event
.
EndPass
):
# save parameters
with
open
(
'params_pass_%d.tar'
%
event
.
pass_id
,
'w'
)
as
f
:
trainer
.
save_parameter_to_tar
(
f
)
result
=
trainer
.
test
(
reader
=
paddle
.
batch
(
paddle
.
dataset
.
cifar
.
test10
(),
batch_size
=
128
),
feeding
=
{
'image'
:
0
,
'label'
:
1
})
print
"
\n
Test with Pass %d, %s"
%
(
event
.
pass_id
,
result
.
metrics
)
# Save the inference topology to protobuf.
inference_topology
=
paddle
.
topology
.
Topology
(
layers
=
out
)
with
open
(
"inference_topology.pkl"
,
'wb'
)
as
f
:
inference_topology
.
serialize_for_inference
(
f
)
if
isinstance
(
event
,
fluid
.
EndEpochEvent
):
avg_cost
,
accuracy
=
trainer
.
test
(
reader
=
test_reader
,
feed_order
=
[
'pixel'
,
'label'
])
print
(
'Loss {0:2.2}, Acc {1:2.2}'
.
format
(
avg_cost
,
accuracy
))
if
params_dirname
is
not
None
:
trainer
.
save_params
(
params_dirname
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
trainer
=
fluid
.
Trainer
(
train_func
=
train_program
,
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
),
place
=
place
)
trainer
.
train
(
reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
cifar
.
train10
(),
buf_size
=
50000
),
batch_size
=
128
),
num_passes
=
200
,
reader
=
train_reader
,
num_epochs
=
EPOCH_NUM
,
event_handler
=
event_handler
,
feeding
=
{
'image'
:
0
,
'label'
:
1
})
feed_order
=
[
'pixel'
,
'label'
])
# inference
def
infer
(
use_cuda
,
inference_program
,
params_dirname
=
None
):
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
inferencer
=
fluid
.
Inferencer
(
infer_func
=
inference_program
,
param_path
=
params_dirname
,
place
=
place
)
# Prepare testing data.
from
PIL
import
Image
import
numpy
as
np
import
os
...
...
@@ -105,6 +97,7 @@ def main():
def
load_image
(
file
):
im
=
Image
.
open
(
file
)
im
=
im
.
resize
((
32
,
32
),
Image
.
ANTIALIAS
)
im
=
np
.
array
(
im
).
astype
(
np
.
float32
)
# The storage order of the loaded image is W(widht),
# H(height), C(channel). PaddlePaddle requires
...
...
@@ -114,23 +107,38 @@ def main():
# image is B(Blue), G(green), R(Red). But PIL open
# image in RGB mode. It must swap the channel order.
im
=
im
[(
2
,
1
,
0
),
:,
:]
# BGR
im
=
im
.
flatten
()
im
=
im
/
255.0
# Add one dimension to mimic the list format.
im
=
numpy
.
expand_dims
(
im
,
axis
=
0
)
return
im
test_data
=
[]
cur_dir
=
os
.
path
.
dirname
(
os
.
path
.
realpath
(
__file__
))
test_data
.
append
((
load_image
(
cur_dir
+
'/image/dog.png'
),
))
img
=
load_image
(
cur_dir
+
'/image/dog.png'
)
# inference
results
=
inferencer
.
infer
({
'pixel'
:
img
})
print
(
"infer results: "
,
results
)
def
main
(
use_cuda
):
if
use_cuda
and
not
fluid
.
core
.
is_compiled_with_cuda
():
return
save_path
=
"image_classification_resnet.inference.model"
# users can remove the comments and change the model name
# with open('params_pass_50.tar', 'r') as f:
# parameters = paddle.parameters.Parameters.from_tar(f)
train
(
use_cuda
=
use_cuda
,
train_program
=
train_network
,
params_dirname
=
save_path
)
probs
=
paddle
.
infer
(
output_layer
=
out
,
parameters
=
parameters
,
input
=
test_data
)
lab
=
np
.
argsort
(
-
probs
)
# probs and lab are the results of one batch data
print
"Label of image/dog.png is: %d"
%
lab
[
0
][
0
]
infer
(
use_cuda
=
use_cuda
,
inference_program
=
inference_network
,
params_dirname
=
save_path
)
if
__name__
==
'__main__'
:
main
()
# For demo purpose, the training runs on CPU
# Please change accordingly.
main
(
use_cuda
=
False
)
03.image_classification/vgg.py
浏览文件 @
e675734c
...
...
@@ -12,36 +12,35 @@
# See the License for the specific language governing permissions and
# limitations under the License.
import
paddle.v2
as
paddle
import
paddle
import
paddle.fluid
as
fluid
__all__
=
[
'vgg_bn_drop'
]
def
vgg_bn_drop
(
input
):
def
conv_block
(
ipt
,
num_filter
,
groups
,
dropouts
,
num_channels
=
None
):
return
paddle
.
network
s
.
img_conv_group
(
def
conv_block
(
ipt
,
num_filter
,
groups
,
dropouts
):
return
fluid
.
net
s
.
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_act
=
'relu'
,
conv_with_batchnorm
=
True
,
conv_batchnorm_drop_rate
=
dropouts
,
pool_type
=
paddle
.
pooling
.
Max
()
)
pool_type
=
'max'
)
conv1
=
conv_block
(
input
,
64
,
2
,
[
0.3
,
0
]
,
3
)
conv1
=
conv_block
(
input
,
64
,
2
,
[
0.3
,
0
])
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
drop
=
fluid
.
layers
.
dropout
(
x
=
conv5
,
dropout_prob
=
0.5
)
fc1
=
fluid
.
layers
.
fc
(
input
=
drop
,
size
=
512
,
act
=
None
)
bn
=
fluid
.
layers
.
batch_norm
(
input
=
fc1
,
act
=
'relu'
)
drop2
=
fluid
.
layers
.
dropout
(
x
=
bn
,
dropout_prob
=
0.5
)
fc2
=
fluid
.
layers
.
fc
(
input
=
drop2
,
size
=
512
,
act
=
None
)
predict
=
fluid
.
layers
.
fc
(
input
=
fc2
,
size
=
10
,
act
=
'softmax'
)
return
predict
\ No newline at end of file
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