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95141467
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体验新版 GitCode,发现更多精彩内容 >>
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95141467
编写于
6月 05, 2018
作者:
L
Luo Tao
浏览文件
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浏览文件
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电子邮件补丁
差异文件
add ParallelDo example for benchmark/fluid
上级
6ada5f48
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
52 addition
and
18 deletion
+52
-18
benchmark/fluid/fluid_benchmark.py
benchmark/fluid/fluid_benchmark.py
+5
-0
benchmark/fluid/models/mnist.py
benchmark/fluid/models/mnist.py
+24
-9
benchmark/fluid/models/resnet.py
benchmark/fluid/models/resnet.py
+22
-7
benchmark/fluid/models/stacked_dynamic_lstm.py
benchmark/fluid/models/stacked_dynamic_lstm.py
+1
-2
未找到文件。
benchmark/fluid/fluid_benchmark.py
浏览文件 @
95141467
...
...
@@ -69,6 +69,11 @@ def parse_args():
type
=
int
,
default
=
1
,
help
=
'If gpus > 1, will use ParallelExecutor to run, else use Executor.'
)
parser
.
add_argument
(
'--cpus'
,
type
=
int
,
default
=
1
,
help
=
'If cpus > 1, will use ParallelDo to run, else use Executor.'
)
parser
.
add_argument
(
'--data_set'
,
type
=
str
,
...
...
benchmark/fluid/models/mnist.py
浏览文件 @
95141467
...
...
@@ -69,15 +69,30 @@ def get_model(args):
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
DTYPE
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
# Train program
predict
=
cnn_model
(
images
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Evaluator
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size_tensor
)
if
args
.
device
==
'CPU'
and
args
.
cpus
>
1
:
places
=
fluid
.
layers
.
get_places
(
args
.
cpus
)
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
predict
=
cnn_model
(
pd
.
read_input
(
images
))
label
=
pd
.
read_input
(
label
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
pd
.
write_output
(
avg_cost
)
pd
.
write_output
(
batch_acc
)
avg_cost
,
batch_acc
=
pd
()
avg_cost
=
fluid
.
layers
.
mean
(
avg_cost
)
batch_acc
=
fluid
.
layers
.
mean
(
batch_acc
)
else
:
# Train program
predict
=
cnn_model
(
images
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
# Evaluator
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
# inference program
inference_program
=
fluid
.
default_main_program
().
clone
()
...
...
benchmark/fluid/models/resnet.py
浏览文件 @
95141467
...
...
@@ -132,18 +132,33 @@ def get_model(args):
input
=
fluid
.
layers
.
data
(
name
=
'data'
,
shape
=
dshape
,
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
predict
=
model
(
input
,
class_dim
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
,
total
=
batch_size_tensor
)
if
args
.
device
==
'CPU'
and
args
.
cpus
>
1
:
places
=
fluid
.
layers
.
get_places
(
args
.
cpus
)
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
predict
=
model
(
pd
.
read_input
(
input
),
class_dim
)
label
=
pd
.
read_input
(
label
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
pd
.
write_output
(
avg_cost
)
pd
.
write_output
(
batch_acc
)
avg_cost
,
batch_acc
=
pd
()
avg_cost
=
fluid
.
layers
.
mean
(
avg_cost
)
batch_acc
=
fluid
.
layers
.
mean
(
batch_acc
)
else
:
predict
=
model
(
input
,
class_dim
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
predict
,
label
=
label
)
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
inference_program
=
fluid
.
io
.
get_inference_program
(
target_vars
=
[
batch_acc
,
batch_size_tensor
])
target_vars
=
[
batch_acc
])
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
0.01
,
momentum
=
0.9
)
...
...
benchmark/fluid/models/stacked_dynamic_lstm.py
浏览文件 @
95141467
...
...
@@ -101,9 +101,8 @@ def get_model(args):
loss
=
fluid
.
layers
.
mean
(
x
=
loss
)
# add acc
batch_size_tensor
=
fluid
.
layers
.
create_tensor
(
dtype
=
'int64'
)
batch_acc
=
fluid
.
layers
.
accuracy
(
input
=
logit
,
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
\
shape
=
[
1
],
dtype
=
'int64'
)
,
total
=
batch_size_tensor
)
shape
=
[
1
],
dtype
=
'int64'
))
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
...
...
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