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体验新版 GitCode,发现更多精彩内容 >>
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e896926b
编写于
6月 05, 2018
作者:
Y
Yancey1989
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add unit test for dist mnist
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d74838bd
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2 changed file
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python/paddle/fluid/tests/unittests/CMakeLists.txt
python/paddle/fluid/tests/unittests/CMakeLists.txt
+1
-0
python/paddle/fluid/tests/unittests/test_dist_mnist.py
python/paddle/fluid/tests/unittests/test_dist_mnist.py
+208
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未找到文件。
python/paddle/fluid/tests/unittests/CMakeLists.txt
浏览文件 @
e896926b
...
...
@@ -52,3 +52,4 @@ py_test_modules(test_dist_train MODULES test_dist_train SERIAL)
# since load cudnn libraries, so we use a longer timeout to make this
# unit test stability.
set_tests_properties
(
test_listen_and_serv_op PROPERTIES TIMEOUT 30
)
set_tests_properties
(
test_dist_mnist PROPERTIES TIMEOUT 60
)
python/paddle/fluid/tests/unittests/test_dist_mnist.py
0 → 100644
浏览文件 @
e896926b
# Copyright (c) 2018 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
numpy
as
np
import
argparse
import
time
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.profiler
as
profiler
from
paddle.fluid
import
core
import
unittest
from
multiprocessing
import
Process
import
os
import
signal
SEED
=
1
DTYPE
=
"float32"
# random seed must set before configuring the network.
# fluid.default_startup_program().random_seed = SEED
def
cnn_model
(
data
):
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
data
,
filter_size
=
5
,
num_filters
=
20
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
conv_pool_2
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
conv_pool_1
,
filter_size
=
5
,
num_filters
=
50
,
pool_size
=
2
,
pool_stride
=
2
,
act
=
"relu"
)
# TODO(dzhwinter) : refine the initializer and random seed settting
SIZE
=
10
input_shape
=
conv_pool_2
.
shape
param_shape
=
[
reduce
(
lambda
a
,
b
:
a
*
b
,
input_shape
[
1
:],
1
)]
+
[
SIZE
]
scale
=
(
2.0
/
(
param_shape
[
0
]
**
2
*
SIZE
))
**
0.5
predict
=
fluid
.
layers
.
fc
(
input
=
conv_pool_2
,
size
=
SIZE
,
act
=
"softmax"
,
param_attr
=
fluid
.
param_attr
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NormalInitializer
(
loc
=
0.0
,
scale
=
scale
)))
return
predict
def
get_model
(
batch_size
):
# Input data
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
)
inference_program
=
fluid
.
default_main_program
().
clone
()
# Optimization
opt
=
fluid
.
optimizer
.
AdamOptimizer
(
learning_rate
=
0.001
,
beta1
=
0.9
,
beta2
=
0.999
)
# Reader
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
batch_size
)
opt
.
minimize
(
avg_cost
)
return
inference_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
def
get_transpiler
(
trainer_id
,
main_program
,
pserver_endpoints
,
trainers
):
t
=
fluid
.
DistributeTranspiler
()
t
.
transpile
(
trainer_id
=
trainer_id
,
program
=
main_program
,
pservers
=
pserver_endpoints
,
trainers
=
trainers
)
return
t
def
run_pserver
(
pserver_endpoints
,
trainers
,
current_endpoint
):
get_model
(
batch_size
=
20
)
t
=
get_transpiler
(
0
,
fluid
.
default_main_program
(),
pserver_endpoints
,
trainers
)
pserver_prog
=
t
.
get_pserver_program
(
current_endpoint
)
startup_prog
=
t
.
get_startup_program
(
current_endpoint
,
pserver_prog
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup_prog
)
exe
.
run
(
pserver_prog
)
class
TestDistMnist
(
unittest
.
TestCase
):
def
setUp
(
self
):
self
.
_trainers
=
1
self
.
_pservers
=
1
self
.
_ps_endpoints
=
"127.0.0.1:9123"
def
start_pserver
(
self
,
endpoint
):
p
=
Process
(
target
=
run_pserver
,
args
=
(
self
.
_ps_endpoints
,
self
.
_trainers
,
endpoint
))
p
.
start
()
return
p
.
pid
def
_wait_ps_ready
(
self
,
pid
):
retry_times
=
5
while
True
:
assert
retry_times
>=
0
,
"wait ps ready failed"
time
.
sleep
(
1
)
try
:
# the listen_and_serv_op would touch a file which contains the listen port
# on the /tmp directory until it was ready to process all the RPC call.
os
.
stat
(
"/tmp/paddle.%d.port"
%
pid
)
return
except
os
.
error
:
retry_times
-=
1
def
stop_pserver
(
self
,
pid
):
os
.
kill
(
pid
,
signal
.
SIGTERM
)
def
test_with_place
(
self
):
p
=
fluid
.
CUDAPlace
()
if
core
.
is_compiled_with_cuda
(
)
else
fluid
.
CPUPlace
()
pserver_pid
=
self
.
start_pserver
(
self
.
_ps_endpoints
)
self
.
_wait_ps_ready
(
pserver_pid
)
self
.
run_trainer
(
p
,
0
)
self
.
stop_pserver
(
pserver_pid
)
def
run_trainer
(
self
,
place
,
trainer_id
):
test_program
,
avg_cost
,
train_reader
,
test_reader
,
batch_acc
,
predict
=
get_model
(
batch_size
=
20
)
t
=
get_transpiler
(
trainer_id
,
fluid
.
default_main_program
(),
self
.
_ps_endpoints
,
self
.
_trainers
)
trainer_prog
=
t
.
get_trainer_program
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
feed_var_list
=
[
var
for
var
in
trainer_prog
.
global_block
().
vars
.
itervalues
()
if
var
.
is_data
]
feeder
=
fluid
.
DataFeeder
(
feed_var_list
,
place
)
for
pass_id
in
xrange
(
10
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
exe
.
run
(
trainer_prog
,
feed
=
feeder
.
feed
(
data
))
if
(
batch_id
+
1
)
%
10
==
0
:
acc_set
=
[]
avg_loss_set
=
[]
for
test_data
in
test_reader
():
acc_np
,
avg_loss_np
=
exe
.
run
(
program
=
test_program
,
feed
=
feeder
.
feed
(
test_data
),
fetch_list
=
[
batch_acc
,
avg_cost
])
acc_set
.
append
(
float
(
acc_np
))
avg_loss_set
.
append
(
float
(
avg_loss_np
))
# get test acc and loss
acc_val
=
np
.
array
(
acc_set
).
mean
()
avg_loss_val
=
np
.
array
(
avg_loss_set
).
mean
()
if
float
(
acc_val
)
>
0.2
:
# Smaller value to increase CI speed
return
else
:
print
(
'PassID {0:1}, BatchID {1:04}, Test Loss {2:2.2}, Acc {3:2.2}'
.
format
(
pass_id
,
batch_id
+
1
,
float
(
avg_loss_val
),
float
(
acc_val
)))
if
math
.
isnan
(
float
(
avg_loss_val
)):
assert
(
"got Nan loss, training failed."
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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