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体验新版 GitCode,发现更多精彩内容 >>
提交
a06205f5
编写于
1月 24, 2018
作者:
Y
Yang Yu
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Add demo for parallel.do
Unify the recognize_digits
上级
06a97957
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
208 addition
and
176 deletion
+208
-176
paddle/operators/parallel_do_op.cc
paddle/operators/parallel_do_op.cc
+30
-5
python/paddle/v2/fluid/tests/book/CMakeLists.txt
python/paddle/v2/fluid/tests/book/CMakeLists.txt
+25
-1
python/paddle/v2/fluid/tests/book/__init__.py
python/paddle/v2/fluid/tests/book/__init__.py
+13
-0
python/paddle/v2/fluid/tests/book/test_recognize_digits.py
python/paddle/v2/fluid/tests/book/test_recognize_digits.py
+140
-0
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
.../paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
+0
-74
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
...n/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
+0
-96
未找到文件。
paddle/operators/parallel_do_op.cc
浏览文件 @
a06205f5
...
...
@@ -17,6 +17,7 @@ limitations under the License. */
#include "paddle/framework/executor.h"
#include "paddle/framework/op_registry.h"
#include "paddle/framework/threadpool.h"
#include "paddle/operators/detail/safe_ref.h"
namespace
paddle
{
namespace
operators
{
...
...
@@ -39,8 +40,10 @@ static void SplitTensorAndMoveTensorToScopes(
const
std
::
vector
<
std
::
string
>
&
names
)
{
size_t
num_sub_scopes
=
0
;
for
(
auto
&
argu
:
names
)
{
auto
*
var
=
scope
.
FindVar
(
argu
);
const
auto
&
tensor
=
var
->
Get
<
LoDTensor
>
();
const
auto
&
tensor
=
detail
::
Ref
(
scope
.
FindVar
(
argu
),
"Cannot find variable %s in the parent scope"
,
argu
)
.
Get
<
LoDTensor
>
();
auto
lod_tensors
=
tensor
.
SplitLoDTensor
(
places
);
for
(
auto
&
lod
:
lod_tensors
)
{
...
...
@@ -60,7 +63,9 @@ static void SplitTensorAndMoveTensorToScopes(
}
for
(
size_t
i
=
0
;
i
<
lod_tensors
.
size
();
++
i
)
{
*
(
*
sub_scopes
)[
i
]
->
Var
(
argu
)
->
GetMutable
<
LoDTensor
>
()
=
lod_tensors
[
i
];
*
detail
::
Ref
(
sub_scopes
->
at
(
i
)
->
Var
(
argu
),
"Cannot find variable in the sub-scope"
,
argu
)
.
GetMutable
<
LoDTensor
>
()
=
lod_tensors
[
i
];
}
}
}
...
...
@@ -287,6 +292,17 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
this
->
InputGrad
(
input_param
,
false
));
}
}
auto
*
g_block
=
this
->
grad_block_
[
0
];
// All variable name that needed by gradient operators
std
::
unordered_set
<
std
::
string
>
all_inputs_in_grad_blocks
;
for
(
size_t
i
=
0
;
i
<
g_block
->
OpSize
();
++
i
)
{
auto
*
op
=
g_block
->
Op
(
i
);
for
(
auto
&
var_name
:
op
->
InputArgumentNames
())
{
all_inputs_in_grad_blocks
.
insert
(
var_name
);
}
}
for
(
auto
&
output_param
:
this
->
OutputNames
())
{
if
(
output_param
==
kParallelScopes
)
{
...
...
@@ -295,8 +311,17 @@ class ParallelDoGradOpDescMaker : public framework::SingleGradOpDescMaker {
this
->
Output
(
output_param
));
}
else
{
grad
->
SetInput
(
output_param
,
this
->
Output
(
output_param
));
grad
->
SetInput
(
framework
::
GradVarName
(
output_param
),
this
->
OutputGrad
(
output_param
));
std
::
vector
<
std
::
string
>
og_names
;
for
(
auto
&
og_name
:
this
->
OutputGrad
(
output_param
))
{
if
(
all_inputs_in_grad_blocks
.
count
(
og_name
)
!=
0
)
{
// there is some gradient operator needs the og, make this og as the
// input of parallel.do
// if there is no operator need this og, just do not make this og as
// input.
og_names
.
push_back
(
og_name
);
}
}
grad
->
SetInput
(
framework
::
GradVarName
(
output_param
),
og_names
);
}
}
grad
->
SetAttrMap
(
this
->
Attrs
());
...
...
python/paddle/v2/fluid/tests/book/CMakeLists.txt
浏览文件 @
a06205f5
file
(
GLOB TEST_OPS RELATIVE
"
${
CMAKE_CURRENT_SOURCE_DIR
}
"
"test_*.py"
)
string
(
REPLACE
".py"
""
TEST_OPS
"
${
TEST_OPS
}
"
)
list
(
REMOVE_ITEM TEST_OPS test_image_classification_train
)
list
(
REMOVE_ITEM TEST_OPS test_image_classification_train
test_recognize_digits
)
py_test
(
test_image_classification_train_resnet SRCS test_image_classification_train.py ARGS resnet
)
py_test
(
test_image_classification_train_vgg SRCS test_image_classification_train.py ARGS vgg
)
py_test
(
test_recognize_digits_mlp_cpu
SRCS test_recognize_digits.py
ARGS mlp
)
py_test
(
test_recognize_digits_mlp_cuda
SRCS test_recognize_digits.py
ARGS mlp --use_cuda
)
py_test
(
test_recognize_digits_conv_cpu
SRCS test_recognize_digits.py
ARGS conv
)
py_test
(
test_recognize_digits_conv_cuda
SRCS test_recognize_digits.py
ARGS conv --use_cuda
)
py_test
(
test_recognize_digits_mlp_cpu_parallel
SRCS test_recognize_digits.py
ARGS mlp --parallel
)
py_test
(
test_recognize_digits_mlp_cuda_parallel
SRCS test_recognize_digits.py
ARGS mlp --use_cuda --parallel
)
py_test
(
test_recognize_digits_conv_cpu_parallel
SRCS test_recognize_digits.py
ARGS conv --parallel
)
py_test
(
test_recognize_digits_conv_cuda_parallel
SRCS test_recognize_digits.py
ARGS conv --use_cuda --parallel
)
# default test
foreach
(
src
${
TEST_OPS
}
)
...
...
python/paddle/v2/fluid/tests/book/
test_fit_a_line_parallel_do
.py
→
python/paddle/v2/fluid/tests/book/
__init__
.py
浏览文件 @
a06205f5
...
...
@@ -11,47 +11,3 @@
# 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
import
paddle.v2.fluid
as
fluid
x
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
13
],
dtype
=
'float32'
)
y
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'float32'
)
places
=
fluid
.
layers
.
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
=
places
)
with
pd
.
do
():
x_
=
pd
.
read_input
(
x
)
y_
=
pd
.
read_input
(
y
)
y_predict
=
fluid
.
layers
.
fc
(
input
=
x_
,
size
=
1
,
act
=
None
)
cost
=
fluid
.
layers
.
square_error_cost
(
input
=
y_predict
,
label
=
y_
)
pd
.
write_output
(
fluid
.
layers
.
mean
(
x
=
cost
))
avg_cost
=
fluid
.
layers
.
mean
(
x
=
pd
())
sgd_optimizer
=
fluid
.
optimizer
.
SGD
(
learning_rate
=
0.001
)
sgd_optimizer
.
minimize
(
avg_cost
)
BATCH_SIZE
=
20
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
uci_housing
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
feeder
=
fluid
.
DataFeeder
(
place
=
place
,
feed_list
=
[
x
,
y
])
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
for
data
in
train_reader
():
avg_loss_value
,
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
])
print
(
avg_loss_value
)
if
avg_loss_value
[
0
]
<
10.0
:
exit
(
0
)
# if avg cost less than 10.0, we think our code is good.
exit
(
1
)
python/paddle/v2/fluid/tests/book/test_recognize_digits.py
0 → 100644
浏览文件 @
a06205f5
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from
__future__
import
print_function
import
argparse
import
paddle.v2.fluid
as
fluid
import
paddle.v2
as
paddle
import
sys
def
parse_arg
():
parser
=
argparse
.
ArgumentParser
()
parser
.
add_argument
(
"nn_type"
,
help
=
"The neural network type, in ['mlp', 'conv']"
,
type
=
str
,
choices
=
[
'mlp'
,
'conv'
])
parser
.
add_argument
(
"--parallel"
,
help
=
'Run in parallel or not'
,
default
=
False
,
action
=
"store_true"
)
parser
.
add_argument
(
"--use_cuda"
,
help
=
"Run the program by using CUDA"
,
default
=
False
,
action
=
"store_true"
)
return
parser
.
parse_args
()
BATCH_SIZE
=
64
def
loss_net
(
hidden
,
label
):
prediction
=
fluid
.
layers
.
fc
(
input
=
hidden
,
size
=
10
,
act
=
'softmax'
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
return
fluid
.
layers
.
mean
(
x
=
loss
),
fluid
.
layers
.
accuracy
(
input
=
prediction
,
label
=
label
)
def
mlp
(
img
,
label
):
hidden
=
fluid
.
layers
.
fc
(
input
=
img
,
size
=
200
,
act
=
'tanh'
)
hidden
=
fluid
.
layers
.
fc
(
input
=
hidden
,
size
=
200
,
act
=
'tanh'
)
return
loss_net
(
hidden
,
label
)
def
conv_net
(
img
,
label
):
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
img
,
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"
)
return
loss_net
(
conv_pool_2
,
label
)
def
main
():
args
=
parse_arg
()
print
(
"recognize digits with args: {0}"
.
format
(
" "
.
join
(
sys
.
argv
[
1
:])))
img
=
fluid
.
layers
.
data
(
name
=
'img'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
if
args
.
nn_type
==
'mlp'
:
net_conf
=
mlp
else
:
net_conf
=
conv_net
if
args
.
parallel
:
places
=
fluid
.
layers
.
get_places
()
pd
=
fluid
.
layers
.
ParallelDo
(
places
)
with
pd
.
do
():
img_
=
pd
.
read_input
(
img
)
label_
=
pd
.
read_input
(
label
)
for
o
in
net_conf
(
img_
,
label_
):
pd
.
write_output
(
o
)
avg_loss
,
acc
=
pd
()
# get mean loss and acc through every devices.
avg_loss
=
fluid
.
layers
.
mean
(
x
=
avg_loss
)
acc
=
fluid
.
layers
.
mean
(
x
=
acc
)
else
:
avg_loss
,
acc
=
net_conf
(
img
,
label
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
optimizer
.
minimize
(
avg_loss
)
place
=
fluid
.
CUDAPlace
(
0
)
if
args
.
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
fluid
.
default_startup_program
())
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
img
,
label
],
place
=
place
)
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
for
batch_id
,
data
in
enumerate
(
train_reader
()):
need_check
=
(
batch_id
+
1
)
%
10
==
0
if
need_check
:
fetch_list
=
[
avg_loss
,
acc
]
else
:
fetch_list
=
[]
outs
=
exe
.
run
(
feed
=
feeder
.
feed
(
data
),
fetch_list
=
fetch_list
)
if
need_check
:
avg_loss_np
,
acc_np
=
outs
if
float
(
acc_np
)
>
0.9
:
exit
(
0
)
else
:
print
(
'PassID {0:1}, BatchID {1:04}, Loss {2:2.2}, Acc {3:2.2}'
.
format
(
pass_id
,
batch_id
+
1
,
float
(
avg_loss_np
),
float
(
acc_np
)))
if
__name__
==
'__main__'
:
main
()
python/paddle/v2/fluid/tests/book/test_recognize_digits_conv.py
已删除
100644 → 0
浏览文件 @
06a97957
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from
__future__
import
print_function
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
images
=
fluid
.
layers
.
data
(
name
=
'pixel'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
input
=
images
,
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"
)
predict
=
fluid
.
layers
.
fc
(
input
=
conv_pool_2
,
size
=
10
,
act
=
"softmax"
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.01
)
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
BATCH_SIZE
=
50
PASS_NUM
=
3
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
500
),
batch_size
=
BATCH_SIZE
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
images
,
label
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
for
data
in
train_reader
():
loss
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"pass_id="
+
str
(
pass_id
)
+
" acc="
+
str
(
acc
)
+
" pass_acc="
+
str
(
pass_acc
))
# print loss, acc
if
loss
<
10.0
and
pass_acc
>
0.9
:
# if avg cost less than 10.0 and accuracy is larger than 0.9, we think our code is good.
exit
(
0
)
pass_acc
=
accuracy
.
eval
(
exe
)
print
(
"pass_id="
+
str
(
pass_id
)
+
" pass_acc="
+
str
(
pass_acc
))
exit
(
1
)
python/paddle/v2/fluid/tests/book/test_recognize_digits_mlp.py
已删除
100644 → 0
浏览文件 @
06a97957
# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
#
# 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.
from
__future__
import
print_function
import
numpy
as
np
import
paddle.v2
as
paddle
import
paddle.v2.fluid
as
fluid
BATCH_SIZE
=
128
image
=
fluid
.
layers
.
data
(
name
=
'x'
,
shape
=
[
784
],
dtype
=
'float32'
)
regularizer
=
fluid
.
regularizer
.
L2Decay
(
0.0005
*
BATCH_SIZE
)
hidden1
=
fluid
.
layers
.
fc
(
input
=
image
,
size
=
128
,
act
=
'relu'
,
param_attr
=
fluid
.
ParamAttr
(
regularizer
=
regularizer
,
gradient_clip
=
fluid
.
clip
.
ClipByValue
(
10
)))
hidden2
=
fluid
.
layers
.
fc
(
input
=
hidden1
,
size
=
64
,
act
=
'relu'
,
param_attr
=
regularizer
)
predict
=
fluid
.
layers
.
fc
(
input
=
hidden2
,
size
=
10
,
act
=
'softmax'
,
param_attr
=
regularizer
)
label
=
fluid
.
layers
.
data
(
name
=
'y'
,
shape
=
[
1
],
dtype
=
'int64'
)
cost
=
fluid
.
layers
.
cross_entropy
(
input
=
predict
,
label
=
label
)
avg_cost
=
fluid
.
layers
.
mean
(
x
=
cost
)
optimizer
=
fluid
.
optimizer
.
Momentum
(
learning_rate
=
0.001
,
momentum
=
0.9
)
opts
=
optimizer
.
minimize
(
avg_cost
)
accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
inference_program
=
fluid
.
default_main_program
().
clone
()
with
fluid
.
program_guard
(
inference_program
):
test_accuracy
=
fluid
.
evaluator
.
Accuracy
(
input
=
predict
,
label
=
label
)
test_target
=
[
avg_cost
]
+
test_accuracy
.
metrics
+
test_accuracy
.
states
inference_program
=
fluid
.
io
.
get_inference_program
(
test_target
)
train_reader
=
paddle
.
batch
(
paddle
.
reader
.
shuffle
(
paddle
.
dataset
.
mnist
.
train
(),
buf_size
=
8192
),
batch_size
=
BATCH_SIZE
)
test_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
test
(),
batch_size
=
128
)
place
=
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
image
,
label
],
place
=
place
)
exe
.
run
(
fluid
.
default_startup_program
())
PASS_NUM
=
100
for
pass_id
in
range
(
PASS_NUM
):
accuracy
.
reset
(
exe
)
for
data
in
train_reader
():
out
,
acc
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
accuracy
.
metrics
)
pass_acc
=
accuracy
.
eval
(
exe
)
test_accuracy
.
reset
(
exe
)
for
data
in
test_reader
():
out
,
acc
=
exe
.
run
(
inference_program
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
avg_cost
]
+
test_accuracy
.
metrics
)
test_pass_acc
=
test_accuracy
.
eval
(
exe
)
print
(
"pass_id="
+
str
(
pass_id
)
+
" train_cost="
+
str
(
out
)
+
" train_acc="
+
str
(
acc
)
+
" train_pass_acc="
+
str
(
pass_acc
)
+
" test_acc="
+
str
(
test_pass_acc
))
if
test_pass_acc
>
0.7
:
fluid
.
io
.
save_inference_model
(
"./recognize_digits_mlp.inference.model/"
,
[
"x"
],
[
predict
],
exe
)
exit
(
0
)
exit
(
1
)
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