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54bd17fe
编写于
6年前
作者:
Y
Yu Yang
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Complete Flowers
上级
50e7e25d
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
144 addition
and
4 deletion
+144
-4
paddle/fluid/framework/details/op_handle_base.cc
paddle/fluid/framework/details/op_handle_base.cc
+7
-1
paddle/fluid/framework/details/ssa_graph_builder.cc
paddle/fluid/framework/details/ssa_graph_builder.cc
+1
-1
python/paddle/fluid/tests/unittests/.gitignore
python/paddle/fluid/tests/unittests/.gitignore
+1
-0
python/paddle/fluid/tests/unittests/test_parallel_executor.py
...on/paddle/fluid/tests/unittests/test_parallel_executor.py
+135
-2
未找到文件。
paddle/fluid/framework/details/op_handle_base.cc
浏览文件 @
54bd17fe
...
@@ -31,7 +31,13 @@ std::string OpHandleBase::DebugString() const {
...
@@ -31,7 +31,13 @@ std::string OpHandleBase::DebugString() const {
return
ss
.
str
();
return
ss
.
str
();
}
}
OpHandleBase
::~
OpHandleBase
()
{}
OpHandleBase
::~
OpHandleBase
()
{
#ifdef PADDLE_WITH_CUDA
for
(
auto
&
ev
:
events_
)
{
cudaEventDestroy
(
ev
.
second
);
}
#endif
}
void
OpHandleBase
::
Run
(
bool
use_event
)
{
void
OpHandleBase
::
Run
(
bool
use_event
)
{
#ifdef PADDLE_WITH_CUDA
#ifdef PADDLE_WITH_CUDA
...
...
This diff is collapsed.
Click to expand it.
paddle/fluid/framework/details/ssa_graph_builder.cc
浏览文件 @
54bd17fe
...
@@ -21,7 +21,7 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(SSAGraph *graph) {
...
@@ -21,7 +21,7 @@ void SSAGraphBuilder::PolishGraphToSupportDataHazards(SSAGraph *graph) {
for
(
auto
&
var_map
:
graph
->
vars_
)
{
for
(
auto
&
var_map
:
graph
->
vars_
)
{
for
(
auto
&
name_pair
:
var_map
)
{
for
(
auto
&
name_pair
:
var_map
)
{
if
(
name_pair
.
second
.
size
()
<=
1
)
{
if
(
name_pair
.
second
.
size
()
<=
1
)
{
return
;
continue
;
}
}
auto
it_new
=
name_pair
.
second
.
rbegin
();
auto
it_new
=
name_pair
.
second
.
rbegin
();
auto
it_old
=
name_pair
.
second
.
rbegin
();
auto
it_old
=
name_pair
.
second
.
rbegin
();
...
...
This diff is collapsed.
Click to expand it.
python/paddle/fluid/tests/unittests/.gitignore
浏览文件 @
54bd17fe
...
@@ -2,3 +2,4 @@ mnist.recordio
...
@@ -2,3 +2,4 @@ mnist.recordio
mnist_0.recordio
mnist_0.recordio
mnist_1.recordio
mnist_1.recordio
mnist_2.recordio
mnist_2.recordio
flowers.recordio
This diff is collapsed.
Click to expand it.
python/paddle/fluid/tests/unittests/test_parallel_executor.py
浏览文件 @
54bd17fe
...
@@ -16,6 +16,7 @@ import unittest
...
@@ -16,6 +16,7 @@ import unittest
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
import
paddle.v2
as
paddle
import
paddle.v2
as
paddle
import
paddle.v2.dataset.mnist
as
mnist
import
paddle.v2.dataset.mnist
as
mnist
import
paddle.v2.dataset.flowers
as
flowers
import
numpy
import
numpy
...
@@ -64,6 +65,119 @@ def fc_with_batchnorm():
...
@@ -64,6 +65,119 @@ def fc_with_batchnorm():
return
loss
return
loss
def
squeeze_excitation
(
input
,
num_channels
,
reduction_ratio
):
# pool = fluid.layers.pool2d(
# input=input, pool_size=0, pool_type='avg', global_pooling=True)
conv
=
input
shape
=
conv
.
shape
reshape
=
fluid
.
layers
.
reshape
(
x
=
conv
,
shape
=
[
-
1
,
shape
[
1
],
shape
[
2
]
*
shape
[
3
]])
pool
=
fluid
.
layers
.
reduce_mean
(
input
=
reshape
,
dim
=
2
)
squeeze
=
fluid
.
layers
.
fc
(
input
=
pool
,
size
=
num_channels
/
reduction_ratio
,
act
=
'relu'
)
excitation
=
fluid
.
layers
.
fc
(
input
=
squeeze
,
size
=
num_channels
,
act
=
'sigmoid'
)
scale
=
fluid
.
layers
.
elementwise_mul
(
x
=
input
,
y
=
excitation
,
axis
=
0
)
return
scale
def
conv_bn_layer
(
input
,
num_filters
,
filter_size
,
stride
=
1
,
groups
=
1
,
act
=
None
):
conv
=
fluid
.
layers
.
conv2d
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
filter_size
,
stride
=
stride
,
padding
=
(
filter_size
-
1
)
/
2
,
groups
=
groups
,
act
=
None
,
bias_attr
=
False
)
return
fluid
.
layers
.
batch_norm
(
input
=
conv
,
act
=
act
,
momentum
=
0.1
)
def
shortcut
(
input
,
ch_out
,
stride
):
ch_in
=
input
.
shape
[
1
]
if
ch_in
!=
ch_out
:
if
stride
==
1
:
filter_size
=
1
else
:
filter_size
=
3
return
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
)
else
:
return
input
def
bottleneck_block
(
input
,
num_filters
,
stride
,
cardinality
,
reduction_ratio
):
# The number of first 1x1 convolutional channels for each bottleneck build block
# was halved to reduce the compution cost.
conv0
=
conv_bn_layer
(
input
=
input
,
num_filters
=
num_filters
,
filter_size
=
1
,
act
=
'relu'
)
conv1
=
conv_bn_layer
(
input
=
conv0
,
num_filters
=
num_filters
*
2
,
filter_size
=
3
,
stride
=
stride
,
groups
=
cardinality
,
act
=
'relu'
)
conv2
=
conv_bn_layer
(
input
=
conv1
,
num_filters
=
num_filters
*
2
,
filter_size
=
1
,
act
=
None
)
scale
=
squeeze_excitation
(
input
=
conv2
,
num_channels
=
num_filters
*
2
,
reduction_ratio
=
reduction_ratio
)
short
=
shortcut
(
input
,
num_filters
*
2
,
stride
)
return
fluid
.
layers
.
elementwise_add
(
x
=
short
,
y
=
scale
,
act
=
'relu'
)
def
SE_ResNeXt152
():
reader
=
fluid
.
layers
.
open_recordio_file
(
filename
=
'./flowers.recordio'
,
shapes
=
[[
-
1
,
3
,
224
,
224
],
[
-
1
,
1
]],
lod_levels
=
[
0
,
0
],
dtypes
=
[
'float32'
,
'int64'
])
img
,
label
=
fluid
.
layers
.
read_file
(
reader
)
conv
=
conv_bn_layer
(
input
=
img
,
num_filters
=
64
,
filter_size
=
3
,
stride
=
2
,
act
=
'relu'
)
conv
=
conv_bn_layer
(
input
=
conv
,
num_filters
=
64
,
filter_size
=
3
,
stride
=
1
,
act
=
'relu'
)
conv
=
conv_bn_layer
(
input
=
conv
,
num_filters
=
128
,
filter_size
=
3
,
stride
=
1
,
act
=
'relu'
)
conv
=
fluid
.
layers
.
pool2d
(
input
=
conv
,
pool_size
=
3
,
pool_stride
=
2
,
pool_padding
=
1
,
pool_type
=
'max'
)
cardinality
=
64
reduction_ratio
=
16
depth
=
[
3
,
8
,
36
,
3
]
num_filters
=
[
128
,
256
,
512
,
1024
]
for
block
in
range
(
len
(
depth
)):
for
i
in
range
(
depth
[
block
]):
conv
=
bottleneck_block
(
input
=
conv
,
num_filters
=
num_filters
[
block
],
stride
=
2
if
i
==
0
and
block
!=
0
else
1
,
cardinality
=
cardinality
,
reduction_ratio
=
reduction_ratio
)
shape
=
conv
.
shape
reshape
=
fluid
.
layers
.
reshape
(
x
=
conv
,
shape
=
[
-
1
,
shape
[
1
],
shape
[
2
]
*
shape
[
3
]])
pool
=
fluid
.
layers
.
reduce_mean
(
input
=
reshape
,
dim
=
2
)
dropout
=
fluid
.
layers
.
dropout
(
x
=
pool
,
dropout_prob
=
0.2
)
# Classifier layer:
prediction
=
fluid
.
layers
.
fc
(
input
=
dropout
,
size
=
1000
,
act
=
'softmax'
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
loss
=
fluid
.
layers
.
mean
(
loss
)
return
loss
class
ParallelExecutor
(
unittest
.
TestCase
):
class
ParallelExecutor
(
unittest
.
TestCase
):
@
classmethod
@
classmethod
def
setUpClass
(
cls
):
def
setUpClass
(
cls
):
...
@@ -81,24 +195,40 @@ class ParallelExecutor(unittest.TestCase):
...
@@ -81,24 +195,40 @@ class ParallelExecutor(unittest.TestCase):
fluid
.
recordio_writer
.
convert_reader_to_recordio_file
(
fluid
.
recordio_writer
.
convert_reader_to_recordio_file
(
'./mnist.recordio'
,
reader
,
feeder
)
'./mnist.recordio'
,
reader
,
feeder
)
with
fluid
.
program_guard
(
fluid
.
Program
(),
fluid
.
Program
()):
reader
=
paddle
.
batch
(
flowers
.
train
(),
batch_size
=
4
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
[
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
3
,
224
,
224
]),
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
),
],
place
=
fluid
.
CPUPlace
())
fluid
.
recordio_writer
.
convert_reader_to_recordio_file
(
"./flowers.recordio"
,
reader
,
feeder
)
def
test_simple_fc
(
self
):
def
test_simple_fc
(
self
):
self
.
check_network_convergence
(
simple_fc_net
)
self
.
check_network_convergence
(
simple_fc_net
)
def
test_batchnorm_fc
(
self
):
def
test_batchnorm_fc
(
self
):
self
.
check_network_convergence
(
fc_with_batchnorm
)
self
.
check_network_convergence
(
fc_with_batchnorm
)
def
check_network_convergence
(
self
,
method
):
def
check_network_convergence
(
self
,
method
,
memory_opt
=
True
,
iter
=
10
):
main
=
fluid
.
Program
()
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
method
()
loss
=
method
()
adam
=
fluid
.
optimizer
.
Adam
()
adam
=
fluid
.
optimizer
.
Adam
()
adam
.
minimize
(
loss
)
adam
.
minimize
(
loss
)
if
memory_opt
:
fluid
.
memory_optimize
(
main
)
exe
=
fluid
.
ParallelExecutor
(
loss_name
=
loss
.
name
,
use_cuda
=
True
)
exe
=
fluid
.
ParallelExecutor
(
loss_name
=
loss
.
name
,
use_cuda
=
True
)
first_loss
,
=
exe
.
run
([
loss
.
name
])
first_loss
,
=
exe
.
run
([
loss
.
name
])
first_loss
=
numpy
.
array
(
first_loss
)
first_loss
=
numpy
.
array
(
first_loss
)
for
i
in
xrange
(
10
):
for
i
in
xrange
(
iter
):
exe
.
run
([])
exe
.
run
([])
last_loss
,
=
exe
.
run
([
loss
.
name
])
last_loss
,
=
exe
.
run
([
loss
.
name
])
...
@@ -106,3 +236,6 @@ class ParallelExecutor(unittest.TestCase):
...
@@ -106,3 +236,6 @@ class ParallelExecutor(unittest.TestCase):
print
first_loss
,
last_loss
print
first_loss
,
last_loss
self
.
assertGreater
(
first_loss
[
0
],
last_loss
[
0
])
self
.
assertGreater
(
first_loss
[
0
],
last_loss
[
0
])
def
test_resnet
(
self
):
self
.
check_network_convergence
(
SE_ResNeXt152
,
iter
=
20
)
This diff is collapsed.
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