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体验新版 GitCode,发现更多精彩内容 >>
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b8d1f503
编写于
3月 11, 2019
作者:
Z
Zhen Wang
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 the executor test for the graph clone API. test=develop
上级
ac6ef06f
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
70 addition
and
38 deletion
+70
-38
python/paddle/fluid/contrib/slim/tests/test_graph.py
python/paddle/fluid/contrib/slim/tests/test_graph.py
+70
-38
未找到文件。
python/paddle/fluid/contrib/slim/tests/test_graph.py
浏览文件 @
b8d1f503
...
...
@@ -13,59 +13,92 @@
# limitations under the license.
from
__future__
import
print_function
import
os
import
six
import
unittest
import
paddle
import
paddle.fluid
as
fluid
import
six
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid
import
core
os
.
environ
[
"CUDA_VISIBLE_DEVICES"
]
=
"0"
os
.
environ
[
"CPU_NUM"
]
=
"1"
def
residual_block
(
num
):
def
conv_bn_layer
(
input
,
ch_out
,
filter_size
,
stride
,
padding
,
act
=
'relu'
,
bias_attr
=
False
):
tmp
=
fluid
.
layers
.
conv2d
(
input
=
input
,
filter_size
=
filter_size
,
num_filters
=
ch_out
,
stride
=
stride
,
padding
=
padding
,
act
=
None
,
bias_attr
=
bias_attr
)
return
fluid
.
layers
.
batch_norm
(
input
=
tmp
,
act
=
act
)
data
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
1
,
32
,
32
],
dtype
=
'float32'
)
def
conv_block
():
img
=
fluid
.
layers
.
data
(
name
=
'image'
,
shape
=
[
1
,
28
,
28
],
dtype
=
'float32'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
hidden
=
data
for
_
in
six
.
moves
.
xrange
(
num
):
conv
=
conv_bn_layer
(
hidden
,
16
,
3
,
1
,
1
,
act
=
None
,
bias_attr
=
True
)
short
=
conv_bn_layer
(
hidden
,
16
,
1
,
1
,
0
,
act
=
None
)
hidden
=
fluid
.
layers
.
elementwise_add
(
x
=
conv
,
y
=
short
,
act
=
'relu'
)
fc
=
fluid
.
layers
.
fc
(
input
=
hidden
,
size
=
10
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
fc
,
label
=
label
)
loss
=
fluid
.
layers
.
mean
(
loss
)
return
loss
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_1
=
fluid
.
layers
.
batch_norm
(
conv_pool_1
)
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"
)
prediction
=
fluid
.
layers
.
fc
(
input
=
conv_pool_2
,
size
=
10
,
act
=
'softmax'
)
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
prediction
,
label
=
label
)
avg_loss
=
fluid
.
layers
.
mean
(
loss
)
return
[
img
,
label
],
avg_loss
class
TestGraph
(
unittest
.
TestCase
):
def
test_graph_functions
(
self
,
for_ci
=
True
):
def
graph_apis
(
self
,
use_cuda
=
False
,
for_ci
=
True
):
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
residual_block
(
2
)
feeds
,
loss
=
conv_block
(
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
backup_graph
=
graph
.
clone
()
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
len
(
backup_graph
.
all_nodes
()))
build_strategy
=
fluid
.
BuildStrategy
()
build_strategy
.
memory_optimize
=
False
build_strategy
.
enable_inplace
=
False
origin_binary
=
fluid
.
CompiledProgram
(
graph
.
graph
).
with_data_parallel
(
loss_name
=
loss
.
name
,
build_strategy
=
build_strategy
)
backup_binary
=
fluid
.
CompiledProgram
(
backup_graph
.
graph
).
with_data_parallel
(
loss_name
=
loss
.
name
,
build_strategy
=
build_strategy
)
place
=
fluid
.
CUDAPlace
(
0
)
if
use_cuda
else
fluid
.
CPUPlace
()
exe
=
fluid
.
Executor
(
place
)
exe
.
run
(
startup
)
iters
=
5
batch_size
=
8
train_reader
=
paddle
.
batch
(
paddle
.
dataset
.
mnist
.
train
(),
batch_size
=
batch_size
)
feeder
=
fluid
.
DataFeeder
(
feed_list
=
feeds
,
place
=
place
)
def
train
(
binary
):
for
_
in
range
(
iters
):
data
=
next
(
train_reader
())
loss_v
=
exe
.
run
(
binary
,
feed
=
feeder
.
feed
(
data
),
fetch_list
=
[
loss
.
name
])
print
(
'{}: {}'
.
format
(
'loss'
,
loss_v
))
train
(
origin_binary
)
train
(
backup_binary
)
marked_nodes
=
set
()
for
op
in
graph
.
all_op_nodes
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
marked_nodes
.
add
(
op
)
if
not
for_ci
:
graph
.
draw
(
'.'
,
'residual'
,
marked_nodes
)
backup_marked_nodes
=
set
()
for
op
in
backup_graph
.
all_op_nodes
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
backup_marked_nodes
.
add
(
op
)
backup_graph
.
draw
(
'.'
,
'backup'
,
backup_marked_nodes
)
self
.
assertFalse
(
graph
.
has_circle
())
self
.
assertEqual
(
graph
.
graph_num
(),
1
)
nodes
=
graph
.
topology_sort
()
...
...
@@ -75,14 +108,13 @@ class TestGraph(unittest.TestCase):
nodes_num
=
len
(
graph
.
all_nodes
())
graph
.
safe_remove_nodes
(
marked_nodes
)
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
nodes_num
-
len
(
marked_nodes
))
backup_graph
=
graph
.
clone
()
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
len
(
backup_graph
.
all_nodes
()))
if
not
for_ci
:
backup_marked_nodes
=
set
()
for
op
in
backup_graph
.
all_op_nodes
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
backup_marked_nodes
.
add
(
op
)
backup_graph
.
draw
(
'.'
,
'backup'
,
backup_marked_nodes
)
def
test_graph_apis_cpu
(
self
):
self
.
graph_apis
(
use_cuda
=
False
,
for_ci
=
True
)
def
test_graph_apis_cuda
(
self
):
if
fluid
.
core
.
is_compiled_with_cuda
():
self
.
graph_apis
(
use_cuda
=
True
,
for_ci
=
True
)
if
__name__
==
'__main__'
:
...
...
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