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b8d1f503
编写于
5年前
作者:
Z
Zhen Wang
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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
无相关合并请求
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1
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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 @@
...
@@ -13,59 +13,92 @@
# limitations under the license.
# limitations under the license.
from
__future__
import
print_function
from
__future__
import
print_function
import
os
import
six
import
unittest
import
unittest
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid
as
fluid
import
six
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid.framework
import
IrGraph
from
paddle.fluid
import
core
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'
)
label
=
fluid
.
layers
.
data
(
name
=
'label'
,
shape
=
[
1
],
dtype
=
'int64'
)
hidden
=
data
conv_pool_1
=
fluid
.
nets
.
simple_img_conv_pool
(
for
_
in
six
.
moves
.
xrange
(
num
):
input
=
img
,
conv
=
conv_bn_layer
(
hidden
,
16
,
3
,
1
,
1
,
act
=
None
,
bias_attr
=
True
)
filter_size
=
5
,
short
=
conv_bn_layer
(
hidden
,
16
,
1
,
1
,
0
,
act
=
None
)
num_filters
=
20
,
hidden
=
fluid
.
layers
.
elementwise_add
(
x
=
conv
,
y
=
short
,
act
=
'relu'
)
pool_size
=
2
,
fc
=
fluid
.
layers
.
fc
(
input
=
hidden
,
size
=
10
)
pool_stride
=
2
,
loss
=
fluid
.
layers
.
cross_entropy
(
input
=
fc
,
label
=
label
)
act
=
"relu"
)
loss
=
fluid
.
layers
.
mean
(
loss
)
conv_pool_1
=
fluid
.
layers
.
batch_norm
(
conv_pool_1
)
return
loss
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
):
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
()
main
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
startup
=
fluid
.
Program
()
with
fluid
.
program_guard
(
main
,
startup
):
with
fluid
.
program_guard
(
main
,
startup
):
loss
=
residual_block
(
2
)
feeds
,
loss
=
conv_block
(
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
=
fluid
.
optimizer
.
Adam
(
learning_rate
=
0.001
)
opt
.
minimize
(
loss
)
opt
.
minimize
(
loss
)
graph
=
IrGraph
(
core
.
Graph
(
main
.
desc
),
for_test
=
False
)
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
()
marked_nodes
=
set
()
for
op
in
graph
.
all_op_nodes
():
for
op
in
graph
.
all_op_nodes
():
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
marked_nodes
.
add
(
op
)
marked_nodes
.
add
(
op
)
if
not
for_ci
:
if
not
for_ci
:
graph
.
draw
(
'.'
,
'residual'
,
marked_nodes
)
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
.
assertFalse
(
graph
.
has_circle
())
self
.
assertEqual
(
graph
.
graph_num
(),
1
)
self
.
assertEqual
(
graph
.
graph_num
(),
1
)
nodes
=
graph
.
topology_sort
()
nodes
=
graph
.
topology_sort
()
...
@@ -75,14 +108,13 @@ class TestGraph(unittest.TestCase):
...
@@ -75,14 +108,13 @@ class TestGraph(unittest.TestCase):
nodes_num
=
len
(
graph
.
all_nodes
())
nodes_num
=
len
(
graph
.
all_nodes
())
graph
.
safe_remove_nodes
(
marked_nodes
)
graph
.
safe_remove_nodes
(
marked_nodes
)
self
.
assertEqual
(
len
(
graph
.
all_nodes
()),
nodes_num
-
len
(
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
()))
def
test_graph_apis_cpu
(
self
):
if
not
for_ci
:
self
.
graph_apis
(
use_cuda
=
False
,
for_ci
=
True
)
backup_marked_nodes
=
set
()
for
op
in
backup_graph
.
all_op_nodes
():
def
test_graph_apis_cuda
(
self
):
if
op
.
name
().
find
(
'conv2d'
)
>
-
1
:
if
fluid
.
core
.
is_compiled_with_cuda
():
backup_marked_nodes
.
add
(
op
)
self
.
graph_apis
(
use_cuda
=
True
,
for_ci
=
True
)
backup_graph
.
draw
(
'.'
,
'backup'
,
backup_marked_nodes
)
if
__name__
==
'__main__'
:
if
__name__
==
'__main__'
:
...
...
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