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d02e3e7f
编写于
4月 02, 2019
作者:
X
Xin Pan
浏览文件
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电子邮件补丁
差异文件
add a simple test
test=develop
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1ebd7434
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python/paddle/fluid/tests/unittests/test_imperative_basic.py
python/paddle/fluid/tests/unittests/test_imperative_basic.py
+49
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未找到文件。
python/paddle/fluid/tests/unittests/test_imperative_basic.py
浏览文件 @
d02e3e7f
...
...
@@ -348,6 +348,55 @@ class TestImperative(unittest.TestCase):
self
.
assertEqual
(
mlp
.
_fc2
,
sublayers
[
1
])
self
.
assertEqual
(
len
(
sublayers
),
2
)
def
test_dygraph_vs_static
(
self
):
inp1
=
np
.
random
.
rand
(
4
,
3
,
3
)
inp2
=
np
.
random
.
rand
(
4
,
3
,
3
)
# dynamic graph
with
fluid
.
dygraph
.
guard
():
if
np
.
sum
(
inp1
)
<
np
.
sum
(
inp2
):
x
=
fluid
.
layers
.
elementwise_add
(
inp1
,
inp2
)
else
:
x
=
fluid
.
layers
.
elementwise_sub
(
inp1
,
inp2
)
dygraph_result
=
x
.
_numpy
()
# static graph
with
new_program_scope
():
inp_data1
=
fluid
.
layers
.
data
(
name
=
'inp1'
,
shape
=
[
3
,
3
],
dtype
=
np
.
float32
)
inp_data2
=
fluid
.
layers
.
data
(
name
=
'inp2'
,
shape
=
[
3
,
3
],
dtype
=
np
.
float32
)
a
=
fluid
.
layers
.
expand
(
fluid
.
layers
.
reshape
(
fluid
.
layers
.
reduce_sum
(
inp_data1
),
[
1
,
1
]),
[
4
,
1
])
b
=
fluid
.
layers
.
expand
(
fluid
.
layers
.
reshape
(
fluid
.
layers
.
reduce_sum
(
inp_data2
),
[
1
,
1
]),
[
4
,
1
])
cond
=
fluid
.
layers
.
less_than
(
x
=
a
,
y
=
b
)
ie
=
fluid
.
layers
.
IfElse
(
cond
)
with
ie
.
true_block
():
d1
=
ie
.
input
(
inp_data1
)
d2
=
ie
.
input
(
inp_data2
)
d3
=
fluid
.
layers
.
elementwise_add
(
d1
,
d2
)
ie
.
output
(
d3
)
with
ie
.
false_block
():
d1
=
ie
.
input
(
inp_data1
)
d2
=
ie
.
input
(
inp_data2
)
d3
=
fluid
.
layers
.
elementwise_sub
(
d1
,
d2
)
ie
.
output
(
d3
)
out
=
ie
()
exe
=
fluid
.
Executor
(
fluid
.
CPUPlace
(
)
if
not
core
.
is_compiled_with_cuda
()
else
fluid
.
CUDAPlace
(
0
))
static_result
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
'inp1'
:
inp1
,
'inp2'
:
inp2
},
fetch_list
=
out
)[
0
]
self
.
assertTrue
(
np
.
allclose
(
dygraph_result
,
static_result
))
def
test_rnn
(
self
):
np_inp
=
np
.
array
([[
1.0
,
2.0
,
3.0
],
[
4.0
,
5.0
,
6.0
],
[
7.0
,
8.0
,
9.0
],
[
10.0
,
11.0
,
12.0
]])
...
...
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