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体验新版 GitCode,发现更多精彩内容 >>
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536a55fa
编写于
2月 15, 2022
作者:
W
Weilong Wu
提交者:
GitHub
2月 15, 2022
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电子邮件补丁
差异文件
Support test_layers 51/55 tests with _test_eager_guard() (#39515)
上级
6549a041
变更
1
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并排
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1 changed file
with
744 addition
and
0 deletion
+744
-0
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+744
-0
未找到文件。
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
536a55fa
...
...
@@ -34,6 +34,7 @@ from test_imperative_base import new_program_scope
from
paddle.fluid.dygraph
import
nn
from
paddle.fluid.dygraph
import
base
from
paddle.fluid.dygraph
import
to_variable
from
paddle.fluid.framework
import
_test_eager_guard
class
LayerTest
(
unittest
.
TestCase
):
...
...
@@ -98,6 +99,14 @@ class TestLayer(LayerTest):
return
ret
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
inp
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
x
=
base
.
to_variable
(
inp
)
custom
=
CustomLayer
(
input_size
=
3
,
linear1_size
=
2
)
ret
=
custom
(
x
,
do_linear2
=
False
)
self
.
assertTrue
(
np
.
array_equal
(
ret
.
numpy
().
shape
,
[
3
,
2
]))
ret
=
custom
(
x
,
do_linear2
=
True
)
self
.
assertTrue
(
np
.
array_equal
(
ret
.
numpy
().
shape
,
[
3
,
1
]))
inp
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
x
=
base
.
to_variable
(
inp
)
custom
=
CustomLayer
(
input_size
=
3
,
linear1_size
=
2
)
...
...
@@ -121,6 +130,15 @@ class TestLayer(LayerTest):
static_ret
,
static_ret2
=
self
.
get_static_graph_result
(
feed
=
{
'data'
:
inp
},
fetch_list
=
[
ret
,
ret2
])
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
t
=
base
.
to_variable
(
inp
)
dropout
=
nn
.
Dropout
(
p
=
0.35
,
seed
=
1
,
is_test
=
False
)
dy_eager_ret
=
dropout
(
t
)
dy_eager_ret2
=
fluid
.
layers
.
dropout
(
t
,
dropout_prob
=
0.35
,
seed
=
1
,
is_test
=
False
)
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
dy_eager_ret2_value
=
dy_eager_ret2
.
numpy
()
t
=
base
.
to_variable
(
inp
)
dropout
=
nn
.
Dropout
(
p
=
0.35
,
seed
=
1
,
is_test
=
False
)
dy_ret
=
dropout
(
t
)
...
...
@@ -129,6 +147,9 @@ class TestLayer(LayerTest):
dy_ret_value
=
dy_ret
.
numpy
()
dy_ret2_value
=
dy_ret2
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
dy_eager_ret_value
,
dy_eager_ret2_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_eager_ret_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
static_ret2
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret_value
,
dy_ret2_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_ret_value
))
...
...
@@ -147,12 +168,22 @@ class TestLayer(LayerTest):
static_ret
=
self
.
get_static_graph_result
(
feed
=
{
'data'
:
inp
},
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
t
=
base
.
to_variable
(
inp
)
linear
=
nn
.
Linear
(
32
,
4
,
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
))
dy_eager_ret
=
linear
(
t
)
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
t
=
base
.
to_variable
(
inp
)
linear
=
nn
.
Linear
(
32
,
4
,
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
))
dy_ret
=
linear
(
t
)
dy_ret_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_eager_ret_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_ret_value
))
with
self
.
static_graph
():
...
...
@@ -193,11 +224,18 @@ class TestLayer(LayerTest):
static_ret
=
self
.
get_static_graph_result
(
feed
=
{
'data'
:
inp
},
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
t
=
base
.
to_variable
(
inp
)
flatten
=
nn
.
Flatten
()
dy_eager_ret
=
flatten
(
t
)
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
t
=
base
.
to_variable
(
inp
)
flatten
=
nn
.
Flatten
()
dy_ret
=
flatten
(
t
)
dy_ret_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_eager_ret_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_ret_value
))
with
self
.
static_graph
():
...
...
@@ -253,13 +291,35 @@ class TestLayer(LayerTest):
static_ret2
=
self
.
get_static_graph_result
(
feed
=
{
'data'
:
inp
},
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
lm
=
nn
.
LayerNorm
(
normalized_shape
=
[
32
,
32
],
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
act
=
'sigmoid'
)
dy_eager_ret
=
lm
(
base
.
to_variable
(
inp
))
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
lm
=
nn
.
LayerNorm
(
normalized_shape
=
[
32
,
32
],
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
act
=
'sigmoid'
)
dy_ret
=
lm
(
base
.
to_variable
(
inp
))
dy_ret_value
=
dy_ret
.
numpy
()
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
lm
=
nn
.
LayerNorm
(
normalized_shape
=
[
32
,
32
],
shift
=
False
,
scale
=
False
,
param_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
act
=
'sigmoid'
)
lm
(
base
.
to_variable
(
inp
))
self
.
assertFalse
(
hasattr
(
lm
,
"_scale_w"
))
self
.
assertFalse
(
hasattr
(
lm
,
"_bias_w"
))
lm
=
nn
.
LayerNorm
(
normalized_shape
=
[
32
,
32
],
shift
=
False
,
...
...
@@ -273,9 +333,18 @@ class TestLayer(LayerTest):
self
.
assertFalse
(
hasattr
(
lm
,
"_bias_w"
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
static_ret2
))
self
.
assertTrue
(
np
.
array_equal
(
dy_eager_ret_value
,
static_ret2
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret_value
,
static_ret2
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
lm
=
nn
.
LayerNorm
(
normalized_shape
=
[
16
,
32
],
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
act
=
'sigmoid'
)
with
self
.
assertRaises
(
ValueError
):
lm
(
base
.
to_variable
(
inp
))
lm
=
nn
.
LayerNorm
(
normalized_shape
=
[
16
,
32
],
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
...
...
@@ -295,11 +364,18 @@ class TestLayer(LayerTest):
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
t
=
np
.
ones
([
3
,
3
,
5
,
5
],
dtype
=
'float32'
)
my_syncbn
=
paddle
.
nn
.
SyncBatchNorm
(
3
)
dy_eager_ret
=
my_syncbn
(
base
.
to_variable
(
t
))
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
t
=
np
.
ones
([
3
,
3
,
5
,
5
],
dtype
=
'float32'
)
my_syncbn
=
paddle
.
nn
.
SyncBatchNorm
(
3
)
dy_ret
=
my_syncbn
(
base
.
to_variable
(
t
))
dy_ret_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_ret_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_ret
,
dy_eager_ret_value
))
def
test_relu
(
self
):
with
self
.
static_graph
():
...
...
@@ -310,11 +386,17 @@ class TestLayer(LayerTest):
[
3
,
3
],
dtype
=
'float32'
)},
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
t
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
dy_eager_ret
=
layers
.
relu
(
base
.
to_variable
(
t
))
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
t
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
dy_ret
=
layers
.
relu
(
base
.
to_variable
(
t
))
dy_ret_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_ret_value
))
def
test_matmul
(
self
):
with
self
.
static_graph
():
...
...
@@ -331,12 +413,20 @@ class TestLayer(LayerTest):
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
t
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
t2
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
dy_eager_ret
=
layers
.
matmul
(
base
.
to_variable
(
t
),
base
.
to_variable
(
t2
))
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
t
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
t2
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
dy_ret
=
layers
.
matmul
(
base
.
to_variable
(
t
),
base
.
to_variable
(
t2
))
dy_ret_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_ret_value
))
def
test_conv2d
(
self
):
with
self
.
static_graph
():
...
...
@@ -358,6 +448,13 @@ class TestLayer(LayerTest):
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
conv2d
=
nn
.
Conv2D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
])
dy_eager_ret
=
conv2d
(
base
.
to_variable
(
images
))
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
conv2d
=
nn
.
Conv2D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
])
...
...
@@ -365,6 +462,16 @@ class TestLayer(LayerTest):
dy_ret_value
=
dy_ret
.
numpy
()
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
conv2d
=
nn
.
Conv2D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
],
bias_attr
=
False
)
dy_ret
=
conv2d
(
base
.
to_variable
(
images
))
self
.
assertTrue
(
conv2d
.
bias
is
None
)
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
conv2d
=
nn
.
Conv2D
(
num_channels
=
3
,
...
...
@@ -396,9 +503,49 @@ class TestLayer(LayerTest):
self
.
assertRaises
(
TypeError
,
test_type
)
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
conv2d1
=
nn
.
Conv2D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
])
conv2d2
=
nn
.
Conv2D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
],
param_attr
=
weight_attr
)
dy_ret1
=
conv2d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv2d2
(
base
.
to_variable
(
images
))
self
.
assertFalse
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv2d1_weight_np
=
conv2d1
.
weight
.
numpy
()
conv2d1_bias
=
conv2d1
.
bias
self
.
assertFalse
(
np
.
array_equal
(
conv2d1_weight_np
,
conv2d2
.
weight
.
numpy
()))
conv2d2
.
weight
.
set_value
(
conv2d1_weight_np
)
self
.
assertTrue
(
np
.
array_equal
(
conv2d1_weight_np
,
conv2d2
.
weight
.
numpy
()))
conv2d2
.
bias
.
set_value
(
conv2d1_bias
)
dy_ret1
=
conv2d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv2d2
(
base
.
to_variable
(
images
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv2d2
.
weight
=
conv2d1
.
weight
conv2d2
.
bias
=
conv2d1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
conv2d1
.
weight
.
numpy
(),
conv2d2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
conv2d1
.
bias
.
numpy
(),
conv2d2
.
bias
.
numpy
()))
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
...
...
@@ -467,6 +614,14 @@ class TestLayer(LayerTest):
fetch_list
=
[
updated_hidden
,
reset_hidden_pre
,
gate
])
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
gru
=
nn
.
GRUUnit
(
size
=
D
*
3
)
dy_eager_ret
=
gru
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
dy_eager_ret_value
=
[]
for
i
in
range
(
len
(
static_ret
)):
dy_eager_ret_value
.
append
(
dy_eager_ret
[
i
].
numpy
())
gru
=
nn
.
GRUUnit
(
size
=
D
*
3
)
dy_ret
=
gru
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
...
...
@@ -477,8 +632,40 @@ class TestLayer(LayerTest):
for
i
in
range
(
len
(
static_ret
)):
self
.
assertTrue
(
np
.
allclose
(
static_ret
[
i
],
static_ret2
[
i
]))
self
.
assertTrue
(
np
.
allclose
(
static_ret
[
i
],
dy_ret_value
[
i
]))
self
.
assertTrue
(
np
.
allclose
(
static_ret
[
i
],
dy_eager_ret_value
[
i
]))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
custom_weight
=
np
.
random
.
randn
(
D
,
D
*
3
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
gru1
=
nn
.
GRUUnit
(
size
=
D
*
3
)
gru2
=
nn
.
GRUUnit
(
size
=
D
*
3
,
param_attr
=
weight_attr
)
dy_ret1
=
gru1
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
dy_ret2
=
gru2
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
self
.
assertFalse
(
np
.
array_equal
(
gru1
.
weight
.
numpy
(),
gru2
.
weight
.
numpy
()))
for
o1
,
o2
in
zip
(
dy_ret1
,
dy_ret2
):
self
.
assertFalse
(
np
.
array_equal
(
o1
.
numpy
(),
o2
.
numpy
()))
gru2
.
weight
.
set_value
(
gru1
.
weight
.
numpy
())
gru2
.
bias
.
set_value
(
gru1
.
bias
)
dy_ret1
=
gru1
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
dy_ret2
=
gru2
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
for
o1
,
o2
in
zip
(
dy_ret1
,
dy_ret2
):
self
.
assertTrue
(
np
.
array_equal
(
o1
.
numpy
(),
o2
.
numpy
()))
gru2
.
weight
=
gru1
.
weight
gru2
.
bias
=
gru1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
gru1
.
weight
.
numpy
(),
gru2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
gru1
.
bias
.
numpy
(),
gru2
.
bias
.
numpy
()))
custom_weight
=
np
.
random
.
randn
(
D
,
D
*
3
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
...
...
@@ -543,19 +730,37 @@ class TestLayer(LayerTest):
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
ret
=
layers
.
elementwise_add
(
to_variable
(
n
),
to_variable
(
n2
))
ret
=
layers
.
elementwise_pow
(
ret
,
to_variable
(
n3
))
ret
=
layers
.
elementwise_div
(
ret
,
to_variable
(
n4
))
ret
=
layers
.
elementwise_sub
(
ret
,
to_variable
(
n5
))
dy_eager_ret
=
layers
.
elementwise_mul
(
ret
,
to_variable
(
n6
))
dy_eager_ret_value
=
dy_eager_ret
.
numpy
()
ret
=
layers
.
elementwise_add
(
to_variable
(
n
),
to_variable
(
n2
))
ret
=
layers
.
elementwise_pow
(
ret
,
to_variable
(
n3
))
ret
=
layers
.
elementwise_div
(
ret
,
to_variable
(
n4
))
ret
=
layers
.
elementwise_sub
(
ret
,
to_variable
(
n5
))
dy_ret
=
layers
.
elementwise_mul
(
ret
,
to_variable
(
n6
))
dy_ret_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_ret_value
))
def
test_elementwise_minmax
(
self
):
n
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
n2
=
np
.
ones
([
3
,
3
],
dtype
=
'float32'
)
*
2
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
min_eager_ret
=
layers
.
elementwise_min
(
to_variable
(
n
),
to_variable
(
n2
))
max_eager_ret
=
layers
.
elementwise_max
(
to_variable
(
n
),
to_variable
(
n2
))
min_eager_ret_value
=
min_eager_ret
.
numpy
()
max_eager_ret_value
=
max_eager_ret
.
numpy
()
min_ret
=
layers
.
elementwise_min
(
to_variable
(
n
),
to_variable
(
n2
))
max_ret
=
layers
.
elementwise_max
(
to_variable
(
n
),
to_variable
(
n2
))
min_ret_value
=
min_ret
.
numpy
()
...
...
@@ -563,6 +768,8 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
allclose
(
n
,
min_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
n2
,
max_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
n
,
min_eager_ret_value
))
self
.
assertTrue
(
np
.
allclose
(
n2
,
max_eager_ret_value
))
def
test_sequence_conv
(
self
):
inp_np
=
np
.
arange
(
12
).
reshape
([
3
,
4
]).
astype
(
'float32'
)
...
...
@@ -633,6 +840,16 @@ class TestLayer(LayerTest):
static_rlt2
=
self
.
get_static_graph_result
(
feed
=
{
'pixel'
:
inp_np
},
fetch_list
=
[
out
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
conv2d_transpose
=
nn
.
Conv2DTranspose
(
num_channels
=
3
,
num_filters
=
10
,
filter_size
=
27
,
act
=
'sigmoid'
,
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
))
dy_eager_rlt
=
conv2d_transpose
(
base
.
to_variable
(
inp_np
))
dy_eager_rlt_value
=
dy_eager_rlt
.
numpy
()
conv2d_transpose
=
nn
.
Conv2DTranspose
(
num_channels
=
3
,
num_filters
=
10
,
...
...
@@ -643,8 +860,48 @@ class TestLayer(LayerTest):
dy_rlt_value
=
dy_rlt
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_rlt2
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_rlt_value
,
static_rlt2
))
self
.
assertTrue
(
np
.
allclose
(
dy_eager_rlt_value
,
static_rlt2
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
conv2d1
=
nn
.
Conv2DTranspose
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
])
conv2d2
=
nn
.
Conv2DTranspose
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
[
2
,
2
],
param_attr
=
weight_attr
)
dy_ret1
=
conv2d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv2d2
(
base
.
to_variable
(
images
))
self
.
assertFalse
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv2d1_weight_np
=
conv2d1
.
weight
.
numpy
()
conv2d1_bias
=
conv2d1
.
bias
self
.
assertFalse
(
np
.
array_equal
(
conv2d1_weight_np
,
conv2d2
.
weight
.
numpy
()))
conv2d2
.
weight
.
set_value
(
conv2d1_weight_np
)
self
.
assertTrue
(
np
.
array_equal
(
conv2d1_weight_np
,
conv2d2
.
weight
.
numpy
()))
conv2d2
.
bias
.
set_value
(
conv2d1_bias
)
dy_ret1
=
conv2d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv2d2
(
base
.
to_variable
(
images
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv2d2
.
weight
=
conv2d1
.
weight
conv2d2
.
bias
=
conv2d1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
conv2d1
.
weight
.
numpy
(),
conv2d2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
conv2d1
.
bias
.
numpy
(),
conv2d2
.
bias
.
numpy
()))
images
=
np
.
ones
([
2
,
3
,
5
,
5
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
...
...
@@ -750,6 +1007,17 @@ class TestLayer(LayerTest):
feed
=
{
'x'
:
inp_np_x
,
'y'
:
inp_np_y
},
fetch_list
=
[
out
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
btp
=
nn
.
BilinearTensorProduct
(
3
,
3
,
6
,
bias_attr
=
fluid
.
initializer
.
ConstantInitializer
(
value
=
1
),
act
=
'sigmoid'
)
dy_eager_rlt
=
btp
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
dy_eager_rlt_value
=
dy_eager_rlt
.
numpy
()
btp
=
nn
.
BilinearTensorProduct
(
3
,
3
,
...
...
@@ -758,11 +1026,19 @@ class TestLayer(LayerTest):
act
=
'sigmoid'
)
dy_rlt
=
btp
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
dy_rlt_value
=
dy_rlt
.
numpy
()
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
btp2
=
nn
.
BilinearTensorProduct
(
3
,
3
,
6
,
act
=
'sigmoid'
)
dy_eager_rlt2
=
btp2
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
dy_eager_rlt2_value
=
dy_eager_rlt2
.
numpy
()
btp2
=
nn
.
BilinearTensorProduct
(
3
,
3
,
6
,
act
=
'sigmoid'
)
dy_rlt2
=
btp2
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
dy_rlt2_value
=
dy_rlt2
.
numpy
()
with
self
.
static_graph
():
data_x2
=
layers
.
data
(
name
=
'x'
,
...
...
@@ -782,10 +1058,42 @@ class TestLayer(LayerTest):
'y'
:
inp_np_y
},
fetch_list
=
[
out2
])[
0
]
self
.
assertTrue
(
np
.
array_equal
(
dy_rlt2_value
,
static_rlt3
))
self
.
assertTrue
(
np
.
array_equal
(
dy_eager_rlt2_value
,
static_rlt3
))
self
.
assertTrue
(
np
.
array_equal
(
static_rlt2
,
static_rlt
))
self
.
assertTrue
(
np
.
array_equal
(
dy_rlt_value
,
static_rlt
))
self
.
assertTrue
(
np
.
array_equal
(
dy_eager_rlt_value
,
static_rlt
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
custom_weight
=
np
.
random
.
randn
(
6
,
3
,
3
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
btp1
=
nn
.
BilinearTensorProduct
(
3
,
3
,
6
,
act
=
'sigmoid'
)
btp2
=
nn
.
BilinearTensorProduct
(
3
,
3
,
6
,
act
=
'sigmoid'
,
param_attr
=
weight_attr
)
dy_rlt1
=
btp1
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
dy_rlt2
=
btp2
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
self
.
assertFalse
(
np
.
array_equal
(
dy_rlt1
.
numpy
(),
dy_rlt2
.
numpy
()))
btp2
.
weight
.
set_value
(
btp1
.
weight
.
numpy
())
btp2
.
bias
.
set_value
(
btp1
.
bias
)
dy_rlt1
=
btp1
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
dy_rlt2
=
btp2
(
base
.
to_variable
(
inp_np_x
),
base
.
to_variable
(
inp_np_y
))
self
.
assertTrue
(
np
.
array_equal
(
dy_rlt1
.
numpy
(),
dy_rlt2
.
numpy
()))
btp2
.
weight
=
btp1
.
weight
btp2
.
bias
=
btp1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
btp1
.
weight
.
numpy
(),
btp2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
btp1
.
bias
.
numpy
(),
btp2
.
bias
.
numpy
()))
custom_weight
=
np
.
random
.
randn
(
6
,
3
,
3
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
...
...
@@ -842,6 +1150,15 @@ class TestLayer(LayerTest):
feed
=
{
"input"
:
inp_np
},
fetch_list
=
[
out
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
prelu
=
nn
.
PRelu
(
mode
=
mode
,
channel
=
inp_np
.
shape
[
1
],
input_shape
=
inp_np
.
shape
,
param_attr
=
ParamAttr
(
initializer
=
Constant
(
1.0
)))
dy_eager_rlt
=
prelu
(
base
.
to_variable
(
inp_np
))
dy_eager_rlt_value
=
dy_eager_rlt
.
numpy
()
prelu
=
nn
.
PRelu
(
mode
=
mode
,
channel
=
inp_np
.
shape
[
1
],
...
...
@@ -852,8 +1169,40 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
allclose
(
static_rlt2
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_rlt_value
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_eager_rlt_value
,
static_rlt
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
inp_np
=
np
.
random
.
randn
(
5
,
200
,
100
,
100
).
astype
(
"float32"
)
inp
=
base
.
to_variable
(
inp_np
)
prelu1
=
nn
.
PRelu
(
mode
=
mode
,
channel
=
inp_np
.
shape
[
1
],
input_shape
=
inp_np
.
shape
,
param_attr
=
ParamAttr
(
initializer
=
Constant
(
2.0
)))
prelu2
=
nn
.
PRelu
(
mode
=
mode
,
channel
=
inp_np
.
shape
[
1
],
input_shape
=
inp_np
.
shape
,
param_attr
=
ParamAttr
(
initializer
=
Constant
(
1.0
)))
dy_rlt1
=
prelu1
(
inp
)
dy_rlt2
=
prelu2
(
inp
)
self
.
assertFalse
(
np
.
array_equal
(
prelu1
.
weight
.
numpy
(),
prelu2
.
weight
.
numpy
(
)))
self
.
assertFalse
(
np
.
array_equal
(
dy_rlt1
.
numpy
(),
dy_rlt2
.
numpy
()))
prelu2
.
weight
.
set_value
(
prelu1
.
weight
.
numpy
())
dy_rlt1
=
prelu1
(
inp
)
dy_rlt2
=
prelu2
(
inp
)
self
.
assertTrue
(
np
.
array_equal
(
dy_rlt1
.
numpy
(),
dy_rlt2
.
numpy
()))
prelu2
.
weight
=
prelu1
.
weight
self
.
assertTrue
(
np
.
array_equal
(
prelu1
.
weight
.
numpy
(),
prelu2
.
weight
.
numpy
(
)))
inp_np
=
np
.
random
.
randn
(
5
,
200
,
100
,
100
).
astype
(
"float32"
)
inp
=
base
.
to_variable
(
inp_np
)
prelu1
=
nn
.
PRelu
(
...
...
@@ -905,6 +1254,14 @@ class TestLayer(LayerTest):
static_rlt2
=
self
.
get_static_graph_result
(
feed
=
{
'word'
:
inp_word
},
fetch_list
=
[
emb_rlt
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
emb2
=
nn
.
Embedding
(
size
=
[
dict_size
,
32
],
param_attr
=
'eager_emb.w'
,
is_sparse
=
False
)
dy_eager_rlt
=
emb2
(
base
.
to_variable
(
inp_word
))
dy_eager_rlt_value
=
dy_eager_rlt
.
numpy
()
emb2
=
nn
.
Embedding
(
size
=
[
dict_size
,
32
],
param_attr
=
'emb.w'
,
is_sparse
=
False
)
dy_rlt
=
emb2
(
base
.
to_variable
(
inp_word
))
...
...
@@ -912,8 +1269,34 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
allclose
(
static_rlt2
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_rlt_value
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_eager_rlt_value
,
static_rlt
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
custom_weight
=
np
.
random
.
randn
(
dict_size
,
32
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
emb1
=
nn
.
Embedding
(
size
=
[
dict_size
,
32
],
is_sparse
=
False
)
emb2
=
nn
.
Embedding
(
size
=
[
dict_size
,
32
],
param_attr
=
weight_attr
,
is_sparse
=
False
)
rep1
=
emb1
(
base
.
to_variable
(
inp_word
))
rep2
=
emb2
(
base
.
to_variable
(
inp_word
))
self
.
assertFalse
(
np
.
array_equal
(
emb1
.
weight
.
numpy
(),
custom_weight
))
self
.
assertTrue
(
np
.
array_equal
(
emb2
.
weight
.
numpy
(),
custom_weight
))
self
.
assertFalse
(
np
.
array_equal
(
rep1
.
numpy
(),
rep2
.
numpy
()))
emb2
.
weight
.
set_value
(
emb1
.
weight
.
numpy
())
rep2
=
emb2
(
base
.
to_variable
(
inp_word
))
self
.
assertTrue
(
np
.
array_equal
(
rep1
.
numpy
(),
rep2
.
numpy
()))
emb2
.
weight
=
emb1
.
weight
self
.
assertTrue
(
np
.
array_equal
(
emb1
.
weight
.
numpy
(),
emb2
.
weight
.
numpy
()))
custom_weight
=
np
.
random
.
randn
(
dict_size
,
32
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
...
...
@@ -1018,6 +1401,7 @@ class TestLayer(LayerTest):
feed
=
feed_dict
,
fetch_list
=
[
nce_loss2
])[
0
]
with
self
.
dynamic_graph
():
# TODO(wuweilong): Add with _test_eager_guard():
words
=
[]
for
i
in
range
(
window_size
):
words
.
append
(
base
.
to_variable
(
inp_word
[
i
]))
...
...
@@ -1054,6 +1438,7 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
allclose
(
dy_rlt_value
,
static_rlt
))
with
self
.
dynamic_graph
():
# TODO(wuweilong): Add with _test_eager_guard():
custom_weight
=
np
.
random
.
randn
(
dict_size
,
128
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
...
...
@@ -1118,6 +1503,17 @@ class TestLayer(LayerTest):
def
test_one_hot
(
self
):
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
label
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([[
1
],
[
1
],
[
3
],
[
0
]]))
one_hot_label1
=
fluid
.
layers
.
one_hot
(
input
=
label
,
depth
=
4
)
one_hot_label2
=
fluid
.
layers
.
one_hot
(
input
=
label
,
depth
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
4
])))
self
.
assertTrue
(
np
.
array_equal
(
one_hot_label1
.
numpy
(),
one_hot_label2
.
numpy
()))
label
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([[
1
],
[
1
],
[
3
],
[
0
]]))
one_hot_label1
=
fluid
.
layers
.
one_hot
(
input
=
label
,
depth
=
4
)
one_hot_label2
=
fluid
.
layers
.
one_hot
(
...
...
@@ -1127,6 +1523,16 @@ class TestLayer(LayerTest):
def
test_split
(
self
):
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
input
=
fluid
.
dygraph
.
to_variable
(
np
.
random
.
random
((
3
,
8
,
5
)))
x0
,
x1
=
fluid
.
layers
.
split
(
input
,
num_or_sections
=
2
,
dim
=
1
)
x00
,
x11
=
fluid
.
layers
.
split
(
input
,
num_or_sections
=
2
,
dim
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
1
])))
self
.
assertTrue
(
np
.
array_equal
(
x0
.
numpy
(),
x00
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
x1
.
numpy
(),
x11
.
numpy
()))
input
=
fluid
.
dygraph
.
to_variable
(
np
.
random
.
random
((
3
,
8
,
5
)))
x0
,
x1
=
fluid
.
layers
.
split
(
input
,
num_or_sections
=
2
,
dim
=
1
)
x00
,
x11
=
fluid
.
layers
.
split
(
...
...
@@ -1138,6 +1544,17 @@ class TestLayer(LayerTest):
def
test_topk
(
self
):
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
input
=
fluid
.
dygraph
.
to_variable
(
np
.
random
.
random
((
13
,
11
)))
top5_values1
,
top5_indices1
=
layers
.
topk
(
input
,
k
=
5
)
top5_values2
,
top5_indices2
=
layers
.
topk
(
input
,
k
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
5
])))
self
.
assertTrue
(
np
.
array_equal
(
top5_values1
.
numpy
(),
top5_values2
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
top5_indices1
.
numpy
(),
top5_indices2
.
numpy
(
)))
input
=
fluid
.
dygraph
.
to_variable
(
np
.
random
.
random
((
13
,
11
)))
top5_values1
,
top5_indices1
=
layers
.
topk
(
input
,
k
=
5
)
top5_values2
,
top5_indices2
=
layers
.
topk
(
...
...
@@ -1168,15 +1585,61 @@ class TestLayer(LayerTest):
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
6
,
6
,
6
],
dtype
=
'float32'
)
conv3d
=
nn
.
Conv3D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
2
)
dy_eager_ret
=
conv3d
(
base
.
to_variable
(
images
))
dy_eager_rlt_value
=
dy_eager_ret
.
numpy
()
images
=
np
.
ones
([
2
,
3
,
6
,
6
,
6
],
dtype
=
'float32'
)
conv3d
=
nn
.
Conv3D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
2
)
dy_ret
=
conv3d
(
base
.
to_variable
(
images
))
dy_rlt_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
6
,
6
,
6
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
conv3d1
=
nn
.
Conv3D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
2
)
conv3d2
=
nn
.
Conv3D
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
2
,
param_attr
=
weight_attr
)
dy_ret1
=
conv3d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv3d2
(
base
.
to_variable
(
images
))
self
.
assertFalse
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv3d1_weight_np
=
conv3d1
.
weight
.
numpy
()
conv3d1_bias
=
conv3d1
.
bias
self
.
assertFalse
(
np
.
array_equal
(
conv3d1_weight_np
,
conv3d2
.
weight
.
numpy
()))
conv3d2
.
weight
.
set_value
(
conv3d1_weight_np
)
self
.
assertTrue
(
np
.
array_equal
(
conv3d1_weight_np
,
conv3d2
.
weight
.
numpy
()))
conv3d1
.
bias
.
set_value
(
conv3d1_bias
)
dy_ret1
=
conv3d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv3d2
(
base
.
to_variable
(
images
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv3d2
.
weight
=
conv3d1
.
weight
conv3d2
.
bias
=
conv3d1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
conv3d1
.
weight
.
numpy
(),
conv3d2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
conv3d1
.
bias
.
numpy
(),
conv3d2
.
bias
.
numpy
()))
images
=
np
.
ones
([
2
,
3
,
6
,
6
,
6
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
...
...
@@ -1309,6 +1772,7 @@ class TestLayer(LayerTest):
with_lod
=
True
)[
0
]
with
self
.
dynamic_graph
():
# TODO(wuweilong): Add with _test_eager_guard():
groupNorm
=
nn
.
GroupNorm
(
channels
=
shape
[
1
],
groups
=
2
,
...
...
@@ -1347,17 +1811,29 @@ class TestLayer(LayerTest):
feed
=
{
'X'
:
input
},
fetch_list
=
[
ret
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
instanceNorm
=
nn
.
InstanceNorm
(
num_channels
=
shape
[
1
])
dy_eager_ret
=
instanceNorm
(
base
.
to_variable
(
input
))
dy_eager_rlt_value
=
dy_eager_ret
.
numpy
()
instanceNorm
=
nn
.
InstanceNorm
(
num_channels
=
shape
[
1
])
dy_ret
=
instanceNorm
(
base
.
to_variable
(
input
))
dy_rlt_value
=
dy_ret
.
numpy
()
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
instanceNorm
=
nn
.
InstanceNorm
(
num_channels
=
shape
[
1
])
dy_eager_ret
=
instanceNorm
(
base
.
to_variable
(
input
))
dy_eager_rlt_value2
=
dy_eager_ret
.
numpy
()
instanceNorm
=
nn
.
InstanceNorm
(
num_channels
=
shape
[
1
])
dy_ret
=
instanceNorm
(
base
.
to_variable
(
input
))
dy_rlt_value2
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_rlt_value2
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_rlt_value2
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
with
self
.
static_graph
():
...
...
@@ -1420,11 +1896,17 @@ class TestLayer(LayerTest):
with_lod
=
True
)[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
spectralNorm
=
nn
.
SpectralNorm
(
shape
,
dim
=
1
,
power_iters
=
2
)
dy_eager_ret
=
spectralNorm
(
base
.
to_variable
(
input
))
dy_eager_rlt_value
=
dy_eager_ret
.
numpy
()
spectralNorm
=
nn
.
SpectralNorm
(
shape
,
dim
=
1
,
power_iters
=
2
)
dy_ret
=
spectralNorm
(
base
.
to_variable
(
input
))
dy_rlt_value
=
dy_ret
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
def
test_tree_conv
(
self
):
...
...
@@ -1492,6 +1974,13 @@ class TestLayer(LayerTest):
with_lod
=
False
)[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
treeConv
=
nn
.
TreeConv
(
feature_size
=
5
,
output_size
=
6
,
num_filters
=
1
,
max_depth
=
2
)
dy_eager_ret
=
treeConv
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
dy_eager_rlt_value
=
dy_eager_ret
.
numpy
()
treeConv
=
nn
.
TreeConv
(
feature_size
=
5
,
output_size
=
6
,
num_filters
=
1
,
max_depth
=
2
)
dy_ret
=
treeConv
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
...
...
@@ -1499,8 +1988,51 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_rlt_value
))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_eager_rlt_value
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
custom_weight
=
np
.
random
.
randn
(
5
,
3
,
6
,
1
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
treeConv1
=
nn
.
TreeConv
(
feature_size
=
5
,
output_size
=
6
,
num_filters
=
1
,
max_depth
=
2
,
bias_attr
=
'eager_tc1_b'
)
treeConv2
=
nn
.
TreeConv
(
feature_size
=
5
,
output_size
=
6
,
num_filters
=
1
,
max_depth
=
2
,
param_attr
=
weight_attr
,
bias_attr
=
'eager_tc2_b'
)
dy_ret1
=
treeConv1
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
dy_ret2
=
treeConv2
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
self
.
assertFalse
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
treeConv2
.
weight
.
set_value
(
treeConv1
.
weight
.
numpy
())
treeConv2
.
bias
.
set_value
(
treeConv1
.
bias
)
dy_ret1
=
treeConv1
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
dy_ret2
=
treeConv2
(
base
.
to_variable
(
vectors
),
base
.
to_variable
(
adj
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
treeConv2
.
weight
=
treeConv1
.
weight
treeConv2
.
bias
=
treeConv1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
treeConv1
.
weight
.
numpy
(),
treeConv2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
treeConv1
.
bias
.
numpy
(),
treeConv2
.
bias
.
numpy
()))
custom_weight
=
np
.
random
.
randn
(
5
,
3
,
6
,
1
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
...
...
@@ -1557,14 +2089,69 @@ class TestLayer(LayerTest):
static_rlt2
=
self
.
get_static_graph_result
(
feed
=
{
'pixel'
:
input_array
},
fetch_list
=
[
out
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
conv3d_transpose
=
nn
.
Conv3DTranspose
(
num_channels
=
3
,
num_filters
=
12
,
filter_size
=
12
,
use_cudnn
=
False
)
dy_eager_rlt
=
conv3d_transpose
(
base
.
to_variable
(
input_array
))
dy_eager_rlt_value
=
dy_eager_rlt
.
numpy
()
conv3d_transpose
=
nn
.
Conv3DTranspose
(
num_channels
=
3
,
num_filters
=
12
,
filter_size
=
12
,
use_cudnn
=
False
)
dy_rlt
=
conv3d_transpose
(
base
.
to_variable
(
input_array
))
dy_rlt_value
=
dy_rlt
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
static_rlt2
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_rlt_value
,
static_rlt
))
self
.
assertTrue
(
np
.
allclose
(
dy_eager_rlt_value
,
static_rlt
))
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
images
=
np
.
ones
([
2
,
3
,
6
,
6
,
6
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
initializer
=
fluid
.
initializer
.
NumpyArrayInitializer
(
custom_weight
))
conv3d1
=
nn
.
Conv3DTranspose
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
2
,
bias_attr
=
'eager_conv3d1_b'
,
use_cudnn
=
False
)
conv3d2
=
nn
.
Conv3DTranspose
(
num_channels
=
3
,
num_filters
=
3
,
filter_size
=
2
,
param_attr
=
weight_attr
,
bias_attr
=
'eager_conv3d2_b'
,
use_cudnn
=
False
)
dy_ret1
=
conv3d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv3d2
(
base
.
to_variable
(
images
))
self
.
assertFalse
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv3d1_weight_np
=
conv3d1
.
weight
.
numpy
()
conv3d1_bias
=
conv3d1
.
bias
self
.
assertFalse
(
np
.
array_equal
(
conv3d1_weight_np
,
conv3d2
.
weight
.
numpy
()))
conv3d2
.
weight
.
set_value
(
conv3d1_weight_np
)
self
.
assertTrue
(
np
.
array_equal
(
conv3d1_weight_np
,
conv3d2
.
weight
.
numpy
()))
conv3d1
.
bias
.
set_value
(
conv3d1_bias
)
dy_ret1
=
conv3d1
(
base
.
to_variable
(
images
))
dy_ret2
=
conv3d2
(
base
.
to_variable
(
images
))
self
.
assertTrue
(
np
.
array_equal
(
dy_ret1
.
numpy
(),
dy_ret2
.
numpy
()))
conv3d2
.
weight
=
conv3d1
.
weight
conv3d2
.
bias
=
conv3d1
.
bias
self
.
assertTrue
(
np
.
array_equal
(
conv3d1
.
weight
.
numpy
(),
conv3d2
.
weight
.
numpy
()))
self
.
assertTrue
(
np
.
array_equal
(
conv3d1
.
bias
.
numpy
(),
conv3d2
.
bias
.
numpy
()))
images
=
np
.
ones
([
2
,
3
,
6
,
6
,
6
],
dtype
=
'float32'
)
custom_weight
=
np
.
random
.
randn
(
3
,
3
,
2
,
2
,
2
).
astype
(
"float32"
)
weight_attr
=
fluid
.
ParamAttr
(
...
...
@@ -1614,6 +2201,20 @@ class TestLayer(LayerTest):
stack_rlt2
=
np
.
stack
(
array_rlt2
,
axis
=
0
)
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
eager_eye_tensor
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
)
eager_eye_tensor_rlt1
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
batch_shape
=
[
3
])
eager_eye_tensor_rlt2
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
batch_shape
=
[
4
,
3
])
eager_diag_tensor
=
layers
.
eye
(
20
)
eager_eye_tensor_value
=
eager_eye_tensor
.
numpy
()
eager_eye_tensor_rlt1_value
=
eager_eye_tensor_rlt1
.
numpy
()
eager_eye_tensor_rlt2_value
=
eager_eye_tensor_rlt2
.
numpy
()
eager_diag_tensor_value
=
eager_diag_tensor
.
numpy
()
eye_tensor
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
)
eye_tensor_rlt1
=
layers
.
eye
(
num_rows
=
3
,
num_columns
=
2
,
...
...
@@ -1626,6 +2227,12 @@ class TestLayer(LayerTest):
eye_tensor_rlt1_value
=
eye_tensor_rlt1
.
numpy
()
eye_tensor_rlt2_value
=
eye_tensor_rlt2
.
numpy
()
diag_tensor_value
=
diag_tensor
.
numpy
()
self
.
assertTrue
(
np
.
allclose
(
eager_eye_tensor_value
,
np_eye
))
self
.
assertTrue
(
np
.
allclose
(
eager_eye_tensor_rlt1_value
,
stack_rlt1
))
self
.
assertTrue
(
np
.
allclose
(
eager_eye_tensor_rlt2_value
,
stack_rlt2
))
self
.
assertTrue
(
np
.
allclose
(
eager_diag_tensor_value
,
np
.
eye
(
20
)))
self
.
assertTrue
(
np
.
allclose
(
eye_tensor_value
,
np_eye
))
self
.
assertTrue
(
np
.
allclose
(
eye_tensor_rlt1_value
,
stack_rlt1
))
self
.
assertTrue
(
np
.
allclose
(
eye_tensor_rlt2_value
,
stack_rlt2
))
...
...
@@ -1655,6 +2262,7 @@ class TestLayer(LayerTest):
static_ret
=
self
.
get_static_graph_result
(
feed
=
{},
fetch_list
=
out
)
with
self
.
dynamic_graph
():
# TODO(wuweilong): Add with _test_eager_guard():
i
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
0
)
ten
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int64'
,
value
=
10
)
...
...
@@ -1687,6 +2295,14 @@ class TestLayer(LayerTest):
feed
=
{
"a"
:
value_a
,
"b"
:
value_b
},
fetch_list
=
[
cond
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
da
=
base
.
to_variable
(
value_a
)
db
=
base
.
to_variable
(
value_b
)
dcond
=
layers
.
less_than
(
x
=
da
,
y
=
db
)
for
i
in
range
(
len
(
static_ret
)):
self
.
assertTrue
(
dcond
.
numpy
()[
i
]
==
static_ret
[
i
])
da
=
base
.
to_variable
(
value_a
)
db
=
base
.
to_variable
(
value_b
)
dcond
=
layers
.
less_than
(
x
=
da
,
y
=
db
)
...
...
@@ -1703,6 +2319,14 @@ class TestLayer(LayerTest):
feed
=
{
"a1"
:
value_a
,
"b1"
:
value_b
},
fetch_list
=
[
cond1
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
da1
=
base
.
to_variable
(
value_a
)
db1
=
base
.
to_variable
(
value_b
)
dcond1
=
layers
.
less_equal
(
x
=
da1
,
y
=
db1
)
for
i
in
range
(
len
(
static_ret1
)):
self
.
assertTrue
(
dcond1
.
numpy
()[
i
]
==
static_ret1
[
i
])
da1
=
base
.
to_variable
(
value_a
)
db1
=
base
.
to_variable
(
value_b
)
dcond1
=
layers
.
less_equal
(
x
=
da1
,
y
=
db1
)
...
...
@@ -1719,6 +2343,14 @@ class TestLayer(LayerTest):
feed
=
{
"a2"
:
value_a
,
"b2"
:
value_b
},
fetch_list
=
[
cond2
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
da2
=
base
.
to_variable
(
value_a
)
db2
=
base
.
to_variable
(
value_b
)
dcond2
=
layers
.
greater_than
(
x
=
da2
,
y
=
db2
)
for
i
in
range
(
len
(
static_ret2
)):
self
.
assertTrue
(
dcond2
.
numpy
()[
i
]
==
static_ret2
[
i
])
da2
=
base
.
to_variable
(
value_a
)
db2
=
base
.
to_variable
(
value_b
)
dcond2
=
layers
.
greater_than
(
x
=
da2
,
y
=
db2
)
...
...
@@ -1735,6 +2367,14 @@ class TestLayer(LayerTest):
feed
=
{
"a3"
:
value_a
,
"b3"
:
value_b
},
fetch_list
=
[
cond3
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
da3
=
base
.
to_variable
(
value_a
)
db3
=
base
.
to_variable
(
value_b
)
dcond3
=
layers
.
greater_equal
(
x
=
da3
,
y
=
db3
)
for
i
in
range
(
len
(
static_ret3
)):
self
.
assertTrue
(
dcond3
.
numpy
()[
i
]
==
static_ret3
[
i
])
da3
=
base
.
to_variable
(
value_a
)
db3
=
base
.
to_variable
(
value_b
)
dcond3
=
layers
.
greater_equal
(
x
=
da3
,
y
=
db3
)
...
...
@@ -1751,6 +2391,14 @@ class TestLayer(LayerTest):
feed
=
{
"a4"
:
value_a
,
"b4"
:
value_b
},
fetch_list
=
[
cond4
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
da4
=
base
.
to_variable
(
value_a
)
db4
=
base
.
to_variable
(
value_b
)
dcond4
=
layers
.
equal
(
x
=
da4
,
y
=
db4
)
for
i
in
range
(
len
(
static_ret4
)):
self
.
assertTrue
(
dcond4
.
numpy
()[
i
]
==
static_ret4
[
i
])
da4
=
base
.
to_variable
(
value_a
)
db4
=
base
.
to_variable
(
value_b
)
dcond4
=
layers
.
equal
(
x
=
da4
,
y
=
db4
)
...
...
@@ -1767,6 +2415,14 @@ class TestLayer(LayerTest):
feed
=
{
"a5"
:
value_a
,
"b5"
:
value_b
},
fetch_list
=
[
cond5
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
da5
=
base
.
to_variable
(
value_a
)
db5
=
base
.
to_variable
(
value_b
)
dcond5
=
layers
.
equal
(
x
=
da5
,
y
=
db5
)
for
i
in
range
(
len
(
static_ret5
)):
self
.
assertTrue
(
dcond5
.
numpy
()[
i
]
==
static_ret5
[
i
])
da5
=
base
.
to_variable
(
value_a
)
db5
=
base
.
to_variable
(
value_b
)
dcond5
=
layers
.
equal
(
x
=
da5
,
y
=
db5
)
...
...
@@ -1795,6 +2451,23 @@ class TestLayer(LayerTest):
static_res
=
ret
[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
a
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
0.1
]).
astype
(
'float32'
))
b
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
0.23
]).
astype
(
'float32'
))
out
=
layers
.
cond
(
a
<
b
,
lambda
:
less_than_branch
(
a
,
b
),
lambda
:
greater_equal_branch
(
a
,
b
))
out2
=
layers
.
cond
(
a
>=
b
,
lambda
:
greater_equal_branch
(
a
,
b
),
lambda
:
less_than_branch
(
a
,
b
))
eager_dynamic_res
=
out
.
numpy
()
eager_dynamic_res2
=
out2
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
eager_dynamic_res
,
eager_dynamic_res2
))
with
self
.
assertRaises
(
TypeError
):
layers
.
cond
(
a
<
b
,
'str'
,
'str'
)
with
self
.
assertRaises
(
TypeError
):
layers
.
cond
(
a
>=
b
,
'str'
,
'str'
)
a
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
0.1
]).
astype
(
'float32'
))
b
=
fluid
.
dygraph
.
to_variable
(
np
.
array
([
0.23
]).
astype
(
'float32'
))
out
=
layers
.
cond
(
a
<
b
,
lambda
:
less_than_branch
(
a
,
b
),
...
...
@@ -1810,6 +2483,7 @@ class TestLayer(LayerTest):
layers
.
cond
(
a
>=
b
,
'str'
,
'str'
)
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dynamic_res
))
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
eager_dynamic_res
))
def
test_case
(
self
):
def
fn_1
():
...
...
@@ -1840,6 +2514,23 @@ class TestLayer(LayerTest):
static_res1
,
static_res2
=
exe
.
run
(
fetch_list
=
[
out_1
,
out_2
])
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
x
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.3
)
y
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.1
)
z
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.2
)
pred_1
=
layers
.
less_than
(
z
,
x
)
# true: 0.2 < 0.3
pred_2
=
layers
.
less_than
(
x
,
y
)
# false: 0.3 < 0.1
pred_3
=
layers
.
equal
(
x
,
y
)
# false: 0.3 == 0.1
out_1
=
layers
.
case
(
pred_fn_pairs
=
[(
pred_1
,
fn_1
),
(
pred_2
,
fn_2
)],
default
=
fn_3
)
out_2
=
layers
.
case
(
pred_fn_pairs
=
[(
pred_2
,
fn_2
),
(
pred_3
,
fn_3
)])
eager_dynamic_res1
=
out_1
.
numpy
()
eager_dynamic_res2
=
out_2
.
numpy
()
x
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.3
)
y
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.1
)
z
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'float32'
,
value
=
0.2
)
...
...
@@ -1856,6 +2547,8 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
array_equal
(
static_res1
,
dynamic_res1
))
self
.
assertTrue
(
np
.
array_equal
(
static_res2
,
dynamic_res2
))
self
.
assertTrue
(
np
.
array_equal
(
static_res1
,
eager_dynamic_res1
))
self
.
assertTrue
(
np
.
array_equal
(
static_res2
,
eager_dynamic_res2
))
def
test_switch_case
(
self
):
def
fn_1
():
...
...
@@ -1891,6 +2584,29 @@ class TestLayer(LayerTest):
fetch_list
=
[
out_1
,
out_2
,
out_3
])
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
index_1
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int32'
,
value
=
1
)
index_2
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int32'
,
value
=
2
)
out_1
=
layers
.
switch_case
(
branch_index
=
index_1
,
branch_fns
=
{
1
:
fn_1
,
2
:
fn_2
},
default
=
fn_3
)
out_2
=
layers
.
switch_case
(
branch_index
=
index_2
,
branch_fns
=
[(
1
,
fn_1
),
(
2
,
fn_2
)],
default
=
fn_3
)
out_3
=
layers
.
switch_case
(
branch_index
=
index_2
,
branch_fns
=
[(
0
,
fn_1
),
(
4
,
fn_2
),
(
7
,
fn_3
)])
eager_dynamic_res1
=
out_1
.
numpy
()
eager_dynamic_res2
=
out_2
.
numpy
()
eager_dynamic_res3
=
out_3
.
numpy
()
index_1
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int32'
,
value
=
1
)
index_2
=
layers
.
fill_constant
(
shape
=
[
1
],
dtype
=
'int32'
,
value
=
2
)
...
...
@@ -1914,6 +2630,9 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
array_equal
(
static_res1
,
dynamic_res1
))
self
.
assertTrue
(
np
.
array_equal
(
static_res2
,
dynamic_res2
))
self
.
assertTrue
(
np
.
array_equal
(
static_res3
,
dynamic_res3
))
self
.
assertTrue
(
np
.
array_equal
(
static_res1
,
eager_dynamic_res1
))
self
.
assertTrue
(
np
.
array_equal
(
static_res2
,
eager_dynamic_res2
))
self
.
assertTrue
(
np
.
array_equal
(
static_res3
,
eager_dynamic_res3
))
def
test_crop_tensor
(
self
):
with
self
.
static_graph
():
...
...
@@ -3281,6 +4000,14 @@ class TestBook(LayerTest):
fetch_list
=
[
output
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
x_dy
=
base
.
to_variable
(
x_np
)
rois_dy
=
base
.
to_variable
(
rois_np
)
rois_num_dy
=
base
.
to_variable
(
rois_num_np
)
dy_eager_res
=
layers
.
roi_pool
(
x_dy
,
rois_dy
,
4
,
4
,
0.5
,
rois_num
=
rois_num_dy
)
dy_eager_res_value
=
dy_eager_res
[
0
].
numpy
()
x_dy
=
base
.
to_variable
(
x_np
)
rois_dy
=
base
.
to_variable
(
rois_np
)
rois_num_dy
=
base
.
to_variable
(
rois_num_np
)
...
...
@@ -3288,6 +4015,7 @@ class TestBook(LayerTest):
x_dy
,
rois_dy
,
4
,
4
,
0.5
,
rois_num
=
rois_num_dy
)
dy_res_value
=
dy_res
[
0
].
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dy_res_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dy_eager_res_value
))
def
test_sequence_enumerate
(
self
):
# TODO(minqiyang): dygraph do not support lod now
...
...
@@ -3312,12 +4040,21 @@ class TestBook(LayerTest):
fetch_list
=
[
output
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
x_dy
=
base
.
to_variable
(
x_np
)
rois_dy
=
base
.
to_variable
(
rois_np
)
rois_num_dy
=
base
.
to_variable
(
rois_num_np
)
dy_eager_res
=
layers
.
roi_align
(
x_dy
,
rois_dy
,
4
,
4
,
0.5
,
2
,
rois_num
=
rois_num_dy
)
dy_eager_res_value
=
dy_eager_res
.
numpy
()
x_dy
=
base
.
to_variable
(
x_np
)
rois_dy
=
base
.
to_variable
(
rois_np
)
rois_num_dy
=
base
.
to_variable
(
rois_num_np
)
dy_res
=
layers
.
roi_align
(
x_dy
,
rois_dy
,
4
,
4
,
0.5
,
2
,
rois_num
=
rois_num_dy
)
dy_res_value
=
dy_res
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dy_eager_res_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dy_res_value
))
def
test_dice_loss
(
self
):
...
...
@@ -3338,11 +4075,18 @@ class TestBook(LayerTest):
fetch_list
=
[
output
])[
0
]
with
self
.
dynamic_graph
():
with
_test_eager_guard
():
input_
=
base
.
to_variable
(
input_np
)
label_
=
base
.
to_variable
(
label_np
)
dy_eager_res
=
layers
.
dice_loss
(
input_
,
label_
,
eps
)
dy_eager_res_value
=
dy_eager_res
.
numpy
()
input_
=
base
.
to_variable
(
input_np
)
label_
=
base
.
to_variable
(
label_np
)
dy_res
=
layers
.
dice_loss
(
input_
,
label_
,
eps
)
dy_res_value
=
dy_res
.
numpy
()
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dy_res_value
))
self
.
assertTrue
(
np
.
array_equal
(
static_res
,
dy_eager_res_value
))
def
test_roi_perspective_transform
(
self
):
# TODO(minqiyang): dygraph do not support lod now
...
...
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