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体验新版 GitCode,发现更多精彩内容 >>
提交
d17bb4e6
编写于
3月 11, 2019
作者:
M
minqiyang
浏览文件
操作
浏览文件
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电子邮件补丁
差异文件
Add unit test for gru unit
test=develop
上级
0d27d204
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
164 addition
and
25 deletion
+164
-25
python/paddle/fluid/imperative/nn.py
python/paddle/fluid/imperative/nn.py
+19
-20
python/paddle/fluid/tests/unittests/op_test.py
python/paddle/fluid/tests/unittests/op_test.py
+103
-4
python/paddle/fluid/tests/unittests/test_gru_op.py
python/paddle/fluid/tests/unittests/test_gru_op.py
+1
-1
python/paddle/fluid/tests/unittests/test_layers.py
python/paddle/fluid/tests/unittests/test_layers.py
+41
-0
未找到文件。
python/paddle/fluid/imperative/nn.py
浏览文件 @
d17bb4e6
...
...
@@ -22,6 +22,7 @@ from . import layers
from
..framework
import
Variable
,
OpProtoHolder
from
..param_attr
import
ParamAttr
from
..initializer
import
Normal
,
Constant
__all__
=
[
'Conv2D'
,
'Pool2D'
,
'FC'
,
'BatchNorm'
,
'Embedding'
,
'GRUUnit'
]
...
...
@@ -548,7 +549,7 @@ class GRUUnit(layers.Layer):
"""
def
__init__
(
self
,
hidden
,
name_scope
,
size
,
param_attr
=
None
,
bias_attr
=
None
,
...
...
@@ -556,8 +557,8 @@ class GRUUnit(layers.Layer):
gate_activation
=
'sigmoid'
,
origin_mode
=
False
,
dtype
=
'float32'
):
super
(
GRUUnit
,
self
).
__init__
(
name_scope
)
super
(
GRUUnit
,
self
).
__init__
()
activation_dict
=
dict
(
identity
=
0
,
sigmoid
=
1
,
...
...
@@ -566,29 +567,27 @@ class GRUUnit(layers.Layer):
activation
=
activation_dict
[
activation
]
gate_activation
=
activation_dict
[
gate_activation
]
helper
=
LayerHelper
(
'gru_unit'
,
**
locals
())
dtype
=
helper
.
input_dtype
()
self
.
_dtype
=
dtype
size
=
size
//
3
# create weight
weight
=
helper
.
create_parameter
(
attr
=
helper
.
param_attr
,
shape
=
[
size
,
3
*
size
],
dtype
=
dtype
)
self
.
_weight
=
self
.
create_parameter
(
attr
=
param_attr
,
shape
=
[
size
,
3
*
size
],
dtype
=
dtype
)
gate
=
helper
.
create_variable_for_type_inference
(
dtype
)
reset_hidden_pre
=
helper
.
create_variable_for_type_inference
(
dtype
)
updated_hidden
=
helper
.
create_variable_for_type_inference
(
dtype
)
inputs
=
{
'Input'
:
input
,
'HiddenPrev'
:
hidden
,
'Weight'
:
weight
}
# create bias
if
helper
.
bias_attr
:
bias_size
=
[
1
,
3
*
size
]
bias
=
helper
.
create_parameter
(
attr
=
helper
.
bias_attr
,
shape
=
bias_size
,
dtype
=
dtype
,
is_bias
=
True
)
inputs
[
'Bias'
]
=
bias
bias_size
=
[
1
,
3
*
size
]
self
.
_bias
=
self
.
create_parameter
(
attr
=
bias_attr
,
shape
=
bias_size
,
dtype
=
dtype
,
is_bias
=
True
)
def
forward
(
self
,
input
):
def
forward
(
self
,
input
,
hidden
):
inputs
=
{
'Input'
:
input
,
'HiddenPrev'
:
hidden
,
'Weight'
:
self
.
_weight
}
if
self
.
_bias
:
inputs
[
'Bias'
]
=
self
.
_bias
gate
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
reset_hidden_pre
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
updated_hidden
=
self
.
_helper
.
create_variable_for_type_inference
(
self
.
_dtype
)
self
.
_helper
.
append_op
(
type
=
'gru_unit'
,
inputs
=
inputs
,
...
...
python/paddle/fluid/tests/unittests/op_test.py
浏览文件 @
d17bb4e6
...
...
@@ -22,6 +22,7 @@ import six
import
time
import
itertools
import
collections
from
collections
import
defaultdict
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
...
...
@@ -257,8 +258,65 @@ class OpTest(unittest.TestCase):
outs
,
_
=
self
.
_calc_output
(
place
)
return
outs
def
_calc_output
(
self
,
place
,
parallel
=
False
,
no_check_set
=
None
):
def
_create_var_from_numpy
(
self
,
value
):
if
isinstance
(
value
,
tuple
):
data
=
value
[
0
]
lod
=
value
[
1
]
v
=
fluid
.
imperative
.
base
.
to_variable
(
value
=
data
)
v
.
_ivar
.
value
().
get_tensor
().
set_recursive_sequence_lengths
(
lod
)
return
v
else
:
return
fluid
.
imperative
.
base
.
to_variable
(
value
)
def
_calc_imperative_output
(
self
,
place
,
parallel
=
False
,
no_check_set
=
None
):
with
fluid
.
imperative
.
base
.
guard
(
place
=
place
):
block
=
fluid
.
default_main_program
().
global_block
()
# prepare input variable
inputs
=
defaultdict
(
list
)
for
name
,
np_value
in
six
.
iteritems
(
self
.
inputs
):
if
not
isinstance
(
np_value
,
list
):
np_value
=
[
np_value
]
for
i
in
range
(
len
(
np_value
)):
inputs
[
name
].
append
(
self
.
_create_var_from_numpy
(
np_value
[
i
]))
# prepare output variable
outputs
=
defaultdict
(
list
)
for
name
,
np_value
in
six
.
iteritems
(
self
.
outputs
):
if
not
isinstance
(
np_value
,
list
):
np_value
=
[
np_value
]
for
i
in
range
(
len
(
np_value
)):
value
=
np_value
[
i
]
if
isinstance
(
value
,
tuple
):
v
=
block
.
create_var
(
name
=
"%s_out%d"
%
(
name
,
i
),
dtype
=
value
[
0
].
dtype
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
persistable
=
False
,
stop_gradient
=
False
)
v
.
_ivar
.
value
().
get_tensor
(
).
set_recursive_sequence_lengths
(
value
[
1
])
else
:
v
=
block
.
create_var
(
name
=
"%s_out%d"
%
(
name
,
i
),
dtype
=
value
.
dtype
,
type
=
core
.
VarDesc
.
VarType
.
LOD_TENSOR
,
persistable
=
False
,
stop_gradient
=
False
)
outputs
[
name
].
append
(
v
)
block
.
append_op
(
type
=
self
.
op_type
,
inputs
=
inputs
,
outputs
=
outputs
,
attrs
=
self
.
attrs
)
return
outputs
def
_calc_output
(
self
,
place
,
parallel
=
False
,
no_check_set
=
None
):
program
=
Program
()
block
=
program
.
global_block
()
self
.
_append_ops
(
block
)
...
...
@@ -305,8 +363,13 @@ class OpTest(unittest.TestCase):
place
,
atol
,
no_check_set
=
None
,
equal_nan
=
False
):
equal_nan
=
False
,
check_imperative
=
False
):
if
check_imperative
:
imperative_outs
=
self
.
_calc_imperative_output
(
place
,
no_check_set
=
no_check_set
)
outs
,
fetch_list
=
self
.
_calc_output
(
place
,
no_check_set
=
no_check_set
)
for
out_name
,
out_dup
in
Operator
.
get_op_outputs
(
self
.
op_type
):
if
out_name
not
in
self
.
outputs
:
continue
...
...
@@ -330,6 +393,10 @@ class OpTest(unittest.TestCase):
type
(
sub_out
))
for
item
in
sub_out
:
sub_out_name
,
expect
=
item
[
0
],
item
[
1
]
if
check_imperative
:
imperative_actual
=
imperative_outs
[
sub_out_name
][
0
]
imperative_actual_t
=
np
.
array
(
imperative_actual
.
_ivar
.
value
().
get_tensor
())
idx
=
find_actual
(
sub_out_name
,
fetch_list
)
actual
=
outs
[
idx
]
actual_t
=
np
.
array
(
actual
)
...
...
@@ -340,12 +407,24 @@ class OpTest(unittest.TestCase):
actual_t
,
expect_t
,
atol
=
atol
,
equal_nan
=
equal_nan
),
"Output ("
+
sub_out_name
+
") has diff at "
+
str
(
place
))
self
.
assertTrue
(
np
.
allclose
(
imperative_actual_t
,
expect_t
,
atol
=
atol
,
equal_nan
=
equal_nan
),
"Output ("
+
sub_out_name
+
") has diff at "
+
str
(
place
)
+
" in imperative mode"
)
if
isinstance
(
expect
,
tuple
):
self
.
assertListEqual
(
actual
.
recursive_sequence_lengths
(),
expect
[
1
],
"Output ("
+
sub_out_name
+
") has different lod at "
+
str
(
place
))
else
:
if
check_imperative
:
imperative_actual
=
imperative_outs
[
out_name
][
0
]
imperative_actual_t
=
np
.
array
(
imperative_actual
.
_ivar
.
value
().
get_tensor
())
idx
=
find_actual
(
out_name
,
fetch_list
)
actual
=
outs
[
idx
]
actual_t
=
np
.
array
(
actual
)
...
...
@@ -357,10 +436,25 @@ class OpTest(unittest.TestCase):
"Output ("
+
out_name
+
") has diff at "
+
str
(
place
)
+
"
\n
Expect "
+
str
(
expect_t
)
+
"
\n
"
+
"But Got"
+
str
(
actual_t
)
+
" in class "
+
self
.
__class__
.
__name__
)
self
.
assertTrue
(
np
.
allclose
(
imperative_actual_t
,
expect_t
,
atol
=
atol
,
equal_nan
=
equal_nan
),
"Output ("
+
out_name
+
") has diff at "
+
str
(
place
)
+
"
\n
Expect "
+
str
(
expect_t
)
+
"
\n
"
+
"But Got"
+
str
(
imperative_actual_t
)
+
" in class "
+
self
.
__class__
.
__name__
)
if
isinstance
(
expect
,
tuple
):
self
.
assertListEqual
(
actual
.
recursive_sequence_lengths
(),
expect
[
1
],
"Output ("
+
out_name
+
") has different lod at "
+
str
(
place
))
if
check_imperative
:
self
.
assertListEqual
(
imperative_actual
.
_ivar
.
value
().
get_tensor
()
.
recursive_sequence_lengths
(),
expect
[
1
],
"Output ("
+
out_name
+
") has different lod at "
+
str
(
place
))
def
_get_places
(
self
):
if
self
.
dtype
==
np
.
float16
:
...
...
@@ -383,10 +477,15 @@ class OpTest(unittest.TestCase):
places
.
append
(
core
.
CUDAPlace
(
0
))
return
places
def
check_output
(
self
,
atol
=
1e-5
,
no_check_set
=
None
,
equal_nan
=
False
):
def
check_output
(
self
,
atol
=
1e-5
,
no_check_set
=
None
,
equal_nan
=
False
,
check_imperative
=
False
):
places
=
self
.
_get_places
()
for
place
in
places
:
self
.
check_output_with_place
(
place
,
atol
,
no_check_set
,
equal_nan
)
self
.
check_output_with_place
(
place
,
atol
,
no_check_set
,
equal_nan
,
check_imperative
)
def
check_output_customized
(
self
,
checker
):
places
=
self
.
_get_places
()
...
...
python/paddle/fluid/tests/unittests/test_gru_op.py
浏览文件 @
d17bb4e6
...
...
@@ -156,7 +156,7 @@ class TestGRUOp(OpTest):
}
def
test_check_output
(
self
):
self
.
check_output
(
atol
=
1e-8
)
self
.
check_output
(
atol
=
1e-8
,
check_imperative
=
True
)
def
test_check_grad
(
self
):
self
.
check_grad
([
'Input'
,
'H0'
,
'Weight'
,
'Bias'
],
[
'Hidden'
])
...
...
python/paddle/fluid/tests/unittests/test_layers.py
浏览文件 @
d17bb4e6
...
...
@@ -112,6 +112,47 @@ class TestLayer(LayerTest):
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
dy_ret
.
_numpy
()))
self
.
assertTrue
(
np
.
allclose
(
static_ret
,
static_ret2
))
def
test_gru_unit
(
self
):
lod
=
[[
2
,
4
,
3
]]
D
=
5
T
=
sum
(
lod
[
0
])
N
=
len
(
lod
[
0
])
input
=
np
.
random
.
rand
(
T
,
3
*
D
).
astype
(
'float32'
)
hidden_input
=
np
.
random
.
rand
(
T
,
D
).
astype
(
'float32'
)
with
self
.
static_graph
():
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
-
1
,
D
*
3
],
dtype
=
'float32'
)
hidden
=
layers
.
data
(
name
=
'hidden'
,
shape
=
[
-
1
,
D
],
dtype
=
'float32'
)
updated_hidden
,
reset_hidden_pre
,
gate
=
layers
.
gru_unit
(
input
=
x
,
hidden
=
hidden
,
size
=
D
*
3
)
static_ret
=
self
.
get_static_graph_result
(
feed
=
{
'x'
:
input
,
'hidden'
:
hidden_input
},
fetch_list
=
[
updated_hidden
,
reset_hidden_pre
,
gate
])
with
self
.
static_graph
():
x
=
layers
.
data
(
name
=
'x'
,
shape
=
[
-
1
,
D
*
3
],
dtype
=
'float32'
)
hidden
=
layers
.
data
(
name
=
'hidden'
,
shape
=
[
-
1
,
D
],
dtype
=
'float32'
)
updated_hidden
,
reset_hidden_pre
,
gate
=
layers
.
gru_unit
(
input
=
x
,
hidden
=
hidden
,
size
=
D
*
3
)
gru
=
nn
.
GRUUnit
(
'gru'
,
size
=
D
*
3
)
updated_hidden
,
reset_hidden_pre
,
gate
=
gru
(
x
,
hidden
)
static_ret2
=
self
.
get_static_graph_result
(
feed
=
{
'x'
:
input
,
'hidden'
:
hidden_input
},
fetch_list
=
[
updated_hidden
,
reset_hidden_pre
,
gate
])
with
self
.
dynamic_graph
():
gru
=
nn
.
GRUUnit
(
'gru'
,
size
=
D
*
3
)
dy_ret
=
gru
(
base
.
to_variable
(
input
),
base
.
to_variable
(
hidden_input
))
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
[
i
].
_numpy
()))
class
TestBook
(
unittest
.
TestCase
):
def
test_fit_a_line
(
self
):
...
...
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