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体验新版 GitCode,发现更多精彩内容 >>
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提交
2b0c22ad
编写于
7月 04, 2022
作者:
L
Leo Guo
提交者:
GitHub
7月 04, 2022
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
Modify the unittests of the conv2d_transpose, gaussian_random op. test=kunlun (#43961)
上级
9e3433bd
变更
2
隐藏空白更改
内联
并排
Showing
2 changed file
with
429 addition
and
166 deletion
+429
-166
python/paddle/fluid/tests/unittests/xpu/test_conv2d_transpose_op_xpu.py
...fluid/tests/unittests/xpu/test_conv2d_transpose_op_xpu.py
+148
-153
python/paddle/fluid/tests/unittests/xpu/test_gaussian_random_op_xpu.py
.../fluid/tests/unittests/xpu/test_gaussian_random_op_xpu.py
+281
-13
未找到文件。
python/paddle/fluid/tests/unittests/xpu/test_conv2d_transpose_op_xpu.py
浏览文件 @
2b0c22ad
...
...
@@ -22,9 +22,11 @@ import numpy as np
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
import
paddle
import
paddle.nn
as
nn
from
paddle.fluid
import
Program
,
program_guard
paddle
.
enable_static
()
def
conv2dtranspose_forward_naive
(
input_
,
filter_
,
attrs
):
...
...
@@ -117,166 +119,159 @@ def conv2dtranspose_forward_naive(input_, filter_, attrs):
return
out
class
TestConv2DTransposeOp
(
XPUOpTest
):
def
setUp
(
self
):
# init as conv transpose
self
.
dtype
=
np
.
float32
self
.
need_check_grad
=
True
self
.
is_test
=
False
self
.
use_cudnn
=
False
self
.
use_mkldnn
=
False
self
.
output_size
=
None
self
.
output_padding
=
[]
self
.
data_format
=
"NCHW"
self
.
pad
=
[
0
,
0
]
self
.
padding_algorithm
=
"EXPLICIT"
self
.
init_op_type
()
self
.
init_test_case
()
self
.
__class__
.
op_type
=
"conv2d_transpose"
input_
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
self
.
dtype
)
filter_
=
np
.
random
.
random
(
self
.
filter_size
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
'padding_algorithm'
:
self
.
padding_algorithm
,
'groups'
:
self
.
groups
,
'dilations'
:
self
.
dilations
,
'use_cudnn'
:
self
.
use_cudnn
,
'is_test'
:
self
.
is_test
,
'use_mkldnn'
:
self
.
use_mkldnn
,
'data_format'
:
self
.
data_format
}
if
self
.
output_size
is
not
None
:
self
.
attrs
[
'output_size'
]
=
self
.
output_size
if
len
(
self
.
output_padding
)
>
0
:
self
.
attrs
[
'output_padding'
]
=
self
.
output_padding
output
=
conv2dtranspose_forward_naive
(
input_
,
filter_
,
self
.
attrs
).
astype
(
self
.
dtype
)
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
if
core
.
is_compiled_with_xpu
():
paddle
.
enable_static
()
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_output_with_place
(
place
)
def
test_check_grad_no_input
(
self
):
if
self
.
need_check_grad
:
if
core
.
is_compiled_with_xpu
():
paddle
.
enable_static
()
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
'Filter'
],
class
XPUTestConv2DTransposeOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'conv2d_transpose'
self
.
use_dynamic_create_class
=
False
class
TestConv2DTransposeOp
(
XPUOpTest
):
def
setUp
(
self
):
# init as conv transpose
self
.
need_check_grad
=
True
self
.
is_test
=
False
self
.
use_cudnn
=
False
self
.
use_mkldnn
=
False
self
.
output_size
=
None
self
.
output_padding
=
[]
self
.
data_format
=
"NCHW"
self
.
pad
=
[
0
,
0
]
self
.
padding_algorithm
=
"EXPLICIT"
self
.
init_op_type
()
self
.
init_test_case
()
self
.
__class__
.
op_type
=
"conv2d_transpose"
input_
=
np
.
random
.
random
(
self
.
input_size
).
astype
(
self
.
dtype
)
filter_
=
np
.
random
.
random
(
self
.
filter_size
).
astype
(
self
.
dtype
)
self
.
inputs
=
{
'Input'
:
input_
,
'Filter'
:
filter_
}
self
.
attrs
=
{
'strides'
:
self
.
stride
,
'paddings'
:
self
.
pad
,
'padding_algorithm'
:
self
.
padding_algorithm
,
'groups'
:
self
.
groups
,
'dilations'
:
self
.
dilations
,
'use_cudnn'
:
self
.
use_cudnn
,
'is_test'
:
self
.
is_test
,
'use_mkldnn'
:
self
.
use_mkldnn
,
'data_format'
:
self
.
data_format
}
if
self
.
output_size
is
not
None
:
self
.
attrs
[
'output_size'
]
=
self
.
output_size
if
len
(
self
.
output_padding
)
>
0
:
self
.
attrs
[
'output_padding'
]
=
self
.
output_padding
output
=
conv2dtranspose_forward_naive
(
input_
,
filter_
,
self
.
attrs
).
astype
(
self
.
dtype
)
self
.
outputs
=
{
'Output'
:
output
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad_no_input
(
self
):
if
self
.
need_check_grad
:
self
.
check_grad_with_place
(
self
.
place
,
[
'Filter'
],
'Output'
,
no_grad_set
=
set
([
'Input'
]))
def
test_check_grad_no_filter
(
self
):
if
self
.
need_check_grad
:
if
core
.
is_compiled_with_xpu
():
paddle
.
enable_static
()
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
[
'Input'
],
def
test_check_grad_no_filter
(
self
):
if
self
.
need_check_grad
:
self
.
check_grad_with_place
(
self
.
place
,
[
'Input'
],
'Output'
,
no_grad_set
=
set
([
'Filter'
]))
def
test_check_grad
(
self
):
if
self
.
need_check_grad
:
if
core
.
is_compiled_with_xpu
():
paddle
.
enable_static
()
place
=
paddle
.
XPUPlace
(
0
)
self
.
check_grad_with_place
(
place
,
set
([
'Input'
,
'Filter'
]),
def
test_check_grad
(
self
):
if
self
.
need_check_grad
:
self
.
check_grad_with_place
(
self
.
place
,
set
([
'Input'
,
'Filter'
]),
'Output'
)
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
def
init_op_type
(
self
):
self
.
op_type
=
"conv2d_transpose"
class
TestWithSymmetricPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithAsymmetricPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
0
,
1
,
2
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithSAMEPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
stride
=
[
2
,
1
]
self
.
dilations
=
[
1
,
2
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
6
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
4
,
3
]
self
.
padding_algorithm
=
'SAME'
class
TestWithVALIDPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
self
.
padding_algorithm
=
'VALID'
class
TestWithGroups
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
]
class
TestWithStride
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
def
init_test_case
(
self
):
self
.
pad
=
[
0
,
0
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
def
init_op_type
(
self
):
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
"conv2d_transpose"
class
TestWithSymmetricPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithAsymmetricPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
0
,
1
,
2
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
class
TestWithSAMEPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
stride
=
[
2
,
1
]
self
.
dilations
=
[
1
,
2
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
6
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
4
,
3
]
self
.
padding_algorithm
=
'SAME'
class
TestWithVALIDPad
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
self
.
padding_algorithm
=
'VALID'
class
TestWithGroups
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
1
,
1
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
2
self
.
input_size
=
[
2
,
4
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
3
,
3
,
3
]
class
TestWithStride
(
TestConv2DTransposeOp
):
def
init_test_case
(
self
):
self
.
pad
=
[
1
,
1
]
self
.
stride
=
[
2
,
2
]
self
.
dilations
=
[
1
,
1
]
self
.
groups
=
1
self
.
input_size
=
[
2
,
3
,
5
,
5
]
# NCHW
f_c
=
self
.
input_size
[
1
]
self
.
filter_size
=
[
f_c
,
6
,
3
,
3
]
support_types
=
get_xpu_op_support_types
(
'conv2d_transpose'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestConv2DTransposeOp
,
stype
)
if
__name__
==
'__main__'
:
unittest
.
main
()
python/paddle/fluid/tests/unittests/xpu/test_gaussian_random_op_xpu.py
浏览文件 @
2b0c22ad
...
...
@@ -21,25 +21,293 @@ import unittest
import
numpy
as
np
import
paddle
import
paddle.fluid
as
fluid
import
paddle.fluid.core
as
core
from
paddle.fluid.op
import
Operator
from
paddle.fluid.executor
import
Executor
from
op_test
import
OpTest
from
test_gaussian_random_op
import
TestGaussianRandomOp
from
op_test_xpu
import
XPUOpTest
from
xpu.get_test_cover_info
import
create_test_class
,
get_xpu_op_support_types
,
XPUOpTestWrapper
import
paddle
paddle
.
enable_static
()
class
TestXPUGaussianRandomOp
(
TestGaussianRandomOp
):
class
XPUTestGaussianRandomOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'gaussian_random'
self
.
use_dynamic_create_class
=
False
class
TestGaussianRandomOp
(
XPUOpTest
):
def
init
(
self
):
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
op_type
=
'gaussian_random'
def
setUp
(
self
):
self
.
init
()
self
.
python_api
=
paddle
.
normal
self
.
set_attrs
()
self
.
inputs
=
{}
self
.
use_mkldnn
=
False
self
.
attrs
=
{
"shape"
:
[
123
,
92
],
"mean"
:
self
.
mean
,
"std"
:
self
.
std
,
"seed"
:
10
,
"use_mkldnn"
:
self
.
use_mkldnn
}
paddle
.
seed
(
10
)
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
123
,
92
),
dtype
=
self
.
dtype
)}
def
set_attrs
(
self
):
self
.
mean
=
1.0
self
.
std
=
2.
def
test_check_output
(
self
):
self
.
check_output_with_place_customized
(
self
.
verify_output
,
self
.
place
)
def
verify_output
(
self
,
outs
):
self
.
assertEqual
(
outs
[
0
].
shape
,
(
123
,
92
))
hist
,
_
=
np
.
histogram
(
outs
[
0
],
range
=
(
-
3
,
5
))
hist
=
hist
.
astype
(
"float32"
)
hist
/=
float
(
outs
[
0
].
size
)
data
=
np
.
random
.
normal
(
size
=
(
123
,
92
),
loc
=
1
,
scale
=
2
)
hist2
,
_
=
np
.
histogram
(
data
,
range
=
(
-
3
,
5
))
hist2
=
hist2
.
astype
(
"float32"
)
hist2
/=
float
(
outs
[
0
].
size
)
self
.
assertTrue
(
np
.
allclose
(
hist
,
hist2
,
rtol
=
0
,
atol
=
0.01
),
"hist: "
+
str
(
hist
)
+
" hist2: "
+
str
(
hist2
))
class
TestMeanStdAreInt
(
TestGaussianRandomOp
):
def
set_attrs
(
self
):
self
.
mean
=
1
self
.
std
=
2
# Situation 2: Attr(shape) is a list(with tensor)
class
TestGaussianRandomOp_ShapeTensorList
(
TestGaussianRandomOp
):
def
setUp
(
self
):
'''Test gaussian_random op with specified value
'''
self
.
init
()
self
.
init_data
()
shape_tensor_list
=
[]
for
index
,
ele
in
enumerate
(
self
.
shape
):
shape_tensor_list
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
attrs
=
{
'shape'
:
self
.
infer_shape
,
'mean'
:
self
.
mean
,
'std'
:
self
.
std
,
'seed'
:
self
.
seed
,
'use_mkldnn'
:
self
.
use_mkldnn
}
self
.
inputs
=
{
"ShapeTensorList"
:
shape_tensor_list
}
self
.
outputs
=
{
'Out'
:
np
.
zeros
(
self
.
shape
,
dtype
=
self
.
dtype
)}
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
-
1
,
92
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
def
test_check_output
(
self
):
self
.
check_output_with_place_customized
(
self
.
verify_output
,
self
.
place
)
class
TestGaussianRandomOp2_ShapeTensorList
(
TestGaussianRandomOp_ShapeTensorList
):
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
-
1
,
-
1
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
class
TestGaussianRandomOp3_ShapeTensorList
(
TestGaussianRandomOp_ShapeTensorList
):
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
123
,
-
1
]
self
.
use_mkldnn
=
True
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
class
TestGaussianRandomOp4_ShapeTensorList
(
TestGaussianRandomOp_ShapeTensorList
):
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
infer_shape
=
[
123
,
-
1
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
# Situation 3: shape is a tensor
class
TestGaussianRandomOp1_ShapeTensor
(
TestGaussianRandomOp
):
def
setUp
(
self
):
'''Test gaussian_random op with specified value
'''
self
.
init
()
self
.
init_data
()
self
.
use_mkldnn
=
False
self
.
inputs
=
{
"ShapeTensor"
:
np
.
array
(
self
.
shape
).
astype
(
"int32"
)}
self
.
attrs
=
{
'mean'
:
self
.
mean
,
'std'
:
self
.
std
,
'seed'
:
self
.
seed
,
'use_mkldnn'
:
self
.
use_mkldnn
}
self
.
outputs
=
{
'Out'
:
np
.
zeros
((
123
,
92
),
dtype
=
self
.
dtype
)}
def
init_data
(
self
):
self
.
shape
=
[
123
,
92
]
self
.
use_mkldnn
=
False
self
.
mean
=
1.0
self
.
std
=
2.0
self
.
seed
=
10
# Test python API
class
TestGaussianRandomAPI
(
unittest
.
TestCase
):
def
test_api
(
self
):
positive_2_int32
=
fluid
.
layers
.
fill_constant
([
1
],
"int32"
,
2000
)
positive_2_int64
=
fluid
.
layers
.
fill_constant
([
1
],
"int64"
,
500
)
shape_tensor_int32
=
fluid
.
data
(
name
=
"shape_tensor_int32"
,
shape
=
[
2
],
dtype
=
"int32"
)
shape_tensor_int64
=
fluid
.
data
(
name
=
"shape_tensor_int64"
,
shape
=
[
2
],
dtype
=
"int64"
)
out_1
=
fluid
.
layers
.
gaussian_random
(
shape
=
[
2000
,
500
],
dtype
=
"float32"
,
mean
=
0.0
,
std
=
1.0
,
seed
=
10
)
out_2
=
fluid
.
layers
.
gaussian_random
(
shape
=
[
2000
,
positive_2_int32
],
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_3
=
fluid
.
layers
.
gaussian_random
(
shape
=
[
2000
,
positive_2_int64
],
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_4
=
fluid
.
layers
.
gaussian_random
(
shape
=
shape_tensor_int32
,
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_5
=
fluid
.
layers
.
gaussian_random
(
shape
=
shape_tensor_int64
,
dtype
=
"float32"
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
out_6
=
fluid
.
layers
.
gaussian_random
(
shape
=
shape_tensor_int64
,
dtype
=
np
.
float32
,
mean
=
0.
,
std
=
1.0
,
seed
=
10
)
exe
=
fluid
.
Executor
(
place
=
fluid
.
XPUPlace
(
0
))
res_1
,
res_2
,
res_3
,
res_4
,
res_5
,
res_6
=
exe
.
run
(
fluid
.
default_main_program
(),
feed
=
{
"shape_tensor_int32"
:
np
.
array
([
2000
,
500
]).
astype
(
"int32"
),
"shape_tensor_int64"
:
np
.
array
([
2000
,
500
]).
astype
(
"int64"
),
},
fetch_list
=
[
out_1
,
out_2
,
out_3
,
out_4
,
out_5
,
out_6
])
self
.
assertAlmostEqual
(
np
.
mean
(
res_1
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_1
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_2
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_2
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_3
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_3
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_4
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_5
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_5
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_5
),
1.
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
mean
(
res_6
),
0.0
,
delta
=
0.1
)
self
.
assertAlmostEqual
(
np
.
std
(
res_6
),
1.
,
delta
=
0.1
)
def
test_default_dtype
(
self
):
paddle
.
disable_static
()
def
test_default_fp16
():
paddle
.
framework
.
set_default_dtype
(
'float16'
)
paddle
.
tensor
.
random
.
gaussian
([
2
,
3
])
self
.
assertRaises
(
TypeError
,
test_default_fp16
)
def
test_default_fp32
():
paddle
.
framework
.
set_default_dtype
(
'float32'
)
out
=
paddle
.
tensor
.
random
.
gaussian
([
2
,
3
])
self
.
assertEqual
(
out
.
dtype
,
fluid
.
core
.
VarDesc
.
VarType
.
FP32
)
def
test_default_fp64
():
paddle
.
framework
.
set_default_dtype
(
'float64'
)
out
=
paddle
.
tensor
.
random
.
gaussian
([
2
,
3
])
self
.
assertEqual
(
out
.
dtype
,
fluid
.
core
.
VarDesc
.
VarType
.
FP64
)
test_default_fp64
()
test_default_fp32
()
paddle
.
enable_static
()
class
TestStandardNormalDtype
(
unittest
.
TestCase
):
def
test_default_dtype
(
self
):
paddle
.
disable_static
()
def
test_default_fp16
():
paddle
.
framework
.
set_default_dtype
(
'float16'
)
paddle
.
tensor
.
random
.
standard_normal
([
2
,
3
])
self
.
assertRaises
(
TypeError
,
test_default_fp16
)
def
test_default_fp32
():
paddle
.
framework
.
set_default_dtype
(
'float32'
)
out
=
paddle
.
tensor
.
random
.
standard_normal
([
2
,
3
])
self
.
assertEqual
(
out
.
dtype
,
fluid
.
core
.
VarDesc
.
VarType
.
FP32
)
def
test_default_fp64
():
paddle
.
framework
.
set_default_dtype
(
'float64'
)
out
=
paddle
.
tensor
.
random
.
standard_normal
([
2
,
3
])
self
.
assertEqual
(
out
.
dtype
,
fluid
.
core
.
VarDesc
.
VarType
.
FP64
)
test_default_fp64
()
test_default_fp32
()
paddle
.
enable_static
()
def
test_check_output
(
self
):
if
paddle
.
is_compiled_with_xpu
():
place
=
paddle
.
XPUPlace
(
0
)
outs
=
self
.
calc_output
(
place
)
outs
=
[
np
.
array
(
out
)
for
out
in
outs
]
outs
.
sort
(
key
=
len
)
self
.
verify_output
(
outs
)
support_types
=
get_xpu_op_support_types
(
'gaussian_random'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestGaussianRandomOp
,
stype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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