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d1a53649
编写于
6月 24, 2022
作者:
C
Chenxiao Niu
提交者:
GitHub
6月 24, 2022
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add UTs for mlu interp_v2(nearest). (#43709)
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python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py
...luid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py
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python/paddle/fluid/tests/unittests/mlu/test_nearest_interp_v2_op_mlu.py
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d1a53649
# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from
__future__
import
print_function
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
'..'
)
from
op_test
import
OpTest
import
paddle.fluid.core
as
core
import
paddle.fluid
as
fluid
import
paddle.nn
as
nn
import
paddle
from
paddle.nn.functional
import
interpolate
paddle
.
enable_static
()
def
nearest_neighbor_interp_np
(
X
,
out_h
,
out_w
,
scale_h
=
0
,
scale_w
=
0
,
out_size
=
None
,
actual_shape
=
None
,
align_corners
=
True
,
data_layout
=
'NCHW'
):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if
data_layout
==
"NHWC"
:
X
=
np
.
transpose
(
X
,
(
0
,
3
,
1
,
2
))
# NHWC => NCHW
if
out_size
is
not
None
:
out_h
=
out_size
[
0
]
out_w
=
out_size
[
1
]
if
actual_shape
is
not
None
:
out_h
=
actual_shape
[
0
]
out_w
=
actual_shape
[
1
]
n
,
c
,
in_h
,
in_w
=
X
.
shape
ratio_h
=
ratio_w
=
0.0
if
(
out_h
>
1
):
if
(
align_corners
):
ratio_h
=
(
in_h
-
1.0
)
/
(
out_h
-
1.0
)
else
:
if
scale_h
>
0
:
ratio_h
=
1.0
/
scale_h
else
:
ratio_h
=
1.0
*
in_h
/
out_h
if
(
out_w
>
1
):
if
(
align_corners
):
ratio_w
=
(
in_w
-
1.0
)
/
(
out_w
-
1.0
)
else
:
if
scale_w
>
0
:
ratio_w
=
1.0
/
scale_w
else
:
ratio_w
=
1.0
*
in_w
/
out_w
out
=
np
.
zeros
((
n
,
c
,
out_h
,
out_w
))
if
align_corners
:
for
i
in
range
(
out_h
):
in_i
=
int
(
ratio_h
*
i
+
0.5
)
for
j
in
range
(
out_w
):
in_j
=
int
(
ratio_w
*
j
+
0.5
)
out
[:,
:,
i
,
j
]
=
X
[:,
:,
in_i
,
in_j
]
else
:
for
i
in
range
(
out_h
):
in_i
=
int
(
ratio_h
*
i
)
for
j
in
range
(
out_w
):
in_j
=
int
(
ratio_w
*
j
)
out
[:,
:,
i
,
j
]
=
X
[:,
:,
in_i
,
in_j
]
if
data_layout
==
"NHWC"
:
out
=
np
.
transpose
(
out
,
(
0
,
2
,
3
,
1
))
# NCHW => NHWC
# out = np.expand_dims(out, 2)
return
out
.
astype
(
X
.
dtype
)
def
nearest_neighbor_interp3d_np
(
X
,
out_d
,
out_h
,
out_w
,
scale_d
=
0
,
scale_h
=
0
,
scale_w
=
0
,
out_size
=
None
,
actual_shape
=
None
,
align_corners
=
True
,
data_layout
=
'NCHW'
):
"""nearest neighbor interpolation implement in shape [N, C, H, W]"""
if
data_layout
==
"NHWC"
:
X
=
np
.
transpose
(
X
,
(
0
,
4
,
1
,
2
,
3
))
# NDHWC => NCDHW
if
out_size
is
not
None
:
out_d
=
out_size
[
0
]
out_h
=
out_size
[
1
]
out_w
=
out_size
[
2
]
if
actual_shape
is
not
None
:
out_d
=
actual_shape
[
0
]
out_h
=
actual_shape
[
1
]
out_w
=
actual_shape
[
2
]
n
,
c
,
in_d
,
in_h
,
in_w
=
X
.
shape
ratio_d
=
ratio_h
=
ratio_w
=
0.0
if
(
out_d
>
1
):
if
(
align_corners
):
ratio_d
=
(
in_d
-
1.0
)
/
(
out_d
-
1.0
)
else
:
if
scale_d
>
0
:
ratio_d
=
1.0
/
scale_d
else
:
ratio_d
=
1.0
*
in_d
/
out_d
if
(
out_h
>
1
):
if
(
align_corners
):
ratio_h
=
(
in_h
-
1.0
)
/
(
out_h
-
1.0
)
else
:
if
scale_h
>
0
:
ratio_h
=
1.0
/
scale_h
else
:
ratio_h
=
1.0
*
in_h
/
out_h
if
(
out_w
>
1
):
if
(
align_corners
):
ratio_w
=
(
in_w
-
1.0
)
/
(
out_w
-
1.0
)
else
:
if
scale_w
>
0
:
ratio_w
=
1.0
/
scale_w
else
:
ratio_w
=
1.0
*
in_w
/
out_w
out
=
np
.
zeros
((
n
,
c
,
out_d
,
out_h
,
out_w
))
if
align_corners
:
for
d
in
range
(
out_d
):
in_d
=
int
(
ratio_d
*
d
+
0.5
)
for
i
in
range
(
out_h
):
in_i
=
int
(
ratio_h
*
i
+
0.5
)
for
j
in
range
(
out_w
):
in_j
=
int
(
ratio_w
*
j
+
0.5
)
out
[:,
:,
d
,
i
,
j
]
=
X
[:,
:,
in_d
,
in_i
,
in_j
]
else
:
for
d
in
range
(
out_d
):
in_d
=
int
(
ratio_d
*
d
)
for
i
in
range
(
out_h
):
in_i
=
int
(
ratio_h
*
i
)
for
j
in
range
(
out_w
):
in_j
=
int
(
ratio_w
*
j
)
out
[:,
:,
d
,
i
,
j
]
=
X
[:,
:,
in_d
,
in_i
,
in_j
]
if
data_layout
==
"NDHWC"
:
out
=
np
.
transpose
(
out
,
(
0
,
2
,
3
,
4
,
1
))
# NCDHW => NDHWC
return
out
.
astype
(
X
.
dtype
)
class
TestNearestInterpOp
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
out_size
=
None
self
.
actual_shape
=
None
self
.
init_test_case
()
self
.
data_layout
=
'NCHW'
if
len
(
self
.
input_shape
)
==
4
else
'NCDHW'
self
.
op_type
=
"nearest_interp_v2"
input_np
=
np
.
random
.
random
(
self
.
input_shape
).
astype
(
"float32"
)
if
self
.
data_layout
==
"NCHW"
and
len
(
self
.
input_shape
)
==
4
:
in_d
=
1
in_h
=
self
.
input_shape
[
2
]
in_w
=
self
.
input_shape
[
3
]
else
:
in_d
=
1
in_h
=
self
.
input_shape
[
1
]
in_w
=
self
.
input_shape
[
2
]
if
self
.
data_layout
==
"NCDHW"
and
len
(
self
.
input_shape
)
==
5
:
in_d
=
self
.
input_shape
[
2
]
in_h
=
self
.
input_shape
[
3
]
in_w
=
self
.
input_shape
[
4
]
else
:
in_d
=
self
.
input_shape
[
1
]
in_h
=
self
.
input_shape
[
2
]
in_w
=
self
.
input_shape
[
3
]
scale_d
=
0
scale_h
=
0
scale_w
=
0
if
self
.
scale
:
if
isinstance
(
self
.
scale
,
float
)
or
isinstance
(
self
.
scale
,
int
):
if
self
.
scale
>
0
:
scale_d
=
scale_h
=
scale_w
=
float
(
self
.
scale
)
if
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
==
1
:
scale_d
=
scale_w
=
scale_h
=
self
.
scale
[
0
]
elif
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
>
1
:
if
len
(
self
.
scale
)
==
5
:
scale_w
=
self
.
scale
[
2
]
scale_h
=
self
.
scale
[
1
]
scale_d
=
self
.
scale
[
0
]
else
:
scale_w
=
self
.
scale
[
1
]
scale_h
=
self
.
scale
[
0
]
out_h
=
int
(
in_h
*
scale_h
)
out_w
=
int
(
in_w
*
scale_w
)
out_d
=
int
(
in_d
*
scale_d
)
else
:
if
len
(
self
.
input_shape
)
==
5
:
out_d
=
self
.
out_d
out_h
=
self
.
out_h
out_w
=
self
.
out_w
if
len
(
self
.
input_shape
)
==
4
:
output_np
=
nearest_neighbor_interp_np
(
input_np
,
out_h
,
out_w
,
scale_h
,
scale_w
,
self
.
out_size
,
self
.
actual_shape
,
self
.
align_corners
,
self
.
data_layout
)
elif
len
(
self
.
input_shape
)
==
5
:
output_np
=
nearest_neighbor_interp3d_np
(
input_np
,
out_d
,
out_h
,
out_w
,
scale_d
,
scale_h
,
scale_w
,
self
.
out_size
,
self
.
actual_shape
,
self
.
align_corners
,
self
.
data_layout
)
self
.
inputs
=
{
'X'
:
input_np
}
if
self
.
out_size
is
not
None
:
self
.
inputs
[
'OutSize'
]
=
self
.
out_size
if
self
.
actual_shape
is
not
None
:
self
.
inputs
[
'OutSize'
]
=
self
.
actual_shape
if
len
(
self
.
input_shape
)
==
5
:
self
.
attrs
=
{
'out_d'
:
self
.
out_d
,
'out_h'
:
self
.
out_h
,
'out_w'
:
self
.
out_w
,
'interp_method'
:
self
.
interp_method
,
'align_corners'
:
self
.
align_corners
,
'data_layout'
:
self
.
data_layout
}
else
:
self
.
attrs
=
{
'out_h'
:
self
.
out_h
,
'out_w'
:
self
.
out_w
,
'interp_method'
:
self
.
interp_method
,
'align_corners'
:
self
.
align_corners
,
'data_layout'
:
self
.
data_layout
}
if
self
.
scale
:
if
isinstance
(
self
.
scale
,
float
)
or
isinstance
(
self
.
scale
,
int
):
if
self
.
scale
>
0
:
self
.
scale
=
[
self
.
scale
]
if
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
==
1
:
self
.
scale
=
[
self
.
scale
[
0
],
self
.
scale
[
0
]]
self
.
attrs
[
'scale'
]
=
self
.
scale
self
.
outputs
=
{
'Out'
:
output_np
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
,
in_place
=
True
)
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
2
,
3
,
4
,
5
]
self
.
out_h
=
2
self
.
out_w
=
2
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
3
,
3
]).
astype
(
"int32"
)
self
.
align_corners
=
True
# class TestNearestNeighborInterpCase1(TestNearestInterpOp):
# def init_test_case(self):
# self.interp_method = 'nearest'
# self.input_shape = [4, 1, 1, 7, 8]
# self.out_d = 1
# self.out_h = 1
# self.out_w = 1
# self.scale = 0.
# self.align_corners = True
class
TestNearestNeighborInterpCase2
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
3
,
9
,
6
]
self
.
out_h
=
12
self
.
out_w
=
12
self
.
scale
=
0.
self
.
align_corners
=
True
class
TestNearestNeighborInterpCase3
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
0.
self
.
align_corners
=
True
class
TestNearestNeighborInterpCase4
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
4
,
1
,
7
,
8
]
self
.
out_h
=
1
self
.
out_w
=
1
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
2
,
2
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestNeighborInterpCase5
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
3
,
9
,
6
]
self
.
out_h
=
12
self
.
out_w
=
12
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
11
,
11
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestNeighborInterpCase6
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
1
,
1
,
32
,
64
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
65
,
129
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestNeighborInterpSame
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
2
,
3
,
32
,
64
]
self
.
out_h
=
32
self
.
out_w
=
64
self
.
scale
=
0.
self
.
align_corners
=
True
class
TestNearestNeighborInterpActualShape
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
2
,
32
,
16
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
66
,
40
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestNeighborInterpDataLayout
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
2
,
4
,
4
,
5
]
self
.
out_h
=
2
self
.
out_w
=
2
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
3
,
8
]).
astype
(
"int32"
)
self
.
align_corners
=
True
self
.
data_layout
=
"NHWC"
class
TestNearestInterpWithoutCorners
(
TestNearestInterpOp
):
def
set_align_corners
(
self
):
self
.
align_corners
=
False
class
TestNearestNeighborInterpScale1
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
2
,
7
,
5
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
2.
self
.
out_size
=
np
.
array
([
66
,
40
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestNeighborInterpScale2
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
2
,
5
,
7
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
1.5
self
.
out_size
=
np
.
array
([
66
,
40
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestNeighborInterpScale3
(
TestNearestInterpOp
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
2
,
7
,
5
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
[
2.0
,
3.0
]
self
.
out_size
=
np
.
array
([
66
,
40
]).
astype
(
"int32"
)
self
.
align_corners
=
True
class
TestNearestInterpOp_attr_tensor
(
OpTest
):
def
setUp
(
self
):
self
.
place
=
paddle
.
device
.
MLUPlace
(
0
)
self
.
__class__
.
use_mlu
=
True
self
.
out_size
=
None
self
.
actual_shape
=
None
self
.
init_test_case
()
self
.
op_type
=
"nearest_interp_v2"
self
.
shape_by_1Dtensor
=
False
self
.
scale_by_1Dtensor
=
False
self
.
attrs
=
{
'interp_method'
:
self
.
interp_method
,
'align_corners'
:
self
.
align_corners
,
}
input_np
=
np
.
random
.
random
(
self
.
input_shape
).
astype
(
"float32"
)
self
.
inputs
=
{
'X'
:
input_np
}
if
self
.
scale_by_1Dtensor
:
self
.
inputs
[
'Scale'
]
=
np
.
array
([
self
.
scale
]).
astype
(
"float32"
)
elif
self
.
scale
:
if
isinstance
(
self
.
scale
,
float
)
or
isinstance
(
self
.
scale
,
int
):
if
self
.
scale
>
0
:
scale_h
=
scale_w
=
float
(
self
.
scale
)
if
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
==
1
:
scale_w
=
scale_h
=
self
.
scale
[
0
]
elif
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
>
1
:
scale_w
=
self
.
scale
[
1
]
scale_h
=
self
.
scale
[
0
]
out_h
=
int
(
self
.
input_shape
[
2
]
*
scale_h
)
out_w
=
int
(
self
.
input_shape
[
3
]
*
scale_w
)
else
:
out_h
=
self
.
out_h
out_w
=
self
.
out_w
if
self
.
shape_by_1Dtensor
:
self
.
inputs
[
'OutSize'
]
=
self
.
out_size
elif
self
.
out_size
is
not
None
:
size_tensor
=
[]
for
index
,
ele
in
enumerate
(
self
.
out_size
):
size_tensor
.
append
((
"x"
+
str
(
index
),
np
.
ones
(
(
1
)).
astype
(
'int32'
)
*
ele
))
self
.
inputs
[
'SizeTensor'
]
=
size_tensor
self
.
attrs
[
'out_h'
]
=
self
.
out_h
self
.
attrs
[
'out_w'
]
=
self
.
out_w
if
self
.
scale
:
if
isinstance
(
self
.
scale
,
float
)
or
isinstance
(
self
.
scale
,
int
):
if
self
.
scale
>
0
:
self
.
scale
=
[
self
.
scale
]
if
isinstance
(
self
.
scale
,
list
)
and
len
(
self
.
scale
)
==
1
:
self
.
scale
=
[
self
.
scale
[
0
],
self
.
scale
[
0
]]
self
.
attrs
[
'scale'
]
=
self
.
scale
output_np
=
nearest_neighbor_interp_np
(
input_np
,
out_h
,
out_w
,
0
,
0
,
self
.
out_size
,
self
.
actual_shape
,
self
.
align_corners
)
self
.
outputs
=
{
'Out'
:
output_np
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
self
.
place
)
def
test_check_grad
(
self
):
self
.
check_grad_with_place
(
self
.
place
,
[
'X'
],
'Out'
,
in_place
=
True
)
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
2
,
5
,
4
,
4
]
self
.
out_h
=
3
self
.
out_w
=
3
self
.
scale
=
0.
self
.
out_size
=
[
3
,
3
]
self
.
align_corners
=
True
# out_size is a tensor list
class
TestNearestInterp_attr_tensor_Case1
(
TestNearestInterpOp_attr_tensor
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
3
,
9
,
6
]
self
.
out_h
=
12
self
.
out_w
=
12
self
.
scale
=
0.
self
.
out_size
=
[
8
,
12
]
self
.
align_corners
=
True
# out_size is a 1-D tensor
class
TestNearestInterp_attr_tensor_Case2
(
TestNearestInterpOp_attr_tensor
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
2
,
32
,
16
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
0.
self
.
out_size
=
np
.
array
([
66
,
40
]).
astype
(
"int32"
)
self
.
align_corners
=
True
self
.
shape_by_1Dtensor
=
True
# scale is a 1-D tensor
class
TestNearestInterp_attr_tensor_Case3
(
TestNearestInterpOp_attr_tensor
):
def
init_test_case
(
self
):
self
.
interp_method
=
'nearest'
self
.
input_shape
=
[
3
,
2
,
32
,
16
]
self
.
out_h
=
64
self
.
out_w
=
32
self
.
scale
=
2.0
self
.
out_size
=
None
self
.
align_corners
=
True
self
.
scale_by_1Dtensor
=
True
#TODO: comment this test for now until nearest_interp_op added.
# class TestNearestAPI(unittest.TestCase):
# def test_case(self):
# x = fluid.data(name="x", shape=[2, 3, 6, 6], dtype="float32")
# y = fluid.data(name="y", shape=[2, 6, 6, 3], dtype="float32")
# dim = fluid.data(name="dim", shape=[1], dtype="int32")
# shape_tensor = fluid.data(name="shape_tensor", shape=[2], dtype="int32")
# actual_size = fluid.data(name="actual_size", shape=[2], dtype="int32")
# scale_tensor = fluid.data(
# name="scale_tensor", shape=[1], dtype="float32")
# out1 = fluid.layers.resize_nearest(
# y, out_shape=[12, 12], data_format='NHWC', align_corners=False)
# out2 = fluid.layers.resize_nearest(
# x, out_shape=[12, dim], align_corners=False)
# out3 = fluid.layers.resize_nearest(
# x, out_shape=shape_tensor, align_corners=False)
# out4 = fluid.layers.resize_nearest(
# x, out_shape=[4, 4], actual_shape=actual_size, align_corners=False)
# out5 = fluid.layers.resize_nearest(
# x, scale=scale_tensor, align_corners=False)
# x_data = np.random.random((2, 3, 6, 6)).astype("float32")
# dim_data = np.array([12]).astype("int32")
# shape_data = np.array([12, 12]).astype("int32")
# actual_size_data = np.array([12, 12]).astype("int32")
# scale_data = np.array([2.0]).astype("float32")
# place = paddle.MLUPlace(0)
# exe = fluid.Executor(place)
# exe.run(fluid.default_startup_program())
# results = exe.run(fluid.default_main_program(),
# feed={
# "x": x_data,
# "y": np.transpose(x_data, (0, 2, 3, 1)),
# "dim": dim_data,
# "shape_tensor": shape_data,
# "actual_size": actual_size_data,
# "scale_tensor": scale_data
# },
# fetch_list=[out1, out2, out3, out4, out5],
# return_numpy=True)
# expect_res = nearest_neighbor_interp_np(
# x_data, out_h=12, out_w=12, align_corners=False)
# self.assertTrue(
# np.allclose(results[0], np.transpose(expect_res, (0, 2, 3, 1))))
# for i in range(len(results) - 1):
# self.assertTrue(np.allclose(results[i + 1], expect_res))
class
TestNearestInterpException
(
unittest
.
TestCase
):
def
test_exception
(
self
):
import
paddle
input
=
fluid
.
data
(
name
=
"input"
,
shape
=
[
1
,
3
,
6
,
6
],
dtype
=
"float32"
)
def
attr_data_format
():
# for 4-D input, data_format can only be NCHW or NHWC
out
=
fluid
.
layers
.
resize_nearest
(
input
,
out_shape
=
[
4
,
8
],
data_format
=
'NDHWC'
)
def
attr_scale_type
():
out
=
fluid
.
layers
.
resize_nearest
(
input
,
scale
=
'scale'
)
def
attr_scale_value
():
out
=
fluid
.
layers
.
resize_nearest
(
input
,
scale
=-
0.3
)
def
input_shape_error
():
x
=
paddle
.
randn
([
1
,
3
])
out
=
paddle
.
nn
.
functional
.
interpolate
(
x
,
scale_factor
=
'scale'
)
def
mode_error
():
x
=
paddle
.
randn
([
1
,
3
])
out
=
paddle
.
nn
.
functional
.
interpolate
(
x
,
scale_factor
=
'scale'
,
mode
=
"BILINEAR"
)
self
.
assertRaises
(
ValueError
,
attr_data_format
)
self
.
assertRaises
(
TypeError
,
attr_scale_type
)
self
.
assertRaises
(
ValueError
,
attr_scale_value
)
self
.
assertRaises
(
ValueError
,
input_shape_error
)
self
.
assertRaises
(
ValueError
,
mode_error
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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