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ba992136
编写于
8月 08, 2023
作者:
L
leolishaohao
提交者:
GitHub
8月 08, 2023
浏览文件
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电子邮件补丁
差异文件
[XPU] register multiclass_nms3 and norm xpu kernel to optimize model (#56064)
上级
c271f26b
变更
5
隐藏空白更改
内联
并排
Showing
5 changed file
with
726 addition
and
0 deletion
+726
-0
paddle/phi/backends/xpu/xpu2_op_list.cc
paddle/phi/backends/xpu/xpu2_op_list.cc
+2
-0
paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc
paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc
+206
-0
paddle/phi/kernels/xpu/norm_kernel.cc
paddle/phi/kernels/xpu/norm_kernel.cc
+74
-0
test/xpu/test_multiclass_nms3_op_xpu.py
test/xpu/test_multiclass_nms3_op_xpu.py
+348
-0
test/xpu/test_norm_op_xpu.py
test/xpu/test_norm_op_xpu.py
+96
-0
未找到文件。
paddle/phi/backends/xpu/xpu2_op_list.cc
浏览文件 @
ba992136
...
...
@@ -542,6 +542,7 @@ XPUOpMap& get_kl2_ops() {
phi
::
DataType
::
INT64
})},
{
"multi_encoder_xpu"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
,
phi
::
DataType
::
FLOAT16
})},
{
"multiclass_nms3"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"nearest_interp_v2"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
,
phi
::
DataType
::
FLOAT16
,
...
...
@@ -549,6 +550,7 @@ XPUOpMap& get_kl2_ops() {
{
"nearest_interp_v2_grad"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"nll_loss"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"nll_loss_grad"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"norm"
,
XPUKernelSet
({
phi
::
DataType
::
FLOAT32
})},
{
"not_equal"
,
XPUKernelSet
({
phi
::
DataType
::
INT64
,
phi
::
DataType
::
INT32
,
...
...
paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc
0 → 100644
浏览文件 @
ba992136
// Copyright (c) 2023 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.
#include "paddle/phi/kernels/multiclass_nms3_kernel.h"
#include <vector>
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/backends/xpu/xpu_context.h"
#include "paddle/phi/common/memory_utils.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
MultiClassNMSKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
bboxes
,
const
DenseTensor
&
scores
,
const
paddle
::
optional
<
DenseTensor
>&
rois_num
,
float
score_threshold
,
int
nums_top_k
,
int
keep_top_k
,
float
nms_threshold
,
bool
normalized
,
float
nms_eta
,
int
background_label
,
DenseTensor
*
out
,
DenseTensor
*
index
,
DenseTensor
*
nms_rois_num
)
{
using
XPUT
=
typename
XPUTypeTrait
<
T
>::
Type
;
const
XPUT
*
bboxes_data
=
reinterpret_cast
<
const
XPUT
*>
(
bboxes
.
data
<
T
>
());
const
XPUT
*
scores_data
=
reinterpret_cast
<
const
XPUT
*>
(
scores
.
data
<
T
>
());
bool
return_index
=
index
!=
nullptr
;
bool
has_rois_num
=
rois_num
.
get_ptr
()
!=
nullptr
;
bool
return_rois_num
=
nms_rois_num
!=
nullptr
;
auto
score_dims
=
phi
::
vectorize
<
int
>
(
scores
.
dims
());
auto
score_size
=
score_dims
.
size
();
bool
is_lod
=
score_size
==
2
?
true
:
false
;
int
n
=
0
;
int
b
=
0
;
int
class_num
=
scores
.
dims
()[
1
];
int
out_dim
=
bboxes
.
dims
()[
2
]
+
2
;
int
boxes_count
=
0
;
std
::
vector
<
int
>
rois_num_vec
;
rois_num_vec
.
clear
();
if
(
is_lod
)
{
if
(
has_rois_num
)
{
n
=
rois_num
.
get_ptr
()
->
numel
();
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
rois_num_vec
.
push_back
(
rois_num
.
get_ptr
()
->
data
<
int
>
()[
i
]);
boxes_count
+=
rois_num
.
get_ptr
()
->
data
<
int
>
()[
i
];
}
}
else
{
auto
lod
=
bboxes
.
lod
().
back
();
boxes_count
=
lod
[
lod
.
size
()
-
1
];
n
=
lod
.
size
()
-
1
;
for
(
int
i
=
0
;
i
<
n
;
i
++
)
{
rois_num_vec
.
push_back
(
lod
[
i
+
1
]
-
lod
[
i
]);
}
}
PADDLE_ENFORCE_EQ
(
boxes_count
==
bboxes
.
dims
()[
0
],
true
,
phi
::
errors
::
InvalidArgument
(
"boxes_count should equal boxes->dims()[0]."
,
"But received: (%d) and (%d)"
,
boxes_count
,
bboxes
.
dims
()[
0
]));
PADDLE_ENFORCE_EQ
(
boxes_count
==
score_dims
[
0
],
true
,
phi
::
errors
::
InvalidArgument
(
"boxes_count shuold equal score_dims[0]."
,
"But received: (%d) and (%d)"
,
boxes_count
,
score_dims
[
0
]));
}
else
{
n
=
bboxes
.
dims
()[
0
];
b
=
bboxes
.
dims
()[
1
];
boxes_count
=
n
*
b
;
}
std
::
vector
<
T
>
outs_vec_
;
std
::
vector
<
int
>
out_index_vec_
;
outs_vec_
.
resize
(
boxes_count
*
out_dim
);
out_index_vec_
.
resize
(
boxes_count
);
std
::
vector
<
size_t
>
batch_starts
;
int
r
=
0
;
r
=
xpu
::
multiclass_nms
<
T
,
int
>
(
ctx
.
x_context
(),
bboxes_data
,
scores_data
,
rois_num_vec
,
outs_vec_
,
out_index_vec_
,
batch_starts
,
n
,
b
,
class_num
,
out_dim
,
nums_top_k
,
score_threshold
,
keep_top_k
,
nms_threshold
,
background_label
,
normalized
,
nms_eta
,
return_index
,
is_lod
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"multiclass_nms"
);
uint64_t
num_kept
=
batch_starts
.
back
();
if
(
num_kept
==
0
)
{
if
(
return_index
)
{
// out_dim may be zero when there is no object in picture, so add some
// zeros to it
// caution: results may differ between cpu and xpu due to this operation
out
->
Resize
({
1
,
out_dim
});
ctx
.
template
Alloc
<
T
>(
out
);
T
*
out_ptr
=
out
->
template
data
<
T
>();
std
::
vector
<
T
>
temp_value
(
out_dim
,
0.0
f
);
memory_utils
::
Copy
(
ctx
.
GetPlace
(),
out_ptr
,
phi
::
CPUPlace
(),
temp_value
.
data
(),
1
*
out_dim
*
sizeof
(
T
));
index
->
Resize
({
1
,
1
});
ctx
.
template
Alloc
<
int
>(
index
);
int
*
out_index_ptr
=
index
->
template
data
<
int
>();
std
::
vector
<
int
>
temp_idx
(
1
,
0
);
memory_utils
::
Copy
(
ctx
.
GetPlace
(),
out_index_ptr
,
phi
::
CPUPlace
(),
temp_idx
.
data
(),
1
*
sizeof
(
int
));
}
else
{
out
->
Resize
({
1
,
1
});
T
*
od
=
ctx
.
template
Alloc
<
T
>(
out
);
od
[
0
]
=
-
1
;
batch_starts
=
{
0
,
1
};
}
}
else
{
out
->
Resize
({
static_cast
<
int64_t
>
(
num_kept
),
out_dim
});
ctx
.
template
Alloc
<
T
>(
out
);
T
*
out_ptr
=
out
->
template
data
<
T
>();
memory_utils
::
Copy
(
ctx
.
GetPlace
(),
out_ptr
,
phi
::
CPUPlace
(),
outs_vec_
.
data
(),
num_kept
*
out_dim
*
sizeof
(
T
));
if
(
return_index
)
{
index
->
Resize
({
static_cast
<
int64_t
>
(
num_kept
),
1
});
ctx
.
template
Alloc
<
int
>(
index
);
int
*
out_index_ptr
=
index
->
template
data
<
int
>();
memory_utils
::
Copy
(
ctx
.
GetPlace
(),
out_index_ptr
,
phi
::
CPUPlace
(),
out_index_vec_
.
data
(),
num_kept
*
sizeof
(
int
));
}
}
if
(
return_rois_num
)
{
nms_rois_num
->
Resize
({
n
});
ctx
.
template
Alloc
<
int
>(
nms_rois_num
);
DenseTensor
nms_rois_num_cpu
;
nms_rois_num_cpu
.
Resize
({
nms_rois_num
->
numel
()});
ctx
.
template
HostAlloc
<
int
>(
&
nms_rois_num_cpu
);
int
*
nms_rois_num_cpu_data
=
nms_rois_num_cpu
.
data
<
int
>
();
for
(
int
i
=
1
;
i
<=
n
;
i
++
)
{
nms_rois_num_cpu_data
[
i
-
1
]
=
batch_starts
[
i
]
-
batch_starts
[
i
-
1
];
}
phi
::
Copy
(
ctx
,
nms_rois_num_cpu
,
nms_rois_num
->
place
(),
true
,
nms_rois_num
);
}
LoD
lod
;
if
(
num_kept
==
0
)
{
batch_starts
[
batch_starts
.
size
()
-
1
]
=
1
;
}
lod
.
emplace_back
(
batch_starts
);
if
(
return_index
)
{
index
->
set_lod
(
lod
);
}
out
->
set_lod
(
lod
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
multiclass_nms3
,
XPU
,
ALL_LAYOUT
,
phi
::
MultiClassNMSKernel
,
float
)
{
kernel
->
OutputAt
(
1
).
SetDataType
(
phi
::
DataType
::
INT32
);
kernel
->
OutputAt
(
2
).
SetDataType
(
phi
::
DataType
::
INT32
);
}
paddle/phi/kernels/xpu/norm_kernel.cc
0 → 100644
浏览文件 @
ba992136
// Copyright (c) 2023 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.
#include "paddle/phi/kernels/norm_kernel.h"
#include <vector>
#include "paddle/phi/backends/xpu/enforce_xpu.h"
#include "paddle/phi/core/kernel_registry.h"
namespace
phi
{
template
<
typename
T
,
typename
Context
>
void
NormKernel
(
const
Context
&
ctx
,
const
DenseTensor
&
x
,
int
axis
,
float
epsilon
,
bool
is_test
,
DenseTensor
*
out
,
DenseTensor
*
norm
)
{
ctx
.
template
Alloc
<
T
>(
out
);
ctx
.
template
Alloc
<
T
>(
norm
);
std
::
vector
<
int
>
xshape
;
auto
x_dims
=
x
.
dims
();
auto
x_dims_size
=
x_dims
.
size
();
xshape
.
resize
(
x_dims_size
);
if
(
axis
<
0
)
{
axis
+=
x_dims_size
;
}
PADDLE_ENFORCE_GE
(
axis
,
0
,
phi
::
errors
::
InvalidArgument
(
"axis must be greater than or equal to 0."
"But received axis: %d."
,
axis
));
PADDLE_ENFORCE_LT
(
axis
,
x_dims_size
,
phi
::
errors
::
InvalidArgument
(
"Attr(axis) value must be less than rank of Input(X)"
"But received axis: %d, rank: %d."
,
axis
,
x_dims_size
));
for
(
int
i
=
0
;
i
<
x_dims_size
;
i
++
)
{
xshape
[
i
]
=
static_cast
<
int
>
(
x_dims
[
i
]);
}
int
r
=
xpu
::
l2_norm
<
T
>
(
ctx
.
x_context
(),
x
.
data
<
T
>
(),
out
->
data
<
T
>
(),
norm
->
data
<
T
>
(),
xshape
,
axis
,
epsilon
);
PADDLE_ENFORCE_XDNN_SUCCESS
(
r
,
"l2_norm"
);
}
}
// namespace phi
PD_REGISTER_KERNEL
(
norm
,
XPU
,
ALL_LAYOUT
,
phi
::
NormKernel
,
float
)
{}
test/xpu/test_multiclass_nms3_op_xpu.py
0 → 100644
浏览文件 @
ba992136
# Copyright (c) 2023 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.
import
copy
import
unittest
import
numpy
as
np
from
get_test_cover_info
import
(
XPUOpTestWrapper
,
create_test_class
,
get_xpu_op_support_types
,
)
from
op_test_xpu
import
XPUOpTest
import
paddle
paddle
.
enable_static
()
def
softmax
(
x
):
# clip to shiftx, otherwise, when calc loss with
# log(exp(shiftx)), may get log(0)=INF
shiftx
=
(
x
-
np
.
max
(
x
)).
clip
(
-
64.0
)
exps
=
np
.
exp
(
shiftx
)
return
exps
/
np
.
sum
(
exps
)
def
iou
(
box_a
,
box_b
,
norm
):
"""Apply intersection-over-union overlap between box_a and box_b."""
xmin_a
=
min
(
box_a
[
0
],
box_a
[
2
])
ymin_a
=
min
(
box_a
[
1
],
box_a
[
3
])
xmax_a
=
max
(
box_a
[
0
],
box_a
[
2
])
ymax_a
=
max
(
box_a
[
1
],
box_a
[
3
])
xmin_b
=
min
(
box_b
[
0
],
box_b
[
2
])
ymin_b
=
min
(
box_b
[
1
],
box_b
[
3
])
xmax_b
=
max
(
box_b
[
0
],
box_b
[
2
])
ymax_b
=
max
(
box_b
[
1
],
box_b
[
3
])
area_a
=
(
ymax_a
-
ymin_a
+
(
not
norm
))
*
(
xmax_a
-
xmin_a
+
(
not
norm
))
area_b
=
(
ymax_b
-
ymin_b
+
(
not
norm
))
*
(
xmax_b
-
xmin_b
+
(
not
norm
))
if
area_a
<=
0
and
area_b
<=
0
:
return
0.0
xa
=
max
(
xmin_a
,
xmin_b
)
ya
=
max
(
ymin_a
,
ymin_b
)
xb
=
min
(
xmax_a
,
xmax_b
)
yb
=
min
(
ymax_a
,
ymax_b
)
inter_area
=
max
(
xb
-
xa
+
(
not
norm
),
0.0
)
*
max
(
yb
-
ya
+
(
not
norm
),
0.0
)
iou_ratio
=
inter_area
/
(
area_a
+
area_b
-
inter_area
)
return
iou_ratio
def
nms
(
boxes
,
scores
,
score_threshold
,
nms_threshold
,
top_k
=
200
,
normalized
=
True
,
eta
=
1.0
,
):
"""Apply non-maximum suppression at test time to avoid detecting too many
overlapping bounding boxes for a given object.
Args:
boxes: (tensor) The location preds for the img, Shape: [num_priors,4].
scores: (tensor) The class predscores for the img, Shape:[num_priors].
score_threshold: (float) The confidence thresh for filtering low
confidence boxes.
nms_threshold: (float) The overlap thresh for suppressing unnecessary
boxes.
top_k: (int) The maximum number of box preds to consider.
eta: (float) The parameter for adaptive NMS.
Return:
The indices of the kept boxes with respect to num_priors.
"""
all_scores
=
copy
.
deepcopy
(
scores
)
all_scores
=
all_scores
.
flatten
()
selected_indices
=
np
.
argwhere
(
all_scores
>
score_threshold
)
selected_indices
=
selected_indices
.
flatten
()
all_scores
=
all_scores
[
selected_indices
]
sorted_indices
=
np
.
argsort
(
-
all_scores
,
axis
=
0
,
kind
=
'mergesort'
)
sorted_scores
=
all_scores
[
sorted_indices
]
sorted_indices
=
selected_indices
[
sorted_indices
]
if
top_k
>
-
1
and
top_k
<
sorted_indices
.
shape
[
0
]:
sorted_indices
=
sorted_indices
[:
top_k
]
sorted_scores
=
sorted_scores
[:
top_k
]
selected_indices
=
[]
adaptive_threshold
=
nms_threshold
for
i
in
range
(
sorted_scores
.
shape
[
0
]):
idx
=
sorted_indices
[
i
]
keep
=
True
for
k
in
range
(
len
(
selected_indices
)):
if
keep
:
kept_idx
=
selected_indices
[
k
]
overlap
=
iou
(
boxes
[
idx
],
boxes
[
kept_idx
],
normalized
)
keep
=
True
if
overlap
<=
adaptive_threshold
else
False
else
:
break
if
keep
:
selected_indices
.
append
(
idx
)
if
keep
and
eta
<
1
and
adaptive_threshold
>
0.5
:
adaptive_threshold
*=
eta
return
selected_indices
def
multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
,
shared
,
):
if
shared
:
class_num
=
scores
.
shape
[
0
]
priorbox_num
=
scores
.
shape
[
1
]
else
:
box_num
=
scores
.
shape
[
0
]
class_num
=
scores
.
shape
[
1
]
selected_indices
=
{}
num_det
=
0
for
c
in
range
(
class_num
):
if
c
==
background
:
continue
if
shared
:
indices
=
nms
(
boxes
,
scores
[
c
],
score_threshold
,
nms_threshold
,
nms_top_k
,
normalized
,
)
else
:
indices
=
nms
(
boxes
[:,
c
,
:],
scores
[:,
c
],
score_threshold
,
nms_threshold
,
nms_top_k
,
normalized
,
)
selected_indices
[
c
]
=
indices
num_det
+=
len
(
indices
)
if
keep_top_k
>
-
1
and
num_det
>
keep_top_k
:
score_index
=
[]
for
c
,
indices
in
selected_indices
.
items
():
for
idx
in
indices
:
if
shared
:
score_index
.
append
((
scores
[
c
][
idx
],
c
,
idx
))
else
:
score_index
.
append
((
scores
[
idx
][
c
],
c
,
idx
))
sorted_score_index
=
sorted
(
score_index
,
key
=
lambda
tup
:
tup
[
0
],
reverse
=
True
)
sorted_score_index
=
sorted_score_index
[:
keep_top_k
]
selected_indices
=
{}
for
_
,
c
,
_
in
sorted_score_index
:
selected_indices
[
c
]
=
[]
for
s
,
c
,
idx
in
sorted_score_index
:
selected_indices
[
c
].
append
(
idx
)
if
not
shared
:
for
labels
in
selected_indices
:
selected_indices
[
labels
].
sort
()
num_det
=
keep_top_k
return
selected_indices
,
num_det
def
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
=
True
,
gpu_logic
=
False
,
):
batch_size
=
scores
.
shape
[
0
]
num_boxes
=
scores
.
shape
[
2
]
det_outs
=
[]
index_outs
=
[]
lod
=
[]
for
n
in
range
(
batch_size
):
nmsed_outs
,
nmsed_num
=
multiclass_nms
(
boxes
[
n
],
scores
[
n
],
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
normalized
,
shared
=
True
,
)
lod
.
append
(
nmsed_num
)
if
nmsed_num
==
0
:
continue
tmp_det_out
=
[]
for
c
,
indices
in
nmsed_outs
.
items
():
for
idx
in
indices
:
xmin
,
ymin
,
xmax
,
ymax
=
boxes
[
n
][
idx
][:]
tmp_det_out
.
append
(
[
c
,
scores
[
n
][
c
][
idx
],
xmin
,
ymin
,
xmax
,
ymax
,
idx
+
n
*
num_boxes
,
]
)
if
gpu_logic
:
sorted_det_out
=
sorted
(
tmp_det_out
,
key
=
lambda
tup
:
tup
[
1
],
reverse
=
True
)
else
:
sorted_det_out
=
sorted
(
tmp_det_out
,
key
=
lambda
tup
:
tup
[
0
],
reverse
=
False
)
det_outs
.
extend
(
sorted_det_out
)
return
det_outs
,
lod
class
TestIOU
(
unittest
.
TestCase
):
def
test_iou
(
self
):
box1
=
np
.
array
([
4.0
,
3.0
,
7.0
,
5.0
]).
astype
(
'float32'
)
box2
=
np
.
array
([
3.0
,
4.0
,
6.0
,
8.0
]).
astype
(
'float32'
)
expt_output
=
np
.
array
([
2.0
/
16.0
]).
astype
(
'float32'
)
calc_output
=
np
.
array
([
iou
(
box1
,
box2
,
True
)]).
astype
(
'float32'
)
np
.
testing
.
assert_allclose
(
calc_output
,
expt_output
,
rtol
=
1e-05
)
class
XPUTestMulticlassNMS3Op
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'multiclass_nms3'
self
.
use_dynamic_create_class
=
False
class
TestXpuMulticlassNMS3Op
(
XPUOpTest
):
def
set_argument
(
self
):
self
.
score_threshold
=
0.01
def
setUp
(
self
):
self
.
op_type
=
"multiclass_nms3"
self
.
dtype
=
self
.
in_type
self
.
set_argument
()
N
=
7
M
=
1200
C
=
21
BOX_SIZE
=
4
background
=
0
nms_threshold
=
0.3
nms_top_k
=
400
keep_top_k
=
(
200
if
not
hasattr
(
self
,
'keep_top_k'
)
else
self
.
keep_top_k
)
score_threshold
=
self
.
score_threshold
scores
=
np
.
random
.
random
((
N
*
M
,
C
)).
astype
(
self
.
dtype
)
scores
=
np
.
apply_along_axis
(
softmax
,
1
,
scores
)
scores
=
np
.
reshape
(
scores
,
(
N
,
M
,
C
))
scores
=
np
.
transpose
(
scores
,
(
0
,
2
,
1
))
boxes
=
np
.
random
.
random
((
N
,
M
,
BOX_SIZE
)).
astype
(
self
.
dtype
)
boxes
[:,
:,
0
:
2
]
=
boxes
[:,
:,
0
:
2
]
*
0.5
boxes
[:,
:,
2
:
4
]
=
boxes
[:,
:,
2
:
4
]
*
0.5
+
0.5
det_outs
,
lod
=
batched_multiclass_nms
(
boxes
,
scores
,
background
,
score_threshold
,
nms_threshold
,
nms_top_k
,
keep_top_k
,
gpu_logic
=
self
.
gpu_logic
if
hasattr
(
self
,
'gpu_logic'
)
else
None
,
)
det_outs
=
np
.
array
(
det_outs
)
nmsed_outs
=
(
det_outs
[:,
:
-
1
].
astype
(
self
.
dtype
)
if
len
(
det_outs
)
else
np
.
array
([],
dtype
=
np
.
float32
).
reshape
([
0
,
BOX_SIZE
+
2
])
)
index_outs
=
(
det_outs
[:,
-
1
:].
astype
(
'int'
)
if
len
(
det_outs
)
else
np
.
array
([],
dtype
=
'int'
).
reshape
([
0
,
1
])
)
self
.
inputs
=
{
'BBoxes'
:
boxes
,
'Scores'
:
scores
}
self
.
outputs
=
{
'Out'
:
nmsed_outs
,
'Index'
:
index_outs
,
'NmsRoisNum'
:
np
.
array
(
lod
).
astype
(
'int32'
),
}
self
.
attrs
=
{
'background_label'
:
0
,
'nms_threshold'
:
nms_threshold
,
'nms_top_k'
:
nms_top_k
,
'keep_top_k'
:
keep_top_k
,
'score_threshold'
:
score_threshold
,
'nms_eta'
:
1.0
,
'normalized'
:
True
,
}
def
test_check_output
(
self
):
self
.
check_output_with_place
(
paddle
.
XPUPlace
(
0
))
support_types
=
get_xpu_op_support_types
(
'multiclass_nms3'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestMulticlassNMS3Op
,
stype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
test/xpu/test_norm_op_xpu.py
0 → 100644
浏览文件 @
ba992136
# Copyright (c) 2023 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.
import
unittest
import
numpy
as
np
from
get_test_cover_info
import
(
XPUOpTestWrapper
,
create_test_class
,
get_xpu_op_support_types
,
)
from
op_test_xpu
import
XPUOpTest
import
paddle
paddle
.
enable_static
()
def
l2_norm
(
x
,
axis
,
epsilon
):
x2
=
x
**
2
s
=
np
.
sum
(
x2
,
axis
=
axis
,
keepdims
=
True
)
r
=
np
.
sqrt
(
s
+
epsilon
)
y
=
x
/
np
.
broadcast_to
(
r
,
x
.
shape
)
return
y
,
r
class
XPUTestNormOp
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
"norm"
self
.
use_dynamic_create_class
=
False
class
TestXPUNormOp
(
XPUOpTest
):
def
setUp
(
self
):
self
.
op_type
=
"norm"
self
.
dtype
=
self
.
in_type
self
.
place
=
paddle
.
XPUPlace
(
0
)
self
.
init_test_case
()
x
=
np
.
random
.
random
(
self
.
shape
).
astype
(
self
.
dtype
)
y
,
norm
=
l2_norm
(
x
,
self
.
axis
,
self
.
epsilon
)
self
.
inputs
=
{
'X'
:
x
}
self
.
attrs
=
{
'epsilon'
:
self
.
epsilon
,
'axis'
:
self
.
axis
}
self
.
outputs
=
{
'Out'
:
y
,
'Norm'
:
norm
}
def
init_test_case
(
self
):
self
.
shape
=
[
2
,
3
,
4
,
5
]
self
.
axis
=
1
self
.
epsilon
=
1e-8
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'
)
class
TestXPUNormOp2
(
TestXPUNormOp
):
def
init_test_case
(
self
):
self
.
shape
=
[
5
,
3
,
9
,
7
]
self
.
axis
=
0
self
.
epsilon
=
1e-8
class
TestXPUNormOp3
(
TestXPUNormOp
):
def
init_test_case
(
self
):
self
.
shape
=
[
5
,
3
,
2
,
7
]
self
.
axis
=
-
1
self
.
epsilon
=
1e-8
class
TestXPUNormOp4
(
TestXPUNormOp
):
def
init_test_case
(
self
):
self
.
shape
=
[
128
,
1024
,
14
,
14
]
self
.
axis
=
2
self
.
epsilon
=
1e-8
class
TestXPUNormOp5
(
TestXPUNormOp
):
def
init_test_case
(
self
):
self
.
shape
=
[
2048
,
2048
]
self
.
axis
=
1
self
.
epsilon
=
1e-8
support_types
=
get_xpu_op_support_types
(
'norm'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUTestNormOp
,
stype
)
if
__name__
==
"__main__"
:
unittest
.
main
()
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