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f10d26e9
编写于
6月 17, 2022
作者:
L
Leo Guo
提交者:
GitHub
6月 17, 2022
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差异文件
Modify the large case in test_generate_proposals_v2_op to small for kunlun. *test=kunlun (#43587)
上级
bba7c5b9
变更
1
隐藏空白更改
内联
并排
Showing
1 changed file
with
42 addition
and
31 deletion
+42
-31
python/paddle/fluid/tests/unittests/xpu/test_generate_proposals_v2_op_xpu.py
.../tests/unittests/xpu/test_generate_proposals_v2_op_xpu.py
+42
-31
未找到文件。
python/paddle/fluid/tests/unittests/xpu/test_generate_proposals_v2_op_xpu.py
浏览文件 @
f10d26e9
...
...
@@ -17,6 +17,7 @@ from __future__ import print_function
import
unittest
import
numpy
as
np
import
sys
sys
.
path
.
append
(
".."
)
import
math
...
...
@@ -48,7 +49,7 @@ def box_coder(all_anchors, bbox_deltas, variances, pixel_offset=True):
anchor_loc
[:,
2
]
=
all_anchors
[:,
0
]
+
0.5
*
anchor_loc
[:,
0
]
anchor_loc
[:,
3
]
=
all_anchors
[:,
1
]
+
0.5
*
anchor_loc
[:,
1
]
# predicted bbox: bbox_center_x, bbox_center_y, bbox_width, bbox_height
# predicted bbox: bbox_center_x, bbox_center_y, bbox_width, bbox_height
pred_bbox
=
np
.
zeros_like
(
bbox_deltas
,
dtype
=
np
.
float32
)
if
variances
is
not
None
:
for
i
in
range
(
bbox_deltas
.
shape
[
0
]):
...
...
@@ -64,10 +65,12 @@ def box_coder(all_anchors, bbox_deltas, variances, pixel_offset=True):
1000
/
16.0
)))
*
anchor_loc
[
i
,
1
]
else
:
for
i
in
range
(
bbox_deltas
.
shape
[
0
]):
pred_bbox
[
i
,
0
]
=
bbox_deltas
[
i
,
0
]
*
anchor_loc
[
i
,
0
]
+
anchor_loc
[
i
,
2
]
pred_bbox
[
i
,
1
]
=
bbox_deltas
[
i
,
1
]
*
anchor_loc
[
i
,
1
]
+
anchor_loc
[
i
,
3
]
pred_bbox
[
i
,
0
]
=
bbox_deltas
[
i
,
0
]
*
anchor_loc
[
i
,
0
]
+
anchor_loc
[
i
,
2
]
pred_bbox
[
i
,
1
]
=
bbox_deltas
[
i
,
1
]
*
anchor_loc
[
i
,
1
]
+
anchor_loc
[
i
,
3
]
pred_bbox
[
i
,
2
]
=
math
.
exp
(
min
(
bbox_deltas
[
i
,
2
],
math
.
log
(
1000
/
16.0
)))
*
anchor_loc
[
i
,
0
]
...
...
@@ -91,17 +94,21 @@ def clip_tiled_boxes(boxes, im_shape, pixel_offset=True):
)
offset
=
1
if
pixel_offset
else
0
# x1 >= 0
boxes
[:,
0
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
0
::
4
],
im_shape
[
1
]
-
offset
),
0
)
boxes
[:,
0
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
0
::
4
],
im_shape
[
1
]
-
offset
),
0
)
# y1 >= 0
boxes
[:,
1
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
1
::
4
],
im_shape
[
0
]
-
offset
),
0
)
boxes
[:,
1
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
1
::
4
],
im_shape
[
0
]
-
offset
),
0
)
# x2 < im_shape[1]
boxes
[:,
2
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
2
::
4
],
im_shape
[
1
]
-
offset
),
0
)
boxes
[:,
2
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
2
::
4
],
im_shape
[
1
]
-
offset
),
0
)
# y2 < im_shape[0]
boxes
[:,
3
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
3
::
4
],
im_shape
[
0
]
-
offset
),
0
)
boxes
[:,
3
::
4
]
=
np
.
maximum
(
np
.
minimum
(
boxes
[:,
3
::
4
],
im_shape
[
0
]
-
offset
),
0
)
return
boxes
...
...
@@ -116,8 +123,8 @@ def filter_boxes(boxes, min_size, im_shape, pixel_offset=True):
if
pixel_offset
:
x_ctr
=
boxes
[:,
0
]
+
ws
/
2.
y_ctr
=
boxes
[:,
1
]
+
hs
/
2.
keep
=
np
.
where
((
ws
>=
min_size
)
&
(
hs
>=
min_size
)
&
(
x_ctr
<
im_shape
[
1
])
&
(
y_ctr
<
im_shape
[
0
]))[
0
]
keep
=
np
.
where
((
ws
>=
min_size
)
&
(
hs
>=
min_size
)
&
(
x_ctr
<
im_shape
[
1
])
&
(
y_ctr
<
im_shape
[
0
]))[
0
]
else
:
keep
=
np
.
where
((
ws
>=
min_size
)
&
(
hs
>=
min_size
))[
0
]
return
keep
...
...
@@ -309,10 +316,11 @@ def anchor_generator_in_python(input_feat, anchor_sizes, aspect_ratios,
scale_h
=
anchor_size
/
stride
[
1
]
w
=
scale_w
*
base_w
h
=
scale_h
*
base_h
out_anchors
[
h_idx
,
w_idx
,
idx
,
:]
=
[
(
x_ctr
-
0.5
*
(
w
-
1
)),
(
y_ctr
-
0.5
*
(
h
-
1
)),
(
x_ctr
+
0.5
*
(
w
-
1
)),
(
y_ctr
+
0.5
*
(
h
-
1
))
]
out_anchors
[
h_idx
,
w_idx
,
idx
,
:]
=
[(
x_ctr
-
0.5
*
(
w
-
1
)),
(
y_ctr
-
0.5
*
(
h
-
1
)),
(
x_ctr
+
0.5
*
(
w
-
1
)),
(
y_ctr
+
0.5
*
(
h
-
1
))]
idx
+=
1
# set the variance.
...
...
@@ -323,11 +331,13 @@ def anchor_generator_in_python(input_feat, anchor_sizes, aspect_ratios,
class
XPUGenerateProposalsV2Op
(
XPUOpTestWrapper
):
def
__init__
(
self
):
self
.
op_name
=
'generate_proposals_v2'
self
.
use_dynamic_create_class
=
False
class
TestGenerateProposalsV2Op
(
XPUOpTest
):
def
set_data
(
self
):
self
.
init_input_shape
()
self
.
init_test_params
()
...
...
@@ -413,6 +423,7 @@ class XPUGenerateProposalsV2Op(XPUOpTestWrapper):
self
.
nms_thresh
,
self
.
min_size
,
self
.
eta
,
self
.
pixel_offset
)
class
TestGenerateProposalsV2OutLodOp
(
TestGenerateProposalsV2Op
):
def
set_data
(
self
):
self
.
init_input_shape
()
self
.
init_test_params
()
...
...
@@ -439,11 +450,11 @@ class XPUGenerateProposalsV2Op(XPUOpTestWrapper):
self
.
outputs
=
{
'RpnRois'
:
(
self
.
rpn_rois
[
0
],
[
self
.
rois_num
]),
'RpnRoiProbs'
:
(
self
.
rpn_roi_probs
[
0
],
[
self
.
rois_num
]),
'RpnRoisNum'
:
(
np
.
asarray
(
self
.
rois_num
,
dtype
=
np
.
int32
))
'RpnRoisNum'
:
(
np
.
asarray
(
self
.
rois_num
,
dtype
=
np
.
int32
))
}
class
TestGenerateProposalsV2OpNoBoxLeft
(
TestGenerateProposalsV2Op
):
def
init_test_params
(
self
):
self
.
pre_nms_topN
=
12000
# train 12000, test 2000
self
.
post_nms_topN
=
5000
# train 6000, test 1000
...
...
@@ -453,6 +464,7 @@ class XPUGenerateProposalsV2Op(XPUOpTestWrapper):
self
.
pixel_offset
=
True
class
TestGenerateProposalsV2OpNoOffset
(
TestGenerateProposalsV2Op
):
def
init_test_params
(
self
):
self
.
pre_nms_topN
=
12000
# train 12000, test 2000
self
.
post_nms_topN
=
5000
# train 6000, test 1000
...
...
@@ -461,11 +473,12 @@ class XPUGenerateProposalsV2Op(XPUOpTestWrapper):
self
.
eta
=
1.
self
.
pixel_offset
=
False
# """
class
TestGenerateProposalsV2OpMaskRcnn1XPU
(
TestGenerateProposalsV2Op
):
def
init_input_shape
(
self
):
self
.
input_feat_shape
=
(
1
,
20
,
48
,
64
)
self
.
im_shape
=
np
.
array
([[
768
,
1024
]]).
astype
(
self
.
dtype
)
# Another case is [768, 1024]
self
.
im_shape
=
np
.
array
([[
192
,
256
]]).
astype
(
self
.
dtype
)
def
init_test_params
(
self
):
self
.
pre_nms_topN
=
12000
# train 12000, test 2000
...
...
@@ -526,19 +539,17 @@ class XPUGenerateProposalsV2Op(XPUOpTestWrapper):
self
.
outputs
=
{
'RpnRois'
:
(
self
.
rpn_rois
[
0
],
[
self
.
rois_num
]),
'RpnRoiProbs'
:
(
self
.
rpn_roi_probs
[
0
],
[
self
.
rois_num
]),
'RpnRoisNum'
:
(
np
.
asarray
(
self
.
rois_num
,
dtype
=
np
.
int32
))
'RpnRoisNum'
:
(
np
.
asarray
(
self
.
rois_num
,
dtype
=
np
.
int32
))
}
support_types
=
get_xpu_op_support_types
(
'generate_proposals_v2'
)
for
stype
in
support_types
:
create_test_class
(
globals
(),
XPUGenerateProposalsV2Op
,
stype
,
test_grad
=
False
,
ignore_deivce_version
=
[
core
.
XPUVersion
.
XPU1
])
create_test_class
(
globals
(),
XPUGenerateProposalsV2Op
,
stype
,
test_grad
=
False
,
ignore_deivce_version
=
[
core
.
XPUVersion
.
XPU1
])
if
__name__
==
'__main__'
:
unittest
.
main
()
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