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体验新版 GitCode,发现更多精彩内容 >>
提交
f06c6193
编写于
10月 23, 2018
作者:
J
jerrywgz
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
fix rpn target assign test=develop
上级
765085d2
变更
4
隐藏空白更改
内联
并排
Showing
4 changed file
with
100 addition
and
37 deletion
+100
-37
paddle/fluid/operators/detection/rpn_target_assign_op.cc
paddle/fluid/operators/detection/rpn_target_assign_op.cc
+52
-16
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+10
-5
python/paddle/fluid/tests/test_detection.py
python/paddle/fluid/tests/test_detection.py
+4
-2
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
...paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
+34
-14
未找到文件。
paddle/fluid/operators/detection/rpn_target_assign_op.cc
浏览文件 @
f06c6193
...
...
@@ -52,6 +52,9 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"TargetBBox"
),
"Output(TargetBBox) of RpnTargetAssignOp should not be null"
);
PADDLE_ENFORCE
(
ctx
->
HasOutput
(
"BBox_inside_weight"
),
"Output(BBox_inside_weight) of RpnTargetAssignOp should not be null"
);
auto
anchor_dims
=
ctx
->
GetInputDim
(
"Anchor"
);
auto
gt_boxes_dims
=
ctx
->
GetInputDim
(
"GtBoxes"
);
...
...
@@ -68,6 +71,7 @@ class RpnTargetAssignOp : public framework::OperatorWithKernel {
ctx
->
SetOutputDim
(
"ScoreIndex"
,
{
-
1
});
ctx
->
SetOutputDim
(
"TargetLabel"
,
{
-
1
,
1
});
ctx
->
SetOutputDim
(
"TargetBBox"
,
{
-
1
,
4
});
ctx
->
SetOutputDim
(
"BBox_inside_weight"
,
{
-
1
,
4
});
}
protected:
...
...
@@ -169,6 +173,7 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
const
float
rpn_positive_overlap
,
const
float
rpn_negative_overlap
,
std
::
vector
<
int
>*
fg_inds
,
std
::
vector
<
int
>*
bg_inds
,
std
::
vector
<
int
>*
tgt_lbl
,
std
::
vector
<
int
>*
fg_fake
,
std
::
vector
<
T
>*
bbox_inside_weight
,
std
::
minstd_rand
engine
,
bool
use_random
)
{
float
epsilon
=
0.00001
;
int
anchor_num
=
anchor_to_gt_max
.
dims
()[
0
];
...
...
@@ -201,12 +206,12 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
// Reservoir Sampling
int
fg_num
=
static_cast
<
int
>
(
rpn_fg_fraction
*
rpn_batch_size_per_im
);
ReservoirSampling
(
fg_num
,
&
fg_inds_fake
,
engine
,
use_random
);
fg
_num
=
static_cast
<
int
>
(
fg_inds_fake
.
size
());
for
(
int64_t
i
=
0
;
i
<
fg_num
;
++
i
)
{
int
fg_fake
_num
=
static_cast
<
int
>
(
fg_inds_fake
.
size
());
for
(
int64_t
i
=
0
;
i
<
fg_
fake_
num
;
++
i
)
{
target_label
[
fg_inds_fake
[
i
]]
=
1
;
}
int
bg_num
=
rpn_batch_size_per_im
-
fg_num
;
int
bg_num
=
rpn_batch_size_per_im
-
fg_
fake_
num
;
for
(
int64_t
i
=
0
;
i
<
anchor_num
;
++
i
)
{
if
(
anchor_to_gt_max_data
[
i
]
<
rpn_negative_overlap
)
{
bg_inds_fake
.
push_back
(
i
);
...
...
@@ -214,12 +219,28 @@ void ScoreAssign(const T* anchor_by_gt_overlap_data,
}
ReservoirSampling
(
bg_num
,
&
bg_inds_fake
,
engine
,
use_random
);
bg_num
=
static_cast
<
int
>
(
bg_inds_fake
.
size
());
int
fake_num
=
0
;
for
(
int64_t
i
=
0
;
i
<
bg_num
;
++
i
)
{
// fg fake found
if
(
target_label
[
bg_inds_fake
[
i
]]
==
1
)
{
fake_num
++
;
fg_fake
->
emplace_back
(
fg_inds_fake
[
0
]);
for
(
int
j
=
0
;
j
<
4
;
++
j
)
{
bbox_inside_weight
->
emplace_back
(
T
(
0.
));
}
}
target_label
[
bg_inds_fake
[
i
]]
=
0
;
}
for
(
int64_t
i
=
0
;
i
<
(
fg_fake_num
-
fake_num
)
*
4
;
++
i
)
{
bbox_inside_weight
->
emplace_back
(
T
(
1.
));
}
for
(
int64_t
i
=
0
;
i
<
anchor_num
;
++
i
)
{
if
(
target_label
[
i
]
==
1
)
fg_inds
->
emplace_back
(
i
);
if
(
target_label
[
i
]
==
1
)
{
fg_inds
->
emplace_back
(
i
);
fg_fake
->
emplace_back
(
i
);
}
if
(
target_label
[
i
]
==
0
)
bg_inds
->
emplace_back
(
i
);
}
fg_num
=
fg_inds
->
size
();
...
...
@@ -248,7 +269,8 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
std
::
vector
<
int
>
bg_inds
;
std
::
vector
<
int
>
gt_inds
;
std
::
vector
<
int
>
tgt_lbl
;
std
::
vector
<
int
>
fg_fake
;
std
::
vector
<
T
>
bbox_inside_weight
;
// Calculate the max IoU between anchors and gt boxes
// Map from anchor to gt box that has highest overlap
auto
place
=
ctx
.
GetPlace
();
...
...
@@ -275,32 +297,37 @@ std::vector<Tensor> SampleRpnFgBgGt(const platform::CPUDeviceContext& ctx,
// Follow the Faster RCNN's implementation
ScoreAssign
(
anchor_by_gt_overlap_data
,
anchor_to_gt_max
,
gt_to_anchor_max
,
rpn_batch_size_per_im
,
rpn_fg_fraction
,
rpn_positive_overlap
,
rpn_negative_overlap
,
&
fg_inds
,
&
bg_inds
,
&
tgt_lbl
,
engin
e
,
use_random
);
rpn_negative_overlap
,
&
fg_inds
,
&
bg_inds
,
&
tgt_lbl
,
&
fg_fak
e
,
&
bbox_inside_weight
,
engine
,
use_random
);
int
fg_num
=
fg_inds
.
size
();
int
bg_num
=
bg_inds
.
size
();
gt_inds
.
reserve
(
fg_num
);
for
(
int
i
=
0
;
i
<
fg_num
;
++
i
)
{
gt_inds
.
emplace_back
(
argmax
[
fg_inds
[
i
]]);
int
fg_fake_num
=
fg_fake
.
size
();
gt_inds
.
reserve
(
fg_fake_num
);
for
(
int
i
=
0
;
i
<
fg_fake_num
;
++
i
)
{
gt_inds
.
emplace_back
(
argmax
[
fg_fake
[
i
]]);
}
Tensor
loc_index_t
,
score_index_t
,
tgt_lbl_t
,
gt_inds_t
;
int
*
loc_index_data
=
loc_index_t
.
mutable_data
<
int
>
({
fg_num
},
place
);
Tensor
loc_index_t
,
score_index_t
,
tgt_lbl_t
,
gt_inds_t
,
bbox_inside_weight_t
;
int
*
loc_index_data
=
loc_index_t
.
mutable_data
<
int
>
({
fg_fake_num
},
place
);
int
*
score_index_data
=
score_index_t
.
mutable_data
<
int
>
({
fg_num
+
bg_num
},
place
);
int
*
tgt_lbl_data
=
tgt_lbl_t
.
mutable_data
<
int
>
({
fg_num
+
bg_num
},
place
);
int
*
gt_inds_data
=
gt_inds_t
.
mutable_data
<
int
>
({
fg_num
},
place
);
std
::
copy
(
fg_inds
.
begin
(),
fg_inds
.
end
(),
loc_index_data
);
int
*
gt_inds_data
=
gt_inds_t
.
mutable_data
<
int
>
({
fg_fake_num
},
place
);
T
*
bbox_inside_weight_data
=
bbox_inside_weight_t
.
mutable_data
<
T
>
({
fg_fake_num
,
4
},
place
);
std
::
copy
(
fg_fake
.
begin
(),
fg_fake
.
end
(),
loc_index_data
);
std
::
copy
(
fg_inds
.
begin
(),
fg_inds
.
end
(),
score_index_data
);
std
::
copy
(
bg_inds
.
begin
(),
bg_inds
.
end
(),
score_index_data
+
fg_num
);
std
::
copy
(
tgt_lbl
.
begin
(),
tgt_lbl
.
end
(),
tgt_lbl_data
);
std
::
copy
(
gt_inds
.
begin
(),
gt_inds
.
end
(),
gt_inds_data
);
std
::
copy
(
bbox_inside_weight
.
begin
(),
bbox_inside_weight
.
end
(),
bbox_inside_weight_data
);
std
::
vector
<
Tensor
>
loc_score_tgtlbl_gt
;
loc_score_tgtlbl_gt
.
emplace_back
(
loc_index_t
);
loc_score_tgtlbl_gt
.
emplace_back
(
score_index_t
);
loc_score_tgtlbl_gt
.
emplace_back
(
tgt_lbl_t
);
loc_score_tgtlbl_gt
.
emplace_back
(
gt_inds_t
);
loc_score_tgtlbl_gt
.
emplace_back
(
bbox_inside_weight_t
);
return
loc_score_tgtlbl_gt
;
}
...
...
@@ -318,6 +345,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
auto
*
score_index
=
context
.
Output
<
LoDTensor
>
(
"ScoreIndex"
);
auto
*
tgt_bbox
=
context
.
Output
<
LoDTensor
>
(
"TargetBBox"
);
auto
*
tgt_lbl
=
context
.
Output
<
LoDTensor
>
(
"TargetLabel"
);
auto
*
bbox_inside_weight
=
context
.
Output
<
LoDTensor
>
(
"BBox_inside_weight"
);
PADDLE_ENFORCE_EQ
(
gt_boxes
->
lod
().
size
(),
1UL
,
"RpnTargetAssignOp gt_boxes needs 1 level of LoD"
);
...
...
@@ -340,7 +368,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index
->
mutable_data
<
int
>
({
max_num
},
place
);
tgt_bbox
->
mutable_data
<
T
>
({
max_num
,
4
},
place
);
tgt_lbl
->
mutable_data
<
int
>
({
max_num
,
1
},
place
);
bbox_inside_weight
->
mutable_data
<
T
>
({
max_num
,
4
},
place
);
auto
&
dev_ctx
=
context
.
device_context
<
platform
::
CPUDeviceContext
>
();
std
::
random_device
rnd
;
...
...
@@ -394,6 +422,7 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
Tensor
sampled_score_index
=
loc_score_tgtlbl_gt
[
1
];
Tensor
sampled_tgtlbl
=
loc_score_tgtlbl_gt
[
2
];
Tensor
sampled_gt_index
=
loc_score_tgtlbl_gt
[
3
];
Tensor
sampled_bbox_inside_weight
=
loc_score_tgtlbl_gt
[
4
];
int
loc_num
=
sampled_loc_index
.
dims
()[
0
];
int
score_num
=
sampled_score_index
.
dims
()[
0
];
...
...
@@ -432,6 +461,8 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
AppendRpns
<
int
>
(
score_index
,
total_score_num
,
&
sampled_score_index_unmap
);
AppendRpns
<
T
>
(
tgt_bbox
,
total_loc_num
*
4
,
&
sampled_tgt_bbox
);
AppendRpns
<
int
>
(
tgt_lbl
,
total_score_num
,
&
sampled_tgtlbl
);
AppendRpns
<
T
>
(
bbox_inside_weight
,
total_loc_num
*
4
,
&
sampled_bbox_inside_weight
);
total_loc_num
+=
loc_num
;
total_score_num
+=
score_num
;
...
...
@@ -448,10 +479,12 @@ class RpnTargetAssignKernel : public framework::OpKernel<T> {
score_index
->
set_lod
(
loc_score
);
tgt_bbox
->
set_lod
(
lod_loc
);
tgt_lbl
->
set_lod
(
loc_score
);
bbox_inside_weight
->
set_lod
(
lod_loc
);
loc_index
->
Resize
({
total_loc_num
});
score_index
->
Resize
({
total_score_num
});
tgt_bbox
->
Resize
({
total_loc_num
,
4
});
tgt_lbl
->
Resize
({
total_score_num
,
1
});
bbox_inside_weight
->
Resize
({
total_loc_num
,
4
});
}
};
...
...
@@ -514,6 +547,9 @@ class RpnTargetAssignOpMaker : public framework::OpProtoAndCheckerMaker {
"TargetLabel"
,
"(Tensor<int>), The target labels of each anchor with shape "
"[F + B, 1], F and B are sampled foreground and backgroud number."
);
AddOutput
(
"BBox_inside_weight"
,
"(Tensor), The bbox inside weight with shape "
"[F, 4], F is the sampled foreground number."
);
AddComment
(
R"DOC(
This operator can be, for a given set of ground truth bboxes and the
anchors, to assign classification and regression targets to each prediction.
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
f06c6193
...
...
@@ -116,8 +116,8 @@ def rpn_target_assign(bbox_pred,
Returns:
tuple:
A tuple(predicted_scores, predicted_location, target_label,
target_bbox
) is returned. The predicted_scores and
predicted_location is the predicted result of the RPN.
target_bbox
, bbox_inside_weight) is returned. The predicted_scores
and
predicted_location is the predicted result of the RPN.
The target_label and target_bbox is the ground truth,
respectively. The predicted_location is a 2D Tensor with shape
[F, 4], and the shape of target_bbox is same as the shape of
...
...
@@ -126,6 +126,8 @@ def rpn_target_assign(bbox_pred,
[F + B, 1], and the shape of target_label is same as the shape
of the predicted_scores, B is the number of the background
anchors, the F and B is depends on the input of this operator.
Bbox_inside_weight represents whether the predicted loc is fake_fg
or not and the shape is [F, 4].
Examples:
.. code-block:: python
...
...
@@ -138,7 +140,7 @@ def rpn_target_assign(bbox_pred,
append_batch_size=False, dtype='float32')
gt_boxes = layers.data(name='gt_boxes', shape=[10, 4],
append_batch_size=False, dtype='float32')
loc_pred, score_pred, loc_target, score_target =
loc_pred, score_pred, loc_target, score_target
, bbox_inside_weight
=
fluid.layers.rpn_target_assign(bbox_pred=bbox_pred,
cls_logits=cls_logits,
anchor_box=anchor_box,
...
...
@@ -151,6 +153,7 @@ def rpn_target_assign(bbox_pred,
score_index
=
helper
.
create_tmp_variable
(
dtype
=
'int32'
)
target_label
=
helper
.
create_tmp_variable
(
dtype
=
'int32'
)
target_bbox
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
bbox_inside_weight
=
helper
.
create_tmp_variable
(
dtype
=
anchor_box
.
dtype
)
helper
.
append_op
(
type
=
"rpn_target_assign"
,
inputs
=
{
...
...
@@ -163,7 +166,8 @@ def rpn_target_assign(bbox_pred,
'LocationIndex'
:
loc_index
,
'ScoreIndex'
:
score_index
,
'TargetLabel'
:
target_label
,
'TargetBBox'
:
target_bbox
'TargetBBox'
:
target_bbox
,
'BBox_inside_weight'
:
bbox_inside_weight
},
attrs
=
{
'rpn_batch_size_per_im'
:
rpn_batch_size_per_im
,
...
...
@@ -178,13 +182,14 @@ def rpn_target_assign(bbox_pred,
score_index
.
stop_gradient
=
True
target_label
.
stop_gradient
=
True
target_bbox
.
stop_gradient
=
True
bbox_inside_weight
.
stop_gradient
=
True
cls_logits
=
nn
.
reshape
(
x
=
cls_logits
,
shape
=
(
-
1
,
1
))
bbox_pred
=
nn
.
reshape
(
x
=
bbox_pred
,
shape
=
(
-
1
,
4
))
predicted_cls_logits
=
nn
.
gather
(
cls_logits
,
score_index
)
predicted_bbox_pred
=
nn
.
gather
(
bbox_pred
,
loc_index
)
return
predicted_cls_logits
,
predicted_bbox_pred
,
target_label
,
target_bbox
return
predicted_cls_logits
,
predicted_bbox_pred
,
target_label
,
target_bbox
,
bbox_inside_weight
def
detection_output
(
loc
,
...
...
python/paddle/fluid/tests/test_detection.py
浏览文件 @
f06c6193
...
...
@@ -301,7 +301,7 @@ class TestRpnTargetAssign(unittest.TestCase):
dtype
=
'float32'
,
lod_level
=
1
,
append_batch_size
=
False
)
pred_scores
,
pred_loc
,
tgt_lbl
,
tgt_bbox
=
layers
.
rpn_target_assign
(
pred_scores
,
pred_loc
,
tgt_lbl
,
tgt_bbox
,
bbox_inside_weight
=
layers
.
rpn_target_assign
(
bbox_pred
=
bbox_pred
,
cls_logits
=
cls_logits
,
anchor_box
=
anchor_box
,
...
...
@@ -313,12 +313,14 @@ class TestRpnTargetAssign(unittest.TestCase):
rpn_straddle_thresh
=
0.0
,
rpn_fg_fraction
=
0.5
,
rpn_positive_overlap
=
0.7
,
rpn_negative_overlap
=
0.3
)
rpn_negative_overlap
=
0.3
,
use_random
=
False
)
self
.
assertIsNotNone
(
pred_scores
)
self
.
assertIsNotNone
(
pred_loc
)
self
.
assertIsNotNone
(
tgt_lbl
)
self
.
assertIsNotNone
(
tgt_bbox
)
self
.
assertIsNotNone
(
bbox_inside_weight
)
assert
pred_scores
.
shape
[
1
]
==
1
assert
pred_loc
.
shape
[
1
]
==
4
assert
pred_loc
.
shape
[
1
]
==
tgt_bbox
.
shape
[
1
]
...
...
python/paddle/fluid/tests/unittests/test_rpn_target_assign_op.py
浏览文件 @
f06c6193
...
...
@@ -50,8 +50,10 @@ def rpn_target_assign(anchor_by_gt_overlap,
fg_inds
,
size
=
(
len
(
fg_inds
)
-
num_fg
),
replace
=
False
)
else
:
disable_inds
=
fg_inds
[
num_fg
:]
labels
[
disable_inds
]
=
-
1
fg_inds
=
np
.
where
(
labels
==
1
)[
0
]
bbox_inside_weight
=
np
.
zeros
((
len
(
fg_inds
),
4
),
dtype
=
np
.
float32
)
num_bg
=
rpn_batch_size_per_im
-
np
.
sum
(
labels
==
1
)
bg_inds
=
np
.
where
(
anchor_to_gt_max
<
rpn_negative_overlap
)[
0
]
...
...
@@ -59,18 +61,27 @@ def rpn_target_assign(anchor_by_gt_overlap,
enable_inds
=
bg_inds
[
np
.
random
.
randint
(
len
(
bg_inds
),
size
=
num_bg
)]
else
:
enable_inds
=
bg_inds
[:
num_bg
]
fg_fake_inds
=
np
.
array
([],
np
.
int32
)
fg_value
=
np
.
array
([
fg_inds
[
0
]],
np
.
int32
)
fake_num
=
0
for
bg_id
in
enable_inds
:
if
bg_id
in
fg_inds
:
fake_num
+=
1
fg_fake_inds
=
np
.
hstack
([
fg_fake_inds
,
fg_value
])
labels
[
enable_inds
]
=
0
bbox_inside_weight
[
fake_num
:,
:]
=
1
fg_inds
=
np
.
where
(
labels
==
1
)[
0
]
bg_inds
=
np
.
where
(
labels
==
0
)[
0
]
loc_index
=
fg_inds
score_index
=
np
.
hstack
((
fg_inds
,
bg_inds
))
loc_index
=
np
.
hstack
([
fg_fake_inds
,
fg_inds
])
score_index
=
np
.
hstack
([
fg_inds
,
bg_inds
])
labels
=
labels
[
score_index
]
assert
not
np
.
any
(
labels
==
-
1
),
"Wrong labels with -1"
gt_inds
=
anchor_to_gt_argmax
[
fg_inds
]
gt_inds
=
anchor_to_gt_argmax
[
loc_index
]
return
loc_index
,
score_index
,
labels
,
gt_inds
return
loc_index
,
score_index
,
labels
,
gt_inds
,
bbox_inside_weight
def
get_anchor
(
n
,
c
,
h
,
w
):
...
...
@@ -123,9 +134,12 @@ def rpn_target_assign_in_python(all_anchors,
gt_boxes_slice
=
gt_boxes_slice
[
not_crowd_inds
]
iou
=
_bbox_overlaps
(
inside_anchors
,
gt_boxes_slice
)
loc_inds
,
score_inds
,
labels
,
gt_inds
=
rpn_target_assign
(
iou
,
rpn_batch_size_per_im
,
rpn_positive_overlap
,
rpn_negative_overlap
,
rpn_fg_fraction
,
use_random
)
loc_inds
,
score_inds
,
labels
,
gt_inds
,
bbox_inside_weight
=
\
rpn_target_assign
(
iou
,
rpn_batch_size_per_im
,
rpn_positive_overlap
,
rpn_negative_overlap
,
rpn_fg_fraction
,
use_random
)
# unmap to all anchor
loc_inds
=
inds_inside
[
loc_inds
]
score_inds
=
inds_inside
[
score_inds
]
...
...
@@ -139,6 +153,7 @@ def rpn_target_assign_in_python(all_anchors,
score_indexes
=
score_inds
tgt_labels
=
labels
tgt_bboxes
=
box_deltas
bbox_inside_weights
=
bbox_inside_weight
else
:
loc_indexes
=
np
.
concatenate
(
[
loc_indexes
,
loc_inds
+
i
*
anchor_num
])
...
...
@@ -146,8 +161,10 @@ def rpn_target_assign_in_python(all_anchors,
[
score_indexes
,
score_inds
+
i
*
anchor_num
])
tgt_labels
=
np
.
concatenate
([
tgt_labels
,
labels
])
tgt_bboxes
=
np
.
vstack
([
tgt_bboxes
,
box_deltas
])
bbox_inside_weights
=
np
.
vstack
([
bbox_inside_weights
,
\
bbox_inside_weight
])
return
loc_indexes
,
score_indexes
,
tgt_bboxes
,
tgt_labels
return
loc_indexes
,
score_indexes
,
tgt_bboxes
,
tgt_labels
,
bbox_inside_weights
class
TestRpnTargetAssignOp
(
OpTest
):
...
...
@@ -182,10 +199,12 @@ class TestRpnTargetAssignOp(OpTest):
rpn_fg_fraction
=
0.5
use_random
=
False
loc_index
,
score_index
,
tgt_bbox
,
labels
=
rpn_target_assign_in_python
(
all_anchors
,
gt_boxes
,
is_crowd
,
im_info
,
lod
,
rpn_straddle_thresh
,
rpn_batch_size_per_im
,
rpn_positive_overlap
,
rpn_negative_overlap
,
rpn_fg_fraction
,
use_random
)
loc_index
,
score_index
,
tgt_bbox
,
labels
,
bbox_inside_weights
=
\
rpn_target_assign_in_python
(
all_anchors
,
gt_boxes
,
is_crowd
,
im_info
,
lod
,
rpn_straddle_thresh
,
rpn_batch_size_per_im
,
rpn_positive_overlap
,
rpn_negative_overlap
,
rpn_fg_fraction
,
use_random
)
labels
=
labels
[:,
np
.
newaxis
]
self
.
op_type
=
"rpn_target_assign"
...
...
@@ -207,7 +226,8 @@ class TestRpnTargetAssignOp(OpTest):
'LocationIndex'
:
loc_index
.
astype
(
'int32'
),
'ScoreIndex'
:
score_index
.
astype
(
'int32'
),
'TargetBBox'
:
tgt_bbox
.
astype
(
'float32'
),
'TargetLabel'
:
labels
.
astype
(
'int32'
)
'TargetLabel'
:
labels
.
astype
(
'int32'
),
'BBox_inside_weight'
:
bbox_inside_weights
.
astype
(
'float32'
)
}
def
test_check_output
(
self
):
...
...
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