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262a7c0a
编写于
6月 18, 2019
作者:
A
AIFollowers
提交者:
qingqing01
6月 18, 2019
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
add cascade rcnn support (#18136)
Add cascade rcnn support.
上级
bc93a209
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
181 addition
and
108 deletion
+181
-108
paddle/fluid/API.spec
paddle/fluid/API.spec
+1
-1
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
.../fluid/operators/detection/generate_proposal_labels_op.cc
+106
-60
python/paddle/fluid/layers/detection.py
python/paddle/fluid/layers/detection.py
+10
-2
python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py
...fluid/tests/unittests/test_generate_proposal_labels_op.py
+64
-45
未找到文件。
paddle/fluid/API.spec
浏览文件 @
262a7c0a
...
...
@@ -350,7 +350,7 @@ paddle.fluid.layers.detection_map (ArgSpec(args=['detect_res', 'label', 'class_n
paddle.fluid.layers.rpn_target_assign (ArgSpec(args=['bbox_pred', 'cls_logits', 'anchor_box', 'anchor_var', 'gt_boxes', 'is_crowd', 'im_info', 'rpn_batch_size_per_im', 'rpn_straddle_thresh', 'rpn_fg_fraction', 'rpn_positive_overlap', 'rpn_negative_overlap', 'use_random'], varargs=None, keywords=None, defaults=(256, 0.0, 0.5, 0.7, 0.3, True)), ('document', '1e164a56fe9376e18a56d22563d9f801'))
paddle.fluid.layers.anchor_generator (ArgSpec(args=['input', 'anchor_sizes', 'aspect_ratios', 'variance', 'stride', 'offset', 'name'], varargs=None, keywords=None, defaults=(None, None, [0.1, 0.1, 0.2, 0.2], None, 0.5, None)), ('document', '82b2aefeeb1b706bc4afec70928a259a'))
paddle.fluid.layers.roi_perspective_transform (ArgSpec(args=['input', 'rois', 'transformed_height', 'transformed_width', 'spatial_scale'], varargs=None, keywords=None, defaults=(1.0,)), ('document', 'd1ddc75629fedee46f82e631e22c79dc'))
paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'
], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True)), ('document', '9c601df88b251f22e9311c52939948cd
'))
paddle.fluid.layers.generate_proposal_labels (ArgSpec(args=['rpn_rois', 'gt_classes', 'is_crowd', 'gt_boxes', 'im_info', 'batch_size_per_im', 'fg_fraction', 'fg_thresh', 'bg_thresh_hi', 'bg_thresh_lo', 'bbox_reg_weights', 'class_nums', 'use_random'
, 'is_cls_agnostic', 'is_cascade_rcnn'], varargs=None, keywords=None, defaults=(256, 0.25, 0.25, 0.5, 0.0, [0.1, 0.1, 0.2, 0.2], None, True, False, False)), ('document', 'e87c1131e98715d3657a96c44db1b910
'))
paddle.fluid.layers.generate_proposals (ArgSpec(args=['scores', 'bbox_deltas', 'im_info', 'anchors', 'variances', 'pre_nms_top_n', 'post_nms_top_n', 'nms_thresh', 'min_size', 'eta', 'name'], varargs=None, keywords=None, defaults=(6000, 1000, 0.5, 0.1, 1.0, None)), ('document', 'b7d707822b6af2a586bce608040235b1'))
paddle.fluid.layers.generate_mask_labels (ArgSpec(args=['im_info', 'gt_classes', 'is_crowd', 'gt_segms', 'rois', 'labels_int32', 'num_classes', 'resolution'], varargs=None, keywords=None, defaults=None), ('document', 'b319b10ddaf17fb4ddf03518685a17ef'))
paddle.fluid.layers.iou_similarity (ArgSpec(args=['x', 'y', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', '72fca4a39ccf82d5c746ae62d1868a99'))
...
...
paddle/fluid/operators/detection/generate_proposal_labels_op.cc
浏览文件 @
262a7c0a
...
...
@@ -109,17 +109,18 @@ std::vector<std::vector<int>> SampleFgBgGt(
const
platform
::
CPUDeviceContext
&
context
,
Tensor
*
iou
,
const
Tensor
&
is_crowd
,
const
int
batch_size_per_im
,
const
float
fg_fraction
,
const
float
fg_thresh
,
const
float
bg_thresh_hi
,
const
float
bg_thresh_lo
,
std
::
minstd_rand
engine
,
const
bool
use_random
)
{
const
float
bg_thresh_lo
,
std
::
minstd_rand
engine
,
const
bool
use_random
,
const
bool
is_cascade_rcnn
,
const
Tensor
&
rpn_rois
)
{
std
::
vector
<
int
>
fg_inds
;
std
::
vector
<
int
>
bg_inds
;
std
::
vector
<
int
>
gt_inds
;
std
::
vector
<
int
>
mapped_
gt_inds
;
int64_t
gt_num
=
is_crowd
.
numel
();
const
int
*
crowd_data
=
is_crowd
.
data
<
int
>
();
T
*
proposal_to_gt_overlaps
=
iou
->
data
<
T
>
();
int64_t
row
=
iou
->
dims
()[
0
];
int64_t
col
=
iou
->
dims
()[
1
];
float
epsilon
=
0.00001
;
const
T
*
rpn_rois_dt
=
rpn_rois
.
data
<
T
>
();
// Follow the Faster RCNN's implementation
for
(
int64_t
i
=
0
;
i
<
row
;
++
i
)
{
const
T
*
v
=
proposal_to_gt_overlaps
+
i
*
col
;
...
...
@@ -127,24 +128,37 @@ std::vector<std::vector<int>> SampleFgBgGt(
if
((
i
<
gt_num
)
&&
(
crowd_data
[
i
]))
{
max_overlap
=
-
1.0
;
}
if
(
max_overlap
>
fg_thresh
)
{
if
(
is_cascade_rcnn
&&
((
rpn_rois_dt
[
i
*
4
+
2
]
-
rpn_rois_dt
[
i
*
4
+
0
]
+
1
)
<=
0
||
(
rpn_rois_dt
[
i
*
4
+
3
]
-
rpn_rois_dt
[
i
*
4
+
1
]
+
1
)
<=
0
))
{
continue
;
}
if
(
max_overlap
>=
fg_thresh
)
{
// fg mapped gt label index
for
(
int64_t
j
=
0
;
j
<
col
;
++
j
)
{
T
val
=
proposal_to_gt_overlaps
[
i
*
col
+
j
];
auto
diff
=
std
::
abs
(
max_overlap
-
val
);
if
(
diff
<
epsilon
)
{
fg_inds
.
emplace_back
(
i
);
gt_inds
.
emplace_back
(
j
);
mapped_
gt_inds
.
emplace_back
(
j
);
break
;
}
}
}
else
{
if
((
max_overlap
>=
bg_thresh_lo
)
&&
(
max_overlap
<
bg_thresh_hi
))
{
}
else
if
((
max_overlap
>=
bg_thresh_lo
)
&&
(
max_overlap
<
bg_thresh_hi
))
{
bg_inds
.
emplace_back
(
i
);
}
}
else
{
continue
;
}
}
std
::
vector
<
std
::
vector
<
int
>>
res
;
if
(
is_cascade_rcnn
)
{
res
.
emplace_back
(
fg_inds
);
res
.
emplace_back
(
bg_inds
);
res
.
emplace_back
(
mapped_gt_inds
);
}
else
{
// Reservoir Sampling
// sampling fg
std
::
uniform_real_distribution
<
float
>
uniform
(
0
,
1
);
int
fg_rois_per_im
=
std
::
floor
(
batch_size_per_im
*
fg_fraction
);
int
fg_rois_this_image
=
fg_inds
.
size
();
...
...
@@ -156,19 +170,22 @@ std::vector<std::vector<int>> SampleFgBgGt(
int
rng_ind
=
std
::
floor
(
uniform
(
engine
)
*
i
);
if
(
rng_ind
<
fg_rois_per_this_image
)
{
std
::
iter_swap
(
fg_inds
.
begin
()
+
rng_ind
,
fg_inds
.
begin
()
+
i
);
std
::
iter_swap
(
gt_inds
.
begin
()
+
rng_ind
,
gt_inds
.
begin
()
+
i
);
std
::
iter_swap
(
mapped_gt_inds
.
begin
()
+
rng_ind
,
mapped_gt_inds
.
begin
()
+
i
);
}
}
}
}
std
::
vector
<
int
>
new_fg_inds
(
fg_inds
.
begin
(),
fg_inds
.
begin
()
+
fg_rois_per_this_image
);
std
::
vector
<
int
>
new_gt_inds
(
gt_inds
.
begin
(),
gt_inds
.
begin
()
+
fg_rois_per_this_image
);
std
::
vector
<
int
>
new_gt_inds
(
mapped_gt_inds
.
begin
(),
mapped_gt_inds
.
begin
()
+
fg_rois_per_this_image
);
// sampling bg
int
bg_rois_per_image
=
batch_size_per_im
-
fg_rois_per_this_image
;
int
bg_rois_this_image
=
bg_inds
.
size
();
int
bg_rois_per_this_image
=
std
::
min
(
bg_rois_per_image
,
bg_rois_this_image
);
int
bg_rois_per_this_image
=
std
::
min
(
bg_rois_per_image
,
bg_rois_this_image
);
if
(
use_random
)
{
const
int64_t
bg_size
=
static_cast
<
int64_t
>
(
bg_inds
.
size
());
if
(
bg_size
>
bg_rois_per_this_image
)
{
...
...
@@ -181,10 +198,12 @@ std::vector<std::vector<int>> SampleFgBgGt(
}
std
::
vector
<
int
>
new_bg_inds
(
bg_inds
.
begin
(),
bg_inds
.
begin
()
+
bg_rois_per_this_image
);
std
::
vector
<
std
::
vector
<
int
>>
res
;
//
res
.
emplace_back
(
new_fg_inds
);
res
.
emplace_back
(
new_bg_inds
);
res
.
emplace_back
(
new_gt_inds
);
}
return
res
;
}
...
...
@@ -231,35 +250,50 @@ std::vector<Tensor> SampleRoisForOneImage(
const
Tensor
&
im_info
,
const
int
batch_size_per_im
,
const
float
fg_fraction
,
const
float
fg_thresh
,
const
float
bg_thresh_hi
,
const
float
bg_thresh_lo
,
const
std
::
vector
<
float
>&
bbox_reg_weights
,
const
int
class_nums
,
std
::
minstd_rand
engine
,
bool
use_random
)
{
std
::
minstd_rand
engine
,
bool
use_random
,
bool
is_cascade_rcnn
,
bool
is_cls_agnostic
)
{
// 1.1 map to original image
auto
im_scale
=
im_info
.
data
<
T
>
()[
2
];
Tensor
rpn_rois_slice
;
Tensor
rpn_rois
;
rpn_rois
.
mutable_data
<
T
>
(
rpn_rois_in
.
dims
(),
context
.
GetPlace
());
if
(
is_cascade_rcnn
)
{
// slice rpn_rois from gt_box_num refer to detectron
rpn_rois_slice
=
rpn_rois_in
.
Slice
(
gt_boxes
.
dims
()[
0
],
rpn_rois_in
.
dims
()[
0
]);
rpn_rois
.
mutable_data
<
T
>
(
rpn_rois_slice
.
dims
(),
context
.
GetPlace
());
const
T
*
rpn_rois_in_dt
=
rpn_rois_slice
.
data
<
T
>
();
T
*
rpn_rois_dt
=
rpn_rois
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
rpn_rois
.
numel
();
++
i
)
{
rpn_rois_dt
[
i
]
=
rpn_rois_in_dt
[
i
]
/
im_scale
;
}
}
else
{
rpn_rois
.
mutable_data
<
T
>
(
rpn_rois_in
.
dims
(),
context
.
GetPlace
());
const
T
*
rpn_rois_in_dt
=
rpn_rois_in
.
data
<
T
>
();
T
*
rpn_rois_dt
=
rpn_rois
.
data
<
T
>
();
for
(
int
i
=
0
;
i
<
rpn_rois
.
numel
();
++
i
)
{
rpn_rois_dt
[
i
]
=
rpn_rois_in_dt
[
i
]
/
im_scale
;
}
}
Tensor
boxes
;
// 1.2 compute overlaps
int
proposals_num
=
gt_boxes
.
dims
()[
0
]
+
rpn_rois
.
dims
()[
0
];
Tensor
boxes
;
boxes
.
mutable_data
<
T
>
({
proposals_num
,
kBoxDim
},
context
.
GetPlace
());
Concat
<
T
>
(
context
,
gt_boxes
,
rpn_rois
,
&
boxes
);
// Overlaps
Tensor
proposal_to_gt_overlaps
;
proposal_to_gt_overlaps
.
mutable_data
<
T
>
({
proposals_num
,
gt_boxes
.
dims
()[
0
]},
context
.
GetPlace
());
BboxOverlaps
<
T
>
(
boxes
,
gt_boxes
,
&
proposal_to_gt_overlaps
);
// Generate proposal index
std
::
vector
<
std
::
vector
<
int
>>
fg_bg_gt
=
SampleFgBgGt
<
T
>
(
context
,
&
proposal_to_gt_overlaps
,
is_crowd
,
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
engine
,
use_random
);
std
::
vector
<
std
::
vector
<
int
>>
fg_bg_gt
=
SampleFgBgGt
<
T
>
(
context
,
&
proposal_to_gt_overlaps
,
is_crowd
,
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
engine
,
use_random
,
is_cascade_rcnn
,
boxes
);
std
::
vector
<
int
>
fg_inds
=
fg_bg_gt
[
0
];
std
::
vector
<
int
>
bg_inds
=
fg_bg_gt
[
1
];
std
::
vector
<
int
>
gt_inds
=
fg_bg_gt
[
2
];
std
::
vector
<
int
>
mapped_gt_inds
=
fg_bg_gt
[
2
];
// mapped_gt_labels
// Gather boxes and labels
Tensor
sampled_boxes
,
sampled_labels
,
sampled_gts
;
...
...
@@ -271,7 +305,8 @@ std::vector<Tensor> SampleRoisForOneImage(
sampled_labels
.
mutable_data
<
int
>
({
boxes_num
},
context
.
GetPlace
());
sampled_gts
.
mutable_data
<
T
>
({
fg_num
,
kBoxDim
},
context
.
GetPlace
());
GatherBoxesLabels
<
T
>
(
context
,
boxes
,
gt_boxes
,
gt_classes
,
fg_inds
,
bg_inds
,
gt_inds
,
&
sampled_boxes
,
&
sampled_labels
,
&
sampled_gts
);
mapped_gt_inds
,
&
sampled_boxes
,
&
sampled_labels
,
&
sampled_gts
);
// Compute targets
Tensor
bbox_targets_single
;
...
...
@@ -305,6 +340,9 @@ std::vector<Tensor> SampleRoisForOneImage(
for
(
int64_t
i
=
0
;
i
<
boxes_num
;
++
i
)
{
int
label
=
sampled_labels_data
[
i
];
if
(
label
>
0
)
{
if
(
is_cls_agnostic
)
{
label
=
1
;
}
int
dst_idx
=
i
*
width
+
kBoxDim
*
label
;
int
src_idx
=
kBoxDim
*
i
;
bbox_targets_data
[
dst_idx
]
=
bbox_targets_single_data
[
src_idx
];
...
...
@@ -356,7 +394,8 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
context
.
Attr
<
std
::
vector
<
float
>>
(
"bbox_reg_weights"
);
int
class_nums
=
context
.
Attr
<
int
>
(
"class_nums"
);
bool
use_random
=
context
.
Attr
<
bool
>
(
"use_random"
);
bool
is_cascade_rcnn
=
context
.
Attr
<
bool
>
(
"is_cascade_rcnn"
);
bool
is_cls_agnostic
=
context
.
Attr
<
bool
>
(
"is_cls_agnostic"
);
PADDLE_ENFORCE_EQ
(
rpn_rois
->
lod
().
size
(),
1UL
,
"GenerateProposalLabelsOp rpn_rois needs 1 level of LoD"
);
PADDLE_ENFORCE_EQ
(
...
...
@@ -411,7 +450,7 @@ class GenerateProposalLabelsKernel : public framework::OpKernel<T> {
dev_ctx
,
rpn_rois_slice
,
gt_classes_slice
,
is_crowd_slice
,
gt_boxes_slice
,
im_info_slice
,
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
,
engine
,
use_random
);
engine
,
use_random
,
is_cascade_rcnn
,
is_cls_agnostic
);
Tensor
sampled_rois
=
tensor_output
[
0
];
Tensor
sampled_labels_int32
=
tensor_output
[
1
];
Tensor
sampled_bbox_targets
=
tensor_output
[
2
];
...
...
@@ -513,6 +552,13 @@ class GenerateProposalLabelsOpMaker : public framework::OpProtoAndCheckerMaker {
"use_random"
,
"Use random sampling to choose foreground and background boxes."
)
.
SetDefault
(
true
);
AddAttr
<
bool
>
(
"is_cascade_rcnn"
,
"cascade rcnn sampling policy changed from stage 2."
)
.
SetDefault
(
false
);
AddAttr
<
bool
>
(
"is_cls_agnostic"
,
"the box regress will only include fg and bg locations if set true "
)
.
SetDefault
(
false
);
AddComment
(
R"DOC(
This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth,
...
...
python/paddle/fluid/layers/detection.py
浏览文件 @
262a7c0a
...
...
@@ -1916,9 +1916,13 @@ def generate_proposal_labels(rpn_rois,
bg_thresh_lo
=
0.0
,
bbox_reg_weights
=
[
0.1
,
0.1
,
0.2
,
0.2
],
class_nums
=
None
,
use_random
=
True
):
use_random
=
True
,
is_cls_agnostic
=
False
,
is_cascade_rcnn
=
False
):
"""
** Generate Proposal Labels of Faster-RCNN **
This operator can be, for given the GenerateProposalOp output bounding boxes and groundtruth,
to sample foreground boxes and background boxes, and compute loss target.
...
...
@@ -1949,6 +1953,8 @@ def generate_proposal_labels(rpn_rois,
bbox_reg_weights(list|tuple): Box regression weights.
class_nums(int): Class number.
use_random(bool): Use random sampling to choose foreground and background boxes.
is_cls_agnostic(bool): class agnostic bbox regression will only represent fg and bg boxes.
is_cascade_rcnn(bool): cascade rcnn model will change sampling policy when settting True.
Examples:
.. code-block:: python
...
...
@@ -2007,7 +2013,9 @@ def generate_proposal_labels(rpn_rois,
'bg_thresh_lo'
:
bg_thresh_lo
,
'bbox_reg_weights'
:
bbox_reg_weights
,
'class_nums'
:
class_nums
,
'use_random'
:
use_random
'use_random'
:
use_random
,
'is_cls_agnostic'
:
is_cls_agnostic
,
'is_cascade_rcnn'
:
is_cascade_rcnn
})
rois
.
stop_gradient
=
True
...
...
python/paddle/fluid/tests/unittests/test_generate_proposal_labels_op.py
浏览文件 @
262a7c0a
...
...
@@ -22,10 +22,10 @@ import paddle.fluid as fluid
from
op_test
import
OpTest
def
generate_proposal_labels_in_python
(
rpn_rois
,
gt_classes
,
is_crowd
,
gt_boxes
,
im_info
,
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
):
def
generate_proposal_labels_in_python
(
rpn_rois
,
gt_classes
,
is_crowd
,
gt_boxes
,
im_info
,
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
,
is_cls_agnostic
,
is_cascade_rcnn
):
rois
=
[]
labels_int32
=
[]
bbox_targets
=
[]
...
...
@@ -36,13 +36,12 @@ def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes,
im_info
),
'batch size of rpn_rois and ground_truth is not matched'
for
im_i
in
range
(
len
(
im_info
)):
frcn_blobs
=
_sample_rois
(
rpn_rois
[
im_i
],
gt_classes
[
im_i
],
is_crowd
[
im_i
],
gt_boxes
[
im_i
],
im_info
[
im_i
],
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
)
frcn_blobs
=
_sample_rois
(
rpn_rois
[
im_i
],
gt_classes
[
im_i
],
is_crowd
[
im_i
],
gt_boxes
[
im_i
],
im_info
[
im_i
],
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
,
is_cls_agnostic
,
is_cascade_rcnn
)
lod
.
append
(
frcn_blobs
[
'rois'
].
shape
[
0
])
rois
.
append
(
frcn_blobs
[
'rois'
])
labels_int32
.
append
(
frcn_blobs
[
'labels_int32'
])
bbox_targets
.
append
(
frcn_blobs
[
'bbox_targets'
])
...
...
@@ -54,7 +53,8 @@ def generate_proposal_labels_in_python(rpn_rois, gt_classes, is_crowd, gt_boxes,
def
_sample_rois
(
rpn_rois
,
gt_classes
,
is_crowd
,
gt_boxes
,
im_info
,
batch_size_per_im
,
fg_fraction
,
fg_thresh
,
bg_thresh_hi
,
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
):
bg_thresh_lo
,
bbox_reg_weights
,
class_nums
,
is_cls_agnostic
,
is_cascade_rcnn
):
rois_per_image
=
int
(
batch_size_per_im
)
fg_rois_per_im
=
int
(
np
.
round
(
fg_fraction
*
rois_per_image
))
...
...
@@ -62,7 +62,8 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
im_scale
=
im_info
[
2
]
inv_im_scale
=
1.
/
im_scale
rpn_rois
=
rpn_rois
*
inv_im_scale
if
is_cascade_rcnn
:
rpn_rois
=
rpn_rois
[
gt_boxes
.
shape
[
0
]:,
:]
boxes
=
np
.
vstack
([
gt_boxes
,
rpn_rois
])
gt_overlaps
=
np
.
zeros
((
boxes
.
shape
[
0
],
class_nums
))
box_to_gt_ind_map
=
np
.
zeros
((
boxes
.
shape
[
0
]),
dtype
=
np
.
int32
)
...
...
@@ -87,15 +88,26 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
max_overlaps
=
gt_overlaps
.
max
(
axis
=
1
)
max_classes
=
gt_overlaps
.
argmax
(
axis
=
1
)
# Cascade RCNN Decode Filter
if
is_cascade_rcnn
:
ws
=
boxes
[:,
2
]
-
boxes
[:,
0
]
+
1
hs
=
boxes
[:,
3
]
-
boxes
[:,
1
]
+
1
keep
=
np
.
where
((
ws
>
0
)
&
(
hs
>
0
))[
0
]
boxes
=
boxes
[
keep
]
fg_inds
=
np
.
where
(
max_overlaps
>=
fg_thresh
)[
0
]
bg_inds
=
np
.
where
((
max_overlaps
<
bg_thresh_hi
)
&
(
max_overlaps
>=
bg_thresh_lo
))[
0
]
fg_rois_per_this_image
=
fg_inds
.
shape
[
0
]
bg_rois_per_this_image
=
bg_inds
.
shape
[
0
]
else
:
# Foreground
fg_inds
=
np
.
where
(
max_overlaps
>=
fg_thresh
)[
0
]
fg_rois_per_this_image
=
np
.
minimum
(
fg_rois_per_im
,
fg_inds
.
shape
[
0
])
# Sample foreground if there are too many
#
if fg_inds.shape[0] > fg_rois_per_this_image:
#
fg_inds = np.random.choice(
#
fg_inds, size=fg_rois_per_this_image, replace=False)
if
fg_inds
.
shape
[
0
]
>
fg_rois_per_this_image
:
fg_inds
=
np
.
random
.
choice
(
fg_inds
,
size
=
fg_rois_per_this_image
,
replace
=
False
)
fg_inds
=
fg_inds
[:
fg_rois_per_this_image
]
# Background
bg_inds
=
np
.
where
((
max_overlaps
<
bg_thresh_hi
)
&
(
max_overlaps
>=
bg_thresh_lo
))[
0
]
...
...
@@ -103,9 +115,9 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
bg_rois_per_this_image
=
np
.
minimum
(
bg_rois_per_this_image
,
bg_inds
.
shape
[
0
])
# Sample background if there are too many
#
if bg_inds.shape[0] > bg_rois_per_this_image:
#
bg_inds = np.random.choice(
#
bg_inds, size=bg_rois_per_this_image, replace=False)
if
bg_inds
.
shape
[
0
]
>
bg_rois_per_this_image
:
bg_inds
=
np
.
random
.
choice
(
bg_inds
,
size
=
bg_rois_per_this_image
,
replace
=
False
)
bg_inds
=
bg_inds
[:
bg_rois_per_this_image
]
keep_inds
=
np
.
append
(
fg_inds
,
bg_inds
)
...
...
@@ -114,14 +126,12 @@ def _sample_rois(rpn_rois, gt_classes, is_crowd, gt_boxes, im_info,
sampled_boxes
=
boxes
[
keep_inds
]
sampled_gts
=
gt_boxes
[
box_to_gt_ind_map
[
keep_inds
]]
sampled_gts
[
fg_rois_per_this_image
:,
:]
=
gt_boxes
[
0
]
bbox_label_targets
=
_compute_targets
(
sampled_boxes
,
sampled_gts
,
sampled_labels
,
bbox_reg_weights
)
bbox_targets
,
bbox_inside_weights
=
_expand_bbox_targets
(
bbox_label_targets
,
class_nums
)
bbox_targets
,
bbox_inside_weights
=
_expand_bbox_targets
(
bbox_label_targets
,
class_nums
,
is_cls_agnostic
)
bbox_outside_weights
=
np
.
array
(
bbox_inside_weights
>
0
,
dtype
=
bbox_inside_weights
.
dtype
)
# Scale rois
sampled_rois
=
sampled_boxes
*
im_scale
...
...
@@ -192,19 +202,22 @@ def _box_to_delta(ex_boxes, gt_boxes, weights):
return
targets
def
_expand_bbox_targets
(
bbox_targets_input
,
class_nums
):
def
_expand_bbox_targets
(
bbox_targets_input
,
class_nums
,
is_cls_agnostic
):
class_labels
=
bbox_targets_input
[:,
0
]
fg_inds
=
np
.
where
(
class_labels
>
0
)[
0
]
bbox_targets
=
np
.
zeros
((
class_labels
.
shape
[
0
],
4
*
class_nums
))
#if is_cls_agnostic:
# class_labels = [1 if ll > 0 else 0 for ll in class_labels]
# class_labels = np.array(class_labels, dtype=np.int32)
# class_nums = 2
bbox_targets
=
np
.
zeros
((
class_labels
.
shape
[
0
],
4
*
class_nums
if
not
is_cls_agnostic
else
4
*
2
))
bbox_inside_weights
=
np
.
zeros
(
bbox_targets
.
shape
)
for
ind
in
fg_inds
:
class_label
=
int
(
class_labels
[
ind
])
class_label
=
int
(
class_labels
[
ind
])
if
not
is_cls_agnostic
else
1
start_ind
=
class_label
*
4
end_ind
=
class_label
*
4
+
4
bbox_targets
[
ind
,
start_ind
:
end_ind
]
=
bbox_targets_input
[
ind
,
1
:]
bbox_inside_weights
[
ind
,
start_ind
:
end_ind
]
=
(
1.0
,
1.0
,
1.0
,
1.0
)
return
bbox_targets
,
bbox_inside_weights
...
...
@@ -228,7 +241,9 @@ class TestGenerateProposalLabelsOp(OpTest):
'bg_thresh_lo'
:
self
.
bg_thresh_lo
,
'bbox_reg_weights'
:
self
.
bbox_reg_weights
,
'class_nums'
:
self
.
class_nums
,
'use_random'
:
False
'use_random'
:
False
,
'is_cls_agnostic'
:
self
.
is_cls_agnostic
,
'is_cascade_rcnn'
:
self
.
is_cascade_rcnn
}
self
.
outputs
=
{
'Rois'
:
(
self
.
rois
,
[
self
.
lod
]),
...
...
@@ -252,12 +267,15 @@ class TestGenerateProposalLabelsOp(OpTest):
self
.
bg_thresh_hi
=
0.5
self
.
bg_thresh_lo
=
0.0
self
.
bbox_reg_weights
=
[
0.1
,
0.1
,
0.2
,
0.2
]
self
.
class_nums
=
81
#self.class_nums = 81
self
.
is_cls_agnostic
=
False
#True
self
.
is_cascade_rcnn
=
True
self
.
class_nums
=
2
if
self
.
is_cls_agnostic
else
81
def
init_test_input
(
self
):
np
.
random
.
seed
(
0
)
gt_nums
=
6
# Keep same with batch_size_per_im for unittest
proposal_nums
=
2000
#self.batch_size_per_im - gt_nums
proposal_nums
=
2000
if
not
self
.
is_cascade_rcnn
else
512
#self.batch_size_per_im - gt_nums
images_shape
=
[[
64
,
64
]]
self
.
im_info
=
np
.
ones
((
len
(
images_shape
),
3
)).
astype
(
np
.
float32
)
for
i
in
range
(
len
(
images_shape
)):
...
...
@@ -280,7 +298,8 @@ class TestGenerateProposalLabelsOp(OpTest):
self
.
rpn_rois
,
self
.
gt_classes
,
self
.
is_crowd
,
self
.
gt_boxes
,
self
.
im_info
,
self
.
batch_size_per_im
,
self
.
fg_fraction
,
self
.
fg_thresh
,
self
.
bg_thresh_hi
,
self
.
bg_thresh_lo
,
self
.
bbox_reg_weights
,
self
.
class_nums
self
.
bbox_reg_weights
,
self
.
class_nums
,
self
.
is_cls_agnostic
,
self
.
is_cascade_rcnn
)
self
.
rois
=
np
.
vstack
(
self
.
rois
)
self
.
labels_int32
=
np
.
hstack
(
self
.
labels_int32
)
...
...
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