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体验新版 GitCode,发现更多精彩内容 >>
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99c63aed
编写于
7月 29, 2021
作者:
C
cnn
提交者:
GitHub
7月 29, 2021
浏览文件
操作
浏览文件
下载
电子邮件补丁
差异文件
s2anet update (#3817)
上级
7f000edb
变更
8
隐藏空白更改
内联
并排
Showing
8 changed file
with
267 addition
and
63 deletion
+267
-63
configs/dota/README.md
configs/dota/README.md
+4
-3
configs/dota/_base_/s2anet.yml
configs/dota/_base_/s2anet.yml
+0
-1
configs/dota/_base_/s2anet_optimizer_2x.yml
configs/dota/_base_/s2anet_optimizer_2x.yml
+20
-0
configs/dota/s2anet_1x_spine.yml
configs/dota/s2anet_1x_spine.yml
+4
-2
configs/dota/s2anet_alignconv_2x_dota.yml
configs/dota/s2anet_alignconv_2x_dota.yml
+8
-5
configs/dota/s2anet_conv_1x_dota.yml
configs/dota/s2anet_conv_1x_dota.yml
+7
-0
ppdet/modeling/heads/s2anet_head.py
ppdet/modeling/heads/s2anet_head.py
+221
-50
ppdet/modeling/proposal_generator/target_layer.py
ppdet/modeling/proposal_generator/target_layer.py
+3
-2
未找到文件。
configs/dota/README.md
浏览文件 @
99c63aed
...
...
@@ -129,9 +129,10 @@ python3.7 tools/infer.py -c configs/dota/s2anet_1x_dota.yml -o weights=./weights
### S2ANet模型
| 模型 | GPU个数 | Conv类型 | mAP | 模型下载 | 配置文件 |
|:-----------:|:-------:|:----------:|:--------:| :----------:| :---------: |
| S2ANet | 8 | Conv | 71.42 |
[
model
](
https://paddledet.bj.bcebos.com/models/s2anet_conv_1x_dota.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/tree/release/2.2/configs/dota/s2anet_conv_1x_dota.yml
)
|
| 模型 | Conv类型 | mAP | 模型下载 | 配置文件 |
|:-----------:|:----------:|:--------:| :----------:| :---------: |
| S2ANet | Conv | 71.42 |
[
model
](
https://paddledet.bj.bcebos.com/models/s2anet_conv_1x_dota.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_conv_1x_dota.yml
)
|
| S2ANet | AlignConv | 74.0 |
[
model
](
https://paddledet.bj.bcebos.com/models/s2anet_alignconv_2x_dota.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/s2anet_alignconv_2x_dota.yml
)
|
**注意:**
这里使用
`multiclass_nms`
,与原作者使用nms略有不同,精度相比原始论文中高0.15 (71.27-->71.42)。
...
...
configs/dota/_base_/s2anet.yml
浏览文件 @
99c63aed
...
...
@@ -53,4 +53,3 @@ S2ANetBBoxPostProcess:
score_threshold
:
0.05
nms_threshold
:
0.1
normalized
:
False
#background_label: -1
configs/dota/_base_/s2anet_optimizer_2x.yml
0 → 100644
浏览文件 @
99c63aed
epoch
:
24
LearningRate
:
base_lr
:
0.005
schedulers
:
-
!PiecewiseDecay
gamma
:
0.1
milestones
:
[
14
,
20
]
-
!LinearWarmup
start_factor
:
0.3333333333333333
steps
:
1000
OptimizerBuilder
:
optimizer
:
momentum
:
0.9
type
:
Momentum
regularizer
:
factor
:
0.0001
type
:
L2
clip_grad_by_norm
:
35
configs/dota/s2anet_1x_spine.yml
浏览文件 @
99c63aed
...
...
@@ -24,5 +24,7 @@ S2ANetHead:
align_conv_type
:
'
AlignConv'
# AlignConv Conv
align_conv_size
:
3
use_sigmoid_cls
:
True
reg_loss_weight
:
[
1.0
,
1.0
,
1.0
,
1.0
,
1.05
]
cls_loss_weight
:
[
1.05
,
1.0
]
reg_loss_weight
:
[
1.0
,
1.0
,
1.0
,
1.0
,
1.05
]
cls_loss_weight
:
[
1.05
,
1.0
]
reg_loss_type
:
gwd
use_paddle_anchor
:
False
configs/dota/s2anet_
1
x_dota.yml
→
configs/dota/s2anet_
alignconv_2
x_dota.yml
浏览文件 @
99c63aed
it
_BASE_
:
[
_BASE_
:
[
'
../datasets/dota.yml'
,
'
../runtime.yml'
,
'
_base_/s2anet_optimizer_
1
x.yml'
,
'
_base_/s2anet_optimizer_
2
x.yml'
,
'
_base_/s2anet.yml'
,
'
_base_/s2anet_reader.yml'
,
]
weights
:
output/s2anet_1x_dota/model_final
pretrain_weights
:
https://paddledet.bj.bcebos.com/models/pretrained/ResNet50_vd_ssld_v2_pretrained.pdparams
weights
:
output/s2anet_alignconv_2x_dota/model_final
S2ANetHead
:
anchor_strides
:
[
8
,
16
,
32
,
64
,
128
]
...
...
@@ -19,5 +21,6 @@ S2ANetHead:
align_conv_type
:
'
AlignConv'
# AlignConv Conv
align_conv_size
:
3
use_sigmoid_cls
:
True
reg_loss_weight
:
[
1.0
,
1.0
,
1.0
,
1.0
,
1.1
]
cls_loss_weight
:
[
1.1
,
1.05
]
reg_loss_weight
:
[
1.0
,
1.0
,
1.0
,
1.0
,
1.05
]
cls_loss_weight
:
[
1.05
,
1.0
]
#reg_loss_type: 'l1' # 'l1' 'gwd'
configs/dota/s2anet_conv_1x_dota.yml
浏览文件 @
99c63aed
...
...
@@ -7,6 +7,13 @@ _BASE_: [
]
weights
:
output/s2anet_1x_dota/model_final
ResNet
:
depth
:
50
variant
:
b
norm_type
:
bn
return_idx
:
[
1
,
2
,
3
]
num_stages
:
4
S2ANetHead
:
anchor_strides
:
[
8
,
16
,
32
,
64
,
128
]
anchor_scales
:
[
4
]
...
...
ppdet/modeling/heads/s2anet_head.py
浏览文件 @
99c63aed
...
...
@@ -102,8 +102,7 @@ class S2ANetAnchorGenerator(nn.Layer):
valid_xx
,
valid_yy
=
self
.
_meshgrid
(
valid_x
,
valid_y
)
valid
=
valid_xx
&
valid_yy
valid
=
paddle
.
reshape
(
valid
,
[
-
1
,
1
])
valid
=
paddle
.
expand
(
valid
,
[
-
1
,
self
.
num_base_anchors
]).
reshape
([
-
1
])
valid
=
paddle
.
expand
(
valid
,
[
-
1
,
self
.
num_base_anchors
]).
reshape
([
-
1
])
return
valid
...
...
@@ -179,9 +178,12 @@ class AlignConv(nn.Layer):
offset_x
=
x_anchor
-
x_conv
offset_y
=
y_anchor
-
y_conv
offset
=
paddle
.
stack
([
offset_y
,
offset_x
],
axis
=-
1
)
offset
=
paddle
.
reshape
(
offset
,
[
feat_h
*
feat_w
,
self
.
kernel_size
*
self
.
kernel_size
*
2
])
offset
=
paddle
.
reshape
(
offset
,
[
feat_h
*
feat_w
,
self
.
kernel_size
*
self
.
kernel_size
*
2
])
offset
=
paddle
.
transpose
(
offset
,
[
1
,
0
])
offset
=
paddle
.
reshape
(
offset
,
[
1
,
self
.
kernel_size
*
self
.
kernel_size
*
2
,
feat_h
,
feat_w
])
offset
=
paddle
.
reshape
(
offset
,
[
1
,
self
.
kernel_size
*
self
.
kernel_size
*
2
,
feat_h
,
feat_w
])
return
offset
def
forward
(
self
,
x
,
refine_anchors
,
featmap_size
,
stride
):
...
...
@@ -260,8 +262,8 @@ class S2ANetHead(nn.Layer):
# anchor
self
.
anchor_generators
=
[]
for
anchor_base
in
self
.
anchor_base_sizes
:
self
.
anchor_generators
.
append
(
S2ANetAnchorGenerator
(
anchor_base
,
anchor_scales
,
self
.
anchor_generators
.
append
(
S2ANetAnchorGenerator
(
anchor_base
,
anchor_scales
,
anchor_ratios
))
self
.
anchor_generators
=
nn
.
LayerList
(
self
.
anchor_generators
)
...
...
@@ -440,8 +442,7 @@ class S2ANetHead(nn.Layer):
init_anchors
=
paddle
.
to_tensor
(
init_anchors
,
dtype
=
'float32'
)
NA
=
featmap_size
[
0
]
*
featmap_size
[
1
]
init_anchors
=
paddle
.
reshape
(
init_anchors
,
[
NA
,
4
])
init_anchors
=
paddle
.
reshape
(
init_anchors
,
[
NA
,
4
])
init_anchors
=
self
.
rect2rbox
(
init_anchors
)
self
.
base_anchors_list
.
append
(
init_anchors
)
...
...
@@ -474,18 +475,19 @@ class S2ANetHead(nn.Layer):
# [N, CLS, H, W] --> [N, H, W, CLS]
odm_cls_score
=
odm_cls_score
.
transpose
([
0
,
2
,
3
,
1
])
odm_cls_score_shape
=
odm_cls_score
.
shape
odm_cls_score_reshape
=
paddle
.
reshape
(
odm_cls_score
,
[
odm_cls_score_shape
[
0
],
odm_cls_score_shape
[
1
]
*
odm_cls_score_shape
[
2
],
self
.
cls_out_channels
])
odm_cls_score_reshape
=
paddle
.
reshape
(
odm_cls_score
,
[
odm_cls_score_shape
[
0
],
odm_cls_score_shape
[
1
]
*
odm_cls_score_shape
[
2
],
self
.
cls_out_channels
])
odm_cls_branch_list
.
append
(
odm_cls_score_reshape
)
odm_bbox_pred
=
self
.
odm_reg
(
odm_reg_feat
)
# [N, 5, H, W] --> [N, H, W, 5]
odm_bbox_pred
=
odm_bbox_pred
.
transpose
([
0
,
2
,
3
,
1
])
odm_bbox_pred_reshape
=
paddle
.
reshape
(
odm_bbox_pred
,
[
-
1
,
5
])
odm_bbox_pred_reshape
=
paddle
.
unsqueeze
(
odm_bbox_pred_reshape
,
axis
=
0
)
odm_bbox_pred_reshape
=
paddle
.
reshape
(
odm_bbox_pred
,
[
-
1
,
5
])
odm_bbox_pred_reshape
=
paddle
.
unsqueeze
(
odm_bbox_pred_reshape
,
axis
=
0
)
odm_reg_branch_list
.
append
(
odm_bbox_pred_reshape
)
self
.
s2anet_head_out
=
(
fam_cls_branch_list
,
fam_reg_branch_list
,
...
...
@@ -499,12 +501,8 @@ class S2ANetHead(nn.Layer):
odm_cls_branch_list
=
self
.
s2anet_head_out
[
2
]
odm_reg_branch_list
=
self
.
s2anet_head_out
[
3
]
pred_scores
,
pred_bboxes
=
self
.
get_bboxes
(
odm_cls_branch_list
,
odm_reg_branch_list
,
refine_anchors
,
nms_pre
,
self
.
cls_out_channels
,
self
.
use_sigmoid_cls
)
odm_cls_branch_list
,
odm_reg_branch_list
,
refine_anchors
,
nms_pre
,
self
.
cls_out_channels
,
self
.
use_sigmoid_cls
)
return
pred_scores
,
pred_bboxes
def
smooth_l1_loss
(
self
,
pred
,
label
,
delta
=
1.0
/
9.0
):
...
...
@@ -523,8 +521,8 @@ class S2ANetHead(nn.Layer):
return
loss
def
get_fam_loss
(
self
,
fam_target
,
s2anet_head_out
,
reg_loss_type
=
'gwd'
):
(
labels
,
label_weights
,
bbox_targets
,
bbox_weights
,
pos_ind
s
,
neg_inds
)
=
fam_target
(
labels
,
label_weights
,
bbox_targets
,
bbox_weights
,
bbox_gt_bboxe
s
,
pos_inds
,
neg_inds
)
=
fam_target
fam_cls_branch_list
,
fam_reg_branch_list
,
odm_cls_branch_list
,
odm_reg_branch_list
=
s2anet_head_out
fam_cls_losses
=
[]
...
...
@@ -543,7 +541,6 @@ class S2ANetHead(nn.Layer):
feat_bbox_targets
=
bbox_targets
[
st_idx
:
st_idx
+
feat_anchor_num
,
:]
feat_bbox_weights
=
bbox_weights
[
st_idx
:
st_idx
+
feat_anchor_num
,
:]
st_idx
+=
feat_anchor_num
# step2: calc cls loss
feat_labels
=
feat_labels
.
reshape
(
-
1
)
...
...
@@ -590,18 +587,53 @@ class S2ANetHead(nn.Layer):
fam_bbox_pred
=
paddle
.
reshape
(
fam_bbox_pred
,
[
-
1
,
5
])
fam_bbox
=
self
.
smooth_l1_loss
(
fam_bbox_pred
,
feat_bbox_targets
)
# iou_factor
fam_bbox
=
self
.
smooth_l1_loss
(
fam_bbox_pred
,
feat_bbox_targets
)
loss_weight
=
paddle
.
to_tensor
(
self
.
reg_loss_weight
,
dtype
=
'float32'
,
stop_gradient
=
True
)
fam_bbox
=
paddle
.
multiply
(
fam_bbox
,
loss_weight
)
feat_bbox_weights
=
paddle
.
to_tensor
(
feat_bbox_weights
,
stop_gradient
=
True
)
if
reg_loss_type
==
'l1'
:
fam_bbox
=
self
.
smooth_l1_loss
(
fam_bbox_pred
,
feat_bbox_targets
)
loss_weight
=
paddle
.
to_tensor
(
self
.
reg_loss_weight
,
dtype
=
'float32'
,
stop_gradient
=
True
)
fam_bbox
=
paddle
.
multiply
(
fam_bbox
,
loss_weight
)
feat_bbox_weights
=
paddle
.
to_tensor
(
feat_bbox_weights
,
stop_gradient
=
True
)
fam_bbox
=
fam_bbox
*
feat_bbox_weights
fam_bbox_total
=
paddle
.
sum
(
fam_bbox
)
/
num_total_samples
elif
reg_loss_type
==
'iou'
or
reg_loss_type
==
'gwd'
:
fam_bbox
=
paddle
.
sum
(
fam_bbox
,
axis
=-
1
)
feat_bbox_weights
=
paddle
.
sum
(
feat_bbox_weights
,
axis
=-
1
)
try
:
from
rbox_iou_ops
import
rbox_iou
except
Exception
as
e
:
print
(
"import custom_ops error, try install rbox_iou_ops "
\
"following ppdet/ext_op/README.md"
,
e
)
sys
.
stdout
.
flush
()
sys
.
exit
(
-
1
)
# calc iou
fam_bbox_decode
=
self
.
delta2rbox
(
self
.
base_anchors_list
[
idx
],
fam_bbox_pred
)
bbox_gt_bboxes
=
paddle
.
to_tensor
(
bbox_gt_bboxes
,
dtype
=
fam_bbox_decode
.
dtype
,
place
=
fam_bbox_decode
.
place
)
bbox_gt_bboxes
.
stop_gradient
=
True
iou
=
rbox_iou
(
fam_bbox_decode
,
bbox_gt_bboxes
)
iou
=
paddle
.
diag
(
iou
)
if
reg_loss_type
==
'iou'
:
EPS
=
paddle
.
to_tensor
(
1e-8
,
dtype
=
'float32'
,
stop_gradient
=
True
)
iou_factor
=
-
1.0
*
paddle
.
log
(
iou
+
EPS
)
/
(
fam_bbox
+
EPS
)
iou_factor
.
stop_gradient
=
True
#fam_bbox = fam_bbox * iou_factor
elif
reg_loss_type
==
'gwd'
:
bbox_gt_bboxes_level
=
bbox_gt_bboxes
[
st_idx
:
st_idx
+
feat_anchor_num
,
:]
fam_bbox_total
=
self
.
gwd_loss
(
fam_bbox_decode
,
bbox_gt_bboxes_level
)
fam_bbox_total
=
fam_bbox_total
*
feat_bbox_weights
fam_bbox_total
=
paddle
.
sum
(
fam_bbox_total
)
fam_bbox_losses
.
append
(
fam_bbox_total
)
st_idx
+=
feat_anchor_num
fam_cls_loss
=
paddle
.
add_n
(
fam_cls_losses
)
fam_cls_loss_weight
=
paddle
.
to_tensor
(
...
...
@@ -611,8 +643,8 @@ class S2ANetHead(nn.Layer):
return
fam_cls_loss
,
fam_reg_loss
def
get_odm_loss
(
self
,
odm_target
,
s2anet_head_out
,
reg_loss_type
=
'gwd'
):
(
labels
,
label_weights
,
bbox_targets
,
bbox_weights
,
pos_ind
s
,
neg_inds
)
=
odm_target
(
labels
,
label_weights
,
bbox_targets
,
bbox_weights
,
bbox_gt_bboxe
s
,
pos_inds
,
neg_inds
)
=
odm_target
fam_cls_branch_list
,
fam_reg_branch_list
,
odm_cls_branch_list
,
odm_reg_branch_list
=
s2anet_head_out
odm_cls_losses
=
[]
...
...
@@ -621,7 +653,7 @@ class S2ANetHead(nn.Layer):
num_total_samples
=
len
(
pos_inds
)
+
len
(
neg_inds
)
if
self
.
sampling
else
len
(
pos_inds
)
num_total_samples
=
max
(
1
,
num_total_samples
)
for
idx
,
feat_size
in
enumerate
(
self
.
featmap_sizes_list
):
feat_anchor_num
=
feat_size
[
0
]
*
feat_size
[
1
]
...
...
@@ -631,7 +663,6 @@ class S2ANetHead(nn.Layer):
feat_bbox_targets
=
bbox_targets
[
st_idx
:
st_idx
+
feat_anchor_num
,
:]
feat_bbox_weights
=
bbox_weights
[
st_idx
:
st_idx
+
feat_anchor_num
,
:]
st_idx
+=
feat_anchor_num
# step2: calc cls loss
feat_labels
=
feat_labels
.
reshape
(
-
1
)
...
...
@@ -676,19 +707,53 @@ class S2ANetHead(nn.Layer):
odm_bbox_pred
=
paddle
.
squeeze
(
odm_bbox_pred
,
axis
=
0
)
odm_bbox_pred
=
paddle
.
reshape
(
odm_bbox_pred
,
[
-
1
,
5
])
odm_bbox
=
self
.
smooth_l1_loss
(
odm_bbox_pred
,
feat_bbox_targets
)
# iou_factor odm not use_iou
loss_weight
=
paddle
.
to_tensor
(
self
.
reg_loss_weight
,
dtype
=
'float32'
,
stop_gradient
=
True
)
odm_bbox
=
paddle
.
multiply
(
odm_bbox
,
loss_weight
)
feat_bbox_weights
=
paddle
.
to_tensor
(
feat_bbox_weights
,
stop_gradient
=
True
)
if
reg_loss_type
==
'l1'
:
odm_bbox
=
self
.
smooth_l1_loss
(
odm_bbox_pred
,
feat_bbox_targets
)
loss_weight
=
paddle
.
to_tensor
(
self
.
reg_loss_weight
,
dtype
=
'float32'
,
stop_gradient
=
True
)
odm_bbox
=
paddle
.
multiply
(
odm_bbox
,
loss_weight
)
feat_bbox_weights
=
paddle
.
to_tensor
(
feat_bbox_weights
,
stop_gradient
=
True
)
odm_bbox
=
odm_bbox
*
feat_bbox_weights
odm_bbox_total
=
paddle
.
sum
(
odm_bbox
)
/
num_total_samples
elif
reg_loss_type
==
'iou'
or
reg_loss_type
==
'gwd'
:
odm_bbox
=
paddle
.
sum
(
odm_bbox
,
axis
=-
1
)
feat_bbox_weights
=
paddle
.
sum
(
feat_bbox_weights
,
axis
=-
1
)
try
:
from
rbox_iou_ops
import
rbox_iou
except
Exception
as
e
:
print
(
"import custom_ops error, try install rbox_iou_ops "
\
"following ppdet/ext_op/README.md"
,
e
)
sys
.
stdout
.
flush
()
sys
.
exit
(
-
1
)
# calc iou
odm_bbox_decode
=
self
.
delta2rbox
(
self
.
refine_anchor_list
[
idx
],
odm_bbox_pred
)
bbox_gt_bboxes
=
paddle
.
to_tensor
(
bbox_gt_bboxes
,
dtype
=
odm_bbox_decode
.
dtype
,
place
=
odm_bbox_decode
.
place
)
bbox_gt_bboxes
.
stop_gradient
=
True
iou
=
rbox_iou
(
odm_bbox_decode
,
bbox_gt_bboxes
)
iou
=
paddle
.
diag
(
iou
)
if
reg_loss_type
==
'iou'
:
EPS
=
paddle
.
to_tensor
(
1e-8
,
dtype
=
'float32'
,
stop_gradient
=
True
)
iou_factor
=
-
1.0
*
paddle
.
log
(
iou
+
EPS
)
/
(
odm_bbox
+
EPS
)
iou_factor
.
stop_gradient
=
True
# odm_bbox = odm_bbox * iou_factor
elif
reg_loss_type
==
'gwd'
:
bbox_gt_bboxes_level
=
bbox_gt_bboxes
[
st_idx
:
st_idx
+
feat_anchor_num
,
:]
odm_bbox_total
=
self
.
gwd_loss
(
odm_bbox_decode
,
bbox_gt_bboxes_level
)
odm_bbox_total
=
odm_bbox_total
*
feat_bbox_weights
odm_bbox_total
=
paddle
.
sum
(
odm_bbox_total
)
odm_bbox_losses
.
append
(
odm_bbox_total
)
st_idx
+=
feat_anchor_num
odm_cls_loss
=
paddle
.
add_n
(
odm_cls_losses
)
odm_cls_loss_weight
=
paddle
.
to_tensor
(
...
...
@@ -737,11 +802,12 @@ class S2ANetHead(nn.Layer):
fam_reg_loss_lst
.
append
(
im_fam_reg_loss
)
# ODM
np_refine_anchors_list
=
paddle
.
concat
(
self
.
refine_anchor_list
).
numpy
()
np_refine_anchors_list
=
paddle
.
concat
(
self
.
refine_anchor_list
).
numpy
()
np_refine_anchors_list
=
np
.
concatenate
(
np_refine_anchors_list
)
np_refine_anchors_list
=
np_refine_anchors_list
.
reshape
(
-
1
,
5
)
im_odm_target
=
self
.
anchor_assign
(
np_refine_anchors_list
,
gt_bboxes
,
gt_labels
,
is_crowd
)
im_odm_target
=
self
.
anchor_assign
(
np_refine_anchors_list
,
gt_
bboxes
,
gt_
labels
,
is_crowd
)
if
im_odm_target
is
not
None
:
im_odm_cls_loss
,
im_odm_reg_loss
=
self
.
get_odm_loss
(
...
...
@@ -841,7 +907,8 @@ class S2ANetHead(nn.Layer):
deltas
=
paddle
.
reshape
(
deltas
,
[
-
1
,
5
])
rrois
=
paddle
.
reshape
(
rrois
,
[
-
1
,
5
])
# fix dy2st bug denorm_deltas = deltas * self.stds + self.means
denorm_deltas
=
paddle
.
add
(
paddle
.
multiply
(
deltas
,
self
.
stds
),
self
.
means
)
denorm_deltas
=
paddle
.
add
(
paddle
.
multiply
(
deltas
,
self
.
stds
),
self
.
means
)
dx
=
denorm_deltas
[:,
0
]
dy
=
denorm_deltas
[:,
1
]
...
...
@@ -872,9 +939,7 @@ class S2ANetHead(nn.Layer):
bboxes
=
paddle
.
stack
([
gx
,
gy
,
gw
,
gh
,
ga
],
axis
=-
1
)
return
bboxes
def
bbox_decode
(
self
,
bbox_preds
,
anchors
):
def
bbox_decode
(
self
,
bbox_preds
,
anchors
):
"""decode bbox from deltas
Args:
bbox_preds: [N,H,W,5]
...
...
@@ -886,3 +951,109 @@ class S2ANetHead(nn.Layer):
bbox_delta
=
paddle
.
reshape
(
bbox_preds
,
[
-
1
,
5
])
bboxes
=
self
.
delta2rbox
(
anchors
,
bbox_delta
)
return
bboxes
def
trace
(
self
,
A
):
tr
=
paddle
.
diagonal
(
A
,
axis1
=-
2
,
axis2
=-
1
)
tr
=
paddle
.
sum
(
tr
,
axis
=-
1
)
return
tr
def
sqrt_newton_schulz_autograd
(
self
,
A
,
numIters
):
A_shape
=
A
.
shape
batchSize
=
A_shape
[
0
]
dim
=
A_shape
[
1
]
normA
=
A
*
A
normA
=
paddle
.
sum
(
normA
,
axis
=
1
)
normA
=
paddle
.
sum
(
normA
,
axis
=
1
)
normA
=
paddle
.
sqrt
(
normA
)
normA1
=
normA
.
reshape
([
batchSize
,
1
,
1
])
Y
=
paddle
.
divide
(
A
,
paddle
.
expand_as
(
normA1
,
A
))
I
=
paddle
.
eye
(
dim
,
dim
).
reshape
([
1
,
dim
,
dim
])
l0
=
[]
for
i
in
range
(
batchSize
):
l0
.
append
(
I
)
I
=
paddle
.
concat
(
l0
,
axis
=
0
)
I
.
stop_gradient
=
False
Z
=
paddle
.
eye
(
dim
,
dim
).
reshape
([
1
,
dim
,
dim
])
l1
=
[]
for
i
in
range
(
batchSize
):
l1
.
append
(
Z
)
Z
=
paddle
.
concat
(
l1
,
axis
=
0
)
Z
.
stop_gradient
=
False
for
i
in
range
(
numIters
):
T
=
0.5
*
(
3.0
*
I
-
Z
.
bmm
(
Y
))
Y
=
Y
.
bmm
(
T
)
Z
=
T
.
bmm
(
Z
)
sA
=
Y
*
paddle
.
sqrt
(
normA1
).
reshape
([
batchSize
,
1
,
1
])
sA
=
paddle
.
expand_as
(
sA
,
A
)
return
sA
def
wasserstein_distance_sigma
(
sigma1
,
sigma2
):
wasserstein_distance_item2
=
paddle
.
matmul
(
sigma1
,
sigma1
)
+
paddle
.
matmul
(
sigma2
,
sigma2
)
-
2
*
self
.
sqrt_newton_schulz_autograd
(
paddle
.
matmul
(
paddle
.
matmul
(
sigma1
,
paddle
.
matmul
(
sigma2
,
sigma2
)),
sigma1
),
10
)
wasserstein_distance_item2
=
self
.
trace
(
wasserstein_distance_item2
)
return
wasserstein_distance_item2
def
xywhr2xyrs
(
self
,
xywhr
):
xywhr
=
paddle
.
reshape
(
xywhr
,
[
-
1
,
5
])
xy
=
xywhr
[:,
:
2
]
wh
=
paddle
.
clip
(
xywhr
[:,
2
:
4
],
min
=
1e-7
,
max
=
1e7
)
r
=
xywhr
[:,
4
]
cos_r
=
paddle
.
cos
(
r
)
sin_r
=
paddle
.
sin
(
r
)
R
=
paddle
.
stack
(
(
cos_r
,
-
sin_r
,
sin_r
,
cos_r
),
axis
=-
1
).
reshape
([
-
1
,
2
,
2
])
S
=
0.5
*
paddle
.
nn
.
functional
.
diag_embed
(
wh
)
return
xy
,
R
,
S
def
gwd_loss
(
self
,
pred
,
target
,
fun
=
'log'
,
tau
=
1.0
,
alpha
=
1.0
,
normalize
=
False
):
xy_p
,
R_p
,
S_p
=
self
.
xywhr2xyrs
(
pred
)
xy_t
,
R_t
,
S_t
=
self
.
xywhr2xyrs
(
target
)
xy_distance
=
(
xy_p
-
xy_t
).
square
().
sum
(
axis
=-
1
)
Sigma_p
=
R_p
.
matmul
(
S_p
.
square
()).
matmul
(
R_p
.
transpose
([
0
,
2
,
1
]))
Sigma_t
=
R_t
.
matmul
(
S_t
.
square
()).
matmul
(
R_t
.
transpose
([
0
,
2
,
1
]))
whr_distance
=
paddle
.
diagonal
(
S_p
,
axis1
=-
2
,
axis2
=-
1
).
square
().
sum
(
axis
=-
1
)
whr_distance
=
whr_distance
+
paddle
.
diagonal
(
S_t
,
axis1
=-
2
,
axis2
=-
1
).
square
().
sum
(
axis
=-
1
)
_t
=
Sigma_p
.
matmul
(
Sigma_t
)
_t_tr
=
paddle
.
diagonal
(
_t
,
axis1
=-
2
,
axis2
=-
1
).
sum
(
axis
=-
1
)
_t_det_sqrt
=
paddle
.
diagonal
(
S_p
,
axis1
=-
2
,
axis2
=-
1
).
prod
(
axis
=-
1
)
_t_det_sqrt
=
_t_det_sqrt
*
paddle
.
diagonal
(
S_t
,
axis1
=-
2
,
axis2
=-
1
).
prod
(
axis
=-
1
)
whr_distance
=
whr_distance
+
(
-
2
)
*
(
(
_t_tr
+
2
*
_t_det_sqrt
).
clip
(
0
).
sqrt
())
distance
=
(
xy_distance
+
alpha
*
alpha
*
whr_distance
).
clip
(
0
)
if
normalize
:
wh_p
=
pred
[...,
2
:
4
].
clip
(
min
=
1e-7
,
max
=
1e7
)
wh_t
=
target
[...,
2
:
4
].
clip
(
min
=
1e-7
,
max
=
1e7
)
scale
=
((
wh_p
.
log
()
+
wh_t
.
log
()).
sum
(
dim
=-
1
)
/
4
).
exp
()
distance
=
distance
/
scale
if
fun
==
'log'
:
distance
=
paddle
.
log1p
(
distance
)
if
tau
>=
1.0
:
return
1
-
1
/
(
tau
+
distance
)
return
distance
ppdet/modeling/proposal_generator/target_layer.py
浏览文件 @
99c63aed
...
...
@@ -451,16 +451,17 @@ class RBoxAssigner(object):
anchors_num
=
anchors
.
shape
[
0
]
bbox_targets
=
np
.
zeros_like
(
anchors
)
bbox_weights
=
np
.
zeros_like
(
anchors
)
bbox_gt_bboxes
=
np
.
zeros_like
(
anchors
)
pos_labels
=
np
.
ones
(
anchors_num
,
dtype
=
np
.
int32
)
*
-
1
pos_labels_weights
=
np
.
zeros
(
anchors_num
,
dtype
=
np
.
float32
)
pos_sampled_anchors
=
anchors
[
pos_inds
]
#print('ancho target pos_inds', pos_inds, len(pos_inds))
pos_sampled_gt_boxes
=
gt_bboxes
[
anchor_gt_bbox_inds
[
pos_inds
]]
if
len
(
pos_inds
)
>
0
:
pos_bbox_targets
=
self
.
rbox2delta
(
pos_sampled_anchors
,
pos_sampled_gt_boxes
)
bbox_targets
[
pos_inds
,
:]
=
pos_bbox_targets
bbox_gt_bboxes
[
pos_inds
,
:]
=
pos_sampled_gt_boxes
bbox_weights
[
pos_inds
,
:]
=
1.0
pos_labels
[
pos_inds
]
=
labels
[
pos_inds
]
...
...
@@ -469,4 +470,4 @@ class RBoxAssigner(object):
if
len
(
neg_inds
)
>
0
:
pos_labels_weights
[
neg_inds
]
=
1.0
return
(
pos_labels
,
pos_labels_weights
,
bbox_targets
,
bbox_weights
,
pos_inds
,
neg_inds
)
bbox_gt_bboxes
,
pos_inds
,
neg_inds
)
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