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体验新版 GitCode,发现更多精彩内容 >>
提交
6b5d7100
编写于
7月 23, 2019
作者:
Y
Yuan Gao
提交者:
wangguanzhong
7月 23, 2019
浏览文件
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电子邮件补丁
差异文件
add rpn recall function and class-aware rpn (#2814)
* add rpn recall function and class-aware rpn
上级
3dd4f349
变更
6
隐藏空白更改
内联
并排
Showing
6 changed file
with
194 addition
and
40 deletion
+194
-40
ppdet/modeling/anchor_heads/rpn_head.py
ppdet/modeling/anchor_heads/rpn_head.py
+88
-32
ppdet/modeling/architectures/cascade_rcnn.py
ppdet/modeling/architectures/cascade_rcnn.py
+8
-0
ppdet/modeling/architectures/faster_rcnn.py
ppdet/modeling/architectures/faster_rcnn.py
+8
-1
ppdet/modeling/architectures/mask_rcnn.py
ppdet/modeling/architectures/mask_rcnn.py
+7
-1
ppdet/utils/coco_eval.py
ppdet/utils/coco_eval.py
+72
-1
ppdet/utils/eval_utils.py
ppdet/utils/eval_utils.py
+11
-5
未找到文件。
ppdet/modeling/anchor_heads/rpn_head.py
浏览文件 @
6b5d7100
...
...
@@ -22,8 +22,8 @@ from paddle.fluid.initializer import Normal
from
paddle.fluid.regularizer
import
L2Decay
from
ppdet.core.workspace
import
register
from
ppdet.modeling.ops
import
(
AnchorGenerator
,
RPNTargetAssign
,
GenerateProposals
)
from
ppdet.modeling.ops
import
(
AnchorGenerator
,
RPNTargetAssign
,
GenerateProposals
)
__all__
=
[
'RPNTargetAssign'
,
'GenerateProposals'
,
'RPNHead'
,
'FPNRPNHead'
]
...
...
@@ -48,12 +48,14 @@ class RPNHead(object):
anchor_generator
=
AnchorGenerator
().
__dict__
,
rpn_target_assign
=
RPNTargetAssign
().
__dict__
,
train_proposal
=
GenerateProposals
(
12000
,
2000
).
__dict__
,
test_proposal
=
GenerateProposals
().
__dict__
):
test_proposal
=
GenerateProposals
().
__dict__
,
num_classes
=
1
):
super
(
RPNHead
,
self
).
__init__
()
self
.
anchor_generator
=
anchor_generator
self
.
rpn_target_assign
=
rpn_target_assign
self
.
train_proposal
=
train_proposal
self
.
test_proposal
=
test_proposal
self
.
num_classes
=
num_classes
if
isinstance
(
anchor_generator
,
dict
):
self
.
anchor_generator
=
AnchorGenerator
(
**
anchor_generator
)
if
isinstance
(
rpn_target_assign
,
dict
):
...
...
@@ -96,7 +98,7 @@ class RPNHead(object):
# Proposal classification scores
self
.
rpn_cls_score
=
fluid
.
layers
.
conv2d
(
rpn_conv
,
num_filters
=
num_anchor
,
num_filters
=
num_anchor
*
self
.
num_classes
,
filter_size
=
1
,
stride
=
1
,
padding
=
0
,
...
...
@@ -147,12 +149,27 @@ class RPNHead(object):
body_feat
=
list
(
body_feats
.
values
())[
-
1
]
rpn_cls_score
,
rpn_bbox_pred
=
self
.
_get_output
(
body_feat
)
rpn_cls_score_prob
=
fluid
.
layers
.
sigmoid
(
rpn_cls_score
,
name
=
'rpn_cls_score_prob'
)
if
self
.
num_classes
==
1
:
rpn_cls_prob
=
fluid
.
layers
.
sigmoid
(
rpn_cls_score
,
name
=
'rpn_cls_prob'
)
else
:
rpn_cls_score
=
fluid
.
layers
.
transpose
(
rpn_cls_score
,
perm
=
[
0
,
2
,
3
,
1
])
rpn_cls_score
=
fluid
.
layers
.
reshape
(
rpn_cls_score
,
shape
=
(
0
,
0
,
0
,
-
1
,
self
.
num_classes
))
rpn_cls_prob_tmp
=
fluid
.
layers
.
softmax
(
rpn_cls_score
,
use_cudnn
=
False
,
name
=
'rpn_cls_prob'
)
rpn_cls_prob_slice
=
fluid
.
layers
.
slice
(
rpn_cls_prob_tmp
,
axes
=
[
4
],
starts
=
[
1
],
ends
=
[
self
.
num_classes
])
rpn_cls_prob
,
_
=
fluid
.
layers
.
topk
(
rpn_cls_prob_slice
,
1
)
rpn_cls_prob
=
fluid
.
layers
.
reshape
(
rpn_cls_prob
,
shape
=
(
0
,
0
,
0
,
-
1
))
rpn_cls_prob
=
fluid
.
layers
.
transpose
(
rpn_cls_prob
,
perm
=
[
0
,
3
,
1
,
2
])
prop_op
=
self
.
train_proposal
if
mode
==
'train'
else
self
.
test_proposal
rpn_rois
,
rpn_roi_probs
=
prop_op
(
scores
=
rpn_cls_
score_
prob
,
scores
=
rpn_cls_prob
,
bbox_deltas
=
rpn_bbox_pred
,
im_info
=
im_info
,
anchors
=
self
.
anchor
,
...
...
@@ -165,7 +182,8 @@ class RPNHead(object):
rpn_bbox_pred
=
fluid
.
layers
.
transpose
(
rpn_bbox_pred
,
perm
=
[
0
,
2
,
3
,
1
])
anchor
=
fluid
.
layers
.
reshape
(
anchor
,
shape
=
(
-
1
,
4
))
anchor_var
=
fluid
.
layers
.
reshape
(
anchor_var
,
shape
=
(
-
1
,
4
))
rpn_cls_score
=
fluid
.
layers
.
reshape
(
x
=
rpn_cls_score
,
shape
=
(
0
,
-
1
,
1
))
rpn_cls_score
=
fluid
.
layers
.
reshape
(
x
=
rpn_cls_score
,
shape
=
(
0
,
-
1
,
self
.
num_classes
))
rpn_bbox_pred
=
fluid
.
layers
.
reshape
(
x
=
rpn_bbox_pred
,
shape
=
(
0
,
-
1
,
4
))
return
rpn_cls_score
,
rpn_bbox_pred
,
anchor
,
anchor_var
...
...
@@ -177,7 +195,7 @@ class RPNHead(object):
return
self
.
_transform_input
(
self
.
rpn_cls_score
,
self
.
rpn_bbox_pred
,
self
.
anchor
,
self
.
anchor_var
)
def
get_loss
(
self
,
im_info
,
gt_box
,
is_crowd
):
def
get_loss
(
self
,
im_info
,
gt_box
,
is_crowd
,
gt_label
=
None
):
"""
Sample proposals and Calculate rpn loss.
...
...
@@ -196,20 +214,37 @@ class RPNHead(object):
"""
rpn_cls
,
rpn_bbox
,
anchor
,
anchor_var
=
self
.
_get_loss_input
()
score_pred
,
loc_pred
,
score_tgt
,
loc_tgt
,
bbox_weight
=
\
self
.
rpn_target_assign
(
bbox_pred
=
rpn_bbox
,
cls_logits
=
rpn_cls
,
anchor_box
=
anchor
,
anchor_var
=
anchor_var
,
gt_boxes
=
gt_box
,
is_crowd
=
is_crowd
,
im_info
=
im_info
)
score_tgt
=
fluid
.
layers
.
cast
(
x
=
score_tgt
,
dtype
=
'float32'
)
score_tgt
.
stop_gradient
=
True
rpn_cls_loss
=
fluid
.
layers
.
sigmoid_cross_entropy_with_logits
(
x
=
score_pred
,
label
=
score_tgt
)
if
self
.
num_classes
==
1
:
score_pred
,
loc_pred
,
score_tgt
,
loc_tgt
,
bbox_weight
=
\
self
.
rpn_target_assign
(
bbox_pred
=
rpn_bbox
,
cls_logits
=
rpn_cls
,
anchor_box
=
anchor
,
anchor_var
=
anchor_var
,
gt_boxes
=
gt_box
,
is_crowd
=
is_crowd
,
im_info
=
im_info
)
score_tgt
=
fluid
.
layers
.
cast
(
x
=
score_tgt
,
dtype
=
'float32'
)
score_tgt
.
stop_gradient
=
True
rpn_cls_loss
=
fluid
.
layers
.
sigmoid_cross_entropy_with_logits
(
x
=
score_pred
,
label
=
score_tgt
)
else
:
score_pred
,
loc_pred
,
score_tgt
,
loc_tgt
,
bbox_weight
=
\
self
.
rpn_target_assign
(
bbox_pred
=
rpn_bbox
,
cls_logits
=
rpn_cls
,
anchor_box
=
anchor
,
anchor_var
=
anchor_var
,
gt_boxes
=
gt_box
,
gt_labels
=
gt_label
,
is_crowd
=
is_crowd
,
num_classes
=
self
.
num_classes
,
im_info
=
im_info
)
labels_int64
=
fluid
.
layers
.
cast
(
x
=
score_tgt
,
dtype
=
'int64'
)
labels_int64
.
stop_gradient
=
True
rpn_cls_loss
=
fluid
.
layers
.
softmax_with_cross_entropy
(
logits
=
score_pred
,
label
=
labels_int64
,
numeric_stable_mode
=
True
)
rpn_cls_loss
=
fluid
.
layers
.
reduce_mean
(
rpn_cls_loss
,
name
=
'loss_rpn_cls'
)
...
...
@@ -261,13 +296,15 @@ class FPNRPNHead(RPNHead):
anchor_start_size
=
32
,
num_chan
=
256
,
min_level
=
2
,
max_level
=
6
):
max_level
=
6
,
num_classes
=
1
):
super
(
FPNRPNHead
,
self
).
__init__
(
anchor_generator
,
rpn_target_assign
,
train_proposal
,
test_proposal
)
self
.
anchor_start_size
=
anchor_start_size
self
.
num_chan
=
num_chan
self
.
min_level
=
min_level
self
.
max_level
=
max_level
self
.
num_classes
=
num_classes
self
.
fpn_rpn_list
=
[]
self
.
anchors_list
=
[]
...
...
@@ -319,9 +356,10 @@ class FPNRPNHead(RPNHead):
**
(
feat_lvl
-
self
.
min_level
),
),
stride
=
(
2.
**
feat_lvl
,
2.
**
feat_lvl
))
cls_num_filters
=
num_anchors
*
self
.
num_classes
self
.
rpn_cls_score
=
fluid
.
layers
.
conv2d
(
input
=
conv_rpn_fpn
,
num_filters
=
num_ancho
rs
,
num_filters
=
cls_num_filte
rs
,
filter_size
=
1
,
act
=
None
,
name
=
cls_name
,
...
...
@@ -366,19 +404,37 @@ class FPNRPNHead(RPNHead):
shape of (rois_num, 1).
"""
rpn_cls_
logits
_fpn
,
rpn_bbox_pred_fpn
=
self
.
_get_output
(
body_feat
,
feat_lvl
)
rpn_cls_
score
_fpn
,
rpn_bbox_pred_fpn
=
self
.
_get_output
(
body_feat
,
feat_lvl
)
prop_op
=
self
.
train_proposal
if
mode
==
'train'
else
self
.
test_proposal
rpn_cls_prob_fpn
=
fluid
.
layers
.
sigmoid
(
rpn_cls_logits_fpn
,
name
=
'rpn_cls_probs_fpn'
+
str
(
feat_lvl
))
rpn_rois_fpn
,
rpn_roi_probs_fpn
=
prop_op
(
if
self
.
num_classes
==
1
:
rpn_cls_prob_fpn
=
fluid
.
layers
.
sigmoid
(
rpn_cls_score_fpn
,
name
=
'rpn_cls_prob_fpn'
+
str
(
feat_lvl
))
else
:
rpn_cls_score_fpn
=
fluid
.
layers
.
transpose
(
rpn_cls_score_fpn
,
perm
=
[
0
,
2
,
3
,
1
])
rpn_cls_score_fpn
=
fluid
.
layers
.
reshape
(
rpn_cls_score_fpn
,
shape
=
(
0
,
0
,
0
,
-
1
,
self
.
num_classes
))
rpn_cls_prob_fpn
=
fluid
.
layers
.
softmax
(
rpn_cls_score_fpn
,
use_cudnn
=
False
,
name
=
'rpn_cls_prob_fpn'
+
str
(
feat_lvl
))
rpn_cls_prob_fpn
=
fluid
.
layers
.
slice
(
rpn_cls_prob_fpn
,
axes
=
[
4
],
starts
=
[
1
],
ends
=
[
self
.
num_classes
])
rpn_cls_prob_fpn
,
_
=
fluid
.
layers
.
topk
(
rpn_cls_prob_fpn
,
1
)
rpn_cls_prob_fpn
=
fluid
.
layers
.
reshape
(
rpn_cls_prob_fpn
,
shape
=
(
0
,
0
,
0
,
-
1
))
rpn_cls_prob_fpn
=
fluid
.
layers
.
transpose
(
rpn_cls_prob_fpn
,
perm
=
[
0
,
3
,
1
,
2
])
rpn_rois_fpn
,
rpn_roi_prob_fpn
=
prop_op
(
scores
=
rpn_cls_prob_fpn
,
bbox_deltas
=
rpn_bbox_pred_fpn
,
im_info
=
im_info
,
anchors
=
self
.
anchors
,
variances
=
self
.
anchor_var
)
return
rpn_rois_fpn
,
rpn_roi_prob
s
_fpn
return
rpn_rois_fpn
,
rpn_roi_prob_fpn
def
get_proposals
(
self
,
fpn_feats
,
im_info
,
mode
=
'train'
):
"""
...
...
ppdet/modeling/architectures/cascade_rcnn.py
浏览文件 @
6b5d7100
...
...
@@ -49,6 +49,7 @@ class CascadeRCNN(object):
roi_extractor
=
'FPNRoIAlign'
,
bbox_head
=
'CascadeBBoxHead'
,
bbox_assigner
=
'CascadeBBoxAssigner'
,
rpn_only
=
False
,
fpn
=
'FPN'
):
super
(
CascadeRCNN
,
self
).
__init__
()
assert
fpn
is
not
None
,
"cascade RCNN requires FPN"
...
...
@@ -58,6 +59,7 @@ class CascadeRCNN(object):
self
.
bbox_assigner
=
bbox_assigner
self
.
roi_extractor
=
roi_extractor
self
.
bbox_head
=
bbox_head
self
.
rpn_only
=
rpn_only
# Cascade local cfg
self
.
cls_agnostic_bbox_reg
=
2
(
brw0
,
brw1
,
brw2
)
=
self
.
bbox_assigner
.
bbox_reg_weights
...
...
@@ -88,6 +90,12 @@ class CascadeRCNN(object):
if
mode
==
'train'
:
rpn_loss
=
self
.
rpn_head
.
get_loss
(
im_info
,
gt_box
,
is_crowd
)
else
:
if
self
.
rpn_only
:
im_scale
=
fluid
.
layers
.
slice
(
im_info
,
[
1
],
starts
=
[
2
],
ends
=
[
3
])
im_scale
=
fluid
.
layers
.
sequence_expand
(
im_scale
,
rois
)
rois
=
rois
/
im_scale
return
{
'proposal'
:
rois
}
proposal_list
=
[]
roi_feat_list
=
[]
...
...
ppdet/modeling/architectures/faster_rcnn.py
浏览文件 @
6b5d7100
...
...
@@ -48,6 +48,7 @@ class FasterRCNN(object):
roi_extractor
,
bbox_head
=
'BBoxHead'
,
bbox_assigner
=
'BBoxAssigner'
,
rpn_only
=
False
,
fpn
=
None
):
super
(
FasterRCNN
,
self
).
__init__
()
self
.
backbone
=
backbone
...
...
@@ -56,6 +57,7 @@ class FasterRCNN(object):
self
.
roi_extractor
=
roi_extractor
self
.
bbox_head
=
bbox_head
self
.
fpn
=
fpn
self
.
rpn_only
=
rpn_only
def
build
(
self
,
feed_vars
,
mode
=
'train'
):
im
=
feed_vars
[
'image'
]
...
...
@@ -90,7 +92,12 @@ class FasterRCNN(object):
bbox_targets
=
outs
[
2
]
bbox_inside_weights
=
outs
[
3
]
bbox_outside_weights
=
outs
[
4
]
else
:
if
self
.
rpn_only
:
im_scale
=
fluid
.
layers
.
slice
(
im_info
,
[
1
],
starts
=
[
2
],
ends
=
[
3
])
im_scale
=
fluid
.
layers
.
sequence_expand
(
im_scale
,
rois
)
rois
=
rois
/
im_scale
return
{
'proposal'
:
rois
}
if
self
.
fpn
is
None
:
# in models without FPN, roi extractor only uses the last level of
# feature maps. And body_feat_names[-1] represents the name of
...
...
ppdet/modeling/architectures/mask_rcnn.py
浏览文件 @
6b5d7100
...
...
@@ -51,6 +51,7 @@ class MaskRCNN(object):
roi_extractor
=
'RoIAlign'
,
mask_assigner
=
'MaskAssigner'
,
mask_head
=
'MaskHead'
,
rpn_only
=
False
,
fpn
=
None
):
super
(
MaskRCNN
,
self
).
__init__
()
self
.
backbone
=
backbone
...
...
@@ -60,6 +61,7 @@ class MaskRCNN(object):
self
.
bbox_head
=
bbox_head
self
.
mask_assigner
=
mask_assigner
self
.
mask_head
=
mask_head
self
.
rpn_only
=
rpn_only
self
.
fpn
=
fpn
def
build
(
self
,
feed_vars
,
mode
=
'train'
):
...
...
@@ -130,7 +132,11 @@ class MaskRCNN(object):
return
loss
else
:
if
self
.
rpn_only
:
im_scale
=
fluid
.
layers
.
slice
(
im_info
,
[
1
],
starts
=
[
2
],
ends
=
[
3
])
im_scale
=
fluid
.
layers
.
sequence_expand
(
im_scale
,
rois
)
rois
=
rois
/
im_scale
return
{
'proposal'
:
rois
}
if
self
.
fpn
is
None
:
last_feat
=
body_feats
[
list
(
body_feats
.
keys
())[
-
1
]]
roi_feat
=
self
.
roi_extractor
(
last_feat
,
rois
)
...
...
ppdet/utils/coco_eval.py
浏览文件 @
6b5d7100
...
...
@@ -30,7 +30,12 @@ import logging
logger
=
logging
.
getLogger
(
__name__
)
__all__
=
[
'bbox_eval'
,
'mask_eval'
,
'bbox2out'
,
'mask2out'
,
'get_category_info'
'bbox_eval'
,
'mask_eval'
,
'bbox2out'
,
'mask2out'
,
'get_category_info'
,
'proposal_eval'
,
]
...
...
@@ -42,6 +47,34 @@ def clip_bbox(bbox):
return
xmin
,
ymin
,
xmax
,
ymax
def
proposal_eval
(
results
,
anno_file
,
outfile
,
max_dets
=
(
100
,
300
,
1000
)):
assert
'proposal'
in
results
[
0
]
assert
outfile
.
endswith
(
'.json'
)
xywh_results
=
proposal2out
(
results
)
assert
len
(
xywh_results
)
>
0
,
"The number of valid proposal detected is zero.
\n
\
Please use reasonable model and check input data."
with
open
(
outfile
,
'w'
)
as
f
:
json
.
dump
(
xywh_results
,
f
)
coco_gt
=
COCO
(
anno_file
)
logger
.
info
(
"Start evaluate..."
)
coco_dt
=
coco_gt
.
loadRes
(
outfile
)
coco_ev
=
COCOeval
(
coco_gt
,
coco_dt
,
'bbox'
)
coco_ev
.
params
.
useCats
=
0
coco_ev
.
params
.
maxDets
=
list
(
max_dets
)
coco_ev
.
evaluate
()
coco_ev
.
accumulate
()
coco_ev
.
summarize
()
# flush coco evaluation result
sys
.
stdout
.
flush
()
def
bbox_eval
(
results
,
anno_file
,
outfile
,
with_background
=
True
):
assert
'bbox'
in
results
[
0
]
assert
outfile
.
endswith
(
'.json'
)
...
...
@@ -96,6 +129,44 @@ def mask_eval(results, anno_file, outfile, resolution, thresh_binarize=0.5):
coco_ev
.
summarize
()
def
proposal2out
(
results
,
is_bbox_normalized
=
False
):
xywh_res
=
[]
for
t
in
results
:
bboxes
=
t
[
'proposal'
][
0
]
lengths
=
t
[
'proposal'
][
1
][
0
]
im_ids
=
np
.
array
(
t
[
'im_id'
][
0
])
if
bboxes
.
shape
==
(
1
,
1
)
or
bboxes
is
None
:
continue
k
=
0
for
i
in
range
(
len
(
lengths
)):
num
=
lengths
[
i
]
im_id
=
int
(
im_ids
[
i
][
0
])
for
j
in
range
(
num
):
dt
=
bboxes
[
k
]
xmin
,
ymin
,
xmax
,
ymax
=
dt
.
tolist
()
if
is_bbox_normalized
:
xmin
,
ymin
,
xmax
,
ymax
=
\
clip_bbox
([
xmin
,
ymin
,
xmax
,
ymax
])
w
=
xmax
-
xmin
h
=
ymax
-
ymin
else
:
w
=
xmax
-
xmin
+
1
h
=
ymax
-
ymin
+
1
bbox
=
[
xmin
,
ymin
,
w
,
h
]
coco_res
=
{
'image_id'
:
im_id
,
'category_id'
:
1
,
'bbox'
:
bbox
,
'score'
:
1.0
}
xywh_res
.
append
(
coco_res
)
k
+=
1
return
xywh_res
def
bbox2out
(
results
,
clsid2catid
,
is_bbox_normalized
=
False
):
xywh_res
=
[]
for
t
in
results
:
...
...
ppdet/utils/eval_utils.py
浏览文件 @
6b5d7100
...
...
@@ -91,13 +91,19 @@ def eval_run(exe, compile_program, pyreader, keys, values, cls):
def
eval_results
(
results
,
feed
,
metric
,
resolution
=
None
,
output_file
=
None
):
"""Evaluation for evaluation program results"""
if
metric
==
'COCO'
:
from
ppdet.utils.coco_eval
import
bbox_eval
,
mask_eval
from
ppdet.utils.coco_eval
import
proposal_eval
,
bbox_eval
,
mask_eval
anno_file
=
getattr
(
feed
.
dataset
,
'annotation'
,
None
)
with_background
=
getattr
(
feed
,
'with_background'
,
True
)
output
=
'bbox.json'
if
output_file
:
output
=
'{}_bbox.json'
.
format
(
output_file
)
bbox_eval
(
results
,
anno_file
,
output
,
with_background
)
if
'proposal'
in
results
[
0
]:
output
=
'proposal.json'
if
output_file
:
output
=
'{}_proposal.json'
.
format
(
output_file
)
proposal_eval
(
results
,
anno_file
,
output
)
if
'bbox'
in
results
[
0
]:
output
=
'bbox.json'
if
output_file
:
output
=
'{}_bbox.json'
.
format
(
output_file
)
bbox_eval
(
results
,
anno_file
,
output
,
with_background
)
if
'mask'
in
results
[
0
]:
output
=
'mask.json'
if
output_file
:
...
...
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