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PaddleDetection
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da0157cf
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da0157cf
编写于
12月 26, 2022
作者:
W
wangguanzhong
提交者:
GitHub
12月 26, 2022
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电子邮件补丁
差异文件
update numpy 1.24 (#7552)
上级
630304e0
变更
12
隐藏空白更改
内联
并排
Showing
12 changed file
with
135 addition
and
101 deletion
+135
-101
deploy/pptracking/python/mot/matching/jde_matching.py
deploy/pptracking/python/mot/matching/jde_matching.py
+5
-5
deploy/pptracking/python/mot/tracker/base_jde_tracker.py
deploy/pptracking/python/mot/tracker/base_jde_tracker.py
+2
-7
deploy/python/utils.py
deploy/python/utils.py
+1
-1
deploy/third_engine/demo_avh/convert_image.py
deploy/third_engine/demo_avh/convert_image.py
+2
-2
ppdet/data/crop_utils/annotation_cropper.py
ppdet/data/crop_utils/annotation_cropper.py
+91
-53
ppdet/data/crop_utils/chip_box_utils.py
ppdet/data/crop_utils/chip_box_utils.py
+10
-6
ppdet/data/source/keypoint_coco.py
ppdet/data/source/keypoint_coco.py
+10
-8
ppdet/data/source/pose3d_cmb.py
ppdet/data/source/pose3d_cmb.py
+2
-2
ppdet/modeling/mot/matching/jde_matching.py
ppdet/modeling/mot/matching/jde_matching.py
+5
-5
ppdet/modeling/mot/tracker/base_jde_tracker.py
ppdet/modeling/mot/tracker/base_jde_tracker.py
+2
-7
ppdet/modeling/post_process.py
ppdet/modeling/post_process.py
+1
-1
ppdet/modeling/proposal_generator/target.py
ppdet/modeling/proposal_generator/target.py
+4
-4
未找到文件。
deploy/pptracking/python/mot/matching/jde_matching.py
浏览文件 @
da0157cf
...
...
@@ -82,8 +82,8 @@ def linear_assignment(cost_matrix, thresh):
def
bbox_ious
(
atlbrs
,
btlbrs
):
boxes
=
np
.
ascontiguousarray
(
atlbrs
,
dtype
=
np
.
float
)
query_boxes
=
np
.
ascontiguousarray
(
btlbrs
,
dtype
=
np
.
float
)
boxes
=
np
.
ascontiguousarray
(
atlbrs
,
dtype
=
np
.
float
32
)
query_boxes
=
np
.
ascontiguousarray
(
btlbrs
,
dtype
=
np
.
float
32
)
N
=
boxes
.
shape
[
0
]
K
=
query_boxes
.
shape
[
0
]
ious
=
np
.
zeros
((
N
,
K
),
dtype
=
boxes
.
dtype
)
...
...
@@ -127,13 +127,13 @@ def embedding_distance(tracks, detections, metric='euclidean'):
"""
Compute cost based on features between two list[STrack].
"""
cost_matrix
=
np
.
zeros
((
len
(
tracks
),
len
(
detections
)),
dtype
=
np
.
float
)
cost_matrix
=
np
.
zeros
((
len
(
tracks
),
len
(
detections
)),
dtype
=
np
.
float
32
)
if
cost_matrix
.
size
==
0
:
return
cost_matrix
det_features
=
np
.
asarray
(
[
track
.
curr_feat
for
track
in
detections
],
dtype
=
np
.
float
)
[
track
.
curr_feat
for
track
in
detections
],
dtype
=
np
.
float
32
)
track_features
=
np
.
asarray
(
[
track
.
smooth_feat
for
track
in
tracks
],
dtype
=
np
.
float
)
[
track
.
smooth_feat
for
track
in
tracks
],
dtype
=
np
.
float
32
)
cost_matrix
=
np
.
maximum
(
0.0
,
cdist
(
track_features
,
det_features
,
metric
))
# Nomalized features
return
cost_matrix
...
...
deploy/pptracking/python/mot/tracker/base_jde_tracker.py
浏览文件 @
da0157cf
...
...
@@ -95,14 +95,9 @@ class BaseTrack(object):
class
STrack
(
BaseTrack
):
def
__init__
(
self
,
tlwh
,
score
,
cls_id
,
buff_size
=
30
,
temp_feat
=
None
):
def
__init__
(
self
,
tlwh
,
score
,
cls_id
,
buff_size
=
30
,
temp_feat
=
None
):
# wait activate
self
.
_tlwh
=
np
.
asarray
(
tlwh
,
dtype
=
np
.
float
)
self
.
_tlwh
=
np
.
asarray
(
tlwh
,
dtype
=
np
.
float
32
)
self
.
score
=
score
self
.
cls_id
=
cls_id
self
.
track_len
=
0
...
...
deploy/python/utils.py
浏览文件 @
da0157cf
...
...
@@ -357,7 +357,7 @@ def nms(dets, match_threshold=0.6, match_metric='iou'):
order
=
scores
.
argsort
()[::
-
1
]
ndets
=
dets
.
shape
[
0
]
suppressed
=
np
.
zeros
((
ndets
),
dtype
=
np
.
int
)
suppressed
=
np
.
zeros
((
ndets
),
dtype
=
np
.
int
32
)
for
_i
in
range
(
ndets
):
i
=
order
[
_i
]
...
...
deploy/third_engine/demo_avh/convert_image.py
浏览文件 @
da0157cf
...
...
@@ -78,13 +78,13 @@ def create_headers(image_name):
# Create input header file
create_header_file
(
"inputs"
,
"input"
,
img_data
,
"./include"
)
# Create output header file
output_data
=
np
.
zeros
([
8500
],
np
.
float
)
output_data
=
np
.
zeros
([
8500
],
np
.
float
32
)
create_header_file
(
"outputs"
,
"output0"
,
output_data
,
"./include"
,
)
output_data
=
np
.
zeros
([
170000
],
np
.
float
)
output_data
=
np
.
zeros
([
170000
],
np
.
float
32
)
create_header_file
(
"outputs"
,
"output1"
,
...
...
ppdet/data/crop_utils/annotation_cropper.py
浏览文件 @
da0157cf
...
...
@@ -27,14 +27,15 @@ from .chip_box_utils import intersection_over_box
class
AnnoCropper
(
object
):
def
__init__
(
self
,
image_target_sizes
:
List
[
int
],
def
__init__
(
self
,
image_target_sizes
:
List
[
int
],
valid_box_ratio_ranges
:
List
[
List
[
float
]],
chip_target_size
:
int
,
chip_target_stride
:
int
,
use_neg_chip
:
bool
=
False
,
max_neg_num_per_im
:
int
=
8
,
max_
per_img
:
int
=
-
1
,
nms_thresh
:
int
=
0.5
):
chip_target_size
:
int
,
chip_target_stride
:
int
,
use_neg_chip
:
bool
=
False
,
max_
neg_num_per_im
:
int
=
8
,
max_per_img
:
int
=-
1
,
nms_thresh
:
int
=
0.5
):
"""
Generate chips by chip_target_size and chip_target_stride.
These two parameters just like kernel_size and stride in cnn.
...
...
@@ -117,7 +118,8 @@ class AnnoCropper(object):
self
.
chip_records
=
[]
self
.
_global_chip_id
=
1
for
r
in
records
:
self
.
_cur_im_pos_chips
=
[]
# element: (chip, boxes_idx), chip is [x1, y1, x2, y2], boxes_ids is List[int]
self
.
_cur_im_pos_chips
=
[
]
# element: (chip, boxes_idx), chip is [x1, y1, x2, y2], boxes_ids is List[int]
self
.
_cur_im_neg_chips
=
[]
# element: (chip, neg_box_num)
for
scale_i
in
range
(
self
.
scale_num
):
self
.
_get_current_scale_parameters
(
scale_i
,
r
)
...
...
@@ -126,12 +128,16 @@ class AnnoCropper(object):
chips
=
self
.
_create_chips
(
r
[
'h'
],
r
[
'w'
],
self
.
_cur_scale
)
# # dict: chipid->[box_id, ...]
pos_chip2boxes_idx
=
self
.
_get_valid_boxes_and_pos_chips
(
r
[
'gt_bbox'
],
chips
)
pos_chip2boxes_idx
=
self
.
_get_valid_boxes_and_pos_chips
(
r
[
'gt_bbox'
],
chips
)
# dict: chipid->neg_box_num
neg_chip2box_num
=
self
.
_get_neg_boxes_and_chips
(
chips
,
list
(
pos_chip2boxes_idx
.
keys
()),
r
.
get
(
'proposals'
,
None
))
neg_chip2box_num
=
self
.
_get_neg_boxes_and_chips
(
chips
,
list
(
pos_chip2boxes_idx
.
keys
()),
r
.
get
(
'proposals'
,
None
))
self
.
_add_to_cur_im_chips
(
chips
,
pos_chip2boxes_idx
,
neg_chip2box_num
)
self
.
_add_to_cur_im_chips
(
chips
,
pos_chip2boxes_idx
,
neg_chip2box_num
)
cur_image_records
=
self
.
_trans_all_chips2annotations
(
r
)
self
.
chip_records
.
extend
(
cur_image_records
)
...
...
@@ -147,7 +153,7 @@ class AnnoCropper(object):
for
neg_chipid
,
neg_box_num
in
neg_chip2box_num
.
items
():
chip
=
np
.
array
(
chips
[
neg_chipid
])
self
.
_cur_im_neg_chips
.
append
((
chip
,
neg_box_num
))
self
.
_cur_im_neg_chips
.
append
((
chip
,
neg_box_num
))
def
_trans_all_chips2annotations
(
self
,
r
):
gt_bbox
=
r
[
'gt_bbox'
]
...
...
@@ -156,20 +162,24 @@ class AnnoCropper(object):
gt_class
=
r
[
'gt_class'
]
# gt_poly = r['gt_poly'] # [None]xN
# remaining keys: im_id, h, w
chip_records
=
self
.
_trans_pos_chips2annotations
(
im_file
,
gt_bbox
,
is_crowd
,
gt_class
)
chip_records
=
self
.
_trans_pos_chips2annotations
(
im_file
,
gt_bbox
,
is_crowd
,
gt_class
)
if
not
self
.
use_neg_chip
:
return
chip_records
sampled_neg_chips
=
self
.
_sample_neg_chips
()
neg_chip_records
=
self
.
_trans_neg_chips2annotations
(
im_file
,
sampled_neg_chips
)
neg_chip_records
=
self
.
_trans_neg_chips2annotations
(
im_file
,
sampled_neg_chips
)
chip_records
.
extend
(
neg_chip_records
)
return
chip_records
def
_trans_pos_chips2annotations
(
self
,
im_file
,
gt_bbox
,
is_crowd
,
gt_class
):
def
_trans_pos_chips2annotations
(
self
,
im_file
,
gt_bbox
,
is_crowd
,
gt_class
):
chip_records
=
[]
for
chip
,
boxes_idx
in
self
.
_cur_im_pos_chips
:
chip_bbox
,
final_boxes_idx
=
transform_chip_box
(
gt_bbox
,
boxes_idx
,
chip
)
chip_bbox
,
final_boxes_idx
=
transform_chip_box
(
gt_bbox
,
boxes_idx
,
chip
)
x1
,
y1
,
x2
,
y2
=
chip
chip_h
=
y2
-
y1
chip_w
=
x2
-
x1
...
...
@@ -197,12 +207,15 @@ class AnnoCropper(object):
return
self
.
_cur_im_neg_chips
candidate_num
=
int
(
sample_num
*
1.5
)
candidate_neg_chips
=
sorted
(
self
.
_cur_im_neg_chips
,
key
=
lambda
x
:
-
x
[
1
])[:
candidate_num
]
candidate_neg_chips
=
sorted
(
self
.
_cur_im_neg_chips
,
key
=
lambda
x
:
-
x
[
1
])[:
candidate_num
]
random
.
shuffle
(
candidate_neg_chips
)
sampled_neg_chips
=
candidate_neg_chips
[:
sample_num
]
return
sampled_neg_chips
def
_trans_neg_chips2annotations
(
self
,
im_file
:
str
,
sampled_neg_chips
:
List
[
Tuple
]):
def
_trans_neg_chips2annotations
(
self
,
im_file
:
str
,
sampled_neg_chips
:
List
[
Tuple
]):
chip_records
=
[]
for
chip
,
neg_box_num
in
sampled_neg_chips
:
x1
,
y1
,
x2
,
y2
=
chip
...
...
@@ -213,9 +226,12 @@ class AnnoCropper(object):
'im_id'
:
np
.
array
([
self
.
_global_chip_id
]),
'h'
:
chip_h
,
'w'
:
chip_w
,
'gt_bbox'
:
np
.
zeros
((
0
,
4
),
dtype
=
np
.
float32
),
'is_crowd'
:
np
.
zeros
((
0
,
1
),
dtype
=
np
.
int32
),
'gt_class'
:
np
.
zeros
((
0
,
1
),
dtype
=
np
.
int32
),
'gt_bbox'
:
np
.
zeros
(
(
0
,
4
),
dtype
=
np
.
float32
),
'is_crowd'
:
np
.
zeros
(
(
0
,
1
),
dtype
=
np
.
int32
),
'gt_class'
:
np
.
zeros
(
(
0
,
1
),
dtype
=
np
.
int32
),
# 'gt_poly': [],
'chip'
:
chip
}
...
...
@@ -247,7 +263,8 @@ class AnnoCropper(object):
assert
chip_size
>=
stride
chip_overlap
=
chip_size
-
stride
if
(
width
-
chip_overlap
)
%
stride
>
min_chip_location_diff
:
# 不能被stride整除的部分比较大,则保留
if
(
width
-
chip_overlap
)
%
stride
>
min_chip_location_diff
:
# 不能被stride整除的部分比较大,则保留
w_steps
=
max
(
1
,
int
(
math
.
ceil
((
width
-
chip_overlap
)
/
stride
)))
else
:
# 不能被stride整除的部分比较小,则丢弃
w_steps
=
max
(
1
,
int
(
math
.
floor
((
width
-
chip_overlap
)
/
stride
)))
...
...
@@ -267,9 +284,10 @@ class AnnoCropper(object):
# check chip size
for
item
in
chips
:
if
item
[
2
]
-
item
[
0
]
>
chip_size
*
1.1
or
item
[
3
]
-
item
[
1
]
>
chip_size
*
1.1
:
if
item
[
2
]
-
item
[
0
]
>
chip_size
*
1.1
or
item
[
3
]
-
item
[
1
]
>
chip_size
*
1.1
:
raise
ValueError
(
item
)
chips
=
np
.
array
(
chips
,
dtype
=
np
.
float
)
chips
=
np
.
array
(
chips
,
dtype
=
np
.
float
32
)
raw_size_chips
=
chips
/
scale
return
raw_size_chips
...
...
@@ -279,12 +297,15 @@ class AnnoCropper(object):
im_size
=
self
.
_cur_im_size
scale
=
self
.
_cur_scale
# Nx4 N
valid_boxes
,
valid_boxes_idx
=
self
.
_validate_boxes
(
valid_ratio_range
,
im_size
,
gt_bbox
,
scale
)
valid_boxes
,
valid_boxes_idx
=
self
.
_validate_boxes
(
valid_ratio_range
,
im_size
,
gt_bbox
,
scale
)
# dict: chipid->[box_id, ...]
pos_chip2boxes_idx
=
self
.
_find_pos_chips
(
chips
,
valid_boxes
,
valid_boxes_idx
)
pos_chip2boxes_idx
=
self
.
_find_pos_chips
(
chips
,
valid_boxes
,
valid_boxes_idx
)
return
pos_chip2boxes_idx
def
_validate_boxes
(
self
,
valid_ratio_range
:
List
[
float
],
def
_validate_boxes
(
self
,
valid_ratio_range
:
List
[
float
],
im_size
:
int
,
gt_boxes
:
'np.array of Nx4'
,
scale
:
float
):
...
...
@@ -299,20 +320,26 @@ class AnnoCropper(object):
target_mins
=
mins
*
scale
low
=
valid_ratio_range
[
0
]
if
valid_ratio_range
[
0
]
>
0
else
0
high
=
valid_ratio_range
[
1
]
if
valid_ratio_range
[
1
]
>
0
else
np
.
finfo
(
np
.
float
).
max
high
=
valid_ratio_range
[
1
]
if
valid_ratio_range
[
1
]
>
0
else
np
.
finfo
(
np
.
float32
).
max
valid_boxes_idx
=
np
.
nonzero
((
low
<=
box_ratio
)
&
(
box_ratio
<
high
)
&
(
target_mins
>=
2
))[
0
]
valid_boxes_idx
=
np
.
nonzero
((
low
<=
box_ratio
)
&
(
box_ratio
<
high
)
&
(
target_mins
>=
2
))[
0
]
valid_boxes
=
gt_boxes
[
valid_boxes_idx
]
return
valid_boxes
,
valid_boxes_idx
def
_find_pos_chips
(
self
,
chips
:
'Cx4'
,
valid_boxes
:
'Bx4'
,
valid_boxes_idx
:
'B'
):
def
_find_pos_chips
(
self
,
chips
:
'Cx4'
,
valid_boxes
:
'Bx4'
,
valid_boxes_idx
:
'B'
):
"""
:return: pos_chip2boxes_idx, dict: chipid->[box_id, ...]
"""
iob
=
intersection_over_box
(
chips
,
valid_boxes
)
# overlap, CxB
iob_threshold_to_find_chips
=
1.
pos_chip_ids
,
_
=
self
.
_find_chips_to_cover_overlaped_boxes
(
iob
,
iob_threshold_to_find_chips
)
pos_chip_ids
,
_
=
self
.
_find_chips_to_cover_overlaped_boxes
(
iob
,
iob_threshold_to_find_chips
)
pos_chip_ids
=
set
(
pos_chip_ids
)
iob_threshold_to_assign_box
=
0.5
...
...
@@ -323,7 +350,8 @@ class AnnoCropper(object):
def
_find_chips_to_cover_overlaped_boxes
(
self
,
iob
,
overlap_threshold
):
return
find_chips_to_cover_overlaped_boxes
(
iob
,
overlap_threshold
)
def
_assign_boxes_to_pos_chips
(
self
,
iob
,
overlap_threshold
,
pos_chip_ids
,
valid_boxes_idx
):
def
_assign_boxes_to_pos_chips
(
self
,
iob
,
overlap_threshold
,
pos_chip_ids
,
valid_boxes_idx
):
chip_ids
,
box_ids
=
np
.
nonzero
(
iob
>=
overlap_threshold
)
pos_chip2boxes_idx
=
defaultdict
(
list
)
for
chip_id
,
box_id
in
zip
(
chip_ids
,
box_ids
):
...
...
@@ -333,7 +361,10 @@ class AnnoCropper(object):
pos_chip2boxes_idx
[
chip_id
].
append
(
raw_gt_box_idx
)
return
pos_chip2boxes_idx
def
_get_neg_boxes_and_chips
(
self
,
chips
:
'Cx4'
,
pos_chip_ids
:
'D'
,
proposals
:
'Px4'
):
def
_get_neg_boxes_and_chips
(
self
,
chips
:
'Cx4'
,
pos_chip_ids
:
'D'
,
proposals
:
'Px4'
):
"""
:param chips:
:param pos_chip_ids:
...
...
@@ -351,12 +382,16 @@ class AnnoCropper(object):
im_size
=
self
.
_cur_im_size
scale
=
self
.
_cur_scale
valid_props
,
_
=
self
.
_validate_boxes
(
valid_ratio_range
,
im_size
,
proposals
,
scale
)
valid_props
,
_
=
self
.
_validate_boxes
(
valid_ratio_range
,
im_size
,
proposals
,
scale
)
neg_boxes
=
self
.
_find_neg_boxes
(
chips
,
pos_chip_ids
,
valid_props
)
neg_chip2box_num
=
self
.
_find_neg_chips
(
chips
,
pos_chip_ids
,
neg_boxes
)
return
neg_chip2box_num
def
_find_neg_boxes
(
self
,
chips
:
'Cx4'
,
pos_chip_ids
:
'D'
,
valid_props
:
'Px4'
):
def
_find_neg_boxes
(
self
,
chips
:
'Cx4'
,
pos_chip_ids
:
'D'
,
valid_props
:
'Px4'
):
"""
:return: neg_boxes: Nx4
"""
...
...
@@ -370,7 +405,8 @@ class AnnoCropper(object):
neg_boxes
=
valid_props
[
non_overlap_props_idx
]
return
neg_boxes
def
_find_neg_chips
(
self
,
chips
:
'Cx4'
,
pos_chip_ids
:
'D'
,
neg_boxes
:
'Nx4'
):
def
_find_neg_chips
(
self
,
chips
:
'Cx4'
,
pos_chip_ids
:
'D'
,
neg_boxes
:
'Nx4'
):
"""
:return: neg_chip2box_num, dict: chipid->neg_box_num
"""
...
...
@@ -469,31 +505,37 @@ class AnnoCropper(object):
for
result
in
results
:
bbox_locs
=
result
[
'bbox'
]
bbox_nums
=
result
[
'bbox_num'
]
if
len
(
bbox_locs
)
==
1
and
bbox_locs
[
0
][
0
]
==
-
1
:
# current batch has no detections
if
len
(
bbox_locs
)
==
1
and
bbox_locs
[
0
][
0
]
==
-
1
:
# current batch has no detections
# bbox_locs = array([[-1.]], dtype=float32); bbox_nums = [[1]]
# MultiClassNMS output: If there is no detected boxes for all images, lod will be set to {1} and Out only contains one value which is -1.
continue
im_ids
=
result
[
'im_id'
]
# replace with range(len(bbox_nums))
im_ids
=
result
[
'im_id'
]
# replace with range(len(bbox_nums))
last_bbox_num
=
0
for
idx
,
im_id
in
enumerate
(
im_ids
):
cur_bbox_len
=
bbox_nums
[
idx
]
bboxes
=
bbox_locs
[
last_bbox_num
:
last_bbox_num
+
cur_bbox_len
]
bboxes
=
bbox_locs
[
last_bbox_num
:
last_bbox_num
+
cur_bbox_len
]
last_bbox_num
+=
cur_bbox_len
# box: [num_id, score, xmin, ymin, xmax, ymax]
if
len
(
bboxes
)
==
0
:
# current image has no detections
continue
chip_rec
=
records
[
int
(
im_id
)
-
1
]
# im_id starts from 1, type is np.int64
chip_rec
=
records
[
int
(
im_id
)
-
1
]
# im_id starts from 1, type is np.int64
image_size
=
max
(
chip_rec
[
"ori_im_h"
],
chip_rec
[
"ori_im_w"
])
bboxes
=
transform_chip_boxes2image_boxes
(
bboxes
,
chip_rec
[
"chip"
],
chip_rec
[
"ori_im_h"
],
chip_rec
[
"ori_im_w"
])
bboxes
=
transform_chip_boxes2image_boxes
(
bboxes
,
chip_rec
[
"chip"
],
chip_rec
[
"ori_im_h"
],
chip_rec
[
"ori_im_w"
])
scale_i
=
chip_rec
[
"scale_i"
]
cur_scale
=
self
.
_get_current_scale
(
self
.
target_sizes
[
scale_i
],
image_size
)
_
,
valid_boxes_idx
=
self
.
_validate_boxes
(
self
.
valid_box_ratio_ranges
[
scale_i
],
image_size
,
bboxes
[:,
2
:],
cur_scale
)
cur_scale
=
self
.
_get_current_scale
(
self
.
target_sizes
[
scale_i
],
image_size
)
_
,
valid_boxes_idx
=
self
.
_validate_boxes
(
self
.
valid_box_ratio_ranges
[
scale_i
],
image_size
,
bboxes
[:,
2
:],
cur_scale
)
ori_img_id
=
self
.
_global_chip_id2img_id
[
int
(
im_id
)]
img_id2bbox
[
ori_img_id
].
append
(
bboxes
[
valid_boxes_idx
])
...
...
@@ -507,7 +549,8 @@ class AnnoCropper(object):
nms_thresh
=
self
.
nms_thresh
for
img_id
in
img_id2bbox
:
box
=
img_id2bbox
[
img_id
]
# list of np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2]
box
=
img_id2bbox
[
img_id
]
# list of np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2]
box
=
np
.
concatenate
(
box
,
axis
=
0
)
nms_dets
=
nms
(
box
,
nms_thresh
)
if
max_per_img
>
0
:
...
...
@@ -525,18 +568,13 @@ class AnnoCropper(object):
results
=
[]
for
img_id
in
im_ids
:
# output by original im_id order
if
len
(
img_id2bbox
[
img_id
])
==
0
:
bbox
=
np
.
array
([[
-
1.
,
0.
,
0.
,
0.
,
0.
,
0.
]])
# edge case: no detections
bbox
=
np
.
array
(
[[
-
1.
,
0.
,
0.
,
0.
,
0.
,
0.
]])
# edge case: no detections
bbox_num
=
np
.
array
([
0
])
else
:
# np.array of shape [N, 6], 6 is [label, score, x1, y1, x2, y2]
bbox
=
img_id2bbox
[
img_id
]
bbox_num
=
np
.
array
([
len
(
bbox
)])
res
=
dict
(
im_id
=
np
.
array
([[
img_id
]]),
bbox
=
bbox
,
bbox_num
=
bbox_num
)
res
=
dict
(
im_id
=
np
.
array
([[
img_id
]]),
bbox
=
bbox
,
bbox_num
=
bbox_num
)
results
.
append
(
res
)
return
results
ppdet/data/crop_utils/chip_box_utils.py
浏览文件 @
da0157cf
...
...
@@ -33,8 +33,10 @@ def intersection_over_box(chips, boxes):
box_area
=
bbox_area
(
boxes
)
# B
inter_x2y2
=
np
.
minimum
(
np
.
expand_dims
(
chips
,
1
)[:,
:,
2
:],
boxes
[:,
2
:])
# CxBX2
inter_x1y1
=
np
.
maximum
(
np
.
expand_dims
(
chips
,
1
)[:,
:,
:
2
],
boxes
[:,
:
2
])
# CxBx2
inter_x2y2
=
np
.
minimum
(
np
.
expand_dims
(
chips
,
1
)[:,
:,
2
:],
boxes
[:,
2
:])
# CxBX2
inter_x1y1
=
np
.
maximum
(
np
.
expand_dims
(
chips
,
1
)[:,
:,
:
2
],
boxes
[:,
:
2
])
# CxBx2
inter_wh
=
inter_x2y2
-
inter_x1y1
inter_wh
=
np
.
clip
(
inter_wh
,
a_min
=
0
,
a_max
=
None
)
inter_area
=
inter_wh
[:,
:,
0
]
*
inter_wh
[:,
:,
1
]
# CxB
...
...
@@ -81,8 +83,9 @@ def transform_chip_box(gt_bbox: 'Gx4', boxes_idx: 'B', chip: '4'):
def
find_chips_to_cover_overlaped_boxes
(
iob
,
overlap_threshold
):
chip_ids
,
box_ids
=
np
.
nonzero
(
iob
>=
overlap_threshold
)
chip_id2overlap_box_num
=
np
.
bincount
(
chip_ids
)
# 1d array
chip_id2overlap_box_num
=
np
.
pad
(
chip_id2overlap_box_num
,
(
0
,
len
(
iob
)
-
len
(
chip_id2overlap_box_num
)),
constant_values
=
0
)
chip_id2overlap_box_num
=
np
.
pad
(
chip_id2overlap_box_num
,
(
0
,
len
(
iob
)
-
len
(
chip_id2overlap_box_num
)),
constant_values
=
0
)
chosen_chip_ids
=
[]
while
len
(
box_ids
)
>
0
:
...
...
@@ -92,7 +95,8 @@ def find_chips_to_cover_overlaped_boxes(iob, overlap_threshold):
chosen_chip_ids
.
append
(
max_count_chip_id
)
box_ids_in_cur_chip
=
box_ids
[
chip_ids
==
max_count_chip_id
]
ids_not_in_cur_boxes_mask
=
np
.
logical_not
(
np
.
isin
(
box_ids
,
box_ids_in_cur_chip
))
ids_not_in_cur_boxes_mask
=
np
.
logical_not
(
np
.
isin
(
box_ids
,
box_ids_in_cur_chip
))
chip_ids
=
chip_ids
[
ids_not_in_cur_boxes_mask
]
box_ids
=
box_ids
[
ids_not_in_cur_boxes_mask
]
return
chosen_chip_ids
,
chip_id2overlap_box_num
...
...
@@ -124,7 +128,7 @@ def nms(dets, thresh):
order
=
scores
.
argsort
()[::
-
1
]
ndets
=
dets
.
shape
[
0
]
suppressed
=
np
.
zeros
((
ndets
),
dtype
=
np
.
int
)
suppressed
=
np
.
zeros
((
ndets
),
dtype
=
np
.
int
32
)
# nominal indices
# _i, _j
...
...
ppdet/data/source/keypoint_coco.py
浏览文件 @
da0157cf
...
...
@@ -487,9 +487,9 @@ class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
continue
joints
=
np
.
zeros
(
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
)
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
32
)
joints_vis
=
np
.
zeros
(
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
)
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
32
)
for
ipt
in
range
(
self
.
ann_info
[
'num_joints'
]):
joints
[
ipt
,
0
]
=
obj
[
'keypoints'
][
ipt
*
3
+
0
]
joints
[
ipt
,
1
]
=
obj
[
'keypoints'
][
ipt
*
3
+
1
]
...
...
@@ -560,9 +560,10 @@ class KeypointTopDownCocoDataset(KeypointTopDownBaseDataset):
continue
center
,
scale
=
self
.
_box2cs
(
box
)
joints
=
np
.
zeros
((
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
)
joints
=
np
.
zeros
(
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float32
)
joints_vis
=
np
.
ones
(
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
)
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
32
)
kpt_db
.
append
({
'image_file'
:
img_name
,
'im_id'
:
im_id
,
...
...
@@ -633,8 +634,8 @@ class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
im_id
=
a
[
'image_id'
]
if
'image_id'
in
a
else
int
(
os
.
path
.
splitext
(
image_name
)[
0
])
c
=
np
.
array
(
a
[
'center'
],
dtype
=
np
.
float
)
s
=
np
.
array
([
a
[
'scale'
],
a
[
'scale'
]],
dtype
=
np
.
float
)
c
=
np
.
array
(
a
[
'center'
],
dtype
=
np
.
float
32
)
s
=
np
.
array
([
a
[
'scale'
],
a
[
'scale'
]],
dtype
=
np
.
float
32
)
# Adjust center/scale slightly to avoid cropping limbs
if
c
[
0
]
!=
-
1
:
...
...
@@ -642,9 +643,10 @@ class KeypointTopDownMPIIDataset(KeypointTopDownBaseDataset):
s
=
s
*
1.25
c
=
c
-
1
joints
=
np
.
zeros
((
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
)
joints
=
np
.
zeros
(
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float32
)
joints_vis
=
np
.
zeros
(
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
)
(
self
.
ann_info
[
'num_joints'
],
3
),
dtype
=
np
.
float
32
)
if
'joints'
in
a
:
joints_
=
np
.
array
(
a
[
'joints'
])
joints_
[:,
0
:
2
]
=
joints_
[:,
0
:
2
]
-
1
...
...
ppdet/data/source/pose3d_cmb.py
浏览文件 @
da0157cf
...
...
@@ -68,7 +68,7 @@ class Pose3DDataset(DetDataset):
def
get_mask
(
self
,
mvm_percent
=
0.3
):
num_joints
=
self
.
num_joints
mjm_mask
=
np
.
ones
((
num_joints
,
1
)).
astype
(
np
.
float
)
mjm_mask
=
np
.
ones
((
num_joints
,
1
)).
astype
(
np
.
float
32
)
if
self
.
test_mode
==
False
:
pb
=
np
.
random
.
random_sample
()
masked_num
=
int
(
...
...
@@ -78,7 +78,7 @@ class Pose3DDataset(DetDataset):
np
.
arange
(
num_joints
),
replace
=
False
,
size
=
masked_num
)
mjm_mask
[
indices
,
:]
=
0.0
mvm_mask
=
np
.
ones
((
10
,
1
)).
astype
(
np
.
float
)
mvm_mask
=
np
.
ones
((
10
,
1
)).
astype
(
np
.
float
32
)
if
self
.
test_mode
==
False
:
num_vertices
=
10
pb
=
np
.
random
.
random_sample
()
...
...
ppdet/modeling/mot/matching/jde_matching.py
浏览文件 @
da0157cf
...
...
@@ -82,8 +82,8 @@ def linear_assignment(cost_matrix, thresh):
def
bbox_ious
(
atlbrs
,
btlbrs
):
boxes
=
np
.
ascontiguousarray
(
atlbrs
,
dtype
=
np
.
float
)
query_boxes
=
np
.
ascontiguousarray
(
btlbrs
,
dtype
=
np
.
float
)
boxes
=
np
.
ascontiguousarray
(
atlbrs
,
dtype
=
np
.
float
32
)
query_boxes
=
np
.
ascontiguousarray
(
btlbrs
,
dtype
=
np
.
float
32
)
N
=
boxes
.
shape
[
0
]
K
=
query_boxes
.
shape
[
0
]
ious
=
np
.
zeros
((
N
,
K
),
dtype
=
boxes
.
dtype
)
...
...
@@ -127,13 +127,13 @@ def embedding_distance(tracks, detections, metric='euclidean'):
"""
Compute cost based on features between two list[STrack].
"""
cost_matrix
=
np
.
zeros
((
len
(
tracks
),
len
(
detections
)),
dtype
=
np
.
float
)
cost_matrix
=
np
.
zeros
((
len
(
tracks
),
len
(
detections
)),
dtype
=
np
.
float
32
)
if
cost_matrix
.
size
==
0
:
return
cost_matrix
det_features
=
np
.
asarray
(
[
track
.
curr_feat
for
track
in
detections
],
dtype
=
np
.
float
)
[
track
.
curr_feat
for
track
in
detections
],
dtype
=
np
.
float
32
)
track_features
=
np
.
asarray
(
[
track
.
smooth_feat
for
track
in
tracks
],
dtype
=
np
.
float
)
[
track
.
smooth_feat
for
track
in
tracks
],
dtype
=
np
.
float
32
)
cost_matrix
=
np
.
maximum
(
0.0
,
cdist
(
track_features
,
det_features
,
metric
))
# Nomalized features
return
cost_matrix
...
...
ppdet/modeling/mot/tracker/base_jde_tracker.py
浏览文件 @
da0157cf
...
...
@@ -102,14 +102,9 @@ class BaseTrack(object):
@
register
@
serializable
class
STrack
(
BaseTrack
):
def
__init__
(
self
,
tlwh
,
score
,
cls_id
,
buff_size
=
30
,
temp_feat
=
None
):
def
__init__
(
self
,
tlwh
,
score
,
cls_id
,
buff_size
=
30
,
temp_feat
=
None
):
# wait activate
self
.
_tlwh
=
np
.
asarray
(
tlwh
,
dtype
=
np
.
float
)
self
.
_tlwh
=
np
.
asarray
(
tlwh
,
dtype
=
np
.
float
32
)
self
.
score
=
score
self
.
cls_id
=
cls_id
self
.
track_len
=
0
...
...
ppdet/modeling/post_process.py
浏览文件 @
da0157cf
...
...
@@ -635,7 +635,7 @@ def nms(dets, match_threshold=0.6, match_metric='iou'):
order
=
scores
.
argsort
()[::
-
1
]
ndets
=
dets
.
shape
[
0
]
suppressed
=
np
.
zeros
((
ndets
),
dtype
=
np
.
int
)
suppressed
=
np
.
zeros
((
ndets
),
dtype
=
np
.
int
32
)
for
_i
in
range
(
ndets
):
i
=
order
[
_i
]
...
...
ppdet/modeling/proposal_generator/target.py
浏览文件 @
da0157cf
...
...
@@ -295,7 +295,7 @@ def polygons_to_mask(polygons, height, width):
assert
len
(
polygons
)
>
0
,
"COCOAPI does not support empty polygons"
rles
=
mask_util
.
frPyObjects
(
polygons
,
height
,
width
)
rle
=
mask_util
.
merge
(
rles
)
return
mask_util
.
decode
(
rle
).
astype
(
np
.
bool
)
return
mask_util
.
decode
(
rle
).
astype
(
np
.
bool
_
)
def
rasterize_polygons_within_box
(
poly
,
box
,
resolution
):
...
...
@@ -448,7 +448,7 @@ def libra_sample_via_interval(max_overlaps, full_set, num_expected, floor_thr,
tmp_sampled_set
=
np
.
random
.
choice
(
tmp_inds
,
size
=
per_num_expected
,
replace
=
False
)
else
:
tmp_sampled_set
=
np
.
array
(
tmp_inds
,
dtype
=
np
.
int
)
tmp_sampled_set
=
np
.
array
(
tmp_inds
,
dtype
=
np
.
int
32
)
sampled_inds
.
append
(
tmp_sampled_set
)
sampled_inds
=
np
.
concatenate
(
sampled_inds
)
...
...
@@ -509,13 +509,13 @@ def libra_sample_neg(max_overlaps,
size
=
num_expected_iou_sampling
,
replace
=
False
)
else
:
iou_sampled_inds
=
np
.
array
(
iou_sampling_neg_inds
,
dtype
=
np
.
int
)
iou_sampled_inds
=
np
.
array
(
iou_sampling_neg_inds
,
dtype
=
np
.
int
32
)
num_expected_floor
=
num_expected
-
len
(
iou_sampled_inds
)
if
len
(
floor_neg_inds
)
>
num_expected_floor
:
sampled_floor_inds
=
np
.
random
.
choice
(
floor_neg_inds
,
size
=
num_expected_floor
,
replace
=
False
)
else
:
sampled_floor_inds
=
np
.
array
(
floor_neg_inds
,
dtype
=
np
.
int
)
sampled_floor_inds
=
np
.
array
(
floor_neg_inds
,
dtype
=
np
.
int
32
)
sampled_inds
=
np
.
concatenate
((
sampled_floor_inds
,
iou_sampled_inds
))
if
len
(
sampled_inds
)
<
num_expected
:
num_extra
=
num_expected
-
len
(
sampled_inds
)
...
...
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