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01d57c6a
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01d57c6a
编写于
2月 05, 2021
作者:
G
Guanghua Yu
提交者:
GitHub
2月 05, 2021
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电子邮件补丁
差异文件
fix RCNN dygraph to static (#2184)
* fix RCNN dygraph to static
上级
9a4fae6d
变更
17
显示空白变更内容
内联
并排
Showing
17 changed file
with
125 addition
and
218 deletion
+125
-218
dygraph/deploy/python/infer.py
dygraph/deploy/python/infer.py
+11
-39
dygraph/deploy/python/visualize.py
dygraph/deploy/python/visualize.py
+21
-63
dygraph/ppdet/engine/export_utils.py
dygraph/ppdet/engine/export_utils.py
+2
-3
dygraph/ppdet/metrics/coco_utils.py
dygraph/ppdet/metrics/coco_utils.py
+4
-11
dygraph/ppdet/modeling/architectures/faster_rcnn.py
dygraph/ppdet/modeling/architectures/faster_rcnn.py
+1
-9
dygraph/ppdet/modeling/architectures/fcos.py
dygraph/ppdet/modeling/architectures/fcos.py
+1
-9
dygraph/ppdet/modeling/architectures/mask_rcnn.py
dygraph/ppdet/modeling/architectures/mask_rcnn.py
+1
-10
dygraph/ppdet/modeling/architectures/ttfnet.py
dygraph/ppdet/modeling/architectures/ttfnet.py
+1
-6
dygraph/ppdet/modeling/architectures/yolo.py
dygraph/ppdet/modeling/architectures/yolo.py
+1
-9
dygraph/ppdet/modeling/bbox_utils.py
dygraph/ppdet/modeling/bbox_utils.py
+6
-7
dygraph/ppdet/modeling/heads/bbox_head.py
dygraph/ppdet/modeling/heads/bbox_head.py
+1
-2
dygraph/ppdet/modeling/heads/mask_head.py
dygraph/ppdet/modeling/heads/mask_head.py
+4
-3
dygraph/ppdet/modeling/layers.py
dygraph/ppdet/modeling/layers.py
+4
-6
dygraph/ppdet/modeling/post_process.py
dygraph/ppdet/modeling/post_process.py
+19
-9
dygraph/ppdet/modeling/proposal_generator/anchor_generator.py
...aph/ppdet/modeling/proposal_generator/anchor_generator.py
+14
-9
dygraph/ppdet/modeling/proposal_generator/rpn_head.py
dygraph/ppdet/modeling/proposal_generator/rpn_head.py
+11
-5
dygraph/ppdet/py_op/post_process.py
dygraph/ppdet/py_op/post_process.py
+23
-18
未找到文件。
dygraph/deploy/python/infer.py
浏览文件 @
01d57c6a
...
...
@@ -84,16 +84,8 @@ class Detector(object):
np_boxes
[:,
3
]
*=
w
np_boxes
[:,
4
]
*=
h
np_boxes
[:,
5
]
*=
w
expect_boxes
=
(
np_boxes
[:,
1
]
>
threshold
)
&
(
np_boxes
[:,
0
]
>
-
1
)
np_boxes
=
np_boxes
[
expect_boxes
,
:]
for
box
in
np_boxes
:
print
(
'class_id:{:d}, confidence:{:.4f},'
'left_top:[{:.2f},{:.2f}],'
' right_bottom:[{:.2f},{:.2f}]'
.
format
(
int
(
box
[
0
]),
box
[
1
],
box
[
2
],
box
[
3
],
box
[
4
],
box
[
5
]))
results
[
'boxes'
]
=
np_boxes
if
np_masks
is
not
None
:
np_masks
=
np_masks
[
expect_boxes
,
:,
:,
:]
results
[
'masks'
]
=
np_masks
return
results
...
...
@@ -111,7 +103,7 @@ class Detector(object):
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape:
[N, class_num, mask_resolution, mask_resolution
]
shape:
[N, im_h, im_w
]
'''
inputs
=
self
.
preprocess
(
image
)
np_boxes
,
np_masks
=
None
,
None
...
...
@@ -125,7 +117,7 @@ class Detector(object):
output_names
=
self
.
predictor
.
get_output_names
()
boxes_tensor
=
self
.
predictor
.
get_output_handle
(
output_names
[
0
])
np_boxes
=
boxes_tensor
.
copy_to_cpu
()
if
self
.
pred_config
.
mask
_resolution
is
not
None
:
if
self
.
pred_config
.
mask
:
masks_tensor
=
self
.
predictor
.
get_output_handle
(
output_names
[
2
])
np_masks
=
masks_tensor
.
copy_to_cpu
()
...
...
@@ -135,14 +127,7 @@ class Detector(object):
output_names
=
self
.
predictor
.
get_output_names
()
boxes_tensor
=
self
.
predictor
.
get_output_handle
(
output_names
[
0
])
np_boxes
=
boxes_tensor
.
copy_to_cpu
()
score_tensor
=
self
.
predictor
.
get_output_handle
(
output_names
[
3
])
np_score
=
score_tensor
.
copy_to_cpu
()
label_tensor
=
self
.
predictor
.
get_output_handle
(
output_names
[
2
])
np_label
=
label_tensor
.
copy_to_cpu
()
np_boxes
=
np
.
concatenate
(
[
np_label
[:,
np
.
newaxis
],
np_score
[:,
np
.
newaxis
],
np_boxes
],
axis
=-
1
)
if
self
.
pred_config
.
mask_resolution
is
not
None
:
if
self
.
pred_config
.
mask
:
masks_tensor
=
self
.
predictor
.
get_output_handle
(
output_names
[
2
])
np_masks
=
masks_tensor
.
copy_to_cpu
()
t2
=
time
.
time
()
...
...
@@ -196,10 +181,9 @@ class DetectorSOLOv2(Detector):
image (str/np.ndarray): path of image/ np.ndarray read by cv2
threshold (float): threshold of predicted box' score
Returns:
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape:[N, class_num, mask_resolution, mask_resolution]
results (dict): 'segm': np.ndarray,shape:[N, im_h, im_w]
'cate_label': label of segm, shape:[N]
'cate_score': confidence score of segm, shape:[N]
'''
inputs
=
self
.
preprocess
(
image
)
np_label
,
np_score
,
np_segms
=
None
,
None
,
None
...
...
@@ -273,9 +257,9 @@ class PredictConfig():
self
.
preprocess_infos
=
yml_conf
[
'Preprocess'
]
self
.
min_subgraph_size
=
yml_conf
[
'min_subgraph_size'
]
self
.
labels
=
yml_conf
[
'label_list'
]
self
.
mask
_resolution
=
Non
e
if
'mask
_resolution
'
in
yml_conf
:
self
.
mask
_resolution
=
yml_conf
[
'mask_resolution
'
]
self
.
mask
=
Fals
e
if
'mask'
in
yml_conf
:
self
.
mask
=
yml_conf
[
'mask
'
]
self
.
input_shape
=
yml_conf
[
'image_shape'
]
self
.
print_config
()
...
...
@@ -355,19 +339,9 @@ def load_predictor(model_dir,
return
predictor
def
visualize
(
image_file
,
results
,
labels
,
mask_resolution
=
14
,
output_dir
=
'output/'
,
threshold
=
0.5
):
def
visualize
(
image_file
,
results
,
labels
,
output_dir
=
'output/'
,
threshold
=
0.5
):
# visualize the predict result
im
=
visualize_box_mask
(
image_file
,
results
,
labels
,
mask_resolution
=
mask_resolution
,
threshold
=
threshold
)
im
=
visualize_box_mask
(
image_file
,
results
,
labels
,
threshold
=
threshold
)
img_name
=
os
.
path
.
split
(
image_file
)[
-
1
]
if
not
os
.
path
.
exists
(
output_dir
):
os
.
makedirs
(
output_dir
)
...
...
@@ -397,7 +371,6 @@ def predict_image(detector):
FLAGS
.
image_file
,
results
,
detector
.
pred_config
.
labels
,
mask_resolution
=
detector
.
pred_config
.
mask_resolution
,
output_dir
=
FLAGS
.
output_dir
,
threshold
=
FLAGS
.
threshold
)
...
...
@@ -431,7 +404,6 @@ def predict_video(detector, camera_id):
frame
,
results
,
detector
.
pred_config
.
labels
,
mask_resolution
=
detector
.
pred_config
.
mask_resolution
,
threshold
=
FLAGS
.
threshold
)
im
=
np
.
array
(
im
)
writer
.
write
(
im
)
...
...
dygraph/deploy/python/visualize.py
浏览文件 @
01d57c6a
...
...
@@ -21,16 +21,15 @@ from PIL import Image, ImageDraw
from
scipy
import
ndimage
def
visualize_box_mask
(
im
,
results
,
labels
,
mask_resolution
=
14
,
threshold
=
0.5
):
def
visualize_box_mask
(
im
,
results
,
labels
,
threshold
=
0.5
):
"""
Args:
im (str/np.ndarray): path of image/np.ndarray read by cv2
results (dict): include 'boxes': np.ndarray: shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
MaskRCNN's results include 'masks': np.ndarray:
shape:[N,
class_num, mask_resolution, mask_resolution
]
shape:[N,
im_h, im_w
]
labels (list): labels:['class1', ..., 'classn']
mask_resolution (int): shape of a mask is:[mask_resolution, mask_resolution]
threshold (float): Threshold of score.
Returns:
im (PIL.Image.Image): visualized image
...
...
@@ -41,13 +40,9 @@ def visualize_box_mask(im, results, labels, mask_resolution=14, threshold=0.5):
im
=
Image
.
fromarray
(
im
)
if
'masks'
in
results
and
'boxes'
in
results
:
im
=
draw_mask
(
im
,
results
[
'boxes'
],
results
[
'masks'
],
labels
,
resolution
=
mask_resolution
)
im
,
results
[
'boxes'
],
results
[
'masks'
],
labels
,
threshold
=
threshold
)
if
'boxes'
in
results
:
im
=
draw_box
(
im
,
results
[
'boxes'
],
labels
)
im
=
draw_box
(
im
,
results
[
'boxes'
],
labels
,
threshold
=
threshold
)
if
'segm'
in
results
:
im
=
draw_segm
(
im
,
...
...
@@ -80,91 +75,49 @@ def get_color_map_list(num_classes):
return
color_map
def
expand_boxes
(
boxes
,
scale
=
0.0
):
"""
Args:
boxes (np.ndarray): shape:[N,4], N:number of box,
matix element:[x_min, y_min, x_max, y_max]
scale (float): scale of boxes
Returns:
boxes_exp (np.ndarray): expanded boxes
"""
w_half
=
(
boxes
[:,
2
]
-
boxes
[:,
0
])
*
.
5
h_half
=
(
boxes
[:,
3
]
-
boxes
[:,
1
])
*
.
5
x_c
=
(
boxes
[:,
2
]
+
boxes
[:,
0
])
*
.
5
y_c
=
(
boxes
[:,
3
]
+
boxes
[:,
1
])
*
.
5
w_half
*=
scale
h_half
*=
scale
boxes_exp
=
np
.
zeros
(
boxes
.
shape
)
boxes_exp
[:,
0
]
=
x_c
-
w_half
boxes_exp
[:,
2
]
=
x_c
+
w_half
boxes_exp
[:,
1
]
=
y_c
-
h_half
boxes_exp
[:,
3
]
=
y_c
+
h_half
return
boxes_exp
def
draw_mask
(
im
,
np_boxes
,
np_masks
,
labels
,
resolution
=
14
,
threshold
=
0.5
):
def
draw_mask
(
im
,
np_boxes
,
np_masks
,
labels
,
threshold
=
0.5
):
"""
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
np_masks (np.ndarray): shape:[N,
class_num, resolution, resolution
]
np_masks (np.ndarray): shape:[N,
im_h, im_w
]
labels (list): labels:['class1', ..., 'classn']
resolution (int): shape of a mask is:[resolution, resolution]
threshold (float): threshold of mask
Returns:
im (PIL.Image.Image): visualized image
"""
color_list
=
get_color_map_list
(
len
(
labels
))
scale
=
(
resolution
+
2.0
)
/
resolution
im_w
,
im_h
=
im
.
size
w_ratio
=
0.4
alpha
=
0.7
im
=
np
.
array
(
im
).
astype
(
'float32'
)
rects
=
np_boxes
[:,
2
:]
expand_rects
=
expand_boxes
(
rects
,
scale
)
expand_rects
=
expand_rects
.
astype
(
np
.
int32
)
clsid_scores
=
np_boxes
[:,
0
:
2
]
padded_mask
=
np
.
zeros
((
resolution
+
2
,
resolution
+
2
),
dtype
=
np
.
float32
)
clsid2color
=
{}
for
idx
in
range
(
len
(
np_boxes
)):
clsid
,
score
=
clsid_scores
[
idx
].
tolist
()
clsid
=
int
(
clsid
)
xmin
,
ymin
,
xmax
,
ymax
=
expand_rects
[
idx
].
tolist
()
w
=
xmax
-
xmin
+
1
h
=
ymax
-
ymin
+
1
w
=
np
.
maximum
(
w
,
1
)
h
=
np
.
maximum
(
h
,
1
)
padded_mask
[
1
:
-
1
,
1
:
-
1
]
=
np_masks
[
idx
,
int
(
clsid
),
:,
:]
resized_mask
=
cv2
.
resize
(
padded_mask
,
(
w
,
h
))
resized_mask
=
np
.
array
(
resized_mask
>
threshold
,
dtype
=
np
.
uint8
)
x0
=
min
(
max
(
xmin
,
0
),
im_w
)
x1
=
min
(
max
(
xmax
+
1
,
0
),
im_w
)
y0
=
min
(
max
(
ymin
,
0
),
im_h
)
y1
=
min
(
max
(
ymax
+
1
,
0
),
im_h
)
im_mask
=
np
.
zeros
((
im_h
,
im_w
),
dtype
=
np
.
uint8
)
im_mask
[
y0
:
y1
,
x0
:
x1
]
=
resized_mask
[(
y0
-
ymin
):(
y1
-
ymin
),
(
x0
-
xmin
):(
x1
-
xmin
)]
expect_boxes
=
(
np_boxes
[:,
1
]
>
threshold
)
&
(
np_boxes
[:,
0
]
>
-
1
)
np_boxes
=
np_boxes
[
expect_boxes
,
:]
np_masks
=
np_masks
[
expect_boxes
,
:,
:]
for
i
in
range
(
len
(
np_masks
)):
clsid
,
score
=
int
(
np_boxes
[
i
][
0
]),
np_boxes
[
i
][
1
]
mask
=
np_masks
[
i
]
if
clsid
not
in
clsid2color
:
clsid2color
[
clsid
]
=
color_list
[
clsid
]
color_mask
=
clsid2color
[
clsid
]
for
c
in
range
(
3
):
color_mask
[
c
]
=
color_mask
[
c
]
*
(
1
-
w_ratio
)
+
w_ratio
*
255
idx
=
np
.
nonzero
(
im_
mask
)
idx
=
np
.
nonzero
(
mask
)
color_mask
=
np
.
array
(
color_mask
)
im
[
idx
[
0
],
idx
[
1
],
:]
*=
1.0
-
alpha
im
[
idx
[
0
],
idx
[
1
],
:]
+=
alpha
*
color_mask
return
Image
.
fromarray
(
im
.
astype
(
'uint8'
))
def
draw_box
(
im
,
np_boxes
,
labels
):
def
draw_box
(
im
,
np_boxes
,
labels
,
threshold
=
0.5
):
"""
Args:
im (PIL.Image.Image): PIL image
np_boxes (np.ndarray): shape:[N,6], N: number of box,
matix element:[class, score, x_min, y_min, x_max, y_max]
labels (list): labels:['class1', ..., 'classn']
threshold (float): threshold of box
Returns:
im (PIL.Image.Image): visualized image
"""
...
...
@@ -172,10 +125,15 @@ def draw_box(im, np_boxes, labels):
draw
=
ImageDraw
.
Draw
(
im
)
clsid2color
=
{}
color_list
=
get_color_map_list
(
len
(
labels
))
expect_boxes
=
(
np_boxes
[:,
1
]
>
threshold
)
&
(
np_boxes
[:,
0
]
>
-
1
)
np_boxes
=
np_boxes
[
expect_boxes
,
:]
for
dt
in
np_boxes
:
clsid
,
bbox
,
score
=
int
(
dt
[
0
]),
dt
[
2
:],
dt
[
1
]
xmin
,
ymin
,
xmax
,
ymax
=
bbox
print
(
'class_id:{:d}, confidence:{:.4f}, left_top:[{:.2f},{:.2f}],'
'right_bottom:[{:.2f},{:.2f}]'
.
format
(
int
(
clsid
),
score
,
xmin
,
ymin
,
xmax
,
ymax
))
w
=
xmax
-
xmin
h
=
ymax
-
ymin
if
clsid
not
in
clsid2color
:
...
...
dygraph/ppdet/engine/export_utils.py
浏览文件 @
01d57c6a
...
...
@@ -98,9 +98,8 @@ def _dump_infer_config(config, path, image_shape, model):
'Architecture: {} is not supported for exporting model now'
.
format
(
infer_arch
))
os
.
_exit
(
0
)
if
'mask_post_process'
in
model
.
__dict__
and
model
.
__dict__
[
'mask_post_process'
]:
infer_cfg
[
'mask_resolution'
]
=
model
.
mask_post_process
.
mask_resolution
if
'Mask'
in
infer_arch
:
infer_cfg
[
'mask'
]
=
True
infer_cfg
[
'Preprocess'
],
infer_cfg
[
'label_list'
],
image_shape
=
_parse_reader
(
config
[
'TestReader'
],
config
[
'TestDataset'
],
config
[
'metric'
],
...
...
dygraph/ppdet/metrics/coco_utils.py
浏览文件 @
01d57c6a
...
...
@@ -42,19 +42,12 @@ def get_infer_results(outs, catid, bias=0):
infer_res
=
{}
if
'bbox'
in
outs
:
infer_res
[
'bbox'
]
=
get_det_res
(
outs
[
'bbox'
],
outs
[
'score'
],
outs
[
'label'
],
outs
[
'bbox_num'
],
im_id
,
catid
,
bias
=
bias
)
outs
[
'bbox'
],
outs
[
'bbox_num'
],
im_id
,
catid
,
bias
=
bias
)
if
'mask'
in
outs
:
# mask post process
infer_res
[
'mask'
]
=
get_seg_res
(
outs
[
'mask'
],
outs
[
'score'
],
outs
[
'label'
],
outs
[
'bbox_num'
],
im_id
,
catid
)
infer_res
[
'mask'
]
=
get_seg_res
(
outs
[
'mask'
],
outs
[
'bbox'
],
outs
[
'bbox_num'
],
im_id
,
catid
)
if
'segm'
in
outs
:
infer_res
[
'segm'
]
=
get_solov2_segm_res
(
outs
,
im_id
,
catid
)
...
...
dygraph/ppdet/modeling/architectures/faster_rcnn.py
浏览文件 @
01d57c6a
...
...
@@ -99,13 +99,5 @@ class FasterRCNN(BaseArch):
def
get_pred
(
self
):
bbox_pred
,
bbox_num
=
self
.
_forward
()
label
=
bbox_pred
[:,
0
]
score
=
bbox_pred
[:,
1
]
bbox
=
bbox_pred
[:,
2
:]
output
=
{
'bbox'
:
bbox
,
'score'
:
score
,
'label'
:
label
,
'bbox_num'
:
bbox_num
}
output
=
{
'bbox'
:
bbox_pred
,
'bbox_num'
:
bbox_num
}
return
output
dygraph/ppdet/modeling/architectures/fcos.py
浏览文件 @
01d57c6a
...
...
@@ -91,13 +91,5 @@ class FCOS(BaseArch):
def
get_pred
(
self
):
bboxes
,
bbox_num
=
self
.
_forward
()
label
=
bboxes
[:,
0
]
score
=
bboxes
[:,
1
]
bbox
=
bboxes
[:,
2
:]
output
=
{
'bbox'
:
bbox
,
'score'
:
score
,
'label'
:
label
,
'bbox_num'
:
bbox_num
}
output
=
{
'bbox'
:
bboxes
,
'bbox_num'
:
bbox_num
}
return
output
dygraph/ppdet/modeling/architectures/mask_rcnn.py
浏览文件 @
01d57c6a
...
...
@@ -124,14 +124,5 @@ class MaskRCNN(BaseArch):
def
get_pred
(
self
):
bbox_pred
,
bbox_num
,
mask_pred
=
self
.
_forward
()
label
=
bbox_pred
[:,
0
]
score
=
bbox_pred
[:,
1
]
bbox
=
bbox_pred
[:,
2
:]
output
=
{
'label'
:
label
,
'score'
:
score
,
'bbox'
:
bbox
,
'bbox_num'
:
bbox_num
,
'mask'
:
mask_pred
,
}
output
=
{
'bbox'
:
bbox_pred
,
'bbox_num'
:
bbox_num
,
'mask'
:
mask_pred
}
return
output
dygraph/ppdet/modeling/architectures/ttfnet.py
浏览文件 @
01d57c6a
...
...
@@ -91,13 +91,8 @@ class TTFNet(BaseArch):
def
get_pred
(
self
):
bbox_pred
,
bbox_num
=
self
.
_forward
()
label
=
bbox_pred
[:,
0
]
score
=
bbox_pred
[:,
1
]
bbox
=
bbox_pred
[:,
2
:]
output
=
{
"bbox"
:
bbox
,
'score'
:
score
,
'label'
:
label
,
"bbox"
:
bbox_pred
,
"bbox_num"
:
bbox_num
,
}
return
output
dygraph/ppdet/modeling/architectures/yolo.py
浏览文件 @
01d57c6a
...
...
@@ -61,13 +61,5 @@ class YOLOv3(BaseArch):
def
get_pred
(
self
):
bbox_pred
,
bbox_num
=
self
.
_forward
()
label
=
bbox_pred
[:,
0
]
score
=
bbox_pred
[:,
1
]
bbox
=
bbox_pred
[:,
2
:]
output
=
{
'bbox'
:
bbox
,
'score'
:
score
,
'label'
:
label
,
'bbox_num'
:
bbox_num
}
output
=
{
'bbox'
:
bbox_pred
,
'bbox_num'
:
bbox_num
}
return
output
dygraph/ppdet/modeling/bbox_utils.py
浏览文件 @
01d57c6a
...
...
@@ -39,8 +39,6 @@ def bbox2delta(src_boxes, tgt_boxes, weights):
def
delta2bbox
(
deltas
,
boxes
,
weights
):
clip_scale
=
math
.
log
(
1000.0
/
16
)
if
boxes
.
shape
[
0
]
==
0
:
return
paddle
.
zeros
((
0
,
deltas
.
shape
[
1
]),
dtype
=
'float32'
)
widths
=
boxes
[:,
2
]
-
boxes
[:,
0
]
heights
=
boxes
[:,
3
]
-
boxes
[:,
1
]
...
...
@@ -61,12 +59,13 @@ def delta2bbox(deltas, boxes, weights):
pred_w
=
paddle
.
exp
(
dw
)
*
widths
.
unsqueeze
(
1
)
pred_h
=
paddle
.
exp
(
dh
)
*
heights
.
unsqueeze
(
1
)
pred_boxes
=
paddle
.
zeros_like
(
deltas
)
pred_boxes
=
[]
pred_boxes
.
append
(
pred_ctr_x
-
0.5
*
pred_w
)
pred_boxes
.
append
(
pred_ctr_y
-
0.5
*
pred_h
)
pred_boxes
.
append
(
pred_ctr_x
+
0.5
*
pred_w
)
pred_boxes
.
append
(
pred_ctr_y
+
0.5
*
pred_h
)
pred_boxes
=
paddle
.
stack
(
pred_boxes
,
axis
=-
1
)
pred_boxes
[:,
0
::
4
]
=
pred_ctr_x
-
0.5
*
pred_w
pred_boxes
[:,
1
::
4
]
=
pred_ctr_y
-
0.5
*
pred_h
pred_boxes
[:,
2
::
4
]
=
pred_ctr_x
+
0.5
*
pred_w
pred_boxes
[:,
3
::
4
]
=
pred_ctr_y
+
0.5
*
pred_h
return
pred_boxes
...
...
dygraph/ppdet/modeling/heads/bbox_head.py
浏览文件 @
01d57c6a
...
...
@@ -141,8 +141,7 @@ class BBoxHead(nn.Layer):
rois_feat
=
self
.
roi_extractor
(
body_feats
,
rois
,
rois_num
)
bbox_feat
=
self
.
head
(
rois_feat
)
#if self.with_pool:
if
len
(
bbox_feat
.
shape
)
>
2
and
bbox_feat
.
shape
[
-
1
]
>
1
:
if
self
.
with_pool
:
feat
=
F
.
adaptive_avg_pool2d
(
bbox_feat
,
output_size
=
1
)
feat
=
paddle
.
squeeze
(
feat
,
axis
=
[
2
,
3
])
else
:
...
...
dygraph/ppdet/modeling/heads/mask_head.py
浏览文件 @
01d57c6a
...
...
@@ -182,11 +182,12 @@ class MaskHead(nn.Layer):
mask_out
=
F
.
sigmoid
(
mask_logit
)
else
:
num_masks
=
mask_logit
.
shape
[
0
]
pred_masks
=
paddle
.
split
(
mask_logit
,
num_masks
)
mask_out
=
[]
# TODO: need to optimize gather
for
i
,
pred_mask
in
enumerate
(
pred_masks
):
mask
=
paddle
.
gather
(
pred_mask
,
labels
[
i
],
axis
=
1
)
for
i
in
range
(
mask_logit
.
shape
[
0
]):
pred_masks
=
paddle
.
unsqueeze
(
mask_logit
[
i
,
:,
:,
:],
axis
=
0
)
mask
=
paddle
.
gather
(
pred_masks
,
labels
[
i
],
axis
=
1
)
mask_out
.
append
(
mask
)
mask_out
=
F
.
sigmoid
(
paddle
.
concat
(
mask_out
))
return
mask_out
...
...
dygraph/ppdet/modeling/layers.py
浏览文件 @
01d57c6a
...
...
@@ -316,14 +316,12 @@ class RCNNBox(object):
# [N, C*4]
bbox
=
paddle
.
concat
(
roi
)
if
bbox
.
shape
[
0
]
==
0
:
bbox
=
paddle
.
zeros
([
0
,
bbox_pred
.
shape
[
1
]],
dtype
=
'float32'
)
else
:
bbox
=
delta2bbox
(
bbox_pred
,
bbox
,
self
.
prior_box_var
)
scores
=
cls_prob
[:,
:
-
1
]
# [N*C, 4]
bbox_num_class
=
bbox
.
shape
[
1
]
//
4
bbox
=
paddle
.
reshape
(
bbox
,
[
-
1
,
bbox_num_class
,
4
])
origin_h
=
paddle
.
unsqueeze
(
origin_shape
[:,
0
],
axis
=
1
)
origin_w
=
paddle
.
unsqueeze
(
origin_shape
[:,
1
],
axis
=
1
)
zeros
=
paddle
.
zeros_like
(
origin_h
)
...
...
dygraph/ppdet/modeling/post_process.py
浏览文件 @
01d57c6a
...
...
@@ -54,8 +54,6 @@ class BBoxPostProcess(object):
including labels, scores and bboxes. The size of
bboxes are corresponding to the original image.
"""
if
bboxes
.
shape
[
0
]
==
0
:
return
paddle
.
zeros
(
shape
=
[
1
,
6
])
origin_shape
=
paddle
.
floor
(
im_shape
/
scale_factor
+
0.5
)
...
...
@@ -65,9 +63,12 @@ class BBoxPostProcess(object):
for
i
in
range
(
bbox_num
.
shape
[
0
]):
expand_shape
=
paddle
.
expand
(
origin_shape
[
i
:
i
+
1
,
:],
[
bbox_num
[
i
],
2
])
scale_y
,
scale_x
=
scale_factor
[
i
]
scale_y
,
scale_x
=
scale_factor
[
i
]
[
0
],
scale_factor
[
i
][
1
]
scale
=
paddle
.
concat
([
scale_x
,
scale_y
,
scale_x
,
scale_y
])
expand_scale
=
paddle
.
expand
(
scale
,
[
bbox_num
[
i
],
4
])
# TODO: Because paddle.expand transform error when dygraph
# to static, use reshape to avoid mistakes.
expand_scale
=
paddle
.
reshape
(
expand_scale
,
[
bbox_num
[
i
],
4
])
origin_shape_list
.
append
(
expand_shape
)
scale_factor_list
.
append
(
expand_scale
)
...
...
@@ -121,6 +122,10 @@ class MaskPostProcess(object):
gx
=
paddle
.
expand
(
img_x
,
[
N
,
img_y
.
shape
[
1
],
img_x
.
shape
[
2
]])
gy
=
paddle
.
expand
(
img_y
,
[
N
,
img_y
.
shape
[
1
],
img_x
.
shape
[
2
]])
# TODO: Because paddle.expand transform error when dygraph
# to static, use reshape to avoid mistakes.
gx
=
paddle
.
reshape
(
gx
,
[
N
,
img_y
.
shape
[
1
],
img_x
.
shape
[
2
]])
gy
=
paddle
.
reshape
(
gy
,
[
N
,
img_y
.
shape
[
1
],
img_x
.
shape
[
2
]])
grid
=
paddle
.
stack
([
gx
,
gy
],
axis
=
3
)
img_masks
=
F
.
grid_sample
(
masks
,
grid
,
align_corners
=
False
)
return
img_masks
[:,
0
]
...
...
@@ -129,19 +134,24 @@ class MaskPostProcess(object):
"""
Paste the mask prediction to the original image.
"""
assert
bboxes
.
shape
[
0
]
>
0
,
'There is no detection output'
num_mask
=
mask_out
.
shape
[
0
]
# TODO: support bs > 1
origin_shape
=
paddle
.
cast
(
origin_shape
,
'int32'
)
# TODO: support bs > 1 and mask output dtype is bool
pred_result
=
paddle
.
zeros
(
[
num_mask
,
origin_shape
[
0
][
0
],
origin_shape
[
0
][
1
]],
dtype
=
'bool'
)
[
num_mask
,
origin_shape
[
0
][
0
],
origin_shape
[
0
][
1
]],
dtype
=
'int32'
)
if
bboxes
.
shape
[
0
]
==
0
:
return
pred_result
# TODO: optimize chunk paste
pred_result
=
[]
for
i
in
range
(
bboxes
.
shape
[
0
]):
im_h
,
im_w
=
origin_shape
[
i
]
im_h
,
im_w
=
origin_shape
[
i
]
[
0
],
origin_shape
[
i
][
1
]
pred_mask
=
self
.
paste_mask
(
mask_out
[
i
],
bboxes
[
i
:
i
+
1
,
2
:],
im_h
,
im_w
)
pred_mask
=
pred_mask
>=
self
.
binary_thresh
pred_result
[
i
]
=
pred_mask
pred_mask
=
paddle
.
cast
(
pred_mask
,
'int32'
)
pred_result
.
append
(
pred_mask
)
pred_result
=
paddle
.
concat
(
pred_result
)
return
pred_result
...
...
dygraph/ppdet/modeling/proposal_generator/anchor_generator.py
浏览文件 @
01d57c6a
...
...
@@ -24,7 +24,7 @@ from .. import ops
@
register
class
AnchorGenerator
(
object
):
class
AnchorGenerator
(
nn
.
Layer
):
def
__init__
(
self
,
anchor_sizes
=
[
32
,
64
,
128
,
256
,
512
],
aspect_ratios
=
[
0.5
,
1.0
,
2.0
],
...
...
@@ -64,17 +64,21 @@ class AnchorGenerator(object):
self
.
generate_cell_anchors
(
s
,
a
)
for
s
,
a
in
zip
(
sizes
,
aspect_ratios
)
]
[
self
.
register_buffer
(
t
.
name
,
t
,
persistable
=
False
)
for
t
in
cell_anchors
]
return
cell_anchors
def
_create_grid_offsets
(
self
,
size
,
stride
,
offset
):
grid_height
,
grid_width
=
size
grid_height
,
grid_width
=
size
[
0
],
size
[
1
]
shifts_x
=
paddle
.
arange
(
offset
*
stride
,
grid_width
*
stride
,
step
=
stride
,
dtype
=
'float32'
)
shifts_y
=
paddle
.
arange
(
offset
*
stride
,
grid_height
*
stride
,
step
=
stride
,
dtype
=
'float32'
)
shift_y
,
shift_x
=
paddle
.
meshgrid
(
shifts_y
,
shifts_x
)
shift_x
=
shift_x
.
reshape
(
[
-
1
])
shift_y
=
shift_y
.
reshape
(
[
-
1
])
shift_x
=
paddle
.
reshape
(
shift_x
,
[
-
1
])
shift_y
=
paddle
.
reshape
(
shift_y
,
[
-
1
])
return
shift_x
,
shift_y
def
_grid_anchors
(
self
,
grid_sizes
):
...
...
@@ -84,14 +88,15 @@ class AnchorGenerator(object):
shift_x
,
shift_y
=
self
.
_create_grid_offsets
(
size
,
stride
,
self
.
offset
)
shifts
=
paddle
.
stack
((
shift_x
,
shift_y
,
shift_x
,
shift_y
),
axis
=
1
)
shifts
=
paddle
.
reshape
(
shifts
,
[
-
1
,
1
,
4
])
base_anchors
=
paddle
.
reshape
(
base_anchors
,
[
1
,
-
1
,
4
])
anchors
.
append
((
shifts
.
reshape
([
-
1
,
1
,
4
])
+
base_anchors
.
reshape
(
[
1
,
-
1
,
4
])).
reshape
([
-
1
,
4
]))
anchors
.
append
(
paddle
.
reshape
(
shifts
+
base_anchors
,
[
-
1
,
4
]))
return
anchors
def
__call__
(
self
,
input
):
grid_sizes
=
[
feature_map
.
shape
[
-
2
:]
for
feature_map
in
input
]
def
forward
(
self
,
input
):
grid_sizes
=
[
paddle
.
shape
(
feature_map
)
[
-
2
:]
for
feature_map
in
input
]
anchors_over_all_feature_maps
=
self
.
_grid_anchors
(
grid_sizes
)
return
anchors_over_all_feature_maps
...
...
@@ -105,4 +110,4 @@ class AnchorGenerator(object):
ratios and 5 sizes, the number of anchors is 15.
For FPN models, `num_anchors` on every feature map is the same.
"""
return
self
.
cell_anchors
[
0
].
shape
[
0
]
return
len
(
self
.
cell_anchors
[
0
])
dygraph/ppdet/modeling/proposal_generator/rpn_head.py
浏览文件 @
01d57c6a
...
...
@@ -108,7 +108,14 @@ class RPNHead(nn.Layer):
anchors
=
self
.
anchor_generator
(
rpn_feats
)
rois
,
rois_num
=
self
.
_gen_proposal
(
scores
,
deltas
,
anchors
,
inputs
)
# TODO: Fix batch_size > 1 when testing.
if
self
.
training
:
batch_size
=
im_shape
.
shape
[
0
]
else
:
batch_size
=
1
rois
,
rois_num
=
self
.
_gen_proposal
(
scores
,
deltas
,
anchors
,
inputs
,
batch_size
)
if
self
.
training
:
loss
=
self
.
get_loss
(
scores
,
deltas
,
anchors
,
inputs
)
...
...
@@ -116,7 +123,7 @@ class RPNHead(nn.Layer):
else
:
return
rois
,
rois_num
,
None
def
_gen_proposal
(
self
,
scores
,
bbox_deltas
,
anchors
,
inputs
):
def
_gen_proposal
(
self
,
scores
,
bbox_deltas
,
anchors
,
inputs
,
batch_size
):
"""
scores (list[Tensor]): Multi-level scores prediction
bbox_deltas (list[Tensor]): Multi-level deltas prediction
...
...
@@ -125,7 +132,6 @@ class RPNHead(nn.Layer):
"""
prop_gen
=
self
.
train_proposal
if
self
.
training
else
self
.
test_proposal
im_shape
=
inputs
[
'im_shape'
]
batch_size
=
im_shape
.
shape
[
0
]
rpn_rois_list
=
[[]
for
i
in
range
(
batch_size
)]
rpn_prob_list
=
[[]
for
i
in
range
(
batch_size
)]
rpn_rois_num_list
=
[[]
for
i
in
range
(
batch_size
)]
...
...
dygraph/ppdet/py_op/post_process.py
浏览文件 @
01d57c6a
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import
six
import
os
import
numpy
as
np
import
cv2
def
get_det_res
(
bboxes
,
scores
,
labels
,
bbox_nums
,
image_id
,
label_to_cat_id_map
,
bias
=
0
):
def
get_det_res
(
bboxes
,
bbox_nums
,
image_id
,
label_to_cat_id_map
,
bias
=
0
):
det_res
=
[]
k
=
0
for
i
in
range
(
len
(
bbox_nums
)):
cur_image_id
=
int
(
image_id
[
i
][
0
])
det_nums
=
bbox_nums
[
i
]
for
j
in
range
(
det_nums
):
box
=
bboxes
[
k
]
score
=
float
(
scores
[
k
])
label
=
int
(
labels
[
k
])
if
label
<
0
:
continue
dt
=
bboxes
[
k
]
k
=
k
+
1
xmin
,
ymin
,
xmax
,
ymax
=
box
.
tolist
()
category_id
=
label_to_cat_id_map
[
label
]
num_id
,
score
,
xmin
,
ymin
,
xmax
,
ymax
=
dt
.
tolist
()
if
int
(
num_id
)
<
0
:
continue
category_id
=
label_to_cat_id_map
[
int
(
num_id
)]
w
=
xmax
-
xmin
+
bias
h
=
ymax
-
ymin
+
bias
bbox
=
[
xmin
,
ymin
,
w
,
h
]
...
...
@@ -37,8 +43,7 @@ def get_det_res(bboxes,
return
det_res
def
get_seg_res
(
masks
,
scores
,
labels
,
mask_nums
,
image_id
,
label_to_cat_id_map
):
def
get_seg_res
(
masks
,
bboxes
,
mask_nums
,
image_id
,
label_to_cat_id_map
):
import
pycocotools.mask
as
mask_util
seg_res
=
[]
k
=
0
...
...
@@ -46,9 +51,9 @@ def get_seg_res(masks, scores, labels, mask_nums, image_id,
cur_image_id
=
int
(
image_id
[
i
][
0
])
det_nums
=
mask_nums
[
i
]
for
j
in
range
(
det_nums
):
mask
=
masks
[
k
]
score
=
float
(
scores
[
k
])
label
=
int
(
labels
[
k
])
mask
=
masks
[
k
]
.
astype
(
np
.
uint8
)
score
=
float
(
bboxes
[
k
][
1
])
label
=
int
(
bboxes
[
k
][
0
])
k
=
k
+
1
cat_id
=
label_to_cat_id_map
[
label
]
rle
=
mask_util
.
encode
(
...
...
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