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1adb26ef
编写于
6月 05, 2021
作者:
G
Guanghua Yu
提交者:
GitHub
6月 05, 2021
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差异文件
add solov2_r101vd model (#3286)
上级
32abf1a0
变更
4
显示空白变更内容
内联
并排
Showing
4 changed file
with
92 addition
and
23 deletion
+92
-23
configs/solov2/README.md
configs/solov2/README.md
+1
-0
configs/solov2/_base_/solov2_r50_fpn.yml
configs/solov2/_base_/solov2_r50_fpn.yml
+0
-1
configs/solov2/solov2_r101_vd_fpn_3x_coco.yml
configs/solov2/solov2_r101_vd_fpn_3x_coco.yml
+66
-0
ppdet/modeling/heads/solov2_head.py
ppdet/modeling/heads/solov2_head.py
+25
-22
未找到文件。
configs/solov2/README.md
浏览文件 @
1adb26ef
...
...
@@ -21,6 +21,7 @@ SOLOv2 (Segmenting Objects by Locations) is a fast instance segmentation framewo
| SOLOv2 (Paper) | X101-DCN-FPN | True | 3x | 42.4 | 5.9 | V100 | - | - |
| SOLOv2 | R50-FPN | False | 1x | 35.5 | 21.9 | V100 |
[
model
](
https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_1x_coco.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r50_fpn_1x_coco.yml
)
|
| SOLOv2 | R50-FPN | True | 3x | 38.0 | 21.9 | V100 |
[
model
](
https://paddledet.bj.bcebos.com/models/solov2_r50_fpn_3x_coco.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r50_fpn_3x_coco.yml
)
|
| SOLOv2 | R101vd-FPN | True | 3x | 42.7 | 12.1 | V100 |
[
model
](
https://paddledet.bj.bcebos.com/models/solov2_r101_vd_fpn_3x_coco.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/solov2/solov2_r101_vd_fpn_3x_coco.yml
)
|
**Notes:**
...
...
configs/solov2/_base_/solov2_r50_fpn.yml
浏览文件 @
1adb26ef
...
...
@@ -9,7 +9,6 @@ SOLOv2:
ResNet
:
depth
:
50
norm_type
:
bn
freeze_at
:
0
return_idx
:
[
0
,
1
,
2
,
3
]
num_stages
:
4
...
...
configs/solov2/solov2_r101_vd_fpn_3x_coco.yml
0 → 100644
浏览文件 @
1adb26ef
_BASE_
:
[
'
../datasets/coco_instance.yml'
,
'
../runtime.yml'
,
'
_base_/solov2_r50_fpn.yml'
,
'
_base_/optimizer_1x.yml'
,
'
_base_/solov2_reader.yml'
,
]
pretrain_weights
:
https://paddledet.bj.bcebos.com/models/pretrained/ResNet101_vd_pretrained.pdparams
weights
:
output/solov2_r101_vd_fpn_3x_coco/model_final
epoch
:
36
use_ema
:
true
ema_decay
:
0.9998
ResNet
:
depth
:
101
variant
:
d
freeze_at
:
0
return_idx
:
[
0
,
1
,
2
,
3
]
dcn_v2_stages
:
[
1
,
2
,
3
]
num_stages
:
4
SOLOv2Head
:
seg_feat_channels
:
512
stacked_convs
:
4
num_grids
:
[
40
,
36
,
24
,
16
,
12
]
kernel_out_channels
:
256
solov2_loss
:
SOLOv2Loss
mask_nms
:
MaskMatrixNMS
dcn_v2_stages
:
[
0
,
1
,
2
,
3
]
SOLOv2MaskHead
:
mid_channels
:
128
out_channels
:
256
start_level
:
0
end_level
:
3
use_dcn_in_tower
:
True
LearningRate
:
base_lr
:
0.01
schedulers
:
-
!PiecewiseDecay
gamma
:
0.1
milestones
:
[
24
,
33
]
-
!LinearWarmup
start_factor
:
0.
steps
:
2000
TrainReader
:
sample_transforms
:
-
Decode
:
{}
-
Poly2Mask
:
{}
-
RandomResize
:
{
interp
:
1
,
target_size
:
[[
640
,
1333
],
[
672
,
1333
],
[
704
,
1333
],
[
736
,
1333
],
[
768
,
1333
],
[
800
,
1333
]],
keep_ratio
:
True
}
-
RandomFlip
:
{}
-
NormalizeImage
:
{
is_scale
:
true
,
mean
:
[
0.485
,
0.456
,
0.406
],
std
:
[
0.229
,
0.224
,
0.225
]}
-
Permute
:
{}
batch_transforms
:
-
PadBatch
:
{
pad_to_stride
:
32
}
-
Gt2Solov2Target
:
{
num_grids
:
[
40
,
36
,
24
,
16
,
12
],
scale_ranges
:
[[
1
,
96
],
[
48
,
192
],
[
96
,
384
],
[
192
,
768
],
[
384
,
2048
]],
coord_sigma
:
0.2
}
batch_size
:
2
shuffle
:
true
drop_last
:
true
ppdet/modeling/heads/solov2_head.py
浏览文件 @
1adb26ef
...
...
@@ -43,6 +43,7 @@ class SOLOv2MaskHead(nn.Layer):
end_level (int): The position where the input ends.
use_dcn_in_tower (bool): Whether to use dcn in tower or not.
"""
__shared__
=
[
'norm_type'
]
def
__init__
(
self
,
in_channels
=
256
,
...
...
@@ -50,7 +51,8 @@ class SOLOv2MaskHead(nn.Layer):
out_channels
=
256
,
start_level
=
0
,
end_level
=
3
,
use_dcn_in_tower
=
False
):
use_dcn_in_tower
=
False
,
norm_type
=
'gn'
):
super
(
SOLOv2MaskHead
,
self
).
__init__
()
assert
start_level
>=
0
and
end_level
>=
start_level
self
.
in_channels
=
in_channels
...
...
@@ -58,24 +60,22 @@ class SOLOv2MaskHead(nn.Layer):
self
.
mid_channels
=
mid_channels
self
.
use_dcn_in_tower
=
use_dcn_in_tower
self
.
range_level
=
end_level
-
start_level
+
1
# TODO: add DeformConvNorm
conv_type
=
[
ConvNormLayer
]
self
.
conv_func
=
conv_type
[
0
]
if
self
.
use_dcn_in_tower
:
self
.
conv_func
=
conv_type
[
1
]
self
.
use_dcn
=
True
if
self
.
use_dcn_in_tower
else
False
self
.
convs_all_levels
=
[]
self
.
norm_type
=
norm_type
for
i
in
range
(
start_level
,
end_level
+
1
):
conv_feat_name
=
'mask_feat_head.convs_all_levels.{}'
.
format
(
i
)
conv_pre_feat
=
nn
.
Sequential
()
if
i
==
start_level
:
conv_pre_feat
.
add_sublayer
(
conv_feat_name
+
'.conv'
+
str
(
i
),
self
.
conv_func
(
ConvNormLayer
(
ch_in
=
self
.
in_channels
,
ch_out
=
self
.
mid_channels
,
filter_size
=
3
,
stride
=
1
,
norm_type
=
'gn'
))
use_dcn
=
self
.
use_dcn
,
norm_type
=
self
.
norm_type
))
self
.
add_sublayer
(
'conv_pre_feat'
+
str
(
i
),
conv_pre_feat
)
self
.
convs_all_levels
.
append
(
conv_pre_feat
)
else
:
...
...
@@ -87,12 +87,13 @@ class SOLOv2MaskHead(nn.Layer):
ch_in
=
self
.
mid_channels
conv_pre_feat
.
add_sublayer
(
conv_feat_name
+
'.conv'
+
str
(
j
),
self
.
conv_func
(
ConvNormLayer
(
ch_in
=
ch_in
,
ch_out
=
self
.
mid_channels
,
filter_size
=
3
,
stride
=
1
,
norm_type
=
'gn'
))
use_dcn
=
self
.
use_dcn
,
norm_type
=
self
.
norm_type
))
conv_pre_feat
.
add_sublayer
(
conv_feat_name
+
'.conv'
+
str
(
j
)
+
'act'
,
nn
.
ReLU
())
conv_pre_feat
.
add_sublayer
(
...
...
@@ -105,12 +106,13 @@ class SOLOv2MaskHead(nn.Layer):
conv_pred_name
=
'mask_feat_head.conv_pred.0'
self
.
conv_pred
=
self
.
add_sublayer
(
conv_pred_name
,
self
.
conv_func
(
ConvNormLayer
(
ch_in
=
self
.
mid_channels
,
ch_out
=
self
.
out_channels
,
filter_size
=
1
,
stride
=
1
,
norm_type
=
'gn'
))
use_dcn
=
self
.
use_dcn
,
norm_type
=
self
.
norm_type
))
def
forward
(
self
,
inputs
):
"""
...
...
@@ -165,7 +167,7 @@ class SOLOv2Head(nn.Layer):
mask_nms (object): MaskMatrixNMS instance.
"""
__inject__
=
[
'solov2_loss'
,
'mask_nms'
]
__shared__
=
[
'num_classes'
]
__shared__
=
[
'n
orm_type'
,
'n
um_classes'
]
def
__init__
(
self
,
num_classes
=
80
,
...
...
@@ -179,7 +181,8 @@ class SOLOv2Head(nn.Layer):
solov2_loss
=
None
,
score_threshold
=
0.1
,
mask_threshold
=
0.5
,
mask_nms
=
None
):
mask_nms
=
None
,
norm_type
=
'gn'
):
super
(
SOLOv2Head
,
self
).
__init__
()
self
.
num_classes
=
num_classes
self
.
in_channels
=
in_channels
...
...
@@ -194,33 +197,33 @@ class SOLOv2Head(nn.Layer):
self
.
mask_nms
=
mask_nms
self
.
score_threshold
=
score_threshold
self
.
mask_threshold
=
mask_threshold
self
.
norm_type
=
norm_type
conv_type
=
[
ConvNormLayer
]
self
.
conv_func
=
conv_type
[
0
]
self
.
kernel_pred_convs
=
[]
self
.
cate_pred_convs
=
[]
for
i
in
range
(
self
.
stacked_convs
):
if
i
in
self
.
dcn_v2_stages
:
self
.
conv_func
=
conv_type
[
1
]
use_dcn
=
True
if
i
in
self
.
dcn_v2_stages
else
False
ch_in
=
self
.
in_channels
+
2
if
i
==
0
else
self
.
seg_feat_channels
kernel_conv
=
self
.
add_sublayer
(
'bbox_head.kernel_convs.'
+
str
(
i
),
self
.
conv_func
(
ConvNormLayer
(
ch_in
=
ch_in
,
ch_out
=
self
.
seg_feat_channels
,
filter_size
=
3
,
stride
=
1
,
norm_type
=
'gn'
))
use_dcn
=
use_dcn
,
norm_type
=
self
.
norm_type
))
self
.
kernel_pred_convs
.
append
(
kernel_conv
)
ch_in
=
self
.
in_channels
if
i
==
0
else
self
.
seg_feat_channels
cate_conv
=
self
.
add_sublayer
(
'bbox_head.cate_convs.'
+
str
(
i
),
self
.
conv_func
(
ConvNormLayer
(
ch_in
=
ch_in
,
ch_out
=
self
.
seg_feat_channels
,
filter_size
=
3
,
stride
=
1
,
norm_type
=
'gn'
))
use_dcn
=
use_dcn
,
norm_type
=
self
.
norm_type
))
self
.
cate_pred_convs
.
append
(
cate_conv
)
self
.
solo_kernel
=
self
.
add_sublayer
(
...
...
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