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4e88bec5
编写于
5月 13, 2021
作者:
S
shangliang Xu
提交者:
GitHub
5月 13, 2021
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add res2net (#2992)
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configs/res2net/README.md
configs/res2net/README.md
+37
-0
configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml
...s/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml
+33
-0
configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml
...igs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml
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configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml
...igs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml
+47
-0
docs/MODEL_ZOO_cn.md
docs/MODEL_ZOO_cn.md
+20
-0
ppdet/modeling/backbones/__init__.py
ppdet/modeling/backbones/__init__.py
+2
-0
ppdet/modeling/backbones/res2net.py
ppdet/modeling/backbones/res2net.py
+357
-0
未找到文件。
configs/res2net/README.md
0 → 100644
浏览文件 @
4e88bec5
# Res2Net
## Introduction
-
Res2Net: A New Multi-scale Backbone Architecture:
[
https://arxiv.org/abs/1904.01169
](
https://arxiv.org/abs/1904.01169
)
```
@article{DBLP:journals/corr/abs-1904-01169,
author = {Shanghua Gao and
Ming{-}Ming Cheng and
Kai Zhao and
Xinyu Zhang and
Ming{-}Hsuan Yang and
Philip H. S. Torr},
title = {Res2Net: {A} New Multi-scale Backbone Architecture},
journal = {CoRR},
volume = {abs/1904.01169},
year = {2019},
url = {http://arxiv.org/abs/1904.01169},
archivePrefix = {arXiv},
eprint = {1904.01169},
timestamp = {Thu, 25 Apr 2019 10:24:54 +0200},
biburl = {https://dblp.org/rec/bib/journals/corr/abs-1904-01169},
bibsource = {dblp computer science bibliography, https://dblp.org}
}
```
## Model Zoo
| Backbone | Type | Image/gpu | Lr schd | Inf time (fps) | Box AP | Mask AP | Download | Configs |
| :---------------------- | :------------- | :-------: | :-----: | :------------: | :----: | :-----: | :----------------------------------------------------------: | :-----: |
| Res2Net50-FPN | Faster | 2 | 1x | - | 40.6 | - |
[
model
](
https://paddledet.bj.bcebos.com/models/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/develop/configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x.yml
)
|
| Res2Net50-FPN | Mask | 2 | 2x | - | 42.4 | 38.1 |
[
model
](
https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/develop/configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml
)
|
| Res2Net50-vd-FPN | Mask | 2 | 2x | - | 42.6 | 38.1 |
[
model
](
https://paddledet.bj.bcebos.com/models/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.pdparams
)
|
[
config
](
https://github.com/PaddlePaddle/PaddleDetection/develop/configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml
)
|
Note: all the above models are trained with 8 gpus.
configs/res2net/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco.yml
0 → 100644
浏览文件 @
4e88bec5
_BASE_
:
[
'
../datasets/coco_detection.yml'
,
'
../runtime.yml'
,
'
../faster_rcnn/_base_/optimizer_1x.yml'
,
'
../faster_rcnn/_base_/faster_rcnn_r50_fpn.yml'
,
'
../faster_rcnn/_base_/faster_fpn_reader.yml'
,
]
pretrain_weights
:
https://paddledet.bj.bcebos.com/models/pretrained/Res2Net50_26w_4s_pretrained.pdparams
weights
:
output/faster_rcnn_res2net50_vb_26w_4s_fpn_1x_coco/model_final
FasterRCNN
:
backbone
:
Res2Net
neck
:
FPN
rpn_head
:
RPNHead
bbox_head
:
BBoxHead
# post process
bbox_post_process
:
BBoxPostProcess
Res2Net
:
# index 0 stands for res2
depth
:
50
width
:
26
scales
:
4
norm_type
:
bn
freeze_at
:
0
return_idx
:
[
0
,
1
,
2
,
3
]
num_stages
:
4
variant
:
b
TrainReader
:
batch_size
:
2
configs/res2net/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco.yml
0 → 100644
浏览文件 @
4e88bec5
_BASE_
:
[
'
../datasets/coco_instance.yml'
,
'
../runtime.yml'
,
'
../mask_rcnn/_base_/optimizer_1x.yml'
,
'
../mask_rcnn/_base_/mask_rcnn_r50_fpn.yml'
,
'
../mask_rcnn/_base_/mask_fpn_reader.yml'
,
]
pretrain_weights
:
https://paddledet.bj.bcebos.com/models/pretrained/Res2Net50_26w_4s_pretrained.pdparams
weights
:
output/mask_rcnn_res2net50_vb_26w_4s_fpn_2x_coco/model_final
MaskRCNN
:
backbone
:
Res2Net
neck
:
FPN
rpn_head
:
RPNHead
bbox_head
:
BBoxHead
mask_head
:
MaskHead
# post process
bbox_post_process
:
BBoxPostProcess
mask_post_process
:
MaskPostProcess
Res2Net
:
# index 0 stands for res2
depth
:
50
width
:
26
scales
:
4
norm_type
:
bn
freeze_at
:
0
return_idx
:
[
0
,
1
,
2
,
3
]
num_stages
:
4
variant
:
b
epoch
:
24
LearningRate
:
base_lr
:
0.01
schedulers
:
-
!PiecewiseDecay
gamma
:
0.1
milestones
:
[
16
,
22
]
-
!LinearWarmup
start_factor
:
0.3333333333333333
steps
:
500
TrainReader
:
batch_size
:
2
configs/res2net/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco.yml
0 → 100644
浏览文件 @
4e88bec5
_BASE_
:
[
'
../datasets/coco_instance.yml'
,
'
../runtime.yml'
,
'
../mask_rcnn/_base_/optimizer_1x.yml'
,
'
../mask_rcnn/_base_/mask_rcnn_r50_fpn.yml'
,
'
../mask_rcnn/_base_/mask_fpn_reader.yml'
,
]
pretrain_weights
:
https://paddledet.bj.bcebos.com/models/pretrained/Res2Net50_vd_26w_4s_pretrained.pdparams
weights
:
output/mask_rcnn_res2net50_vd_26w_4s_fpn_2x_coco/model_final
MaskRCNN
:
backbone
:
Res2Net
neck
:
FPN
rpn_head
:
RPNHead
bbox_head
:
BBoxHead
mask_head
:
MaskHead
# post process
bbox_post_process
:
BBoxPostProcess
mask_post_process
:
MaskPostProcess
Res2Net
:
# index 0 stands for res2
depth
:
50
width
:
26
scales
:
4
norm_type
:
bn
freeze_at
:
0
return_idx
:
[
0
,
1
,
2
,
3
]
num_stages
:
4
variant
:
d
epoch
:
24
LearningRate
:
base_lr
:
0.01
schedulers
:
-
!PiecewiseDecay
gamma
:
0.1
milestones
:
[
16
,
22
]
-
!LinearWarmup
start_factor
:
0.3333333333333333
steps
:
500
TrainReader
:
batch_size
:
2
docs/MODEL_ZOO_cn.md
浏览文件 @
4e88bec5
...
@@ -63,3 +63,23 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
...
@@ -63,3 +63,23 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
### TTFNet
### TTFNet
请参考
[
TTFNet
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/
)
请参考
[
TTFNet
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/ttfnet/
)
### Group Normalization
请参考
[
Group Normalization
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/gn/
)
### Deformable ConvNets v2
请参考
[
Deformable ConvNets v2
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dcn/
)
### HRNets
请参考
[
HRNets
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/hrnet/
)
### S2ANet
请参考
[
S2ANet
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/dota/
)
### Res2Net
请参考
[
Res2Net
](
https://github.com/PaddlePaddle/PaddleDetection/tree/develop/configs/res2net/
)
ppdet/modeling/backbones/__init__.py
浏览文件 @
4e88bec5
...
@@ -21,6 +21,7 @@ from . import hrnet
...
@@ -21,6 +21,7 @@ from . import hrnet
from
.
import
blazenet
from
.
import
blazenet
from
.
import
ghostnet
from
.
import
ghostnet
from
.
import
senet
from
.
import
senet
from
.
import
res2net
from
.vgg
import
*
from
.vgg
import
*
from
.resnet
import
*
from
.resnet
import
*
...
@@ -31,3 +32,4 @@ from .hrnet import *
...
@@ -31,3 +32,4 @@ from .hrnet import *
from
.blazenet
import
*
from
.blazenet
import
*
from
.ghostnet
import
*
from
.ghostnet
import
*
from
.senet
import
*
from
.senet
import
*
from
.res2net
import
*
ppdet/modeling/backbones/res2net.py
0 → 100644
浏览文件 @
4e88bec5
# Copyright (c) 2021 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.
from
numbers
import
Integral
import
paddle
import
paddle.nn
as
nn
import
paddle.nn.functional
as
F
from
ppdet.core.workspace
import
register
,
serializable
from
..shape_spec
import
ShapeSpec
from
.resnet
import
ConvNormLayer
__all__
=
[
'Res2Net'
,
'Res2NetC5'
]
Res2Net_cfg
=
{
50
:
[
3
,
4
,
6
,
3
],
101
:
[
3
,
4
,
23
,
3
],
152
:
[
3
,
8
,
36
,
3
],
200
:
[
3
,
12
,
48
,
3
]
}
class
BottleNeck
(
nn
.
Layer
):
def
__init__
(
self
,
ch_in
,
ch_out
,
stride
,
shortcut
,
width
,
scales
=
4
,
variant
=
'b'
,
groups
=
1
,
lr
=
1.0
,
norm_type
=
'bn'
,
norm_decay
=
0.
,
freeze_norm
=
True
,
dcn_v2
=
False
):
super
(
BottleNeck
,
self
).
__init__
()
self
.
shortcut
=
shortcut
self
.
scales
=
scales
self
.
stride
=
stride
if
not
shortcut
:
if
variant
==
'd'
and
stride
==
2
:
self
.
branch1
=
nn
.
Sequential
()
self
.
branch1
.
add_sublayer
(
'pool'
,
nn
.
AvgPool2D
(
kernel_size
=
2
,
stride
=
2
,
padding
=
0
,
ceil_mode
=
True
))
self
.
branch1
.
add_sublayer
(
'conv'
,
ConvNormLayer
(
ch_in
=
ch_in
,
ch_out
=
ch_out
,
filter_size
=
1
,
stride
=
1
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
lr
=
lr
))
else
:
self
.
branch1
=
ConvNormLayer
(
ch_in
=
ch_in
,
ch_out
=
ch_out
,
filter_size
=
1
,
stride
=
stride
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
lr
=
lr
)
self
.
branch2a
=
ConvNormLayer
(
ch_in
=
ch_in
,
ch_out
=
width
*
scales
,
filter_size
=
1
,
stride
=
stride
if
variant
==
'a'
else
1
,
groups
=
1
,
act
=
'relu'
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
lr
=
lr
)
self
.
branch2b
=
nn
.
LayerList
([
ConvNormLayer
(
ch_in
=
width
,
ch_out
=
width
,
filter_size
=
3
,
stride
=
1
if
variant
==
'a'
else
stride
,
groups
=
groups
,
act
=
'relu'
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
lr
=
lr
,
dcn_v2
=
dcn_v2
)
for
_
in
range
(
self
.
scales
-
1
)
])
self
.
branch2c
=
ConvNormLayer
(
ch_in
=
width
*
scales
,
ch_out
=
ch_out
,
filter_size
=
1
,
stride
=
1
,
groups
=
1
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
lr
=
lr
)
def
forward
(
self
,
inputs
):
out
=
self
.
branch2a
(
inputs
)
feature_split
=
paddle
.
split
(
out
,
self
.
scales
,
1
)
out_split
=
[]
for
i
in
range
(
self
.
scales
-
1
):
if
i
==
0
or
self
.
stride
==
2
:
out_split
.
append
(
self
.
branch2b
[
i
](
feature_split
[
i
]))
else
:
out_split
.
append
(
self
.
branch2b
[
i
](
paddle
.
add
(
feature_split
[
i
],
out_split
[
-
1
])))
if
self
.
stride
==
1
:
out_split
.
append
(
feature_split
[
-
1
])
else
:
out_split
.
append
(
F
.
avg_pool2d
(
feature_split
[
-
1
],
3
,
self
.
stride
,
1
))
out
=
self
.
branch2c
(
paddle
.
concat
(
out_split
,
1
))
if
self
.
shortcut
:
short
=
inputs
else
:
short
=
self
.
branch1
(
inputs
)
out
=
paddle
.
add
(
out
,
short
)
out
=
F
.
relu
(
out
)
return
out
class
Blocks
(
nn
.
Layer
):
def
__init__
(
self
,
ch_in
,
ch_out
,
count
,
stage_num
,
width
,
scales
=
4
,
variant
=
'b'
,
groups
=
1
,
lr
=
1.0
,
norm_type
=
'bn'
,
norm_decay
=
0.
,
freeze_norm
=
True
,
dcn_v2
=
False
):
super
(
Blocks
,
self
).
__init__
()
self
.
blocks
=
nn
.
Sequential
()
for
i
in
range
(
count
):
self
.
blocks
.
add_sublayer
(
str
(
i
),
BottleNeck
(
ch_in
=
ch_in
if
i
==
0
else
ch_out
,
ch_out
=
ch_out
,
stride
=
2
if
i
==
0
and
stage_num
!=
2
else
1
,
shortcut
=
False
if
i
==
0
else
True
,
width
=
width
*
(
2
**
(
stage_num
-
2
)),
scales
=
scales
,
variant
=
variant
,
groups
=
groups
,
lr
=
lr
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
dcn_v2
=
dcn_v2
))
def
forward
(
self
,
inputs
):
return
self
.
blocks
(
inputs
)
@
register
@
serializable
class
Res2Net
(
nn
.
Layer
):
"""
Res2Net, see https://arxiv.org/abs/1904.01169
Args:
depth (int): Res2Net depth, should be 50, 101, 152, 200.
width (int): Res2Net width
scales (int): Res2Net scale
variant (str): Res2Net variant, supports 'a', 'b', 'c', 'd' currently
lr_mult_list (list): learning rate ratio of different resnet stages(2,3,4,5),
lower learning rate ratio is need for pretrained model
got using distillation(default as [1.0, 1.0, 1.0, 1.0]).
groups (int): The groups number of the Conv Layer.
norm_type (str): normalization type, 'bn' or 'sync_bn'
norm_decay (float): weight decay for normalization layer weights
freeze_norm (bool): freeze normalization layers
freeze_at (int): freeze the backbone at which stage
return_idx (list): index of stages whose feature maps are returned,
index 0 stands for res2
dcn_v2_stages (list): index of stages who select deformable conv v2
num_stages (int): number of stages created
"""
__shared__
=
[
'norm_type'
]
def
__init__
(
self
,
depth
=
50
,
width
=
26
,
scales
=
4
,
variant
=
'b'
,
lr_mult_list
=
[
1.0
,
1.0
,
1.0
,
1.0
],
groups
=
1
,
norm_type
=
'bn'
,
norm_decay
=
0.
,
freeze_norm
=
True
,
freeze_at
=
0
,
return_idx
=
[
0
,
1
,
2
,
3
],
dcn_v2_stages
=
[
-
1
],
num_stages
=
4
):
super
(
Res2Net
,
self
).
__init__
()
self
.
_model_type
=
'Res2Net'
if
groups
==
1
else
'Res2NeXt'
assert
depth
in
[
50
,
101
,
152
,
200
],
\
"depth {} not in [50, 101, 152, 200]"
assert
variant
in
[
'a'
,
'b'
,
'c'
,
'd'
],
"invalid Res2Net variant"
assert
num_stages
>=
1
and
num_stages
<=
4
self
.
depth
=
depth
self
.
variant
=
variant
self
.
norm_type
=
norm_type
self
.
norm_decay
=
norm_decay
self
.
freeze_norm
=
freeze_norm
self
.
freeze_at
=
freeze_at
if
isinstance
(
return_idx
,
Integral
):
return_idx
=
[
return_idx
]
assert
max
(
return_idx
)
<
num_stages
,
\
'the maximum return index must smaller than num_stages, '
\
'but received maximum return index is {} and num_stages '
\
'is {}'
.
format
(
max
(
return_idx
),
num_stages
)
self
.
return_idx
=
return_idx
self
.
num_stages
=
num_stages
assert
len
(
lr_mult_list
)
==
4
,
\
"lr_mult_list length must be 4 but got {}"
.
format
(
len
(
lr_mult_list
))
if
isinstance
(
dcn_v2_stages
,
Integral
):
dcn_v2_stages
=
[
dcn_v2_stages
]
assert
max
(
dcn_v2_stages
)
<
num_stages
self
.
dcn_v2_stages
=
dcn_v2_stages
block_nums
=
Res2Net_cfg
[
depth
]
# C1 stage
if
self
.
variant
in
[
'c'
,
'd'
]:
conv_def
=
[
[
3
,
32
,
3
,
2
,
"conv1_1"
],
[
32
,
32
,
3
,
1
,
"conv1_2"
],
[
32
,
64
,
3
,
1
,
"conv1_3"
],
]
else
:
conv_def
=
[[
3
,
64
,
7
,
2
,
"conv1"
]]
self
.
res1
=
nn
.
Sequential
()
for
(
c_in
,
c_out
,
k
,
s
,
_name
)
in
conv_def
:
self
.
res1
.
add_sublayer
(
_name
,
ConvNormLayer
(
ch_in
=
c_in
,
ch_out
=
c_out
,
filter_size
=
k
,
stride
=
s
,
groups
=
1
,
act
=
'relu'
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
lr
=
1.0
))
self
.
_in_channels
=
[
64
,
256
,
512
,
1024
]
self
.
_out_channels
=
[
256
,
512
,
1024
,
2048
]
self
.
_out_strides
=
[
4
,
8
,
16
,
32
]
# C2-C5 stages
self
.
res_layers
=
[]
for
i
in
range
(
num_stages
):
lr_mult
=
lr_mult_list
[
i
]
stage_num
=
i
+
2
self
.
res_layers
.
append
(
self
.
add_sublayer
(
"res{}"
.
format
(
stage_num
),
Blocks
(
self
.
_in_channels
[
i
],
self
.
_out_channels
[
i
],
count
=
block_nums
[
i
],
stage_num
=
stage_num
,
width
=
width
,
scales
=
scales
,
groups
=
groups
,
lr
=
lr_mult
,
norm_type
=
norm_type
,
norm_decay
=
norm_decay
,
freeze_norm
=
freeze_norm
,
dcn_v2
=
(
i
in
self
.
dcn_v2_stages
))))
@
property
def
out_shape
(
self
):
return
[
ShapeSpec
(
channels
=
self
.
_out_channels
[
i
],
stride
=
self
.
_out_strides
[
i
])
for
i
in
self
.
return_idx
]
def
forward
(
self
,
inputs
):
x
=
inputs
[
'image'
]
res1
=
self
.
res1
(
x
)
x
=
F
.
max_pool2d
(
res1
,
kernel_size
=
3
,
stride
=
2
,
padding
=
1
)
outs
=
[]
for
idx
,
stage
in
enumerate
(
self
.
res_layers
):
x
=
stage
(
x
)
if
idx
==
self
.
freeze_at
:
x
.
stop_gradient
=
True
if
idx
in
self
.
return_idx
:
outs
.
append
(
x
)
return
outs
@
register
class
Res2NetC5
(
nn
.
Layer
):
def
__init__
(
self
,
depth
=
50
,
width
=
26
,
scales
=
4
,
variant
=
'b'
):
super
(
Res2NetC5
,
self
).
__init__
()
feat_in
,
feat_out
=
[
1024
,
2048
]
self
.
res5
=
Blocks
(
feat_in
,
feat_out
,
count
=
3
,
stage_num
=
5
,
width
=
width
,
scales
=
scales
,
variant
=
variant
)
self
.
feat_out
=
feat_out
@
property
def
out_shape
(
self
):
return
[
ShapeSpec
(
channels
=
self
.
feat_out
,
stride
=
32
,
)]
def
forward
(
self
,
roi_feat
,
stage
=
0
):
y
=
self
.
res5
(
roi_feat
)
return
y
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