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[Feature] Support MobileNetV2 backbone #86

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Sep 4, 2020
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[Feature] Support MobileNetV2 backbone #86

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1310ff3
[Feature] Support MobileNetV2 backbone
xvjiarui Aug 23, 2020
3c8f828
Fixed import
xvjiarui Aug 23, 2020
89f230e
Fixed test
xvjiarui Aug 23, 2020
8aeffc3
Fixed test
xvjiarui Aug 23, 2020
2eb1ba3
Fixed dilate
xvjiarui Aug 25, 2020
21c2287
upload model
xvjiarui Aug 31, 2020
88b76e5
update table
xvjiarui Aug 31, 2020
0ed7b4b
update table
xvjiarui Aug 31, 2020
e8b0b7f
update bibtex
xvjiarui Sep 2, 2020
20e97f2
Merge branch 'master' into mobilenet_v2
xvjiarui Sep 2, 2020
aedf362
update MMCV requirement
xvjiarui Sep 2, 2020
169c23b
merge master
xvjiarui Sep 3, 2020
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32 changes: 32 additions & 0 deletions configs/mobilenet_v2/README.md
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# MobileNetV2: Inverted Residuals and Linear Bottlenecks

## Introduction

```
@inproceedings{sandler2018mobilenetv2,
title={Mobilenetv2: Inverted residuals and linear bottlenecks},
author={Sandler, Mark and Howard, Andrew and Zhu, Menglong and Zhmoginov, Andrey and Chen, Liang-Chieh},
booktitle={Proceedings of the IEEE conference on computer vision and pattern recognition},
pages={4510--4520},
year={2018}
}
```


## Results and models

### Cityscapes
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| FCN | M-V2-D8 | 512x1024 | 80000 | 3.4 | 14.2 | 61.54 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-d24c28c1.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes/fcn_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) |
| PSPNet | M-V2-D8 | 512x1024 | 80000 | 3.6 | 11.2 | 70.23 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes_20200825_124817-19e81d51.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes/pspnet_m-v2-d8_512x1024_80k_cityscapes-20200825_124817.log.json) |
| DeepLabV3 | M-V2-D8 | 512x1024 | 80000 | 3.9 | 8.4 | 73.84 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-bef03590.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes/deeplabv3_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) |
| DeepLabV3+ | M-V2-D8 | 512x1024 | 80000 | 5.1 | 8.4 | 75.20 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes_20200825_124836-d256dd4b.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes-20200825_124836.log.json) |

### ADE20k
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | download |
|------------|----------|-----------|--------:|---------:|----------------|------:|---------------|--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| FCN | M-V2-D8 | 512x512 | 160000 | 6.5 | 64.4 | 19.71 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k_20200825_214953-c40e1095.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k/fcn_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json) |
| PSPNet | M-V2-D8 | 512x512 | 160000 | 6.5 | 57.7 | 29.68 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k_20200825_214953-f5942f7a.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k/pspnet_m-v2-d8_512x512_160k_ade20k-20200825_214953.log.json) |
| DeepLabV3 | M-V2-D8 | 512x512 | 160000 | 6.8 | 39.9 | 34.08 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k_20200825_223255-63986343.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k/deeplabv3_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) |
| DeepLabV3+ | M-V2-D8 | 512x512 | 160000 | 8.2 | 43.1 | 34.02 | - | [model](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k_20200825_223255-465a01d4.pth) | [log](https://openmmlab.oss-accelerate.aliyuncs.com/mmsegmentation/v0.5/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k/deeplabv3plus_m-v2-d8_512x512_160k_ade20k-20200825_223255.log.json) |
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/deeplabv3_m-v2-d8_512x1024_80k_cityscapes.py
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_base_ = '../deeplabv3/deeplabv3_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/deeplabv3_m-v2-d8_512x512_160k_ade20k.py
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_base_ = '../deeplabv3/deeplabv3_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x1024_80k_cityscapes.py
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_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320, c1_in_channels=24),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/deeplabv3plus_m-v2-d8_512x512_160k_ade20k.py
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_base_ = '../deeplabv3plus/deeplabv3plus_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320, c1_in_channels=24),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/fcn_m-v2-d8_512x1024_80k_cityscapes.py
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_base_ = '../fcn/fcn_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/fcn_m-v2-d8_512x512_160k_ade20k.py
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_base_ = '../fcn/fcn_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/pspnet_m-v2-d8_512x1024_80k_cityscapes.py
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_base_ = '../pspnet/pspnet_r101-d8_512x1024_80k_cityscapes.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
12 changes: 12 additions & 0 deletions configs/mobilenet_v2/pspnet_m-v2-d8_512x512_160k_ade20k.py
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_base_ = '../pspnet/pspnet_r101-d8_512x512_160k_ade20k.py'
model = dict(
pretrained='mmcls://mobilenet_v2',
backbone=dict(
_delete_=True,
type='MobileNetV2',
widen_factor=1.,
strides=(1, 2, 2, 1, 1, 1, 1),
dilations=(1, 1, 1, 2, 2, 4, 4),
out_indices=(1, 2, 4, 6)),
decode_head=dict(in_channels=320),
auxiliary_head=dict(in_channels=96))
4 changes: 2 additions & 2 deletions mmseg/__init__.py
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Expand Up @@ -2,8 +2,8 @@

from .version import __version__, version_info

MMCV_MIN = '1.0.5'
MMCV_MAX = '1.1.1'
MMCV_MIN = '1.1.2'
MMCV_MAX = '1.2.0'


def digit_version(version_str):
Expand Down
3 changes: 2 additions & 1 deletion mmseg/models/backbones/__init__.py
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@@ -1,10 +1,11 @@
from .fast_scnn import FastSCNN
from .hrnet import HRNet
from .mobilenet_v2 import MobileNetV2
from .resnest import ResNeSt
from .resnet import ResNet, ResNetV1c, ResNetV1d
from .resnext import ResNeXt

__all__ = [
'ResNet', 'ResNetV1c', 'ResNetV1d', 'ResNeXt', 'HRNet', 'FastSCNN',
'ResNeSt'
'ResNeSt', 'MobileNetV2'
]
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