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[Benchmark] Uploading FastFCN on ADE20K (#972)

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* Uploading FastFCN on ADE20K

* fixing lint error
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MengzhangLI 2021-10-20 11:27:33 +08:00 committed by GitHub
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11
configs/fastfcn/README.md
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@ -35,6 +35,17 @@ year={2019}
| EncNet + JPU | R-50-D32 | 512x1024 | 80000 | 8.15 | 4.77 | 77.97 |79.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036-78da5046.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_512x1024_80k_cityscapes_20210928_030036.log.json) |
| EncNet + JPU (4x4)| R-50-D32 | 512x1024 | 80000 | 15.45 | - | 78.6 | 80.25 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217.log.json) |
### ADE20K
| Method | Backbone | Crop Size | Lr schd | Mem (GB) | Inf time (fps) | mIoU | mIoU(ms+flip) | config | download |
| --------- | --------- | --------- | ------: | -------- | -------------- | ----: | ------------- | --------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- |
| DeepLabV3 + JPU | R-50-D32 | 512x1024 | 80000 | 8.46 | 12.06 | 41.88 | 42.91 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619.log.json) |
| DeepLabV3 + JPU | R-50-D32 | 512x1024 | 160000 | - | - | 43.58 | 44.92 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246.log.json) |
| PSPNet + JPU | R-50-D32 | 512x1024 | 80000 | 8.02 | 19.21 | 41.40 | 42.12 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137.log.json) |
| PSPNet + JPU | R-50-D32 | 512x1024 | 160000 | - | - | 42.63 | 43.71 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455.log.json) |
| EncNet + JPU | R-50-D32 | 512x1024 | 80000 | 9.67 | 17.23 | 40.88 | 42.36 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214.log.json) |
| EncNet + JPU | R-50-D32 | 512x1024 | 160000 | - | - | 42.50 | 44.21 | [config](https://github.com/open-mmlab/mmsegmentation/blob/master/configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py) | [model](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth) | [log](https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456.log.json) |
Note:
- `4x4` means 4 GPUs with 4 samples per GPU in training, default setting is 4 GPUs with 2 samples per GPU in training.

109
configs/fastfcn/fastfcn.yml
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@ -3,6 +3,7 @@ Collections:
Metadata:
Training Data:
- Cityscapes
- ADE20K
Paper:
URL: https://arxiv.org/abs/1903.11816
Title: 'FastFCN: Rethinking Dilated Convolution in the Backbone for Semantic Segmentation'
@ -124,3 +125,111 @@ Models:
mIoU(ms+flip): 80.25
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes/fastfcn_r50-d32_jpu_enc_4x4_512x1024_80k_cityscapes_20210926_093217-e1eb6dbb.pth
- Name: fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k
In Collection: fastfcn
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 82.92
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.46
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.88
mIoU(ms+flip): 42.91
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k_20211013_190619-3aa40f2d.pth
- Name: fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k
In Collection: fastfcn
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 43.58
mIoU(ms+flip): 44.92
Config: configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k_20211008_152246-27036aee.pth
- Name: fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k
In Collection: fastfcn
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 52.06
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 8.02
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 41.4
mIoU(ms+flip): 42.12
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k_20210930_225137-993d07c8.pth
- Name: fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k
In Collection: fastfcn
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.63
mIoU(ms+flip): 43.71
Config: configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k_20211008_105455-e8f5a2fd.pth
- Name: fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k
In Collection: fastfcn
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 80000
inference time (ms/im):
- value: 58.04
hardware: V100
backend: PyTorch
batch size: 1
mode: FP32
resolution: (512,1024)
memory (GB): 9.67
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 40.88
mIoU(ms+flip): 42.36
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k_20210930_225214-65aef6dd.pth
- Name: fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k
In Collection: fastfcn
Metadata:
backbone: R-50-D32
crop size: (512,1024)
lr schd: 160000
Results:
- Task: Semantic Segmentation
Dataset: ADE20K
Metrics:
mIoU: 42.5
mIoU(ms+flip): 44.21
Config: configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py
Weights: https://download.openmmlab.com/mmsegmentation/v0.5/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k_20211008_105456-d875ce3c.pth

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configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_160k_ade20k.py Normal file
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@ -0,0 +1,20 @@
# model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='ASPPHead',
in_channels=2048,
in_index=2,
channels=512,
dilations=(1, 12, 24, 36),
dropout_ratio=0.1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))

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configs/fastfcn/fastfcn_r50-d32_jpu_aspp_512x512_80k_ade20k.py Normal file
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@ -0,0 +1,20 @@
# model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='ASPPHead',
in_channels=2048,
in_index=2,
channels=512,
dilations=(1, 12, 24, 36),
dropout_ratio=0.1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))

24
configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_160k_ade20k.py Normal file
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@ -0,0 +1,24 @@
# model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='EncHead',
in_channels=[512, 1024, 2048],
in_index=(0, 1, 2),
channels=512,
num_codes=32,
use_se_loss=True,
add_lateral=False,
dropout_ratio=0.1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_se_decode=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))

24
configs/fastfcn/fastfcn_r50-d32_jpu_enc_512x512_80k_ade20k.py Normal file
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@ -0,0 +1,24 @@
# model settings
_base_ = './fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py'
norm_cfg = dict(type='SyncBN', requires_grad=True)
model = dict(
decode_head=dict(
_delete_=True,
type='EncHead',
in_channels=[512, 1024, 2048],
in_index=(0, 1, 2),
channels=512,
num_codes=32,
use_se_loss=True,
add_lateral=False,
dropout_ratio=0.1,
num_classes=150,
norm_cfg=norm_cfg,
align_corners=False,
loss_decode=dict(
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0),
loss_se_decode=dict(
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=0.2)),
# model training and testing settings
train_cfg=dict(),
test_cfg=dict(mode='whole'))

7
configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_160k_ade20k.py Normal file
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@ -0,0 +1,7 @@
_base_ = [
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_160k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))

7
configs/fastfcn/fastfcn_r50-d32_jpu_psp_512x512_80k_ade20k.py Normal file
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@ -0,0 +1,7 @@
_base_ = [
'../_base_/models/fastfcn_r50-d32_jpu_psp.py',
'../_base_/datasets/ade20k.py', '../_base_/default_runtime.py',
'../_base_/schedules/schedule_80k.py'
]
model = dict(
decode_head=dict(num_classes=150), auxiliary_head=dict(num_classes=150))