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[Feature] Support DDRNet #1722
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[Feature] Support DDRNet #1722
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,50 @@ | ||
| # model settings | ||
| # default : DDRNet23slim | ||
|
|
||
| norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| backbone=dict( | ||
| type='DualResNet', | ||
| layers=[2, 2, (2,), 2], | ||
| planes=32, | ||
| spp_planes=128, | ||
| norm_cfg=norm_cfg , | ||
| align_corners=False, | ||
| init_cfg= dict(type='Pretrained', checkpoint='mmseg_ddr23s.pth'), | ||
| ), | ||
|
|
||
| decode_head=dict( | ||
| type='FCNHead', | ||
| init_cfg= dict(type='Kaiming', distribution='normal'), | ||
| in_index=-1, | ||
| concat_input=False, | ||
| dropout_ratio=0, | ||
| input_transform=None, | ||
| in_channels=32*4, | ||
| channels=64, | ||
| num_convs=1, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| norm_cfg=norm_cfg , | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=dict( | ||
| type='FCNHead', | ||
| init_cfg= dict(type='Kaiming', distribution='normal'), | ||
| in_index=-2, | ||
| concat_input=False, | ||
| dropout_ratio=0, | ||
| input_transform=None, | ||
| in_channels=32*2, | ||
| channels=64, | ||
| num_convs=1, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| norm_cfg=norm_cfg , | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
|
|
||
| # model training and testing settings | ||
| train_cfg=dict(sampler=None), | ||
| test_cfg=dict(mode='whole')) |
16 changes: 16 additions & 0 deletions
16
configs/ddrnet/ddrnet-23-slim_in1k-pre_2x8_1024x1024_160k_cityscapes.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,16 @@ | ||
| _base_ = [ | ||
| '../_base_/models/ddrnet.py', | ||
| '../_base_/datasets/cityscapes_1024x1024.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | ||
| ] | ||
|
|
||
| data = dict( | ||
| samples_per_gpu=8, | ||
| workers_per_gpu=8, | ||
| ) | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| dict(type='TensorboardLoggerHook') | ||
| ]) | ||
59 changes: 59 additions & 0 deletions
59
configs/ddrnet/ddrnet-23_in1k-pre_2x8_1024x1024_160k_cityscapes.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,59 @@ | ||
| _base_ = [ | ||
| '../_base_/models/ddrnet.py', | ||
| '../_base_/datasets/cityscapes_1024x1024.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | ||
| ] | ||
|
|
||
| data = dict( | ||
| samples_per_gpu=8, | ||
| workers_per_gpu=8, | ||
| ) | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| dict(type='TensorboardLoggerHook') | ||
| ]) | ||
|
|
||
| # model settings | ||
| # default : DDRNet23 | ||
| norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| backbone=dict( | ||
| type='DualResNet', | ||
| layers=[2, 2, (2,), 2], | ||
| planes=64, | ||
| spp_planes=128, | ||
| norm_cfg=norm_cfg , | ||
| align_corners=False, | ||
| init_cfg= dict(type='Pretrained', checkpoint='/home/yyq/mmseg_ddr23.pth'), | ||
| ), | ||
| decode_head=dict( | ||
| type='FCNHead', | ||
| in_index=-1, | ||
| dropout_ratio=0, | ||
| input_transform=None, | ||
| in_channels=64*4, | ||
| channels=128, | ||
| num_convs=1, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=dict( | ||
| type='FCNHead', | ||
| in_index=-2, | ||
| dropout_ratio=0, | ||
| input_transform=None, | ||
| in_channels=64*2, | ||
| channels=128, | ||
| num_convs=1, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
|
|
||
| # model training and testing settings | ||
| train_cfg=dict(sampler=None), | ||
| test_cfg=dict(mode='whole')) |
58 changes: 58 additions & 0 deletions
58
configs/ddrnet/ddrnet-39_in1k-pre_2x8_1024x1024_160k_cityscapes.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| _base_ = [ | ||
| '../_base_/models/ddrnet.py', | ||
| '../_base_/datasets/cityscapes_1024x1024.py', | ||
| '../_base_/default_runtime.py', '../_base_/schedules/schedule_160k.py' | ||
| ] | ||
|
|
||
| data = dict( | ||
| samples_per_gpu=8, | ||
| workers_per_gpu=8, | ||
| ) | ||
| log_config = dict( | ||
| interval=50, | ||
| hooks=[ | ||
| dict(type='TextLoggerHook'), | ||
| dict(type='TensorboardLoggerHook') | ||
| ]) | ||
| # model settings | ||
| # default : DDRNet39 | ||
| norm_cfg = dict(type='SyncBN', eps=1e-03, requires_grad=True) | ||
| model = dict( | ||
| type='EncoderDecoder', | ||
| backbone=dict( | ||
| type='DualResNet', | ||
| layers=(3, 4, (3,3), 3), | ||
| planes=64, | ||
| spp_planes=128, | ||
| norm_cfg=norm_cfg , | ||
| align_corners=False, | ||
| init_cfg= dict(type='Pretrained', checkpoint='mmseg_ddr39.pth'), | ||
| ), | ||
| decode_head=dict( | ||
| type='FCNHead', | ||
| in_index=-1, | ||
| dropout_ratio=0, | ||
| input_transform=None, | ||
| in_channels=64*4, | ||
| channels=256, | ||
| num_convs=1, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0)), | ||
| auxiliary_head=dict( | ||
| type='FCNHead', | ||
| in_index=-2, | ||
| dropout_ratio=0, | ||
| input_transform=None, | ||
| in_channels=64*2, | ||
| channels=256, | ||
| num_convs=1, | ||
| num_classes=19, | ||
| align_corners=False, | ||
| loss_decode=dict( | ||
| type='CrossEntropyLoss', use_sigmoid=False, loss_weight=0.4)), | ||
|
|
||
| # model training and testing settings | ||
| train_cfg=dict(sampler=None), | ||
| test_cfg=dict(mode='whole')) |
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To keep the same with former Cityscapes dataset config, we may set default setting 4 GPUs with 4 samplers per GPU.
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Sorry, i just evaluate models on the Cityscapes val set. DDRNet-23-slim gains 77.37% mIoU ,and DDRNet-23 gains 78.66% mIoU .Compared with val results (77.8%,79.5%) from DDRNet repo ,i think results are acceptable,because some basic training tricks such as OHEM are not used.DDRNet-39 gains 79.46% mIoU on Cityscapes val set.
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Indeed a great job! You successfully transferred pretrained weights and launched DDRNet training in mmseg. ;)