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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,37 @@ | ||
| # dataset settings | ||
| _base_ = './mapillary_v1.py' | ||
| metainfo = dict( | ||
| classes=('Bird', 'Ground Animal', 'Curb', 'Fence', 'Guard Rail', 'Barrier', | ||
| 'Wall', 'Bike Lane', 'Crosswalk - Plain', 'Curb Cut', 'Parking', | ||
| 'Pedestrian Area', 'Rail Track', 'Road', 'Service Lane', | ||
| 'Sidewalk', 'Bridge', 'Building', 'Tunnel', 'Person', 'Bicyclist', | ||
| 'Motorcyclist', 'Other Rider', 'Lane Marking - Crosswalk', | ||
| 'Lane Marking - General', 'Mountain', 'Sand', 'Sky', 'Snow', | ||
| 'Terrain', 'Vegetation', 'Water', 'Banner', 'Bench', 'Bike Rack', | ||
| 'Billboard', 'Catch Basin', 'CCTV Camera', 'Fire Hydrant', | ||
| 'Junction Box', 'Mailbox', 'Manhole', 'Phone Booth', 'Pothole', | ||
| 'Street Light', 'Pole', 'Traffic Sign Frame', 'Utility Pole', | ||
| 'Traffic Light', 'Traffic Sign (Back)', 'Traffic Sign (Front)', | ||
| 'Trash Can', 'Bicycle', 'Boat', 'Bus', 'Car', 'Caravan', | ||
| 'Motorcycle', 'On Rails', 'Other Vehicle', 'Trailer', 'Truck', | ||
| 'Wheeled Slow', 'Car Mount', 'Ego Vehicle'), | ||
| palette=[[165, 42, 42], [0, 192, 0], [196, 196, 196], [190, 153, 153], | ||
| [180, 165, 180], [90, 120, 150], [102, 102, 156], [128, 64, 255], | ||
| [140, 140, 200], [170, 170, 170], [250, 170, 160], [96, 96, 96], | ||
| [230, 150, 140], [128, 64, 128], [110, 110, 110], [244, 35, 232], | ||
| [150, 100, 100], [70, 70, 70], [150, 120, 90], [220, 20, 60], | ||
| [255, 0, 0], [255, 0, 100], [255, 0, 200], [200, 128, 128], | ||
| [255, 255, 255], [64, 170, 64], [230, 160, 50], [70, 130, 180], | ||
| [190, 255, 255], [152, 251, 152], [107, 142, 35], [0, 170, 30], | ||
| [255, 255, 128], [250, 0, 30], [100, 140, 180], [220, 220, 220], | ||
| [220, 128, 128], [222, 40, 40], [100, 170, 30], [40, 40, 40], | ||
| [33, 33, 33], [100, 128, 160], [142, 0, 0], [70, 100, 150], | ||
| [210, 170, 100], [153, 153, 153], [128, 128, 128], [0, 0, 80], | ||
| [250, 170, 30], [192, 192, 192], [220, 220, 0], [140, 140, 20], | ||
| [119, 11, 32], [150, 0, 255], [0, 60, 100], [0, 0, 142], | ||
| [0, 0, 90], [0, 0, 230], [0, 80, 100], [128, 64, 64], [0, 0, 110], | ||
| [0, 0, 70], [0, 0, 192], [32, 32, 32], [120, 10, 10]]) | ||
|
|
||
| train_dataloader = dict(dataset=dict(metainfo=metainfo)) | ||
| val_dataloader = dict(dataset=dict(metainfo=metainfo)) | ||
| test_dataloader = val_dataloader |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,25 @@ | ||
| # optimizer | ||
| optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0005) | ||
| optim_wrapper = dict(type='OptimWrapper', optimizer=optimizer, clip_grad=None) | ||
| # learning policy | ||
| param_scheduler = [ | ||
| dict( | ||
| type='PolyLR', | ||
| eta_min=1e-4, | ||
| power=0.9, | ||
| begin=0, | ||
| end=240000, | ||
| by_epoch=False) | ||
| ] | ||
| # training schedule for 240k | ||
| train_cfg = dict( | ||
| type='IterBasedTrainLoop', max_iters=240000, val_interval=24000) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') | ||
| default_hooks = dict( | ||
| timer=dict(type='IterTimerHook'), | ||
| logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), | ||
| param_scheduler=dict(type='ParamSchedulerHook'), | ||
| checkpoint=dict(type='CheckpointHook', by_epoch=False, interval=24000), | ||
| sampler_seed=dict(type='DistSamplerSeedHook'), | ||
| visualization=dict(type='SegVisualizationHook')) |
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58 changes: 58 additions & 0 deletions
58
configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-300k_mapillay_v1_65-1280x1280.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| _base_ = [ | ||
| '../_base_/models/deeplabv3plus_r50-d8.py', | ||
| '../_base_/datasets/mapillary_v1_65.py', | ||
| '../_base_/default_runtime.py', | ||
| ] | ||
|
|
||
| crop_size = (1280, 1280) | ||
| data_preprocessor = dict(size=crop_size) | ||
| model = dict( | ||
| data_preprocessor=data_preprocessor, | ||
| pretrained='open-mmlab://resnet50_v1c', | ||
| backbone=dict(depth=50), | ||
| decode_head=dict(num_classes=65), | ||
| auxiliary_head=dict(num_classes=65)) | ||
|
|
||
| iters = 300000 | ||
| # optimizer | ||
| optimizer = dict( | ||
| type='AdamW', lr=0.0001, betas=(0.9, 0.999), weight_decay=0.0001) | ||
| # optimizer | ||
| optim_wrapper = dict( | ||
| type='OptimWrapper', | ||
| optimizer=optimizer, | ||
| clip_grad=dict(max_norm=0.01, norm_type=2), | ||
| paramwise_cfg=dict( | ||
| custom_keys={'backbone': dict(lr_mult=0.1, decay_mult=1.0)})) | ||
| param_scheduler = [ | ||
| dict( | ||
| type='PolyLR', | ||
| eta_min=0, | ||
| power=0.9, | ||
| begin=0, | ||
| end=iters, | ||
| by_epoch=False) | ||
| ] | ||
|
|
||
| # training schedule for 300k | ||
| train_cfg = dict( | ||
| type='IterBasedTrainLoop', max_iters=iters, val_interval=iters // 10) | ||
| val_cfg = dict(type='ValLoop') | ||
| test_cfg = dict(type='TestLoop') | ||
|
|
||
| default_hooks = dict( | ||
| timer=dict(type='IterTimerHook'), | ||
| logger=dict(type='LoggerHook', interval=50, log_metric_by_epoch=False), | ||
| param_scheduler=dict(type='ParamSchedulerHook'), | ||
| checkpoint=dict( | ||
| type='CheckpointHook', by_epoch=False, interval=iters // 10), | ||
| sampler_seed=dict(type='DistSamplerSeedHook'), | ||
| visualization=dict(type='SegVisualizationHook')) | ||
|
|
||
| train_dataloader = dict(batch_size=2) | ||
|
|
||
| # Default setting for scaling LR automatically | ||
| # - `enable` means enable scaling LR automatically | ||
| # or not by default. | ||
| # - `base_batch_size` = (4 GPUs) x (2 samples per GPU). | ||
| auto_scale_lr = dict(enable=False, base_batch_size=8) |
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