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75 changes: 75 additions & 0 deletions
75
configs/classification/aircrafts/adautomix/basic/r18_adautomix_bilinear.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| _base_ = [ | ||
| '../../../_base_/datasets/aircrafts/sz224_bs16.py', | ||
| '../../../_base_/default_runtime.py', | ||
| ] | ||
|
|
||
| # models settings | ||
| model = dict( | ||
| type='AdAutoMix', | ||
| pretrained='tools/resnet18-pytorch.pth', | ||
| alpha=1.0, | ||
| mix_samples=3, | ||
| is_random=True, | ||
| momentum=0.999, # 0.999 to 0.999999 | ||
| lam_margin=0.00, # degenerate to mixup when | ||
| mixup_radio=0.5, | ||
| beta_radio=0.3, | ||
| debug=False, | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=18, | ||
| num_stages=4, | ||
| out_indices=(2,3), | ||
| style='pytorch'), | ||
| mix_block=dict( | ||
| type='AdaptiveMask', | ||
| in_channel=256, | ||
| reduction=2, | ||
| lam_concat=True, | ||
| use_scale=True, unsampling_mode='bilinear', | ||
| scale_factor=16, | ||
| frozen=False), | ||
| head_one=dict( | ||
| type='ClsHead', # default CE | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=512, num_classes=100), | ||
| head_mix=dict( | ||
| type='ClsMixupHead', | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=512, num_classes=100), | ||
| head_weights=dict( | ||
| head_mix_q=1, head_one_q=1, head_mix_k=1, head_one_k=1), | ||
| ) | ||
|
|
||
| # additional hooks | ||
| custom_hooks = [ | ||
| dict(type='SAVEHook', | ||
| iter_per_epoch=400, | ||
| save_interval=4000, # plot every 500 x 10 ep | ||
| ), | ||
| dict(type='CosineScheduleHook', | ||
| end_momentum=0.99999, | ||
| adjust_scope=[0.1, 1.0], | ||
| warming_up="constant", | ||
| interval=1) | ||
| ] | ||
|
|
||
| # optimizer | ||
| optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005, | ||
| paramwise_options={ | ||
| 'mix_block': dict(lr=0.1, momentum=0.9)},) # required parawise_option | ||
| # apex | ||
| use_fp16 = False | ||
| optimizer_config = dict(update_interval=1, grad_clip=None) | ||
|
|
||
| # learning policy | ||
| lr_config = dict(policy='CosineAnnealing', min_lr=0.0005) | ||
|
|
||
| # additional scheduler | ||
| addtional_scheduler = dict( | ||
| policy='CosineAnnealing', min_lr=0.001, # 0.1 x 1/100 | ||
| paramwise_options=['mix_block'], | ||
| ) | ||
|
|
||
| # runtime settings | ||
| runner = dict(type='EpochBasedRunner', max_epochs=200) |
75 changes: 75 additions & 0 deletions
75
configs/classification/aircrafts/adautomix/basic/rx50_adautomix_bilinear.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| _base_ = [ | ||
| '../../../_base_/datasets/aircrafts/sz224_bs16.py', | ||
| '../../../_base_/default_runtime.py', | ||
| ] | ||
|
|
||
| # models settings | ||
| model = dict( | ||
| type='AdAutoMix', | ||
| pretrained='tools/resnext50-pytorch.pth', | ||
| alpha=1.0, | ||
| mix_samples=3, | ||
| is_random=True, | ||
| momentum=0.999, # 0.999 to 0.999999 | ||
| lam_margin=0.03, | ||
| mixup_radio=0.5, | ||
| beta_radio=0.6, | ||
| debug=True, | ||
| backbone=dict( | ||
| type='ResNeXt', # normal | ||
| depth=50, | ||
| groups=32, width_per_group=4, # 32x4d | ||
| out_indices=(2,3), # no conv-1, x-1: stage-x | ||
| style='pytorch'), | ||
| mix_block=dict( | ||
| type='AdaptiveMask', | ||
| in_channel=1024, | ||
| reduction=2, | ||
| lam_concat=True, | ||
| use_scale=True, unsampling_mode='bilinear', | ||
| scale_factor=16, | ||
| frozen=False), | ||
| head_one=dict( | ||
| type='ClsHead', # default CE | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=2048, num_classes=100), | ||
| head_mix=dict( | ||
| type='ClsMixupHead', | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=2048, num_classes=100), | ||
| head_weights=dict( | ||
| head_mix_q=1, head_one_q=1, head_mix_k=1, head_one_k=1), | ||
| ) | ||
|
|
||
| # additional hooks | ||
| custom_hooks = [ | ||
| dict(type='SAVEHook', | ||
| iter_per_epoch=500, | ||
| save_interval=5000, # plot every 500 x 10 ep | ||
| ), | ||
| dict(type='CosineScheduleHook', | ||
| end_momentum=0.99999, | ||
| adjust_scope=[0.1, 1.0], | ||
| warming_up="constant", | ||
| interval=1) | ||
| ] | ||
|
|
||
| # optimizer | ||
| optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005, | ||
| paramwise_options={ | ||
| 'mix_block': dict(lr=0.1, momentum=0.9, weight_decay=0.0005)},) # required parawise_option | ||
| # apex | ||
| use_fp16 = False | ||
| optimizer_config = dict(update_interval=1, grad_clip=None) | ||
|
|
||
| # learning policy | ||
| lr_config = dict(policy='CosineAnnealing', min_lr=0.) | ||
|
|
||
| # additional scheduler | ||
| addtional_scheduler = dict( | ||
| policy='CosineAnnealing', min_lr=0.001, # 0.1 x 1/100 | ||
| paramwise_options=['mix_block'], | ||
| ) | ||
|
|
||
| # runtime settings | ||
| runner = dict(type='EpochBasedRunner', max_epochs=200) |
75 changes: 75 additions & 0 deletions
75
configs/classification/cars/adautomix/basic/r18_adautomix_bilinear.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| _base_ = [ | ||
| '../../../_base_/datasets/cars/sz224_bs16.py', | ||
| '../../../_base_/default_runtime.py', | ||
| ] | ||
|
|
||
| # models settings | ||
| model = dict( | ||
| type='AdAutoMix', | ||
| pretrained='tools/resnet18-pytorch.pth', | ||
| alpha=1.0, | ||
| mix_samples=3, | ||
| is_random=False, | ||
| momentum=0.999, # 0.999 to 0.999999 | ||
| lam_margin=0.03, # degenerate to mixup when | ||
| mixed_radio=0.5, | ||
| beta_radio=0.5, | ||
| debug=True, | ||
| backbone=dict( | ||
| type='ResNet', | ||
| depth=18, | ||
| num_stages=4, | ||
| out_indices=(2,3), | ||
| style='pytorch'), | ||
| mix_block=dict( | ||
| type='AdaptiveMask', | ||
| in_channel=256, | ||
| reduction=2, | ||
| lam_concat=True, | ||
| use_scale=True, unsampling_mode='bilinear', | ||
| scale_factor=16, | ||
| frozen=False), | ||
| head_one=dict( | ||
| type='ClsHead', # default CE | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=512, num_classes=196), | ||
| head_mix=dict( | ||
| type='ClsMixupHead', | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=512, num_classes=196), | ||
| head_weights=dict( | ||
| head_mix_q=1, head_one_q=1, head_mix_k=1, head_one_k=1), | ||
| ) | ||
|
|
||
| # additional hooks | ||
| custom_hooks = [ | ||
| dict(type='SAVEHook', | ||
| iter_per_epoch=500, | ||
| save_interval=5000, # plot every 500 x 10 ep | ||
| ), | ||
| dict(type='CosineScheduleHook', | ||
| end_momentum=0.99999, | ||
| adjust_scope=[0.1, 1.0], | ||
| warming_up="constant", | ||
| interval=1) | ||
| ] | ||
|
|
||
| # optimizer | ||
| optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005, | ||
| paramwise_options={ | ||
| 'mix_block': dict(lr=0.1, momentum=0.9)},) # required parawise_option | ||
| # apex | ||
| use_fp16 = False | ||
| optimizer_config = dict(update_interval=1, grad_clip=None) | ||
|
|
||
| # learning policy | ||
| lr_config = dict(policy='CosineAnnealing', min_lr=0.0) | ||
|
|
||
| # additional scheduler | ||
| addtional_scheduler = dict( | ||
| policy='CosineAnnealing', min_lr=0.001, # 0.1 x 1/100 | ||
| paramwise_options=['mix_block'], | ||
| ) | ||
|
|
||
| # runtime settings | ||
| runner = dict(type='EpochBasedRunner', max_epochs=200) |
76 changes: 76 additions & 0 deletions
76
configs/classification/cars/adautomix/basic/rx50_adautomix_bilinear.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,76 @@ | ||
| _base_ = [ | ||
| '../../../_base_/datasets/cars/sz224_bs16.py', | ||
| '../../../_base_/default_runtime.py', | ||
| ] | ||
|
|
||
| # models settings | ||
| model = dict( | ||
| type='AdAutoMix', | ||
| pretrained='tools/resnext50-pytorch.pth', | ||
| alpha=1.0, | ||
| mix_samples=3, | ||
| is_random=False, | ||
| momentum=0.999, # 0.999 to 0.999999 | ||
| lam_margin=0.03, # degenerate to mixup when | ||
| mixed_radio=0.5, | ||
| beta_radio=0.3, | ||
| debug=True, | ||
| backbone=dict( | ||
| type='ResNeXt', # normal | ||
| depth=50, | ||
| groups=32, width_per_group=4, # 32x4d | ||
| out_indices=(2, 3), # no conv-1, x-1: stage-x | ||
| style='pytorch'), | ||
| mix_block=dict( | ||
| type='AdaptiveMask', | ||
| in_channel=1024, | ||
| reduction=2, | ||
| lam_concat=True, | ||
| use_scale=True, unsampling_mode='bilinear', | ||
| scale_factor=16, | ||
| frozen=False), | ||
| head_one=dict( | ||
| type='ClsHead', # default CE | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=2048, num_classes=196), | ||
| head_mix=dict( | ||
| type='ClsMixupHead', | ||
| loss=dict(type='CrossEntropyLoss', use_soft=False, use_sigmoid=False, loss_weight=1.0), | ||
| with_avg_pool=True, multi_label=False, in_channels=2048, num_classes=196), | ||
| head_weights=dict( | ||
| head_mix_q=1, head_one_q=1, head_mix_k=1, head_one_k=1), | ||
| ) | ||
|
|
||
| # additional hooks | ||
| custom_hooks = [ | ||
| dict(type='SAVEHook', | ||
| iter_per_epoch=500, | ||
| save_interval=5000, # plot every 500 x 10 ep | ||
| ), | ||
| dict(type='CosineScheduleHook', | ||
| end_momentum=0.99999, | ||
| adjust_scope=[0.1, 1.0], | ||
| warming_up="constant", | ||
| interval=1) | ||
| ] | ||
|
|
||
| # optimizer | ||
| optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0005, | ||
| paramwise_options={ | ||
| 'mix_block': dict(lr=0.1, momentum=0.9)},) # required parawise_option | ||
| # apex | ||
| use_fp16 = True | ||
| fp16 = dict(type='mmcv', loss_scale='dynamic') | ||
| optimizer_config = dict(update_interval=1, grad_clip=None) | ||
|
|
||
| # learning policy | ||
| lr_config = dict(policy='CosineAnnealing', min_lr=0.0) | ||
|
|
||
| # additional scheduler | ||
| addtional_scheduler = dict( | ||
| policy='CosineAnnealing', min_lr=0.001, # 0.1 x 1/100 | ||
| paramwise_options=['mix_block'], | ||
| ) | ||
|
|
||
| # runtime settings | ||
| runner = dict(type='EpochBasedRunner', max_epochs=200) |
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