_base_ = [ '../../_base_/models/swin_transformer/base_224.py', '../../_base_/datasets/imagenet_bs256_swin_192.py', '../../_base_/default_runtime.py' ] # dataset settings train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', scale=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', prob=0.5, direction='horizontal'), dict( type='RandAugment', policies='timm_increasing', num_policies=2, total_level=10, magnitude_level=9, magnitude_std=0.5, hparams=dict(pad_val=[104, 116, 124], interpolation='bicubic')), dict( type='RandomErasing', erase_prob=0.25, mode='rand', min_area_ratio=0.02, max_area_ratio=0.3333333333333333, fill_color=[103.53, 116.28, 123.675], fill_std=[57.375, 57.12, 58.395]), dict(type='PackInputs') ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='ResizeEdge', scale=256, edge='short', backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict(type='PackInputs') ] train_dataloader = dict(dataset=dict(pipeline=train_pipeline)) val_dataloader = dict(dataset=dict(pipeline=test_pipeline)) test_dataloader = val_dataloader # model settings model = dict( backbone=dict( img_size=224, drop_path_rate=0.1, stage_cfgs=dict(block_cfgs=dict(window_size=7)), init_cfg=dict(type='Pretrained', checkpoint='', prefix='backbone.'))) # optimizer settings optim_wrapper = dict( type='AmpOptimWrapper', optimizer=dict(type='AdamW', lr=5e-3, weight_decay=0.05), clip_grad=dict(max_norm=5.0), constructor='LearningRateDecayOptimWrapperConstructor', paramwise_cfg=dict( layer_decay_rate=0.9, custom_keys={ '.norm': dict(decay_mult=0.0), '.bias': dict(decay_mult=0.0), '.absolute_pos_embed': dict(decay_mult=0.0), '.relative_position_bias_table': dict(decay_mult=0.0) })) # learning rate scheduler param_scheduler = [ dict( type='LinearLR', start_factor=2.5e-7 / 1.25e-3, by_epoch=True, begin=0, end=20, convert_to_iter_based=True), dict( type='CosineAnnealingLR', T_max=80, eta_min=2.5e-7 * 2048 / 512, by_epoch=True, begin=20, end=100, convert_to_iter_based=True) ] # runtime settings train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=100) val_cfg = dict() test_cfg = dict() default_hooks = dict( # save checkpoint per epoch. checkpoint=dict(type='CheckpointHook', interval=1, max_keep_ckpts=3), logger=dict(type='LoggerHook', interval=100)) randomness = dict(seed=0)