rand_increasing_policies = [ dict(type='AutoContrast'), dict(type='Equalize'), dict(type='Invert'), dict(type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)), dict(type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)), dict(type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)), dict( type='SolarizeAdd', magnitude_key='magnitude', magnitude_range=(0, 110)), dict( type='ColorTransform', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict(type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Brightness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Shear', magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='horizontal'), dict( type='Shear', magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='vertical'), dict( type='Translate', magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='horizontal'), dict( type='Translate', magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='vertical') ] dataset_type = 'ImageNet' img_norm_cfg = dict( mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) train_pipeline = [ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', size=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='RandAugment', policies=[ dict(type='AutoContrast'), dict(type='Equalize'), dict(type='Invert'), dict( type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)), dict( type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)), dict( type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)), dict( type='SolarizeAdd', magnitude_key='magnitude', magnitude_range=(0, 110)), dict( type='ColorTransform', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Brightness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Shear', magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='horizontal'), dict( type='Shear', magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='vertical'), dict( type='Translate', magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='horizontal'), dict( type='Translate', magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='vertical') ], 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='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ] test_pipeline = [ dict(type='LoadImageFromFile'), dict( type='Resize', size=(256, -1), backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ] data = dict( samples_per_gpu=32, workers_per_gpu=16, train=dict( type='ImageNet', data_prefix='/data/oss_bucket_0/', pipeline=[ dict(type='LoadImageFromFile'), dict( type='RandomResizedCrop', size=224, backend='pillow', interpolation='bicubic'), dict(type='RandomFlip', flip_prob=0.5, direction='horizontal'), dict( type='RandAugment', policies=[ dict(type='AutoContrast'), dict(type='Equalize'), dict(type='Invert'), dict( type='Rotate', magnitude_key='angle', magnitude_range=(0, 30)), dict( type='Posterize', magnitude_key='bits', magnitude_range=(4, 0)), dict( type='Solarize', magnitude_key='thr', magnitude_range=(256, 0)), dict( type='SolarizeAdd', magnitude_key='magnitude', magnitude_range=(0, 110)), dict( type='ColorTransform', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Contrast', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Brightness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Sharpness', magnitude_key='magnitude', magnitude_range=(0, 0.9)), dict( type='Shear', magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='horizontal'), dict( type='Shear', magnitude_key='magnitude', magnitude_range=(0, 0.3), direction='vertical'), dict( type='Translate', magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='horizontal'), dict( type='Translate', magnitude_key='magnitude', magnitude_range=(0, 0.45), direction='vertical') ], 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='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='ToTensor', keys=['gt_label']), dict(type='Collect', keys=['img', 'gt_label']) ], ann_file='/data/oss_bucket_0/virgo_data/dailytags/train_mmcls.txt', classes='/data/oss_bucket_0/virgo_data/dailytags/classname.txt'), val=dict( type='ImageNet', data_prefix='/data/oss_bucket_0/', ann_file='/data/oss_bucket_0/virgo_data/dailytags/val_mmcls.txt', pipeline=[ dict(type='LoadImageFromFile'), dict( type='Resize', size=(256, -1), backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ], classes='/data/oss_bucket_0/virgo_data/dailytags/classname.txt'), test=dict( type='ImageNet', data_prefix='/data/oss_bucket_0/', ann_file='/data/oss_bucket_0/virgo_data/dailytags/val_mmcls.txt', pipeline=[ dict(type='LoadImageFromFile'), dict( type='Resize', size=(256, -1), backend='pillow', interpolation='bicubic'), dict(type='CenterCrop', crop_size=224), dict( type='Normalize', mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True), dict(type='ImageToTensor', keys=['img']), dict(type='Collect', keys=['img']) ], classes='/data/oss_bucket_0/virgo_data/dailytags/classname.txt')) evaluation = dict(interval=2, metric='accuracy', save_best='auto') paramwise_cfg = dict( norm_decay_mult=0.0, bias_decay_mult=0.0, custom_keys=dict({ '.absolute_pos_embed': dict(decay_mult=0.0), '.relative_position_bias_table': dict(decay_mult=0.0), '.cls_token': dict(decay_mult=0.0), '.pos_embed': dict(decay_mult=0.0) })) optimizer = dict( type='AdamW', lr=0.001, weight_decay=0.1, eps=1e-08, betas=(0.9, 0.999), paramwise_cfg=dict( norm_decay_mult=0.0, bias_decay_mult=0.0, custom_keys=dict({ '.absolute_pos_embed': dict(decay_mult=0.0), '.relative_position_bias_table': dict(decay_mult=0.0), '.cls_token': dict(decay_mult=0.0), '.pos_embed': dict(decay_mult=0.0) }))) optimizer_config = dict(grad_clip=dict(max_norm=5.0)) lr_config = dict( policy='CosineAnnealing', by_epoch=False, min_lr_ratio=0.01, warmup='linear', warmup_ratio=0.001, warmup_iters=20, warmup_by_epoch=True) runner = dict(type='EpochBasedRunner', max_epochs=300) checkpoint_config = dict(interval=1, max_keep_ckpts=20, create_symlink=False) log_config = dict(interval=100, hooks=[dict(type='TextLoggerHook')]) dist_params = dict(backend='nccl') log_level = 'INFO' load_from = None resume_from = '/data/oss_bucket_1/emian/VisualContentRecognition/mmlab/work_dirs/dailytags_deit-base_pt-16xb64_in1k_gzn2_pai_wd0.1/epoch_296.pth' workflow = [('train', 1)] model = dict( type='ImageClassifier', backbone=dict( type='VisionTransformer', arch='deit-base', img_size=224, patch_size=16, drop_path_rate=0.1), neck=None, head=dict( type='VisionTransformerClsHead', num_classes=1296, in_channels=768, loss=dict( type='LabelSmoothLoss', label_smooth_val=0.1, mode='original')), init_cfg=[ dict(type='TruncNormal', layer='Linear', std=0.02), dict(type='Constant', layer='LayerNorm', val=1.0, bias=0.0) ], train_cfg=dict(augments=[ dict(type='BatchMixup', alpha=0.8, num_classes=1296, prob=0.5), dict(type='BatchCutMix', alpha=1.0, num_classes=1296, prob=0.5) ])) custom_hooks = [dict(type='EMAHook', momentum=4e-05, priority='ABOVE_NORMAL')] work_dir = '/data/oss_bucket_1/emian/VisualContentRecognition/mmlab/work_dirs/dailytags_deit-base_pt-16xb64_in1k_gzn2_pai_wd0.1/' gpu_ids = range(0, 32)