| auto_scale_lr = dict(base_batch_size=96) |
| custom_hooks = [ |
| dict(momentum=0.0001, priority='ABOVE_NORMAL', type='EMAHook'), |
| ] |
| data_preprocessor = dict( |
| mean=[ |
| 123.675, |
| 116.28, |
| 103.53, |
| ], |
| num_classes=2, |
| std=[ |
| 58.395, |
| 57.12, |
| 57.375, |
| ], |
| to_rgb=True) |
| dataset_type = 'CustomDataset' |
| default_hooks = dict( |
| checkpoint=dict(interval=2, type='CheckpointHook'), |
| logger=dict(interval=100, type='LoggerHook'), |
| param_scheduler=dict(type='ParamSchedulerHook'), |
| sampler_seed=dict(type='DistSamplerSeedHook'), |
| timer=dict(type='IterTimerHook'), |
| visualization=dict( |
| enable=True, |
| interval=1, |
| out_dir=None, |
| type='VisualizationHook', |
| wait_time=2)) |
| default_scope = 'mmpretrain' |
| env_cfg = dict( |
| cudnn_benchmark=False, |
| dist_cfg=dict(backend='nccl'), |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0)) |
| launcher = 'none' |
| load_from = './ConvNeXt_v2-v2_ep90.pth' |
| log_level = 'INFO' |
| model = dict( |
| backbone=dict( |
| arch='tiny', |
| drop_path_rate=0.5, |
| layer_scale_init_value=0.0, |
| type='ConvNeXt', |
| use_grn=True), |
| head=dict( |
| in_channels=768, |
| init_cfg=None, |
| loss=dict(label_smooth_val=0.2, type='LabelSmoothLoss'), |
| num_classes=2, |
| type='LinearClsHead'), |
| init_cfg=dict( |
| bias=0.0, layer=[ |
| 'Conv2d', |
| 'Linear', |
| ], std=0.02, type='TruncNormal'), |
| train_cfg=dict(augments=[ |
| dict(alpha=0.8, type='Mixup'), |
| dict(alpha=1.0, type='CutMix'), |
| ]), |
| type='ImageClassifier') |
| optim_wrapper = dict( |
| accumulative_counts=3, |
| clip_grad=None, |
| loss_scale='dynamic', |
| optimizer=dict( |
| betas=( |
| 0.9, |
| 0.999, |
| ), |
| eps=1e-08, |
| lr=0.00032, |
| type='AdamW', |
| weight_decay=0.05), |
| paramwise_cfg=dict( |
| 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) |
| }), |
| flat_decay_mult=0.0, |
| norm_decay_mult=0.0), |
| type='AmpOptimWrapper') |
| param_scheduler = [ |
| dict( |
| by_epoch=True, |
| convert_to_iter_based=True, |
| end=2, |
| start_factor=0.001, |
| type='LinearLR'), |
| dict(begin=2, by_epoch=True, eta_min=8e-05, type='CosineAnnealingLR'), |
| ] |
| randomness = dict(deterministic=False, seed=None) |
| resume = False |
| test_cfg = dict() |
| test_dataloader = dict( |
| batch_size=16, |
| collate_fn=dict(type='default_collate'), |
| dataset=dict( |
| data_root='./testimgs', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict( |
| backend='pillow', |
| interpolation='bicubic', |
| scale=384, |
| type='Resize'), |
| dict(type='PackInputs'), |
| ], |
| type='CustomDataset'), |
| num_workers=5, |
| persistent_workers=True, |
| pin_memory=True, |
| sampler=dict(shuffle=False, type='DefaultSampler')) |
| test_evaluator = dict(topk=(1, ), type='Accuracy') |
| test_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict(backend='pillow', interpolation='bicubic', scale=384, type='Resize'), |
| dict(type='PackInputs'), |
| ] |
| train_cfg = dict(by_epoch=True, max_epochs=120, val_interval=1) |
| train_dataloader = dict( |
| batch_size=32, |
| collate_fn=dict(type='default_collate'), |
| dataset=dict( |
| data_root='./procset', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict( |
| backend='pillow', |
| interpolation='bicubic', |
| scale=384, |
| type='RandomResizedCrop'), |
| dict(direction='horizontal', prob=0.5, type='RandomFlip'), |
| dict(type='PackInputs'), |
| ], |
| type='CustomDataset'), |
| num_workers=5, |
| persistent_workers=True, |
| pin_memory=True, |
| sampler=dict(shuffle=True, type='DefaultSampler')) |
| train_pipeline = [ |
| dict(type='LoadImageFromFile'), |
| dict( |
| backend='pillow', |
| interpolation='bicubic', |
| scale=384, |
| type='RandomResizedCrop'), |
| dict(direction='horizontal', prob=0.5, type='RandomFlip'), |
| dict(type='PackInputs'), |
| ] |
| val_cfg = dict() |
| val_dataloader = dict( |
| batch_size=16, |
| collate_fn=dict(type='default_collate'), |
| dataset=dict( |
| data_root='./valset', |
| pipeline=[ |
| dict(type='LoadImageFromFile'), |
| dict( |
| backend='pillow', |
| interpolation='bicubic', |
| scale=384, |
| type='Resize'), |
| dict(type='PackInputs'), |
| ], |
| type='CustomDataset'), |
| num_workers=5, |
| persistent_workers=True, |
| pin_memory=True, |
| sampler=dict(shuffle=False, type='DefaultSampler')) |
| val_evaluator = dict(topk=(1, ), type='Accuracy') |
| vis_backends = [ |
| dict(type='LocalVisBackend'), |
| ] |
| visualizer = dict( |
| type='UniversalVisualizer', vis_backends=[ |
| dict(type='LocalVisBackend'), |
| ]) |
| work_dir = './work_dirs\\convnext-v2-tiny_32xb32_in1k-384px' |
|
|
