Sentry_image_models
/
convnext_small_4xb256_IF_1m_lr4e-4_aug_5e-1
/convnext_small_4xb256_IF_1m_lr4e-4_aug_5e-1.py
| optim_wrapper = dict( | |
| optimizer=dict( | |
| type='AdamW', | |
| lr=0.0004, | |
| weight_decay=0.05, | |
| eps=1e-08, | |
| betas=(0.9, 0.999), | |
| _scope_='mmpretrain'), | |
| paramwise_cfg=dict( | |
| norm_decay_mult=0.0, | |
| bias_decay_mult=0.0, | |
| flat_decay_mult=0.0, | |
| custom_keys=dict({ | |
| '.absolute_pos_embed': dict(decay_mult=0.0), | |
| '.relative_position_bias_table': dict(decay_mult=0.0) | |
| })), | |
| type='AmpOptimWrapper', | |
| dtype='bfloat16', | |
| clip_grad=None) | |
| param_scheduler = [ | |
| dict(type='CosineAnnealingLR', eta_min=1e-05, by_epoch=False, begin=0) | |
| ] | |
| train_cfg = dict(by_epoch=True, max_epochs=10, val_interval=1) | |
| val_cfg = dict() | |
| test_cfg = dict() | |
| auto_scale_lr = dict(base_batch_size=4096) | |
| model = dict( | |
| type='ImageClassifier', | |
| backbone=dict( | |
| frozen_stages=2, | |
| type='ConvNeXt', | |
| arch='small', | |
| drop_path_rate=0.4, | |
| init_cfg=dict( | |
| type='Pretrained', | |
| checkpoint= | |
| 'https://download.openmmlab.com/mmclassification/v0/convnext/convnext-small_in21k-pre_3rdparty_in1k_20221219-aeca4c93.pth', | |
| prefix='backbone')), | |
| head=dict( | |
| type='LinearClsHead', | |
| num_classes=2, | |
| in_channels=768, | |
| loss=dict(type='CrossEntropyLoss', loss_weight=1.0), | |
| init_cfg=None), | |
| init_cfg=dict( | |
| type='TruncNormal', layer=['Conv2d', 'Linear'], std=0.02, bias=0.0), | |
| train_cfg=None) | |
| dataset_type = 'CustomDataset' | |
| data_preprocessor = dict( | |
| num_classes=2, | |
| mean=[123.675, 116.28, 103.53], | |
| std=[58.395, 57.12, 57.375], | |
| to_rgb=True) | |
| bgr_mean = [103.53, 116.28, 123.675] | |
| bgr_std = [57.375, 57.12, 58.395] | |
| train_pipeline = [ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| 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( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/IF80w.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/if-dpmsolver++-50-20w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/train/cc1m.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]) | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=True)) | |
| val_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/if-dpmsolver++-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]) | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=False)) | |
| val_evaluator = [ | |
| dict(type='Accuracy', topk=1), | |
| dict(type='SingleLabelMetric', average=None) | |
| ] | |
| test_dataloader = dict( | |
| pin_memory=True, | |
| persistent_workers=True, | |
| collate_fn=dict(type='default_collate'), | |
| batch_size=256, | |
| num_workers=10, | |
| dataset=dict( | |
| type='ConcatDataset', | |
| datasets=[ | |
| dict( | |
| type='CustomDataset', | |
| data_root='/mnt/petrelfs/luzeyu/workspace/fakebench/dataset', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/if-dpmsolver++-25-1w.tsv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]), | |
| dict( | |
| type='CustomDataset', | |
| data_root='', | |
| ann_file= | |
| '/mnt/petrelfs/luzeyu/workspace/fakebench/dataset/meta/val/cc1w.csv', | |
| pipeline=[ | |
| dict(type='LoadImageFromFile'), | |
| dict( | |
| type='RandomResizedCrop', | |
| scale=224, | |
| backend='pillow', | |
| interpolation='bicubic'), | |
| dict(type='RandomFlip', prob=0.5, direction='horizontal'), | |
| dict(type='JPEG', compress_val=65, prob=0.5), | |
| dict(type='GaussianBlur', radius=1.5, prob=0.5), | |
| dict(type='PackInputs') | |
| ]) | |
| ]), | |
| sampler=dict(type='DefaultSampler', shuffle=False)) | |
| test_evaluator = [ | |
| dict(type='Accuracy', topk=1), | |
| dict(type='SingleLabelMetric', average=None) | |
| ] | |
| custom_hooks = [dict(type='EMAHook', momentum=0.0001, priority='ABOVE_NORMAL')] | |
| default_scope = 'mmpretrain' | |
| default_hooks = dict( | |
| timer=dict(type='IterTimerHook'), | |
| logger=dict(type='LoggerHook', interval=100), | |
| param_scheduler=dict(type='ParamSchedulerHook'), | |
| checkpoint=dict(type='CheckpointHook', interval=1), | |
| sampler_seed=dict(type='DistSamplerSeedHook'), | |
| visualization=dict(type='VisualizationHook', enable=True)) | |
| env_cfg = dict( | |
| cudnn_benchmark=True, | |
| mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0), | |
| dist_cfg=dict(backend='nccl')) | |
| vis_backends = [dict(type='LocalVisBackend')] | |
| visualizer = dict( | |
| type='UniversalVisualizer', | |
| vis_backends=[ | |
| dict(type='LocalVisBackend'), | |
| dict(type='TensorboardVisBackend') | |
| ]) | |
| log_level = 'INFO' | |
| load_from = None | |
| resume = False | |
| randomness = dict(seed=None, deterministic=False) | |
| launcher = 'slurm' | |
| work_dir = 'workdir/convnext_small_4xb256_IF_1m_lr4e-4_aug_5e-1' | |