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| _base_ = './yolox_s_fast_8xb8-300e_coco.py' | |
| # ========================modified parameters====================== | |
| # Batch size of a single GPU during training | |
| # 8 -> 32 | |
| train_batch_size_per_gpu = 32 | |
| # Multi-scale training intervals | |
| # 10 -> 1 | |
| batch_augments_interval = 1 | |
| # Last epoch number to switch training pipeline | |
| # 15 -> 20 | |
| num_last_epochs = 20 | |
| # Base learning rate for optim_wrapper. Corresponding to 8xb32=256 bs | |
| base_lr = 0.004 | |
| # SGD -> AdamW | |
| optim_wrapper = dict( | |
| _delete_=True, | |
| type='OptimWrapper', | |
| optimizer=dict(type='AdamW', lr=base_lr, weight_decay=0.05), | |
| paramwise_cfg=dict( | |
| norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True)) | |
| # 0.0001 -> 0.0002 | |
| ema_momentum = 0.0002 | |
| # ============================== Unmodified in most cases =================== | |
| model = dict( | |
| data_preprocessor=dict(batch_augments=[ | |
| dict( | |
| type='YOLOXBatchSyncRandomResize', | |
| random_size_range=(480, 800), | |
| size_divisor=32, | |
| interval=batch_augments_interval) | |
| ])) | |
| param_scheduler = [ | |
| dict( | |
| # use quadratic formula to warm up 5 epochs | |
| # and lr is updated by iteration | |
| # TODO: fix default scope in get function | |
| type='mmdet.QuadraticWarmupLR', | |
| by_epoch=True, | |
| begin=0, | |
| end=5, | |
| convert_to_iter_based=True), | |
| dict( | |
| # use cosine lr from 5 to 285 epoch | |
| type='CosineAnnealingLR', | |
| eta_min=base_lr * 0.05, | |
| begin=5, | |
| T_max=_base_.max_epochs - num_last_epochs, | |
| end=_base_.max_epochs - num_last_epochs, | |
| by_epoch=True, | |
| convert_to_iter_based=True), | |
| dict( | |
| # use fixed lr during last num_last_epochs epochs | |
| type='ConstantLR', | |
| by_epoch=True, | |
| factor=1, | |
| begin=_base_.max_epochs - num_last_epochs, | |
| end=_base_.max_epochs, | |
| ) | |
| ] | |
| custom_hooks = [ | |
| dict( | |
| type='YOLOXModeSwitchHook', | |
| num_last_epochs=num_last_epochs, | |
| new_train_pipeline=_base_.train_pipeline_stage2, | |
| priority=48), | |
| dict(type='mmdet.SyncNormHook', priority=48), | |
| dict( | |
| type='EMAHook', | |
| ema_type='ExpMomentumEMA', | |
| momentum=ema_momentum, | |
| update_buffers=True, | |
| strict_load=False, | |
| priority=49) | |
| ] | |
| train_dataloader = dict(batch_size=train_batch_size_per_gpu) | |
| train_cfg = dict(dynamic_intervals=[(_base_.max_epochs - num_last_epochs, 1)]) | |
| auto_scale_lr = dict(base_batch_size=8 * train_batch_size_per_gpu) | |