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| _base_ = './yolox_s_fast_8xb32-300e-rtmdet-hyp_coco.py' | |
| data_root = './data/cat/' | |
| class_name = ('cat', ) | |
| num_classes = len(class_name) | |
| metainfo = dict(classes=class_name, palette=[(20, 220, 60)]) | |
| num_last_epochs = 5 | |
| max_epochs = 40 | |
| train_batch_size_per_gpu = 12 | |
| train_num_workers = 4 | |
| load_from = 'https://download.openmmlab.com/mmyolo/v0/yolox/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco/yolox_s_fast_8xb32-300e-rtmdet-hyp_coco_20230210_134645-3a8dfbd7.pth' # noqa | |
| model = dict( | |
| backbone=dict(frozen_stages=4), | |
| bbox_head=dict(head_module=dict(num_classes=num_classes))) | |
| train_dataloader = dict( | |
| batch_size=train_batch_size_per_gpu, | |
| num_workers=train_num_workers, | |
| dataset=dict( | |
| data_root=data_root, | |
| metainfo=metainfo, | |
| ann_file='annotations/trainval.json', | |
| data_prefix=dict(img='images/'))) | |
| val_dataloader = dict( | |
| dataset=dict( | |
| metainfo=metainfo, | |
| data_root=data_root, | |
| ann_file='annotations/test.json', | |
| data_prefix=dict(img='images/'))) | |
| test_dataloader = val_dataloader | |
| param_scheduler = [ | |
| dict( | |
| # use quadratic formula to warm up 3 epochs | |
| # and lr is updated by iteration | |
| # TODO: fix default scope in get function | |
| type='mmdet.QuadraticWarmupLR', | |
| by_epoch=True, | |
| begin=0, | |
| end=3, | |
| convert_to_iter_based=True), | |
| dict( | |
| # use cosine lr from 5 to 35 epoch | |
| type='CosineAnnealingLR', | |
| eta_min=_base_.base_lr * 0.05, | |
| begin=5, | |
| T_max=max_epochs - num_last_epochs, | |
| end=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=max_epochs - num_last_epochs, | |
| end=max_epochs, | |
| ) | |
| ] | |
| _base_.custom_hooks[0].num_last_epochs = num_last_epochs | |
| val_evaluator = dict(ann_file=data_root + 'annotations/test.json') | |
| test_evaluator = val_evaluator | |
| default_hooks = dict( | |
| checkpoint=dict(interval=10, max_keep_ckpts=2, save_best='auto'), | |
| logger=dict(type='LoggerHook', interval=5)) | |
| train_cfg = dict(max_epochs=max_epochs, val_interval=10) | |
| # visualizer = dict(vis_backends = [dict(type='LocalVisBackend'), dict(type='WandbVisBackend')]) # noqa | |