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update: finetune high-resolution models
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184
.../finetune_coco/yolo_world_v2_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,184 @@ | ||
| _base_ = ( | ||
| '../../third_party/mmyolo/configs/yolov8/' | ||
| 'yolov8_l_mask-refine_syncbn_fast_8xb16-500e_coco.py') | ||
| custom_imports = dict( | ||
| imports=['yolo_world'], | ||
| allow_failed_imports=False) | ||
|
|
||
| # hyper-parameters | ||
| num_classes = 80 | ||
| num_training_classes = 80 | ||
| max_epochs = 80 # Maximum training epochs | ||
| close_mosaic_epochs = 10 | ||
| save_epoch_intervals = 5 | ||
| text_channels = 512 | ||
| neck_embed_channels = [128, 256, _base_.last_stage_out_channels // 2] | ||
| neck_num_heads = [4, 8, _base_.last_stage_out_channels // 2 // 32] | ||
| base_lr = 2e-4 | ||
| weight_decay = 0.05 | ||
| train_batch_size_per_gpu = 16 | ||
| load_from = 'pretrained_models/yolo_world_l_clip_t2i_bn_2e-3adamw_32xb16-100e_obj365v1_goldg_cc3mlite_train-ca93cd1f.pth' | ||
| text_model_name = '../pretrained_models/clip-vit-base-patch32-projection' | ||
| # text_model_name = 'openai/clip-vit-base-patch32' | ||
| persistent_workers = False | ||
|
|
||
| # model settings | ||
| model = dict( | ||
| type='YOLOWorldDetector', | ||
| mm_neck=True, | ||
| num_train_classes=num_training_classes, | ||
| num_test_classes=num_classes, | ||
| data_preprocessor=dict(type='YOLOWDetDataPreprocessor'), | ||
| backbone=dict( | ||
| _delete_=True, | ||
| type='MultiModalYOLOBackbone', | ||
| image_model={{_base_.model.backbone}}, | ||
| text_model=dict( | ||
| type='HuggingCLIPLanguageBackbone', | ||
| model_name=text_model_name, | ||
| frozen_modules=['all'])), | ||
| neck=dict(type='YOLOWorldPAFPN', | ||
| guide_channels=text_channels, | ||
| embed_channels=neck_embed_channels, | ||
| num_heads=neck_num_heads, | ||
| block_cfg=dict(type='EfficientCSPLayerWithTwoConv')), | ||
| bbox_head=dict(type='YOLOWorldHead', | ||
| head_module=dict(type='YOLOWorldHeadModule', | ||
| use_bn_head=True, | ||
| embed_dims=text_channels, | ||
| num_classes=num_training_classes)), | ||
| train_cfg=dict(assigner=dict(num_classes=num_training_classes))) | ||
|
|
||
| # dataset settings | ||
| text_transform = [ | ||
| dict(type='RandomLoadText', | ||
| num_neg_samples=(num_classes, num_classes), | ||
| max_num_samples=num_training_classes, | ||
| padding_to_max=True, | ||
| padding_value=''), | ||
| dict(type='mmdet.PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', 'flip', | ||
| 'flip_direction', 'texts')) | ||
| ] | ||
| mosaic_affine_transform = [ | ||
| dict( | ||
| type='MultiModalMosaic', | ||
| img_scale=_base_.img_scale, | ||
| pad_val=114.0, | ||
| pre_transform=_base_.pre_transform), | ||
| dict(type='YOLOv5CopyPaste', prob=_base_.copypaste_prob), | ||
| dict( | ||
| type='YOLOv5RandomAffine', | ||
| max_rotate_degree=0.0, | ||
| max_shear_degree=0.0, | ||
| max_aspect_ratio=100., | ||
| scaling_ratio_range=(1 - _base_.affine_scale, | ||
| 1 + _base_.affine_scale), | ||
| # img_scale is (width, height) | ||
| border=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2), | ||
| border_val=(114, 114, 114), | ||
| min_area_ratio=_base_.min_area_ratio, | ||
| use_mask_refine=_base_.use_mask2refine) | ||
| ] | ||
| train_pipeline = [ | ||
| *_base_.pre_transform, | ||
| *mosaic_affine_transform, | ||
| dict( | ||
| type='YOLOv5MultiModalMixUp', | ||
| prob=_base_.mixup_prob, | ||
| pre_transform=[*_base_.pre_transform, | ||
| *mosaic_affine_transform]), | ||
| *_base_.last_transform[:-1], | ||
| *text_transform | ||
| ] | ||
| train_pipeline_stage2 = [ | ||
| *_base_.train_pipeline_stage2[:-1], | ||
| *text_transform | ||
| ] | ||
| coco_train_dataset = dict( | ||
| _delete_=True, | ||
| type='MultiModalDataset', | ||
| dataset=dict( | ||
| type='YOLOv5CocoDataset', | ||
| data_root='data/coco', | ||
| ann_file='annotations/instances_train2017.json', | ||
| data_prefix=dict(img='train2017/'), | ||
| filter_cfg=dict(filter_empty_gt=False, min_size=32)), | ||
| class_text_path='data/texts/coco_class_texts.json', | ||
| pipeline=train_pipeline) | ||
|
|
||
| train_dataloader = dict( | ||
| persistent_workers=persistent_workers, | ||
| batch_size=train_batch_size_per_gpu, | ||
| collate_fn=dict(type='yolow_collate'), | ||
| dataset=coco_train_dataset) | ||
| test_pipeline = [ | ||
| *_base_.test_pipeline[:-1], | ||
| dict(type='LoadText'), | ||
| dict( | ||
| type='mmdet.PackDetInputs', | ||
| meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape', | ||
| 'scale_factor', 'pad_param', 'texts')) | ||
| ] | ||
| coco_val_dataset = dict( | ||
| _delete_=True, | ||
| type='MultiModalDataset', | ||
| dataset=dict( | ||
| type='YOLOv5CocoDataset', | ||
| data_root='data/coco', | ||
| ann_file='annotations/instances_val2017.json', | ||
| data_prefix=dict(img='val2017/'), | ||
| filter_cfg=dict(filter_empty_gt=False, min_size=32)), | ||
| class_text_path='data/texts/coco_class_texts.json', | ||
| pipeline=test_pipeline) | ||
| val_dataloader = dict(dataset=coco_val_dataset) | ||
| test_dataloader = val_dataloader | ||
| # training settings | ||
| default_hooks = dict( | ||
| param_scheduler=dict( | ||
| scheduler_type='linear', | ||
| lr_factor=0.01, | ||
| max_epochs=max_epochs), | ||
| checkpoint=dict( | ||
| max_keep_ckpts=-1, | ||
| save_best=None, | ||
| interval=save_epoch_intervals)) | ||
| custom_hooks = [ | ||
| dict( | ||
| type='EMAHook', | ||
| ema_type='ExpMomentumEMA', | ||
| momentum=0.0001, | ||
| update_buffers=True, | ||
| strict_load=False, | ||
| priority=49), | ||
| dict( | ||
| type='mmdet.PipelineSwitchHook', | ||
| switch_epoch=max_epochs - close_mosaic_epochs, | ||
| switch_pipeline=train_pipeline_stage2) | ||
| ] | ||
| train_cfg = dict( | ||
| max_epochs=max_epochs, | ||
| val_interval=5, | ||
| dynamic_intervals=[((max_epochs - close_mosaic_epochs), | ||
| _base_.val_interval_stage2)]) | ||
| optim_wrapper = dict( | ||
| optimizer=dict( | ||
| _delete_=True, | ||
| type='AdamW', | ||
| lr=base_lr, | ||
| weight_decay=weight_decay, | ||
| batch_size_per_gpu=train_batch_size_per_gpu), | ||
| paramwise_cfg=dict( | ||
| bias_decay_mult=0.0, | ||
| norm_decay_mult=0.0, | ||
| custom_keys={'backbone.text_model': dict(lr_mult=0.01), | ||
| 'logit_scale': dict(weight_decay=0.0)}), | ||
| constructor='YOLOWv5OptimizerConstructor') | ||
|
|
||
| # evaluation settings | ||
| val_evaluator = dict( | ||
| _delete_=True, | ||
| type='mmdet.CocoMetric', | ||
| proposal_nums=(100, 1, 10), | ||
| ann_file='data/coco/annotations/instances_val2017.json', | ||
| metric='bbox') |
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