-
Notifications
You must be signed in to change notification settings - Fork 426
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
update: finetune high-resolution models
- Loading branch information
1 parent
c960c05
commit 31f85f8
Showing
26 changed files
with
1,787 additions
and
70 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
184 changes: 184 additions & 0 deletions
184
.../finetune_coco/yolo_world_v2_l_efficient_neck_2e-4_80e_8gpus_mask-refine_finetune_coco.py
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| 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') |
Oops, something went wrong.