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199
...in/yolo_world_v2_x_vlpan_bn_2e-3_100e_4x8gpus_obj365v1_goldg_train_1280ft_lvis_minival.py
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,199 @@ | ||
| _base_ = ('../../third_party/mmyolo/configs/yolov8/' | ||
| 'yolov8_x_syncbn_fast_8xb16-500e_coco.py') | ||
| custom_imports = dict(imports=['yolo_world'], | ||
| allow_failed_imports=False) | ||
|
|
||
| # hyper-parameters | ||
| num_classes = 1203 | ||
| num_training_classes = 80 | ||
| max_epochs = 100 # Maximum training epochs | ||
| close_mosaic_epochs = 2 | ||
| save_epoch_intervals = 2 | ||
| 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-3 | ||
| weight_decay = 0.05 / 2 | ||
| train_batch_size_per_gpu = 16 | ||
| text_model_name = '../pretrained_models/clip-vit-base-patch32-projection' | ||
| # text_model_name = 'openai/clip-vit-base-patch32' | ||
| img_scale = (1280, 1280) | ||
|
|
||
| # 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='MaxSigmoidCSPLayerWithTwoConv')), | ||
| 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')) | ||
| ] | ||
| train_pipeline = [ | ||
| *_base_.pre_transform, | ||
| dict(type='MultiModalMosaic', | ||
| img_scale=img_scale, | ||
| pad_val=114.0, | ||
| pre_transform=_base_.pre_transform), | ||
| dict( | ||
| type='YOLOv5RandomAffine', | ||
| max_rotate_degree=0.0, | ||
| max_shear_degree=0.0, | ||
| scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale), | ||
| max_aspect_ratio=_base_.max_aspect_ratio, | ||
| border=(-img_scale[0] // 2, -img_scale[1] // 2), | ||
| border_val=(114, 114, 114)), | ||
| *_base_.last_transform[:-1], | ||
| *text_transform, | ||
| ] | ||
| train_pipeline_stage2 = [ | ||
| *_base_.pre_transform, | ||
| dict(type='YOLOv5KeepRatioResize', scale=img_scale), | ||
| dict( | ||
| type='LetterResize', | ||
| scale=img_scale, | ||
| allow_scale_up=True, | ||
| pad_val=dict(img=114.0)), | ||
| dict( | ||
| type='YOLOv5RandomAffine', | ||
| max_rotate_degree=0.0, | ||
| max_shear_degree=0.0, | ||
| scaling_ratio_range=(1 - _base_.affine_scale, 1 + _base_.affine_scale), | ||
| max_aspect_ratio=_base_.max_aspect_ratio, | ||
| border_val=(114, 114, 114)), | ||
| *_base_.last_transform[:-1], | ||
| *text_transform | ||
| ] | ||
|
|
||
| obj365v1_train_dataset = dict( | ||
| type='MultiModalDataset', | ||
| dataset=dict( | ||
| type='YOLOv5Objects365V1Dataset', | ||
| data_root='data/objects365v1/', | ||
| ann_file='annotations/objects365_train.json', | ||
| data_prefix=dict(img='train/'), | ||
| filter_cfg=dict(filter_empty_gt=False, min_size=32)), | ||
| class_text_path='data/texts/obj365v1_class_texts.json', | ||
| pipeline=train_pipeline) | ||
|
|
||
| mg_train_dataset = dict(type='YOLOv5MixedGroundingDataset', | ||
| data_root='data/mixed_grounding/', | ||
| ann_file='annotations/final_mixed_train_no_coco.json', | ||
| data_prefix=dict(img='gqa/images/'), | ||
| filter_cfg=dict(filter_empty_gt=False, min_size=32), | ||
| pipeline=train_pipeline) | ||
|
|
||
| flickr_train_dataset = dict( | ||
| type='YOLOv5MixedGroundingDataset', | ||
| data_root='data/flickr/', | ||
| ann_file='annotations/final_flickr_separateGT_train.json', | ||
| data_prefix=dict(img='full_images/'), | ||
| filter_cfg=dict(filter_empty_gt=True, min_size=32), | ||
| pipeline=train_pipeline) | ||
|
|
||
| train_dataloader = dict(batch_size=train_batch_size_per_gpu, | ||
| collate_fn=dict(type='yolow_collate'), | ||
| dataset=dict(_delete_=True, | ||
| type='ConcatDataset', | ||
| datasets=[ | ||
| obj365v1_train_dataset, | ||
| flickr_train_dataset, mg_train_dataset | ||
| ], | ||
| ignore_keys=['classes', 'palette'])) | ||
|
|
||
| test_pipeline = [ | ||
| dict(type='LoadImageFromFile'), | ||
| dict(type='YOLOv5KeepRatioResize', scale=img_scale), | ||
| dict( | ||
| type='LetterResize', | ||
| scale=img_scale, | ||
| allow_scale_up=False, | ||
| pad_val=dict(img=114)), | ||
| dict(type='LoadAnnotations', with_bbox=True, _scope_='mmdet'), | ||
| 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='YOLOv5LVISV1Dataset', | ||
| data_root='data/coco/', | ||
| test_mode=True, | ||
| ann_file='lvis/lvis_v1_minival_inserted_image_name.json', | ||
| data_prefix=dict(img=''), | ||
| batch_shapes_cfg=None), | ||
| class_text_path='data/texts/lvis_v1_class_texts.json', | ||
| pipeline=test_pipeline) | ||
| val_dataloader = dict(dataset=coco_val_dataset) | ||
| test_dataloader = val_dataloader | ||
|
|
||
| val_evaluator = dict(type='mmdet.LVISMetric', | ||
| ann_file='data/coco/lvis/lvis_v1_minival_inserted_image_name.json', | ||
| metric='bbox') | ||
| test_evaluator = val_evaluator | ||
|
|
||
| # training settings | ||
| default_hooks = dict(param_scheduler=dict(max_epochs=max_epochs), | ||
| checkpoint=dict(interval=save_epoch_intervals, | ||
| rule='greater')) | ||
| 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=10, | ||
| 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') |
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