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_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_base_dual_vlpan_2e-3adamw_32xb16_100e_o365_goldg_train_pretrained-0e566235.pth' | |
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='openai/clip-vit-base-patch32', | |
frozen_modules=['all'])), | |
neck=dict(type='YOLOWolrdDualPAFPN', | |
guide_channels=text_channels, | |
embed_channels=neck_embed_channels, | |
num_heads=neck_num_heads, | |
block_cfg=dict(type='MaxSigmoidCSPLayerWithTwoConv'), | |
text_enhancder=dict(type='ImagePoolingAttentionModule', | |
embed_channels=256, | |
num_heads=8)), | |
bbox_head=dict(type='YOLOWorldHead', | |
head_module=dict(type='YOLOWorldHeadModule', | |
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') | |