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_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
deepen_factor = 1.0
widen_factor = 1.5
# model settings
image_backbone = _base_.model.backbone
image_backbone.update(
deepen_factor=deepen_factor,
widen_factor=widen_factor
)
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=image_backbone,
text_model=dict(
type='HuggingCLIPLanguageBackbone',
model_name='openai/clip-vit-base-patch32',
frozen_modules=['all'])),
neck=dict(type='YOLOWorldPAFPN',
deepen_factor=deepen_factor,
widen_factor=widen_factor,
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',
widen_factor=widen_factor,
embed_dims=text_channels,
use_bn_head=True,
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=_base_.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=(-_base_.img_scale[0] // 2, -_base_.img_scale[1] // 2),
border_val=(114, 114, 114)),
*_base_.last_transform[:-1],
*text_transform,
]
train_pipeline_stage2 = [*_base_.train_pipeline_stage2[:-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/captions/obj365v1_class_captions.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='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 = [
*_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='YOLOv5LVISV1Dataset',
data_root='data/lvis/',
test_mode=True,
ann_file='annotations/'
'lvis_v1_minival_inserted_image_name.json',
data_prefix=dict(img=''),
batch_shapes_cfg=None),
class_text_path='data/captions/lvis_v1_class_captions.json',
pipeline=test_pipeline)
val_dataloader = dict(dataset=coco_val_dataset)
test_dataloader = val_dataloader
val_evaluator = dict(
type='mmdet.LVISMetric',
ann_file='data/lvis/annotations/'
'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') |