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dino_4scale_uniperceiver_adapter_large_6ep_gqa.py
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_base_ = [
'_base_/datasets/grounding_gqa.py',
'_base_/default_runtime.py'
]
# https://github.com/czczup/ViT-Adapter/releases/download/wsdm2023/uni-perceiver-large-L24-H1024-224size-pretrained_converted.pth
pretrained = 'pretrained/uni-perceiver-large-L24-H1024-224size-pretrained_converted.pth'
model = dict(
type='GroundingDINO',
with_aux_loss=True,
backbone=dict(
type='UniPerceiverAdapter',
patch_size=16,
embed_dim=1024,
depth=24,
num_heads=16,
mlp_ratio=4,
drop_path_rate=0.3,
conv_inplane=64,
n_points=4,
deform_num_heads=16,
cffn_ratio=0.25,
deform_ratio=0.5,
num_classes=1,
with_cp=True,
out_indices=(1, 2, 3),
num_cross_attn=0,
interaction_indexes=[[0, 5], [6, 11], [12, 17], [18, 23]],
window_attn=[False] * 24,
window_size=[None] * 24,
pretrained=pretrained),
neck=dict(
type='ChannelMapper',
in_channels=[1024, 1024, 1024],
kernel_size=1,
out_channels=256,
act_cfg=None,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
bbox_head=dict(
type='DINOHead',
num_query=100,
num_classes=1,
in_channels=2048, # TODO
sync_cls_avg_factor=True,
as_two_stage=True,
with_box_refine=True,
dn_cfg=dict(
type='CdnQueryGenerator',
noise_scale=dict(label=0.5, box=1.0), # 0.5, 0.4 for DN-DETR
group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100)),
transformer=dict(
type='DinoTransformer',
two_stage_num_proposals=100,
encoder=dict(
type='DetrTransformerEncoder',
num_layers=6,
transformerlayers=dict(
type='BaseTransformerLayer',
attn_cfgs=dict(
type='MultiScaleDeformableAttention',
embed_dims=256,
dropout=0.0), # 0.1 for DeformDETR
feedforward_channels=2048, # 1024 for DeformDETR
ffn_cfgs=dict(
type='FFN',
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.,
use_checkpoint=True,
act_cfg=dict(type='ReLU', inplace=True)),
ffn_dropout=0.0, # 0.1 for DeformDETR
operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
decoder=dict(
type='DinoTransformerDecoder',
num_layers=6,
return_intermediate=True,
transformerlayers=dict(
type='DetrTransformerDecoderLayer',
attn_cfgs=[
dict(
type='MultiheadAttention',
embed_dims=256,
num_heads=8,
dropout=0.0), # 0.1 for DeformDETR
dict(
type='MultiScaleDeformableAttention',
embed_dims=256,
dropout=0.0), # 0.1 for DeformDETR
],
feedforward_channels=2048, # 1024 for DeformDETR
ffn_cfgs=dict(
type='FFN',
embed_dims=256,
feedforward_channels=2048,
num_fcs=2,
ffn_drop=0.,
use_checkpoint=True,
act_cfg=dict(type='ReLU', inplace=True)),
ffn_dropout=0.0, # 0.1 for DeformDETR
operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
'ffn', 'norm')))),
positional_encoding=dict(
type='SinePositionalEncoding',
num_feats=128,
temperature=20,
normalize=True),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0), # 2.0 in DeformDETR
loss_bbox=dict(type='L1Loss', loss_weight=5.0),
loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
cls_cost=dict(type='FocalLossCost', weight=2.0),
reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
test_cfg=dict(max_per_img=1)) # TODO: Originally 100
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='LoadRefer'),
dict(type='RandomFlipWithRefer', flip_ratio=0.5),
dict(
type='AutoAugment',
policies=[
[
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
keep_ratio=True)
],
[
dict(
type='Resize',
# The radio of all image in train dataset < 7
# follow the original impl
img_scale=[(400, 4200), (500, 4200), (600, 4200)],
multiscale_mode='value',
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=False),
dict(
type='Resize',
img_scale=[(480, 1333), (512, 1333), (544, 1333),
(576, 1333), (608, 1333), (640, 1333),
(672, 1333), (704, 1333), (736, 1333),
(768, 1333), (800, 1333)],
multiscale_mode='value',
override=True,
keep_ratio=True)
]
]),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='TokenizeRefer', max_sent_len=64),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'refer',
'r_mask', 'gt_bboxes', 'gt_labels'])
]
# test_pipeline, NOTE the Pad's size_divisor is different from the default
# setting (size_divisor=32). While there is little effect on the performance
# whether we use the default setting or use size_divisor=1.
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadRefer'),
dict(
type='MultiScaleFlipAug',
img_scale=[(1333, 600), (1333, 800), (1333, 1000)],
flip=True,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlipWithRefer'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='TokenizeRefer', max_sent_len=64),
dict(type='Collect', keys=['img', 'refer', 'r_mask'])
])
]
data = dict(
samples_per_gpu=2,
workers_per_gpu=2,
train=dict(filter_empty_gt=True, pipeline=train_pipeline),
val=dict(pipeline=test_pipeline),
test=dict(pipeline=test_pipeline))
# optimizer
optimizer = dict(
type='AdamW', lr=0.0001, weight_decay=0.05,
constructor='LayerDecayOptimizerConstructor',
paramwise_cfg=dict(num_layers=24, layer_decay_rate=0.8))
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[])
runner = dict(type='EpochBasedRunner', max_epochs=6)
checkpoint_config = dict(
interval=1,
max_keep_ckpts=3,
save_last=True,
)
evaluation = dict(save_best='auto')
custom_hooks = [
dict(
type='ExpMomentumEMAHook',
resume_from=None,
momentum=0.0001,
priority=49)
]