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grounding_dino_swin-t_finetune_16xb2_1x_coco.py
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_base_ = [
'../_base_/datasets/coco_detection.py',
'../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]
load_from = 'https://download.openmmlab.com/mmdetection/v3.0/grounding_dino/groundingdino_swint_ogc_mmdet-822d7e9d.pth' # noqa
lang_model_name = 'bert-base-uncased'
model = dict(
type='GroundingDINO',
num_queries=900,
with_box_refine=True,
as_two_stage=True,
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[123.675, 116.28, 103.53],
std=[58.395, 57.12, 57.375],
bgr_to_rgb=True,
pad_mask=False,
),
language_model=dict(
type='BertModel',
name=lang_model_name,
pad_to_max=False,
use_sub_sentence_represent=True,
special_tokens_list=['[CLS]', '[SEP]', '.', '?'],
add_pooling_layer=False,
),
backbone=dict(
type='SwinTransformer',
embed_dims=96,
depths=[2, 2, 6, 2],
num_heads=[3, 6, 12, 24],
window_size=7,
mlp_ratio=4,
qkv_bias=True,
qk_scale=None,
drop_rate=0.,
attn_drop_rate=0.,
drop_path_rate=0.2,
patch_norm=True,
out_indices=(1, 2, 3),
with_cp=True,
convert_weights=False),
neck=dict(
type='ChannelMapper',
in_channels=[192, 384, 768],
kernel_size=1,
out_channels=256,
act_cfg=None,
bias=True,
norm_cfg=dict(type='GN', num_groups=32),
num_outs=4),
encoder=dict(
num_layers=6,
num_cp=6,
# visual layer config
layer_cfg=dict(
self_attn_cfg=dict(embed_dims=256, num_levels=4, dropout=0.0),
ffn_cfg=dict(
embed_dims=256, feedforward_channels=2048, ffn_drop=0.0)),
# text layer config
text_layer_cfg=dict(
self_attn_cfg=dict(num_heads=4, embed_dims=256, dropout=0.0),
ffn_cfg=dict(
embed_dims=256, feedforward_channels=1024, ffn_drop=0.0)),
# fusion layer config
fusion_layer_cfg=dict(
v_dim=256,
l_dim=256,
embed_dim=1024,
num_heads=4,
init_values=1e-4),
),
decoder=dict(
num_layers=6,
return_intermediate=True,
layer_cfg=dict(
# query self attention layer
self_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
# cross attention layer query to text
cross_attn_text_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
# cross attention layer query to image
cross_attn_cfg=dict(embed_dims=256, num_heads=8, dropout=0.0),
ffn_cfg=dict(
embed_dims=256, feedforward_channels=2048, ffn_drop=0.0)),
post_norm_cfg=None),
positional_encoding=dict(
num_feats=128, normalize=True, offset=0.0, temperature=20),
bbox_head=dict(
type='GroundingDINOHead',
num_classes=80,
sync_cls_avg_factor=True,
contrastive_cfg=dict(max_text_len=256, log_scale=0.0, bias=False),
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)),
dn_cfg=dict( # TODO: Move to model.train_cfg ?
label_noise_scale=0.5,
box_noise_scale=1.0, # 0.4 for DN-DETR
group_cfg=dict(dynamic=True, num_groups=None,
num_dn_queries=100)), # TODO: half num_dn_queries
# training and testing settings
train_cfg=dict(
assigner=dict(
type='HungarianAssigner',
match_costs=[
dict(type='BinaryFocalLossCost', weight=2.0),
dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
dict(type='IoUCost', iou_mode='giou', weight=2.0)
])),
test_cfg=dict(max_per_img=300))
# dataset settings
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='RandomFlip', prob=0.5),
dict(
type='RandomChoice',
transforms=[
[
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
],
[
dict(
type='RandomChoiceResize',
# The radio of all image in train dataset < 7
# follow the original implement
scales=[(400, 4200), (500, 4200), (600, 4200)],
keep_ratio=True),
dict(
type='RandomCrop',
crop_type='absolute_range',
crop_size=(384, 600),
allow_negative_crop=True),
dict(
type='RandomChoiceResize',
scales=[(480, 1333), (512, 1333), (544, 1333), (576, 1333),
(608, 1333), (640, 1333), (672, 1333), (704, 1333),
(736, 1333), (768, 1333), (800, 1333)],
keep_ratio=True)
]
]),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction', 'text',
'custom_entities'))
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=_base_.backend_args),
dict(type='FixScaleResize', scale=(800, 1333), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'text', 'custom_entities'))
]
train_dataloader = dict(
dataset=dict(
filter_cfg=dict(filter_empty_gt=False),
pipeline=train_pipeline,
return_classes=True))
val_dataloader = dict(
dataset=dict(pipeline=test_pipeline, return_classes=True))
test_dataloader = val_dataloader
optim_wrapper = dict(
_delete_=True,
type='OptimWrapper',
optimizer=dict(type='AdamW', lr=0.0001, weight_decay=0.0001),
clip_grad=dict(max_norm=0.1, norm_type=2),
paramwise_cfg=dict(custom_keys={
'absolute_pos_embed': dict(decay_mult=0.),
'backbone': dict(lr_mult=0.1)
}))
# learning policy
max_epochs = 12
param_scheduler = [
dict(
type='MultiStepLR',
begin=0,
end=max_epochs,
by_epoch=True,
milestones=[11],
gamma=0.1)
]
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (16 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=32)