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grounding_dino_swin-l_pretrain_obj365_goldg.py
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_base_ = 'grounding_dino_swin-t_pretrain_obj365.py'
pretrained = 'https://github.com/SwinTransformer/storage/releases/download/v1.0.0/swin_large_patch4_window12_384_22k.pth' # noqa
num_levels = 5
model = dict(
use_autocast=True,
num_feature_levels=num_levels,
backbone=dict(
_delete_=True,
type='SwinTransformer',
pretrain_img_size=384,
embed_dims=192,
depths=[2, 2, 18, 2],
num_heads=[6, 12, 24, 48],
window_size=12,
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=(0, 1, 2, 3),
# Please only add indices that would be used
# in FPN, otherwise some parameter will not be used
with_cp=True,
convert_weights=True,
frozen_stages=-1,
init_cfg=dict(type='Pretrained', checkpoint=pretrained)),
neck=dict(in_channels=[192, 384, 768, 1536], num_outs=num_levels),
encoder=dict(layer_cfg=dict(self_attn_cfg=dict(num_levels=num_levels))),
decoder=dict(layer_cfg=dict(cross_attn_cfg=dict(num_levels=num_levels))))
# --------------------------- object365v2 od dataset---------------------------
# objv2_backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/objects365v2/': 'yudong:s3://wangyudong/obj365_v2/',
# 'data/objects365v2/': 'yudong:s3://wangyudong/obj365_v2/'
# }))
objv2_backend_args = None
objv2_train_pipeline = [
dict(type='LoadImageFromFile', backend_args=objv2_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='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(
type='RandomSamplingNegPos',
tokenizer_name=_base_.lang_model_name,
num_sample_negative=85,
# change this
label_map_file='data/objects365v2/annotations/o365v2_label_map.json',
max_tokens=256),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction', 'text',
'custom_entities', 'tokens_positive', 'dataset_mode'))
]
o365v2_dataset = dict(
type='ODVGDataset',
data_root='data/objects365v2/',
ann_file='annotations/zhiyuan_objv2_train_od.json',
label_map_file='annotations/o365v2_label_map.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=objv2_train_pipeline,
return_classes=True,
need_text=False,
backend_args=None,
)
# --------------------------- openimagev6 od dataset---------------------------
# oi_backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
oi_backend_args = None
oi_train_pipeline = [
dict(type='LoadImageFromFile', backend_args=oi_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='FilterAnnotations', min_gt_bbox_wh=(1e-2, 1e-2)),
dict(
type='RandomSamplingNegPos',
tokenizer_name=_base_.lang_model_name,
num_sample_negative=85,
# change this
label_map_file='data/OpenImages/annotations/openimages_label_map.json',
max_tokens=256),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'flip', 'flip_direction', 'text',
'custom_entities', 'tokens_positive', 'dataset_mode'))
]
oiv6_dataset = dict(
type='ODVGDataset',
data_root='data/OpenImages/',
ann_file='annotations/oidv6-train-annotations_od.json',
label_map_file='annotations/openimages_label_map.json',
data_prefix=dict(img='OpenImages/train/'),
filter_cfg=dict(filter_empty_gt=False),
need_text=False,
pipeline=oi_train_pipeline,
return_classes=True,
backend_args=None)
flickr30k_dataset = dict(
type='ODVGDataset',
data_root='data/flickr30k_entities/',
ann_file='final_flickr_separateGT_train_vg.json',
label_map_file=None,
data_prefix=dict(img='flickr30k_images/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base_.train_pipeline,
return_classes=True,
backend_args=None)
gqa_dataset = dict(
type='ODVGDataset',
data_root='data/gqa/',
ann_file='final_mixed_train_no_coco_vg.json',
label_map_file=None,
data_prefix=dict(img='images/'),
filter_cfg=dict(filter_empty_gt=False),
pipeline=_base_.train_pipeline,
return_classes=True,
backend_args=None)
train_dataloader = dict(
dataset=dict(datasets=[
o365v2_dataset, oiv6_dataset, flickr30k_dataset, gqa_dataset
]))
# 4Nodex8GPU
optim_wrapper = dict(optimizer=dict(lr=0.0002))
max_iter = 200000
train_cfg = dict(
_delete_=True,
type='IterBasedTrainLoop',
max_iters=max_iter,
val_interval=13000)
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
dict(
type='MultiStepLR',
begin=0,
end=max_iter,
by_epoch=False,
milestones=[156100],
gamma=0.5)
]
default_hooks = dict(
checkpoint=dict(by_epoch=False, interval=13000, max_keep_ckpts=30))
log_processor = dict(by_epoch=False)