grounding_dino_swin-t_pretrain_obj365_goldg_cap4m.py

_base_ = [
    '../_base_/datasets/coco_detection.py',
    '../_base_/schedules/schedule_1x.py', '../_base_/default_runtime.py'
]

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=True,
    ),
    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=False,
        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,
        # 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),
        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)),
    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=None,
    test_cfg=dict(max_per_img=300))

test_pipeline = [
    dict(
        type='LoadImageFromFile', backend_args=None,
        imdecode_backend='pillow'),
    dict(
        type='FixScaleResize',
        scale=(800, 1333),
        keep_ratio=True,
        backend='pillow'),
    dict(type='LoadAnnotations', with_bbox=True),
    dict(
        type='PackDetInputs',
        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
                   'scale_factor', 'text', 'custom_entities',
                   'tokens_positive'))
]

val_dataloader = dict(
    dataset=dict(pipeline=test_pipeline, return_classes=True))
test_dataloader = val_dataloader