Delete grounding_dino_swin-t_finetune_16xb2_1x_coco.py

grounding_dino_swin-t_finetune_16xb2_1x_coco.py

@@ -1,204 +0,0 @@
-_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)