Upload grounding_dino_swin-t_finetune_16xb2_1x_coco.py with huggingface_hub

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)