link = 'https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth'

model = dict(
    detector=dict(
    init_cfg=dict(type='Pretrained', checkpoint=link),
    type='DETR',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(3, ),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    bbox_head=dict(
        type='DETRHead',
        num_classes=80,
        in_channels=2048,
        transformer=dict(
            type='Transformer',
            encoder=dict(
                type='DetrTransformerEncoder',
                num_layers=6,
                transformerlayers=dict(
                    type='BaseTransformerLayer',
                    attn_cfgs=[
                        dict(
                            type='MultiheadAttention',
                            embed_dims=256,
                            num_heads=8,
                            dropout=0.1)
                    ],
                    feedforward_channels=2048,
                    ffn_dropout=0.1,
                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
            decoder=dict(
                type='DetrTransformerDecoder',
                return_intermediate=True,
                num_layers=6,
                transformerlayers=dict(
                    type='DetrTransformerDecoderLayer',
                    attn_cfgs=dict(
                        type='MultiheadAttention',
                        embed_dims=256,
                        num_heads=8,
                        dropout=0.1),
                    feedforward_channels=2048,
                    ffn_dropout=0.1,
                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
                                     'ffn', 'norm')),
            )),
        positional_encoding=dict(
            type='SinePositionalEncoding', num_feats=128, normalize=True),
        loss_cls=dict(
            type='CrossEntropyLoss',
            bg_cls_weight=0.1,
            use_sigmoid=False,
            loss_weight=1.0,
            class_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='HungarianAssigner',
            cls_cost=dict(type='ClassificationCost', weight=1.),
            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
    test_cfg=dict(max_per_img=100)))

link = 'https://download.openmmlab.com/mmdetection/v2.0/detr/detr_r50_8x2_150e_coco/detr_r50_8x2_150e_coco_20201130_194835-2c4b8974.pth'

model = dict(
    detector=dict(
    init_cfg=dict(type='Pretrained', checkpoint=link),
    type='DETR',
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(3, ),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=False),
        norm_eval=True,
        style='pytorch',
        init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
    bbox_head=dict(
        type='DETRHead',
        num_classes=80,
        in_channels=2048,
        transformer=dict(
            type='Transformer',
            encoder=dict(
                type='DetrTransformerEncoder',
                num_layers=6,
                transformerlayers=dict(
                    type='BaseTransformerLayer',
                    attn_cfgs=[
                        dict(
                            type='MultiheadAttention',
                            embed_dims=256,
                            num_heads=8,
                            dropout=0.1)
                    ],
                    feedforward_channels=2048,
                    ffn_dropout=0.1,
                    operation_order=('self_attn', 'norm', 'ffn', 'norm'))),
            decoder=dict(
                type='DetrTransformerDecoder',
                return_intermediate=True,
                num_layers=6,
                transformerlayers=dict(
                    type='DetrTransformerDecoderLayer',
                    attn_cfgs=dict(
                        type='MultiheadAttention',
                        embed_dims=256,
                        num_heads=8,
                        dropout=0.1),
                    feedforward_channels=2048,
                    ffn_dropout=0.1,
                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
                                     'ffn', 'norm')),
            )),
        positional_encoding=dict(
            type='SinePositionalEncoding', num_feats=128, normalize=True),
        loss_cls=dict(
            type='CrossEntropyLoss',
            bg_cls_weight=0.1,
            use_sigmoid=False,
            loss_weight=1.0,
            class_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='HungarianAssigner',
            cls_cost=dict(type='ClassificationCost', weight=1.),
            reg_cost=dict(type='BBoxL1Cost', weight=5.0, box_format='xywh'),
            iou_cost=dict(type='IoUCost', iou_mode='giou', weight=2.0))),
    test_cfg=dict(max_per_img=100)))

# training and testing settings