_base_ = [
    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
    type='DDQDETR',
    num_queries=900,  # num_matching_queries
    # ratio of num_dense queries to num_queries
    dense_topk_ratio=1.5,
    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_size_divisor=1),
    backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(1, 2, 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')),
    neck=dict(
        type='ChannelMapper',
        in_channels=[512, 1024, 2048],
        kernel_size=1,
        out_channels=256,
        act_cfg=None,
        norm_cfg=dict(type='GN', num_groups=32),
        num_outs=4),
    # encoder class name: DeformableDetrTransformerEncoder
    encoder=dict(
        num_layers=6,
        layer_cfg=dict(
            self_attn_cfg=dict(embed_dims=256, num_levels=4,
                               dropout=0.0),  # 0.1 for DeformDETR
            ffn_cfg=dict(
                embed_dims=256,
                feedforward_channels=2048,  # 1024 for DeformDETR
                ffn_drop=0.0))),  # 0.1 for DeformDETR
    # decoder class name: DDQTransformerDecoder
    decoder=dict(
        # `num_layers` >= 2, because attention masks of the last
        #   `num_layers` - 1 layers are used for distinct query selection
        num_layers=6,
        return_intermediate=True,
        layer_cfg=dict(
            self_attn_cfg=dict(embed_dims=256, num_heads=8,
                               dropout=0.0),  # 0.1 for DeformDETR
            cross_attn_cfg=dict(embed_dims=256, num_levels=4,
                                dropout=0.0),  # 0.1 for DeformDETR
            ffn_cfg=dict(
                embed_dims=256,
                feedforward_channels=2048,  # 1024 for DeformDETR
                ffn_drop=0.0)),  # 0.1 for DeformDETR
        post_norm_cfg=None),
    positional_encoding=dict(
        num_feats=128,
        normalize=True,
        offset=0.0,  # -0.5 for DeformDETR
        temperature=20),  # 10000 for DeformDETR
    bbox_head=dict(
        type='DDQDETRHead',
        num_classes=80,
        sync_cls_avg_factor=True,
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=1.0),
        loss_bbox=dict(type='L1Loss', loss_weight=5.0),
        loss_iou=dict(type='GIoULoss', loss_weight=2.0)),
    dn_cfg=dict(
        label_noise_scale=0.5,
        box_noise_scale=1.0,
        group_cfg=dict(dynamic=True, num_groups=None, num_dn_queries=100)),
    dqs_cfg=dict(type='nms', iou_threshold=0.8),
    # training and testing settings
    train_cfg=dict(
        assigner=dict(
            type='HungarianAssigner',
            match_costs=[
                dict(type='FocalLossCost', 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))

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')
]

train_dataloader = dict(
    dataset=dict(
        filter_cfg=dict(filter_empty_gt=False), pipeline=train_pipeline))

# optimizer
optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=0.0002, weight_decay=0.05),
    clip_grad=dict(max_norm=0.1, norm_type=2),
    paramwise_cfg=dict(custom_keys={'backbone': dict(lr_mult=0.1)}))

# learning policy
max_epochs = 12
train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=max_epochs, val_interval=1)

val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')

param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=0.0001,
        by_epoch=False,
        begin=0,
        end=2000),
    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 = (8 GPUs) x (2 samples per GPU)
auto_scale_lr = dict(base_batch_size=16)
