_base_ = [
    "mmdet::_base_/default_runtime.py",
    "mmdet::_base_/schedules/schedule_1x.py",
    "mmdet::_base_/datasets/coco_detection.py",
    "mmdet::rtmdet/rtmdet_tta.py",
]
model = dict(
    type="RTMDet",
    data_preprocessor=dict(
        type="DetDataPreprocessor",
        mean=[103.53, 116.28, 123.675],
        std=[57.375, 57.12, 58.395],
        bgr_to_rgb=False,
        batch_augments=None,
    ),
    backbone=dict(
        type="CSPNeXt",
        arch="P5",
        expand_ratio=0.5,
        deepen_factor=0.67,
        widen_factor=0.75,
        channel_attention=True,
        norm_cfg=dict(type="SyncBN"),
        act_cfg=dict(type="SiLU", inplace=True),
    ),
    neck=dict(
        type="CSPNeXtPAFPN",
        in_channels=[192, 384, 768],
        out_channels=192,
        num_csp_blocks=2,
        expand_ratio=0.5,
        norm_cfg=dict(type="SyncBN"),
        act_cfg=dict(type="SiLU", inplace=True),
    ),
    bbox_head=dict(
        type="RTMDetSepBNHead",
        num_classes=80,
        in_channels=192,
        stacked_convs=2,
        feat_channels=192,
        anchor_generator=dict(type="MlvlPointGenerator", offset=0, strides=[8, 16, 32]),
        bbox_coder=dict(type="DistancePointBBoxCoder"),
        loss_cls=dict(
            type="QualityFocalLoss", use_sigmoid=True, beta=2.0, loss_weight=1.0
        ),
        loss_bbox=dict(type="GIoULoss", loss_weight=2.0),
        with_objectness=False,
        exp_on_reg=True,
        share_conv=True,
        pred_kernel_size=1,
        norm_cfg=dict(type="SyncBN"),
        act_cfg=dict(type="SiLU", inplace=True),
    ),
    train_cfg=dict(
        assigner=dict(type="DynamicSoftLabelAssigner", topk=13),
        allowed_border=-1,
        pos_weight=-1,
        debug=False,
    ),
    test_cfg=dict(
        nms_pre=30000,
        min_bbox_size=0,
        score_thr=0.001,
        nms=dict(type="nms", iou_threshold=0.65),
        max_per_img=300,
    ),
)

train_pipeline = [
    dict(type="LoadImageFromFile", backend_args={{_base_.backend_args}}),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(type="CachedMosaic", img_scale=(640, 640), pad_val=114.0),
    dict(
        type="RandomResize", scale=(1280, 1280), ratio_range=(0.1, 2.0), keep_ratio=True
    ),
    dict(type="RandomCrop", crop_size=(640, 640)),
    dict(type="YOLOXHSVRandomAug"),
    dict(type="RandomFlip", prob=0.5),
    dict(type="Pad", size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type="CachedMixUp",
        img_scale=(640, 640),
        ratio_range=(1.0, 1.0),
        max_cached_images=20,
        pad_val=(114, 114, 114),
    ),
    dict(type="mmdet.PackDetInputs"),
]

train_pipeline_stage2 = [
    dict(type="LoadImageFromFile", backend_args={{_base_.backend_args}}),
    dict(type="LoadAnnotations", with_bbox=True),
    dict(
        type="RandomResize", scale=(640, 640), ratio_range=(0.1, 2.0), keep_ratio=True
    ),
    dict(type="RandomCrop", crop_size=(640, 640)),
    dict(type="YOLOXHSVRandomAug"),
    dict(type="RandomFlip", prob=0.5),
    dict(type="Pad", size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(type="mmdet.PackDetInputs"),
]

test_pipeline = [
    dict(type="LoadImageFromFile", backend_args={{_base_.backend_args}}),
    dict(type="Resize", scale=(640, 640), keep_ratio=True),
    dict(type="Pad", size=(640, 640), pad_val=dict(img=(114, 114, 114))),
    dict(
        type="mmdet.PackDetInputs",
        meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"),
    ),
]

train_dataloader = dict(
    batch_size=32,
    num_workers=10,
    batch_sampler=None,
    pin_memory=True,
    dataset=dict(pipeline=train_pipeline),
)
val_dataloader = dict(
    batch_size=5, num_workers=10, dataset=dict(pipeline=test_pipeline)
)
test_dataloader = val_dataloader

max_epochs = 300
stage2_num_epochs = 20
base_lr = 0.004
interval = 10

train_cfg = dict(
    max_epochs=max_epochs,
    val_interval=interval,
    dynamic_intervals=[(max_epochs - stage2_num_epochs, 1)],
)

val_evaluator = dict(proposal_nums=(100, 1, 10))
test_evaluator = val_evaluator

# optimizer
optim_wrapper = dict(
    _delete_=True,
    type="OptimWrapper",
    optimizer=dict(type="AdamW", lr=base_lr, weight_decay=0.05),
    paramwise_cfg=dict(norm_decay_mult=0, bias_decay_mult=0, bypass_duplicate=True),
)

# learning rate
param_scheduler = [
    dict(type="LinearLR", start_factor=1.0e-5, by_epoch=False, begin=0, end=1000),
    dict(
        # use cosine lr from 150 to 300 epoch
        type="CosineAnnealingLR",
        eta_min=base_lr * 0.05,
        begin=max_epochs // 2,
        end=max_epochs,
        T_max=max_epochs // 2,
        by_epoch=True,
        convert_to_iter_based=True,
    ),
]

# hooks
default_hooks = dict(
    checkpoint=dict(
        interval=interval, max_keep_ckpts=3  # only keep latest 3 checkpoints
    )
)
custom_hooks = [
    dict(
        type="EMAHook",
        ema_type="ExpMomentumEMA",
        momentum=0.0002,
        update_buffers=True,
        priority=49,
    ),
    dict(
        type="PipelineSwitchHook",
        switch_epoch=max_epochs - stage2_num_epochs,
        switch_pipeline=train_pipeline_stage2,
    ),
]