_base_ = [
    '../_base_/datasets/coco_detection.py', '../_base_/default_runtime.py'
]
model = dict(
    type='DeformableDETR',
    num_queries=300,
    num_feature_levels=4,
    with_box_refine=False,
    as_two_stage=False,
    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=dict(  # DeformableDetrTransformerEncoder
        num_layers=6,
        layer_cfg=dict(  # DeformableDetrTransformerEncoderLayer
            self_attn_cfg=dict(  # MultiScaleDeformableAttention
                embed_dims=256,
                batch_first=True),
            ffn_cfg=dict(
                embed_dims=256, feedforward_channels=1024, ffn_drop=0.1))),
    decoder=dict(  # DeformableDetrTransformerDecoder
        num_layers=6,
        return_intermediate=True,
        layer_cfg=dict(  # DeformableDetrTransformerDecoderLayer
            self_attn_cfg=dict(  # MultiheadAttention
                embed_dims=256,
                num_heads=8,
                dropout=0.1,
                batch_first=True),
            cross_attn_cfg=dict(  # MultiScaleDeformableAttention
                embed_dims=256,
                batch_first=True),
            ffn_cfg=dict(
                embed_dims=256, feedforward_channels=1024, ffn_drop=0.1)),
        post_norm_cfg=None),
    positional_encoding=dict(num_feats=128, normalize=True, offset=-0.5),
    bbox_head=dict(
        type='DeformableDETRHead',
        num_classes=80,
        sync_cls_avg_factor=True,
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=2.0),
        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',
            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=100))

# train_pipeline, NOTE the img_scale and the Pad's size_divisor is different
# from the default setting in mmdet.
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=0.0001),
    clip_grad=dict(max_norm=0.1, norm_type=2),
    paramwise_cfg=dict(
        custom_keys={
            'backbone': dict(lr_mult=0.1),
            'sampling_offsets': dict(lr_mult=0.1),
            'reference_points': dict(lr_mult=0.1)
        }))

# learning policy
max_epochs = 50
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='MultiStepLR',
        begin=0,
        end=max_epochs,
        by_epoch=True,
        milestones=[40],
        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)
