_base_ = [
    # 'mmdet3d::_base_/datasets/nus-3d.py',
    '../../../configs/_base_/default_runtime.py'
]

custom_imports = dict(imports=['projects.DETR3D.detr3d'])
# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-51.2, -51.2, -5.0, 51.2, 51.2, 3.0]
voxel_size = [0.2, 0.2, 8]

img_norm_cfg = dict(
    mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], bgr_to_rgb=False)
# For nuScenes we usually do 10-class detection
class_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]

input_modality = dict(
    use_lidar=False,
    use_camera=True,
    use_radar=False,
    use_map=False,
    use_external=False)
# this means type='DETR3D' will be processed as 'mmdet3d.DETR3D'
default_scope = 'mmdet3d'
model = dict(
    type='DETR3D',
    use_grid_mask=True,
    data_preprocessor=dict(
        type='Det3DDataPreprocessor', **img_norm_cfg, pad_size_divisor=32),
    img_backbone=dict(
        type='mmdet.ResNet',
        depth=101,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN2d', requires_grad=False),
        norm_eval=True,
        style='caffe',
        dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
        stage_with_dcn=(False, False, True, True)),
    img_neck=dict(
        type='mmdet.FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        start_level=1,
        add_extra_convs='on_output',
        num_outs=4,
        relu_before_extra_convs=True),
    pts_bbox_head=dict(
        type='DETR3DHead',
        num_query=900,
        num_classes=10,
        in_channels=256,
        sync_cls_avg_factor=True,
        with_box_refine=True,
        as_two_stage=False,
        transformer=dict(
            type='Detr3DTransformer',
            decoder=dict(
                type='Detr3DTransformerDecoder',
                num_layers=6,
                return_intermediate=True,
                transformerlayers=dict(
                    type='mmdet.DetrTransformerDecoderLayer',
                    attn_cfgs=[
                        dict(
                            type='MultiheadAttention',  # mmcv.
                            embed_dims=256,
                            num_heads=8,
                            dropout=0.1),
                        dict(
                            type='Detr3DCrossAtten',
                            pc_range=point_cloud_range,
                            num_points=1,
                            embed_dims=256)
                    ],
                    feedforward_channels=512,
                    ffn_dropout=0.1,
                    operation_order=('self_attn', 'norm', 'cross_attn', 'norm',
                                     'ffn', 'norm')))),
        bbox_coder=dict(
            type='NMSFreeCoder',
            post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
            pc_range=point_cloud_range,
            max_num=300,
            voxel_size=voxel_size,
            num_classes=10),
        positional_encoding=dict(
            type='mmdet.SinePositionalEncoding',
            num_feats=128,
            normalize=True,
            offset=-0.5),
        loss_cls=dict(
            type='mmdet.FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=2.0),
        loss_bbox=dict(type='mmdet.L1Loss', loss_weight=0.25),
        loss_iou=dict(type='mmdet.GIoULoss', loss_weight=0.0)),
    # model training and testing settings
    train_cfg=dict(
        pts=dict(
            grid_size=[512, 512, 1],
            voxel_size=voxel_size,
            point_cloud_range=point_cloud_range,
            out_size_factor=4,
            assigner=dict(
                type='HungarianAssigner3D',
                cls_cost=dict(type='mmdet.FocalLossCost', weight=2.0),
                reg_cost=dict(type='BBox3DL1Cost', weight=0.25),
                # ↓ Fake cost. This is just to get compatible with DETR head
                iou_cost=dict(type='mmdet.IoUCost', weight=0.0),
                pc_range=point_cloud_range))))

dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'

test_transforms = [
    dict(
        type='RandomResize3D',
        scale=(1600, 900),
        ratio_range=(1., 1.),
        keep_ratio=True)
]
train_transforms = [dict(type='PhotoMetricDistortion3D')] + test_transforms

backend_args = None
train_pipeline = [
    dict(
        type='LoadMultiViewImageFromFiles',
        to_float32=True,
        num_views=6,
        backend_args=backend_args),
    dict(
        type='LoadAnnotations3D',
        with_bbox_3d=True,
        with_label_3d=True,
        with_attr_label=False),
    dict(type='MultiViewWrapper', transforms=train_transforms),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectNameFilter', classes=class_names),
    dict(type='Pack3DDetInputs', keys=['img', 'gt_bboxes_3d', 'gt_labels_3d'])
]

test_pipeline = [
    dict(
        type='LoadMultiViewImageFromFiles',
        to_float32=True,
        num_views=6,
        backend_args=backend_args),
    dict(type='MultiViewWrapper', transforms=test_transforms),
    dict(type='Pack3DDetInputs', keys=['img'])
]

metainfo = dict(classes=class_names)
data_prefix = dict(
    pts='',
    CAM_FRONT='samples/CAM_FRONT',
    CAM_FRONT_LEFT='samples/CAM_FRONT_LEFT',
    CAM_FRONT_RIGHT='samples/CAM_FRONT_RIGHT',
    CAM_BACK='samples/CAM_BACK',
    CAM_BACK_RIGHT='samples/CAM_BACK_RIGHT',
    CAM_BACK_LEFT='samples/CAM_BACK_LEFT')

train_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=True),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='nuscenes_infos_train.pkl',
        pipeline=train_pipeline,
        load_type='frame_based',
        metainfo=metainfo,
        modality=input_modality,
        test_mode=False,
        data_prefix=data_prefix,
        # we use box_type_3d='LiDAR' in kitti and nuscenes dataset
        # and box_type_3d='Depth' in sunrgbd and scannet dataset.
        box_type_3d='LiDAR',
        backend_args=backend_args))

val_dataloader = dict(
    batch_size=1,
    num_workers=4,
    persistent_workers=True,
    drop_last=False,
    sampler=dict(type='DefaultSampler', shuffle=False),
    dataset=dict(
        type=dataset_type,
        data_root=data_root,
        ann_file='nuscenes_infos_val.pkl',
        load_type='frame_based',
        pipeline=test_pipeline,
        metainfo=metainfo,
        modality=input_modality,
        test_mode=True,
        data_prefix=data_prefix,
        box_type_3d='LiDAR',
        backend_args=backend_args))

test_dataloader = val_dataloader

val_evaluator = dict(
    type='NuScenesMetric',
    data_root=data_root,
    ann_file=data_root + 'nuscenes_infos_val.pkl',
    metric='bbox',
    backend_args=backend_args)
test_evaluator = val_evaluator

optim_wrapper = dict(
    type='OptimWrapper',
    optimizer=dict(type='AdamW', lr=2e-4, weight_decay=0.01),
    paramwise_cfg=dict(custom_keys={'img_backbone': dict(lr_mult=0.1)}),
    clip_grad=dict(max_norm=35, norm_type=2),
)

# learning policy
param_scheduler = [
    dict(
        type='LinearLR',
        start_factor=1.0 / 3,
        by_epoch=False,
        begin=0,
        end=500),
    dict(
        type='CosineAnnealingLR',
        by_epoch=True,
        begin=0,
        end=24,
        T_max=24,
        eta_min_ratio=1e-3)
]

total_epochs = 24

train_cfg = dict(
    type='EpochBasedTrainLoop', max_epochs=total_epochs, val_interval=2)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
default_hooks = dict(
    checkpoint=dict(
        type='CheckpointHook', interval=1, max_keep_ckpts=1, save_last=True))
load_from = 'ckpts/fcos3d.pth'

# setuptools 65 downgrades to 58.
# In mmlab-node we use setuptools 61 but occurs NO errors
vis_backends = [dict(type='TensorboardVisBackend')]
visualizer = dict(
    type='Det3DLocalVisualizer', vis_backends=vis_backends, name='visualizer')