_base_ = [
    '../_base_/datasets/nusc_LC.py',
]
point_cloud_range = [-54.0, -54.0, -5.0, 54.0, 54.0, 3.0]
class_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]
voxel_size = [0.075, 0.075, 0.2]
out_size_factor = 8
evaluation = dict(interval=12)
dataset_type = 'NuScenesDataset'
data_root = 'data/nuscenes/'
input_modality = dict(
    use_lidar=True,
    use_camera=True,
    use_radar=False,
    use_map=False,
    use_external=False)
num_views = 6
model = dict(
    type='TransFusionDetector',
    freeze_img=True,
    # img_backbone=dict(
    #     type='DLASeg',
    #     num_layers=34,
    #     heads={},
    #     head_convs=-1,
    #     ),
    img_backbone=dict(
        type='ResNet',
        depth=50,
        num_stages=4,
        out_indices=(0, 1, 2, 3),
        frozen_stages=1,
        norm_cfg=dict(type='BN', requires_grad=True),
        norm_eval=True,
        with_cp=True,
        style='pytorch'),
    img_neck=dict(
        type='FPN',
        in_channels=[256, 512, 1024, 2048],
        out_channels=256,
        num_outs=5),
    pts_voxel_layer=dict(
        max_num_points=10,
        voxel_size=voxel_size,
        max_voxels=(120000, 160000),
        point_cloud_range=point_cloud_range),
    pts_voxel_encoder=dict(
        type='HardSimpleVFE',
        num_features=5,
    ),
    pts_middle_encoder=dict(
        type='SparseEncoder',
        in_channels=5,
        sparse_shape=[41, 1440, 1440],
        output_channels=128,
        order=('conv', 'norm', 'act'),
        encoder_channels=((16, 16, 32), (32, 32, 64), (64, 64, 128), (128, 128)),
        encoder_paddings=((0, 0, 1), (0, 0, 1), (0, 0, [0, 1, 1]), (0, 0)),
        block_type='basicblock'),
    pts_backbone=dict(
        type='SECOND',
        in_channels=256,
        out_channels=[128, 256],
        layer_nums=[5, 5],
        layer_strides=[1, 2],
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),
        conv_cfg=dict(type='Conv2d', bias=False)),
    pts_neck=dict(
        type='SECONDFPN',
        in_channels=[128, 256],
        out_channels=[256, 256],
        upsample_strides=[1, 2],
        norm_cfg=dict(type='BN', eps=0.001, momentum=0.01),
        upsample_cfg=dict(type='deconv', bias=False),
        use_conv_for_no_stride=True),
    pts_bbox_head=dict(
        type='TransFusionHead',
        fuse_img=True,
        num_views=num_views,
        in_channels_img=256,
        out_size_factor_img=4,
        num_proposals=200,
        auxiliary=True,
        in_channels=256 * 2,
        hidden_channel=128,
        num_classes=len(class_names),
        num_decoder_layers=1,
        num_heads=8,
        learnable_query_pos=False,
        initialize_by_heatmap=True,
        nms_kernel_size=3,
        ffn_channel=256,
        dropout=0.1,
        bn_momentum=0.1,
        activation='relu',
        common_heads=dict(center=(2, 2), height=(1, 2), dim=(3, 2), rot=(2, 2), vel=(2, 2)),
        bbox_coder=dict(
            type='TransFusionBBoxCoder',
            pc_range=point_cloud_range[:2],
            voxel_size=voxel_size[:2],
            out_size_factor=out_size_factor,
            post_center_range=[-61.2, -61.2, -10.0, 61.2, 61.2, 10.0],
            score_threshold=0.0,
            code_size=10,
        ),
        loss_cls=dict(type='FocalLoss', use_sigmoid=True, gamma=2, alpha=0.25, reduction='mean', loss_weight=1.0),
        # loss_iou=dict(type='CrossEntropyLoss', use_sigmoid=True, reduction='mean', loss_weight=0.0),
        loss_bbox=dict(type='L1Loss', reduction='mean', loss_weight=0.25),
        loss_heatmap=dict(type='GaussianFocalLoss', reduction='mean', loss_weight=1.0),
    ),
    train_cfg=dict(
        pts=dict(
            dataset='nuScenes',
            assigner=dict(
                type='HungarianAssigner3D',
                iou_calculator=dict(type='BboxOverlaps3D', coordinate='lidar'),
                cls_cost=dict(type='FocalLossCost', gamma=2, alpha=0.25, weight=0.15),
                reg_cost=dict(type='BBoxBEVL1Cost', weight=0.25),
                iou_cost=dict(type='IoU3DCost', weight=0.25)
            ),
            pos_weight=-1,
            gaussian_overlap=0.1,
            min_radius=2,
            grid_size=[1440, 1440, 40],  # [x_len, y_len, 1]
            voxel_size=voxel_size,
            out_size_factor=out_size_factor,
            code_weights=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 1.0, 0.2, 0.2],
            point_cloud_range=point_cloud_range)),
    test_cfg=dict(
        pts=dict(
            dataset='nuScenes',
            grid_size=[1440, 1440, 40],
            out_size_factor=out_size_factor,
            pc_range=point_cloud_range[0:2],
            voxel_size=voxel_size[:2],
            nms_type=None,
        )))
optimizer = dict(type='AdamW', lr=0.0001, weight_decay=0.01)  # for 8gpu * 2sample_per_gpu
optimizer_config = dict(grad_clip=dict(max_norm=0.1, norm_type=2))
lr_config = dict(
    policy='cyclic',
    target_ratio=(10, 0.0001),
    cyclic_times=1,
    step_ratio_up=0.4)
momentum_config = dict(
    policy='cyclic',
    target_ratio=(0.8947368421052632, 1),
    cyclic_times=1,
    step_ratio_up=0.4)
total_epochs = 6
checkpoint_config = dict(interval=1)
log_config = dict(
    interval=50,
    hooks=[dict(type='TextLoggerHook'),
           dict(type='TensorboardLoggerHook')])
dist_params = dict(backend='nccl')
log_level = 'INFO'

load_from = 'work_dirs/transfusion_nusc_voxel_L_20e_nodb/epoch_20.pth'
load_img_from = 'work_dirs/mask_rcnn_r50_fpn_1x_nuim/epoch_12.pth'
resume_from = None
workflow = [('train', 1)]
gpu_ids = range(0, 8)
freeze_lidar_components = True
find_unused_parameters = True

data = dict(
    samples_per_gpu=2,
    workers_per_gpu=6,)

