_base_ = './hv_pointpillars_fpn_sbn-all_free-anchor_4x8_2x_nus-3d.py'

model = dict(
    pretrained=dict(pts='open-mmlab://regnetx_1.6gf'),
    pts_backbone=dict(
        _delete_=True,
        type='NoStemRegNet',
        arch='regnetx_1.6gf',
        out_indices=(1, 2, 3),
        frozen_stages=-1,
        strides=(1, 2, 2, 2),
        base_channels=64,
        stem_channels=64,
        norm_cfg=dict(type='naiveSyncBN2d', eps=1e-3, momentum=0.01),
        norm_eval=False,
        style='pytorch'),
    pts_neck=dict(in_channels=[168, 408, 912]))

# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-50, -50, -5, 50, 50, 3]
# For nuScenes we usually do 10-class detection
class_names = [
    'car', 'truck', 'trailer', 'bus', 'construction_vehicle', 'bicycle',
    'motorcycle', 'pedestrian', 'traffic_cone', 'barrier'
]
# file_client_args = dict(backend='disk')
# Uncomment the following if use ceph or other file clients.
# See https://mmcv.readthedocs.io/en/latest/api.html#mmcv.fileio.FileClient
# for more details.
file_client_args = dict(
    backend='petrel',
    path_mapping=dict({
        './data/nuscenes/': 's3://nuscenes/nuscenes/',
        'data/nuscenes/': 's3://nuscenes/nuscenes/'
    }))
train_pipeline = [
    dict(
        type='LoadPointsFromFile',
        coord_type='LIDAR',
        load_dim=5,
        use_dim=5,
        file_client_args=file_client_args),
    dict(
        type='LoadPointsFromMultiSweeps',
        sweeps_num=10,
        file_client_args=file_client_args),
    dict(type='LoadAnnotations3D', with_bbox_3d=True, with_label_3d=True),
    dict(
        type='GlobalRotScaleTrans',
        rot_range=[-0.7854, 0.7854],
        scale_ratio_range=[0.95, 1.05],
        translation_std=[0.2, 0.2, 0.2]),
    dict(
        type='RandomFlip3D',
        flip_ratio_bev_horizontal=0.5,
        flip_ratio_bev_vertical=0.5),
    dict(type='PointsRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectRangeFilter', point_cloud_range=point_cloud_range),
    dict(type='ObjectNameFilter', classes=class_names),
    dict(type='PointShuffle'),
    dict(type='DefaultFormatBundle3D', class_names=class_names),
    dict(type='Collect3D', keys=['points', 'gt_bboxes_3d', 'gt_labels_3d'])
]
data = dict(train=dict(pipeline=train_pipeline))

lr_config = dict(step=[28, 34])
runner = dict(max_epochs=36)
evaluation = dict(interval=36)
