opusv1-t_r50_704x256_8f_nusc-occ3d_100e.py

dataset_type = 'NuScenesOcc3DDataset'
dataset_root = 'data/nuscenes/'
occ_root = 'data/nuscenes/gts/'

input_modality = dict(
    use_lidar=False,
    use_camera=True,
    use_radar=False,
    use_map=False,
    use_external=True
)

# For nuScenes we usually do 10-class detection
object_names = [
    'car', 'truck', 'construction_vehicle', 'bus', 'trailer', 'barrier',
    'motorcycle', 'bicycle', 'pedestrian', 'traffic_cone'
]

occ_names = [
    'others', 'barrier', 'bicycle', 'bus', 'car', 'construction_vehicle',
    'motorcycle', 'pedestrian', 'traffic_cone', 'trailer', 'truck',
    'driveable_surface', 'other_flat', 'sidewalk', 'terrain', 'manmade',
    'vegetation'
]

# If point cloud range is changed, the models should also change their point
# cloud range accordingly
point_cloud_range = [-40.0, -40.0, -1.0, 40.0, 40.0, 5.4]
voxel_size = [0.4, 0.4, 0.4]

# arch config
embed_dims = 256
num_layers = 6
num_query = 600
num_frames = 8
num_levels = 4
num_points = 4
num_refines = [1, 4, 16, 32, 64, 128]

img_backbone = dict(
    type='ResNet',
    depth=50,
    num_stages=4,
    out_indices=(0, 1, 2, 3),
    frozen_stages=1,
    norm_cfg=dict(type='BN2d', requires_grad=True),
    norm_eval=True,
    style='pytorch',
    with_cp=True)
img_neck = dict(
    type='FPN',
    in_channels=[256, 512, 1024, 2048],
    out_channels=embed_dims,
    num_outs=num_levels)
img_norm_cfg = dict(
    mean=[123.675, 116.280, 103.530],
    std=[58.395, 57.120, 57.375],
    to_rgb=True)

model = dict(
    type='OPUSV1',
    use_grid_mask=False,
    data_aug=dict(
        img_color_aug=True,  # Move some augmentations to GPU
        img_norm_cfg=img_norm_cfg,
        img_pad_cfg=dict(size_divisor=32)),
    stop_prev_grad=0,
    img_backbone=img_backbone,
    img_neck=img_neck,
    pts_bbox_head=dict(
        type='OPUSV1Head',
        num_classes=len(occ_names),
        in_channels=embed_dims,
        num_query=num_query,
        pc_range=point_cloud_range,
        voxel_size=voxel_size,
        transformer=dict(
            type='OPUSV1Transformer',
            embed_dims=embed_dims,
            num_frames=num_frames,
            num_points=num_points,
            num_layers=num_layers,
            num_levels=num_levels,
            num_classes=len(occ_names),
            num_refines=num_refines,
            scales=[0.5],
            pc_range=point_cloud_range),
        loss_cls=dict(
            type='FocalLoss',
            use_sigmoid=True,
            gamma=2.0,
            alpha=0.25,
            loss_weight=2.0),
        loss_pts=dict(type='SmoothL1Loss', beta=0.2, loss_weight=0.5)),
    train_cfg=dict(
        pts=dict(
            cls_weights=[
                10, 5, 10, 5, 5, 10, 10, 5, 10, 5, 5, 1, 5, 1, 1, 2, 1],
            )
        ),
    test_cfg=dict(
        pts=dict(
            score_thr=0.5,
            padding=True
        )
    )
)

ida_aug_conf = {
    'resize_lim': (0.38, 0.55),
    'final_dim': (256, 704),
    'bot_pct_lim': (0.0, 0.0),
    'rot_lim': (0.0, 0.0),
    'H': 900, 'W': 1600,
    'rand_flip': True,
}

train_pipeline = [
    dict(type='LoadMVImageWithSweeps', prev_sweeps_num=num_frames-1),
    dict(type='LoadOcc3DFromFile', occ_root=occ_root), 
    dict(type='RandomTransformImage', ida_aug_conf=ida_aug_conf, training=True),
    dict(type='FinalFormatting', keys=['img', 'voxel_semantics', 'mask_camera'],
         meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'ego2occ',
                    'ego2img', 'ego2lidar', 'img_timestamp'))
]

test_pipeline = [
    dict(type='LoadMVImageWithSweeps', prev_sweeps_num=num_frames-1, test_mode=True),
    dict(type='RandomTransformImage', ida_aug_conf=ida_aug_conf, training=False),
    dict(type='FinalFormatting', test_mode=True, keys=['img'],
         meta_keys=('filename', 'ori_shape', 'img_shape', 'pad_shape', 'ego2occ',
                    'ego2img', 'ego2lidar', 'img_timestamp'))
]

data = dict(
    workers_per_gpu=4,
    train=dict(
        type=dataset_type,
        data_root=dataset_root,
        ann_file=dataset_root + 'nuscenes_infos_train_sweep.pkl',
        pipeline=train_pipeline,
        classes=object_names,
        modality=input_modality,
        test_mode=False,
        use_valid_flag=True),
    val=dict(
        type=dataset_type,
        data_root=dataset_root,
        ann_file=dataset_root + 'nuscenes_infos_val_sweep.pkl',
        pipeline=test_pipeline,
        classes=object_names,
        modality=input_modality,
        test_mode=True),
    test=dict(
        type=dataset_type,
        data_root=dataset_root,
        ann_file=dataset_root + 'nuscenes_infos_test_sweep.pkl',
        pipeline=test_pipeline,
        classes=object_names,
        modality=input_modality,
        test_mode=True)
)

optimizer = dict(
    type='AdamW',
    lr=2e-4,
    paramwise_cfg=dict(custom_keys={
        'img_backbone': dict(lr_mult=0.1),
        'sampling_offset': dict(lr_mult=0.1),
    }),
    weight_decay=0.01
)

optimizer_config = dict(
    type='Fp16OptimizerHook',
    loss_scale=512.0,
    grad_clip=dict(max_norm=35, norm_type=2)
)

# learning policy
lr_config = dict(
    policy='CosineAnnealing',
    warmup='linear',
    warmup_iters=500,
    warmup_ratio=1.0 / 3,
    min_lr_ratio=1e-3
)
total_epochs = 100
batch_size = 8

# load pretrained weights
load_from = 'pretrain/cascade_mask_rcnn_r50_fpn_coco-20e_20e_nuim_20201009_124951-40963960.pth'
revise_keys = [('backbone', 'img_backbone')]

# resume the last training
resume_from = None

# checkpointing
checkpoint_config = dict(interval=1, max_keep_ckpts=1)

# logging
log_config = dict(
    interval=1,
    hooks=[
        dict(type='TextLoggerHook', interval=50, reset_flag=True),
        dict(type='MyTensorboardLoggerHook', interval=500, reset_flag=True)
    ]
)

# evaluation
eval_config = dict(interval=total_epochs)

# other flags
debug = False