bev_backbone

ResidualBlock2d(
    in_channels: int,
    out_channels: int,
    *,
    stride: int = 1,
    downsample: bool = False,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: Module

Residual 2D conv block (the reference's BasicBlock) of the BEV residual backbone.

Parameters:

• in_channels (int) –

  Input channels.

• out_channels (int) –

  Output channels.

• stride (int, default: 1 ) –

  Stride of the first conv (and the optional projection shortcut).

• downsample (bool, default: False ) –

  Add a \(1\times1\) projection shortcut to match channels / stride.

• norm (Union[str, Callable, None], default: 'batch_norm' ) –

  Normalization type or callable.

• norm_kwargs (Optional[Dict[str, Any]], default: None ) –

  Extra normalization arguments.

• act (Union[str, Callable, None], default: 'relu' ) –

  Activation type or callable.

• act_kwargs (Optional[Dict[str, Any]], default: None ) –

  Extra activation arguments.

BEVBackbone(
    input_channels: int,
    layer_nums: Sequence[int],
    layer_strides: Sequence[int],
    num_filters: Sequence[int],
    upsample_strides: Sequence[float],
    num_upsample_filters: Sequence[int],
    *,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: Module

SSD-style multi-scale 2D BEV backbone.

Each level downsamples the BEV pseudo-image with a strided \(3\times3\) conv followed by layer_nums residual-free \(3\times3\) convs, then upsamples back to a common stride; the level outputs are concatenated along the channel dim. An upsample factor \(\geq 1\) uses a transposed conv, a factor \(< 1\) (e.g. \(0.5\)) a strided down-conv (nuScenes configs use both).

Parameters:

• input_channels (int) –

  Channels of the input BEV feature map.

• layer_nums (Sequence[int]) –

  Number of \(3\times3\) convs after the strided conv, per level.

• layer_strides (Sequence[int]) –

  Downsample stride of the leading conv, per level.

• num_filters (Sequence[int]) –

  Channel width, per level.

• upsample_strides (Sequence[float]) –

  Upsample factor per level.

• num_upsample_filters (Sequence[int]) –

  Channels of each upsampled level.

• act (Union[str, Callable, None], default: 'relu' ) –

  Activation type or callable for every conv block.

• act_kwargs (Optional[Dict[str, Any]], default: None ) –

  Extra activation arguments.

• norm (Union[str, Callable, None], default: 'batch_norm' ) –

  Normalization type or callable for every conv block.

• norm_kwargs (Optional[Dict[str, Any]], default: None ) –

  Extra normalization arguments.

BEVResidualBackbone(
    input_channels: int,
    layer_nums: Sequence[int],
    layer_strides: Sequence[int],
    num_filters: Sequence[int],
    upsample_strides: Sequence[float],
    num_upsample_filters: Sequence[int],
    *,
    act: Union[str, Callable, None] = "relu",
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: Module

Residual SSD-style 2D BEV backbone used by Voxel Mamba.

Same scaffolding as BEVBackbone (per-level block then upsample, concatenated), but each level is a stack of residual ResidualBlock2ds instead of plain \(3\times3\) convs.

Parameters:

• input_channels (int) –

  Channels of the input BEV feature map.

• layer_nums (Sequence[int]) –

  Number of residual blocks after the strided block, per level.

• layer_strides (Sequence[int]) –

  Downsample stride of the leading block, per level.

• num_filters (Sequence[int]) –

  Channel width, per level.

• upsample_strides (Sequence[float]) –

  Upsample factor per level.

• num_upsample_filters (Sequence[int]) –

  Channels of each upsampled level.

• act (Union[str, Callable, None], default: 'relu' ) –

  Activation type or callable for every conv block.

• act_kwargs (Optional[Dict[str, Any]], default: None ) –

  Extra activation arguments.

• norm (Union[str, Callable, None], default: 'batch_norm' ) –

  Normalization type or callable for every conv block.

• norm_kwargs (Optional[Dict[str, Any]], default: None ) –

  Extra normalization arguments.