conv2d_blocks

Conv2dBlock(
    in_channels: int,
    out_channels: int,
    kernel_size: int = 3,
    *,
    stride: int = 1,
    padding: int = 0,
    transposed: bool = False,
    act: Union[str, Callable, None] = "relu",
    act_first: bool = False,
    act_kwargs: Optional[Dict[str, Any]] = None,
    norm: Union[str, Callable, None] = "batch_norm",
    norm_kwargs: Optional[Dict[str, Any]] = None,
    bias: bool = False,
)

Bases: Module

Single nn.Conv2d (or nn.ConvTranspose2d) + optional norm + optional activation.

The 2D analogue of Conv3dBlock, with stride, padding and transposed exposed so it can express the strided down-convs and transposed up-convs of an SSD-style BEV backbone.

Shape

Input: \((B, C_\text{in}, H, W)\) Output: \((B, C_\text{out}, H', W')\)

Parameters:

• in_channels (int) –

  Input channel count.

• out_channels (int) –

  Output channel count.

• kernel_size (int, default: 3 ) –

  Conv kernel size.

• stride (int, default: 1 ) –

  Conv stride.

• padding (int, default: 0 ) –

  Conv padding.

• transposed (bool, default: False ) –

  Use nn.ConvTranspose2d instead of nn.Conv2d (for upsampling).

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

  Activation, name resolved by create_act. None disables.

• act_first (bool, default: False ) –

  If True, run activation before normalization.

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

  Extra kwargs for the activation.

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

  Normalization, name resolved by create_norm (with dim=2). None disables.

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

  Extra kwargs for the normalization.

• bias (bool, default: False ) –

  Whether the conv has a bias term.