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linear_blocks

Linear block with optional normalization and activation.

Classes:

  • LinearBlock –

    Linear block consisting of a linear layer, normalization and activation.

LinearBlock 

LinearBlock(
    in_channels: int,
    out_channels: int,
    act: Union[str, Callable, None] = None,
    act_kwargs: Optional[Dict[str, Any]] = None,
    act_first: bool = False,
    bias: bool = True,
    norm: Union[str, Callable, None] = None,
    norm_kwargs: Optional[Dict[str, Any]] = None,
)

Bases: Module

Linear block consisting of a linear layer, normalization and activation.

Parameters:

  • in_channels (int) –

    Number of input channels.

  • out_channels (int) –

    Number of output channels.

  • bias (bool, default: True ) –

    Whether to use a bias for the linear layer.

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

    Activation function to use. If None, no activation is applied.

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

    Extra arguments for the activation function.

  • act_first (bool, default: False ) –

    Whether to apply the activation function before the normalization.

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

    Normalization layer to use. If None, no normalization is applied.

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

    Extra arguments for the normalization layer.

Example 
from torch_pointcloud.layers import LinearBlock

block = LinearBlock(64, 128, act="relu", norm="batch_norm", bias=False)
x = torch.randn(32, 64)
y = block(x)
print(y.shape)