mmagic.models.archs.linear_module

Module Contents

Classes

LinearModule

A linear block that contains linear/norm/activation layers.

class mmagic.models.archs.linear_module.LinearModule(in_features: int, out_features: int, bias: bool = True, act_cfg: Optional[dict] = dict(type='ReLU'), inplace: bool = True, with_spectral_norm: bool = False, order: Tuple[str, str] = ('linear', 'act'))

Bases: torch.nn.Module

A linear block that contains linear/norm/activation layers.

For low level vision, we add spectral norm and padding layer.

Parameters

• in_features (int) – Same as nn.Linear.

• out_features (int) – Same as nn.Linear.

• bias (bool) – Same as nn.Linear. Default: True.

• act_cfg (dict) – Config dict for activation layer, “relu” by default.

• inplace (bool) – Whether to use inplace mode for activation. Default: True.

• with_spectral_norm (bool) – Whether use spectral norm in linear module. Default: False.

• order (tuple[str]) – The order of linear/activation layers. It is a sequence of “linear”, “norm” and “act”. Examples are (“linear”, “act”) and (“act”, “linear”).

init_weights() → None

Init weights for the model.

forward(x: torch.Tensor, activate: Optional[bool] = True) → torch.Tensor

Forward Function.

Parameters

• x (torch.Tensor) – Input tensor with shape of \((n, *, c)\). Same as torch.nn.Linear.

• activate (bool, optional) – Whether to use activation layer. Defaults to True.

Returns

Same as torch.nn.Linear.

Return type

torch.Tensor