mmagic.models.editors.fba

Package Contents

Classes

FBADecoder	Decoder for FBA matting.
FBAResnetDilated	ResNet-based encoder for FBA image matting.

class mmagic.models.editors.fba.FBADecoder(pool_scales, in_channels, channels, conv_cfg=None, norm_cfg=dict(type='BN'), act_cfg=dict(type='ReLU'), align_corners=False)

Bases: torch.nn.Module

Decoder for FBA matting.

Parameters

• pool_scales (tuple[int]) – Pooling scales used in

• Module. (Pooling Pyramid) –

• in_channels (int) – Input channels.

• channels (int) – Channels after modules, before conv_seg.

• conv_cfg (dict|None) – Config of conv layers.

• norm_cfg (dict|None) – Config of norm layers.

• act_cfg (dict) – Config of activation layers.

• align_corners (bool) – align_corners argument of F.interpolate.

init_weights(pretrained=None)

Init weights for the model.

Parameters

pretrained (str, optional) – Path for pretrained weights. If given None, pretrained weights will not be loaded. Defaults to None.

forward(inputs)

Forward function.

Parameters

inputs (dict) – Output dict of FbaEncoder.

Returns

Predicted alpha, fg and bg of the current batch.

Return type

tuple(Tensor)

class mmagic.models.editors.fba.FBAResnetDilated(depth: int, in_channels: int = 3, stem_channels: int = 64, base_channels: int = 64, num_stages: int = 4, strides: Sequence[int] = (1, 2, 2, 2), dilations: Sequence[int] = (1, 1, 2, 4), deep_stem: bool = False, avg_down: bool = False, frozen_stages: int = - 1, act_cfg: dict = dict(type='ReLU'), conv_cfg: Optional[dict] = None, norm_cfg: dict = dict(type='BN'), with_cp: bool = False, multi_grid: Optional[Sequence[int]] = None, contract_dilation: bool = False, zero_init_residual: bool = True)

Bases: mmagic.models.archs.ResNet

ResNet-based encoder for FBA image matting.

forward(x)

Forward function.

Parameters

x (Tensor) – Input tensor with shape (N, C, H, W).

Returns

Output tensor.

Return type

Tensor