mmagic.models.editors.gca.gca

Module Contents

Classes

GCA	Guided Contextual Attention image matting model.

class mmagic.models.editors.gca.gca.GCA(data_preprocessor, backbone, loss_alpha=None, init_cfg: Optional[dict] = None, train_cfg=None, test_cfg=None)

Bases: mmagic.models.base_models.BaseMattor

Guided Contextual Attention image matting model.

https://arxiv.org/abs/2001.04069

Parameters

• data_preprocessor (dict, optional) – The pre-process config of BaseDataPreprocessor.

• backbone (dict) – Config of backbone.

• loss_alpha (dict) – Config of the alpha prediction loss. Default: None.

• init_cfg (dict, optional) – Initialization config dict. Default: None.

• train_cfg (dict) – Config of training. In train_cfg, train_backbone should be specified. If the model has a refiner, train_refiner should be specified.

• test_cfg (dict) – Config of testing. In test_cfg, If the model has a refiner, train_refiner should be specified.

_forward(inputs)

Forward function.

Parameters

inputs (torch.Tensor) – Input tensor.

Returns

Output tensor.

Return type

Tensor

_forward_test(inputs)

Forward function for testing GCA model.

Parameters

inputs (torch.Tensor) – batch input tensor.

Returns

Output tensor of model.

Return type

Tensor

_forward_train(inputs, data_samples)

Forward function for training GCA model.

Parameters

• inputs (torch.Tensor) – batch input tensor collated by data_preprocessor.

• data_samples (List[BaseDataElement]) – data samples collated by data_preprocessor.

Returns

Contains the loss items and batch information.

Return type

dict