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mmedit.models.editors.liif

Package Contents

Classes

LIIF

LIIF model for single image super-resolution.

LIIFEDSRNet

LIIF net based on EDSR.

LIIFRDNNet

LIIF net based on RDN.

MLPRefiner

Multilayer perceptrons (MLPs), refiner used in LIIF.

class mmedit.models.editors.liif.LIIF(generator: dict, pixel_loss: dict, train_cfg: Optional[dict] = None, test_cfg: Optional[dict] = None, init_cfg: Optional[dict] = None, data_preprocessor: Optional[dict] = None)[源代码]

Bases: mmedit.models.base_models.BaseEditModel

LIIF model for single image super-resolution.

Paper: Learning Continuous Image Representation with

Local Implicit Image Function

参数
  • generator (dict) – Config for the generator.

  • pixel_loss (dict) – Config for the pixel loss.

  • pretrained (str) – Path for pretrained model. Default: None.

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

forward_tensor(inputs, data_samples=None, **kwargs)

Forward tensor. Returns result of simple forward.

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

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

返回

result of simple forward.

返回类型

Tensor

forward_inference(inputs, data_samples=None, **kwargs)

Forward inference. Returns predictions of validation, testing, and simple inference.

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

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

返回

predictions.

返回类型

List[EditDataSample]

class mmedit.models.editors.liif.LIIFEDSRNet(encoder, imnet, local_ensemble=True, feat_unfold=True, cell_decode=True, eval_bsize=None)[源代码]

Bases: LIIFNet

LIIF net based on EDSR.

Paper: Learning Continuous Image Representation with

Local Implicit Image Function

参数
  • encoder (dict) – Config for the generator.

  • imnet (dict) – Config for the imnet.

  • local_ensemble (bool) – Whether to use local ensemble. Default: True.

  • feat_unfold (bool) – Whether to use feature unfold. Default: True.

  • cell_decode (bool) – Whether to use cell decode. Default: True.

  • eval_bsize (int) – Size of batched predict. Default: None.

gen_feature(x)

Generate feature.

参数

x (Tensor) – Input tensor with shape (n, c, h, w).

返回

Forward results.

返回类型

Tensor

class mmedit.models.editors.liif.LIIFRDNNet(encoder, imnet, local_ensemble=True, feat_unfold=True, cell_decode=True, eval_bsize=None)[源代码]

Bases: LIIFNet

LIIF net based on RDN.

Paper: Learning Continuous Image Representation with

Local Implicit Image Function

参数
  • encoder (dict) – Config for the generator.

  • imnet (dict) – Config for the imnet.

  • local_ensemble (bool) – Whether to use local ensemble. Default: True.

  • feat_unfold (bool) – Whether to use feat unfold. Default: True.

  • cell_decode (bool) – Whether to use cell decode. Default: True.

  • eval_bsize (int) – Size of batched predict. Default: None.

gen_feature(x)

Generate feature.

参数

x (Tensor) – Input tensor with shape (n, c, h, w).

返回

Forward results.

返回类型

Tensor

class mmedit.models.editors.liif.MLPRefiner(in_dim, out_dim, hidden_list)[源代码]

Bases: mmengine.model.BaseModule

Multilayer perceptrons (MLPs), refiner used in LIIF.

参数
  • in_dim (int) – Input dimension.

  • out_dim (int) – Output dimension.

  • hidden_list (list[int]) – List of hidden dimensions.

forward(x)

Forward function.

参数

x (Tensor) – The input of MLP.

返回

The output of MLP.

返回类型

Tensor

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