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mmedit.models.editors.cyclegan.cyclegan

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Classes

CycleGAN

CycleGAN model for unpaired image-to-image translation.

class mmedit.models.editors.cyclegan.cyclegan.CycleGAN(*args, buffer_size=50, loss_config=dict(cycle_loss_weight=10.0, id_loss_weight=0.5), **kwargs)[source]

Bases: mmedit.models.base_models.BaseTranslationModel

CycleGAN model for unpaired image-to-image translation.

Ref: Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks

forward_test(img, target_domain, **kwargs)[source]

Forward function for testing.

Parameters
  • img (tensor) – Input image tensor.

  • target_domain (str) – Target domain of output image.

  • kwargs (dict) – Other arguments.

Returns

Forward results.

Return type

dict

_get_disc_loss(outputs)[source]

Backward function for the discriminators.

Parameters

outputs (dict) – Dict of forward results.

Returns

Discriminators’ loss and loss dict.

Return type

dict

_get_gen_loss(outputs)[source]

Backward function for the generators.

Parameters

outputs (dict) – Dict of forward results.

Returns

Generators’ loss and loss dict.

Return type

dict

_get_opposite_domain(domain)[source]

Get the opposite domain respect to the input domain.

Parameters

domain (str) – The input domain.

Returns

The opposite domain.

Return type

str

train_step(data: dict, optim_wrapper: mmengine.optim.OptimWrapperDict)[source]

Training step function.

Parameters
  • data_batch (dict) – Dict of the input data batch.

  • optimizer (dict[torch.optim.Optimizer]) – Dict of optimizers for the generators and discriminators.

  • ddp_reducer (Reducer | None, optional) – Reducer from ddp. It is used to prepare for backward() in ddp. Defaults to None.

  • running_status (dict | None, optional) – Contains necessary basic information for training, e.g., iteration number. Defaults to None.

Returns

Dict of loss, information for logger, the number of samples and results for visualization.

Return type

dict

test_step(data: dict) mmedit.utils.typing.SampleList[source]

Gets the generated image of given data. Same as val_step().

Parameters

data (dict) – Data sampled from metric specific sampler. More detials in Metrics and Evaluator.

Returns

A list of EditDataSample contain generated results.

Return type

SampleList

val_step(data: dict) mmedit.utils.typing.SampleList[source]

Gets the generated image of given data. Same as val_step().

Parameters

data (dict) – Data sampled from metric specific sampler. More detials in Metrics and Evaluator.

Returns

A list of EditDataSample contain generated results.

Return type

SampleList

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