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mmedit.models.editors.cyclegan

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Classes

CycleGAN

CycleGAN model for unpaired image-to-image translation.

ResnetGenerator

Construct a Resnet-based generator that consists of residual blocks

class mmedit.models.editors.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

class mmedit.models.editors.cyclegan.ResnetGenerator(in_channels, out_channels, base_channels=64, norm_cfg=dict(type='IN'), use_dropout=False, num_blocks=9, padding_mode='reflect', init_cfg=dict(type='normal', gain=0.02))[source]

Bases: torch.nn.Module

Construct a Resnet-based generator that consists of residual blocks between a few downsampling/upsampling operations.

Parameters
  • in_channels (int) – Number of channels in input images.

  • out_channels (int) – Number of channels in output images.

  • base_channels (int) – Number of filters at the last conv layer. Default: 64.

  • norm_cfg (dict) – Config dict to build norm layer. Default: dict(type=’IN’).

  • use_dropout (bool) – Whether to use dropout layers. Default: False.

  • num_blocks (int) – Number of residual blocks. Default: 9.

  • padding_mode (str) – The name of padding layer in conv layers: ‘reflect’ | ‘replicate’ | ‘zeros’. Default: ‘reflect’.

  • init_cfg (dict) – Config dict for initialization. type: The name of our initialization method. Default: ‘normal’. gain: Scaling factor for normal, xavier and orthogonal. Default: 0.02.

forward(x)[source]

Forward function.

Parameters

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

Returns

Forward results.

Return type

Tensor

init_weights(pretrained=None, strict=True)[source]

Initialize weights for the model.

Parameters
  • pretrained (str, optional) – Path for pretrained weights. If given None, pretrained weights will not be loaded. Default: None.

  • strict (bool, optional) – Whether to allow different params for the model and checkpoint. Default: True.

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