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mmedit.models.editors.cyclegan

Package Contents

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)[源代码]

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)

Forward function for testing.

参数
  • img (tensor) – Input image tensor.

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

  • kwargs (dict) – Other arguments.

返回

Forward results.

返回类型

dict

_get_disc_loss(outputs)

Backward function for the discriminators.

参数

outputs (dict) – Dict of forward results.

返回

Discriminators’ loss and loss dict.

返回类型

dict

_get_gen_loss(outputs)

Backward function for the generators.

参数

outputs (dict) – Dict of forward results.

返回

Generators’ loss and loss dict.

返回类型

dict

_get_opposite_domain(domain)

Get the opposite domain respect to the input domain.

参数

domain (str) – The input domain.

返回

The opposite domain.

返回类型

str

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

Training step function.

参数
  • 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.

返回

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

返回类型

dict

test_step(data: dict) mmedit.utils.typing.SampleList

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

参数

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

返回

A list of EditDataSample contain generated results.

返回类型

SampleList

val_step(data: dict) mmedit.utils.typing.SampleList

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

参数

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

返回

A list of EditDataSample contain generated results.

返回类型

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))[源代码]

Bases: torch.nn.Module

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

参数
  • 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)

Forward function.

参数

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

返回

Forward results.

返回类型

Tensor

init_weights(pretrained=None, strict=True)

Initialize weights for the model.

参数
  • 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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