mmagic.models.editors.cyclegan¶
Package Contents¶
Classes¶
CycleGAN model for unpaired image-to-image translation. |
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Construct a Resnet-based generator that consists of residual blocks |
- class mmagic.models.editors.cyclegan.CycleGAN(*args, buffer_size=50, loss_config=dict(cycle_loss_weight=10.0, id_loss_weight=0.5), **kwargs)[source]¶
Bases:
mmagic.models.base_models.BaseTranslationModelCycleGAN 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 forbackward()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) mmagic.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 details in Metrics and Evaluator.
- Returns
A list of
DataSamplecontain generated results.- Return type
SampleList
- val_step(data: dict) mmagic.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 details in Metrics and Evaluator.
- Returns
A list of
DataSamplecontain generated results.- Return type
SampleList
- class mmagic.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:
mmengine.model.BaseModuleConstruct 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.