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mmedit.models.base_models.base_gan

Module Contents

Classes

BaseGAN

Base class for GAN models.

Attributes

ModelType

mmedit.models.base_models.base_gan.ModelType[源代码]
class mmedit.models.base_models.base_gan.BaseGAN(generator: ModelType, discriminator: Optional[ModelType] = None, data_preprocessor: Optional[Union[dict, mmengine.Config]] = None, generator_steps: int = 1, discriminator_steps: int = 1, noise_size: Optional[int] = None, ema_config: Optional[Dict] = None, loss_config: Optional[Dict] = None)[源代码]

Bases: mmengine.model.BaseModel

Base class for GAN models.

参数
  • generator (ModelType) – The config or model of the generator.

  • discriminator (Optional[ModelType]) – The config or model of the discriminator. Defaults to None.

  • data_preprocessor (Optional[Union[dict, Config]]) – The pre-process config or GenDataPreprocessor.

  • generator_steps (int) – The number of times the generator is completely updated before the discriminator is updated. Defaults to 1.

  • discriminator_steps (int) – The number of times the discriminator is completely updated before the generator is updated. Defaults to 1.

  • ema_config (Optional[Dict]) – The config for generator’s exponential moving average setting. Defaults to None.

property generator_steps: int[源代码]

The number of times the generator is completely updated before the discriminator is updated.

Type

int

property discriminator_steps: int[源代码]

The number of times the discriminator is completely updated before the generator is updated.

Type

int

property device: torch.device[源代码]

Get current device of the model.

返回

The current device of the model.

返回类型

torch.device

property with_ema_gen: bool[源代码]

Whether the GAN adopts exponential moving average.

返回

If True, means this GAN model is adopted to exponential

moving average and vice versa.

返回类型

bool

static gather_log_vars(log_vars_list: List[Dict[str, torch.Tensor]]) Dict[str, torch.Tensor][源代码]

Gather a list of log_vars. :param log_vars_list: List[Dict[str, Tensor]]

返回

Dict[str, Tensor]

_init_loss(loss_config: Optional[Dict] = None) None[源代码]

Initialize customized loss modules.

If loss_config is a dict, we allow kinds of value for each field.

  1. loss_config is None: Users will implement all loss calculations

    in their own function. Weights for each loss terms are hard coded.

  2. loss_config is dict of scalar or string: Users will implement all

    loss calculations and use passed loss_config to control the weight or behavior of the loss calculation. Users will unpack and use each field in this dict by themself.

    loss_config = dict(gp_norm_mode=’HWC’, gp_loss_weight=10)

  3. loss_config is dict of dict: Each field in loss_config will

    used to build a corresponding loss module. And use loss calculation function predefined by BaseGAN to calculate the loss.

    loss_config = dict()

示例

loss_config = dict(

# BaseGAN pre-defined fields gan_loss=dict(type=’GANLoss’, gan_type=’wgan-logistic-ns’), disc_auxiliary_loss=dict(

type=’R1GradientPenalty’, loss_weight=10. / 2., interval=2, norm_mode=’HWC’, data_info=dict(

real_data=’real_imgs’, discriminator=’disc’)),

gen_auxiliary_loss=dict(

type=’GeneratorPathRegularizer’, loss_weight=2, pl_batch_shrink=2, interval=g_reg_interval, data_info=dict(

generator=’gen’, num_batches=’batch_size’)),

# user-defined field for loss weights or loss calculation my_loss_2=dict(weight=2, norm_mode=’L1’), my_loss_3=2, my_loss_4_norm_type=’L2’)

参数

loss_config (Optional[Dict], optional) – Loss config used to build loss modules or define the loss weights. Defaults to None.

noise_fn(noise: mmedit.utils.typing.NoiseVar = None, num_batches: int = 1)[源代码]

Sampling function for noise. There are three scenarios in this function:

  • If noise is a callable function, sample num_batches of noise with passed noise.

  • If noise is None, sample num_batches of noise from gaussian distribution.

  • If noise is a torch.Tensor, directly return noise.

参数
  • noise (Union[Tensor, Callable, List[int], None]) – You can directly give a batch of label through a torch.Tensor or offer a callable function to sample a batch of label data. Otherwise, the None indicates to use the default noise sampler. Defaults to None.

  • num_batches (int, optional) – The number of batches label want to sample. If label is a Tensor, this will be ignored. Defaults to 1.

返回

Sampled noise tensor.

返回类型

Tensor

_init_ema_model(ema_config: dict)[源代码]

Initialize a EMA model corresponding to the given ema_config. If ema_config is an empty dict or None, EMA model will not be initialized.

参数

ema_config (dict) – Config to initialize the EMA model.

_get_valid_model(batch_inputs: mmedit.utils.typing.ForwardInputs) str[源代码]

Try to get the valid forward model from inputs.

  • If forward model is defined in batch_inputs, it will be used as forward model.

  • If forward model is not defined in batch_inputs, ‘ema’ will returned if :property:`with_ema_gen` is true. Otherwise, ‘orig’ will be returned.

参数

batch_inputs (ForwardInputs) – Inputs passed to forward().

返回

Forward model to generate image. (‘orig’, ‘ema’ or

’ema/orig’).

返回类型

str

forward(inputs: mmedit.utils.typing.ForwardInputs, data_samples: Optional[list] = None, mode: Optional[str] = None) mmedit.utils.typing.SampleList[源代码]

Sample images with the given inputs. If forward mode is ‘ema’ or ‘orig’, the image generated by corresponding generator will be returned. If forward mode is ‘ema/orig’, images generated by original generator and EMA generator will both be returned in a dict.

参数
  • batch_inputs (ForwardInputs) – Dict containing the necessary information (e.g. noise, num_batches, mode) to generate image.

  • data_samples (Optional[list]) – Data samples collated by data_preprocessor. Defaults to None.

  • mode (Optional[str]) – mode is not used in BaseGAN. Defaults to None.

返回

A list of EditDataSample contain generated results.

返回类型

SampleList

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

Gets the generated image of given data.

Calls self.data_preprocessor(data) and self(inputs, data_sample, mode=None) in order. Return the generated results which will be passed to evaluator.

参数

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

返回

Generated image or image dict.

返回类型

SampleList

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

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

Generated image or image dict.

返回类型

List[EditDataSample]

train_step(data: dict, optim_wrapper: mmengine.optim.OptimWrapperDict) Dict[str, torch.Tensor][源代码]

Train GAN model. In the training of GAN models, generator and discriminator are updated alternatively. In MMEditing’s design, self.train_step is called with data input. Therefore we always update discriminator, whose updating is relay on real data, and then determine if the generator needs to be updated based on the current number of iterations. More details about whether to update generator can be found in should_gen_update().

参数
  • data (dict) – Data sampled from dataloader.

  • optim_wrapper (OptimWrapperDict) – OptimWrapperDict instance contains OptimWrapper of generator and discriminator.

返回

A dict of tensor for logging.

返回类型

Dict[str, torch.Tensor]

_get_gen_loss(out_dict)[源代码]
_get_disc_loss(out_dict)[源代码]
train_generator(inputs: dict, data_samples: List[mmedit.structures.EditDataSample], optimizer_wrapper: mmengine.optim.OptimWrapper) Dict[str, torch.Tensor][源代码]

Training function for discriminator. All GANs should implement this function by themselves.

参数
  • inputs (dict) – Inputs from dataloader.

  • data_samples (List[EditDataSample]) – Data samples from dataloader.

  • optim_wrapper (OptimWrapper) – OptimWrapper instance used to update model parameters.

返回

A dict of tensor for logging.

返回类型

Dict[str, Tensor]

train_discriminator(inputs: dict, data_samples: List[mmedit.structures.EditDataSample], optimizer_wrapper: mmengine.optim.OptimWrapper) Dict[str, torch.Tensor][源代码]

Training function for discriminator. All GANs should implement this function by themselves.

参数
  • inputs (dict) – Inputs from dataloader.

  • data_samples (List[EditDataSample]) – Data samples from dataloader.

  • optim_wrapper (OptimWrapper) – OptimWrapper instance used to update model parameters.

返回

A dict of tensor for logging.

返回类型

Dict[str, Tensor]

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