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mmedit.models.editors.real_esrgan.real_esrgan

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Classes

RealESRGAN

Real-ESRGAN model for single image super-resolution.

class mmedit.models.editors.real_esrgan.real_esrgan.RealESRGAN(generator, discriminator=None, gan_loss=None, pixel_loss=None, perceptual_loss=None, is_use_sharpened_gt_in_pixel=False, is_use_sharpened_gt_in_percep=False, is_use_sharpened_gt_in_gan=False, is_use_ema=True, train_cfg=None, test_cfg=None, init_cfg=None, data_preprocessor=None)[源代码]

Bases: mmedit.models.editors.srgan.SRGAN

Real-ESRGAN model for single image super-resolution.

Ref: Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data, 2021.

Note: generator_ema is realized in EMA_HOOK

参数
  • generator (dict) – Config for the generator.

  • discriminator (dict, optional) – Config for the discriminator. Default: None.

  • gan_loss (dict, optional) – Config for the gan loss. Note that the loss weight in gan loss is only for the generator.

  • pixel_loss (dict, optional) – Config for the pixel loss. Default: None.

  • perceptual_loss (dict, optional) – Config for the perceptual loss. Default: None.

  • is_use_sharpened_gt_in_pixel (bool, optional) – Whether to use the image sharpened by unsharp masking as the GT for pixel loss. Default: False.

  • is_use_sharpened_gt_in_percep (bool, optional) – Whether to use the image sharpened by unsharp masking as the GT for perceptual loss. Default: False.

  • is_use_sharpened_gt_in_gan (bool, optional) – Whether to use the image sharpened by unsharp masking as the GT for adversarial loss. Default: False.

  • is_use_ema (bool, optional) – When to apply exponential moving average on the network weights. Default: True.

  • train_cfg (dict) – Config for training. Default: None. You may change the training of gan by setting: disc_steps: how many discriminator updates after one generate update; disc_init_steps: how many discriminator updates at the start of the training. These two keys are useful when training with WGAN.

  • test_cfg (dict) – Config for testing. Default: None.

  • init_cfg (dict, optional) – The weight initialized config for BaseModule. Default: None.

  • data_preprocessor (dict, optional) – The pre-process config of BaseDataPreprocessor. Default: None.

forward_tensor(inputs, data_samples=None, training=False)[源代码]

Forward tensor. Returns result of simple forward.

参数
  • inputs (torch.Tensor) – batch input tensor collated by data_preprocessor.

  • data_samples (List[BaseDataElement], optional) – data samples collated by data_preprocessor.

  • training (bool) – Whether is training. Default: False.

返回

result of simple forward.

返回类型

Tensor

g_step(batch_outputs, batch_gt_data)[源代码]

G step of GAN: Calculate losses of generator.

参数
  • batch_outputs (Tensor) – Batch output of generator.

  • batch_gt_data (Tuple[Tensor]) – Batch GT data.

返回

Dict of losses.

返回类型

dict

d_step_real(batch_outputs, batch_gt_data: torch.Tensor)[源代码]

Real part of D step.

参数
  • batch_outputs (Tensor) – Batch output of generator.

  • batch_gt_data (Tuple[Tensor]) – Batch GT data.

返回

Real part of gan_loss for discriminator.

返回类型

Tensor

d_step_fake(batch_outputs, batch_gt_data)[源代码]

Fake part of D step.

参数
  • batch_outputs (Tensor) – Output of generator.

  • batch_gt_data (Tuple[Tensor]) – Batch GT data.

返回

Fake part of gan_loss for discriminator.

返回类型

Tensor

extract_gt_data(data_samples)[源代码]

extract gt data from data samples.

参数

data_samples (list) – List of EditDataSample.

返回

Extract gt data.

返回类型

Tensor

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