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mmedit.models.editors.esrgan

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Classes

ESRGAN

Enhanced SRGAN model for single image super-resolution.

RRDBNet

Networks consisting of Residual in Residual Dense Block, which is used

class mmedit.models.editors.esrgan.ESRGAN(generator, discriminator=None, gan_loss=None, pixel_loss=None, perceptual_loss=None, train_cfg=None, test_cfg=None, init_cfg=None, data_preprocessor=None)[source]

Bases: mmedit.models.editors.srgan.SRGAN

Enhanced SRGAN model for single image super-resolution.

Ref: ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. It uses RaGAN for GAN updates: The relativistic discriminator: a key element missing from standard GAN.

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

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

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

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

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

  • 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.

g_step(batch_outputs: torch.Tensor, batch_gt_data: torch.Tensor)[source]

G step of GAN: Calculate losses of generator.

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

  • batch_gt_data (Tensor) – Batch GT data.

Returns

Dict of losses.

Return type

dict

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

D step of real data.

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

  • batch_gt_data (Tensor) – Batch GT data.

Returns

Dict of losses.

Return type

dict

d_step_fake(batch_outputs: torch.Tensor, batch_gt_data)[source]

D step of fake data.

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

  • batch_gt_data (Tensor) – Batch GT data.

Returns

Dict of losses.

Return type

dict

class mmedit.models.editors.esrgan.RRDBNet(in_channels, out_channels, mid_channels=64, num_blocks=23, growth_channels=32, upscale_factor=4, init_cfg=None)[source]

Bases: mmengine.model.BaseModule

Networks consisting of Residual in Residual Dense Block, which is used in ESRGAN and Real-ESRGAN.

ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks. Real-ESRGAN: Training Real-World Blind Super-Resolution with Pure Synthetic Data. # noqa: E501 Currently, it supports [x1/x2/x4] upsampling scale factor.

Parameters
  • in_channels (int) – Channel number of inputs.

  • out_channels (int) – Channel number of outputs.

  • mid_channels (int) – Channel number of intermediate features. Default: 64

  • num_blocks (int) – Block number in the trunk network. Defaults: 23

  • growth_channels (int) – Channels for each growth. Default: 32.

  • upscale_factor (int) – Upsampling factor. Support x1, x2 and x4. Default: 4.

_supported_upscale_factors = [1, 2, 4]
forward(x)[source]

Forward function.

Parameters

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

Returns

Forward results.

Return type

Tensor

init_weights()[source]

Init weights for models.

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