mmagic.models.editors.real_esrgan.real_esrgan

Module Contents

Classes

RealESRGAN

Real-ESRGAN model for single image super-resolution.

class mmagic.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)

Bases: mmagic.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

Parameters

• 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)

Forward tensor. Returns result of simple forward.

Parameters

• 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.

Returns

result of simple forward.

Return type

Tensor

g_step(batch_outputs, batch_gt_data)

G step of GAN: Calculate losses of generator.

Parameters

• batch_outputs (Tensor) – Batch output of generator.

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

Returns

Dict of losses.

Return type

dict

d_step_real(batch_outputs, batch_gt_data: torch.Tensor)

Real part of D step.

Parameters

• batch_outputs (Tensor) – Batch output of generator.

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

Returns

Real part of gan_loss for discriminator.

Return type

Tensor

d_step_fake(batch_outputs, batch_gt_data)

Fake part of D step.

Parameters

• batch_outputs (Tensor) – Output of generator.

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

Returns

Fake part of gan_loss for discriminator.

Return type

Tensor

extract_gt_data(data_samples)

extract gt data from data samples.

Parameters

data_samples (list) – List of DataSample.

Returns

Extract gt data.

Return type

Tensor