Source code for mmagic.models.editors.biggan.biggan
# Copyright (c) OpenMMLab. All rights reserved.
from typing import Dict, Optional, Tuple, Union
import torch
import torch.nn as nn
import torch.nn.functional as F
from mmengine import Config
from mmengine.optim import OptimWrapper
from torch import Tensor
from mmagic.registry import MODELS
from mmagic.structures import DataSample
from ...base_models import BaseConditionalGAN
@MODELS.register_module()
[docs]class BigGAN(BaseConditionalGAN):
"""Implementation of `Large Scale GAN Training for High Fidelity Natural
Image Synthesis <https://arxiv.org/abs/1809.11096>`_ (BigGAN).
Detailed architecture can be found in
:class:`~mmagic.models.editors.biggan.BigGANGenerator`
and
:class:`~mmagic.models.editors.biggan.BigGANDiscriminator`
Args:
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 :class:`~mmagic.models.DataPreprocessor`.
generator_steps (int): Number of times the generator was completely
updated before the discriminator is updated. Defaults to 1.
discriminator_steps (int): Number of times the discriminator was
completely updated before the generator is updated. Defaults to 1.
noise_size (Optional[int]): Size of the input noise vector.
Default to 128.
num_classes (Optional[int]): The number classes you would like to
generate. Defaults to None.
ema_config (Optional[Dict]): The config for generator's exponential
moving average setting. Defaults to None.
"""
def __init__(self,
generator: ModelType,
discriminator: Optional[ModelType] = None,
data_preprocessor: Optional[Union[dict, Config]] = None,
generator_steps: int = 1,
discriminator_steps: int = 1,
noise_size: Optional[int] = None,
num_classes: Optional[int] = None,
ema_config: Optional[Dict] = None):
super().__init__(generator, discriminator, data_preprocessor,
generator_steps, discriminator_steps, noise_size,
num_classes, ema_config)
[docs] def disc_loss(self, disc_pred_fake: Tensor,
disc_pred_real: Tensor) -> Tuple:
r"""Get disc loss. BigGAN use hinge loss to train
the discriminator.
.. math:
L_{D} = -\mathbb{E}_{\left(x, y\right)\sim{p}_{data}}
\left[\min\left(0, -1 + D\left(x, y\right)\right)\right]
-\mathbb{E}_{z\sim{p_{z}}, y\sim{p_{data}}}\left[\min
\left(0, -1 - D\left(G\left(z\right), y\right)\right)\right]
Args:
disc_pred_fake (Tensor): Discriminator's prediction of the fake
images.
disc_pred_real (Tensor): Discriminator's prediction of the real
images.
Returns:
tuple[Tensor, dict]: Loss value and a dict of log variables.
"""
losses_dict = dict()
losses_dict['loss_disc_fake'] = F.relu(1 + disc_pred_fake).mean()
losses_dict['loss_disc_real'] = F.relu(1 - disc_pred_real).mean()
loss, log_var = self.parse_losses(losses_dict)
return loss, log_var
[docs] def gen_loss(self, disc_pred_fake):
r"""Get disc loss. BigGAN use hinge loss to train
the generator.
.. math:
L_{G} = -\mathbb{E}_{z\sim{p_{z}}, y\sim{p_{data}}}
D\left(G\left(z\right), y\right)
Args:
disc_pred_fake (Tensor): Discriminator's prediction of the fake
images.
Returns:
tuple[Tensor, dict]: Loss value and a dict of log variables.
"""
losses_dict = dict()
losses_dict['loss_gen'] = -disc_pred_fake.mean()
loss, log_var = self.parse_losses(losses_dict)
return loss, log_var
[docs] def train_discriminator(self, inputs: dict, data_samples: DataSample,
optimizer_wrapper: OptimWrapper
) -> Dict[str, Tensor]:
"""Train discriminator.
Args:
inputs (dict): Inputs from dataloader.
data_samples (DataSample): Data samples from dataloader.
optim_wrapper (OptimWrapper): OptimWrapper instance used to update
model parameters.
Returns:
Dict[str, Tensor]: A ``dict`` of tensor for logging.
"""
real_imgs = data_samples.gt_img
real_labels = self.data_sample_to_label(data_samples)
assert real_labels is not None, (
'Cannot found \'gt_label\' in \'data_sample\'.')
num_batches = real_imgs.shape[0]
noise_batch = self.noise_fn(num_batches=num_batches)
fake_labels = self.label_fn(num_batches=num_batches)
with torch.no_grad():
fake_imgs = self.generator(
noise=noise_batch, label=fake_labels, return_noise=False)
disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)
disc_pred_real = self.discriminator(real_imgs, label=real_labels)
parsed_losses, log_vars = self.disc_loss(disc_pred_fake,
disc_pred_real)
optimizer_wrapper.update_params(parsed_losses)
return log_vars
[docs] def train_generator(self, inputs: dict, data_samples: DataSample,
optimizer_wrapper: OptimWrapper) -> Dict[str, Tensor]:
"""Train generator.
Args:
inputs (dict): Inputs from dataloader.
data_samples (DataSample): Data samples from dataloader.
Do not used in generator's training.
optim_wrapper (OptimWrapper): OptimWrapper instance used to update
model parameters.
Returns:
Dict[str, Tensor]: A ``dict`` of tensor for logging.
"""
num_batches = len(data_samples)
noise = self.noise_fn(num_batches=num_batches)
fake_labels = self.label_fn(num_batches=num_batches)
fake_imgs = self.generator(
noise=noise, label=fake_labels, return_noise=False)
disc_pred_fake = self.discriminator(fake_imgs, label=fake_labels)
parsed_loss, log_vars = self.gen_loss(disc_pred_fake)
optimizer_wrapper.update_params(parsed_loss)
return log_vars