GANs¶

Collection of Generative Adversarial Networks

Basic GAN¶

This is a vanilla GAN. This model can work on any dataset size but results are shown for MNIST. Replace the encoder, decoder or any part of the training loop to build a new method, or simply finetune on your data.

Implemented by:

William Falcon

Example outputs:

Loss curves:

from pl_bolts.models.gans import GAN
...
gan = GAN()
trainer = Trainer()
trainer.fit(gan)

class pl_bolts.models.gans.GAN(input_channels, input_height, input_width, latent_dim=32, learning_rate=0.0002, **kwargs)[source]

Bases: pytorch_lightning.

Vanilla GAN implementation.

Example:

from pl_bolts.models.gans import GAN

m = GAN()
Trainer(gpus=2).fit(m)

Example CLI:

# mnist
python  basic_gan_module.py --gpus 1

# imagenet
python  basic_gan_module.py --gpus 1 --dataset 'imagenet2012'
--data_dir /path/to/imagenet/folder/ --meta_dir ~/path/to/meta/bin/folder
--batch_size 256 --learning_rate 0.0001

Parameters

input_channels¶ (int) – number of channels of an image
input_height¶ (int) – image height
input_width¶ (int) – image width
latent_dim¶ (int) – emb dim for encoder
learning_rate¶ (float) – the learning rate

forward(z)[source]

Generates an image given input noise z.

Example:

z = torch.rand(batch_size, latent_dim)
gan = GAN.load_from_checkpoint(PATH)
img = gan(z)

DCGAN¶

DCGAN implementation from the paper Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks. The implementation is based on the version from PyTorch’s examples.

Implemented by:

Christoph Clement

Example MNIST outputs:

Example LSUN bedroom outputs:

MNIST Loss curves:

LSUN Loss curves:

class pl_bolts.models.gans.DCGAN(beta1=0.5, feature_maps_gen=64, feature_maps_disc=64, image_channels=1, latent_dim=100, learning_rate=0.0002, **kwargs)[source]

Bases: pytorch_lightning.

DCGAN implementation.

Example:

from pl_bolts.models.gans import DCGAN

m = DCGAN()
Trainer(gpus=2).fit(m)

Example CLI:

# mnist
python dcgan_module.py --gpus 1

# cifar10
python dcgan_module.py --gpus 1 --dataset cifar10 --image_channels 3

Parameters

beta1¶ (float) – Beta1 value for Adam optimizer
feature_maps_gen¶ (int) – Number of feature maps to use for the generator
feature_maps_disc¶ (int) – Number of feature maps to use for the discriminator
image_channels¶ (int) – Number of channels of the images from the dataset
latent_dim¶ (int) – Dimension of the latent space
learning_rate¶ (float) – Learning rate

forward(noise)[source]

Generates an image given input noise.

Example:

noise = torch.rand(batch_size, latent_dim)
gan = GAN.load_from_checkpoint(PATH)
img = gan(noise)

Return type: Tensor