Generative Adversarial Networks

GenerativeAdversarialNetworkBasePyTorch

class GenerativeAdversarialNetworkBasePyTorch(n_channels, noise_length, **kwargs)

Bases: delira.models.abstract_network.AbstractPyTorchNetwork

Implementation of Vanilla DC-GAN to create 64x64 pixel images

Notes

The fully connected part in the discriminator has been replaced with an equivalent convolutional part

References

https://arxiv.org/abs/1511.06434

See also

AbstractPyTorchNetwork

static _build_models(in_channels, noise_length, **kwargs)

Builds actual generator and discriminator models

Parameters

• in_channels (int) – number of channels for generated images by generator and inputs of discriminator

• noise_length (int) – length of noise vector (generator input)

• **kwargs – additional keyword arguments

Returns

• torch.nn.Sequential – generator

• torch.nn.Sequential – discriminator

_init_kwargs = {}

static closure(model, data_dict: dict, optimizers: dict, losses=None, metrics=None, fold=0, **kwargs)

closure method to do a single backpropagation step

Parameters

• model (ClassificationNetworkBase) – trainable model

• data_dict (dict) – dictionary containing data

• optimizers (dict) – dictionary of optimizers to optimize model’s parameters

• losses (dict) – dict holding the losses to calculate errors (gradients from different losses will be accumulated)

• metrics (dict) – dict holding the metrics to calculate

• fold (int) – Current Fold in Crossvalidation (default: 0)

• kwargs (dict) – additional keyword arguments

Returns

• dict – Metric values (with same keys as input dict metrics)

• dict – Loss values (with same keys as input dict losses)

• list – Arbitrary number of predictions as torch.Tensor

Raises

AssertionError – if optimizers or losses are empty or the optimizers are not specified

forward(real_image_batch)

Create fake images by feeding noise through generator and feed results and real images through discriminator

Parameters

real_image_batch (torch.Tensor) – batch of real images

Returns

• torch.Tensor – Generated fake images

• torch.Tensor – Discriminator prediction of fake images

• torch.Tensor – Discriminator prediction of real images

property init_kwargs

Returns all arguments registered as init kwargs

Returns

init kwargs

Return type

dict

static prepare_batch(batch: dict, input_device, output_device)

Helper Function to prepare Network Inputs and Labels (convert them to correct type and shape and push them to correct devices)

Parameters

• batch (dict) – dictionary containing all the data

• input_device (torch.device) – device for network inputs

• output_device (torch.device) – device for network outputs

Returns

dictionary containing data in correct type and shape and on correct device

Return type

dict