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ldctbench.utils.training_utils

PerceptualLoss(network, device, in_ch=3, layers=[3, 8, 15, 22], norm='l1', return_features=False)

Bases: Module

The layers argument defines where to extract the activations. In the default paper, style losses are computed at: 3: "relu1_2", 8: "relu2_2", 15: "relu3_3", 22: "relu4_3" and perceptual (content) loss is evaluated at: 15: "relu3_3". In1 the content is evaluated in vgg19 after the 16th (last) conv layer (layer 34)

  1. Q. Yang et al., “Low-dose CT image denoising using a generative adversarial network with wasserstein distance and perceptual loss,” IEEE Transactions on Medical Imaging, vol. 37, no. 6, pp. 1348–1357, Jun. 2018. ↩

Parameters:

  • network (str) –

    Which VGG flavor to use, must be "vgg16" or "vgg19"

  • device (device) –

    Torch device to use

  • in_ch (int, default: 3 ) –

    Number of input channels, by default 3

  • layers (List[int], default: [3, 8, 15, 22] ) –

    Number of layers at which to extract features, by default [3, 8, 15, 22]

  • norm (str, default: 'l1' ) –

    Pixelwise, must be "l1" or "mse", by default "l1"

  • return_features (bool, default: False ) –

    description, by default False

Raises:

  • ValueError –

    norm is neither "l1" nor "mse".

repeat_ch(in_ch)

Bases: object

Class to repeat input 3 times in channel dimension if in_ch == 1

Parameters:

  • in_ch (int) –

    Number of input channels.

setup_dataloader(args, datasets)

Returns dict of dataloaders

Parameters:

  • args (Namespace) –

    Command line arguments

  • datasets (Dict[str, Dataset]) –

    Dictionary of datasets for each phase.

Returns:

  • Dict[str, DataLoader] –

    Dictionray of dataloaders for each phase.

setup_optimizer(args, parameters)

Setup optimizer for given model parameters

Parameters:

  • args (Namespace) –

    Command line arguments

  • parameters (Iterator[Parameter]) –

    Parameters to be optimized. For some model: nn.Module these can be received via model.parameters()

Returns:

  • Optimizer –

    Optimizer for the given parameters

Raises:

  • ValueError –

    If args.optimizer is not in "sgd" | "adam" | "rmsprop"