Segmentation

UNet2dPyTorch

class UNet2dPyTorch(num_classes, in_channels=1, depth=5, start_filts=64, up_mode='transpose', merge_mode='concat')

Bases: delira.models.abstract_network.AbstractPyTorchNetwork

The UNet2dPyTorch is a convolutional encoder-decoder neural network. Contextual spatial information (from the decoding, expansive pathway) about an input tensor is merged with information representing the localization of details (from the encoding, compressive pathway).

Notes

Differences to the original paper:

• padding is used in 3x3 convolutions to prevent loss of border

  pixels

• merging outputs does not require cropping due to (1)

• residual connections can be used by specifying

  merge_mode='add'

• if non-parametric upsampling is used in the decoder pathway (

  specified by upmode=’upsample’), then an additional 1x1 2d convolution occurs after upsampling to reduce channel dimensionality by a factor of 2. This channel halving happens with the convolution in the tranpose convolution (specified by upmode='transpose')

References

https://arxiv.org/abs/1505.04597

See also

UNet3dPyTorch

_build_model(num_classes, in_channels=3, depth=5, start_filts=64)

Builds the actual model

Parameters

• num_classes (int) – number of output classes

• in_channels (int) – number of channels for the input tensor (default: 1)

• depth (int) – number of MaxPools in the U-Net (default: 5)

• start_filts (int) – number of convolutional filters for the first conv (affects all other conv-filter numbers too; default: 64)

Notes

The Helper functions and classes are defined within this function because delira once offered a possibility to save the source code along the weights to completely recover the network without needing a manually created network instance and these helper functions had to be saved too.

_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 (ClassificationNetworkBasePyTorch) – trainable model

• data_dict (dict) – dictionary containing the 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 – 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(x)

Feed tensor through network

Parameters

x (torch.Tensor) –

Returns

Prediction

Return type

torch.Tensor

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

reset_params()

Initialize all parameters

static weight_init(m)

Initializes weights with xavier_normal and bias with zeros

Parameters

m (torch.nn.Module) – module to initialize

UNet3dPyTorch

class UNet3dPyTorch(num_classes, in_channels=3, depth=5, start_filts=64, up_mode='transpose', merge_mode='concat')

Bases: delira.models.abstract_network.AbstractPyTorchNetwork

The UNet3dPyTorch is a convolutional encoder-decoder neural network. Contextual spatial information (from the decoding, expansive pathway) about an input tensor is merged with information representing the localization of details (from the encoding, compressive pathway).

Notes

Differences to the original paper:

• Working on 3D data instead of 2D slices

• padding is used in 3x3x3 convolutions to prevent loss of border

  pixels

• merging outputs does not require cropping due to (1)

• residual connections can be used by specifying

  merge_mode='add'

• if non-parametric upsampling is used in the decoder pathway (

  specified by upmode=’upsample’), then an additional 1x1x1 3d convolution occurs after upsampling to reduce channel dimensionality by a factor of 2. This channel halving happens with the convolution in the tranpose convolution (specified by upmode='transpose')

References

https://arxiv.org/abs/1505.04597

See also

UNet2dPyTorch

_build_model(num_classes, in_channels=3, depth=5, start_filts=64)

Builds the actual model

Parameters

• num_classes (int) – number of output classes

• in_channels (int) – number of channels for the input tensor (default: 1)

• depth (int) – number of MaxPools in the U-Net (default: 5)

• start_filts (int) – number of convolutional filters for the first conv (affects all other conv-filter numbers too; default: 64)

Notes

The Helper functions and classes are defined within this function because delira once offered a possibility to save the source code along the weights to completely recover the network without needing a manually created network instance and these helper functions had to be saved too.

_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 (ClassificationNetworkBasePyTorch) – trainable model

• data_dict (dict) – dictionary containing the 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 – 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(x)

Feed tensor through network

Parameters

x (torch.Tensor) –

Returns

Prediction

Return type

torch.Tensor

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

reset_params()

Initialize all parameters

static weight_init(m)

Initializes weights with xavier_normal and bias with zeros

Parameters

m (torch.nn.Module) – module to initialize