FastSurferCNN.models.losses

class FastSurferCNN.models.losses.CombinedLoss(weight_dice=1, weight_ce=1)

For CrossEntropy the input has to be a long tensor.

Attributes

cross_entropy_loss	Results of cross entropy loss.
dice_loss	Results of dice loss.
weight_dice	Weight for dice loss.
weight_ce	Weight for float.

Methods

add_module(name, module)	Add a child module to the current module.
apply(fn)	Apply fn recursively to every submodule (as returned by .children()) as well as self.
bfloat16()	Casts all floating point parameters and buffers to bfloat16 datatype.
buffers([recurse])	Return an iterator over module buffers.
children()	Return an iterator over immediate children modules.
compile(*args, **kwargs)	Compile this Module's forward using torch.compile().
cpu()	Move all model parameters and buffers to the CPU.
cuda([device])	Move all model parameters and buffers to the GPU.
double()	Casts all floating point parameters and buffers to double datatype.
eval()	Set the module in evaluation mode.
extra_repr()	Set the extra representation of the module.
float()	Casts all floating point parameters and buffers to float datatype.
forward(inputx, target, weight)	Calculate the total loss, dice loss and cross entropy value for the given input.
get_buffer(target)	Return the buffer given by target if it exists, otherwise throw an error.
get_extra_state()	Return any extra state to include in the module's state_dict.
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_submodule(target)	Return the submodule given by target if it exists, otherwise throw an error.
half()	Casts all floating point parameters and buffers to half datatype.
ipu([device])	Move all model parameters and buffers to the IPU.
load_state_dict(state_dict[, strict, assign])	Copy parameters and buffers from state_dict into this module and its descendants.
modules()	Return an iterator over all modules in the network.
named_buffers([prefix, recurse, ...])	Return an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children()	Return an iterator over immediate children modules, yielding both the name of the module as well as the module itself.
named_modules([memo, prefix, remove_duplicate])	Return an iterator over all modules in the network, yielding both the name of the module as well as the module itself.
named_parameters([prefix, recurse, ...])	Return an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters([recurse])	Return an iterator over module parameters.
register_backward_hook(hook)	Register a backward hook on the module.
register_buffer(name, tensor[, persistent])	Add a buffer to the module.
register_forward_hook(hook, *[, prepend, ...])	Register a forward hook on the module.
register_forward_pre_hook(hook, *[, ...])	Register a forward pre-hook on the module.
register_full_backward_hook(hook[, prepend])	Register a backward hook on the module.
register_full_backward_pre_hook(hook[, prepend])	Register a backward pre-hook on the module.
register_load_state_dict_post_hook(hook)	Register a post hook to be run after module's load_state_dict is called.
register_module(name, module)	Alias for add_module().
register_parameter(name, param)	Add a parameter to the module.
register_state_dict_pre_hook(hook)	Register a pre-hook for the load_state_dict() method.
requires_grad_([requires_grad])	Change if autograd should record operations on parameters in this module.
set_extra_state(state)	Set extra state contained in the loaded state_dict.
share_memory()	See torch.Tensor.share_memory_().
state_dict(*args[, destination, prefix, ...])	Return a dictionary containing references to the whole state of the module.
to(*args, **kwargs)	Move and/or cast the parameters and buffers.
to_empty(*, device[, recurse])	Move the parameters and buffers to the specified device without copying storage.
train([mode])	Set the module in training mode.
type(dst_type)	Casts all parameters and buffers to dst_type.
xpu([device])	Move all model parameters and buffers to the XPU.
zero_grad([set_to_none])	Reset gradients of all model parameters.

__call__

forward(inputx, target, weight)

Calculate the total loss, dice loss and cross entropy value for the given input.

Parameters:

inputxTensor

A Tensor of shape N x C x H x W containing the input x values.

targetTensor

A Tensor of shape N x H x W of integers containing the target.

weightTensor

A Tensor of shape N x H x W of floats containg the weights.

Returns:

Tensor

Total loss.

Tensor

Dice loss.

Tensor

Cross entropy value.

class FastSurferCNN.models.losses.CrossEntropy2D(weight=None, reduction='none')

2D Cross-entropy loss implemented as negative log likelihood.

Attributes

nll_loss

Calculated cross-entropy loss.

Methods

forward(inputs, targets)

Feedforward.

forward(inputs, targets)

Feedforward.

class FastSurferCNN.models.losses.DiceLoss(size_average=None, reduce=None, reduction='mean')

Calculate Dice Loss.

Methods

forward(output, target[, weights, ignore_index])

Calulate the DiceLoss.

forward(output, target, weights=None, ignore_index=None)

Calulate the DiceLoss.

Parameters:

outputTensor

N x C x H x W Variable.

targetTensor

N x C x W LongTensor with starting class at 0.

weightsint, optional

C FloatTensor with class wise weights(Default value = None).

ignore_indexint, optional

Ignore label with index x in the loss calculation (Default value = None).

Returns:

torch.Tensor

Calculated Diceloss.

FastSurferCNN.models.losses.get_loss_func(cfg)

Give a default object of the loss function.

Parameters:

cfgyacs.config.CfgNode

Configuration node, containing searched loss function. The model loss function can either be ‘combined’, ‘ce’ or ‘dice’.

Returns:

CombinedLoss

Total loss.

CrossEntropy2D

Cross entropy value.

DiceLoss

Dice loss.

Raises:

NotImplementedError

Requested loss function is not implemented.