trw.train.guided_back_propagation

Module Contents

Classes

GuidedBackprop

Produces gradients generated with guided back propagation from the given image

Functions

post_process_output_id(output: trw.train.outputs_trw.Output) → torch.Tensor

post_process_output_for_gradient_attribution(output: trw.train.outputs_trw.Output)

Postptocess the output to be suitable for gradient attribution.

Attributes

logger

trw.train.guided_back_propagation.logger
trw.train.guided_back_propagation.post_process_output_id(output: trw.train.outputs_trw.Output) → torch.Tensor
trw.train.guided_back_propagation.post_process_output_for_gradient_attribution(output: trw.train.outputs_trw.Output)

Postptocess the output to be suitable for gradient attribution.

In particular, if we have a trw.train.OutputClassification, we need to apply a softmax operation so that we can backpropagate the loss of a particular class with the appropriate value (1.0).

Parameters

output – a trw.train.OutputClassification

Returns

a torch.Tensor

class trw.train.guided_back_propagation.GuidedBackprop(model: torch.nn.Module, unguided_gradient: bool = False, post_process_output: Callable[[Any], torch.Tensor] = post_process_output_id)

Produces gradients generated with guided back propagation from the given image

update_relus(self) → None
Updates relu activation functions so that

1- stores output in forward pass 2- imputes zero for gradient values that are less than zero

static get_floating_inputs_with_gradients(inputs)

Extract inputs that have a gradient

Parameters

inputs – a tensor of dictionary of tensors

Returns

Return a list of tuple (name, input) for the input that have a gradient

__call__(self, inputs: Tuple[torch.Tensor, trw.basic_typing.Batch], target_class: int, target_class_name: str) → Optional[Tuple[str, Mapping]]

Generate the guided back-propagation gradient

Parameters

  • inputs – a tensor or dictionary of tensors

  • target_class – the target class to be explained

  • target_class_name – the name of the output class if multiple outputs

Returns

a tuple (output_name, dictionary (input, gradient))

static get_positive_negative_saliency(gradient: torch.Tensor) → Tuple[torch.Tensor, torch.Tensor]

Generates positive and negative saliency maps based on the gradient

Parameters

gradient (numpy arr) – Gradient of the operation to visualize

Returns

pos_saliency ( )