-
-
-
-
-
-
-
-
-
-
-
-
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
inputs (Tensor or tuple[Tensor, ...]) – Input for which attribution +
inputs (Tensor or tuple[Tensor, ...]) – Input for which attribution is computed. It can be provided as a single tensor or a tuple of multiple tensors. If multiple input tensors are provided, the batch sizes must be aligned across all @@ -90,7 +90,7 @@
attributions (Tensor or tuple[Tensor, ...]) – Attribution scores that +
attributions (Tensor or tuple[Tensor, ...]) – Attribution scores that are precomputed by an attribution algorithm. Attributions can be provided in form of a single tensor or a tuple of those. It is assumed that attribution @@ -171,10 +171,10 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
layer (torch.nn.Module) – Layer for which output attributions are computed. +
layer (torch.nn.Module) – Layer for which output attributions are computed. Output size of attribute matches that of layer output.
device_ids (list[int]) – Device ID list, necessary only if forward_func applies a DataParallel model, which allows reconstruction of @@ -193,7 +193,7 @@
layer_attribution (Tensor) – Tensor of given layer attributions.
layer_attribution (Tensor) – Tensor of given layer attributions.
interpolate_dims (int or tuple) – Upsampled dimensions. The number of elements must be the number of dimensions of layer_attribution - 2, since the first dimension @@ -242,10 +242,10 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
layer (torch.nn.Module) – Layer for which output attributions are computed. +
layer (torch.nn.Module) – Layer for which output attributions are computed. Output size of attribute matches that of layer output.
device_ids (list[int]) – Device ID list, necessary only if forward_func applies a DataParallel model, which allows reconstruction of @@ -303,7 +303,7 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
attributions (Tensor or tuple[Tensor, ...]) – Precomputed attribution +
attributions (Tensor or tuple[Tensor, ...]) – Precomputed attribution scores. The user can compute those using any attribution algorithm. It is assumed the shape and the dimensionality of attributions must match the shape and @@ -334,17 +334,17 @@
start_point (Tensor or tuple[Tensor, ...], optional) – start_point +
start_point (Tensor or tuple[Tensor, ...], optional) – start_point is passed as an input to model’s forward function. It is the starting point of attributions’ approximation. It is assumed that both start_point and end_point have the same shape and dimensionality.
end_point (Tensor or tuple[Tensor, ...]) – end_point is passed as an input to model’s forward function. It is the end point of attributions’ approximation. It is assumed that both start_point and end_point have the same shape and dimensionality.
target (int, tuple, Tensor, or list, optional) –
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -413,7 +413,7 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
inputs (Tensor or tuple[Tensor, ...]) – Input for which attribution +
inputs (Tensor or tuple[Tensor, ...]) – Input for which attribution is computed. It can be provided as a single tensor or a tuple of multiple tensors. If multiple input tensors are provided, the batch sizes must be aligned across all @@ -90,7 +90,7 @@
attributions (Tensor or tuple[Tensor, ...]) – Attribution scores that +
attributions (Tensor or tuple[Tensor, ...]) – Attribution scores that are precomputed by an attribution algorithm. Attributions can be provided in form of a single tensor or a tuple of those. It is assumed that attribution @@ -171,10 +171,10 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
layer (torch.nn.Module) – Layer for which output attributions are computed. +
layer (torch.nn.Module) – Layer for which output attributions are computed. Output size of attribute matches that of layer output.
device_ids (list[int]) – Device ID list, necessary only if forward_func applies a DataParallel model, which allows reconstruction of @@ -193,7 +193,7 @@
layer_attribution (Tensor) – Tensor of given layer attributions.
layer_attribution (Tensor) – Tensor of given layer attributions.
interpolate_dims (int or tuple) – Upsampled dimensions. The number of elements must be the number of dimensions of layer_attribution - 2, since the first dimension @@ -242,10 +242,10 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
layer (torch.nn.Module) – Layer for which output attributions are computed. +
layer (torch.nn.Module) – Layer for which output attributions are computed. Output size of attribute matches that of layer output.
device_ids (list[int]) – Device ID list, necessary only if forward_func applies a DataParallel model, which allows reconstruction of @@ -303,7 +303,7 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
attributions (Tensor or tuple[Tensor, ...]) – Precomputed attribution +
attributions (Tensor or tuple[Tensor, ...]) – Precomputed attribution scores. The user can compute those using any attribution algorithm. It is assumed the shape and the dimensionality of attributions must match the shape and @@ -334,17 +334,17 @@
start_point (Tensor or tuple[Tensor, ...], optional) – start_point +
start_point (Tensor or tuple[Tensor, ...], optional) – start_point is passed as an input to model’s forward function. It is the starting point of attributions’ approximation. It is assumed that both start_point and end_point have the same shape and dimensionality.
end_point (Tensor or tuple[Tensor, ...]) – end_point is passed as an input to model’s forward function. It is the end point of attributions’ approximation. It is assumed that both start_point and end_point have the same shape and dimensionality.
target (int, tuple, Tensor, or list, optional) –
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -413,7 +413,7 @@
forward_func (Callable or torch.nn.Module) – This can either be an instance +
forward_func (Callable or torch.nn.Module) – This can either be an instance of pytorch model or any modification of model’s forward function.
n_gates (int) – number of gates.
mask (Tensor, optional) – If provided, this allows grouping multiple +
mask (Tensor, optional) – If provided, this allows grouping multiple input tensor elements to share the same stochastic gate. This tensor should be broadcastable to match the input shape and contain integers in the range 0 to n_gates - 1. @@ -103,7 +103,7 @@
input_tensor (Tensor) – Tensor to be gated with stochastic gates
+input_tensor (Tensor) – Tensor to be gated with stochastic gates
n_gates (int) – number of gates.
mask (Tensor, optional) – If provided, this allows grouping multiple +
mask (Tensor, optional) – If provided, this allows grouping multiple input tensor elements to share the same stochastic gate. This tensor should be broadcastable to match the input shape and contain integers in the range 0 to n_gates - 1. @@ -103,7 +103,7 @@
input_tensor (Tensor) – Tensor to be gated with stochastic gates
+input_tensor (Tensor) – Tensor to be gated with stochastic gates
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which predictions +
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which predictions are performed and attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. @@ -232,7 +232,7 @@
experimental_sets (list[list[Concept]]) – A list of list of Concept instances.
target (int, tuple, Tensor, or list, optional) –
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which attributions are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -364,7 +364,7 @@
model (torch.nn.Module) – An instance of pytorch model.
+model (torch.nn.Module) – An instance of pytorch model.
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which concept-based +
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which concept-based interpretation scores are computed. It can be provided as a single tensor or a tuple of multiple tensors. If multiple input tensors are provided, the batch size (the first @@ -548,7 +548,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which predictions +
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which predictions are performed and attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. @@ -232,7 +232,7 @@
experimental_sets (list[list[Concept]]) – A list of list of Concept instances.
target (int, tuple, Tensor, or list, optional) –
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which attributions are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -364,7 +364,7 @@
model (torch.nn.Module) – An instance of pytorch model.
+model (torch.nn.Module) – An instance of pytorch model.
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which concept-based +
inputs (Tensor or tuple[Tensor, ...]) – Inputs for which concept-based interpretation scores are computed. It can be provided as a single tensor or a tuple of multiple tensors. If multiple input tensors are provided, the batch size (the first @@ -548,7 +548,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -67,7 +67,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -143,7 +143,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -67,7 +67,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -143,7 +143,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -100,7 +100,7 @@
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional) –
Baselines define reference samples that are compared with +
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional) –
Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. @@ -130,7 +130,7 @@
Default: None
target (int, tuple, Tensor, or list, optional) –
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -263,7 +263,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -100,7 +100,7 @@
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional) –
Baselines define reference samples that are compared with +
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional) –
Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. @@ -130,7 +130,7 @@
Default: None
target (int, tuple, Tensor, or list, optional) –
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -263,7 +263,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -89,7 +89,7 @@
Baselines define reference samples that are compared with +
baselines (Tensor, tuple[Tensor, ...], or Callable) –
Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. Baselines can be provided as:
@@ -112,7 +112,7 @@Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -231,7 +231,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -89,7 +89,7 @@
Baselines define reference samples that are compared with +
baselines (Tensor, tuple[Tensor, ...], or Callable) –
Baselines define reference samples that are compared with the inputs. In order to assign attribution scores DeepLift computes the differences between the inputs/outputs and corresponding references. Baselines can be provided as:
@@ -112,7 +112,7 @@Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -231,7 +231,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which ablation +
inputs (Tensor or tuple[Tensor, ...]) – Input for which ablation attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple @@ -72,7 +72,7 @@
Baselines define reference value which replaces each +
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional) –
Baselines define reference value which replaces each feature when ablated. Baselines can be provided as:
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -138,7 +138,7 @@
feature_mask (Tensor or tuple[Tensor, ...], optional) – feature_mask defines a mask for the input, grouping features which should be ablated together. feature_mask should contain the same number of tensors as inputs. Each tensor should @@ -254,7 +254,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which ablation +
inputs (Tensor or tuple[Tensor, ...]) – Input for which ablation attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple @@ -72,7 +72,7 @@
Baselines define reference value which replaces each +
baselines (scalar, Tensor, tuple of scalar, or Tensor, optional) –
Baselines define reference value which replaces each feature when ablated. Baselines can be provided as:
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -138,7 +138,7 @@
feature_mask (Tensor or tuple[Tensor, ...], optional) – feature_mask defines a mask for the input, grouping features which should be ablated together. feature_mask should contain the same number of tensors as inputs. Each tensor should @@ -254,7 +254,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which permutation attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If @@ -108,7 +108,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which difference is computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -147,7 +147,7 @@
feature_mask defines a mask for the input, grouping +
feature_mask (Tensor or tuple[Tensor, ...], optional) –
feature_mask defines a mask for the input, grouping features which should be ablated together. feature_mask should contain the same number of tensors as inputs. Each tensor should be the same size as the @@ -258,7 +258,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which permutation attributions are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If @@ -108,7 +108,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which difference is computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -147,7 +147,7 @@
feature_mask defines a mask for the input, grouping +
feature_mask (Tensor or tuple[Tensor, ...], optional) –
feature_mask defines a mask for the input, grouping features which should be ablated together. feature_mask should contain the same number of tensors as inputs. Each tensor should be the same size as the @@ -258,7 +258,15 @@
n_gates (int) – number of gates.
mask (Tensor, optional) – If provided, this allows grouping multiple +
mask (Tensor, optional) – If provided, this allows grouping multiple input tensor elements to share the same stochastic gate. This tensor should be broadcastable to match the input shape and contain integers in the range 0 to n_gates - 1. @@ -90,7 +90,7 @@
input_tensor (Tensor) – Tensor to be gated with stochastic gates
+input_tensor (Tensor) – Tensor to be gated with stochastic gates
n_gates (int) – number of gates.
mask (Tensor, optional) – If provided, this allows grouping multiple +
mask (Tensor, optional) – If provided, this allows grouping multiple input tensor elements to share the same stochastic gate. This tensor should be broadcastable to match the input shape and contain integers in the range 0 to n_gates - 1. @@ -90,7 +90,7 @@
input_tensor (Tensor) – Tensor to be gated with stochastic gates
+input_tensor (Tensor) – Tensor to be gated with stochastic gates
inputs (Tensor or tuple[Tensor, ...]) – Input for which SHAP attribution +
inputs (Tensor or tuple[Tensor, ...]) – Input for which SHAP attribution values are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple @@ -92,7 +92,7 @@
Baselines define the starting point from which expectation +
baselines (Tensor, tuple[Tensor, ...], or Callable) –
Baselines define the starting point from which expectation is computed and can be provided as:
a single tensor, if inputs is a single tensor, with @@ -126,7 +126,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -245,7 +245,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which SHAP attribution +
inputs (Tensor or tuple[Tensor, ...]) – Input for which SHAP attribution values are computed. If forward_func takes a single tensor as input, a single input tensor should be provided. If forward_func takes multiple tensors as input, a tuple @@ -92,7 +92,7 @@
Baselines define the starting point from which expectation +
baselines (Tensor, tuple[Tensor, ...], or Callable) –
Baselines define the starting point from which expectation is computed and can be provided as:
a single tensor, if inputs is a single tensor, with @@ -126,7 +126,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -245,7 +245,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -64,7 +64,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -140,7 +140,15 @@
inputs (Tensor or tuple[Tensor, ...]) – Input for which attributions are computed. If model takes a single tensor as input, a single input tensor should be provided. If model takes multiple tensors as input, a tuple @@ -64,7 +64,7 @@
Output indices for +
target (int, tuple, Tensor, or list, optional) –
Output indices for which gradients are computed (for classification cases, this is usually the target class). If the network returns a scalar value per example, @@ -140,7 +140,15 @@