incerto.bayesian.MCDropout

incerto.bayesian.MCDropout#

class incerto.bayesian.MCDropout(model, num_samples=20, dropout_rate=0.1)[source]#

Bases: Module

Monte Carlo Dropout for uncertainty estimation.

Applies dropout at test time and aggregates predictions from multiple forward passes to estimate predictive uncertainty.

Reference:

Gal & Ghahramani, “Dropout as a Bayesian Approximation” (ICML 2016)

Parameters:

  • model (Module) – Base neural network

  • num_samples (int) – Number of MC samples for inference (default: 20)

  • dropout_rate (float) – Dropout probability (default: 0.1)

Example

>>> backbone = ResNet18(num_classes=10)
>>> mc_model = MCDropout(backbone, num_samples=20)
>>> mean, variance = mc_model.predict(x)
__init__(model, num_samples=20, dropout_rate=0.1)[source]#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Parameters:

Methods

__init__(model[, num_samples, dropout_rate])

Initialize internal Module state, shared by both nn.Module and ScriptModule.

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()

Return the extra representation of the module.

float()

Casts all floating point parameters and buffers to float datatype.

forward(x)

Single forward pass (for training).

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.

mtia([device])

Move all model parameters and buffers to the MTIA.

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.

predict(x[, return_samples])

Monte Carlo prediction with uncertainty estimation.

predict_entropy(x)

Compute predictive entropy (total uncertainty).

predict_mutual_information(x)

Compute mutual information (epistemic uncertainty).

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_load_state_dict_pre_hook(hook)

Register a pre-hook to be run before 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_post_hook(hook)

Register a post-hook for the state_dict() method.

register_state_dict_pre_hook(hook)

Register a pre-hook for the 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.

set_submodule(target, module[, strict])

Set the submodule given by target if it exists, otherwise throw an error.

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.

Attributes

T_destination

call_super_init

dump_patches

training

__init__(model, num_samples=20, dropout_rate=0.1)[source]#

Initialize internal Module state, shared by both nn.Module and ScriptModule.

Parameters:
forward(x)[source]#

Single forward pass (for training).

Parameters:

x (Tensor)

Return type:

Tensor

predict(x, return_samples=False)[source]#

Monte Carlo prediction with uncertainty estimation.

Parameters:

  • x (Tensor) – Input tensor (N, *)

  • return_samples (bool) – If True, return all MC samples

Return type:

Tuple[Tensor, Tensor]

Returns:

Tuple of (mean_prediction, predictive_variance) If return_samples=True: (mean, variance, samples)

predict_entropy(x)[source]#

Compute predictive entropy (total uncertainty).

Parameters:

x (Tensor) – Input tensor

Return type:

Tensor

Returns:

Predictive entropy for each sample

predict_mutual_information(x)[source]#

Compute mutual information (epistemic uncertainty).

MI = H[y|x] - E[H[y|x,θ]]

Parameters:

x (Tensor) – Input tensor

Return type:

Tensor

Returns:

Mutual information for each sample

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