avalanche.models.MultiHeadClassifier

class avalanche.models.MultiHeadClassifier(in_features, initial_out_features=2, masking=True, mask_value=-1000)

Multi-head classifier with separate heads for each task.

Typically used in task-incremental benchmarks where task labels are available and provided to the model.

Note

Each output head may have a different shape, and the number of classes can be determined automatically.

However, since pytorch doest not support jagged tensors, when you compute a minibatch’s output you must ensure that each sample has the same output size, otherwise the model will fail to concatenate the samples together.

These can be easily ensured in two possible ways:

• each minibatch contains a single task, which is the case in most

  common benchmarks in Avalanche. Some exceptions to this setting are multi-task replay or cumulative strategies.

• each head has the same size, which can be enforced by setting a

  large enough initial_out_features.

__init__(in_features, initial_out_features=2, masking=True, mask_value=-1000)

Init.

Parameters:

• in_features – number of input features.

• initial_out_features – initial number of classes (can be dynamically expanded).

• masking – whether unused units should be masked (default=True).

• mask_value – the value used for masked units (default=-1000).

Methods

__init__(in_features[, ...])	Init.
adaptation(experience)	If dataset contains new tasks, a new head is initialized.
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(x, task_labels)	compute the output given the input x and task labels.
forward_all_tasks(x)	compute the output given the input x and task label.
forward_single_task(x, task_label)	compute the output given the input x.
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.
pre_adapt(agent, experience)	Calls self.adaptation recursively accross the hierarchy of pytorch module childrens
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 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.

Attributes

T_destination
active_units
call_super_init
dump_patches
task_masks
training
known_train_tasks_labels	Set of task labels encountered up to now.