norse.torch.module.receptive_field.TemporalReceptiveField#
- class norse.torch.module.receptive_field.TemporalReceptiveField(shape: ~torch.Size, n_scales: int = 4, activation: ~typing.Type[~norse.torch.module.snn.SNNCell] = <class 'norse.torch.module.leaky_integrator_box.LIBoxCell'>, activation_state_map: ~typing.Callable[[~torch.Tensor], ~typing.NamedTuple] = <function TemporalReceptiveField.<lambda>>, min_scale: float = 1, max_scale: ~typing.Optional[float] = None, c: float = 1.41421, time_constants: ~typing.Optional[~torch.Tensor] = None, dt: float = 0.001)[source]#
Creates
n_scales
temporal receptive fields for arbitrary n-dimensional inputs. The scale spaces are selected in a range of [min_scale, max_scale] using an exponential distribution, scattered usingtorch.linspace
.- Parameters:
shape (torch.Size): The shape of the incoming tensor, where the first dimension denote channels n_scales (int): The number of temporal scale spaces to iterate over. activation (SNNCell): The activation neuron. Defaults to LIBoxCell activation_state_map (Callable): A function that takes a tensor and provides a neuron parameter tuple.
Required if activation is changed, since the default behaviour provides LIBoxParameters.
min_scale (float): The minimum scale space. Defaults to 1. max_scale (Optional[float]): The maximum scale. Defaults to None. If set, c is ignored. c (Optional[float]): The base from which to generate scale values. Should be a value
between 1 to 2, exclusive. Defaults to sqrt(2). Ignored if max_scale is set.
time_constants (Optional[torch.Tensor]): Hardcoded time constants. Will overwrite the automatically generated, logarithmically distributed scales, if set. Defaults to None. dt (float): Neuron simulation timestep. Defaults to 0.001.
- __init__(shape: ~torch.Size, n_scales: int = 4, activation: ~typing.Type[~norse.torch.module.snn.SNNCell] = <class 'norse.torch.module.leaky_integrator_box.LIBoxCell'>, activation_state_map: ~typing.Callable[[~torch.Tensor], ~typing.NamedTuple] = <function TemporalReceptiveField.<lambda>>, min_scale: float = 1, max_scale: ~typing.Optional[float] = None, c: float = 1.41421, time_constants: ~typing.Optional[~torch.Tensor] = None, dt: float = 0.001)[source]#
Initializes internal Module state, shared by both nn.Module and ScriptModule.
Methods
__init__
(shape[, n_scales, activation, ...])Initializes internal Module state, shared by both nn.Module and ScriptModule.
add_module
(name, module)Adds a child module to the current module.
apply
(fn)Applies
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])Returns an iterator over module buffers.
children
()Returns an iterator over immediate children modules.
compile
(*args, **kwargs)Compile this Module's forward using
torch.compile()
.cpu
()Moves all model parameters and buffers to the CPU.
cuda
([device])Moves all model parameters and buffers to the GPU.
double
()Casts all floating point parameters and buffers to
double
datatype.eval
()Sets 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[, state])Defines the computation performed at every call.
get_buffer
(target)Returns the buffer given by
target
if it exists, otherwise throws an error.get_extra_state
()Returns any extra state to include in the module's state_dict.
get_parameter
(target)Returns the parameter given by
target
if it exists, otherwise throws an error.get_submodule
(target)Returns the submodule given by
target
if it exists, otherwise throws an error.half
()Casts all floating point parameters and buffers to
half
datatype.ipu
([device])Moves all model parameters and buffers to the IPU.
load_state_dict
(state_dict[, strict, assign])Copies parameters and buffers from
state_dict
into this module and its descendants.modules
()Returns an iterator over all modules in the network.
named_buffers
([prefix, recurse, ...])Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.
named_children
()Returns 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])Returns 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, ...])Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.
parameters
([recurse])Returns an iterator over module parameters.
register_backward_hook
(hook)Registers a backward hook on the module.
register_buffer
(name, tensor[, persistent])Adds a buffer to the module.
register_forward_hook
(hook, *[, prepend, ...])Registers a forward hook on the module.
register_forward_pre_hook
(hook, *[, ...])Registers a forward pre-hook on the module.
register_full_backward_hook
(hook[, prepend])Registers a backward hook on the module.
register_full_backward_pre_hook
(hook[, prepend])Registers a backward pre-hook on the module.
register_load_state_dict_post_hook
(hook)Registers 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)Adds a parameter to the module.
register_state_dict_pre_hook
(hook)These hooks will be called with arguments:
self
,prefix
, andkeep_vars
before callingstate_dict
onself
.requires_grad_
([requires_grad])Change if autograd should record operations on parameters in this module.
set_extra_state
(state)This function is called from
load_state_dict()
to handle any extra state found within the state_dict.share_memory
()state_dict
(*args[, destination, prefix, ...])Returns a dictionary containing references to the whole state of the module.
to
(*args, **kwargs)Moves and/or casts the parameters and buffers.
to_empty
(*, device[, recurse])Moves the parameters and buffers to the specified device without copying storage.
train
([mode])Sets the module in training mode.
type
(dst_type)Casts all parameters and buffers to
dst_type
.xpu
([device])Moves all model parameters and buffers to the XPU.
zero_grad
([set_to_none])Resets gradients of all model parameters.
Attributes
T_destination
call_super_init
dump_patches