torchlogix.layers.LogicConv2d

class torchlogix.layers.LogicConv2d(in_dim, device='cuda', grad_factor=1.0, channels=1, num_kernels=16, tree_depth=None, receptive_field_size=None, implementation=None, connections='random', weight_init='residual', stride=1, padding=0, parametrization='raw', temperature=1.0, forward_sampling='soft')

2d convolutional layer with differentiable logic operations.

This layer implements a 2d convolution with differentiable logic operations. It uses a binary tree structure to combine input features using logical operations.

__init__(in_dim, device='cuda', grad_factor=1.0, channels=1, num_kernels=16, tree_depth=None, receptive_field_size=None, implementation=None, connections='random', weight_init='residual', stride=1, padding=0, parametrization='raw', temperature=1.0, forward_sampling='soft')

Initialize the 2d logic convolutional layer.

Parameters:

• in_dim (Union[int, tuple[int, int]]) – Input dimensions (height, width)

• device (str) – Device to run the layer on

• grad_factor (float) – Gradient factor for the logic operations

• channels (int) – Number of input channels

• num_kernels (int) – Number of output kernels

• tree_depth (int) – Depth of the binary tree

• receptive_field_size (int) – Size of the receptive field

• implementation (str) – Implementation type (“python” or “cuda”)

• connections (str) – Connection type: “random” or “unique”. The latter will overwrite the tree_depth parameter and use a full binary tree of all possible connections within the receptive field.

• stride (int) – Stride of the convolution

• padding (int) – Padding of the convolution

• parametrization (str) – Parametrization to use (“raw” or “walsh”)

Methods

__init__(in_dim[, device, grad_factor, ...])	Initialize the 2d logic convolutional layer.
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.
apply_sliding_window(pairs_tuple)	Apply sliding window to the receptive field pairs across all kernel positions.
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)	Implement the binary tree using the pre-selected indices.
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_indices_from_kernel_pairs(pairs_tuple)
get_parameter(target)	Return the parameter given by target if it exists, otherwise throw an error.
get_random_receptive_field_pairs()	Generate random index pairs within the receptive field for each kernel.
get_random_unique_receptive_field_pairs()	Generate random unique index pairs within the receptive field for each kernel.
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.
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__(in_dim, device='cuda', grad_factor=1.0, channels=1, num_kernels=16, tree_depth=None, receptive_field_size=None, implementation=None, connections='random', weight_init='residual', stride=1, padding=0, parametrization='raw', temperature=1.0, forward_sampling='soft')

Initialize the 2d logic convolutional layer.

Parameters:

• in_dim (Union[int, tuple[int, int]]) – Input dimensions (height, width)

• device (str) – Device to run the layer on

• grad_factor (float) – Gradient factor for the logic operations

• channels (int) – Number of input channels

• num_kernels (int) – Number of output kernels

• tree_depth (int) – Depth of the binary tree

• receptive_field_size (int) – Size of the receptive field

• implementation (str) – Implementation type (“python” or “cuda”)

• connections (str) – Connection type: “random” or “unique”. The latter will overwrite the tree_depth parameter and use a full binary tree of all possible connections within the receptive field.

• stride (int) – Stride of the convolution

• padding (int) – Padding of the convolution

• parametrization (str) – Parametrization to use (“raw” or “walsh”)

forward(x)

Implement the binary tree using the pre-selected indices.

get_random_receptive_field_pairs()

Generate random index pairs within the receptive field for each kernel. May contain self connections and duplicate connections.

get_random_unique_receptive_field_pairs()

Generate random unique index pairs within the receptive field for each kernel. No self-connections or duplicate pairs.

apply_sliding_window(pairs_tuple)

Apply sliding window to the receptive field pairs across all kernel positions.

get_indices_from_kernel_pairs(pairs_tuple)