`tltorch.factorized_conv`.TTConv

class tltorch.factorized_conv.TTConv(*args, **kwargs)[source]

Create a convolution of arbitrary order with a Tucker kernel.

Parameters:

Parameters:	in_channelsint out_channelsint kernel_sizeint or int list if int, order MUST be specified if int list, then the conv will use order = len(kernel_size) rankint rank of the factorized kernel orderint, optional if kernel_size is a list see kernel_size implementation = {‘factorized’, ‘reconstructed’} strategy to use for the forward pass - factorized : the TT conv is expressed as a series of 1D convolutions - reconstructed : full kernel is reconstructed from the decomposition. the reconstruction is used to perform a regular forward pass strideint, default is 1 paddingint, default is 0 dilationint, default is 0

in_channelsint
out_channelsint
kernel_sizeint or int list: if int, order MUST be specified if int list, then the conv will use order = len(kernel_size)
rankint: rank of the factorized kernel
orderint, optional if kernel_size is a list: see kernel_size
implementation = {‘factorized’, ‘reconstructed’}: strategy to use for the forward pass - factorized : the TT conv is expressed as a series of 1D convolutions - reconstructed : full kernel is reconstructed from the decomposition. the reconstruction is used to perform a regular forward pass
strideint, default is 1
paddingint, default is 0
dilationint, default is 0

See also

References

[2]	Jean Kossaifi, Antoine Toisoul, Adrian Bulat, Yannis Panagakis, Timothy M. Hospedales, Maja Pantic; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 6060-6069

Attributes:	kernel_shapeint tuple shape of the kernel weight parametrizing the full convolution rankint rank of the TT decomposition

Methods

`full_weights`()	Returns the reconstructed full convolutional kernel
`get_decomposition`([return_bias])	Transpose back factors from a factorized version
`init_from_decomposition`(tt_tensor[, bias])	Transpose the factors from a full weight to the factorized version
`init_from_random`([decompose_full_weight])	Initialize the factorized convolution’s parameter randomly
`init_from_tensor`(kernel_tensor[, bias, …])	Initialize the factorized convolutional layer from a regular convolutional layer
`transduct`(kernel_size[, mode, padding, …])	Transduction of the factorized convolution to add a new dimension

init_from_tensor(kernel_tensor, bias=None, decomposition_kwargs={})[source]: Initialize the factorized convolutional layer from a regular convolutional layer

init_from_random(decompose_full_weight=True)[source]

Initialize the factorized convolution’s parameter randomly

Parameters:	decompose_full_weightbool If True, a full weight is randomly created and decomposed to intialize the parameters (slower) Otherwise, the parameters are initialized directly (faster) so the reconstruction has a set variance.

init_from_decomposition(tt_tensor, bias=None)[source]

Transpose the factors from a full weight to the factorized version

Parameters:	factorstt_tensor

get_decomposition(return_bias=False)[source]

Transpose back factors from a factorized version

Parameters:	return_biasbool, default is False if True also return the bias
Returns:	factors, bias if return_bias: factors otherwise

full_weights()[source]: Returns the reconstructed full convolutional kernel

transduct(kernel_size, mode=0, padding=0, stride=1, dilation=1)[source]

Transduction of the factorized convolution to add a new dimension

Parameters:	kernel_sizeint size of the additional dimension modewhere to insert the new dimension, after the channels, default is 0 by default, insert the new dimensions before the existing ones (e.g. add time before height and width) paddingint, default is 0 strideint: default is 1
Returns:	self