o %Ý«i)ã@sTdZddlZddlmZmZmZmZmZddlm Z e e ƒZ Gdd„dej j ƒZdS)z^Library implementing complex-valued linear transformation. Authors * Titouan Parcollet 2020 éN)Ú affect_initÚcheck_complex_inputÚ complex_initÚcomplex_linear_opÚ unitary_init)Ú get_loggercs0eZdZdZ   d ‡fdd„ Zdd„Z‡ZS) ÚCLineara†This function implements a fully connected complex-valued linear layer: y = Wx + b. y, W, x and b are thus complex numbers. A complex number is written as: r + xi. A tensor of complex numbers x = [batch, 32] can be understood as [batch, 0:15] = R and [batch, 16:31] = Xi. Thus the features dimension is cut in half (must be divisible by 2). Arguments --------- n_neurons : int It is the number of output neurons (i.e, the dimensionality of the output). Please note that these are complex-valued neurons. If 256 neurons are specified, the output dimension will be 512. input_shape : tuple Expected size of the input. bias : bool if True, the additive bias b is adopted. init_criterion : str , optional (glorot, he). This parameter controls the initialization criterion of the weights. It is combined with weights_init to build the initialization method of the complex-valued weights (default "glorot"). weight_init : str, optional (complex, unitary). This parameter defines the initialization procedure of the complex-valued weights (default "complex"). "complex" will generate random complex-valued weights following the init_criterion and the complex polar form. "unitary" will normalize the weights to lie on the unit circle. More details in: "Deep Complex Networks", Trabelsi C. et al. Example ------- >>> inputs = torch.rand(10, 50, 40) >>> lin = CLinear(n_neurons=100, input_shape=inputs.shape) >>> output = lin(inputs) >>> output.shape torch.Size([10, 50, 200]) TÚglorotÚcomplexcsêtƒ ¡||_||_||_||_t|tƒrd|g}t|ƒ|dd|_ |j|_ t j   t  |j |j ¡¡|_t j   t  |j |j ¡¡|_|jrTt j   t  d|j ¡¡|_n t  d|j ¡ d¡|_ttdœ|j|_t|j|j|j|ƒdS)NééÿÿÿÿéF)r Úunitary)ÚsuperÚ__init__Ú n_neuronsÚbiasÚinit_criterionÚ weight_initÚ isinstanceÚintrÚ in_featuresÚ out_featuresÚtorchÚnnÚ ParameterÚTensorÚ real_weightÚ imag_weightÚbÚrequires_grad_rrÚwinitr)ÚselfrÚ input_shaperrr©Ú __class__©ú^/home/ubuntu/.local/lib/python3.10/site-packages/speechbrain/nnet/complex_networks/c_linear.pyr=s2  ÿÿÿÿzCLinear.__init__cCst||j|j|jƒ}|S)züReturns the linear transformation of input tensor. Arguments --------- x : torch.Tensor Input to transform linearly. Returns ------- The complex linear transformation of the inputs. )rrrr)r"ÚxÚwxr&r&r'Úforwardns zCLinear.forward)Tr r )Ú__name__Ú __module__Ú __qualname__Ú__doc__rr*Ú __classcell__r&r&r$r'rs+ú1r)r.rÚ'speechbrain.nnet.complex_networks.c_opsrrrrrÚspeechbrain.utils.loggerrr+ÚloggerrÚModulerr&r&r&r'Ús