o wif@sddlZddlmZddlmZmZmZddlmZmZm Z m Z m Z m Z ddl mZddgZGdddejeeZGd ddejeeZdS) N)nn) SerializationTyping typecheck) LabelsType LogitsType LogprobsTypeLossTypeMaskType NeuralType)loggingCrossEntropyLossNLLLosscNeZdZdZeddZeddZdfd d Zedfd d Z Z S)r z CrossEntropyLoss cCs^tdgdg|jdttdgdg|jdttdgdg|jdtdddS)3Returns definitions of module input ports. BANYToptional)logitslabels loss_mask)r _logits_dimrrr selfri/home/ubuntu/sommelier/.venv/lib/python3.10/site-packages/nemo/collections/common/losses/cross_entropy.py input_typess zCrossEntropyLoss.input_typescCdttdiSz4Returns definitions of module output ports. loss) elements_typer r rrrr output_types(zCrossEntropyLoss.output_typesrNmeancsH|durt|st|}td|tj|||d||_dS)z Args: logits_ndim (int): number of dimensions (or rank) of the logits tensor weight (list): list of rescaling weight given to each class reduction (str): type of the reduction over the batch Nz(Weighted Cross Entropy loss with weight weight reduction ignore_index)torch is_tensor FloatTensorr infosuper__init__r)r logits_ndimr*r+r, __class__rrr2.s   zCrossEntropyLoss.__init__ctj|ddd}tj|ddd}|dur.|jtjur|dk}tj|ddd}||}||}t|dkr@t|tj|ddSt||}|S)z Args: logits (float): output of the classifier labels (long): ground truth labels loss_mask (bool/float/int): tensor to specify the masking r start_dimend_dimN?dimr-flattendtypeboollenr1forwardargmax)rrrrlogits_flattenlabels_flattenloss_mask_flattenr"r4rrrD;  zCrossEntropyLoss.forwardrNr'r(N __name__ __module__ __qualname____doc__propertyrr%r2rrD __classcell__rrr4rr    cr)rz NLLLoss cCs(tdttdttdtdddS)r)rTD)rrTTr) log_probsr output_mask)r rrr rrrrrYs  zNLLLoss.input_typescCr r!r$rrrrr%cr&zNLLLoss.output_typesrNr'r(cs8|durt|st|}tj|||d||_dS)aO Args: log_probs_ndim (int): number of dimensions (or rank) of the logprobs tensor weight (list): list of rescaling weight given to each class reduction (str): type of the reduction over the batch ignore_index (int): mask out loss computation where labels = ignore_index Nr))r-r.r/r1r2_log_probs_dim)rlog_probs_ndimr*r+r,r4rrr2is  zNLLLoss.__init__cr6)z Args: log_probs (float): output log probability tensor labels (long): ground truth labels loss_mask (bool/float/int): tensor to specify the masking rr7r8r;Nr<r=r?)rrVrrlog_probs_flattenrGrHr"r4rrrDvrIzNLLLoss.forwardrJrKrLrrr4rrTrS)r-rnemo.core.classesrrrnemo.core.neural_typesrrrr r r nemo.utilsr __all__r rrrrrs   ;