o pi!@shddlmZddlmZmZddlmZmZddlZddl m Z ddl m m Z Gddde jZdS))cached_property) combinations permutations)DictTupleNcseZdZdZdedeffdd ZedeeefddZ edefd d Z de j fd d Z de j fd dZdde j dede j fddZdde j dede j fddZde j de j fddZdeedfdeedffddZedeeedfeedfffddZZS) PowersetzPowerset to multilabel conversion, and back. Parameters ---------- num_classes : int Number of regular classes. max_set_size : int Maximum number of classes in each set. num_classes max_set_sizecsBt||_||_|jd|dd|jd|dddS)NmappingF) persistent cardinality)super__init__rr register_buffer build_mappingbuild_cardinality)selfrr  __class__Q/home/ubuntu/.local/lib/python3.10/site-packages/pyannote/audio/utils/powerset.pyr0s zPowerset.__init__returncCsBg}td|jdD]}tt|j|D] }|t|qq |S)a3List of powerset classes e.g. with num_classes = 3 and max_set_size = 2: {}, {0}, {1}, {2}, {0, 1}, {0, 2}, {1, 2} Returns ------- powerset_classes : list of set[int] List of powerset classes, each represented as a set of regular class indices. r)ranger rrappendset)rpowerset_classesset_size current_setrrrr9s  zPowerset.powerset_classescCs t|jS)zNumber of powerset classes)lenrrrrrnum_powerset_classesKs zPowerset.num_powerset_classescCsXt|j|j}d}td|jdD]}tt|j|D] }d|||f<|d7}qq|S)atCompute powerset to regular mapping Returns ------- mapping : (num_powerset_classes, num_classes) torch.Tensor mapping[i, j] == 1 if jth regular class is a member of ith powerset class mapping[i, j] == 0 otherwise Example ------- With num_classes == 3 and max_set_size == 2, returns [0, 0, 0] # none [1, 0, 0] # class #1 [0, 1, 0] # class #2 [0, 0, 1] # class #3 [1, 1, 0] # classes #1 and #2 [1, 0, 1] # classes #1 and #3 [0, 1, 1] # classes #2 and #3 rr)torchzerosr!rrr r)rr powerset_krrrrrrPs  zPowerset.build_mappingcCstj|jddS)z#Compute size of each powerset classrdim)r"sumr r rrrroszPowerset.build_cardinalityFpowersetsoftcCs>|rt|}ntjjtj|dd|j}t||j S)a/Convert predictions from powerset to multi-label Parameter --------- powerset : (batch_size, num_frames, num_powerset_classes) torch.Tensor Soft predictions in "powerset" space. soft : bool, optional Return soft multi-label predictions. Defaults to False (i.e. hard predictions) Assumes that `powerset` are "log probabilities". Returns ------- multi_label : (batch_size, num_frames, num_classes) torch.Tensor Predictions in "multi-label" space. r%) r"expnn functionalone_hotargmaxr!floatmatmulr )rr(r)powerset_probsrrr to_multilabelss  zPowerset.to_multilabelcCs|j||dS)zAlias for `to_multilabel`)r))r3)rr(r)rrrforwardszPowerset.forward multilabelcCs&tjtjt||jjdd|jdS)aConvert (hard) predictions from multi-label to powerset Parameter --------- multi_label : (batch_size, num_frames, num_classes) torch.Tensor Prediction in "multi-label" space. Returns ------- powerset : (batch_size, num_frames, num_powerset_classes) torch.Tensor Hard, one-hot prediction in "powerset" space. Note ---- This method will not complain if `multilabel` is provided a soft predictions (e.g. the output of a sigmoid-ed classifier). However, in that particular case, the resulting powerset output will most likely not make much sense. r*r%)r)Fr.r"r/r1r Tr!)rr5rrr to_powersetszPowerset.to_powersetmultilabel_permutation.cCs|jdd|f}tj|j|jjtjd}d||jdf}tj|j|dd}tj||dd}|d|dddfkj dd}t | S)aHelper function for `permutation_mapping` property Takes a (num_classes,)-shaped permutation in multilabel space and returns the corresponding (num_powerset_classes,)-shaped permutation in powerset space. This does not cache anything and only works on one single permutation at a time. Parameters ---------- multilabel_permutation : tuple of int Permutation in multilabel space. Returns ------- powerset_permutation : tuple of int Permutation in powerset space. Example ------- >>> powerset = Powerset(3, 2) >>> powerset._permutation_powerset((1, 0, 2)) # (0, 2, 1, 3, 4, 6, 5) N)devicedtyperr*r%r) r r"arangerr:inttiler!r'r/tupletolist)rr9permutated_mappingr= powers_of_twobeforeafterpowerset_permutationrrr_permutation_powersets$ zPowerset._permutation_powersetcCs2i}tt|j|jD] }|||t|<q |S)aMapping between multilabel and powerset permutations Example ------- With num_classes == 3 and max_set_size == 2, returns { (0, 1, 2): (0, 1, 2, 3, 4, 5, 6), (0, 2, 1): (0, 1, 3, 2, 5, 4, 6), (1, 0, 2): (0, 2, 1, 3, 4, 6, 5), (1, 2, 0): (0, 2, 3, 1, 6, 4, 5), (2, 0, 1): (0, 3, 1, 2, 5, 6, 4), (2, 1, 0): (0, 3, 2, 1, 6, 5, 4) } )rrrrGr@)rpermutation_mappingr9rrrrHs zPowerset.permutation_mapping)F)__name__ __module__ __qualname____doc__r>rrlistrrr!r"Tensorrrboolr3r4r8rrGrrH __classcell__rrrrr%s&     -0r) functoolsr itertoolsrrtypingrrr"torch.nnr,torch.nn.functionalr-r6Modulerrrrrs