a ɝh@sLddlmZmZddlZddlmZGdddejZGdddejZdS))print_functiondivisionNcs(eZdZdZfddZddZZS) ConvLayerz+ Convolutional operation on graphs cs|tt|||_||_td|j|jd|j|_t|_ t |_ t d|j|_ t |j|_t |_dS)z Initialize ConvLayer. Parameters ---------- atom_fea_len: int Number of atom hidden features. nbr_fea_len: int Number of bond features. N)superr__init__ atom_fea_len nbr_fea_lennnLinearfc_fullSigmoidsigmoidSoftplus softplus1 BatchNorm1dbn1bn2 softplus2)selfrr  __class__%/var/www/html/AI4Kappa/cgcnn/model.pyr s   zConvLayer.__init__c Cs|j\}}||ddf}tj|d|||j||gdd}||}||d|jd|||jd}|j ddd\} } | | } | | } tj | | dd} | | } ||| } | S)ar Forward pass N: Total number of atoms in the batch M: Max number of neighbors Parameters ---------- atom_in_fea: Variable(torch.Tensor) shape (N, atom_fea_len) Atom hidden features before convolution nbr_fea: Variable(torch.Tensor) shape (N, M, nbr_fea_len) Bond features of each atom's M neighbors nbr_fea_idx: torch.LongTensor shape (N, M) Indices of M neighbors of each atom Returns ------- atom_out_fea: nn.Variable shape (N, atom_fea_len) Atom hidden features after convolution Nrdim)shapetorchcat unsqueezeexpandrr rviewchunkrrsumrr) rZ atom_in_feanbr_fea nbr_fea_idxNMZ atom_nbr_feaZ total_nbr_feaZtotal_gated_feaZ nbr_filterZnbr_coreZ nbr_sumedoutrrrforward"s*       zConvLayer.forward)__name__ __module__ __qualname____doc__rr+ __classcell__rrrrrs rcs2eZdZdZd fdd Zd d Zd d ZZS)CrystalGraphConvNetzk Create a crystal graph convolutional neural network for predicting total material properties. @rFcstt|||_t||_tfddt|D|_ t|_ t |_ |dkrtfddt|dD|_ tddt|dD|_|jrtd|_ntd|_|jrtjdd|_t|_dS) a  Initialize CrystalGraphConvNet. Parameters ---------- orig_atom_fea_len: int Number of atom features in the input. nbr_fea_len: int Number of bond features. atom_fea_len: int Number of hidden atom features in the convolutional layers n_conv: int Number of convolutional layers h_fea_len: int Number of hidden features after pooling n_h: int Number of hidden layers after pooling csg|]}tdqS)rr )r.0_r5rr ksz0CrystalGraphConvNet.__init__..rcsg|]}tqSr)r r r6) h_fea_lenrrr9qscSsg|] }tqSr)r rr6rrrr9ssrrN)rr1rclassificationr r embedding ModuleListrangeconvs conv_to_fcrconv_to_fc_softplusfcs softplusesfc_out LogSoftmax logsoftmaxDropoutdropout)rorig_atom_fea_lenr rn_convr:n_hr;r)rr:r rrRs*     zCrystalGraphConvNet.__init__c Cs||}|jD]}||||}q|||}|||}||}|jrX||}t|drt|drt|j |j D]\}}|||}qz| |} |jr| | } | S)a Forward pass N: Total number of atoms in the batch M: Max number of neighbors N0: Total number of crystals in the batch Parameters ---------- atom_fea: Variable(torch.Tensor) shape (N, orig_atom_fea_len) Atom features from atom type nbr_fea: Variable(torch.Tensor) shape (N, M, nbr_fea_len) Bond features of each atom's M neighbors nbr_fea_idx: torch.LongTensor shape (N, M) Indices of M neighbors of each atom crystal_atom_idx: list of torch.LongTensor of length N0 Mapping from the crystal idx to atom idx Returns ------- prediction: nn.Variable shape (N, ) Atom hidden features after convolution rBrC) r<r?poolingr@rAr;rHhasattrziprBrCrDrF) ratom_fear&r'crystal_atom_idxZ conv_funcZcrys_feafcsoftplusr*rrrr+}s       zCrystalGraphConvNet.forwardcsBtdd|Djjdks"Jfdd|D}tj|ddS)a Pooling the atom features to crystal features N: Total number of atoms in the batch N0: Total number of crystals in the batch Parameters ---------- atom_fea: Variable(torch.Tensor) shape (N, atom_fea_len) Atom feature vectors of the batch crystal_atom_idx: list of torch.LongTensor of length N0 Mapping from the crystal idx to atom idx cSsg|] }t|qSr)lenr7idx_maprrrr9z/CrystalGraphConvNet.pooling..rcs g|]}tj|dddqS)rT)rkeepdim)rmeanrTrOrrr9sr)r%datarrr )rrOrPZ summed_fearrYrrLs  zCrystalGraphConvNet.pooling)r2r3r4rF)r,r-r.r/rr+rLr0rrrrr1Ms ++r1) __future__rrrtorch.nnr Modulerr1rrrrs F