o ®Ÿ?e‰4ć@stdZddlmZddlmZddlmZddlmZddlmZddlmZddlm Z e  d ”e  d ”e  d ”d d „ƒZ e  d”dd„ƒZ e  d”dd„ƒZe  d”dd„ƒZe  d”dd„ƒZe  d”dd„ƒZe  d”dd„ƒZd d!„Ze  d"”d#d$„ƒZe  d%”d&d'„ƒZe  d(”d)d*„ƒZe  d+”d,d-„ƒZe  d.”d/d0„ƒZe  d1”d2d3„ƒZe  d4”d5d6„ƒZd7S)8z1Gradients for operators defined in sparse_ops.py.é)Śdtypes)Śops)Ś sparse_tensor)Ś array_ops)Śgen_sparse_ops)Śmath_ops)Ś sparse_opsZ SparseAddGradZ SparseConcatZ SparseReorderc Csb|jd}|jd}t |”d}t |”}t |||”}t |”}t  |j ”} dt  || ”dfS)a…Gradients for the SparseReorder op. Args: op: the SparseReorder op unused_output_indices_grad: the incoming gradients of the output indices output_values_grad: the incoming gradients of the output values Returns: Gradient for each of the 3 input tensors: (input_indices, input_values, input_shape) The gradients for input_indices and input_shape is None. réN) ŚinputsrŚshaperŚrangerŚ SparseTensorrZsparse_reorderZinvert_permutationŚvaluesŚgather) ŚopZunused_output_indices_gradZoutput_values_gradŚ input_indicesZ input_shapeZ num_entriesZ entry_indicesZ sp_unorderedZ sp_orderedZinverted_permutation©rśb/home/www/facesmatcher.com/pyenv/lib/python3.10/site-packages/tensorflow/python/ops/sparse_grad.pyŚ_SparseReorderGrads   ’  ’’rZ SparseAddcGst|d}|jd}|jd}|jd}t ||||”\}}| |jd ””| |jd ””d|dd|ddfS)amThe backward operator for the SparseAdd op. The SparseAdd op calculates A + B, where A, B, and the sum are all represented as `SparseTensor` objects. This op takes in the upstream gradient w.r.t. non-empty values of the sum, and outputs the gradients w.r.t. the non-empty values of A and B. Args: op: the SparseAdd op *grads: the incoming gradients, one element per output of `op` Returns: Gradient for each of the 6 input tensors of SparseAdd: (a_indices, a_values, a_shape, b_indices, b_values, b_shape, thresh) The gradients for the indices, shapes, and the threshold are None. érééN)r ŚoutputsrZsparse_add_gradŚ set_shapeŚ get_shape)rŚgradsŚval_gradŚ a_indicesZ b_indicesZ sum_indicesZ a_val_gradZ b_val_gradrrrŚ_SparseAddGrad:s   ’rZSparseTensorDenseAddcCs|jd}dt ||”d|fS)Nr)r rŚ gather_nd)rŚout_gradŚ sp_indicesrrrŚ_SparseTensorDenseAddGrad\s r"ZSparseReduceSumcCs\|jd}|jd}t ||jd”}t ||”}|t |tj”}dt |||”ddfS)z:Similar to gradient for the Sum Op (i.e. tf.reduce_sum()).rr rN) r rZ reduced_shaperZreshapeŚcastrŚint64r)rr r!Zsp_shapeZoutput_shape_kept_dimsZout_grad_reshapedŚscalerrrŚ_SparseReduceSumGradcs   ’’r&Z SparseSlicecGsX|d}|jd}|jd}|jd}t ||||”}| |jd ””d|dddfS)aThe backward operator for the SparseSlice op. This op takes in the upstream gradient w.r.t. non-empty values of the sliced `SparseTensor`, and outputs the gradients w.r.t. the non-empty values of input `SparseTensor`. Args: op: the SparseSlice op *grads: the incoming gradients, one element per output of `op` Returns: Gradient for each of the 5 input tensors of SparseSlice: (indices, values, shape, start, size) The gradients for the indices, shape, start and the size are None. rrrN)r rrZsparse_slice_gradrr)rrZbackprop_val_gradrZ input_startZoutput_indicesrrrrŚ_SparseSliceGradps   ’r'ZSparseTensorDenseMatMulcCsœ|jdd…\}}}|jd}| d”}| d”}|jj}|jj} || kr0td|›d| ›dƒ‚tj||||| d} |rDtj| d d } |dd…d f} |dd…d f} t  ||s[| n| ”} t  |sd|nt  |”|sl| n| ”}|s„|s„t j t  | d ”t  |d ”d d}n=|r™|s™t j t  | d”t  |d”d d}n(|s¬|r¬t   t  | d ”t  |d””}n|rĮ|rĮt j t  | d”t  |d ”d d d}dtj|d dgdd| fS)aŒGradients for the dense tensor in the SparseTensorDenseMatMul op. Args: op: the SparseTensorDenseMatMul op grad: the incoming gradient Returns: Gradient for each of the 4 input tensors: (sparse_indices, sparse_values, sparse_shape, dense_tensor) The gradients for indices and shape are None. Raises: TypeError: When the two operands don't have the same type. NrŚ adjoint_aŚ adjoint_bzDSparseTensorDenseMatMul op received operands with different types: `z` and `z`.)r(T)Ś conjugaterréž’’’)r)é’’’’)r(r))Zaxis)r Zget_attrZdtypeZ base_dtypeŚ TypeErrorrZsparse_tensor_dense_mat_mulrZmatrix_transposerZ transposerŚmatmulŚ expand_dimsZsqueeze)rŚgradrZa_valuesZa_shapeŚbZadj_aZadj_bŚa_typeŚb_typeZb_gradŚrowsŚcolsZparts_aZparts_bZ a_values_gradrrrŚ_SparseTensorDenseMatMulGradsb   ’’’ ’’  ż  ż’  ür6ZSparseDenseCwiseAddcCótdƒ‚)Nz4Gradient for SparseDenseCwiseAdd is not implemented.©ŚNotImplementedError©Z unused_opZ unused_gradrrrŚ_SparseDenseCwiseAddGradēó’r;cCs |jd}|jd}|jd}t t |”tj”}t t |”t |”d”}t  t  |t jj”|gd”}||} || } t  | t  dg|gd”ddg”} t  || ”} |ra|| } ||jd} n|| } ||jd t | ”} t t |”t | | |””}d| d|fS)z4Common code for SparseDenseCwise{Mul,Div} gradients.rr rr,rN)r rr#rr rr$r/ŚsizeŚconcatZonesrŚslicerZsquarerZ sparse_addŚ zeros_likerr )rr0Zis_mulZ x_indicesZx_shapeŚyZy_shapeZnum_added_dimsZaugmented_y_shapeZscalingZscaled_indicesZ dense_valsZdxZdy_valZdyrrrŚ_SparseDenseCwiseMulOrDivGradķs6   ’’ž  ž rBZSparseDenseCwiseMulcCó t||dƒS)z"Gradients for SparseDenseCwiseMul.T©rB©rr0rrrŚ_SparseDenseCwiseMulGradó rFZSparseDenseCwiseDivcCrC)z"Gradients for SparseDenseCwiseDiv.FrDrErrrŚ_SparseDenseCwiseDivGradrGrHZ SparseSoftmaxc Csˆ|jd|jd}}|jd}t |||”}t |||”}t ||j|j|”}tj|dgdd }t ||”} | j|j} d| dgS)a³Gradients for SparseSoftmax. The calculation is the same as SoftmaxGrad: grad_x = grad_softmax * softmax - sum(grad_softmax * softmax) * softmax where we now only operate on the non-zero values present in the SparseTensors. Args: op: the SparseSoftmax op. grad: the upstream gradient w.r.t. the non-zero SparseSoftmax output values. Returns: Gradients w.r.t. the input (sp_indices, sp_values, sp_shape). rr r,T)ZkeepdimsN)r rrr rrZsparse_reduce_sumZsparse_dense_cwise_add) rr0Śindicesr Zout_valsZ sp_outputZsp_gradZ sp_productZ sum_reducedZsp_sumZgrad_xrrrŚ_SparseSoftmaxGrads  ž   rJZSparseSparseMaximumcCr7)Nz4Gradient for SparseSparseMaximum is not implemented.r8r:rrrŚ_SparseSparseMaximumGrad=r<rKZSparseSparseMinimumcCr7)Nz4Gradient for SparseSparseMinimum is not implemented.r8r:rrrŚ_SparseSparseMinimumGradCr<rLZSparseFillEmptyRowscCs(|jd}tj||d\}}d|d|gS)z"Gradients for SparseFillEmptyRows.r)Śreverse_index_mapZ grad_valuesN)rrZsparse_fill_empty_rows_grad)rZunused_grad_output_indicesZoutput_grad_valuesZunused_grad_empty_row_indicatorZunused_grad_reverse_index_maprMZd_valuesZd_default_valuerrrŚ_SparseFillEmptyRowsGradIs  ’ rNZ SparseToDensecCsF|j\}}}}t ||”}t |”t |”}t |”t |”||gS)N)r rrrZ reduce_sumr@)rr0Zsparse_indicesZ output_shapeŚ_Zsparse_values_gradZdefault_value_gradrrrŚ_SparseToDenseGradWs  ’ žrPN)Ś__doc__Ztensorflow.python.frameworkrrrZtensorflow.python.opsrrrrZNotDifferentiableZRegisterGradientrrr"r&r'r6r;rBrFrHrJrKrLrNrPrrrrŚsN           !    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