\;jI-ddlZddlZddlmZmZddlmZddlmZm Z m Z m Z ddl m Z ddlmZmZddgZd Zd Zd Zd Z dd Ze ddZy)N)_C_opsin_dynamic_mode) convert_dtype)_current_expected_place_get_paddle_placecore dygraph_only) LayerHelper)max to_tensorsparse_coo_tensorsparse_csr_tensorc|r;t|t|jk7r|jt|S|S)N)rdtypeastype)datars _G:\00. PROJECTS\API\Inventory\templateJSON\kerjaOCR\Lib\site-packages\paddle/sparse/creation.py _handle_dtyper#s4  =#< <;;}U34 4 Kct|jdk(sJt|d}|dz}|j}t|jdkDr#t j ||jdd}t |S)N)axis)lenshaper numpynpappendlist)indicesvalueslenss r_infer_dense_shaper#*so w}}  "" " wQ D !8D ::>> import paddle >>> indices = [[0, 1, 2], [1, 2, 0]] >>> values = [1.0, 2.0, 3.0] >>> dense_shape = [3, 3] >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) >>> print(coo) Tensor(shape=[3, 3], dtype=paddle.float32, place=Place(cpu), stop_gradient=True, indices=[[0, 1, 2], [1, 2, 0]], values=[1., 2., 3.]) NTrr+ stop_gradientrz'indices' must be 2-D.rrzKthe indices and values must have same number of non-zero, but get {} and {}Fzthe minimun shape required is z , but get zVthe number of dimensions(len(shape) must be sparse_dim({}) + dense_dim({}), but get {}sparse_sparse_coo_tensor)r!r rout)typeinputsoutputsattrs)rr,r%reagerTensorr rrr*r4rformatr+_equals_copy_torr7r#rrr8r )create_sparse_variable_for_type_inference append_op)r r!rrr+r7nnz sparse_dim dense_dim min_shapeop_typer<r>helperr:s rr r HsQX5!'4::#4#45t5G&$**"3"34vue]CF w}}  "56 6mmA]]1% W]]+ &,,q/ !]dda   %) }}$$U+&&ue4G||##E*__UE2Fvu-,&w7 =EKEy  4YKz%Q5zZ)33 lss"Is5z ..vwFF-"w7 8 E!H% W%>>uE%U   rct|}t|tjjst |d|d}t|tjjst |d|d}t|tjjst ||||}t |jt |jt|dk7rt|dk7rtd||t|dz }|jj|s|j|d}|jj|s|j|d}|jj|s|j|d}t||}||_t|jdk7s0t|jdk7st|jdk7r td t|t|k7r td t|dk(rp|jd |dzk7r(td j!|jd ||d d k7r td |d|jd k7rKtd|jd |dzzd k7r(tdj!|jd |tjj#|||||S)a< Constructs a sparse ``paddle.Tensor`` in CSR(Compressed Sparse Row) format according to the ``crows``, ``cols`` and ``values``. Currently, the crows and cols of each batch must be incrementd. Args: crows(list|tuple|ndarray|Tensor): 1-D array, each element in the rows represents the starting position of the first non-zero element of each row in values. Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. cols(list|tuple|ndarray|Tensor): 1-D array, the column of non-zero elements. Can be a list, tuple, numpy\.ndarray, paddle\.Tensor. values(list|tuple|ndarray|Tensor): 1-D array, the non-zero elements. Can be a scalar, list, tuple, numpy\.ndarray, paddle\.Tensor. shape(list|tuple, optional): The shape of the sparse tensor also represents the shape of original dense tensor. hold all elements. dtype(str|np.dtype, optional): The desired data type of returned tensor. Can be 'bool' , 'float16' , 'float32' , 'float64' , 'int8' , 'int16' , 'int32' , 'int64' , 'uint8', 'complex64' , 'complex128'. Default: None, infers dtype from ``data`` except for python float number which gets dtype from ``get_default_type`` . place(CPUPlace|CUDAPinnedPlace|CUDAPlace|str, optional): The place to allocate Tensor. Can be CPUPlace, CUDAPinnedPlace, CUDAPlace. Default: None, means global place. If ``place`` is string, It can be ``cpu``, ``gpu:x`` and ``gpu_pinned``, where ``x`` is the index of the GPUs. stop_gradient(bool, optional): Whether to block the gradient propagation of Autograd. Default: True. Returns: Tensor: A Tensor constructed from ``crows``, ``cols`` and ``values`` . Examples: .. code-block:: python >>> import paddle >>> crows = [0, 2, 3, 5] >>> cols = [1, 3, 2, 0, 1] >>> values = [1, 2, 3, 4, 5] >>> dense_shape = [3, 4] >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) >>> print(csr) Tensor(shape=[3, 4], dtype=paddle.int64, place=Place(cpu), stop_gradient=True, crows=[0, 2, 3, 5], cols=[1, 3, 2, 0, 1], values=[1, 2, 3, 4, 5]) NTr6rz>SparseCsrTensor only support 2-D or 3-D matrix. but get shape Frz-The 'crows', 'cols' and 'values' must be 1-D.z3the length of cols must be same as length of valuesrzBThe length({}) of crows must be equal to the rows({})+1 of matrix.z the 0th value of crows must be 0r9zTAX %T[[KKND  8q=?@ @ 9 Q 'N  ;;q>TAX &! +U\\KKND  :: ' ' tVUM r)NNNT)NNT)rrr.rrpaddle.base.data_feederrpaddle.base.frameworkrrrr paddle.base.layer_helperr paddle.tensorr r __all__rr#r,r4r rrrrWso *1 1(    HLl`FJmmr