o Qeq@sddlmZddlmZddlmZmZddlmZm Z ddl m Z m Z m Z ddlmZddlmZmZmZdd lmZmZmZmZdd lmZdd lZdd lmZdd lmZdd lZddlm Z m!Z!ddl"m#Z#m$Z$m%Z%dd l&Z&ddl'm(Z(ddl'm)Z)ddl'm*Z*gZ+ddZ,ddZ-dddZ.dddZ/dddZ0ddd Z1ed!d"Z2ed#d$Z3edd&d'Z4dd(d)Z5dd*d+Z6dd,d-Z7ed.d/Z8dd0d1Z9dd2d3Z:dd4d5Z;dddgd fd6d7Zddd?Z@dd@dAZAddBdCZBeddDdEZC % % F ddGdHZD % % % F ddIdJZEddKdLZFeddMdNZGddOdPZHddQdRZIddTdUZJeddVdWZKddXdYZLddZd[ZMdd\d]ZNdd^d_ZOdd`daZPddbdcZQddddeZRddfdgZSeddhdiZTddjdkZUddldmZVddndoZWddpdqZXddrdsZYddtduZZddvdwZ[dxdyZ\dzd{Z]d|d}Z^dddZ_edddZ`ddZadddZbedddZce2e3e4e6e7e8dZdedeD]\ZfZgehejiefegehejjjkefegqd S))print_function)Counter)Variable device_guard)corein_dygraph_mode)_in_legacy_dygraph_in_eager_without_dygraph_check_non_static_mode) LayerHelper) OpProtoHolderconvert_np_dtype_to_dtype_ dygraph_only) convert_dtypecheck_variable_and_dtype check_type check_dtype)utilsN)_elementwise_op_in_dygraph)inplace_apis_in_dygraph_only)_C_ops _legacy_C_ops) dygraph_utils fill_constant_varbase_creator)zeros)_complex_to_real_dtype)_real_to_complex_dtypecCstrt|tjjst|}t||Str.t|tjjs"t|}t |d|j d|}|St |dgddt |dgddt dit}|j||jd}|jdd |gid |gi|j |j d d |S)a This OP takes in the Tensor :attr:`x` with :attr:`x.dtype` and casts it to the output with :attr:`dtype`. It's meaningless if the output dtype equals the input dtype, but it's fine if you do so. Args: x (Tensor): An input N-D Tensor with data type bool, float16, float32, float64, int32, int64, uint8. dtype (np.dtype|str): Data type of the output: bool, float16, float32, float64, int8, int32, int64, uint8. Returns: Tensor: A Tensor with the same shape as input's. Examples: .. code-block:: python import paddle x = paddle.to_tensor([2, 3, 4], 'float64') y = paddle.cast(x, 'uint8') in_dtype out_dtypex) boolfloat16float32float64int16int32int64uint8uint16castdtype) r#r$r%r&int8r'r(r)r*r+r- stop_gradientXOut)r r!typeinputsoutputsattrsN)r,)r isinstancerVarDescVarTyperrr,r rr-rrr locals"create_variable_for_type_inferencer0 append_op)r"r-outhelperr@JD:\Projects\ConvertPro\env\Lib\site-packages\paddle/tensor/manipulation.pyr,2s@    r,c sZtrd}d}d}t|ttfrKt|}t|dkrtdtt|D]%}||dkr;td||t|j||<q$t t|jd||||<q$n td t |tddtt|D}t j jt|ttfrvfd d |D}nt|r|} d d | D}td dtt|D}t|ttfrfd d |D}nt|r|} dd | D}tddtt|D}t|||||gStrd}d}d}t|ttfrt|}t|dkrtdtt|D]&}||dkr td||t|j||<qt t|jd||||<qn td t |tddtt|D}tt|ttfrIfdd |D}|d|f7}nt|ra|}d|_tddtt|D}t|ttfryfdd |D}|d|f7}nt|r|}d|_tddtt|D}tj|||ddd|d|g |RSt|tttfstdt|tttfstdtd*it} d|i} d|i}tddtt|D}t|trd|_|| d<td dtt|D}nBt|ttfr6g|d<t|r2t|| d!<t|D]\}} t| tr(|dd"d"||<q|d| qn||d<t|trQd|_|| d#<td$dtt|D}nBt|ttfrg|d<t|rt|| d%<t|D]\}} t| tr|dd"d"||<qn|d| qnn||d<||d<| j| d&d'} | jd| |d(| id)| S)+aN This operator produces a slice of ``input`` along multiple axes. Similar to numpy: https://docs.scipy.org/doc/numpy/reference/arrays.indexing.html Slice uses ``axes``, ``starts`` and ``ends`` attributes to specify the start and end dimension for each axis in the list of axes and Slice uses this information to slice the input data tensor. If a negative value is passed to ``starts`` or ``ends`` such as :math:`-i`, it represents the reverse position of the axis :math:`i-1` (here 0 is the initial position). If the value passed to ``starts`` or ``ends`` is greater than n (the number of elements in this dimension), it represents n. For slicing to the end of a dimension with unknown size, it is recommended to pass in INT_MAX. The size of ``axes`` must be equal to ``starts`` and ``ends``. Following examples will explain how slice works: .. code-block:: text Case1: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [1, 0] ends = [2, 3] Then: result = [ [5, 6, 7], ] Case2: Given: data = [ [1, 2, 3, 4], [5, 6, 7, 8], ] axes = [0, 1] starts = [0, 1] ends = [-1, 1000] # -1 denotes the reverse 0th position of dimension 0. Then: result = [ [2, 3, 4], ] # result = data[0:1, 1:4] Args: input (Tensor): A ``Tensor`` . The data type is ``float16``, ``float32``, ``float64``, ``int32`` or ``int64``. axes (list|tuple): The data type is ``int32`` . Axes that `starts` and `ends` apply to . starts (list|tuple|Tensor): The data type is ``int32`` . If ``starts`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``starts`` is an Tensor, it should be an 1-D Tensor. It represents starting indices of corresponding axis in ``axes``. ends (list|tuple|Tensor): The data type is ``int32`` . If ``ends`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``ends`` is an Tensor, it should be an 1-D Tensor . It represents ending indices of corresponding axis in ``axes``. Returns: Tensor: A ``Tensor``. The data type is same as ``input``. Examples: .. code-block:: python import paddle input = paddle.rand(shape=[4, 5, 6], dtype='float32') # example 1: # attr starts is a list which doesn't contain tensor. axes = [0, 1, 2] starts = [-3, 0, 2] ends = [3, 2, 4] sliced_1 = paddle.slice(input, axes=axes, starts=starts, ends=ends) # sliced_1 is input[0:3, 0:2, 2:4]. # example 2: # attr starts is a list which contain tensor. minus_3 = paddle.full([1], -3, "int32") sliced_2 = paddle.slice(input, axes=axes, starts=[minus_3, 0, 2], ends=ends) # sliced_2 is input[0:3, 0:2, 2:4]. r@Nrz-Input axes should not be an empty list/tuple.rz:Input axes must be a python list or tuple, but reveived {}cs|]}dVqdSrNr@.0ir@r@rA zslice..c(g|]}t|r|dn|qSrr8numpyitemrErMtmp_tensor_typer@rA zslice..cSg|]}|qSr@r@rEeler@r@rArQcsrBNr@rDr@r@rArGrHcrIrJrKrNrOr@rArQrRcSrSr@r@rTr@r@rArQrVcsrBrWr@rDr@r@rArGrHcsrBrCr@rDr@r@rArGrHcrIrJrKrNrOr@rArQrRstartsTcsrBrWr@rDr@r@rArG%rHcrIrJrKrNrOr@rArQ(rRendscsrBrWr@rDr@r@rArG2rHaxes infer_flagsz7Input starts must be an Variable, python list or tuple.z5Input ends must be an Variable, python list or tuple.sliceInputcsrBrCr@rDr@r@rArGNrH StartsTensorcsrBrWr@rDr@r@rArGTrHStartsTensorListrX EndsTensorcsrBrWr@rDr@r@rArGfrHEndsTensorListinputr-r2r4r5r7r6)r]) rr8listtuplelen ValueErrorrangemaxshapeminformatr4reagerTensorrLrr]r rr0rr r;r _contain_var_convert_to_tensor_list enumerateappendr< input_dtyper=)rcr[rYrZr7Z starts_tensorZ ends_tensorrFr\Ztensor_tr?r5dimr>r@rOrAr]sD                      r]c Cstr t||Strt|d|\}}|St|dgddt|dtt fdt |t r2t|}t |t |j krHt dt |j t |ft|D]\}}|t |j kret d|||t |j fqLtd it}||j}||j}|jdd |gi|g|gd d|id |S)a Permute the data dimensions of `input` according to `perm`. The `i`-th dimension of the returned tensor will correspond to the perm[i]-th dimension of `input`. Args: x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, float32, float64, int32. perm (list|tuple): Permute the input according to the data of perm. name (str): The name of this layer. It is optional. Returns: Tensor: A transposed n-D Tensor, with data type being bool, float32, float64, int32, int64. For Example: .. code-block:: text x = [[[ 1 2 3 4] [ 5 6 7 8] [ 9 10 11 12]] [[13 14 15 16] [17 18 19 20] [21 22 23 24]]] shape(x) = [2,3,4] # Example 1 perm0 = [1,0,2] y_perm0 = [[[ 1 2 3 4] [13 14 15 16]] [[ 5 6 7 8] [17 18 19 20]] [[ 9 10 11 12] [21 22 23 24]]] shape(y_perm0) = [3,2,4] # Example 2 perm1 = [2,1,0] y_perm1 = [[[ 1 13] [ 5 17] [ 9 21]] [[ 2 14] [ 6 18] [10 22]] [[ 3 15] [ 7 19] [11 23]] [[ 4 16] [ 8 20] [12 24]]] shape(y_perm1) = [4,3,2] Examples: .. code-block:: python import paddle x = paddle.randn([2, 3, 4]) x_transposed = paddle.transpose(x, perm=[1, 0, 2]) print(x_transposed.shape) # [3L, 2L, 4L] axisr"r#r$r%r&r(r) complex64 complex128 transposepermzInput(perm) is the permutation of dimensions of Input(x), its length should be equal to dimensions of Input(x), but received dimension of Input(x) is %s, the length of Input(perm) is %s.zEach element in Input(perm) should be less than Input(x)'s dimension, but %d-th element in Input(perm) is %d which exceeds Input(x)'s dimension %d. transpose2r1r2ZXShaper3N)r{)rrr{r rr}rrrfrgr8rhrlrirsr r;r<r-r=) r"r|namer>_idxrvr?x_shaper@r@rAr{sL2      r{cCstr|dkr |j|}|dkrgSt|||Str7|dkr%|j|}|dkr+gSt||dt|d|Std it}|durW|dusN|j|dkrRt d|j|}g}t |D] }| | |j q]|jdd|gid|i||d d |S) a :alias_main: paddle.unstack :alias: paddle.unstack,paddle.tensor.unstack,paddle.tensor.manipulation.unstack :old_api: paddle.fluid.layers.unstack **UnStack Layer** This layer unstacks input Tensor :code:`x` into several Tensors along :code:`axis`. If :code:`axis` < 0, it would be replaced with :code:`axis+rank(x)`. If :code:`num` is None, it would be inferred from :code:`x.shape[axis]`, and if :code:`x.shape[axis]` <= 0 or is unknown, :code:`ValueError` is raised. Args: x (Tensor): Input Tensor. It is a N-D Tensors of data types float32, float64, int32, int64. axis (int): The axis along which the input is unstacked. num (int|None): The number of output variables. Returns: list(Tensor): The unstacked Tensors list. The list elements are N-D Tensors of data types float32, float64, int32, int64. Examples: .. code-block:: python import paddle x = paddle.ones(name='x', shape=[2, 3, 5], dtype='float32') # create a tensor with shape=[2, 3, 5] y = paddle.unstack(x, axis=1) # unstack with second axis, which results 3 tensors with shape=[2, 5] Nrrwnumunstackzunknown unstack numberr1Y)rwrr3)r)rrlrrr rintr r;rirjrtr<r-r=)r"rwrr?outsrr@r@rArs6    rrXc Cstr t|||||St|dddgdd}t|fit}|dks(||kr0td||f|j|jd}|j |d|gid |i||||d d d |S) a Reset the values of `input` according to the shard it beloning to. Every value in `input` must be a non-negative integer, and the parameter `index_num` represents the integer above the maximum value of `input`. Thus, all values in `input` must be in the range [0, index_num) and each value can be regarded as the offset to the beginning of the range. The range is further split into multiple shards. Specifically, we first compute the `shard_size` according to the following formula, which represents the number of integers each shard can hold. So for the i'th shard, it can hold values in the range [i*shard_size, (i+1)*shard_size). :: shard_size = (index_num + nshards - 1) // nshards For each value `v` in `input`, we reset it to a new value according to the following formula: :: v = v - shard_id * shard_size if shard_id * shard_size <= v < (shard_id+1) * shard_size else ignore_value That is, the value `v` is set to the new offset within the range represented by the shard `shard_id` if it in the range. Otherwise, we reset it to be `ignore_value`. Args: input (Tensor): Input tensor with data type int64 or int32. It's last dimension must be 1. index_num (int): An integer represents the integer above the maximum value of `input`. nshards (int): The number of shards. shard_id (int): The index of the current shard. ignore_value (int): An integer value out of sharded index range. Returns: Tensor. Examples: .. code-block:: python import paddle label = paddle.to_tensor([[16], [1]], "int64") shard_label = paddle.shard_index(input=label, index_num=20, nshards=2, shard_id=0) print(shard_label) # [[-1], [1]] rcr)r( shard_indexrz%The shard_id(%d) should be in [0, %d)rdr1r2) index_numnshardsshard_id ignore_valueT)r4r5r6r7r0) rrrrr r;rir<r-r=)rcrrrrop_typer?r>r@r@rAr's0.   rcCsltdit}t|dgddt|dtttfdt|dttttdfd|dur4dgt|j }t r>t |||S| |j}d|i}i}d d }d d } t|trid |_||d<dgt|j |d<nTt|rg} g} |D]2} t| trd | _| | | dqt| | | d} tdgd| d | d| | | | qt| |d<| |d<n |D]}| |q||d<t|trd |_||d<nVt|rg}g}|D]2}t|trd |_|||dq||| d} tdgd|d | d|| ||q||d<||d<n|D]}||q||d<|jd|d|it|dkr0dn|d|S)a Crop input into output, as specified by offsets and shape. .. code-block:: text * Case 1 (input is a 2-D Tensor): Input: X.shape = [3, 5] X.data = [[0, 1, 2, 0, 0], [0, 3, 4, 0, 0], [0, 0, 0, 0, 0]] Parameters: shape = [2, 2] offsets = [0, 1] Output: Out.shape = [2, 2] Out.data = [[1, 2], [3, 4]] * Case 2 (input is a 3-D Tensor): Input: X.shape = [2, 3, 4] X.data = [[[0, 1, 2, 3], [0, 5, 6, 7], [0, 0, 0, 0]], [[0, 3, 4, 5], [0, 6, 7, 8], [0, 0, 0, 0]]] Parameters: shape = [2, 2, -1] offsets = [0, 0, 1] Output: Out.shape = [2, 2, 3] Out.data = [[[1, 2, 3], [5, 6, 7]], [[3, 4, 5], [6, 7, 8]]] Parameters: x (Tensor): 1-D to 6-D Tensor, the data type is float32, float64, int32 or int64. shape (list|tuple|Tensor, optional): The output shape is specified by `shape`. Its data type is int32. If a list/tuple, it's length must be the same as the dimension size of `x`. If a Tensor, it should be a 1-D Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1]. If Variable contained, it is suitable for the case that the shape may be changed each iteration. offsets (list|tuple|Variable, optional): Specifies the cropping offsets at each dimension. Its data type is int32. If a list/tuple, it's length must be the same as the dimension size of `x`. If a Tensor, it should be a 1-D Tensor. When it is a list, each element can be an integer or a Tensor of shape: [1]. If Variable contained, it is suitable for the case that the offsets may be changed each iteration. Default: None, the offsets are 0 at each dimension. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The cropped Tensor has same data type with `x`. Examples: .. code-block:: python import paddle x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7, 8, 9]]) # x.shape = [3, 3] # x = [[1, 2, 3], [4, 5, 6], [7, 8, 9]] # shape can be a 1-D Tensor or list or tuple. shape = paddle.to_tensor([2, 2], dtype='int32') # shape = [2, 2] # shape = (2, 2) out = paddle.crop(x, shape) # out.shape = [2, 2] # out = [[1,2], [4,5]] # offsets can be a 1-D Tensor or list or tuple. offsets = paddle.to_tensor([0, 1], dtype='int32') # offsets = [1, 0] # offsets = (1, 1) out = paddle.crop(x, shape, offsets) # out.shape = [2, 2] # if offsets = [0, 0], out = [[1,2], [4,5]] # if offsets = [0, 1], out = [[2,3], [5,6]] # if offsets = [1, 0], out = [[4,5], [7,8]] # if offsets = [1, 1], out = [[5,6], [8,9]] crop_tensorr"r%r&r(r)rloffsetsNrr1cSsNt|ts tdt||dkrtdt||dkr%tdt|dS)NzIAttr(shape)'s dtype of Op(crop_tensor) should be int32, but received: %s.rzDAttr(shape) of Op(crop_tensor) should not be zero, but received: %s.rXzgWhen the element in Attr(shape) of Op(crop_tensor) is negative, only -1 is supported, but received: %s.r8r TypeErrorr4ristr)Z shape_valr@r@rA_attr_shape_checks& zcrop.._attr_shape_checkcSs6t|ts tdt||dkrtdt|dS)NzKAttr(offsets)'s dtype of Op(crop_tensor) should be int32, but received: %s.rzVAttr(offsets) of Op(crop_tensor) should be greater or equal to zero, but received: %s.r)Z offset_valr@r@rA_attr_offsets_checks z!crop.._attr_offsets_checkTZOffsetsrXr(rZ force_cpur>Z OffsetsTensorShape ShapeTensorr2r3)r)r r;rrrfrgrr4rhrlrrrr<r-r8r0rrqrtrr=)r"rlrrr?r>Ziptsr7rrZnew_offsets_tensorZ offsets_attrrvtemp_outoffsetZnew_shape_tensorZ shape_attrdim_sizer@r@rAcroprsW                        rcCsJt|ttfstdt|trt||St |dt|dt|S)a- **Notes**: **This API is ONLY available in Dygraph mode** This function fill the Tensor with value inplace. Args: x (Tensor): ``x`` is the Tensor we want to filled data inplace value (Scale): ``value`` is the value to be filled in x Returns: x(Tensor): Tensor x filled with value inplace Examples: .. code-block:: python import paddle tensor = paddle.to_tensor([0, 1, 2, 3, 4]) tensor.fill_(0) print(tensor.tolist()) #[0, 0, 0, 0, 0] z;The type of 'value' must be int or float, but received %s. value_float value_int) r8floatrrr4rrfill_r fill_any_)r"valuer@r@rAr5s rcCs(tr t|dSt|dddtdS)a **Notes**: **This API is ONLY available in Dygraph mode** This function fill the Tensor with zero inplace. Args: x (Tensor): ``x`` is the Tensor we want to filled with zero inplace Returns: x (Tensor): Tensor x filled with zero inplace Examples: .. code-block:: python import paddle tensor = paddle.to_tensor([0, 1, 2, 3, 4]) tensor.zero_() print(tensor.tolist()) #[0, 0, 0, 0, 0] grrr)rrrrrrr"r@r@rAzero_\s  rFc Cstdit}t|dtd|d}t|dgddt|dtttfdt|dtd|j }t |}t |dks?Jdt |dkrOt |d ksOJd t rht |dkr`t ||||St |||d St |dkryt|d|d |d|St|d|d |dd S)aA Note: This API is ONLY available in Dygraph mode. This function fill the value into the x Tensor's diagonal inplace. Args: x(Tensor): ``x`` is the original Tensor value(Scale): ``value`` is the value to filled in x offset(int,optional): the offset to the main diagonal. Default: 0 (main diagonal). wrap(bool,optional): the diagonal 'wrapped' after N columns for tall matrices. name(str,optional): Name for the operation (optional, default is None) Returns: Tensor: Tensor with diagonal filled with value. Examples: .. code-block:: python import paddle x = paddle.ones((4, 3)) * 2 x.fill_diagonal_(1.0) print(x.tolist()) #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]] fill_diagonal_r1r"r#r$r%r&r(r)rwraprz-Tensor dims should >= 2 in fill_diagonal_ APIrzFTensor dims should be equal while input dims > 2 in fill_diagonal_ APITrN)r)r r;rrrurr#rrrlsetrhrrrr) r"rrrrr?r-inshapeZ inshapesetr@r@rAr}s:    rc Cs|j}|t|kr|t| ksJd|t|kr!|t| ks%Jdt|dks/Jd|t|;}|t|;}g}tt|D]}||krT||krT|||qCtt|||||t|||||} || t|t|jksJd|t|jdkr|ddg}|rtrt |||||St ||d|d |d |Strt |||||St ||d|d |d |S) Nz9dim1 should between [-rank,rank) in fill_diagonal_tensor_z9dim2 should between [-rank,rank) in fill_diagonal_tensor_rz0Tensor dims should >= 2 in fill_diagonal_tensor_zthe y shape should be {}rrXrdim1dim2) rlrhrjrtrmrgrnreshaperrfill_diagonal_tensor_rfill_diagonal_tensor) r"yrrrinplacerZ predshaperFZdiaglenr@r@rA_fill_diagonal_tensor_implsT     rcCt|||||ddS)a Note: This API is ONLY available in Dygraph mode. This function fill the source Tensor y into the x Tensor's diagonal inplace. Args: x (Tensor): ``x`` is the original Tensor y (Tensor): ``y`` is the Tensor to filled in x dim1 (int,optional): first dimension with respect to which to fill diagonal. Default: 0. dim2 (int,optional): second dimension with respect to which to fill diagonal. Default: 1. offset (int,optional): the offset to the main diagonal. Default: 0 (main diagonal). name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Tensor with diagonal filled with y. Examples: .. code-block:: python import paddle x = paddle.ones((4, 3)) * 2 y = paddle.ones((3,)) x.fill_diagonal_tensor_(y) print(x.tolist()) #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]] Trrrrrr"rrrrrr@r@rArs rcCr)a This function fill the source Tensor y into the x Tensor's diagonal. Args: x (Tensor): ``x`` is the original Tensor y (Tensor): ``y`` is the Tensor to filled in x dim1 (int,optional): first dimension with respect to which to fill diagonal. Default: 0. dim2 (int,optional): second dimension with respect to which to fill diagonal. Default: 1. offset (int,optional): the offset to the main diagonal. Default: 0 (main diagonal). name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Tensor with diagonal filled with y. Examples: .. code-block:: python import paddle x = paddle.ones((4, 3)) * 2 y = paddle.ones((3,)) nx = x.fill_diagonal_tensor(y) print(nx.tolist()) #[[1.0, 2.0, 2.0], [2.0, 1.0, 2.0], [2.0, 2.0, 1.0], [2.0, 2.0, 2.0]] Frrrr@r@rArs rcCs |S)aP Note: This API is ONLY available in Dygraph mode. This function translate the paddle.Tensor to python list. Args: x (Tensor): ``x`` is the Tensor we want to translate to list. Returns: list: A list that contain the same value of current Tensor. Examples: .. code-block:: python import paddle t = paddle.to_tensor([0,1,2,3,4]) expectlist = t.tolist() print(expectlist) #[0, 1, 2, 3, 4] expectlist = paddle.tolist(t) print(expectlist) #[0, 1, 2, 3, 4] )rLtolistrr@r@rAr#s rc Cs|}tr%t|tr|}|d}t|tsdd|D}t||StrOt|tr6|}|d}t|tsBdd|D}t}t ||d||St |dt t tfdt|tst |D]\}}t|dt|d gd d|j|djkrtd qbn|g}t |dttfdt|trt|jdd d gddtdit}|j|d}|djtjjjkrt|dksJdt||jd d}|jdd|di|g|gd|ddd|Sd|i}i} t|trd|_ ||d<n|| d<|jd|d|gi| d|S)a Concatenates the input along the axis. Args: x (list|tuple): ``x`` is a Tensor list or Tensor tuple which is with data type bool, float16, float32, float64, int32, int64, int8, uint8. All the Tensors in ``x`` must have same data type. axis (int|Tensor, optional): Specify the axis to operate on the input Tensors. It's a scalar with data type int or a Tensor with shape [1] and data type int32 or int64. The effective range is [-R, R), where R is Rank(x). When ``axis < 0``, it works the same way as ``axis+R``. Default is 0. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor with the same data type as ``x``. Examples: .. code-block:: python import paddle x1 = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) x2 = paddle.to_tensor([[11, 12, 13], [14, 15, 16]]) x3 = paddle.to_tensor([[21, 22], [23, 24]]) zero = paddle.full(shape=[1], dtype='int32', fill_value=0) # When the axis is negative, the real axis is (axis + Rank(x)) # As follow, axis is -1, Rank(x) is 2, the real axis is 1 out1 = paddle.concat(x=[x1, x2, x3], axis=-1) out2 = paddle.concat(x=[x1, x2], axis=0) out3 = paddle.concat(x=[x1, x2], axis=zero) # out1 # [[ 1 2 3 11 12 13 21 22] # [ 4 5 6 14 15 16 23 24]] # out2 out3 # [[ 1 2 3] # [ 4 5 6] # [11 12 13] # [14 15 16]] rcS g|] }|jddkr|qSrJrlcountrEtr@r@rArQs zconcat..cSrrJrrr@r@rArQ{rrwrcconcatinput[])r#r$r%r&r(r)r.Zunit8:All the Tensors in the input must have the same data type.r(r)zBThe data type of axis must be int32 or int64 when axis is a TensorrdrzuIf the elements of 'input' in concat are Variable(LoDTensorArray), number of the elements must be 1, but received %s.tensor_array_to_tensorr1r2ZOutIndexFrwZ use_stackr3T AxisTensorr2N)r)!rr8rrLrMrrr rrrrfrgrsrrr-rrrr r;r<rudescr4rr9r:LOD_TENSOR_ARRAYrhr=r0) r"rwrrcr>idr? out_indexr5r7r@r@rArBs+               rcCst|}tjrt|Stjrt||St|dt t fd|dkr*t dt |D]\}}t |dt|dgdd|j|djkrMt d q.g}g}d}|t|kr||}t t|j} d} | t| krt|| kr|| | ||n0|| | | ko|| dko| | dk} | r|| } t d || | | kr| | || <||| <| d7} | t| ksm|d7}|t|ksZtdit} d} g}| |kr|| j| d | d7} | |ksd |i}| jd|d |iid|S)a This OP broadcast a list of tensors following broadcast semantics Note: If you want know more about broadcasting, please refer to `Introduction to Tensor`_ . .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor Args: input (list|tuple): ``input`` is a Tensor list or Tensor tuple which is with data type bool, float16, float32, float64, int32, int64. All the Tensors in ``input`` must have same data type. Currently we only support tensors with rank no greater than 5. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: list(Tensor): The list of broadcasted tensors following the same order as ``input``. Examples: .. code-block:: python import paddle x1 = paddle.rand([1, 2, 3, 4]).astype('float32') x2 = paddle.rand([1, 2, 1, 4]).astype('float32') x3 = paddle.rand([1, 1, 3, 1]).astype('float32') out1, out2, out3 = paddle.broadcast_tensors(input=[x1, x2, x3]) # out1, out2, out3: tensors broadcasted from x1, x2, x3 with shape [1,2,3,4] rcbroadcast_tensorsrz8At least 1 tensor is needed to perform broadcast_tensorsrrr#r%r&r(r)rrzInput tensors to broadcast_tensors does not follow bcast semanticsTensor {last_index} conflicts with Tensor {j} in reversed dimension {i}rdr1r2r3N)r)rhpaddle frameworkrrrr rrrfrgrrsrrr-reversedrlrtr r;r<rur=)rcrZ num_inputsrr"Z output_shape_r_last_tensor_indexZoutput_shape_rjZtensorrlrFinvalid last_indexr?r>r5r@r@rArs              rcCst|tr|g}trt||Strt|d|Std it }t |dt d| d}t |dgddt |dttfd|durL||}n|j||dd}|jdd|id |id|id |S) a( Reverse the order of a n-D tensor along given axis in axis. Args: x (Tensor): A Tensor(or LoDTensor) with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor x should be float32, float64, int32, int64, bool. axis (list|tuple|int): The axis(axes) to flip on. Negative indices for indexing from the end are accepted. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: Tensor or LoDTensor calculated by flip layer. The data type is same with input x. Examples: .. code-block:: python import paddle image_shape=(3, 2, 2) img = paddle.arange(image_shape[0] * image_shape[1] * image_shape[2]).reshape(image_shape) tmp = paddle.flip(img, [0,1]) print(tmp) # [[[10,11],[8, 9]], [[6, 7],[4, 5]], [[2, 3],[0, 1]]] out = paddle.flip(tmp,-1) print(out) # [[[11,10],[9, 8]], [[7, 6],[5, 4]], [[3, 2],[1, 0]]] rwflipr1r"r$r%r&r(r)r#NF)rr-Z persistabler2r3)r)r8rrrrrin_dynamic_moderr r;rrrurrfrgr<Zcreate_variabler=)r"rwrr?r-r>r@r@rAr/s.    rc Cstdit}t|dtd|d}t|dgddt|dttfdt|j }t|}|dkr:t d ||dkrEt d ||d |d krYt |d |d |kset d |d |d |d |krr|d | ks{t d |d |d |kr|d | kst d |d |d;}|d kr|S|dkrt t ||d |d Sttd |}||d ||d ||d <||d <|d krtt ||d |St t|||d S)a Rotate a n-D tensor by 90 degrees. The rotation direction and times are specified by axes and the absolute value of k. Rotation direction is from axes[0] towards axes[1] if k > 0, and from axes[1] towards axes[0] for k < 0. Args: x (Tensor): The input Tensor(or LoDTensor). The data type of the input Tensor x should be float16, float32, float64, int32, int64, bool. float16 is only supported on gpu. k (int, optional): Direction and number of times to rotate, default value: 1. axes (list|tuple, optional): Axes to rotate, dimension must be 2. default value: [0, 1]. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: Tensor or LoDTensor calculated by rot90 layer. The data type is same with input x. Examples: .. code-block:: python import paddle data = paddle.arange(4) data = paddle.reshape(data, (2, 2)) print(data) #[[0, 1], # [2, 3]] y = paddle.rot90(data, 1, [0, 1]) print(y) #[[1, 3], # [0, 2]] y= paddle.rot90(data, -1, [0, 1]) print(y) #[[2, 0], # [3, 1]] data2 = paddle.arange(8) data2 = paddle.reshape(data2, (2,2,2)) print(data2) #[[[0, 1], # [2, 3]], # [[4, 5], # [6, 7]]] y = paddle.rot90(data2, 1, [1, 2]) print(y) #[[[1, 3], # [0, 2]], # [[5, 7], # [4, 6]]] rot90r1r"rr[rz4expected total rotation axes == 2, but got axes = {}z1expected total dims >= 2, but got total dims = {}rrzJexpected rotation axes to be different, but got axis0 = {}, and axis1 = {}z'Rotation axis0 out of range, axis0 = {}z'Rotation axis1 out of range, axis1 = {}N)r)r r;rrrurrfrgrhrlrirnabsrrjr{) r"kr[rr?r-Zinput_total_dimsZtotal_rot_dimsZ axes_listr@r@rArgsd4  (     rc Cs8t|ts tdtst|dgddt|j}t|tr+||dks+|| kr/tdt|tr?||dks?|| krCtd|dkrK||}|dkrS||}||kr[td t ret |||St rut |d |d |\}}|Stdit}||j}||j} |jd d |i|| d||dd|S)a Flattens a contiguous range of axes in a tensor according to start_axis and stop_axis. Note: The output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``flatten_clone_x = x.flatten().clone()``. For Example: .. code-block:: text Case 1: Given X.shape = (3, 100, 100, 4) and start_axis = 1 end_axis = 2 We get: Out.shape = (3, 1000 * 100, 2) Case 2: Given X.shape = (3, 100, 100, 4) and start_axis = 0 stop_axis = -1 We get: Out.shape = (3 * 100 * 100 * 4) Args: x (Tensor): A tensor of number of dimentions >= axis. A tensor with data type float32, float64, int8, int32, int64, uint8. start_axis (int): the start axis to flatten stop_axis (int): the stop axis to flatten name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A tensor with the contents of the input tensor, with input \ axes flattened by indicated start axis and end axis. \ A Tensor with data type same as input x. Examples: .. code-block:: python import paddle image_shape=(2, 3, 4, 4) x = paddle.arange(end=image_shape[0] * image_shape[1] * image_shape[2] * image_shape[3]) img = paddle.reshape(x, image_shape) out = paddle.flatten(img, start_axis=1, stop_axis=2) # out shape is [2, 12, 4] # out shares data with img in dygraph mode img[0, 0, 0, 0] = -1 print(out[0, 0, 0]) # [-1] The input x should be a Tensorr")r%r&r.r'r(r)r*flattenr@The start_axis should be a int, and in range [-rank(x), rank(x))?The stop_axis should be a int, and in range [-rank(x), rank(x))r-The stop_axis should be larger than stat_axis start_axis stop_axisflatten_contiguous_ranger1r~)rrr3N)r)r8rrirrrrhrlrrrrr rrr r;r<r-r=) r"rrrx_dimdy_outrr?r>rr@r@rArs^ B        rcCst|ts tdt|j}t|tr||dks|| kr"tdt|tr2||dks2|| kr6td|dkr>||}|dkrF||}||krNtdtrXt|||St rht |d|d|\}}|Sd S) z Inplace version of ``flatten`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_tensor_flatten`. rrrrrrrrN) r8rrirhrlrrrflatten_r rZflatten_contiguous_range_)r"rrrrrrr@r@rArQs>       rc Cs>|j}t|tkr |g}t|tkr|g}t|}|dur>tt|D]}|||ks2||| krr@r@rAr|sP+    rcCsT|durdn|}trt||Strt|d|St|tsDt|tsDt|tr8|j t j j jkr8|g}n tddddt |ftdit}||dj}|dj t j j jkrt|dksnJd t||jd d }|D] }t|dgd dqv|jd d|di|g|gd|ddd|S|jdd|id|id|id|S)a Stacks all the input tensors ``x`` along ``axis`` dimemsion. All tensors must be of the same shape and same dtype. For example, given N tensors of shape [A, B], if ``axis == 0``, the shape of stacked tensor is [N, A, B]; if ``axis == 1``, the shape of stacked tensor is [A, N, B], etc. .. code-block:: text Case 1: Input: x[0].shape = [1, 2] x[0].data = [ [1.0 , 2.0 ] ] x[1].shape = [1, 2] x[1].data = [ [3.0 , 4.0 ] ] x[2].shape = [1, 2] x[2].data = [ [5.0 , 6.0 ] ] Attrs: axis = 0 Output: Out.dims = [3, 1, 2] Out.data =[ [ [1.0, 2.0] ], [ [3.0, 4.0] ], [ [5.0, 6.0] ] ] Case 2: Input: x[0].shape = [1, 2] x[0].data = [ [1.0 , 2.0 ] ] x[1].shape = [1, 2] x[1].data = [ [3.0 , 4.0 ] ] x[2].shape = [1, 2] x[2].data = [ [5.0 , 6.0 ] ] Attrs: axis = 1 or axis = -2 # If axis = -2, axis = axis+ndim(x[0])+1 = -2+2+1 = 1. Output: Out.shape = [1, 3, 2] Out.data =[ [ [1.0, 2.0] [3.0, 4.0] [5.0, 6.0] ] ] Args: x (list[Tensor]|tuple[Tensor]): Input ``x`` can be a ``list`` or ``tuple`` of tensors, the Tensors in ``x`` must be of the same shape and dtype. Supported data types: float32, float64, int32, int64. axis (int, optional): The axis along which all inputs are stacked. ``axis`` range is ``[-(R+1), R+1)``, where ``R`` is the number of dimensions of the first input tensor ``x[0]``. If ``axis < 0``, ``axis = axis+R+1``. The default value of axis is 0. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The stacked tensor with same data type as input. Example: .. code-block:: python import paddle x1 = paddle.to_tensor([[1.0, 2.0]]) x2 = paddle.to_tensor([[3.0, 4.0]]) x3 = paddle.to_tensor([[5.0, 6.0]]) out = paddle.stack([x1, x2, x3], axis=0) print(out.shape) # [3, 1, 2] print(out) # [[[1., 2.]], # [[3., 4.]], # [[5., 6.]]] out = paddle.stack([x1, x2, x3], axis=-2) print(out.shape) # [1, 3, 2] print(out) # [[[1., 2.], # [3., 4.], # [5., 6.]]] Nrrwz2The type of '%s' in %s must be %s, but received %sr"stackz*list[Tensor], tuple[Tensor] or TensorArrayrzpIf the elements of 'x' in stack are Variable(LoDTensorArray), number of the elements must be 1, but received %s.r(rdr$r%r&r(r)rr1rTrr3r)r)rrrr rr8rfrgrrr4rr9r:rrr r;r<r-rhrr=)r"rwrr?r>rrFr@r@rArsbV     rcs\|}|}trd}d}t|tr|}|d}t|j|dks&Jd|dkr1t|j|n|}|d|f7}t|trG|}|d|f7}nBt|tt frt|}t |rxt |D]\}} t| trn||d||<q[|dt|f7}n|dt|f7}nt dt|trt|trt|||St|||Strd d t|D} tj|| g|R| St|d gd d t|dttt fd t|dttfd t|trt|jdddgd td!it|j} d|i} dt|tr|ndi}fdd} t|trd|_|| d<nt|j|dksJd|dkr)t| |n|}||d<t|tra|dks>Jdt|tr^| |dkr^| ||dks^Jd|| |f|}n5t|tr{| |dkr{t|| |ks{Jdt|}ttdd||d<t |r| || d<fdd t|D}jd | d|i|d |S)"a Split the input tensor into multiple sub-Tensors. Args: x (Tensor): A N-D Tensor. The data type is bool, float16, float32, float64, uint8, int8, int32 or int64. num_or_sections (int|list|tuple): If ``num_or_sections`` is an int, then ``num_or_sections`` indicates the number of equal sized sub-Tensors that the ``x`` will be divided into. If ``num_or_sections`` is a list or tuple, the length of it indicates the number of sub-Tensors and the elements in it indicate the sizes of sub-Tensors' dimension orderly. The length of the list must not be larger than the ``x`` 's size of specified ``axis``. axis (int|Tensor, optional): The axis along which to split, it can be a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` or ``int64``. If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list(Tensor): The list of segmented Tensors. Example: .. code-block:: python import paddle # x is a Tensor of shape [3, 9, 5] x = paddle.rand([3, 9, 5]) out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=1) print(out0.shape) # [3, 3, 5] print(out1.shape) # [3, 3, 5] print(out2.shape) # [3, 3, 5] out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, 4], axis=1) print(out0.shape) # [3, 2, 5] print(out1.shape) # [3, 3, 5] print(out2.shape) # [3, 4, 5] out0, out1, out2 = paddle.split(x, num_or_sections=[2, 3, -1], axis=1) print(out0.shape) # [3, 2, 5] print(out1.shape) # [3, 3, 5] print(out2.shape) # [3, 4, 5] # axis is negative, the real axis is (rank(x) + axis)=1 out0, out1, out2 = paddle.split(x, num_or_sections=3, axis=-2) print(out0.shape) # [3, 3, 5] print(out1.shape) # [3, 3, 5] print(out2.shape) # [3, 3, 5] Nr@rz(rank(x) + axis) must >= 0rwrsectionszfThe type of 'num_or_sections' in split must be int, list or tuple in imperative mode, but received %s.cSsg|]}tqSr@)r)rEnr@r@rArQzsplit..rc)r#r$r%r&r(r)r*r.splitnum_or_sectionsrvr(r)r1csg}d}t|D]=\}}t|trd|_||qt|ts!J|dkr1|dks/Jd||}d}tdgd|d|d||q|S)NrXTzbOnly one value of 'num_or_section' in split can be -1. But received num_or_section[%d] is also -1.r(rr)rsr8rr0rtrr<r)Zone_listZ tensor_list unk_dim_idxrrrr?r@rA_get_SectionsTensorLists(      z&split.._get_SectionsTensorListTrrz$num_or_sections must be more than 1.zThe input's size along the split dimension must be evenly divisible by Attr(num_or_sections). But %d is not evenly divisible by %d. zrzsplit..ZSectionsTensorListcg|] }jdqSrdr<rurDrr@rArQr2r3)r)r r8rrLrMrhrlrrfrgrrqrsrr4rrZsplit_with_numrr rjrrrrr-r r;r0mapr=)r"rrwrrcrvrr7indexrMr> input_shaper5rrr@rrArjs0                  rc Cs*|durg}nt|tr|g}n t|trt|}|}|}tr&t||Str4t |d|\}}|St d it }t |dgddt |dttttfdi}t|tr_d|_||d<nt|ttfrwt|rst||d<n||d<|j|jd}|j|jd} |jd d |i||| d d |S)a Squeeze the dimension(s) of size 1 of input tensor x's shape. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``squeeze_clone_x = x.squeeze().clone()``. If axis is provided, it will remove the dimension(s) by given axis that of size 1. If the dimension of given axis is not of size 1, the dimension remain unchanged. If axis is not provided, all dims equal of size 1 will be removed. .. code-block:: text Case1: Input: x.shape = [1, 3, 1, 5] # If axis is not provided, all dims equal of size 1 will be removed. axis = None Output: out.shape = [3, 5] Case2: Input: x.shape = [1, 3, 1, 5] # If axis is provided, it will remove the dimension(s) by given axis that of size 1. axis = 0 Output: out.shape = [3, 1, 5] Case4: Input: x.shape = [1, 3, 1, 5] # If the dimension of one given axis (3) is not of size 1, the dimension remain unchanged. axis = [0, 2, 3] Output: out.shape = [3, 5] Case4: Input: x.shape = [1, 3, 1, 5] # If axis is negative, axis = axis + ndim (number of dimensions in x). axis = [-2] Output: out.shape = [1, 3, 5] Args: x (Tensor): The input Tensor. Supported data type: float32, float64, bool, int8, int32, int64. axis (int|list|tuple, optional): An integer or list/tuple of integers, indicating the dimensions to be squeezed. Default is None. The range of axis is :math:`[-ndim(x), ndim(x))`. If axis is negative, :math:`axis = axis + ndim(x)`. If axis is None, all the dimensions of x of size 1 will be removed. name (str, optional): Please refer to :ref:`api_guide_Name`, Default None. Returns: Tensor: Squeezed Tensor with the same data type as input Tensor. Examples: .. code-block:: python import paddle x = paddle.rand([5, 1, 10]) output = paddle.squeeze(x, axis=1) print(x.shape) # [5, 1, 10] print(output.shape) # [5, 10] # output shares data with x in dygraph mode x[0, 0, 0] = 10. print(output[0, 0]) # [10.] Nr[squeezerc) r$r%r&r#r.r(r)ryrz axis/axesTrdsqueeze2r1r~re)r)r8rrgrfrrrr rrr r;rrrr0rrqrrr<r-r=) r"rwrrcr[r>rr?r7rr@r@rAr"sLI       rcCsl|durg}nt|tr|g}n t|trt|}|}|}tr&t||Str4t |d|\}}|SdS)z Inplace version of ``squeeze`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_squeeze`. Nr[) r8rrgrfrrsqueeze_r rZ squeeze2_r"rwrrcr[r>rr@r@rArs   rr)c Cs|durg}n|g}t|}trinversecountsrr?r7r6r@r@rArsC                rcCs|durg}n|g}t|}tr`tr"t||||||\}} } } tr:t|d|d|d|d|d|dd \}} } } |g} |rD| | |rK| | |rR| | t| d kr\| d St | St |d gd d t |dt d t |dt d t |dt d t |dddgd t|d krt |d dtd tdit} |||||dd}| j|jdd}| j|dd} | j|dd} | j|dd} || | | d}|g} |r| | |r| | |r| | | jd d|i||dt| d kr| d St | S)a Returns the unique elements of `x` in ascending order. Args: x(Tensor): The input tensor, it's data type should be float32, float64, int32, int64. return_index(bool, optional): If True, also return the indices of the input tensor that result in the unique Tensor. return_inverse(bool, optional): If True, also return the indices for where elements in the original input ended up in the returned unique tensor. return_counts(bool, optional): If True, also return the counts for each unique element. axis(int, optional): The axis to apply unique. If None, the input will be flattened. Default: None. dtype(np.dtype|str, optional): The date type of `indices` or `inverse` tensor: int32 or int64. Default: int64. name(str, optional): Name for the operation. For more information, please refer to :ref:`api_guide_Name`. Default: None. Returns: tuple (out, indices, inverse, counts). `out` is the unique tensor for `x`. `indices` is \ provided only if `return_index` is True. `inverse` is provided only if `return_inverse` \ is True. `counts` is provided only if `return_counts` is True. Examples: .. code-block:: python import paddle x = paddle.to_tensor([2, 3, 3, 1, 5, 3]) unique = paddle.unique(x) np_unique = unique.numpy() # [1 2 3 5] _, indices, inverse, counts = paddle.unique(x, return_index=True, return_inverse=True, return_counts=True) print(indices) # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, # [3, 0, 1, 4]) print(inverse) # Tensor(shape=[6], dtype=int64, place=Place(gpu:0), stop_gradient=True, # [1, 2, 2, 0, 3, 2]) print(counts) # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, # [1, 1, 3, 1]) x = paddle.to_tensor([[2, 1, 3], [3, 0, 1], [2, 1, 3]]) unique = paddle.unique(x) print(unique) # Tensor(shape=[4], dtype=int64, place=Place(gpu:0), stop_gradient=True, # [0, 1, 2, 3]) unique = paddle.unique(x, axis=0) print(unique) # Tensor(shape=[2, 3], dtype=int64, place=Place(gpu:0), stop_gradient=True, # [[2, 1, 3], # [3, 0, 1]]) Nr- return_indexrrrw is_sortedTrrrcruniquer(r))r-rrrrwrr/)r2ZIndicesrrr1re)r)rr rrrr rrtrhrgrrr#rrr r;r<r-r=)r"rrrrwr-rrr>indicesrrrr?r7r6r@r@rArJ s>              rc CsR|}|}tr>t|tr|g}nt|tr|}nt|ttfr*dd|D}tr8t |d|\}}|St ||St |dttttfdt|dgddtdit}d |i}i} t|trh|g}t|trud |_||d <nt|ttfrt|rt||d <n|| d<|j|jd }|j|jd } |jd|| || dd|S)a Insert single-dimensional entries to the shape of input Tensor ``x``. Takes one required argument axis, a dimension or list of dimensions that will be inserted. Dimension indices in axis are as seen in the output tensor. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``unsqueeze_clone_x = x.unsqueeze(-1).clone()``. Args: x (Tensor): The input Tensor to be unsqueezed. Supported data type: float32, float64, bool, int8, int32, int64. axis (int|list|tuple|Tensor): Indicates the dimensions to be inserted. The data type is ``int32`` . If ``axis`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``axis`` is a Tensor, it should be an 1-D Tensor . If ``axis`` is negative, ``axis = axis + ndim(x) + 1``. name (str|None): Name for this layer. Please refer to :ref:`api_guide_Name`, Default None. Returns: Tensor: Unsqueezed Tensor with the same data type as input Tensor. Examples: .. code-block:: python import paddle x = paddle.rand([5, 10]) print(x.shape) # [5, 10] out1 = paddle.unsqueeze(x, axis=0) print(out1.shape) # [1, 5, 10] out2 = paddle.unsqueeze(x, axis=[0, 2]) print(out2.shape) # [1, 5, 1, 10] axis = paddle.to_tensor([0, 1, 2]) out3 = paddle.unsqueeze(x, axis=axis) print(out3.shape) # [1, 1, 1, 5, 10] # out1, out2, out3 share data with x in dygraph mode x[0, 0] = 10. print(out1[0, 0, 0]) # [10.] print(out2[0, 0, 0, 0]) # [10.] print(out3[0, 0, 0, 0, 0]) # [10.] cS(g|]}t|tr|dn|qSrJr8rrLrMrNr@r@rArQ zunsqueeze..r[r unsqueezerc) r$r%r&r#r.r'r(r)ryrz unsqueeze2r1TZ AxesTensorZAxesTensorListrdr~reN)r )r r8rrrLrrfrgr rr rr rrr r;r0rrqrrr<r-r=) r"rwrrcr[r>rr?r5r7rr@r@rAr  sV.        r cCsv|}|}t|tr |g}nt|tr|}nt|ttfr'dd|D}tr0t ||St |d|\}}|S)z Inplace version of ``unsqueeze`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_unsqueeze`. cSrrJr rNr@r@rArQ_ r zunsqueeze_..r[) r8rrrLrrfrgrr unsqueeze_rZ unsqueeze2_rr@r@rAr R s   r c Cs|durd}trt|||Str*t|tjr|n|}t||dd|ddSt |dgddt |d d d gdt|t rJt |dd d gdt dit }| d}||}t|t st|jd||d |dd d|id|S|jd|||dddid|id|S)a Output is obtained by gathering entries of ``axis`` of ``x`` indexed by ``index`` and concatenate them together. .. code-block:: text Given: x = [[1, 2], [3, 4], [5, 6]] index = [1, 2] axis=[0] Then: out = [[3, 4], [5, 6]] Args: x (Tensor): The source input tensor with rank>=1. Supported data type is int32, int64, float32, float64 and uint8 (only for CPU), float16 (only for GPU). index (Tensor): The index input tensor with rank=1. Data type is int32 or int64. axis (Tensor|int, optional): The axis of input to be gathered, it's can be int or a Tensor with data type is int32 or int64. The default value is None, if None, the ``axis`` is 0. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: output (Tensor): The output is a tensor with the same rank as ``x``. Examples: .. code-block:: python import paddle input = paddle.to_tensor([[1,2],[3,4],[5,6]]) index = paddle.to_tensor([0,1]) output = paddle.gather(input, index, axis=0) # expected output: [[1,2],[3,4]] Nrrw overwriteFr")r$r%r&r'r(r)r*gatherrr(r)r1r)rwrr2re)r1rAxis)r)rrrr r8rrprMrrrr r;rur<r=)r"rrwrr?r-r>r@r@rAri sH-     rcstr t||St|tstdt|t|tjr!t |}|j }|dkr*|nt ||}||}t r?t ||d|Std itt|dtd}t|dgddfddt|D}jdd |id |id|id |S)a Removes a tensor dimension, then split the input tensor into multiple sub-Tensors. Args: input (Tensor): The input variable which is an N-D Tensor, data type being float32, float64, int32 or int64. axis (int32|int64, optional): A scalar with type ``int32|int64`` shape [1]. The dimension along which to unbind. If :math:`axis < 0`, the dimension to unbind along is :math:`rank(input) + axis`. Default is 0. Returns: list(Tensor): The list of segmented Tensor variables. Example: .. code-block:: python import paddle # input is a Tensor which shape is [3, 4, 5] input = paddle.rand([3, 4, 5]) [x0, x1, x2] = paddle.unbind(input, axis=0) # x0.shape [4, 5] # x1.shape [4, 5] # x2.shape [4, 5] [x0, x1, x2, x3] = paddle.unbind(input, axis=1) # x0.shape [3, 5] # x1.shape [3, 5] # x2.shape [3, 5] # x3.shape [3, 5] z1The type of 'axis' must be int, but received %s.rrwunbindrcrcrrrrDrr@rArQ rzunbind..r1r2r3N)r)rrrr8rrr4npZgenericZasscalarrlrhr rr r;rrrurrjr=)rcrwrZaxis_rr-rr@rrAr s:       rTcCstr t||||Strt|||d|St|dgddt|dtdtd it }| |j }|j d|||dd|id|id|S) an **Scatter Layer** Output is obtained by updating the input on selected indices based on updates. .. code-block:: python import numpy as np #input: x = np.array([[1, 1], [2, 2], [3, 3]]) index = np.array([2, 1, 0, 1]) # shape of updates should be the same as x # shape of updates with dim > 1 should be the same as input updates = np.array([[1, 1], [2, 2], [3, 3], [4, 4]]) overwrite = False # calculation: if not overwrite: for i in range(len(index)): x[index[i]] = np.zeros((2)) for i in range(len(index)): if (overwrite): x[index[i]] = updates[i] else: x[index[i]] += updates[i] # output: out = np.array([[3, 3], [6, 6], [1, 1]]) out.shape # [3, 2] **NOTICE**: The order in which updates are applied is nondeterministic, so the output will be nondeterministic if index contains duplicates. Args: x (Tensor): The input N-D Tensor with ndim>=1. Data type can be float32, float64. index (Tensor): The index 1-D Tensor. Data type can be int32, int64. The length of index cannot exceed updates's length, and the value in index cannot exceed input's length. updates (Tensor): update input with updates parameter based on index. shape should be the same as input, and dim value with dim > 1 should be the same as input. overwrite (bool): The mode that updating the output when there are same indices. If True, use the overwrite mode to update the output of the same index, if False, use the accumulate mode to update the output of the same index.Default value is True. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: Tensor: The output is a Tensor with the same shape as x. Examples: .. code-block:: python import paddle x = paddle.to_tensor([[1, 1], [2, 2], [3, 3]], dtype='float32') index = paddle.to_tensor([2, 1, 0, 1], dtype='int64') updates = paddle.to_tensor([[1, 1], [2, 2], [3, 3], [4, 4]], dtype='float32') output1 = paddle.scatter(x, index, updates, overwrite=False) # [[3., 3.], # [6., 6.], # [1., 1.]] output2 = paddle.scatter(x, index, updates, overwrite=True) # CPU device: # [[3., 3.], # [4., 4.], # [1., 1.]] # GPU device maybe have two results because of the repeated numbers in index # result 1: # [[3., 3.], # [4., 4.], # [1., 1.]] # result 2: # [[3., 3.], # [2., 2.], # [1., 1.]] rr-)r%r&r$r(r)scatter)r1ZIdsUpdatesr2reN)r) rrrr rrrr#r r;r<r-r=)r"rupdatesrrr?r>r@r@rAr s,J   rcCs(tr t||||St|||d|S)z Inplace version of ``scatter`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_scatter`. r)rrscatter_r)r"rrrrr@r@rArf srcCstr t|||Strttd}||||S|j|jkr"tdtd it }|j dd}| |}|j d|||dd|id|S) aI Output is obtained by applying sparse addition to a single value or slice in a Tensor. :attr:`x` is a Tensor with ndim :math:`R` and :attr:`index` is a Tensor with ndim :math:`K` . Thus, :attr:`index` has shape :math:`[i_0, i_1, ..., i_{K-2}, Q]` where :math:`Q \leq R` . :attr:`updates` is a Tensor with ndim :math:`K - 1 + R - Q` and its shape is :math:`index.shape[:-1] + x.shape[index.shape[-1]:]` . According to the :math:`[i_0, i_1, ..., i_{K-2}]` of :attr:`index` , add the corresponding :attr:`updates` slice to the :attr:`x` slice which is obtained by the last one dimension of :attr:`index` . .. code-block:: text Given: * Case 1: x = [0, 1, 2, 3, 4, 5] index = [[1], [2], [3], [1]] updates = [9, 10, 11, 12] we get: output = [0, 22, 12, 14, 4, 5] * Case 2: x = [[65, 17], [-14, -25]] index = [[], []] updates = [[[-1, -2], [1, 2]], [[3, 4], [-3, -4]]] x.shape = (2, 2) index.shape = (2, 0) updates.shape = (2, 2, 2) we get: output = [[67, 19], [-16, -27]] Args: x (Tensor): The x input. Its dtype should be int32, int64, float32, float64. index (Tensor): The index input with ndim > 1 and index.shape[-1] <= x.ndim. Its dtype should be int32 or int64 as it is used as indexes. updates (Tensor): The updated value of scatter_nd_add op, and it must have the same dtype as x. It must have the shape index.shape[:-1] + x.shape[index.shape[-1]:]. name (str|None): The output tensor name. If set None, the layer will be named automatically. Returns: output (Tensor): The output is a tensor with the same shape and dtype as x. Examples: .. code-block:: python import paddle x = paddle.rand(shape=[3, 5, 9, 10], dtype='float32') updates = paddle.rand(shape=[3, 9, 10], dtype='float32') index = paddle.to_tensor([[1, 1], [0, 1], [1, 3]], dtype='int64') output = paddle.scatter_nd_add(x, index, updates) print(output.shape) # [3, 5, 9, 10] scatter_nd_addz'x and updates must have same data type.r"Zinput_param_name)r1rrr2r4r5r6N)r) rrrr getattrrr-rir r;rur<r=)r"rrropr?r-outputr@r@rArq s E      rcCstt||j|||S)a **Scatter_nd Layer** Output is obtained by scattering the :attr:`updates` in a new tensor according to :attr:`index` . This op is similar to :code:`scatter_nd_add`, except the tensor of :attr:`shape` is zero-initialized. Correspondingly, :code:`scatter_nd(index, updates, shape)` is equal to :code:`scatter_nd_add(paddle.zeros(shape, updates.dtype), index, updates)` . If :attr:`index` has repeated elements, then the corresponding updates are accumulated. Because of the numerical approximation issues, the different order of repeated elements in :attr:`index` may cause different results. The specific calculation method can be seen :code:`scatter_nd_add` . This op is the inverse of the :code:`gather_nd` op. Args: index (Tensor): The index input with ndim > 1 and index.shape[-1] <= len(shape). Its dtype should be int32 or int64 as it is used as indexes. updates (Tensor): The updated value of scatter_nd op. Its dtype should be float32, float64. It must have the shape index.shape[:-1] + shape[index.shape[-1]:] shape(tuple|list): Shape of output tensor. name (str|None): The output Tensor name. If set None, the layer will be named automatically. Returns: output (Tensor): The output is a tensor with the same type as :attr:`updates` . Examples: .. code-block:: python import paddle index = paddle.to_tensor([[1, 1], [0, 1], [1, 3]], dtype="int64") updates = paddle.rand(shape=[3, 9, 10], dtype='float32') shape = [3, 5, 9, 10] output = paddle.scatter_nd(index, updates, shape) )rrr-)rrrlrr@r@rA scatter_nd s&rcCst|dtdt||||dS)aE Split the input tensor into multiple sub-Tensors. Args: x (Tensor): A N-D Tensor. The data type is bool, float16, float32, float64, int32 or int64. chunks(int): The number of tensor to be split along the certain axis. axis (int|Tensor, optional): The axis along which to split, it can be a scalar with type ``int`` or a ``Tensor`` with shape [1] and data type ``int32`` or ``int64``. If :math::`axis < 0`, the axis to split along is :math:`rank(x) + axis`. Default is 0. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: list(Tensor): The list of segmented Tensors. Examples: .. code-block:: python import paddle x = paddle.rand([3, 9, 5]) out0, out1, out2 = paddle.chunk(x, chunks=3, axis=1) # out0.shape [3, 3, 5] # out1.shape [3, 3, 5] # out2.shape [3, 3, 5] # axis is negative, the real axis is (rank(x) + axis) which real # value is 1. out0, out1, out2 = paddle.chunk(x, chunks=3, axis=-2) # out0.shape [3, 3, 5] # out1.shape [3, 3, 5] # out2.shape [3, 3, 5] chunkschunk)rrwr)rrr)r"rrwrr@r@rAr  s#r c Cstrt|tjjr|jdksJd|}t ||St r)t |d|St |dt ttfdt|trDt|jdksCJdn&|D]#}t|trYt|jdksXJdqFttjtjf}t||siJdqFt|dgddt|jd kr|jd krtd tdit}d |gi}i}d d}t|trd|_||d<dg|d<nt|t tfr|||d<t|rt||d<|j dd} |!| } |j"d|d| i|d| S)a Construct a new Tensor by repeating ``x`` the number of times given by ``repeat_times``. After tiling, the value of the i'th dimension of the output is equal to ``x.shape[i]*repeat_times[i]``. Both the number of dimensions of ``x`` and the number of elements in ``repeat_times`` should be less than or equal to 6. Args: x (Tensor): The input tensor, its data type should be bool, float32, float64, int32 or int64. repeat_times (list|tuple|Tensor): The number of repeating times. If repeat_times is a list or tuple, all its elements should be integers or 1-D Tensors with the data type int32. If repeat_times is a Tensor, it should be an 1-D Tensor with the data type int32. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor. The data type is the same as ``x``. The size of the i-th dimension is equal to ``x[i] * repeat_times[i]``. Examples: .. code-block:: python import paddle data = paddle.to_tensor([1, 2, 3], dtype='int32') out = paddle.tile(data, repeat_times=[2, 1]) print(out) # Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True, # [[1, 2, 3], # [1, 2, 3]]) out = paddle.tile(data, repeat_times=(2, 2)) print(out) # Tensor(shape=[2, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True, # [[1, 2, 3, 1, 2, 3], # [1, 2, 3, 1, 2, 3]]) repeat_times = paddle.to_tensor([1, 2], dtype='int32') out = paddle.tile(data, repeat_times=repeat_times) print(out) # Tensor(shape=[1, 6], dtype=int32, place=Place(gpu:0), stop_gradient=True, # [[1, 2, 3, 1, 2, 3]]) rz6Only support ndim == 1 while repeat_times is a Tensor. repeat_timestilez#repeat_times must be an 1-D Tensor.z9Elements in repeat_times must be 1-D Tensors or integers.r"rr#FzWhen the date type is bool for the input 'x' of tile op, you must set its stop_gradient to be True by some_var.stop_gradient == True supporting some_var is the input.r1cSsJg}t|D]\}}t|tr|dq|||dks"Jdq|S)NrXrz7All elements in repeat_times must be positive for tile.rsr8rrt)Zlist_repeat_timesZattrs_repeat_timesrtimesr@r@rAget_attr_repeat_timesp s    z#tile..get_attr_repeat_timesTZ RepeatTimesrXZrepeat_times_tensorrr2r3N)r")#rr8rrorpndimrLrrr"r rrrfrgrrhrlrrr(r)rrr-r0rir r;rrqrrrur<r=) r"r!relem type_tupler?r5r7r%r-r>r@r@rAr" sh)              r"cCstr t|d|jStrt|d|jSt|dgddt|dt dt |j dkr6|j dkr6t d |g|gd }tdit}|jdd }||}|jd |d|jid |id|S)a Expand the input tensor ``x`` to the same shape as the input tensor ``y``. Both the number of dimensions of ``x`` and ``y`` must be less than or equal to 6, and the number of dimensions of ``y`` must be greather than or equal to that of ``x``. The dimension to expand must have a value of 1. Args: x (Tensor): The input tensor, its data type is bool, float32, float64, int32 or int64. y (Tensor): The input tensor that gives the shape to expand to. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor: A Tensor with the same shape as ``y``. The data type is the same as ``x``. Examples: .. code-block:: python import paddle data_x = paddle.to_tensor([1, 2, 3], 'int32') data_y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], 'int32') out = paddle.expand_as(data_x, data_y) print(out) # Tensor(shape=[2, 3], dtype=int32, place=Place(gpu:0), stop_gradient=True, # [[1, 2, 3], # [1, 2, 3]]) NZ target_shaper"r expand_asrr#FzWhen the data type of input 'x' for expand_as is bool, you must set its stop_gradient to be False by some_var.stop_gradient = True, supporting some_var as the input 'x'.)r1rr expand_as_v2r2re)r))rrr)rlr rr*rrrrr-r0rir r;rur<r=)r"rrr5r?r-r>r@r@rAr) s.   r)c Cs|tr t||Strt|d|St|tr$t|j dks#Jdn&|D]#}t|tr9t|j dks8Jdq&t t j t j f}t||sIJdq&t|dgddt|dtttfdt|jdkrm|jd krmtd d |gi}i}tdit}d d}t|trd|_||d<nt|ttfr|||d<t|rt||d<|jdd} || } |jd|d| i|d| S)a Broadcast the input tensor to a given shape. Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. The dimension to broadcast to must have a value 1. Args: x (Tensor): The input tensor, its data type is bool, float32, float64, int32 or int64. shape (list|tuple|Tensor): The result shape after broadcasting. The data type is int32. If shape is a list or tuple, all its elements should be integers or 1-D Tensors with the data type int32. If shape is a Tensor, it should be an 1-D Tensor with the data type int32. The value -1 in shape means keeping the corresponding dimension unchanged. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: N-D Tensor: A Tensor with the given shape. The data type is the same as ``x``. Examples: .. code-block:: python import paddle data = paddle.to_tensor([1, 2, 3], dtype='int32') out = paddle.broadcast_to(data, shape=[2, 3]) print(out) # [[1, 2, 3], [1, 2, 3]] rlrshape must be an 1-D Tensor.2Elements in shape must be 1-D Tensors or integers.r"r broadcast_tor#FzWhen the data type of input 'x' for broadcast_to is bool, you must set its stop_gradient to be False by some_var.stop_gradient = True, supporting some_var as the input.r1expandcSsRg}t|D] \}}t|tr|dq|||dks&|dks&Jdq|S)NrXrz=All elements in shape of broadcast_to must be positive or -1.r#Zlist_expand_shapeZattrs_expand_shaperrlr@r@rAget_attr_expand_shape    z+broadcast_to..get_attr_expand_shapeTrexpand_shapes_tensorr expand_v2r2r3Nr.)rrr.r rr3r8rrhrlrrr(r)rrrfrgrr-r0rir r;rrqrrrur<r= r"rlrr'r(r5r7r?r0r-r>r@r@rAr- sX            r-c Cs~tr t||Strt|d|St|tr%t |j dks$Jdn&|D]#}t|tr:t |j dks9Jdq't t j t jf}t||sJJdq't|dgddt|dtttfdt|jdkrn|jd krntd d |gi}i}tdit}d d }t|trd|_||d<nt|ttfr|||d<t|rt||d<|jdd} || } |jd|d| i|d| S)a$ Expand the input tensor to a given shape. Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. And the number of dimensions of ``x`` should be less than the number of elements in ``shape``. The dimension to expand must have a value 1. Args: x (Tensor): The input Tensor, its data type is bool, float32, float64, int32 or int64. shape (list|tuple|Tensor): The result shape after expanding. The data type is int32. If shape is a list or tuple, all its elements should be integers or 1-D Tensors with the data type int32. If shape is a Tensor, it should be an 1-D Tensor with the data type int32. The value -1 in shape means keeping the corresponding dimension unchanged. name (str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Returns: N-D Tensor: A Tensor with the given shape. The data type is the same as ``x``. Examples: .. code-block:: python import paddle data = paddle.to_tensor([1, 2, 3], dtype='int32') out = paddle.expand(data, shape=[2, 3]) print(out) # [[1, 2, 3], [1, 2, 3]] rlrr+r,r"rr.r#FzWhen the data type of input 'x' for expand is bool, you must set its stop_gradient to be False by some_var.stop_gradient = True, supporting some_var as the input.r1cSsRg}t|D] \}}t|tr|dq|||dks&|dks&Jdq|S)NrrXz7All elements in shape of expand must be positive or -1.r#r/r@r@rAr0l r1z%expand..get_attr_expand_shapeTrr2rr3r2r3Nr4)rrr.rrrr3r8rrhrlrrr(r)rrrfrgrr-r0rir r;rrqrrrur<r=r5r@r@rAr.( s^           r.c sNd}d}d}trItjj|rtdt|ttfr+fdd|D}t |}nt|r:d|_ t |}n t d t|t||Strt|rUtdt|ttfrndd|D}tdd |\}}nt|rd|_ t|\}}n t d t|t||Std gd d t|d tttfd t|d ttdfd tdit}fdd} di} i} t|trd|_ || d<n6t|ttfrt|dksJdt|| || d <t|rt|| d<n t|trd|_ || d<|rn|jjd}|jjd} |jd| | || dd| |S)a; Changes the shape of ``x`` without changing its data. Note that the output Tensor will share data with origin Tensor and doesn't have a Tensor copy in ``dygraph`` mode. If you want to use the Tensor copy version, please use `Tensor.clone` like ``reshape_clone_x = x.reshape([-1]).clone()``. Some tricks exist when specifying the target shape. - 1. -1 means the value of this dimension is inferred from the total element number of x and remaining dimensions. Thus one and only one dimension can be set -1. - 2. 0 means the actual dimension value is going to be copied from the corresponding dimension of x. The index of 0s in shape can not exceed the dimension of x. Here are some examples to explain it. - 1. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [6, 8], the reshape operator will transform x into a 2-D tensor with shape [6, 8] and leaving x's data unchanged. - 2. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape specified is [2, 3, -1, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 3, 4, 2] and leaving x's data unchanged. In this case, one dimension of the target shape is set to -1, the value of this dimension is inferred from the total element number of x and remaining dimensions. - 3. Given a 3-D tensor x with a shape [2, 4, 6], and the target shape is [-1, 0, 3, 2], the reshape operator will transform x into a 4-D tensor with shape [2, 4, 3, 2] and leaving x's data unchanged. In this case, besides -1, 0 means the actual dimension value is going to be copied from the corresponding dimension of x. Args: x (Tensor): An N-D Tensor. The data type is ``float32``, ``float64``, ``int32``, ``int64`` or ``bool`` shape (list|tuple|Tensor): Define the target shape. At most one dimension of the target shape can be -1. The data type is ``int32`` . If ``shape`` is a list or tuple, the elements of it should be integers or Tensors with shape [1]. If ``shape`` is an Tensor, it should be an 1-D Tensor . name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A reshaped Tensor with the same data type as ``x``. Examples: .. code-block:: python import paddle x = paddle.rand([2, 4, 6], dtype="float32") positive_four = paddle.full([1], 4, "int32") out = paddle.reshape(x, [-1, 0, 3, 2]) print(out) # the shape is [2,4,3,2]. out = paddle.reshape(x, shape=[positive_four, 12]) print(out) # the shape of out_2 is [4, 12]. shape_tensor = paddle.to_tensor([8, 6], dtype=paddle.int32) out = paddle.reshape(x, shape=shape_tensor) print(out.shape) # the shape is [8, 6]. # out shares data with x in dygraph mode x[0, 0, 0] = 10. print(out[0, 0]) # the value is [10.] NFzRInplace on reshape is not allowed and will be discarded in dygraph mode currently.crIrJrKrNrOr@rArQ rRzreshape..TEshape must be an instance of `list`, `tuple` or `Variable`, got '{}.'cSrrJr rNr@r@rArQ r rlr")r$r%r&r'r(r)r#r+r actual_shapereshape2csd}g}t|D]J\}}t|tr|dq|||dkr-|dks*Jd||}q|dkrD|tjksCJd|tjfq|dksRJd|t|fq|S)NrXaOnly one dimension value of 'shape' in reshape can be -1. But received shape[%d] is also -1. # N = x.shape()[2] # N is an int. (NOT recommend under @to_static) N = paddle.shape(x)[2] # N is a Tensor. (Recommend) z = paddle.reshape([N, -1, 4]) # z.shape is [-1, -1, 4] If your target shape in Reshape represents dynamic shape, please turn it into a Tensor under @to_static. See above example for details.rz}The index of 0 in `shape` must be less than the input tensor X's dimensions. But received shape[%d] = 0, X's dimensions = %d.zzEach dimension value of 'shape' in reshape must not be negative except one unknown dimension. But received shape[%d] = %s.)rsr8rrtrhrlr)Z list_shaperZ attrs_shapeZdim_idxrrr@rAget_attr_shapes2        zreshape..get_attr_shaper1rrz>The size of 'shape' in reshape can't be zero, but received %s.rrdr~re)r9)!rrrorpwarningswarnr8rfrgrrr0rirnr4rZ_append_activation_in_dygraphr rrr9rrr r;rhrrqrrr<r-r=Zappend_activation) r"rlrr8Zactrr>rr?r:r5r7rr@)rPr"rAr s;         %       rcstr8tjjt|ttfrfdd|D}t||}|St|r/d|_ t||}|St d t |t|ttfrRdd|D}t |dd|\}}|St|trld|_ |}t |dd|\}}|SdS)z Inplace version of ``reshape`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_reshape`. crIrJrKrNrOr@rArQ]rRzreshape_..Tr7cSrrJr rNr@r@rArQpr Nrl)rrrorpr8rfrgrreshape_r0rirnr4rZ reshape2_rrLr)r"rlrr>rZ shape_listr@rOrAr=Ts8       r=cCstr t||Strt||St|dgddt|dddgdtd it}|}| |}|j d||dd |id |S) a" This function is actually a high-dimensional extension of :code:`gather` and supports for simultaneous indexing by multiple axes. :attr:`index` is a K-dimensional integer tensor, which is regarded as a (K-1)-dimensional tensor of :attr:`index` into :attr:`input`, where each element defines a slice of params: .. math:: output[(i_0, ..., i_{K-2})] = input[index[(i_0, ..., i_{K-2})]] Obviously, :code:`index.shape[-1] <= input.rank` . And, the output tensor has shape :code:`index.shape[:-1] + input.shape[index.shape[-1]:]` . .. code-block:: text Given: x = [[[ 0, 1, 2, 3], [ 4, 5, 6, 7], [ 8, 9, 10, 11]], [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]]] x.shape = (2, 3, 4) * Case 1: index = [[1]] gather_nd(x, index) = [x[1, :, :]] = [[12, 13, 14, 15], [16, 17, 18, 19], [20, 21, 22, 23]] * Case 2: index = [[0,2]] gather_nd(x, index) = [x[0, 2, :]] = [8, 9, 10, 11] * Case 3: index = [[1, 2, 3]] gather_nd(x, index) = [x[1, 2, 3]] = [23] Args: x (Tensor): The input Tensor which it's data type should be bool, float32, float64, int32, int64. index (Tensor): The index input with rank > 1, index.shape[-1] <= input.rank. Its dtype should be int32, int64. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: output (Tensor): A tensor with the shape index.shape[:-1] + input.shape[index.shape[-1]:] Examples: .. code-block:: python import paddle x = paddle.to_tensor([[[1, 2], [3, 4], [5, 6]], [[7, 8], [9, 10], [11, 12]]]) index = paddle.to_tensor([[0, 1]]) output = paddle.gather_nd(x, index) #[[3, 4]] r")r#r%r&r'r(r)Z gather_nprr(r) gather_ndrr2rN)r>) rrr>r rrr r;rur<r=)r"rrr?r-rr@r@rAr>s(H   r>cs tr t|||||Stditt|dgddt|dttfdt|dttt fdt|dttt fdt|dttt fddd }||d||d||d||dfd d }d |i}d|i} td dt t |D} t rd |i}||||| d} nt |t rd|_||d<n.check_list_elements_dtypecsdg}|D]+}t|trd|_||qt|tsJd}tdgd|d|d||q|S)NTr(rr)r8rr0rtrr<r)Zold_listZnew_list_tensorrvrrr@rAget_new_list_tensorSs    z*strided_slice..get_new_list_tensorr^csrBrCr@rDr@r@rArGbrHz strided_slice..)r[rYrZr@r\Tr_r`rXrarbZ StridesTensorZStridesTensorListr\rdr2reN)r?)rrr?r r;rrrfrgrrjrhr r8r0rrqrsrtr<rur=)r"r[rYrZr@rrDrEr5r7r\rFrvr>r@rrAr?sR                        r?c sfdddg}t|d|t|d|t|dttttffdd}g}g}tt|tj rP|d ksBJd d |d t |j ||j }t |}n9t |trY||}|rftt|d tj ri|}n |d }t |d krw|d }t |tr||}t |tr||}t|t|}}t |t |}} || kr|||dn | |kr||| dt|jt|j} } tj|j td} tj|j td} d }t t |D]_}||||}}| || |}}|d krd | |<||| }n,|d krd | |<||| }n||ks*Jdd|d|d|d|d d| |<d| |<|| |9}qg}g}g}d }d }t |j D]}| |sa|||| ||| |9}qH||||t |j D]}| |s|||| ||| |9}qr|sd g}|j|d||g}|j|d||g}|||}|S)aX This function computes a contraction, which sum the product of elements from two tensors along the given axes. Args: x (Tensor): The left tensor for contraction with data type ``float32`` or ``float64``. y (Tensor): The right tensor for contraction with the same data type as ``x``. axes (int|tuple|list|Tensor, optional): The axes to contract for ``x`` and ``y``, defaulted to integer ``2``. 1. It could be a non-negative integer ``n``, in which the function will sum over the last ``n`` axes of ``x`` and the first ``n`` axes of ``y`` in order. 2. It could be a 1-d tuple or list with data type ``int``, in which ``x`` and ``y`` will be contracted along the same given axes. For example, ``axes`` =[0, 1] applies contraction along the first two axes for ``x`` and the first two axes for ``y``. 3. It could be a tuple or list containing one or two 1-d tuple|list|Tensor with data type ``int``. When containing one tuple|list|Tensor, the data in tuple|list|Tensor specified the same axes for ``x`` and ``y`` to contract. When containing two tuple|list|Tensor, the first will be applied to ``x`` and the second to ``y``. When containing more than two tuple|list|Tensor, only the first two axis sequences will be used while the others will be ignored. 4. It could be a tensor, in which the ``axes`` tensor will be translated to a python list and applied the same rules described above to determine the contraction axes. Note that the ``axes`` with Tensor type is ONLY available in Dygraph mode. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name` . Return: Output (Tensor): The contraction result with the same data type as ``x`` and ``y``. In general, :math:`output.ndim = x.ndim + y.ndim - 2 \times n_{axes}`, where :math:`n_{axes}` denotes the number of axes to be contracted. NOTES: 1. This function supports tensor broadcast, the size in the corresponding dimensions of ``x`` and ``y`` should be equal, or applies to the broadcast rules. 2. This function also supports axes expansion, when the two given axis sequences for ``x`` and ``y`` are of different lengths, the shorter sequence will expand the same axes as the longer one at the end. For example, if ``axes`` =[[0, 1, 2, 3], [1, 0]], the axis sequence for ``x`` is [0, 1, 2, 3], while the corresponding axis sequences for ``y`` will be expanded from [1, 0] to [1, 0, 2, 3]. Examples: .. code-block:: python import paddle data_type = 'float64' # For two 2-d tensor x and y, the case axes=0 is equivalent to outer product. # Note that tensordot supports empty axis sequence, so all the axes=0, axes=[], axes=[[]], and axes=[[],[]] are equivalent cases. x = paddle.arange(4, dtype=data_type).reshape([2, 2]) y = paddle.arange(4, dtype=data_type).reshape([2, 2]) z = paddle.tensordot(x, y, axes=0) # z = [[[[0., 0.], # [0., 0.]], # # [[0., 1.], # [2., 3.]]], # # # [[[0., 2.], # [4., 6.]], # # [[0., 3.], # [6., 9.]]]] # For two 1-d tensor x and y, the case axes=1 is equivalent to inner product. x = paddle.arange(10, dtype=data_type) y = paddle.arange(10, dtype=data_type) z1 = paddle.tensordot(x, y, axes=1) z2 = paddle.dot(x, y) # z1 = z2 = [285.] # For two 2-d tensor x and y, the case axes=1 is equivalent to matrix multiplication. x = paddle.arange(6, dtype=data_type).reshape([2, 3]) y = paddle.arange(12, dtype=data_type).reshape([3, 4]) z1 = paddle.tensordot(x, y, axes=1) z2 = paddle.matmul(x, y) # z1 = z2 = [[20., 23., 26., 29.], # [56., 68., 80., 92.]] # When axes is a 1-d int list, x and y will be contracted along the same given axes. # Note that axes=[1, 2] is equivalent to axes=[[1, 2]], axes=[[1, 2], []], axes=[[1, 2], [1]], and axes=[[1, 2], [1, 2]]. x = paddle.arange(24, dtype=data_type).reshape([2, 3, 4]) y = paddle.arange(36, dtype=data_type).reshape([3, 3, 4]) z = paddle.tensordot(x, y, axes=[1, 2]) # z = [[506. , 1298., 2090.], # [1298., 3818., 6338.]] # When axes is a list containing two 1-d int list, the first will be applied to x and the second to y. x = paddle.arange(60, dtype=data_type).reshape([3, 4, 5]) y = paddle.arange(24, dtype=data_type).reshape([4, 3, 2]) z = paddle.tensordot(x, y, axes=([1, 0], [0, 1])) # z = [[4400., 4730.], # [4532., 4874.], # [4664., 5018.], # [4796., 5162.], # [4928., 5306.]] # Thanks to the support of axes expansion, axes=[[0, 1, 3, 4], [1, 0, 3, 4]] can be abbreviated as axes= [[0, 1, 3, 4], [1, 0]]. x = paddle.arange(720, dtype=data_type).reshape([2, 3, 4, 5, 6]) y = paddle.arange(720, dtype=data_type).reshape([3, 2, 4, 5, 6]) z = paddle.tensordot(x, y, axes=[[0, 1, 3, 4], [1, 0]]) # z = [[23217330., 24915630., 26613930., 28312230.], # [24915630., 26775930., 28636230., 30496530.], # [26613930., 28636230., 30658530., 32680830.], # [28312230., 30496530., 32680830., 34865130.]] tensordotr%r&r"rr[cs trt|Stdd)Nz!The 'axes' with type 'Tensor' in zm is not available in static graph mode, please convert its type to int|Tuple|List, or use dynamic graph mode.)rrrr)rBrr@rA _var_to_list"sztensordot.._var_to_listrzThe 'axes' in z+ should not be negative, but received axes=.rNrdz(The dimensional size for 'x' and 'y' in z+ should match each other, but 'x' has size z in dim z while 'y' has size T)r|)rrrrgrfrrZ issubdtyper4integerrjr&r8rhextendrlrr#sumrrtr{matmul)r"rr[rrurHZaxes_xZaxes_yZ len_axes_xZ len_axes_yZshape_xZshape_yZneed_contracted_dim_xZneed_contracted_dim_yZcontraction_sizerFZdim_xZdim_ysxZsyZperm_xZperm_yZ shape_outZnot_contraction_size_xZnot_contraction_size_yr>r@rGrArFsp                     rFcCstrt|Strt|St|dddgdd}t|fit}d|i}|jt |j d}d|i}i}|j ||||d|S) aTransform a real tensor to a complex tensor. The data type of the input tensor is 'float32' or 'float64', and the data type of the returned tensor is 'complex64' or 'complex128', respectively. The shape of the input tensor is ``(* ,2)``, (``*`` means arbitary shape), i.e. the size of the last axis shoule be 2, which represent the real and imag part of a complex number. The shape of the returned tensor is ``(*,)``. Args: x (Tensor): The input tensor. Data type is 'float32' or 'float64'. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output. Data type is 'complex64' or 'complex128', with the same precision as the input. Examples: .. code-block:: python import paddle x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) y = paddle.as_complex(x) print(y) # Tensor(shape=[2, 3], dtype=complex64, place=Place(gpu:0), stop_gradient=True, # [[1j , (2+3j) , (4+5j) ], # [(6+7j) , (8+9j) , (10+11j)]]) r"r%r& as_complexr1rdr2re) rrrOr rrr r;r<rr-r=)r"rrr?r5r>r6r7r@r@rArOs  rOcCs~trt|Strt|St|dddgdd}t|fit}d|i}|jt |j d}d|i}|j |||d|S) a.Transform a complex tensor to a real tensor. The data type of the input tensor is 'complex64' or 'complex128', and the data type of the returned tensor is 'float32' or 'float64', respectively. When the shape of the input tensor is ``(*, )``, (``*`` means arbitary shape), the shape of the output tensor is ``(*, 2)``, i.e. the shape of the output is the shape of the input appended by an extra ``2``. Args: x (Tensor): The input tensor. Data type is 'complex64' or 'complex128'. name (str, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: The output. Data type is 'float32' or 'float64', with the same precision as the input. Examples: .. code-block:: python import paddle x = paddle.arange(12, dtype=paddle.float32).reshape([2, 3, 2]) y = paddle.as_complex(x) z = paddle.as_real(y) print(z) # Tensor(shape=[2, 3, 2], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [[[0. , 1. ], # [2. , 3. ], # [4. , 5. ]], # [[6. , 7. ], # [8. , 9. ], # [10., 11.]]]) r"ryrzas_realr1rdr2r) rrrPr rrr r;r<rr-r=)r"rrr?r5r>r6r@r@rArPs#  rPcCs|dur t|}d}tr!t|trt|||St|||Std it }t |dgdd| |j }|j d|t|trC|nddd|i|t|trQ|ndd d |S) a+ Returns a new tensor which repeats the ``x`` tensor along dimension ``axis`` using the entries in ``repeats`` which is a int or a Tensor. Args: x (Tensor): The input Tensor to be operated. The data of ``x`` can be one of float32, float64, int32, int64. repeats (Tensor or int): The number of repetitions for each element. repeats is broadcasted to fit the shape of the given axis. axis (int, optional): The dimension in which we manipulate. Default: None, the output tensor is flatten. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A Tensor with same data type as ``x``. Examples: .. code-block:: python import paddle x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]]) repeats = paddle.to_tensor([3, 2, 1], dtype='int32') paddle.repeat_interleave(x, repeats, 1) # [[1, 1, 1, 2, 2, 3], # [4, 4, 4, 5, 5, 6]] paddle.repeat_interleave(x, 2, 0) # [[1, 2, 3], [1, 2, 3], [4, 5, 6], [4, 5, 6]] paddle.repeat_interleave(x, 2, None) # [1, 1, 2, 2, 3, 3, 4, 4, 5, 5, 6, 6] Nrrepeat_interleaver"rz,paddle.tensor.manipulation.repeat_interleave)r1Z RepeatsTensorr2)rvZRepeatsr3)rQ)rrrr8rrZ#repeat_interleave_with_tensor_indexrQr r;rr<r-r=r)r"Zrepeatsrwrr?r>r@r@rArQs4$    rQcCst|tr|gn|}t|tr|gn|}t|t|ks Jdt|d}|dddkr3tdt|d}|dddkrFtdt|j}tt|}tt|} tt|} t t ||D]\} } t| dtssJd| ddkr| d| ksJd ||| |7<n | d|ksJd |t| dtsJd| ddkr| d| ksJd ||| |7<n | d|ksJd ||| ||| <| || | || qdtt| D] } | | || | <qt rt||} | Strt|d|\} }| St|d gd d tdit}||j} ||j}|jd d |gi| g|gdd|id| S)ao Move the axis of tensor from ``source`` position to ``destination`` position. Other axis that have not been moved remain their original order. Args: x (Tensor): The input Tensor. It is a N-D Tensor of data types bool, int32, int64, float32, float64, complex64, complex128. source(int|tuple|list): ``source`` position of axis that will be moved. Each element must be unique and integer. destination(int|tuple|list(int)): ``destination`` position of axis that has been moved. Each element must be unique and integer. name(str, optional): The default value is None. Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A new tensor whose axis have been moved. Examples: .. code-block:: python import paddle x = paddle.ones([3, 2, 4]) paddle.moveaxis(x, [0, 1], [1, 2]).shape # [4, 3, 2] x = paddle.ones([2, 3]) paddle.moveaxis(x, 0, 1).shape # equivalent to paddle.t(x) # [3, 2] z5'source' must have the same number with 'destination'rrz)Each elemment of 'source' must be unique!z.Each elemment of 'destination' must be unique!z*Each elemment of 'source' must be integer.z,'source' must be in the range of [-{0}, {0})rwr"rxmoveaxisr}r1r~r3N)rR)r8rrhr most_commonrirlrfrjrsziprnremoverrr{r rr}rr r;r<r-r=)r"source destinationrsrcdstrr&r|Zsrc_dimsZdst_dimsrFrwr>rr?rr@r@rArR-s           rRcCsPt|j}|dkr||ksJd||S|| ks"Jd|||7}|S)Nrz+'axis' must be in the range of [-{0}, {0}))rhrlrn)arrrwr&r@r@rAnon_negative_axiss   r[cCsVt|j}t|j|||<t|}tt|jD]}|j||j|kr(dSq|SN)rfrlrgrjrh)rZrrwbroadcast_shape_listbroadcast_shaperFr@r@rAinfer_broadcast_shapes r_cCsDt|jt|jkrtdt||}t|||}|s|j}trPt||}t|}t|j|||<t |}t||}t sHt |||St ||d|St|dgddt|dddgdt||}t|}t|j|||<t |}t||}td it}|}||}|jd||d d|id |id |S)a= Take values from the input array by given indices matrix along the designated axis. Args: arr (Tensor) : The input Tensor. Supported data types are float32 and float64. indices (Tensor) : Indices to take along each 1d slice of arr. This must match the dimension of arr, and need to broadcast against arr. Supported data type are int and int64. axis (int) : The axis to take 1d slices along. Returns: Tensor: The indexed element, same dtype with arr Examples: .. code-block:: python import paddle x = paddle.to_tensor([[1, 2, 3], [4, 5, 6], [7,8,9]]) index = paddle.to_tensor([[0]]) axis = 0 result = paddle.take_along_axis(x, index, axis) print(result) # [[1, 2, 3]] <`indices` and `arr` must have the same number of dimensions!rr"r$r%r&r(r)r*take_along_axisrr(r))r^rResultreN)rb)rhrlrir[r_r rr-rfrgr rrbrrr r;rur<r=)rZrrwr^r]r?r-resultr@r@rArbsR        rbassignc Cs*t|jt|jkrtdt||}t|||}trOt|tjs't |n|}|r1t ||}t ||j}t rDt |||||St |||d|d|St|dgddt|ddd gd|rit ||}t ||j}tdit}|}||}|jd|||d ||d d |id |S)a Put values into the destination array by given indices matrix along the designated axis. Args: arr (Tensor) : The Destination Tensor. Supported data types are float32 and float64. indices (Tensor) : Indices to put along each 1d slice of arr. This must match the dimension of arr, and need to broadcast against arr. Supported data type are int and int64. axis (int) : The axis to put 1d slices along. reduce (str, optional): The reduce operation, default is 'assign', support 'add', 'assign', 'mul' and 'multiply'. Returns: Tensor: The indexed element, same dtype with arr Examples: .. code-block:: python import paddle x = paddle.to_tensor([[10, 30, 20], [60, 40, 50]]) index = paddle.to_tensor([[0]]) value = 99 axis = 0 result = paddle.put_along_axis(x, index, value, axis) print(result) # [[99, 99, 99], # [60, 40, 50]] r`rReducer"raput_along_axisrr(r))r^rValue)rrfrcreN)rg)rhrlrir[r_r r8rrp to_tensorr-rrrgrrr r;rur<r=) rZrvaluesrwreducer^r?r-rdr@r@rArgsR         rgc Cst|jt|jkrtdt||}t|||}t|tjs$t|n|}|r.t ||}t ||j}t rAt |||||St |||d|d|S)z Inplace version of ``put_along_axis`` API, the output Tensor will be inplaced with input ``arr``. Please refer to :ref:`api_tensor_put_along_axis`. r`rrf)rhrlrir[r_r8rrprir-rrput_along_axis_r)rZrrjrwrkr^r@r@rArlNs$     rlc Cst|j}t|j}|dkr|}n||}|}||ks || kr$tdt|trA|jtjtjfvr6t dt|jdkrAtd||krItdt |D]}||kra|j||j|kratdqMdS)Nrz,Axis should be in range [-rank(x), rank(x)).z)The index dtype should be int32 or int64.rz!The index should be a 1-D Tensor.zSThe add_value does not support broadcast now. It must have the same dimension as x.zDThe add_value.shape[i] should be equal to x.shape[i] when i != axis.) rhrlrir8rr-rr)r(rrj) r"rZ input_axis add_valuedimsZadd_value_dimsrwZ check_axisrFr@r@rA_index_add_params_checkis0    rocCst||||trt||||Stdit}t|dgddt|dddgdt|dgdd||j}|j d|||d d |id |id |S)a6 Adds the elements of the input tensor with value tensor by selecting the indices in the order given in index. Args: x (Tensor) : The Destination Tensor. Supported data types are int32, int64, float16, float32, float64. index (Tensor): The 1-D Tensor containing the indices to index. The data type of ``index`` must be int32 or int64. axis (int): The dimension in which we index. value (Tensor): The tensor used to add the elements along the target axis. name(str, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: Tensor: same dimention and dtype with x. Examples: .. code-block:: python # required: gpu import paddle input_tensor = paddle.to_tensor(paddle.ones((3, 3)), dtype="float32") index = paddle.to_tensor([0, 2], dtype="int32") value = paddle.to_tensor([[1, 1, 1], [1, 1, 1]], dtype="float32") outplace_res = paddle.index_add(input_tensor, index, 0, value) print(outplace_res) # Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [[2., 2., 2.], # [1., 1., 1.], # [2., 2., 2.]]) index_addr"rz$paddle.tensor.manipulation.index_addrr(r)rm)r1rZAddValuer2rwr3N)rp) rorrrpr r;rr<r-r=)r"rrwrrr?r>r@r@rArpsB  rpcCst||||t||||S)a Inplace version of ``index_add`` API, the output Tensor will be inplaced with input ``x``. Please refer to :ref:`api_paddle_tensor_index_add`. Examples: .. code-block:: python # required: gpu import paddle input_tensor = paddle.to_tensor(paddle.ones((3, 3)), dtype="float32") index = paddle.to_tensor([0, 2], dtype="int32") value = paddle.to_tensor([[1, 1], [1, 1], [1, 1]], dtype="float32") inplace_res = paddle.index_add_(input_tensor, index, 1, value) print(inplace_res) # Tensor(shape=[3, 3], dtype=float32, place=Place(gpu:0), stop_gradient=True, # [[2., 1., 2.], # [2., 1., 2.], # [2., 1., 2.]]) )ror index_add_)r"rrwrrr@r@rArqsrq)rrrrrrr\)rN)rX)NNN)rFN)rrrF)rrrN)rrXN)NN)FFNr)N)FFFNr)NrJ)TN)rN)re)l __future__r collectionsrZstaticrrrrrZfluid.frameworkr r r r r rrZfluid.data_feederrrrrZ fluid.layersrrLrZfluid.layers.nnrZfluid.dygraph.inplace_utilsrrrrZcommon_ops_importrrrr;Zcreationrrr__all__r,r]r{rrrrrrrrrrrrrrrrrrrrrrrr r rrrrrrr r"r)r-r.rr=r>r?rFrOrPrQrRr[r_rbrgrlrorprqZ __METHODSitemsrfuncsetattrZVarBaserorpr@r@r@rAs         R } f A KD &   : * "    j8 pz  * Z  9    l   X Ad  Z ) ' t < ] bK  - _ K Z / 4 Fw EJ  G