o OeQ,@sddlmZmZddlmZddlmZmZddlm Z m Z ddl m m Z gZe dddd d dd d Ze dd ddd dddZe ddddd dddZe ddddd dddZdS)) LayerHelper_non_static_mode)check_variable_and_dtype)_C_ops _legacy_C_ops)_in_legacy_dygraphin_dygraph_modeNz2.4.0zpaddle.geometric.segment_sumz5paddle.incubate.segment_sum will be removed in future)ZsinceZ update_tolevelreasoncCtr t||ddStrt||dd\}}|St|dddt|dddtdit}|j|j d }|j|j d }|j d||d ||d ddid |S)aj Segment Sum Operator. This operator sums the elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\sum_{j} data_{j}$ where sum is over j such that `segment_ids[j] == i`. Args: data (Tensor): A tensor, available data type float32, float64, int32, int64. segment_ids (Tensor): A 1-D tensor, which have the same size with the first dimension of input data. Available data type is 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): the reduced result. Examples: .. code-block:: python import paddle data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') out = paddle.incubate.segment_sum(data, segment_ids) #Outputs: [[4., 4., 4.], [4., 5., 6.]] ZSUMrpooltypeXZfloat32Zfloat64int32int64 segment_pool SegmentIdsrr segment_sumdtyperrZOutZ SummedIdstypeZinputsZoutputsattrsN)r) rrrrrrrlocals"create_variable_for_type_inferencer append_opdataZ segment_idsnameouttmphelperZ summed_idsr&KD:\Projects\ConvertPro\env\Lib\site-packages\paddle/incubate/tensor/math.pyrs6#  rzpaddle.geometric.segment_meanz6paddle.incubate.segment_mean will be removed in futurecCr )a Segment mean Operator. Ihis operator calculate the mean value of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\frac{1}{n_i} \\sum_{j} data[j]$ where sum is over j such that 'segment_ids[j] == i' and $n_i$ is the number of all index 'segment_ids[j] == i'. Args: data (tensor): a tensor, available data type float32, float64, int32, int64. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is 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): the reduced result. Examples: .. code-block:: python import paddle data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') out = paddle.incubate.segment_mean(data, segment_ids) #Outputs: [[2., 2., 2.], [4., 5., 6.]] ZMEANrr rrrrr segment_meanrrrrN)r( rrrrrrrrrrrr r&r&r'r(Xs6%  r(zpaddle.geometric.segment_minz5paddle.incubate.segment_min will be removed in futurecCr )ax Segment min operator. This operator calculate the minimum elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\min_{j} data_{j}$ where min is over j such that `segment_ids[j] == i`. Args: data (tensor): a tensor, available data type float32, float64, int32, int64. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is 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): the reduced result. Examples: .. code-block:: python import paddle data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') out = paddle.incubate.segment_min(data, segment_ids) #Outputs: [[1., 2., 1.], [4., 5., 6.]] ZMINrr rrrrr segment_minrrrrN)r*r)r r&r&r'r*s6$  r*zpaddle.geometric.segment_maxz5paddle.incubate.segment_max will be removed in futurecCstrt||d\}}|Strt||dd\}}|St|dddt|dddtdit}|j|j d }|j|j d }|j d||d ||d ddid |S)aw Segment max operator. This operator calculate the maximum elements of input `data` which with the same index in `segment_ids`. It computes a tensor such that $out_i = \\max_{j} data_{j}$ where max is over j such that `segment_ids[j] == i`. Args: data (tensor): a tensor, available data type float32, float64, int32, int64. segment_ids (tensor): a 1-d tensor, which have the same size with the first dimension of input data. available data type is 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): the reduced result. Examples: .. code-block:: python import paddle data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32') segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32') out = paddle.incubate.segment_max(data, segment_ids) #Outputs: [[3., 2., 3.], [4., 5., 6.]] MAXr rrrrr segment_maxrrrrN)r,r)r r&r&r'r,s8$  r,)N)Zpaddle.fluid.layer_helperrrZpaddle.fluid.data_feederrZpaddlerrZpaddle.fluid.frameworkrrZpaddle.utils.deprecatedutils deprecated__all__rr(r*r,r&r&r&r's<  < > >