§ z”jÏ%ãó’—ddlmZddlmZddlmZddlmZddlm Z ddl m Z erddlm Z gZ ddd„Z ddd„Z ddd„Z ddd„ZdS)é)Ú annotations)Ú TYPE_CHECKING)Ú_C_ops)Úcheck_variable_and_dtype)Ú LayerHelper)Úin_dynamic_or_pir_mode)ÚTensorNÚdatar Ú segment_idsÚnameú str | NoneÚreturncót—t¦«rtj||d¦«St|ddd¦«t|ddd¦«t dit ¦«¤Ž}| |j¬¦«}| |j¬¦«}| d||d œ||d œd di¬ ¦«|S)a¥ 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, float16. 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.geometric.segment_sum(data, segment_ids) >>> print(out.numpy()) [[4. 4. 4.] [4. 5. 6.]] ÚSUMÚX©Úfloat32Úfloat64Úint32Úint64Úfloat16Úuint16Ú segment_poolÚ SegmentIds©rrÚ segment_sum©Údtype©rr©ÚOutÚ SummedIdsÚpooltype©ÚtypeÚinputsÚoutputsÚattrsN)r© rrrrrÚlocalsÚ"create_variable_for_type_inferencerÚ append_op©r r r ÚhelperÚoutÚ summed_idss úe/lsinfo/ai/hellotax_ai/data_center/backend/venv/lib/python3.11/site-packages/paddle/geometric/math.pyrrsò€õDÑÔðÝÔ" 4¨°eÑ<Ô<Ð<å Ø Ø Ø IØ ñ  ô ð õ !Ø ˜Ð'9¸>ñ ô ð õÐ7Ð7­f©h¬hÐ7Ð7ˆØ×7Ò7¸d¼jÐ7ÑIÔIˆØ×>Ò>ÀTÄZÐ>ÑPÔPˆ Ø×ÒØب[Ð9Ð9بjÐ9Ð9ؘuÐ%ð ñ ô ð ð ˆ ócót—t¦«rtj||d¦«St|ddd¦«t|ddd¦«t dit ¦«¤Ž}| |j¬¦«}| |j¬¦«}| d||d œ||d œd di¬ ¦«|S)aü Segment mean Operator. This 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, float16. 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.geometric.segment_mean(data, segment_ids) >>> print(out.numpy()) [[2. 2. 2.] [4. 5. 6.]] ÚMEANrrrrrÚ segment_meanrrr r#r$N)r5r)r-s r1r5r5Xsò€õHÑÔðÝÔ" 4¨°fÑ=Ô=Ð=å Ø Ø Ø IØ ñ  ô ð õ !Ø ˜Ð'9¸>ñ ô ð õÐ8Ð8­v©x¬xÐ8Ð8ˆØ×7Ò7¸d¼jÐ7ÑIÔIˆØ×>Ò>ÀTÄZÐ>ÑPÔPˆ Ø×ÒØب[Ð9Ð9بjÐ9Ð9ؘvÐ&ð ñ ô ð ð ˆ r2cót—t¦«rtj||d¦«St|ddd¦«t|ddd¦«t dit ¦«¤Ž}| |j¬¦«}| |j¬¦«}| d||d œ||d œd di¬ ¦«|S)a² 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, float16. 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.geometric.segment_min(data, segment_ids) >>> print(out.numpy()) [[1. 2. 1.] [4. 5. 6.]] ÚMINrrrrrÚ segment_minrrr r#r$N)r8r)r-s r1r8r8•óò€õFÑÔðÝÔ" 4¨°eÑ<Ô<Ð<å Ø Ø Ø IØ ñ  ô ð õ !Ø ˜Ð'9¸>ñ ô ð õÐ7Ð7­f©h¬hÐ7Ð7ˆØ×7Ò7¸d¼jÐ7ÑIÔIˆØ×>Ò>ÀTÄZÐ>ÑPÔPˆ Ø×ÒØب[Ð9Ð9بjÐ9Ð9ؘuÐ%ð ñ ô ð ð ˆ r2cót—t¦«rtj||d¦«St|ddd¦«t|ddd¦«t dit ¦«¤Ž}| |j¬¦«}| |j¬¦«}| d||d œ||d œd di¬ ¦«|S)a² 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, float16. 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.geometric.segment_max(data, segment_ids) >>> print(out.numpy()) [[3. 2. 3.] [4. 5. 6.]] ÚMAXrrrrrÚ segment_maxrrr r#r$N)r<r)r-s r1r<r<Ñr9r2)N)r r r r r r rr )Ú __future__rÚtypingrÚpaddlerÚpaddle.base.data_feederrÚpaddle.base.layer_helperrÚpaddle.frameworkrr Ú__all__rr5r8r<©r2r1úrEsðð#Ð"Ð"Ð"Ð"Ð"à Ð Ð Ð Ð Ð àÐÐÐÐÐØ<Ð<Ð<Ð<Ð<Ð<Ø0Ð0Ð0Ð0Ð0Ð0Ø3Ð3Ð3Ð3Ð3Ð3àðØÐÐÐÐÐà €ð;?ð8ð8ð8ð8ð8ðx;?ð:ð:ð:ð:ð:ð|;?ð9ð9ð9ð9ð9ðz;?ð9ð9ð9ð9ð9ð9ð9r2