chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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from __future__ import annotations
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from typing import TYPE_CHECKING
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from paddle import _C_ops
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from paddle.base.data_feeder import check_variable_and_dtype
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from paddle.base.layer_helper import LayerHelper
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from paddle.framework import in_dynamic_or_pir_mode
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if TYPE_CHECKING:
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from paddle import Tensor
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__all__ = []
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def segment_sum(
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data: Tensor, segment_ids: Tensor, name: str | None = None
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) -> Tensor:
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r"""
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Segment Sum Operator.
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This operator sums the elements of input `data` which with
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the same index in `segment_ids`.
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It computes a tensor such that $out_i = \\sum_{j} data_{j}$
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where sum is over j such that `segment_ids[j] == i`.
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Args:
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data (Tensor): A tensor, available data type float32, float64, int32, int64, float16.
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segment_ids (Tensor): A 1-D tensor, which have the same size
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with the first dimension of input data.
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Available data type is int32, int64.
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name (str, optional): Name for the operation (optional, default is None).
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For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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- output (Tensor), the reduced result.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
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>>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
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>>> out = paddle.geometric.segment_sum(data, segment_ids)
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>>> print(out.numpy())
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[[4. 4. 4.]
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[4. 5. 6.]]
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.segment_pool(data, segment_ids, "SUM")
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else:
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check_variable_and_dtype(
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data,
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"X",
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("float32", "float64", "int32", "int64", "float16", "uint16"),
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"segment_pool",
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)
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check_variable_and_dtype(
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segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
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)
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helper = LayerHelper("segment_sum", **locals())
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out = helper.create_variable_for_type_inference(dtype=data.dtype)
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summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
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helper.append_op(
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type="segment_pool",
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inputs={"X": data, "SegmentIds": segment_ids},
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outputs={"Out": out, "SummedIds": summed_ids},
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attrs={"pooltype": "SUM"},
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)
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return out
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def segment_mean(
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data: Tensor, segment_ids: Tensor, name: str | None = None
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) -> Tensor:
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r"""
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Segment mean Operator.
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This operator calculate the mean value of input `data` which
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with the same index in `segment_ids`.
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It computes a tensor such that $out_i = \\frac{1}{n_i} \\sum_{j} data[j]$
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where sum is over j such that 'segment_ids[j] == i' and $n_i$ is the number
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of all index 'segment_ids[j] == i'.
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Args:
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data (tensor): a tensor, available data type float32, float64, int32, int64, float16.
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segment_ids (tensor): a 1-d tensor, which have the same size
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with the first dimension of input data.
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available data type is int32, int64.
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name (str, optional): Name for the operation (optional, default is None).
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For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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- output (Tensor), the reduced result.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
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>>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
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>>> out = paddle.geometric.segment_mean(data, segment_ids)
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>>> print(out.numpy())
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[[2. 2. 2.]
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[4. 5. 6.]]
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.segment_pool(data, segment_ids, "MEAN")
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else:
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check_variable_and_dtype(
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data,
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"X",
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("float32", "float64", "int32", "int64", "float16", "uint16"),
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"segment_pool",
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)
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check_variable_and_dtype(
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segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
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)
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helper = LayerHelper("segment_mean", **locals())
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out = helper.create_variable_for_type_inference(dtype=data.dtype)
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summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
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helper.append_op(
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type="segment_pool",
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inputs={"X": data, "SegmentIds": segment_ids},
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outputs={"Out": out, "SummedIds": summed_ids},
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attrs={"pooltype": "MEAN"},
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)
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return out
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def segment_min(
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data: Tensor, segment_ids: Tensor, name: str | None = None
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) -> Tensor:
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r"""
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Segment min operator.
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This operator calculate the minimum elements of input `data` which with
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the same index in `segment_ids`.
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It computes a tensor such that $out_i = \\min_{j} data_{j}$
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where min is over j such that `segment_ids[j] == i`.
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Args:
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data (tensor): a tensor, available data type float32, float64, int32, int64, float16.
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segment_ids (tensor): a 1-d tensor, which have the same size
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with the first dimension of input data.
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available data type is int32, int64.
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name (str, optional): Name for the operation (optional, default is None).
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For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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- output (Tensor), the reduced result.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
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>>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
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>>> out = paddle.geometric.segment_min(data, segment_ids)
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>>> print(out.numpy())
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[[1. 2. 1.]
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[4. 5. 6.]]
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.segment_pool(data, segment_ids, "MIN")
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else:
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check_variable_and_dtype(
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data,
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"X",
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("float32", "float64", "int32", "int64", "float16", "uint16"),
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"segment_pool",
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)
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check_variable_and_dtype(
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segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
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)
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helper = LayerHelper("segment_min", **locals())
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out = helper.create_variable_for_type_inference(dtype=data.dtype)
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summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
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helper.append_op(
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type="segment_pool",
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inputs={"X": data, "SegmentIds": segment_ids},
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outputs={"Out": out, "SummedIds": summed_ids},
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attrs={"pooltype": "MIN"},
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)
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return out
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def segment_max(
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data: Tensor, segment_ids: Tensor, name: str | None = None
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) -> Tensor:
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r"""
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Segment max operator.
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This operator calculate the maximum elements of input `data` which with
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the same index in `segment_ids`.
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It computes a tensor such that $out_i = \\max_{j} data_{j}$
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where max is over j such that `segment_ids[j] == i`.
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Args:
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data (tensor): a tensor, available data type float32, float64, int32, int64, float16.
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segment_ids (tensor): a 1-d tensor, which have the same size
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with the first dimension of input data.
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available data type is int32, int64.
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name (str, optional): Name for the operation (optional, default is None).
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For more information, please refer to :ref:`api_guide_Name`.
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Returns:
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- output (Tensor), the reduced result.
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Examples:
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.. code-block:: pycon
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>>> import paddle
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>>> data = paddle.to_tensor([[1, 2, 3], [3, 2, 1], [4, 5, 6]], dtype='float32')
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>>> segment_ids = paddle.to_tensor([0, 0, 1], dtype='int32')
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>>> out = paddle.geometric.segment_max(data, segment_ids)
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>>> print(out.numpy())
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[[3. 2. 3.]
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[4. 5. 6.]]
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"""
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if in_dynamic_or_pir_mode():
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return _C_ops.segment_pool(data, segment_ids, "MAX")
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else:
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check_variable_and_dtype(
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data,
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"X",
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("float32", "float64", "int32", "int64", "float16", "uint16"),
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"segment_pool",
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)
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check_variable_and_dtype(
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segment_ids, "SegmentIds", ("int32", "int64"), "segment_pool"
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)
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helper = LayerHelper("segment_max", **locals())
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out = helper.create_variable_for_type_inference(dtype=data.dtype)
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summed_ids = helper.create_variable_for_type_inference(dtype=data.dtype)
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helper.append_op(
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type="segment_pool",
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inputs={"X": data, "SegmentIds": segment_ids},
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outputs={"Out": out, "SummedIds": summed_ids},
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attrs={"pooltype": "MAX"},
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)
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return out
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