!38130 fix doc string
Merge pull request !38130 from hezhenhao1/code_docs
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5dd00c5219
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@ -364,6 +364,7 @@ Array操作
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mindspore.ops.size
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mindspore.ops.slice
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mindspore.ops.space_to_batch_nd
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mindspore.ops.sparse_segment_mean
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mindspore.ops.split
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mindspore.ops.stack
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mindspore.ops.tensor_scatter_add
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@ -841,9 +841,6 @@ mindspore.Tensor
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返回一个布尔型Tensor，表示当前Tensor与 `x2` 的对应元素的差异是否在容忍度内相等。
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.. note::
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目前，Ascend后端不支持包含 inf 或 NaN 的输入数组。因此，当输入包含NaN或inf时，结果是不确定的。在Ascend后端上， `equal_nan` 必须为真。
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**参数：**
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- **x2** (Tensor) - 对比的第二个输入，支持的类型有float32，float16，int32。
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@ -864,7 +861,6 @@ mindspore.Tensor
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- **TypeError** - 当前Tensor和 `x2` 的数据类型不同。
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- **ValueError** - 当前Tensor和 `x2` 无法广播。
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- **ValueError** - `atol` 和 `rtol` 中的任何一个小于零。
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- **ValueError** - Ascend平台上的 `equal_nan` 为False。
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.. py:method:: item(index=None)
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@ -0,0 +1,29 @@
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mindspore.ops.sparse_segment_mean
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=================================
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.. py:function:: mindspore.ops.sparse_segment_mean(x, indices, segment_ids)
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计算输出Tensor :math:`output_i = \frac{\sum_j x_{indices[j]}}{N}` ，其中平均是对所有满足 :math:`segment\_ids[j] == i` 的元素， :math:`N` 表示相加的元素个数。如果给定的分段ID :math:`i` 不存在，则有 :math:`output[i] = 0` 。
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.. note::
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- 在CPU上， `segment_ids` 中的值会被校验是否排序，若索引值不是升序的，则抛出错误。另外， `indices` 中的值也会被校验是否在界限内，若索引值超出范围[0, x.shape[0])，则抛出错误。
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- 在GPU平台，对于 `segment_ids` 未排序和 `indices` 越界则不抛出错误。如果，无序的 `segment_ids` 会导致安全但未指定的行为，而超出范围的 `indices` 将被忽略。
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**参数：**
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- **x** (Tensor) - Tensor，其维度必须大于或等于1。
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- **indices** (Tensor) - 1维Tensor，数据类型为int32或int64。
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- **segment_ids** (Tensor) - 1维Tensor，与 `indices` 有同样的数据类型。索引值应当是已排序的，并且可以重复。
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**返回：**
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Tensor，其数据类型和维数与 `x` 相同。第一维度等于 `segment_ids` 的最后一个元素的值加一，其他维度与 `x` 的对应维度相同。
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**异常：**
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- **TypeError** - `x` 、 `indices` 或 `segment_ids` 不是Tensor类型。
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- **TypeError** - `x` 的数据类型不是float16、float32、float64的任一类型。
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- **TypeError** - `indices` 和 `segment_ids` 的数据类型不是float16、float32、float64的任一类型。
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- **TypeError** - `indices` 和 `segment_ids` 的数据类型不相同。
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- **ValueError** - `x` 、 `indices` 或 `segment_ids` 的维度不满足上述要求。
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- **ValueError** - `indices` 或 `segment_ids` 的shape不相同。
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@ -361,6 +361,7 @@ Array Operation
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mindspore.ops.size
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mindspore.ops.slice
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mindspore.ops.space_to_batch_nd
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mindspore.ops.sparse_segment_mean
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mindspore.ops.split
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mindspore.ops.tensor_scatter_add
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mindspore.ops.tensor_scatter_min
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@ -1384,10 +1384,6 @@ class Tensor(Tensor_):
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"""
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Returns a boolean Tensor where two Tensors are element-wise equal within a tolerance.
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Note:
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On Ascend, input arrays containing inf or NaN are not supported. Therefore, when the input is NaN or inf,
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the result is uncertain. And `equal_nan` must be True on Ascend.
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Args:
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x2 (Tensor): Second Tensor to compare, with data type belongs to float32, float16, int32.
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rtol (float, optional): Relative tolerance. Default: 1e-05.
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@ -1405,17 +1401,16 @@ class Tensor(Tensor_):
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TypeError: If the dtype of self Tensor is not same as the `x2`.
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ValueError: If self Tensor and `x2` can not be broadcast.
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ValueError: If either of `atol` and `rtol` is less than zero.
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ValueError: If `equal_nan` is False on Ascend platform.
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Supported Platforms:
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``Ascend`` ``CPU``
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``CPU``
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Examples:
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>>> input = Tensor(np.array([1.3, 2.1, 3.2, 4.1, 5.1]), mindspore.float16)
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>>> other = Tensor(np.array([1.3, 3.3, 2.3, 3.1, 5.1]), mindspore.float16)
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>>> output = ops.isclose(input, other)
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>>> output = input.isclose(other)
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>>> print(output)
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[True False False False True]
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[ True False False False True]
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"""
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self._init_check()
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return tensor_operator_registry.get('isclose')(self, x2, rtol, atol, equal_nan)
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@ -1958,13 +1958,13 @@ def scatter_nd_max(input_x, indices, updates, use_locking=False):
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> input_x = Parameter(Tensor(np.ones(8) * 10, mindspore.float32), name="x")
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>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
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>>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
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>>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
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>>> output = ops.scatter_nd_max(input_x, indices, updates, False)
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>>> print(output)
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[ 1. 8. 6. 4. 7. 6. 7. 9.]
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>>> input_x = Parameter(Tensor(np.ones((4, 4, 4)) * 10, mindspore.int32))
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[1. 8. 6. 4. 7. 6. 7. 9.]
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>>> input_x = Parameter(Tensor(np.ones((4, 4, 4)), mindspore.int32))
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>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
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>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
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... [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
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@ -2750,10 +2750,6 @@ def isclose(x1, x2, rtol=1e-05, atol=1e-08, equal_nan=False):
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∣x1−x2∣ ≤ atol + rtol × ∣x2∣
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Note:
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On Ascend, input arrays containing inf or NaN are not supported. Therefore, when the input is NaN or inf,
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the result is uncertain. And `equal_nan` must be True on Ascend.
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Args:
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x1 (Tensor): First Tensor to compare, with data type belongs to float32, float16, int32.
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x2 (Tensor): Second Tensor to compare, with data type belongs to float32, float16, int32.
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@ -2772,17 +2768,16 @@ def isclose(x1, x2, rtol=1e-05, atol=1e-08, equal_nan=False):
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TypeError: If the dtype of `x1` is not same as the `x2`.
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ValueError: If `x1` and `x2` can not be broadcast.
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ValueError: If either of `atol` and `rtol` is less than zero.
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ValueError: If `equal_nan` is False on Ascend platform.
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Supported Platforms:
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``Ascend`` ``CPU``
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``CPU``
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Examples:
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>>> input = Tensor(np.array([1.3, 2.1, 3.2, 4.1, 5.1]), mindspore.float16)
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>>> other = Tensor(np.array([1.3, 3.3, 2.3, 3.1, 5.1]), mindspore.float16)
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>>> output = ops.isclose(input, other)
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>>> print(output)
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[True False False False True]
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[ True False False False True]
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"""
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is_close = _get_cache_prim(P.IsClose)(rtol=rtol, atol=atol, equal_nan=equal_nan)
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return is_close(x1, x2)
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@ -3761,8 +3756,48 @@ def cummax(x, axis):
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def sparse_segment_mean(x, indices, segment_ids):
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"""
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Computes the mean along sparse segments of a tensor.
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r"""
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Computes a Tensor such that :math:`output_i = \frac{\sum_j x_{indices[j]}}{N}` where mean is over :math:`j` such
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that :math:`segment\_ids[j] == i` and :math:`N` is the total number of values summed. If the mean is empty for
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a given segment ID :math:`i`, :math:`output[i] = 0`.
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Note:
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- On CPU, values in `segment_ids` are always validated to be sorted, and an error is thrown for indices that
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are not increasing. Moreover, values in `indices` are validated to be bounded, and an error is thrown when
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`indices` are out of range[0, x.shape[0]).
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- On GPU, this does not throw an error for unsorted `segment_ids` and out-of-bound `indices`. Out-of-order
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`segment_ids` result in safe but unspecified behavior, while out-of-range `indices` will be ignored.
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Args:
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x (Tensor): A Tensor, and its rank must be greater than or equal to 1.
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indices (Tensor): A 1-D Tensor, with int32 or int64 data type.
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segment_ids (Tensor): A 1-D Tensor, must have the same dtype as `indices`.
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Values should be sorted and can be repeated.
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Returns:
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Tensor, whose dtype and rank is the same as `x`. The first dimension is equal to the value of the last element
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of `segment_ids` plus one, and the other dimensions are the same as those of `x`.
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Raises:
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TypeError: If `x`, `indices` or `segment_ids` is not a Tensor.
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TypeError: If the dtype of `x` is not one of the following dtype: float16, float32, float64.
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TypeError: If the dtype of `indices` and `segment_ids` are not one of the following dtype: int32, int64.
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TypeError: If the dtype of `indices` and `segment_ids` are not the same.
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ValueError: If the shape of `x`, 'indices' or `segment_ids` don't meet the parameter description.
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ValueError: If the size of 'indices' and `segment_ids` are not the same.
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Supported Platforms:
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``GPU`` ``CPU``
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Examples:
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>>> x = Tensor([[0, 1, 2], [1, 2, 3], [3, 6, 7]], dtype=mindspore.float32)
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>>> indices = Tensor([0, 1, 2], dtype=mindspore.int32)
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>>> segment_ids = Tensor([1,2,2], dtype=mindspore.int32)
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>>> out = ops.sparse_segment_mean(x, indices, segment_ids)
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>>> print(out)
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[[0. 0. 0.]
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[0. 1. 2.]
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[2. 4. 5.]]
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"""
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return sparse_segment_mean_(x, indices, segment_ids)
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@ -5067,7 +5067,7 @@ class ScatterNdMul(_ScatterNdOp):
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>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
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>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
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... [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
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>>> scatter_nd_mul = ops.ScatterNdMul()
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>>> scatter_nd_mul = ScatterNdMul()
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>>> output = scatter_nd_mul(input_x, indices, updates)
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>>> print(output)
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[[[1 1 1 1]
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@ -5151,10 +5151,11 @@ class ScatterNdMax(_ScatterNdOp):
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> from mindspore.ops.operations.array_ops import ScatterNdMax
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>>> input_x = Parameter(Tensor(np.array([1, 2, 3, 4, 5, 6, 7, 8]), mindspore.float32), name="x")
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>>> indices = Tensor(np.array([[2], [4], [1], [7]]), mindspore.int32)
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>>> updates = Tensor(np.array([6, 7, 8, 9]), mindspore.float32)
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>>> scatter_nd_max = ops.ScatterNdMax()
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>>> scatter_nd_max = ScatterNdMax()
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>>> output = scatter_nd_max(input_x, indices, updates)
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>>> print(output)
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[ 1. 8. 6. 4. 7. 6. 7. 9.]
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@ -5162,7 +5163,7 @@ class ScatterNdMax(_ScatterNdOp):
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>>> indices = Tensor(np.array([[0], [2]]), mindspore.int32)
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>>> updates = Tensor(np.array([[[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
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... [[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]]]), mindspore.int32)
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>>> scatter_nd_max = ops.ScatterNdMax()
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>>> scatter_nd_max = ScatterNdMax()
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>>> output = scatter_nd_max(input_x, indices, updates)
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>>> print(output)
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[[[1 1 1 1]
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@ -5763,11 +5763,15 @@ class IsClose(Primitive):
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``Ascend`` ``CPU``
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Examples:
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>>> import numpy as np
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>>> from mindspore import Tensor
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>>> from mindspore.ops.operations.math_ops import IsClose
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>>> input = Tensor(np.array([1.3, 2.1, 3.2, 4.1, 5.1]), mindspore.float16)
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>>> other = Tensor(np.array([1.3, 3.3, 2.3, 3.1, 5.1]), mindspore.float16)
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>>> output = ops.IsClose()(input, other)
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>>> isclose = IsClose()
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>>> output = isclose(input, other)
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>>> print(output)
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[True False False False True]
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[ True False False False True]
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"""
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@prim_attr_register
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@ -6072,35 +6076,20 @@ class Trace(Primitive):
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class SparseSegmentMean(Primitive):
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"""
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Computes the mean along sparse segments of a tensor.
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Computes the mean along sparse segments of a Tensor.
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Inputs:
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- **x** (Tensor) - A Tensor, and its rank must be greater equal than 1.
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- **indices** (Tensor) - A 1-D Tensor, has same rank as `segment_ids`.
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- **segment_ids** (Tensor) - A 1-D Tensor, must have the same type as `indices`.
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Values should be sorted and can be repeated.
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Outputs:
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Tensor, with the same data type and shape as input 'x', except for dimension 0
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which is the number of segments.
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Raises:
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TypeError: If the dtype of `x` is not one of the following dtype: float32, float64.
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TypeError: If the dtype of `indices` and `segment_ids` are not one of the following dtype: int32, int64.
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TypeError: If the dtype of `indices` and `segment_ids` are not the same.
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ValueError: If the shape of `x`, 'indices' or `segment_ids` don't meet the parameter description.
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ValueError: If the size of 'indices' and `segment_ids` are not the same.
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RuntimeError: If the value of `indices` are out of range[0, x.shape[0]).
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RuntimeError: If the value of `segment_ids` are not sorted or negative.
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Refer to :func:`mindspore.ops.sparse_segment_mean` for more detail.
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Supported Platforms:
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``CPU``
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``GPU`` ``CPU``
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Examples:
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>>> from mindspore import Tensor
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>>> from mindspore.ops.operations.math_ops import SparseSegmentMean
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>>> x = Tensor([[0, 1, 2], [1, 2, 3], [3, 6, 7]], dtype=mindspore.float32)
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>>> indices = Tensor([0, 1, 2], dtype=mindspore.int32)
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>>> segment_ids = Tensor([1,2,2], dtype=mindspore.int32)
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>>> sparse_segment_mean = ops.SparseSegmentMean()
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>>> sparse_segment_mean = SparseSegmentMean()
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>>> out = sparse_segment_mean(x, indices, segment_ids)
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>>> print(out)
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[[0. 0. 0.]
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