CodeDocs 中文API资料
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@ -521,9 +521,12 @@ Array操作
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mindspore.ops.MaskedSelect
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mindspore.ops.MatrixBandPart
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mindspore.ops.MatrixDeterminant
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mindspore.ops.MatrixDiagPartV3
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mindspore.ops.MatrixDiagV3
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mindspore.ops.MatrixExp
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mindspore.ops.MatrixLogarithm
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mindspore.ops.MatrixPower
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mindspore.ops.MatrixSetDiagV3
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mindspore.ops.MatrixSolve
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mindspore.ops.MatrixTriangularSolve
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mindspore.ops.Meshgrid
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@ -3,6 +3,6 @@ mindspore.ops.GridSampler3D
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.. py:class:: mindspore.ops.GridSampler3D
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给定一个输入和一个网格，使用网格中的输入值和像素位置计算输出。
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给定一个输入和一个网格，使用网格中的输入值和像素位置计算输出。只支持体积(5-D)的输入。
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更多参考详见 :func:`mindspore.ops.grid_sample`。
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@ -0,0 +1,8 @@
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mindspore.ops.MatrixDiagPartV3
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==============================
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.. py:class:: mindspore.ops.MatrixDiagPartV3
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返回Tensor的对角线部分。
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更多参考详见 :func:`mindspore.ops.matrix_diag_part`。
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@ -0,0 +1,8 @@
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mindspore.ops.MatrixDiagV3
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==========================
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.. py:class:: mindspore.ops.MatrixDiagV3
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返回一个batch的对角Tensor，其具有给定的对角线值。
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更多参考详见 :func:`mindspore.ops.matrix_diag`。
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@ -0,0 +1,54 @@
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mindspore.ops.MatrixSetDiagV3
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=============================
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.. py:class:: mindspore.ops.MatrixSetDiagV3(align="RIGHT_LEFT")
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返回具有新的对角线值的批处理矩阵Tensor。
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给定输入 `x` 和对角线 `diagonal` ，此操作返回与 `x` 具有相同shape和值的Tensor，但返回的Tensor除开最内层矩阵的对角线，
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这些值将被对角线中的值覆盖。如果某些对角线比 `max_diag_len` 短，则需要被填充，其中 `max_diag_len` 指对角线的最长长度。
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`diagonal` 的维度 :math:`shape[-2]` 必须等于对角线个数 `num_diags` ， :math:`num_diags = k[1] - k[0] + 1`，
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`diagonal` 的维度 :math:`shape[-1]` 必须等于最长对角线值 `max_diag_len` ，
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:math:`max_diag_len = min(x.shape[-2] + min(k[1], 0), x.shape[-1] + min(-k[0], 0))` 。
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设 `x` 具有 `r + 1` 维 :math:`[I, J, ..., L, M, N]` 。
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当 `k` 是整数或 :math:`k[0] == k[1]` 时，对角线 `diagonal` 的shape为 :math:`[I, J, ..., L, max\_diag\_len]` 。
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否则，其shape为 :math:`[I, J, ... L, num\_diags, max\_diag\_len]` 。
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参数：
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- **align** (str，可选) - 字符串，指定超对角线和次对角线的对齐方式。
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可选值："RIGHT_LEFT"、"LEFT_RIGHT"、"LEFT_LEFT"、"RIGHT_RIGHT"。
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默认值："RIGHT_LEFT"。
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- "RIGHT_LEFT"表示将超对角线与右侧对齐（左侧填充行），将次对角线与左侧对齐（右侧填充行）。
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- "LEFT_RIGHT"表示将超对角线与左侧对齐（右侧填充行），将次对角线与右侧对齐（左侧填充行）。
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- "LEFT_LEFT"表示将超对角线与左侧对齐（右侧填充行），将次对角线与左侧对齐（右侧填充行）。
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- "RIGHT_RIGHT"表示将超对角线与右侧对齐（左侧填充行），将次对角线与右侧对齐（左侧填充行）。
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输入：
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- **x** (Tensor) - Tensor，其维度为 `r+1` 需要满足 `r >=1` 。
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- **diagonal** (Tensor) - 输入对角线Tensor，具有与 `x` 相同的数据类型。
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当 `k` 是整数或 :math:`k[0] == k[1]` 时，其为维度 `r` ，否则，其维度为 `r + 1` 。
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- **k** (Tensor) - int32类型的Tensor。对角线偏移量。正值表示超对角线，0表示主对角线，负值表示次对角线。
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`k` 可以是单个整数（对于单个对角线）或一对整数，分别指定矩阵带的上界和下界，且 `k[0]` 不得大于 `k[1]` 。
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其值必须在 :math:`(-x.shape[-2], x.shape[-1])` 中。采用图模式时，输入 `k` 必须是常量Tensor。
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输出：
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Tensor，与 `x` 的类型相同。
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设 `x` 有 `r+1` 维 :math:`[I， J， ...， M， N]` 。
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则，输出Tensor的维度为 `r+1` 维 :math:`[I, J, ..., L, M, N]` ，与输入 `x` 相同。
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异常：
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- **TypeError** - 若任一输入不是Tensor。
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- **TypeError** - `x` 与 `diagonal` 数据类型不同。
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- **TypeError** - `k` 的数据类型不为int32。
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- **ValueError** - `align` 取值不在合法值集合内。
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- **ValueError** - `k` 的维度不为0或1。
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- **ValueError** - `x` 的维度不大于等于2。
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- **ValueError** - `k` 的大小不为1或2。
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- **ValueError** - 当 `k` 的大小为2时， `k[1]` 小于 `k[0]` 。
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- **ValueError** - 对角线 `diagonal` 的维度与输入 `x` 的维度不匹配。
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- **ValueError** - 对角线 `diagonal` 的shape与输入 `x` 不匹配。
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- **ValueError** - 对角线 `diagonal` 的维度 :math:`shape[-2]` 不等于与对角线个数 `num_diags` ，
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:math:`num_diags = k[1]-k[0]+1` 。
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- **ValueError** - `k` 的取值不在 :math:`(-x.shape[-2], x.shape[-1])` 范围内。
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- **ValueError** - 对角线 `diagonal` 的维度 :math:`shape[-1]` 不等于最长对角线长度 `max_diag_len`，
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:math:`max_diag_len = min(x.shape[-2] + min(k[1], 0), x.shape[-1] + min(-k[0], 0))` 。
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@ -520,9 +520,12 @@ Array Operation
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mindspore.ops.MaskedSelect
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mindspore.ops.MatrixBandPart
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mindspore.ops.MatrixDeterminant
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mindspore.ops.MatrixDiagPartV3
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mindspore.ops.MatrixDiagV3
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mindspore.ops.MatrixExp
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mindspore.ops.MatrixLogarithm
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mindspore.ops.MatrixPower
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mindspore.ops.MatrixSetDiagV3
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mindspore.ops.MatrixSolve
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mindspore.ops.MatrixTriangularSolve
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mindspore.ops.Meshgrid
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@ -1350,17 +1350,29 @@ class MatrixSetDiagV3(Primitive):
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Returns a batched matrix tensor with new batched diagonal values.
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Given x and diagonal, this operation returns a tensor with the same shape and values as x, except for the specified
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diagonals of the innermost matrices. These will be overwritten by the values in diagonal. Some diagonals are shorter
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than max_diag_len and need to be padded.
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The diagonal.shape[-2] must be equal to num_diags calculated by k[1] - k[0] + 1. The diagonal.shape[-1] must be
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equal to the longest diagonal value max_diag_len calculated by min(x.shape[-2] + min(k[1], 0), x.shape[-1] +
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min(-k[0], 0)). Let x have r + 1 dimensions [I, J, ..., L, M, N]. The diagonal tensor has rank r with shape [I, J,
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..., L, max_diag_len] when k is an integer or k[0] == k[1]. Otherwise, it has rank r + 1 with shape [I, J, ..., L,
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num_diags, max_diag_len].
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than `max_diag_len` and need to be padded, where `max_diag_len` is the longest diagonal value.
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The diagonal.shape[-2] must be equal to num_diags calculated by :math:`k[1] - k[0] + 1` .
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The diagonal.shape[-1] must be
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equal to the longest diagonal value `max_diag_len` calculated
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by :math:`min(x.shape[-2] + min(k[1], 0), x.shape[-1] + min(-k[0], 0))` .
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Let x have r + 1 dimensions [I, J, ..., L, M, N].
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The diagonal tensor has rank r with shape :math:`[I, J, ..., L, max_diag_len]`
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when k is an integer or :math:`k[0] == k[1]` . Otherwise, it has rank r + 1
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with shape :math:`[I, J, ..., L, num_diags, max_diag_len]` .
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Args:
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align (string): An optional string from: "RIGHT_LEFT"(default), "LEFT_RIGHT", "LEFT_LEFT", "RIGHT_RIGHT". Align
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is a string specifying how superdiagonals and subdiagonals should be aligned, respectively. "RIGHT_LEFT"
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aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left (right-pads the row).
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align (str, optional): An optional string from: "RIGHT_LEFT", "LEFT_RIGHT", "LEFT_LEFT", "RIGHT_RIGHT".
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Align is a string specifying how superdiagonals and subdiagonals should be aligned, respectively.
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Default: "RIGHT_LEFT".
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- "RIGHT_LEFT" aligns superdiagonals to the right (left-pads the row) and subdiagonals to the left
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(right-pads the row).
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- "LEFT_RIGHT" aligns superdiagonals to the left (right-pads the row) and subdiagonals to the right
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(left-pads the row).
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- "LEFT_LEFT" aligns superdiagonals to the left (right-pads the row) and subdiagonals to the left
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(right-pads the row).
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- "RIGHT_RIGHT" aligns superdiagonals to the right (left-pads the row) and subdiagonals to the right
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(left-pads the row).
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Inputs:
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- **x** (Tensor) - Rank r + 1, where r >= 1.
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@ -1368,14 +1380,14 @@ class MatrixSetDiagV3(Primitive):
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Otherwise, it has rank r + 1.
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- **k** (Tensor) - A Tensor of type int32. Diagonal offset(s). Positive value means superdiagonal, 0 refers to
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the main diagonal, and negative value means subdiagonals. k can be a single integer (for a single diagonal) or
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a pair of integers specifying the low and high ends of a matrix band. k[0] must not be larger than k[1]. The
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value of k has restructions, meaning value of k must be in (-x.shape[-2], x.shape[-1]). Input k must be const
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Tensor when taking Graph mode.
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a pair of integers specifying the low and high ends of a matrix band. `k[0]` must not be larger than `k[1]` .
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The value of `k` has restructions, meaning value of k must be in (-x.shape[-2], x.shape[-1]).
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Input k must be const Tensor when taking Graph mode.
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Outputs:
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A Tensor. Has the same type as x.
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Let x has r+1 dimensions [I, J, ..., L, M, N].
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The output is a tensor of rank r+1 with dimensions [I, J, ..., L, M, N], the same as input x.
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Tensor. The same type as x.
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Let x has r+1 dimensions :math:`[I, J, ..., L, M, N]` .
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The output is a tensor of rank r+1 with dimensions :math:`[I, J, ..., L, M, N]` , the same as input x.
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Raises:
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TypeError: If any input is not Tensor.
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@ -1385,13 +1397,13 @@ class MatrixSetDiagV3(Primitive):
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ValueError: If rank of `k` is not equal to 0 or 1.
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ValueError: If rank of `x` is not greater equal to 2.
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ValueError: If size of `k` is not equal to 1 or 2.
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ValueError: If k[1] is not greater equal to k[0] in case the size of `k` is 2.
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ValueError: If `k[1]` is not greater equal to `k[0]` in case the size of `k` is 2.
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ValueError: If the `diagonal` rank size don't match with input `x` rank size.
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ValueError: If the `diagonal` shape value don't match with input `x` shape value.
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ValueError: If the diagonal.shape[-2] is not equal to num_diags calculated by k[1] - k[0] + 1.
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ValueError: If the diagonal.shape[-2] is not equal to num_diags calculated by :math:`k[1] - k[0] + 1` .
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ValueError: If the value of `k` is not in (-x.shape[-2], x.shape[-1]).
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ValueError: If the diagonal.shape[-1] is not equal to the max_diag_len calculated by min(x.shape[-2] + min(k[1],
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0), x.shape[-1] + min(-k[0], 0)).
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ValueError: If the diagonal.shape[-1] is not equal to the max_diag_len calculated by
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:math:`min(x.shape[-2] + min(k[1], 0), x.shape[-1] + min(-k[0], 0))` .
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Supported Platforms:
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``GPU`` ``CPU``
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@ -7225,24 +7237,6 @@ class IndexFill(Primitive):
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Refer to :func:`mindspore.ops.index_fill` for more details.
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Inputs:
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- **x** (Tensor) - Input tensor.
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The shape is :math:`(N,*)` where :math:`*` means, any number of additional dimensions.
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- **dim** (Union[int, Tensor]) - Dimension along which to fill the input tensor. Only supports
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a 0-D tensor or an int number.
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- **index** (Tensor) - Indices of the input tensor to fill in. Only supports a 0-D or 1-D tensor.
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- **value** (Tensor) - Value to fill the returned tensor. Only supports a 0-D tensor or a scalar.
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Outputs:
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Tensor, has the same type and shape as input tensor.
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Raises:
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TypeError: If `x` is not a Tensor.
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TypeError: If `dim` is neither a int number nor a tensor.
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TypeError: If `index` is not a Tensor.
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TypeError: If `value` is not a Tensor/Scalar.
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TypeError: If dtype of `index` is not int32.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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@ -68,30 +68,10 @@ class Geqrf(Primitive):
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class Svd(Primitive):
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"""
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Computes the singular value decompositions of one or more matrices.
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Refer to :func:`mindspore.ops.svd` for more details.
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Args:
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full_matrices (bool, optional): If true, compute full-sized :math:`U` and :math:`V`. If false, compute
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only the leading P singular vectors. P is the minimum of M and N.
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M, N is the row, col of the input matrix. Default: False.
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compute_uv (bool, optional): If true, compute the left and right singular vectors.
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If false, compute only the singular values. Default: True.
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Inputs:
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- **a** (Tensor): Tensor of the matrices to be decomposed. The shape should be :math:`(*, M, N)`.
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Outputs:
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- **s** (Tensor) - Singular values. The shape is :math:`(*, P)`.
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- **u** (Tensor) - Left singular vectors. If compute_uv is False, u will be an empty tensor.
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The shape is :math:`(*, M, P)`. If full_matrices is True, the shape will be :math:`(*, M, M)`.
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- **v** (Tensor) - Right singular vectors. If compute_uv is False, v will be an empty tensor.
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The shape is :math:`(*, N, P)`. If full_matrices is True, the shape will be :math:`(*, N, N)`.
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Raises:
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TypeError: If full_matrices or compute_uv is not the type of bool.
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TypeError: If the rank of input less than 2.
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TypeError: If the type of input is not one of the following dtype: mstype.float32, mstype.float64.
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Supported Platforms:
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``GPU``
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@ -6175,24 +6175,6 @@ class MatrixExp(Primitive):
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Refer to :func:`mindspore.ops.matrix_exp` for more details.
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.. math::
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matrix\_exp(x) = \sum_{k=0}^{\infty} \frac{1}{k !} x^{k} \in \mathbb{K}^{n \times n}
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Inputs:
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- **x** (Tensor) - The shape of tensor is :math:`(*, n, n)` where * is zero or more batch dimensions.
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Must be one of the following types: float64, float32, float16, complex64, complex128.
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Outputs:
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Tensor, has the same shape and dtype as the `x`.
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Raises:
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TypeError: If `x` is not a Tensor.
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TypeError: If the dtype of `x` is not one of the following dtype:
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float16, float32, float64, complex64, complex128.
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ValueError: If the rank of `x` is less than 2.
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ValueError: If the last two dimensions of `x` are not equal.
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Supported Platforms:
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``Ascend`` ``CPU``
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@ -7124,8 +7106,8 @@ class FFTWithSize(Primitive):
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- "ortho" has both direct and inverse transforms are scaled by 1/sqrt(n).
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- "forward" has the direct transforms scaled by 1/n and the inverse transforms unscaled.
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onesided (bool): Controls whether the input is halved to avoid redundancy. Default: True.
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signal_sizes (list): Size of the original signal (the signal before rfft, no batch dimension),
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onesided (bool, optional): Controls whether the input is halved to avoid redundancy. Default: True.
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signal_sizes (list, optional): Size of the original signal (the signal before rfft, no batch dimension),
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only in irfft mode and set onesided=true requires the parameter. Default: [].
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Inputs:
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@ -8992,54 +8992,10 @@ class ApplyAdamWithAmsgrad(Primitive):
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class GridSampler3D(Primitive):
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"""
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Given an `input_x` and a flow-field `grid`, computes the `output` using `input_x` values and pixel locations from
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`grid`. Only volumetric (5-D) `input_x` is supported.
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Given an input and a grid, the output is calculated using the input values
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and pixel positions in the grid. Only volume (5-D) input is supported.
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For `input_x` with shape :math:`(N, C, D_{in}, H_{in}, W_{in})` and `grid` with shape :math:`(N, D_{out}, H_{out},
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W_{out}, 3)`, the `output` will have shape :math:`(N, C, D_{out}, H_{out}, W_{out})`.
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For each output location `output[n, :, d, h, w]`, the size-3 vector `grid[n, d, h, w]` specifies `input_x` pixel
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locations x, y, z, which are used to interpolate the output value `output[n, :, d, h, w]`. And `interpolation_mode`
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argument specifies "nearest" or "bilinear" interpolation method to sample the input pixels.
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`grid` specifies the sampling pixel locations normalized by the `input_x` spatial dimensions. Therefore, it should
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have most values in the range of :math:`[-1, 1]`.
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If `grid` has values outside the range of :math:`[-1, 1]`, the corresponding outputs are handled as defined by
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`padding_mode`. If `padding_mode` is set to be "zeros", use :math:`0` for out-of-bound grid locations. If
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`padding_mode` is set to be "border", use border values for out-of-bound grid locations. If `padding_mode` is set
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to be "reflection", use values at locations reflected by the border for out-of-bound grid locations. For location
|
||||
far away from the border, it will keep being reflected until becoming in bound.
|
||||
|
||||
Args:
|
||||
interpolation_mode (str): An optional string specifying the interpolation method. The optional values are
|
||||
"bilinear" or "nearest". Default: "bilinear".
|
||||
padding_mode (str): An optional string specifying the pad method. The optional values are "zeros", "border" or
|
||||
"reflection". Default: "zeros".
|
||||
align_corners (bool): An optional bool. If set to `True`, the extrema (-1 and 1) are considered as referring to
|
||||
the center points of the input’s corner pixels. If set to `False`, they are instead considered as referring
|
||||
to the corner points of the input’s corner pixels, making the sampling more resolution agnostic. Default:
|
||||
`False`.
|
||||
|
||||
Inputs:
|
||||
- **input_x** (Tensor) - A 5-D tensor with dtype of float32 or float64 and shape of :math:`(N, C, D_{in},
|
||||
H_{in}, W_{in})`.
|
||||
- **grid** (Tensor) - A 5-D tensor whose dtype is the same as `input_x` and whose shape is :math:`(N, D_{out},
|
||||
H_{out}, W_{out}, 3)`.
|
||||
|
||||
Outputs:
|
||||
A 5-D Tensor whose dtype is the same as `input_x` and whose shape is :math:`(N, C, D_{out}, H_{out}, W_{out})`.
|
||||
|
||||
Raises:
|
||||
TypeError: If `input_x` or `grid` is not a Tensor.
|
||||
TypeError: If the dtypes of `input_x` and `grid` are inconsistent.
|
||||
TypeError: If the dtype of `input_x` or `grid` is not a valid type.
|
||||
TypeError: If `align_corners` is not a boolean value.
|
||||
ValueError: If the rank of `input_x` or `grid` is not equal to 5.
|
||||
ValueError: If the first dimension of `input_x` is not equal to that of `grid`.
|
||||
ValueError: If the last dimension of `grid` is not equal to 3.
|
||||
ValueError: If `interpolation_mode` is not "bilinear", "nearest" or a string value.
|
||||
ValueError: If `padding_mode` is not "zeros", "border", "reflection" or a string value.
|
||||
Refer to :func:`mindspore.ops.grid_sample` for more details.
|
||||
|
||||
Supported Platforms:
|
||||
``Ascend`` ``GPU`` ``CPU``
|
||||
|
|
|
|||
Loading…
Reference in New Issue