add triu, true_divide, subtract to Tensor
This commit is contained in:
shaojunsong
parent
2e0a40557b
commit
2967aa99a5
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@ -0,0 +1,20 @@
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mindspore.Tensor.subtract
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==========================
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.. py:method:: mindspore.Tensor.subtract(other, *, alpha=1)
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对Tensor进行逐元素的减法。
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.. math::
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output[i] = x[i] - alpha * y[i]
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参数：
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- **other** (Union[Tensor, number.Number]) - 参与减法的Tensor或者Number。
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- **alpha** (Number) - :math:`other` 的乘数。默认值：1。
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返回：
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Tensor，shape与广播后的shape相同，数据类型为输入中精度较高的类型。
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异常：
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- **TypeError** - `other` 不是Tensor、number.Number。
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@ -0,0 +1,20 @@
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mindspore.Tensor.triu
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=====================
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.. py:method:: mindspore.Tensor.triu(diagonal=0)
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根据对角线返回相应的三角矩阵。默认为主对角线。
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参数：
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-**diagonal** (int) - 对角线的系数。默认值：0。
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返回：
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Tensor，shape和dtype与输入相同。
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异常：
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-**TypeError** - 如果 `diagonal` 不是int。
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-**TypeError** - 如果 `x` 不是Tensor。
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-**ValueError** - 如果shape的长度小于1。
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@ -0,0 +1,9 @@
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mindspore.Tensor.true_divide
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============================
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.. py:method:: mindspore.Tensor.true_divide(value)
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当Tensor.div()的 :math:`rounding_mode=None` 时的别名。
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参考`Tensor.div() <https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.div.html>`_。
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@ -57,6 +57,7 @@ mindspore.Tensor
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mindspore.Tensor.less_equal
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mindspore.Tensor.igamma
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mindspore.Tensor.igammac
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mindspore.Tensor.true_divide
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逐元素运算
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^^^^^^^^^^^^^
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@ -104,6 +105,7 @@ mindspore.Tensor
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mindspore.Tensor.square
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mindspore.Tensor.std
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mindspore.Tensor.sub
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mindspore.Tensor.subtract
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mindspore.Tensor.svd
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mindspore.Tensor.tan
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mindspore.Tensor.tanh
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@ -158,10 +160,10 @@ Reduction方法
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:toctree: Tensor
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:nosignatures:
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mindspore.Tensor.det
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mindspore.Tensor.ger
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mindspore.Tensor.log_matrix_determinant
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mindspore.Tensor.matrix_determinant
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mindspore.Tensor.det
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Tensor操作方法
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----------------
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@ -253,6 +255,7 @@ Array操作
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mindspore.Tensor.to_tensor
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mindspore.Tensor.trace
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mindspore.Tensor.transpose
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mindspore.Tensor.triu
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mindspore.Tensor.unfold
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mindspore.Tensor.unique_consecutive
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mindspore.Tensor.unique_with_pad
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@ -62,6 +62,7 @@ Mathematical Methods
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mindspore.Tensor.less_equal
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mindspore.Tensor.igamma
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mindspore.Tensor.igammac
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mindspore.Tensor.true_divide
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Element-wise Methods
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^^^^^^^^^^^^^^^^^^^^
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@ -108,6 +109,7 @@ Element-wise Methods
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mindspore.Tensor.sqrt
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mindspore.Tensor.std
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mindspore.Tensor.sub
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mindspore.Tensor.subtract
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mindspore.Tensor.svd
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mindspore.Tensor.square
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mindspore.Tensor.tan
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@ -163,10 +165,10 @@ Linear Algebraic Methods
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:toctree: Tensor
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:nosignatures:
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mindspore.Tensor.det
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mindspore.Tensor.ger
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mindspore.Tensor.log_matrix_determinant
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mindspore.Tensor.matrix_determinant
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mindspore.Tensor.det
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Tensor Operation Methods
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------------------------
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@ -258,6 +260,7 @@ Array Methods
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mindspore.Tensor.to_tensor
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mindspore.Tensor.trace
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mindspore.Tensor.transpose
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mindspore.Tensor.triu
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mindspore.Tensor.unfold
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mindspore.Tensor.unique_consecutive
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mindspore.Tensor.unique_with_pad
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@ -280,6 +280,9 @@ BuiltInTypeMap &GetMethodMap() {
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{"sqrt", std::string("sqrt")}, // P.Sqrt()
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{"square", std::string("square")}, // P.Square()
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{"sub", std::string("sub")}, // P.Sub()
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{"true_divide", std::string("true_divide")}, // true_divide()
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{"triu", std::string("triu")}, // triu()
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{"subtract", std::string("subtract")}, // true_divide()
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{"exp", std::string("exp")}, // P.Exp()
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{"repeat", std::string("repeat")}, // C.repeat_elements
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{"bernoulli", prim::kPrimBernoulli}, // P.Bernoulli()
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@ -2908,6 +2908,28 @@ def top_k(input_x, k, sorted=True):
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return F.top_k(input_x, k, sorted)
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def subtract(x, other, *, alpha=1):
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r"""
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Computes the element-wise subtraction of input tensors.
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"""
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return F.sub(x, other * alpha)
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def true_divide(divident, divisor):
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r"""
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Computes the element-wise division of input tensors.
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"""
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return F.div(divident, divisor, None)
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# pylint: disable=redefined-outer-name
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def triu(x, diagonal=0):
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r"""
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Returns the triangular matrix based on the diagonal.
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"""
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return F.Triu(diagonal)(x)
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#############
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# Iteration #
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#############
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@ -3227,7 +3249,7 @@ def addr(x, vec1, vec2, beta=1, alpha=1):
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r"""
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Computes the outer-product of `vec1` and `vec2` and adds it to `x`.
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"""
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return F.addr(x, vec1, vec2, beta=1, alpha=1)
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return F.addr(x, vec1, vec2, beta=beta, alpha=alpha)
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def addmv(x, mat, vec, beta=1, alpha=1):
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@ -630,7 +630,7 @@ class Tensor(Tensor_):
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def ndimension(self):
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r"""
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Refer to `Tensor.ndim()
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<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.ndim.html>` _.
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<https://www.mindspore.cn/docs/en/master/api_python/mindspore/Tensor/mindspore.Tensor.ndim.html>`_.
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"""
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return len(self._shape)
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@ -970,6 +970,67 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('add')()(self, y)
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def subtract(self, other, *, alpha=1):
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r"""
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Performs the element-wise subtraction of input tensors.
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.. math::
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output[i] = x[i] - alpha * y[i]
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Args:
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other (Tensor, Number): The tensor or number to be subtracted.
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alpha (Number): The multiplier for `other`. Default: 1.
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Returns:
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Tensor, has the same shape and dtype as input tensors.
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Raises:
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TypeError: `other` is not Tensor, number.Number.
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Supported Platforms:
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``Ascend`` ``GPU`` ``CPU``
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Examples:
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>>> x = Tensor(np.array([4, 5, 6]), mindspore.float32)
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>>> y = Tensor(np.array([1, 2, 3]), mindspore.float32)
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>>> z = x.subtract(y, alpha=1)
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>>> print(z)
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[3. 3. 3.]
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"""
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self._init_check()
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return tensor_operator_registry.get('sub')(self, alpha * other)
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def true_divide(self, value):
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r"""
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Alias for Tensor.div() with :math:`rounding_mode=None`.
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Refer to `Tensor.div()
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<https://www.mindspore.cn/docs/en/master/api_python/mindspore/Tensor/mindspore.Tensor.div.html>`_.
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"""
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self._init_check()
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return tensor_operator_registry.get('div')(self, value, None)
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def triu(self, diagonal=0):
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r"""
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Returns a triangular matrix based on the diagonal. Default is the main diagonal.
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Args:
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diagonal (int): The index of diagonal. Default: 0.
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Returns:
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Tensor, a tensor has the same shape and data type as input.
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Raises:
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TypeError: If `diagonal` is not an int.
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TypeError: If `x` is not an Tensor.
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ValueError: If length of shape of x is less than 1.
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Supported Platforms:
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``GPU`` ``CPU``
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"""
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self._init_check()
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validator.check_value_type('diagonal', diagonal, [int], 'triu')
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return tensor_operator_registry.get('triu')(diagonal)(self)
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def addr(self, vec1, vec2, beta=1, alpha=1):
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r"""
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Executes the outer-product of `vec1` and `vec2` and adds it to the input tensor.
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@ -1015,7 +1076,7 @@ class Tensor(Tensor_):
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"""
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self._init_check()
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return tensor_operator_registry.get('addr')(self, vec1, vec2, beta=1, alpha=1)
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return tensor_operator_registry.get('addr')(self, vec1, vec2, beta=beta, alpha=alpha)
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def all(self, axis=(), keep_dims=False):
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"""
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@ -4273,8 +4334,8 @@ class Tensor(Tensor_):
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def det(self):
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r"""
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Refer to `Tensor.matrix_determinant()
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<https://www.mindspore.cn/docs/zh-CN/master/api_python/mindspore/Tensor/mindspore.Tensor.matrix_determinant.html
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>` _.
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<https://www.mindspore.cn/docs/en/master/api_python/mindspore/Tensor/mindspore.Tensor.matrix_determinant.html
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>`_.
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"""
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self._init_check()
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return tensor_operator_registry.get('matrix_determinant')(self)
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@ -5214,7 +5275,7 @@ class Tensor(Tensor_):
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dtype_ = self.dtype
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x = self.asnumpy()
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n = x.strides[1]
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strides = tuple(np.array(strides)*n)
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strides = tuple(np.array(strides) * n)
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return Tensor(np.lib.stride_tricks.as_strided(x, shape, strides, subok, writeable), dtype=dtype_)
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def randperm(self, max_length=1, pad=-1):
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@ -6368,7 +6429,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('bmm')(self, mat2)
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def to(self, dtype):
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r"""
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Performs tensor dtype conversion.
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@ -6396,7 +6456,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('to')()(self, dtype)
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def bool(self):
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r"""
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Converts input tensor dtype to `bool`.
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@ -6417,7 +6476,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('bool')()(self, mstype.bool_)
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def float(self):
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r"""
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Converts input tensor dtype to `float32`.
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@ -6437,7 +6495,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('float')()(self, mstype.float32)
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def half(self):
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r"""
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Converts input tensor dtype to `float16`.
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@ -6457,7 +6514,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('half')()(self, mstype.float16)
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def int(self):
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r"""
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Converts input tensor dtype to `int32`. If the value in tensor is float or half, the decimal will be discarded.
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@ -6477,7 +6533,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('int')()(self, mstype.int32)
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def long(self):
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r"""
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Converts input tensor dtype to `int64`. If the value in tensor is float or half, the decimal will be discarded.
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@ -6497,7 +6552,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('long')()(self, mstype.int64)
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def cholesky(self, upper=False):
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r"""
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Computes the Cholesky decomposition of a symmetric positive-definite matrix :math:`A`
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@ -6541,7 +6595,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('cholesky')(upper=upper)(self)
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def cholesky_inverse(self, upper=False):
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r"""
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Returns the inverse of the positive definite matrix using cholesky matrix factorization.
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@ -6583,7 +6636,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('cholesky_inverse')(upper=upper)(self)
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def conj(self):
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r"""
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Returns a tensor of complex numbers that are the complex conjugate of each element in input.
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@ -6611,7 +6663,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('conj')(self)
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def cross(self, other, dim=None):
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r"""
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Returns the cross product of vectors in dimension `dim` of input tensor and `other`. input tensor and `other`
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@ -6647,7 +6698,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('cross')(self, other, dim)
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def erfinv(self):
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r"""
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Computes the inverse error function of input. The inverse error function is defined in the range `(-1, 1)` as:
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@ -6674,7 +6724,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('erfinv')(self)
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def less_equal(self, other):
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r"""
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Computes the boolean value of :math:`input <= other` element-wise.
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@ -6709,7 +6758,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('less_equal')(self, other)
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def fold(self, output_size, kernel_size, dilation=1, padding=0, stride=1):
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r"""
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Combines an array of sliding local blocks into a large containing tensor.
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@ -6753,7 +6801,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('fold')(self, output_size, kernel_size, dilation, padding, stride)
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def unfold(self, kernel_size, dilation=1, padding=0, stride=1):
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r"""
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Extracts sliding local blocks from a batched input tensor.
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@ -6799,7 +6846,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('unfold')(self, kernel_size, dilation, padding, stride)
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def expand(self, size):
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r"""
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Returns a new view of the self tensor with singleton dimensions expanded to a larger size.
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@ -6839,7 +6885,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('expand')(self, size)
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def cumprod(self, dim, dtype=None):
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r"""
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Computes the cumulative product of the tensor along dimension `dim`. For example, if input tensor is a
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@ -6871,7 +6916,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('cumprod')(self, dim, dtype)
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def div(self, other, rounding_mode=None):
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r"""
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Divides the tensor `input` by the given input tensor `other` in floating-point type element-wise.
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@ -6917,7 +6961,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('div')(self, other, rounding_mode)
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def equal(self, other):
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r"""
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Computes the equivalence between the tensor input tensor `input` and the
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@ -6959,7 +7002,6 @@ class Tensor(Tensor_):
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self._init_check()
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return tensor_operator_registry.get('equal')(self, other)
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def expm1(self):
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r"""
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Returns exponential then minus 1 of a tensor element-wise.
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@ -36,6 +36,7 @@ from mindspore.ops.operations import _inner_ops
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from mindspore.ops.operations import linalg_ops
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from mindspore.ops.operations.math_ops import Median
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from mindspore.ops.operations.array_ops import UniqueConsecutive
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from mindspore.ops.operations.array_ops import Triu
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from mindspore.ops.operations.nn_ops import AdaptiveMaxPool2D
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from mindspore.ops.composite import _Vmap
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@ -338,6 +339,7 @@ tensor_operator_registry.register('abs', P.Abs)
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tensor_operator_registry.register('sqrt', sqrt)
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tensor_operator_registry.register('square', square)
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||||
tensor_operator_registry.register('sub', sub)
|
||||
tensor_operator_registry.register('triu', Triu)
|
||||
tensor_operator_registry.register('tan', P.Tan)
|
||||
tensor_operator_registry.register('acos', acos)
|
||||
tensor_operator_registry.register('cos', cos)
|
||||
|
|
|
|||
|
|
@ -0,0 +1,34 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore import context
|
||||
|
||||
|
||||
class Net(nn.Cell):
|
||||
def construct(self, x, other):
|
||||
return x.subtract(other, alpha=2)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_cpu
|
||||
@pytest.mark.platform_arm_cpu
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
||||
def test_subtract(mode):
|
||||
"""
|
||||
Feature: tensor.subtract()
|
||||
Description: Verify the result of tensor.subtract
|
||||
Expectation: success
|
||||
"""
|
||||
context.set_context(mode=mode)
|
||||
net = Net()
|
||||
x = Tensor([4, 5, 6], dtype=mstype.float32)
|
||||
y = Tensor([1, 2, 3], dtype=mstype.float32)
|
||||
output = net(x, y)
|
||||
expected = np.array([2, 1, 0], dtype=np.float32)
|
||||
assert np.allclose(output.asnumpy(), expected)
|
||||
|
|
@ -0,0 +1,36 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore import context
|
||||
|
||||
|
||||
class Net(nn.Cell):
|
||||
def construct(self, x):
|
||||
return x.triu(diagonal=1)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_cpu
|
||||
@pytest.mark.platform_arm_cpu
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.env_onecard
|
||||
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
||||
def test_subtract(mode):
|
||||
"""
|
||||
Feature: tensor.subtract()
|
||||
Description: Verify the result of tensor.subtract
|
||||
Expectation: success
|
||||
"""
|
||||
context.set_context(mode=mode)
|
||||
net = Net()
|
||||
x = Tensor(np.array([[1, 2, 3, 4],
|
||||
[5, 6, 7, 8],
|
||||
[10, 11, 12, 13],
|
||||
[14, 15, 16, 17]]))
|
||||
output = net(x)
|
||||
expected = np.array([[0, 2, 3, 4],
|
||||
[0, 0, 7, 8],
|
||||
[0, 0, 0, 13],
|
||||
[0, 0, 0, 0]])
|
||||
assert np.array_equal(output.asnumpy(), expected)
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
import numpy as np
|
||||
import pytest
|
||||
import mindspore.common.dtype as mstype
|
||||
import mindspore.nn as nn
|
||||
from mindspore import Tensor
|
||||
from mindspore import context
|
||||
|
||||
|
||||
class Net(nn.Cell):
|
||||
def construct(self, x, other):
|
||||
return x.true_divide(other)
|
||||
|
||||
|
||||
@pytest.mark.level0
|
||||
@pytest.mark.platform_x86_cpu
|
||||
@pytest.mark.platform_arm_cpu
|
||||
@pytest.mark.platform_x86_gpu_training
|
||||
@pytest.mark.platform_arm_ascend_training
|
||||
@pytest.mark.platform_x86_ascend_training
|
||||
@pytest.mark.env_onecard
|
||||
@pytest.mark.parametrize('mode', [context.GRAPH_MODE, context.PYNATIVE_MODE])
|
||||
def test_subtract(mode):
|
||||
"""
|
||||
Feature: tensor.subtract()
|
||||
Description: Verify the result of tensor.subtract
|
||||
Expectation: success
|
||||
"""
|
||||
context.set_context(mode=mode)
|
||||
net = Net()
|
||||
x = Tensor(np.array([1.0, 2.0, 3.0]), mstype.float32)
|
||||
y = Tensor(np.array([4.0, 5.0, 6.0]), mstype.float32)
|
||||
output = net(x, y)
|
||||
expected = np.array([0.25, 0.4, 0.5], dtype=np.float32)
|
||||
assert np.allclose(output.asnumpy(), expected)
|
||||
Loading…
Reference in New Issue