From e0ba571318848bfc1b635a442031ade534b4a2a9 Mon Sep 17 00:00:00 2001 From: zhangyi Date: Mon, 22 Aug 2022 16:51:34 +0800 Subject: [PATCH] modify the supported platforms --- .../Tensor/mindspore.Tensor.atan2.rst | 2 +- .../Tensor/mindspore.Tensor.bernoulli.rst | 12 ++-- .../mindspore/Tensor/mindspore.Tensor.inv.rst | 2 +- .../Tensor/mindspore.Tensor.invert.rst | 2 +- .../Tensor/mindspore.Tensor.lerp.rst | 6 +- .../Tensor/mindspore.Tensor.log1p.rst | 2 + .../Tensor/mindspore.Tensor.logit.rst | 2 +- .../mindspore/Tensor/mindspore.Tensor.std.rst | 2 +- .../mindspore/Tensor/mindspore.Tensor.svd.rst | 4 +- .../mindspore/Tensor/mindspore.Tensor.tan.rst | 2 +- .../mindspore/Tensor/mindspore.Tensor.var.rst | 2 +- mindspore/python/mindspore/common/tensor.py | 56 ++++++++++--------- .../python/mindspore/nn/layer/activation.py | 6 +- mindspore/python/mindspore/nn/layer/conv.py | 6 +- .../mindspore/ops/function/math_func.py | 6 +- .../python/mindspore/ops/function/nn_func.py | 4 +- .../mindspore/ops/operations/array_ops.py | 11 ++-- .../mindspore/ops/operations/math_ops.py | 13 ++++- .../python/mindspore/ops/operations/nn_ops.py | 5 +- 19 files changed, 84 insertions(+), 61 deletions(-) diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.atan2.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.atan2.rst index adb2397ccd8..1693c79aa01 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.atan2.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.atan2.rst @@ -11,7 +11,7 @@ mindspore.Tensor.atan2 输入 `x` 和 `y` 会通过隐式数据类型转换使数据类型保持一致。如果数据类型不同，低精度的数据类型会被转换到高精度的数据类型。 参数： - - **y** (Tensor) - 输入Tensor，shape应能在广播后与 `x` 相同，或 `x` 的shape在广播后与 `y` 相同。 + - **y** (Tensor) - 输入Tensor。shape应能在广播后与 `x` 相同，或 `x` 的shape在广播后与 `y` 相同。 返回： Tensor，与广播后的输入shape相同，和 `x` 数据类型相同。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.bernoulli.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.bernoulli.rst index 348cefdfb2a..c512df22212 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.bernoulli.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.bernoulli.rst @@ -10,15 +10,15 @@ mindspore.Tensor.bernoulli out_{i} \sim Bernoulli(p_{i}) 参数： - - **p** (Union[Tensor, float], 可选) - shape需要可以被广播到当前Tensor。其数据类型为float32或float64。`p` 中每个值代表输出Tensor中对应广播位置为1的概率，数值范围在0到1之间。默认值：0.5。 - - **seed** (int, 可选) - 随机种子，用于生成随机数，数值范围是-1或正整数。默认值：-1，代表取当前时间戳。 + - **p** (Union[Tensor, float], 可选) - shape需要可以被广播到当前Tensor。其数据类型为float32或float64。`p` 中每个值代表输出Tensor中对应广播位置为1的概率。数值范围在0到1之间。默认值：0.5。 + - **seed** (int, 可选) - 随机种子，用于生成随机数。数值范围是-1或正整数。默认值：-1，代表取当前时间戳。 返回： Tensor，shape和数据类型与当前Tensor相同。 异常： - - **TypeError** - 当前Tensor的数据类型不在int8, uint8, int16, int32, int64, bool, float32和float64中。 + - **TypeError** - 当前Tensor的数据类型不在int8、uint8、int16、int32、int64、bool、float32和float64中。 - **TypeError** - `p` 的数据类型既不是float32也不是float64。 - - **TypeError** - `seed` 不是int。 - - **ValueError** - `seed` 是负数且不为-1。 - - **ValueError** - `p` 数值范围不在0到1之间。 \ No newline at end of file + - **TypeError** - `seed` 的数据类型不是int。 + - **ValueError** - `p` 数值范围不在0到1之间。 + - **ValueError** - `seed` 是负数且不为-1。 \ No newline at end of file diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.inv.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.inv.rst index df6a1dc7156..93a3be4d1ed 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.inv.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.inv.rst @@ -3,7 +3,7 @@ mindspore.Tensor.inv .. py:method:: mindspore.Tensor.inv() - 计算当前Tensor的倒数。 + 逐元素计算当前Tensor的倒数。 .. math:: out_i = \frac{1}{x_{i} } diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.invert.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.invert.rst index 1f42bf844e7..0005fa54718 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.invert.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.invert.rst @@ -3,7 +3,7 @@ mindspore.Tensor.invert .. py:method:: mindspore.Tensor.invert() - 按位翻转当前Tensor。 + 逐元素按位翻转当前Tensor。 .. math:: out_i = \sim x_{i} diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.lerp.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.lerp.rst index e2e463f49e8..9c7cecb1da8 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.lerp.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.lerp.rst @@ -3,13 +3,13 @@ mindspore.Tensor.lerp .. py:method:: mindspore.Tensor.lerp(end, weight) - 基于某个浮点数Scalar或权重Tensor的值， 计算当前Tensor和 `end` Tensor之间的线性插值。 + 基于某个浮点数Scalar或权重Tensor的值，计算当前Tensor和 `end` Tensor之间的线性插值。 如果参数 `weight` 是一个Tensor，那么另两个输入的维度信息可以被广播到当前Tensor。 - 如果参数 `weight` 是一个Scalar， 那么 `end` 的维度信息可以被广播到当前Tensor。 + 如果参数 `weight` 是一个Scalar，那么 `end` 的维度信息可以被广播到当前Tensor。 参数： - - **end** (Tensor) - 进行线性插值的Tensor结束点，其数据类型必须为float16或者float32。 + - **end** (Tensor) - 进行线性插值的Tensor结束点。数据类型必须为float16或者float32。 - **weight** (Union[float, Tensor]) - 线性插值公式的权重参数。当为Scalar时，其数据类型为float，当为Tensor时，其数据类型为float16或者float32。 返回： diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.log1p.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.log1p.rst index f036203091f..53d139371ed 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.log1p.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.log1p.rst @@ -5,6 +5,8 @@ mindspore.Tensor.log1p 对当前Tensor逐元素加一后计算自然对数。 + 其中 `x` 表示当前Tensor。 + .. math:: out_i = {log_e}(x_i + 1) diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.logit.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.logit.rst index 4b3c126c506..b5079617f61 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.logit.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.logit.rst @@ -3,7 +3,7 @@ mindspore.Tensor.logit .. py:method:: mindspore.Tensor.logit(eps=None) - 逐元素计算张量的logit值，当 eps 不是 None 时， `x` 中的元素被截断到范围[eps, 1-eps]内。 + 逐元素计算张量的logit值。当 eps 不是 None 时， `x` 中的元素被截断到范围[eps, 1-eps]内。 当 eps 为 None 时，输入 `x` 不进行数值截断。 `x` 指的当前 Tensor。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.std.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.std.rst index 4e2b71a0c87..e0c0d7fc660 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.std.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.std.rst @@ -6,7 +6,7 @@ mindspore.Tensor.std 计算指定维度的标准差。 标准差是方差的算术平方根，如：:math:`std = sqrt(mean(abs(x - x.mean())**2))` 。 - 返回标准差。默认情况下计算展开数组的标准差，否则在指定维度上计算。 + 返回标准差，默认情况下计算展开数组的标准差，否则在指定维度上计算。 .. note:: 不支持NumPy参数 `dtype` 、 `out` 和 `where` 。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.svd.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.svd.rst index f66ccc1e08b..f61babe764f 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.svd.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.svd.rst @@ -8,8 +8,8 @@ mindspore.Tensor.svd 更多参考详见 :func:`mindspore.ops.svd`。 参数： - - **full_matrices** (bool, optional) - 如果这个参数为True，则计算完整的 :math:`U` 和 :math:`V` 。否则 :math:`U` 和 :math:`V` 的shape和P有关，P是M和N的较小值, M和N是输入矩阵的行和列。默认值：False。 - - **compute_uv** (bool, optional) - 如果这个参数为True，则计算 :math:`U` 和 :math:`V` ，否则只计算 :math:`S` 。默认值：True。 + - **full_matrices** (bool, optional) - 如果这个参数为True，则计算完整的 :math:`U` 和 :math:`V` 。否则 :math:`U` 和 :math:`V` 的shape和P有关。P是M和N的较小值。M和N是输入矩阵的行和列。默认值：False。 + - **compute_uv** (bool, optional) - 如果这个参数为True，则计算 :math:`U` 和 :math:`V` 。如果为false，只计算 :math:`S` 。默认值：True。 返回： - **s** (Tensor) - 奇异值。shape为 :math:`(*, P)`。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tan.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tan.rst index 4525aecefb7..69b96ca3a1b 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tan.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.tan.rst @@ -10,7 +10,7 @@ mindspore.Tensor.tan out_i = tan(x_i) 返回： - Tensor。 + Tensor，和当前输入的shape一样。 异常： - **TypeError** - 当前输入不是Tensor。 \ No newline at end of file diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.var.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.var.rst index 5cf5f14e9f8..3edf9afa84f 100644 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.var.rst +++ b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.var.rst @@ -7,7 +7,7 @@ mindspore.Tensor.var 方差是平均值的平方偏差的平均值，即：:math:`var = mean(abs(x - x.mean())**2)` 。 - 返回方差值。默认情况下计算展开Tensor的方差，否则在指定维度上计算。 + 返回方差值，默认情况下计算展开Tensor的方差，否则在指定维度上计算。 .. note:: 不支持NumPy参数 `dtype` 、 `out` 和 `where` 。 diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py index 15aa402c4cd..f573bb38620 100644 --- a/mindspore/python/mindspore/common/tensor.py +++ b/mindspore/python/mindspore/common/tensor.py @@ -421,6 +421,7 @@ class Tensor(Tensor_): Convert numpy array to Tensor. If the data is not C contiguous, the data will be copied to C contiguous to construct the tensor. Otherwise, The tensor will be constructed using this numpy array without copy. + Args: array (numpy.array): The input array. @@ -696,7 +697,7 @@ class Tensor(Tensor_): r""" Returns arctangent of x/y element-wise. - `x` refer to self tensor. + `x` refers to self tensor. It returns :math:`\theta\ \in\ [-\pi, \pi]` such that :math:`x = r*\sin(\theta), y = r*\cos(\theta)`, where :math:`r = \sqrt{x^2 + y^2}`. @@ -706,10 +707,11 @@ class Tensor(Tensor_): the relatively highest precision data type. Args: - y (Tensor): The input tensor. It has the same shape with `x`. + y (Tensor): The input tensor. It has the same shape with `x` after broadcasting, + or the shape of `x` is the same as `y` after broadcasting. Returns: - Tensor, the shape is the same as the one after broadcasting,and the data type is same as `x`. + Tensor, the shape is the same as the one after broadcasting, and the data type is same as `x`. Raises: TypeError: If `x` or `y` is not a Tensor. @@ -1246,7 +1248,7 @@ class Tensor(Tensor_): """ Does a linear interpolation of two tensors start and end based on a float or tensor weight. - If `weight` is a tensor, the shapes of two inputs need to be broadcast; + If `weight` is a tensor, the shapes of two inputs need to be broadcast. If `weight` is a float, the shapes of `end` need to be broadcast. Args: @@ -1407,7 +1409,7 @@ class Tensor(Tensor_): r""" Computes the determinant of one or more square matrices. - `x` refer to self tensor. + `x` refers to self tensor. Returns: @@ -1435,7 +1437,7 @@ class Tensor(Tensor_): r""" Returns the natural logarithm of one plus the input tensor element-wise. - `x` refer to self tensor. + `x` refers to self tensor. .. math:: out_i = {log_e}(x_i + 1) @@ -1465,7 +1467,7 @@ class Tensor(Tensor_): Calculate the logit of a tensor element-wise. When eps is not None, element in 'x' is clamped to [eps, 1-eps]. When eps is None, input 'x' is not clamped. - `x` refer to self tensor. + `x` refers to self tensor. .. math:: \begin{align} @@ -1508,7 +1510,7 @@ class Tensor(Tensor_): r""" Computes the sign and the log of the absolute value of the determinant of one or more square matrices. - `x` refer to self tensor. + `x` refers to self tensor. Returns: Tensor, The signs of the log determinants. The shape is :math:`x\_shape[:-2]`, the dtype is same as `x`. @@ -1601,6 +1603,8 @@ class Tensor(Tensor_): .. math:: out_i = \frac{1}{x_{i} } + where `x` refers to self Tensor. + Returns: Tensor, has the same type and shape as self Tensor. @@ -1626,6 +1630,8 @@ class Tensor(Tensor_): .. math:: out_i = \sim x_{i} + where `x` refers to self Tensor. + Returns: Tensor, has the same shape as as self Tensor. @@ -2191,9 +2197,6 @@ class Tensor(Tensor_): Returns: Tensor, has the same data type as input. - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - Raises: TypeError: If `order` is not string type. ValueError: If `order` is string type, but not 'C' or 'F'. @@ -2203,6 +2206,9 @@ class Tensor(Tensor_): :func:`mindspore.Tensor.ravel`: Return a contiguous flattened tensor. + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` + Examples: >>> import numpy as np >>> from mindspore import Tensor @@ -2582,12 +2588,12 @@ class Tensor(Tensor_): Returns: Tensor. - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - See also: :func:`mindspore.Tensor.sum`: Return sum of tensor elements over a given axis. + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` + Examples: >>> import numpy as np >>> from mindspore import Tensor @@ -3773,12 +3779,12 @@ class Tensor(Tensor_): Returns: Tensor, the merged result. - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - Raises: ValueError: If the input tensor and any of the `choices` cannot be broadcast. + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` + Examples: >>> import numpy as np >>> from mindspore import Tensor @@ -4572,7 +4578,7 @@ class Tensor(Tensor_): Only 2-D tensor is supported for now. Returns: - COOTensor, a sparse representation of the original dense tensor, containing: + COOTensor, a sparse representation of the original dense tensor, containing the following parts. - indices (Tensor): 2-D integer tensor, indicates the positions of `values` of the dense tensor. - values (Tensor): 1-D tensor, indicates the non-zero values of the dense tensor. @@ -4606,7 +4612,7 @@ class Tensor(Tensor_): Only 2-D tensor is supported for now. Returns: - CSRTensor, a sparse representation of the original dense tensor, containing: + CSRTensor, a sparse representation of the original dense tensor, containing the following parts. - indptr (Tensor): 1-D integer tensor, indicates the start and end point for `values` in each row. - indices (Tensor): 1-D integer tensor, indicates the column positions of all non-zero values of the input. @@ -4764,7 +4770,7 @@ class Tensor(Tensor_): RuntimeError: If `axis` is not in the range of :math:`[-ndim, ndim-1]`. Supported Platforms: - ``GPU`` + ``Ascend`` ``GPU`` Examples: >>> import numpy as np @@ -4861,20 +4867,20 @@ class Tensor(Tensor_): return tensor_operator_registry.get('diag')()(self) def xdivy(self, y): - """ + r""" Divides self tensor by the input tensor element-wise. Returns zero when self is zero. The dtype of original Tensor must be one of float, complex or bool. For simplicity, denote the original Tensor by x. .. math:: - out_i = x_{i} / {y_{i}} + out_i = x_{i}\y_{i} `x` and `y` comply with the implicit type conversion rules to make the data types consistent. 'y' must be tensor or scalar, when y is tensor, dtypes of x and y cannot be bool at the same time, and the shapes of them could be broadcast. When y is scalar, the scalar can only be a constant. Args: - - **y** (Union[Tensor, Number, bool]) - The second input y is a Number, + y (Union[Tensor, number.Number, bool]): The second input y is a Number, or a bool when the first input x is a tensor, or a tensor whose data type is float16, float32, float64, complex64, complex128 or bool. @@ -4964,7 +4970,7 @@ class Tensor(Tensor_): - On Ascend, the data type of `x` and `y` must be float16 or float32. Args: - - **y** (Union[Tensor, number.Number, bool]) - The `y` input is a number.Number or + y (Union[Tensor, number.Number, bool]): The `y` input is a number.Number or a bool or a tensor whose data type is number or bool. Returns: @@ -5039,7 +5045,7 @@ class Tensor(Tensor_): .. warning:: - If sorted is set to 'False', it will use the aicpu operator, the performance may be reduced. - `input_x` refer to self tensor. + `input_x` refers to self tensor. If the `input_x` is a one-dimensional Tensor, finds the `k` largest entries in the Tensor, and outputs its value and index as a Tensor. Therefore, values[`k`] is the `k` largest item in `input_x`, diff --git a/mindspore/python/mindspore/nn/layer/activation.py b/mindspore/python/mindspore/nn/layer/activation.py index ff2cd2406f0..e0538fc96a5 100644 --- a/mindspore/python/mindspore/nn/layer/activation.py +++ b/mindspore/python/mindspore/nn/layer/activation.py @@ -90,7 +90,7 @@ class CELU(Cell): TypeError: If the dtype of 'input_x' is neither float16 nor float32. Supported Platforms: - ``Ascend`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> x = Tensor(np.array([-2.0, -1.0, 1.0, 2.0]), mindspore.float32) @@ -967,7 +967,7 @@ class Softsign(Cell): Refer to :func:`mindspore.ops.softsign` for more details. Supported Platforms: - ``Ascend`` ``CPU`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> x = Tensor(np.array([0, -1, 2, 30, -30]), mindspore.float32) @@ -1409,7 +1409,7 @@ class Mish(Cell): Refer to :func:`mindspore.ops.mish` for more details. Supported Platforms: - ``Ascend`` ``CPU`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) diff --git a/mindspore/python/mindspore/nn/layer/conv.py b/mindspore/python/mindspore/nn/layer/conv.py index f80e3ea51d6..07b6e75bfe2 100644 --- a/mindspore/python/mindspore/nn/layer/conv.py +++ b/mindspore/python/mindspore/nn/layer/conv.py @@ -839,9 +839,6 @@ class Conv3dTranspose(_Conv): \text{kernel_size[2]} - 1 }{\text{stride[2]}} + 1} \right \rfloor \\ \end{array} - Supported Platforms: - ``Ascend`` ``GPU`` - Raises: TypeError: If `in_channels`, `out_channels` or `group` is not an int. TypeError: If `kernel_size`, `stride`, `padding` , `dilation` or `output_padding` @@ -854,6 +851,9 @@ class Conv3dTranspose(_Conv): ValueError: If `pad_mode` is not equal to 'pad' and `padding` is not equal to (0, 0, 0, 0, 0, 0). ValueError: If `data_format` is not 'NCDHW'. + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` + Examples: >>> x = Tensor(np.ones([32, 16, 10, 32, 32]), mindspore.float32) >>> conv3d_transpose = nn.Conv3dTranspose(in_channels=16, out_channels=3, kernel_size=(4, 6, 2), diff --git a/mindspore/python/mindspore/ops/function/math_func.py b/mindspore/python/mindspore/ops/function/math_func.py index 33f134a067d..8eb2db92ac8 100644 --- a/mindspore/python/mindspore/ops/function/math_func.py +++ b/mindspore/python/mindspore/ops/function/math_func.py @@ -409,7 +409,7 @@ def argmin(x, axis=-1): TypeError: If `axis` is not an int. Supported Platforms: - ``Ascend`` ``CPU`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> input_x = Tensor(np.array([2.0, 3.1, 1.2]), mindspore.float32) @@ -1998,7 +1998,7 @@ def bessel_i0e(x): TypeError: If dtype of `x` is not float16, float32 or float64. Supported Platforms: - ``CPU`` + ``GPU`` ``CPU`` Examples: >>> x = Tensor(np.array([-1, -0.5, 0.5, 1]), mindspore.float32) @@ -2405,7 +2405,7 @@ def trunc(input_x): TypeError: If `input_x` is not a Tensor. Supported Platforms: - ``Ascend`` ``CPU`` + ``CPU`` Examples: >>> input_x = Tensor(np.array([3.4742, 0.5466, -0.8008, -3.9079]),mindspore.float32) diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py index fd8e9d1baac..30692073d62 100644 --- a/mindspore/python/mindspore/ops/function/nn_func.py +++ b/mindspore/python/mindspore/ops/function/nn_func.py @@ -851,7 +851,7 @@ def softsign(x): TypeError: If dtype of `x` is neither float16 nor float32. Supported Platforms: - ``Ascend`` ``CPU`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> from mindspore.ops import functional as F @@ -1729,7 +1729,7 @@ def mish(x): TypeError: If dtype of `x` is neither float16 nor float32. Supported Platforms: - ``Ascend`` ``CPU`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> input_x = Tensor(np.array([[-1.0, 4.0, -8.0], [2.0, -5.0, 9.0]]), mindspore.float32) diff --git a/mindspore/python/mindspore/ops/operations/array_ops.py b/mindspore/python/mindspore/ops/operations/array_ops.py index b7245b86af8..e2a3baafec7 100755 --- a/mindspore/python/mindspore/ops/operations/array_ops.py +++ b/mindspore/python/mindspore/ops/operations/array_ops.py @@ -588,8 +588,8 @@ class Im2Col(Primitive): Default: 0. Inputs: - - **x** (Tensor) : input tensor, only 4-D input tensors (batched image-like tensors) are supported. - support all real number data type. + - **x** (Tensor) - input tensor, only 4-D input tensors (batched image-like tensors) are supported. + support all real number data type. Outputs: Tensor, a 4-D Tensor with same type of input `x`. @@ -3348,7 +3348,7 @@ class StridedSlice(PrimitiveWithInfer): `x[2:,...]` is equivalent to `x[2:5,:,:,:]`. If the ith bit of `new_axis_mask` is set, `begin`, `end` and `strides` are ignored and a new length 1 - dimension is added at the specified position in tthe output tensor. + dimension is added at the specified position in the output tensor. As for a 5*6*7 tensor, `x[:2, newaxis, :6]` will produce a tensor with shape :math:`(2, 1, 7)` . @@ -6460,7 +6460,7 @@ class SearchSorted(PrimitiveWithInfer): Inputs: - **sequence** (Tensor) - The shape of tensor is :math:`(x_1, x_2, ..., x_R-1, x_R)` or `(x_1)`. - It must contain monitonically increasing sequence on the innermost dimension. + It must contain monitonically increasing sequence on the innermost dimension. - **values** (Tensor) - The shape of tensor is : math:`(x_1, x_2, ..., x_R-1, x_S)`. Outputs: @@ -8120,6 +8120,9 @@ class PopulationCount(Primitive): Computes element-wise population count(a.k.a bitsum, bitcount). Refer to :func:`mindspore.ops.population_count` for more detail. + + Supported Platforms: + ``Ascend`` ``GPU`` ``CPU`` """ @prim_attr_register diff --git a/mindspore/python/mindspore/ops/operations/math_ops.py b/mindspore/python/mindspore/ops/operations/math_ops.py index 5d3bae5b49c..2681318cb9f 100644 --- a/mindspore/python/mindspore/ops/operations/math_ops.py +++ b/mindspore/python/mindspore/ops/operations/math_ops.py @@ -1864,6 +1864,9 @@ class InplaceAdd(PrimitiveWithInfer): Refer to :func:`mindspore.ops.inplace_add` for more detail. + Supported Platforms: + ``Ascend`` ``CPU`` + Examples: >>> import numpy as np >>> import mindspore @@ -1917,6 +1920,9 @@ class InplaceSub(PrimitiveWithInfer): Refer to :func:`mindspore.ops.inplace_sub` for more detail. + Supported Platforms: + ``Ascend`` ``CPU`` + Examples: >>> import numpy as np >>> import mindspore @@ -4928,7 +4934,7 @@ class BesselI0e(Primitive): TypeError: If dtype of `x` is not float16, float32 or float64. Supported Platforms: - ``Ascend`` ``CPU`` ``GPU`` + ``GPU`` ``CPU`` Examples: >>> bessel_i0e = ops.BesselI0e() @@ -4968,7 +4974,7 @@ class BesselI1e(Primitive): TypeError: If dtype of `x` is not float16, float32 or float64. Supported Platforms: - ``Ascend`` ``CPU`` ``GPU`` + ``CPU`` Examples: >>> bessel_i1e = ops.BesselI1e() @@ -5778,6 +5784,9 @@ class Trunc(Primitive): Returns a new tensor with the truncated integer values of the elements of input. Refer to :func:`mindspore.ops.trunc` for more detail. + + Supported Platforms: + ``CPU`` """ @prim_attr_register diff --git a/mindspore/python/mindspore/ops/operations/nn_ops.py b/mindspore/python/mindspore/ops/operations/nn_ops.py index e54eb52c01e..5dab39a1935 100644 --- a/mindspore/python/mindspore/ops/operations/nn_ops.py +++ b/mindspore/python/mindspore/ops/operations/nn_ops.py @@ -7252,6 +7252,9 @@ class CTCGreedyDecoder(Primitive): Performs greedy decoding on the logits given in inputs. Refer to :func:`mindspore.ops.ctc_greedy_decoder` for more detail. + + Supported Platforms: + ``Ascend`` ``CPU`` """ @prim_attr_register @@ -8584,7 +8587,7 @@ class Conv3DTranspose(Primitive): ValueError: If bias is not none. The rank of dout and weight is not 5. Supported Platforms: - ``Ascend`` ``GPU`` + ``Ascend`` ``GPU`` ``CPU`` Examples: >>> dout = Tensor(np.ones([32, 16, 10, 32, 32]), mindspore.float16)