From 38f418d8b788beec2d7d09d25d19481718f7a7c8 Mon Sep 17 00:00:00 2001 From: fandawei Date: Sun, 12 Feb 2023 09:54:45 +0800 Subject: [PATCH] fix float_power/interpolate/baddbmm/fmod/heaviside/index_select doc --- .../ops/mindspore.ops.func_baddbmm.rst | 4 +-- .../ops/mindspore.ops.func_float_power.rst | 5 +--- .../ops/mindspore.ops.func_index_select.rst | 2 +- .../ops/mindspore.ops.func_interpolate.rst | 21 +++++++------- .../mindspore/ops/function/array_func.py | 28 +++++++++++-------- .../mindspore/ops/function/math_func.py | 22 +++++++-------- .../python/mindspore/ops/function/nn_func.py | 28 +++++++++---------- 7 files changed, 56 insertions(+), 54 deletions(-) diff --git a/docs/api/api_python/ops/mindspore.ops.func_baddbmm.rst b/docs/api/api_python/ops/mindspore.ops.func_baddbmm.rst index f701d77711a..017be9d5597 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_baddbmm.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_baddbmm.rst @@ -11,8 +11,8 @@ mindspore.ops.baddbmm 参数： - **x** (Tensor) - 输入Tensor，shape为 :math:`(C, W, H)` 。 - - **batch1** (Tensor) - 输入Tensor，shape为 :math:`(C, W, T)` 。 - - **batch2** (Tensor) - 输入Tensor，shape为 :math:`(C, T, H)` 。 + - **batch1** (Tensor) - 公式中的 :math:`batch1` ，shape为 :math:`(C, W, T)` 。 + - **batch2** (Tensor) - 公式中的 :math:`batch2` ，shape为 :math:`(C, T, H)` 。 - **beta** (Union[float, int]，可选) - `x` 的系数，默认值为1。 - **alpha** (Union[float, int]，可选) - :math:`batch1 @ batch2` 的系数，默认值为1。 diff --git a/docs/api/api_python/ops/mindspore.ops.func_float_power.rst b/docs/api/api_python/ops/mindspore.ops.func_float_power.rst index 69e3d33bbf0..e56afae0af9 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_float_power.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_float_power.rst @@ -3,10 +3,7 @@ mindspore.ops.float_power .. py:function:: mindspore.ops.float_power(x, exponent) - 计算 `x` 的指数幂。对于实数类型，使用mindspore.float64计算。对于复数类型，使用输入数据相同类型计算。 - - .. note:: - 目前GPU平台不支持数据类型complex。 + 计算 `x` 的指数幂。对于实数类型，转换 `x` 和 `exponent` 到mindspore.float64计算。目前不支持复数运算。 参数： - **x** (Union[Tensor, Number]) - 第一个输入，为Tensor或数值型数据类型。 diff --git a/docs/api/api_python/ops/mindspore.ops.func_index_select.rst b/docs/api/api_python/ops/mindspore.ops.func_index_select.rst index bbeea72e58e..7daba21e097 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_index_select.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_index_select.rst @@ -13,7 +13,7 @@ mindspore.ops.index_select 参数： - **x** (Tensor) - 输入Tensor。 - **axis** (int) - `index` 的维度。 - - **index** (Tensor) - 包含索引的一维Tensor。数据类型为int32或int64。 + - **index** (Tensor) - 包含索引的一维Tensor。 返回： Tensor，数据类型与输入 `x` 相同。 diff --git a/docs/api/api_python/ops/mindspore.ops.func_interpolate.rst b/docs/api/api_python/ops/mindspore.ops.func_interpolate.rst index fcd810ca6ac..1f0eb5986e0 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_interpolate.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_interpolate.rst @@ -14,7 +14,8 @@ mindspore.ops.interpolate .. code-block:: - old_i = new_length != 1 ? new_i * (old_length - 1) / (new_length - 1) : 0 # 'align_corners' 为 True + old_i = new_length != 1 ? new_i * (old_length - 1) / (new_length - 1) : 0 # 'align_corners' 为 True + old_i = new_length > 1 ? (new_x + 0.5) * old_length / new_length - 0.5 : 0 # 'align_corners' 为 False 此选项只对'linear'、'bilinear'、'bicubic'和'trilinear'模式有效。默认值：False。 @@ -59,12 +60,12 @@ mindspore.ops.interpolate 异常： - **TypeError** - `x` 不是Tensor。 - - **TypeError** - `x` 的数据类型不支持。 - - **TypeError** - `scales` 不是float类型的tuple。 - - **ValueError** - `scales` 中的数不全是正数。 - - **TypeError** - `sizes` 不是int64类型的tuple。 - - **ValueError** - `sizes` 中的数不全是正数。 - - **TypeError** - `coordinate_transformation_mode` 不是string。 - - **ValueError** - `coordinate_transformation_mode` 不在支持的列表中。 - - **TypeError** - `mode` 不是string类型。 - - **ValueError** - `mode` 不在支持的列表中。 + - **ValueError** - `size` 和 `scale_factor` 都不为空。 + - **ValueError** - `size` 和 `scale_factor` 都为空。 + - **ValueError** - `size` 为元组或列表类型时长度不等于 `x.ndim - 2` 。 + - **ValueError** - `scale_factor` 为元组或列表类型时长度不等于 `x.ndim - 2` 。 + - **ValueError** - `mode` 不在模式支持列表中。 + - **ValueError** - `x.ndim` 不在模式对应维度的支持列表中。 + - **ValueError** - `size` 不为空， `recompute_scale_factor` 不为空。 + - **ValueError** - `scale_factor` 不在对应的支持列表中。 + - **ValueError** - `align_corners` 不在对应的支持列表中。 diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py index 6eac9feb9c4..1821dafde56 100644 --- a/mindspore/python/mindspore/ops/function/array_func.py +++ b/mindspore/python/mindspore/ops/function/array_func.py @@ -4437,18 +4437,18 @@ def _check_check_axis_in_range(axis, ndim): def index_select(x, axis, index): """ - Returns a new Tensor which indexes the `x` Tensor along dimension `axis` using the entries in `index` . - - The returned Tensor has the same number of dimensions as the original Tensor ( `x` ). The `axis` th dimension - has the same size as the length of `index` ; other dimensions have the same size as in the original Tensor. + Generates a new Tensor that accesses the values of `x` along the specified `axis` dimension + using the indices specified in `index`. The new Tensor has the same number of dimensions as `x`, + with the size of the `axis` dimension being equal to the length of `index`, and the size of all other + dimensions will be unchanged from the original `x` Tensor. .. note:: The value of index must be in the range of `[0, x.shape[axis])`, the result is undefined out of range. Args: - x (Tensor): Input Tensor. - axis (int): Dimension in which we index. - index (Tensor): The 1-D Tensor containing the indices to index. The data type can be int32 or int64. + x (Tensor): The input Tensor. + axis (int): The dimension to be indexed. + index (Tensor): A 1-D Tensor with the indices to access in `x` along the specified axis. Returns: Tensor, has the same dtype as input Tensor. @@ -4466,13 +4466,17 @@ def index_select(x, axis, index): >>> import mindspore >>> from mindspore import Tensor, ops >>> import numpy as np - >>> x = Tensor(np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]], dtype=np.float32)) - >>> index = Tensor([0, 2], mindspore.int32) + >>> x = Tensor(np.arange(16).astype(np.float32).reshape(2, 2, 4)) + >>> print(x) + [[[ 0. 1. 2. 3.] + [ 4. 5. 6. 7.]] + [[ 8. 9. 10. 11.] + [12. 13. 14. 15.]]] + >>> index = Tensor([0,], mindspore.int32) >>> y = ops.index_select(x, 1, index) >>> print(y) - [[1. 3.] - [4. 6.] - [7. 9.]] + [[[ 0. 1. 2. 3.]] + [[ 8. 9. 10. 11.]]] """ if not (isinstance(x, Tensor) and isinstance(index, Tensor)): raise TypeError(f"For 'index_select', inputs `x` and `index` must be all tensors.") diff --git a/mindspore/python/mindspore/ops/function/math_func.py b/mindspore/python/mindspore/ops/function/math_func.py index 14cfafeac42..b485f69a03e 100644 --- a/mindspore/python/mindspore/ops/function/math_func.py +++ b/mindspore/python/mindspore/ops/function/math_func.py @@ -954,7 +954,7 @@ def divide(x, other, *, rounding_mode=None): def float_power(x, exponent): """ Computes `x` to the power of the exponent. - For the real number type, cast x and expoent to mindspore.float64 to calculate. + For the real number type, cast `x` and `exponent` to mindspore.float64 to calculate. Currently, complex type calculation is not supported. Args: @@ -1069,10 +1069,10 @@ def fmod(x, other): ``Ascend`` ``GPU`` ``CPU`` Examples: - >>> x = Tensor(np.array([-3., -2, -1, 1, 2, 3]), mindspore.float32) - >>> output = ops.fmod(x, 1.5) + >>> x = Tensor(np.array([-4., -3.5, 0, 3.5, 4]), mindspore.float32) + >>> output = ops.fmod(x, 2.5) >>> print(output) - [ 0. -0.5 -1. 1. 0.5 0. ] + [-1.5 -1. 0. 1. 1.5] """ if not (isinstance(x, (Tensor, Tensor_)) or isinstance(other, (Tensor, Tensor_))): raise TypeError("At least one of the types of inputs must be tensor, " + \ @@ -4527,11 +4527,11 @@ def heaviside(x, values): ``GPU`` ``CPU`` Examples: - >>> x = Tensor(np.array([-1.5, 0., 2.])) - >>> values = Tensor(np.array([0.5])) + >>> x = Tensor(np.array([-5., 1., 0., 2., 0.])) + >>> values = Tensor(np.array([3.])) >>> y = ops.heaviside(x, values) >>> print(y) - [ 0. 0.5 1. ] + [0. 1. 3. 1. 3.] """ heaviside_ = Heaviside() @@ -8111,10 +8111,10 @@ def baddbmm(x, batch1, batch2, beta=1, alpha=1): \text{out}_{i} = \beta \text{input}_{i} + \alpha (\text{batch1}_{i} \mathbin{@} \text{batch2}_{i}) Args: - x (Tensor): The tensor to be added. When batch1 is a (CxWxT) tensor and batch2 is a (CxTxH) tensor, - x must be broadcastable with (CxWxH) tensor. - batch1 (Tensor): The first batch of matrices to be multiplied. Must be 3-D tensors, dtype is same as x. - batch2 (Tensor): The second batch of matrices to be multiplied. Must be 3-D tensors, dtype is same as x. + x (Tensor): The input Tensor. When batch1 is a (CxWxT) Tensor and batch2 is a (CxTxH) Tensor, + x must be broadcastable with (CxWxH) Tensor. + batch1 (Tensor): :math:`batch1` in the above formula. Must be 3-D Tensor, dtype is same as x. + batch2 (Tensor): :math:`batch2` in the above formula. Must be 3-D Tensor, dtype is same as x. beta (Union[float, int], optional): multiplier for input. The default is 1. alpha (Union[float, int], optional): multiplier for `batch1 @ batch2`. The default is 1. Arguments beta and alpha must be integers when inputs of type not FloatTensor, otherwise they should diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py index 13be6a22519..e47b88aa154 100644 --- a/mindspore/python/mindspore/ops/function/nn_func.py +++ b/mindspore/python/mindspore/ops/function/nn_func.py @@ -1953,10 +1953,11 @@ def interpolate(x, size=None, scale_factor=None, mode="nearest", align_corners=N .. code-block:: - old_i = new_length != 1 ? new_i * (old_length - 1) / (new_length - 1) : 0 # 'align_corners' = True - old_i = new_length > 1 ? (new_x + 0.5) * old_length / new_length - 0.5 : 0 # 'align_corners' = False + old_i = new_length != 1 ? new_i * (old_length - 1) / (new_length - 1) : 0 # 'align_corners' = True - This is only valid for 'linear', 'bilinear', 'bicubic', or 'trilinear' modes. Default: False. + old_i = new_length > 1 ? (new_x + 0.5) * old_length / new_length - 0.5 : 0 # 'align_corners' = False + + This is only valid for 'linear', 'bilinear', 'bicubic', or 'trilinear' modes. Default: False. recompute_scale_factor (bool, optional): Recalculate `scale_factor`. If True, the parameter `size` will be calculated using the value of the `scale_factor`, and finally scaled using the value of `size`. @@ -2000,17 +2001,16 @@ def interpolate(x, size=None, scale_factor=None, mode="nearest", align_corners=N Tensor, resized, whose dimensions and dtype are the same as `x`. Raises: - TypeError: If `x` is not Tensor. - TypeError: If `mode` is not one of 'nearest', 'linear', 'bilinear', 'bicubic', - 'trilinear', 'area', nor 'nearest-exact'. - TypeError: If `size` is not one of tuple, list, None. - TypeError: If `scale_factor` is neither int nor None. - TypeError: If `align_corners` is not a bool. - TypeError: If dtype of `x` is neither float16 nor float32. - ValueError: If `size` and `scale_factor` are both None or not None. - ValueError: If shape of `x` or `size` and `mode` are not match. - ValueError: If `scale_factor` is an int which is less than 0. - ValueError: If `size` is a list or tuple whose length is not match `mode`. + TypeError: `x` is not a Tensor. + ValueError: Both `size` and `scale_factor` are not empty. + ValueError: Both `size` and `scale_factor` are empty. + ValueError: When `size` is a tuple or list, its length is not equal to `x.ndim - 2`. + ValueError: When `scale_factor` is a tuple or list, its length is not equal to `x.ndim - 2`. + ValueError: `mode` is not in the list of supported modes. + ValueError: `x.ndim` is not in the list of supported dimensions for the corresponding mode. + ValueError: `size` is not empty, `recompute_scale_factor` is not empty. + ValueError: `scale_factor` is not in the corresponding list of supported values. + ValueError: `align_corners` is not in the corresponding list of supported values. Examples: >>> import mindspore