From 22507e7eb6ae71052dc857ee0995880fdb171fc5 Mon Sep 17 00:00:00 2001 From: shaojunsong Date: Fri, 23 Dec 2022 16:13:06 +0800 Subject: [PATCH] Remove needless tensor api --- .../Tensor/mindspore.Tensor.as_tensor.rst | 13 - .../Tensor/mindspore.Tensor.empty_strided.rst | 14 - .../Tensor/mindspore.Tensor.frombuffer.rst | 15 - .../Tensor/mindspore.Tensor.multinomial.rst | 19 - .../Tensor/mindspore.Tensor.poisson.rst | 23 -- .../Tensor/mindspore.Tensor.rand_like.rst | 15 - .../Tensor/mindspore.Tensor.randint_like.rst | 17 - .../Tensor/mindspore.Tensor.randn_like.rst | 15 - .../Tensor/mindspore.Tensor.randperm.rst | 19 - mindspore/python/mindspore/common/tensor.py | 344 ------------------ .../ascend/test_tensor_func_ascend.py | 159 -------- .../tensor_func/cpu/test_tensor_func_cpu.py | 125 ------- .../tensor_func/gpu/test_tensor_func_gpu.py | 149 -------- 13 files changed, 927 deletions(-) delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.as_tensor.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.empty_strided.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.frombuffer.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.multinomial.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.poisson.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.rand_like.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randint_like.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randn_like.rst delete mode 100644 docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randperm.rst delete mode 100644 tests/st/tensor_func/ascend/test_tensor_func_ascend.py delete mode 100644 tests/st/tensor_func/cpu/test_tensor_func_cpu.py delete mode 100644 tests/st/tensor_func/gpu/test_tensor_func_gpu.py diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.as_tensor.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.as_tensor.rst deleted file mode 100644 index 565ec642b41..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.as_tensor.rst +++ /dev/null @@ -1,13 +0,0 @@ -mindspore.Tensor.as_tensor -========================== - -.. py:method:: mindspore.Tensor.as_tensor(data, dtype=None) - - 将数据转换为mindspore中的张量。 - - 参数： - - **data** (array_like) - 张量的初始数据。可以是列表、元组、NumPy.ndarray、标量和其他类型。 - - **dtype** (mindspore.dtype, 可选) - 返回张量的所需数据类型。默认值：如果为"None"，则从数据推断数据类型。 - - 返回： - Tensor，数据类型在mindspore的数据类型中。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.empty_strided.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.empty_strided.rst deleted file mode 100644 index 38ef29aad22..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.empty_strided.rst +++ /dev/null @@ -1,14 +0,0 @@ -mindspore.Tensor.empty_strided -=============================== - -.. py:method:: mindspore.Tensor.empty_strided(size, stride, dtype=mstype.float64, seed=None) - - 使用指定的“大小”和“步幅”创建张量，并用未定义的数据填充。 - - 参数： - - **size** (tuple[ints]) - 输出张量的形状。 - - **stride** (tuple[ints]) - 输出张量的步幅。 - - **dtype** (mindspore.dtype, 可选) - 返回张量的所需数据类型。 - - 返回： - 具有指定大小和步幅并填充了未定义数据的张量。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.frombuffer.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.frombuffer.rst deleted file mode 100644 index b027099ef39..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.frombuffer.rst +++ /dev/null @@ -1,15 +0,0 @@ -mindspore.Tensor.frombuffer -============================ - -.. py:method:: mindspore.Tensor.frombuffer(buffer, dtype=mstype.float64, count=-1, offset=0) - - 从实现Python缓冲区协议的对象创建一维 `Tensor`。跳过缓冲区中的 `offset` 字节，并且取出数据类型为 `dtype` 的 `count` 个数据。 - - 参数： - - **buffer** (object) - 公开缓冲区接口的Python对象。 - - **dtype** (mindspore.dtype) - 返回张量的所需数据类型。 - - **count** (int, 可选) - 要读取的所需元素的数量。如果为负值，将读取所有元素（直到缓冲区结束）。默认值：-1。 - - **offset** (int, 可选) - 缓冲区开始时要跳过的字节数。默认值：0。 - - 返回： - 来自实现Python缓冲协议的对象的一维张量。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.multinomial.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.multinomial.rst deleted file mode 100644 index a5d6b539da0..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.multinomial.rst +++ /dev/null @@ -1,19 +0,0 @@ -mindspore.Tensor.multinomial -============================= - -.. py:method:: mindspore.Tensor.multinomial(num_samples, seed=0, seed2=0) - - 返回从相应的张量输入行。输入行不需要求和为1(在这种情况下，我们使用这些值作为权重)，但必须是非负的、有限的并且具有非零和。self必须是输入张量包含概率总和的，必须是1或2维。 - - 参数： - - **num_samples** (int32) - 要绘制的样本数。 - - **seed** (int) - 随机种子，必须为非负数。默认值：0。 - - **seed2** (int) - 随机seed2，必须为非负数。默认值：0。 - - 返回： - 与self具有相同行的张量，每行具有num_samples采样索引。 - - 异常： - - **TypeError** - 如果 `seed` 和 `seed2` 都不是int。 - - **TypeError** - 如果 `self` 不是数据类型为float32的Tensor。 - - **TypeError** - 如果 `num_samples` 的数据类型不是int32。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.poisson.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.poisson.rst deleted file mode 100644 index d090235a60d..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.poisson.rst +++ /dev/null @@ -1,23 +0,0 @@ -mindspore.Tensor.poisson -========================== - -.. py:method:: mindspore.Tensor.poisson(shape, mean, seed=0, seed2=0) - - 返回与input大小相同的张量，其中每个元素都是从泊松采样的input中相应元素给出的速率参数分布。张量self的数值作为泊松分布的μ参数。 - - .. math:: - - \text{out}_i \sim \text{Poisson}(\text{input}_i)out*i*∼Poisson(input*i*) - - 参数： - - **shape** (tuple) - 要生成的随机张量的形状。只允许使用常量值。 - - **seed** (int, option) - 设置随机种子（0到2**32）。 - - **seed2** (int, option) - 将随机seed2设置为（0到2**32）。 - - 返回： - Tensor，形状与input_Tensor相同。 - - 异常： - - **TypeError** - 如果 `seed` 和 `seed2` 都不是int。 - - **TypeError** - 如果 `shape` 不是元组。 - - **TypeError** - 如果 `mean` 不是数据类型不是float32的Tensor。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.rand_like.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.rand_like.rst deleted file mode 100644 index 0b8dc353f35..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.rand_like.rst +++ /dev/null @@ -1,15 +0,0 @@ -mindspore.Tensor.rand_like -========================== - -.. py:method:: mindspore.Tensor.rand_like(seed=None) - - 返回与填充的输入大小相同的张量，数值为区间[0,1)上均匀分布的随机数。 - - 参数： - - **seed** (int, option) - 设置随机种子(0到2**32)。 - - 返回： - Tensor，形状与self相同。 - - 异常： - - **TypeError** - 如果input_sensor的数据类型不是int或float diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randint_like.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randint_like.rst deleted file mode 100644 index d2ae2a06fb0..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randint_like.rst +++ /dev/null @@ -1,17 +0,0 @@ -mindspore.Tensor.randint_like -============================== - -.. py:method:: mindspore.Tensor.randint_like(high, low=0, seed=None) - - 返回与输入张量大小相同的张量，数值为区间[low，high]上的随机数，如果只输入一个int类型的数据，默认值为high，如果输入两个整数，则分别为low和high。 - - 参数： - - **low** (int, 可选) - 要从分布中提取的最小整数。默认值：0。 - - **high** (int) - 高于要从分布中提取的最高整数的一个。 - - **seed** (int, 可选) - 设置随机种子(0到2**32)。 - - 返回： - Tensor，形状与self相同。 - - 异常： - - **TypeError** - 如果input_sensor的数据类型不是int或float。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randn_like.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randn_like.rst deleted file mode 100644 index 72bf3c59f86..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randn_like.rst +++ /dev/null @@ -1,15 +0,0 @@ -mindspore.Tensor.randn_like -============================ - -.. py:method:: mindspore.Tensor.randn_like(seed=None) - - 返回一个与输入大小相同的张量，该张量由均值为0、方差为1的正态分布中的随机数填充。 - - 参数： - - **seed** (int, 可选) - 设置随机种子(0到2**32)。 - - 返回： - Tensor，形状与self相同。 - - 异常： - - **TypeError** - 如果self的数据类型不是int或float。 diff --git a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randperm.rst b/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randperm.rst deleted file mode 100644 index 23688282b06..00000000000 --- a/docs/api/api_python/mindspore/Tensor/mindspore.Tensor.randperm.rst +++ /dev/null @@ -1,19 +0,0 @@ -mindspore.Tensor.randperm -========================== - -.. py:method:: mindspore.Tensor.randperm(max_length=1, pad=-1) - - 生成从0到n-1的n个随机样本，不重复。如果 `max_length` >n，最后的 `maxlength-n` 元素将填充 `pad`。 - - 参数： - - **max_length** (int) - 预期获取的项数，该数字必须大于0。默认值：1。 - - **pad** (int) - 要填充的pad值。默认值：-1。 - - **dtype** (mindspore.dtype) - 输出的类型。默认值：mindspore.int32。 - - 返回： - Tensor，shape为：(`max_length`,)，类型为：`dtype`。 - - 异常： - - **TypeError** - 如果 `max_length` 或 `pad` 不是int。 - - **TypeError** - 如果 `self` 有非int元素。 - - **TypeError** - 如果 `self` 有负数元素。 diff --git a/mindspore/python/mindspore/common/tensor.py b/mindspore/python/mindspore/common/tensor.py index a43b7630b97..a17304ebdd2 100644 --- a/mindspore/python/mindspore/common/tensor.py +++ b/mindspore/python/mindspore/common/tensor.py @@ -513,149 +513,6 @@ class Tensor(Tensor_): return Tensor(Tensor_.from_numpy(array)) - @staticmethod - def frombuffer(buffer, dtype=mstype.float64, count=-1, offset=0): - r""" - Creates a 1-dimensional :class:`Tensor` from an object that implements - the Python buffer protocol. - Skips the first :attr:`offset` bytes in the buffer, and interprets the rest of - the raw bytes as a 1-dimensional tensor of type :attr:`dtype` with :attr:`count` - elements. - - Args: - buffer (object): a Python object that exposes the buffer interface. - dtype (mindspore.dtype): the desired data type of returned tensor. - count (int, optional): the number of desired elements to be read. If negative, - all the elements (until the end of the buffer) will be read. Default: -1. - offset (int, optional): the number of bytes to skip at the start of the buffer. Default: 0. - - Returns: - a 1-dimensional Tensor from an object that implements the Python buffer protocol. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> from array import array - >>> import numpy as np - >>> import mindspore - >>> from mindspore import Tensor - >>> input_array = array("d", [1, 2, 3, 4]) - >>> input_array - array('d', [1.0, 2.0, 3.0, 4.0]) - >>> output = Tensor.frombuffer(input_array, mindspore.int32) - >>> print(output) - [1 2 3 4] - """ - res = np.frombuffer(buffer=buffer, dtype=np.float64, count=count, offset=offset) - result = Tensor(res, dtype=dtype) - return result - - @staticmethod - def empty_strided(size, stride, dtype=mstype.float64, seed=None): - r""" - Creates a tensor with the specified :attr:`size` and :attr:`stride` and filled with undefined data. - - Args: - size (tuple of python:ints): the shape of the output tensor. - stride (tuple of python:ints): the strides of the output tensor. - dtype (mindspore.dtype, optional): the desired data type of returned tensor. - - Returns: - a tensor with the specified size and stride and filled with undefined data. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> from mindspore import Tensor - >>> size = (3, 3) - >>> stride = (1, 3) - >>> output = Tensor.empty_strided(size, stride, seed = 0) - >>> print(output) - [[0.00000000e+00 7.15189366e+10 0.00000000e+00] - [0.00000000e+00 0.00000000e+00 6.45894113e+10] - [0.00000000e+00 8.91773001e+10 9.63662761e+10]] - """ - np.random.seed(seed) - tensor_ = Tensor(np.random.uniform(low=0, high=10e10, size=size)) - tensor_array = tensor_.asnumpy() - stride_tensor = tensor_.as_strided(shape=size, strides=stride) - stride_array = stride_tensor.asnumpy() - stride_array.resize(len(stride_array) * len(stride_array[0])) - for i in range(size[0]): - for j in range(size[1]): - if not sum(stride_array - tensor_array[i][j]) < 0.01: - tensor_array[i][j] = 0.0 - return Tensor(tensor_array, dtype=dtype) - - @staticmethod - def poisson(shape, mean, seed=0, seed2=0): - r""" - Returns a tensor of the same size as `input` with each element sampled from a Poisson - distribution with rate parameter given by the corresponding element in `input` i.e., - \text{out}_i \sim \text{Poisson}(\text{input}_i)out*i*∼Poisson(input*i*), - and self as a tensor is the μ parameter .the distribution was constructed with. - The parameter defines mean number of occurrences of the event. - It must be greater than 0. With float32 data type. - - Args: - seed (int, option): set the random seed (0 to 2**32) - seed2 (int, option): set the random seed2 (0 to 2**32) - - Inputs: - - **shape** (tuple) - The shape of random tensor to be generated. Only constant value is allowed. - - Returns: - out (Union[Tensor, int]), with the same shape as input_tensor. - - Raises: - TypeError: If neither `seed` nor `seed2` is an int. - TypeError: If `shape` is not a tuple. - TypeError: If `mean` is not a Tensor whose dtype is not float32. - - Supported Platforms: - ``Ascend`` - - Examples: - >>> shape = (4, 1) - >>> mean = Tensor(np.array([5.0, 10.0]), mstype.float32) - >>> output = Tensor.Poisson(shape, mean, seed=5) - >>> result = output.shape - >>> print(result) - (4, 2) - """ - return tensor_operator_registry.get('poisson')(seed, seed2)(shape, mean) - - @staticmethod - def as_tensor(data, dtype=None): - r""" - convert data to tensor in mindspore. - - Args: - data (array_like): Initial data for the tensor. Can be a list, tuple, - NumPy ndarray, scalar, and other types. - dtype (mindspore.dtype, optional): the desired data type of returned tensor. - Default: if None, infers data type from data. - - Returns: - Tensor contains the data and the dtype is in mindspore. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> import numpy as np - >>> import mindspore as ms - >>> import mindspore.nn as nn - >>> from mindspore import Tensor - >>> input_data = np.array([1, 2, 3]) - >>> ms_tensor = Tensor.as_tensor(input_data) - >>> ms_tensor - Tensor(shape=[3], dtype=Int64, value= [1, 2, 3]) - """ - return Tensor(data, dtype=dtype) - @staticmethod def _use_logical_kernel(me, other) -> bool: """ @@ -3222,170 +3079,6 @@ class Tensor(Tensor_): validator.check_is_int(seed, 'seed') return tensor_operator_registry.get('bernoulli')(self, p, seed) - def multinomial(self, num_samples, seed=0, seed2=0): - r""" - Returns a tensor sampled from the multinomial probability distribution located in the corresponding - row of tensor input. - - Note: - The rows of input do not need to sum to one (in which case we use the values as weights), - but must be non-negative, finite and have a non-zero sum. self must be the input tensor - containing the cumsum of probabilities, must be 1 or 2 dimensions. - - Args: - seed (int): Random seed, must be non-negative. Default: 0. - seed2 (int): Random seed2, must be non-negative. Default: 0. - - Inputs: - - **num_samples** (int32) - number of samples to draw. - - Outputs: - Tensor with the same rows as `self`, each row has num_samples sampled indices. - - Raises: - TypeError: If neither `seed` nor `seed2` is an int. - TypeError: If `self` is not a Tensor whose dtype is float32. - TypeError: If dtype of `num_samples` is not int32. - - Supported Platforms: - ``GPU`` - - Examples: - >>> from mindspore import Tensor - >>> import mindspore - >>> x = Tensor([0., 9., 4., 0.], mindspore.float32) - >>> output = x.multinomial(num_samples=2,seed=10) - >>> print(output) - [2 1] - """ - self._init_check() - validator.check_non_negative_int(seed, 'seed') - validator.check_non_negative_int(seed2, 'seed') - return tensor_operator_registry.get('multinomial')(seed, seed2)(self, num_samples) - - def rand_like(self, seed=None): - r""" - Returns a tensor with the same size as input that is filled with - random numbers from a uniform distribution on the interval [0, 1) - - Args: - input_tensor (Union[Tensor, int, float]): the input tensor. - seed (int, option): set the random seed (0 to 2**32). - - Returns: - out (Union[Tensor, float]), with the same shape as input_tensor. - - Raises: - TypeError: If dtype of the input_tensor is not int or float. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> import mindspore - >>> import numpy as np - >>> from mindspore import Tensor - >>> input_x = Tensor(np.array([[1, 2, 3, 9], [1, 2, 3, 9]]), mindspore.int8) - >>> output = input_x.rand_like(seed = 0) - >>> print(output) - [[0.5488135 0.71518937 0.60276338 0.54488318] - [0.4236548 0.64589411 0.43758721 0.891773 ]] - >>> input_p = Tensor(np.array([1.0, 2.0, 3.0]), mindspore.float32) - >>> output = input_p.rand_like(seed = 0) - >>> print(output) - [0.5488135 0.71518937 0.60276338] - """ - input_tensor = self - input_tensor = np.array(input_tensor) - shape_ = input_tensor.shape - input_tensor = input_tensor.reshape(-1) - x = len(input_tensor) - np.random.seed(seed) - return Tensor(np.array([np.random.rand(1) for i in range(x)]).reshape(shape_)) - - def randint_like(self, high, low=0, seed=None): - r""" - Returns a tensor with the same size as the input tensor, - and the numerical value is a random number on the interval [low, high], - if only one int type data is entered, the default value is high, - if two integers are entered, they are low and high respectively. - - Args: - input_tensor (Union[Tensor, int, float]): the size of input will determine size of the output tensor. - low (int, optional) – Lowest integer to be drawn from the distribution. Default: 0. - high (int) – One above the highest integer to be drawn from the distribution. - seed (int, optional): set the random seed (0 to 2**32). - - Returns: - out (Union[Tensor, int]), with the same shape as input_tensor. - - Raises: - TypeError: If dtype of the input_tensor is not int or float. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> import mindspore - >>> import numpy as np - >>> from mindspore import Tensor - >>> input_x = Tensor(np.array([1., 2., 3., 4., 5.]), mindspore.float32) - >>> output = input_x.randint_like(20, seed = 0) - >>> print(output) - [12 15 0 3 3] - >>> output = input_x.randint_like(20, 100, seed = 0) - >>> print(output) - [64 67 84 87 87] - """ - input_tensor = self - input_tensor = np.array(input_tensor) - shape_ = input_tensor.shape - input_tensor = input_tensor.reshape(-1) - if low > high: - high, low = low, high - x = len(input_tensor) - np.random.seed(seed) - return Tensor(np.array([np.random.randint(low, high) for i in range(x)]).reshape(shape_)) - - def randn_like(self, seed=None): - r""" - Returns a tensor with the same size as input that is filled with random - numbers from a normal distribution with mean 0 and variance 1. - - Args: - input_tensor (Union[Tensor, int, float]): the size of input will determine size of the output tensor. - seed (int, optional): set the random seed (0 to 2**32). - - Returns: - out (Union[Tensor, int]), with the same shape as input_tensor. - - Raises: - TypeError: If dtype of the input_tensor is not int or float. - - Supported Platforms: - ``Ascend`` ``GPU`` ``CPU`` - - Examples: - >>> import mindspore - >>> import numpy as np - >>> from mindspore import Tensor - >>> input_x = Tensor(np.array([1., 2., 3., 4., 5.]), mindspore.float32) - >>> output = input_x.randn_like(seed = 0) - >>> print(output) - [1.7640524 0.4001572 0.978738 2.2408931 1.867558 ] - >>> input_p = Tensor(np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]), mindspore.int32) - >>> output = input_p.randn_like(seed = 0) - >>> print(output) - [[ 1.7640524 0.4001572 0.978738 2.2408931 1.867558 ] - [-0.9772779 0.95008844 -0.1513572 -0.10321885 0.41059852]] - """ - input_tensor = np.array(self) - shape_ = input_tensor.shape - input_tensor = input_tensor.reshape(-1) - x = len(input_tensor) - np.random.seed(seed) - return Tensor([np.random.randn() for i in range(x)]).reshape(shape_) - def as_strided(self, shape=None, strides=None, subok=False, writeable=True): r""" as_strided(input, size, stride, storage_offset=0) -> Tensor @@ -3419,43 +3112,6 @@ class Tensor(Tensor_): strides = tuple(np.array(strides) * n) return Tensor(np.lib.stride_tricks.as_strided(x, shape, strides, subok, writeable), dtype=dtype_) - def randperm(self, max_length=1, pad=-1): - r""" - Generates n random samples from 0 to n-1 without repeating. If `max_length` > n, - the last `max_length-n` elements will be filled with `pad`. - - Args: - max_length (int): Number of items expected to get and the number must be greater than 0. Default: 1. - pad (int): The pad value to be filled. Default: -1. - dtype (mindspore.dtype): The type of output. Default: mindspore.int32. - - Inputs: - - **n** (Tensor[int32]) - The input tensor with shape: (1,) and the number must be in [0, `max_length`]. - - Outputs: - - **output** (Tensor) - The output Tensor with shape: (`max_length`,) and type: `dtype`. - - Raises: - TypeError: If neither `max_length` nor `pad` is an int. - TypeError: If `self` has non-Int elements. - TypeError: If `self` has negative elements. - - Supported Platforms: - ``Ascend`` ``GPU`` - - Examples: - >>> # The result of every execution is different because this operator will generate n random samples. - >>> from mindspore import Tensor - >>> import mindspore - >>> n = Tensor([20], dtype=mindspore.int32) - >>> output = n.randperm(max_length=30, pad=-1) - >>> print(output) - [15 6 11 19 14 16 9 5 13 18 4 10 8 0 17 2 1 12 3 7 - -1 -1 -1 -1 -1 -1 -1 -1 -1 -1] - """ - self._init_check() - return tensor_operator_registry.get('randperm')(max_length, pad)(self) - def random_categorical(self, num_sample, seed=0, dtype=mstype.int64): r""" For details, please refer to :func:`mindspore.ops.random_categorical`. diff --git a/tests/st/tensor_func/ascend/test_tensor_func_ascend.py b/tests/st/tensor_func/ascend/test_tensor_func_ascend.py deleted file mode 100644 index 0c4a50a9fac..00000000000 --- a/tests/st/tensor_func/ascend/test_tensor_func_ascend.py +++ /dev/null @@ -1,159 +0,0 @@ -from array import array -import numpy as np -import pytest -from mindspore import context, Tensor -import mindspore.common.dtype as mstype - - -context.set_context(mode=context.GRAPH_MODE, device_target="Ascend") - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_poisson(): - """ - Feature: poisson - Description: test the function of poisson. - Expectation: success - """ - shape = (4, 1) - mean = Tensor(np.array([5.0, 10.0]), mstype.float32) - output = Tensor.poisson(shape, mean, seed=5) - result = output.shape - assert result == (4, 2) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_rand_like(): - """ - Feature: rand_like - Description: test the function of rand_like. - Expectation: success - """ - input_x = Tensor(np.array([[1, 2, 3, 9], [1, 2, 3, 9]]), mstype.int32) - output = input_x.rand_like(seed=0) - expect_res = np.array([[5.48813504e-01, 7.15189366e-01, 6.02763376e-01, 5.44883183e-01], - [4.23654799e-01, 6.45894113e-01, 4.37587211e-01, 8.91773001e-01]]).astype(np.float64) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_randint_like(): - """ - Feature: randint_like - Description: test the function of randint_like. - Expectation: success - """ - input_x = Tensor(np.array([1., 2., 3., 4., 5.]), mstype.float32) - output = input_x.randint_like(20, 100, seed=0) - expect_res = np.array([64, 67, 84, 87, 87]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_randn_like(): - """ - Feature: randn_like - Description: test the function of randn_like. - Expectation: success - """ - input_p = Tensor(np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]), mstype.int32) - output = input_p.randn_like(seed=0) - expect_res = np.array([[1.76405239e+00, 4.00157213e-01, 9.78738010e-01, 2.24089313e+00, 1.86755800e+00], - [-9.77277875e-01, 9.50088441e-01, -1.51357204e-01, -1.03218853e-01, 4.10598516e-01]]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_as_tensor(): - """ - Feature: as_tensor - Description: test the function of as_tensor. - Expectation: success - """ - input_data = np.array([1, 2, 3]) - ms_tensor = Tensor.as_tensor(input_data) - expect_res = np.array([1, 2, 3]) - assert np.allclose(ms_tensor.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_as_strided(): - """ - Feature: rand_like - Description: test the function of as_stride. - Expectation: success - """ - input_array = np.arange(9, dtype=np.int32).reshape(3, 3) - output = Tensor(input_array).as_strided((2, 2), (1, 1)) - expect_res = np.array([[0, 1], [1, 2]]).astype(np.int32) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_frombuffer(): - """ - Feature: rand_like - Description: test the function of frombuffer. - Expectation: success - """ - input_array = array("d", [1, 2, 3, 4]) - output = Tensor.frombuffer(input_array, mstype.int32) - expect_res = np.array([1, 2, 3, 4]).astype(np.int32) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_empty_strided(): - """ - Feature: rand_like - Description: test the function of empty_strided. - Expectation: success - """ - size = (3, 3) - stride = (1, 3) - output = Tensor.empty_strided(size, stride, seed=0) - expect_res = np.array([[0.00000000e+00, 7.15189366e+10, 0.00000000e+00], - [0.00000000e+00, 0.00000000e+00, 6.45894113e+10], - [0.00000000e+00, 8.91773001e+10, 9.63662761e+10]]).astype(np.float64) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_arm_ascend_training -@pytest.mark.platform_x86_ascend_training -@pytest.mark.env_onecard -def test_randpermn(): - """ - Feature: rand_like - Description: test the function of randpermn. - Expectation: success - """ - n = Tensor([20], dtype=mstype.int32) - output = n.randperm(max_length=30, pad=-1) - set_a = set(np.linspace(0, 19, 20).astype(int)) - set_b = set(output.asnumpy()) - assert set_a.issubset(set_b) diff --git a/tests/st/tensor_func/cpu/test_tensor_func_cpu.py b/tests/st/tensor_func/cpu/test_tensor_func_cpu.py deleted file mode 100644 index 184f455daf7..00000000000 --- a/tests/st/tensor_func/cpu/test_tensor_func_cpu.py +++ /dev/null @@ -1,125 +0,0 @@ -from array import array -import numpy as np -import pytest -import mindspore.common.dtype as mstype -from mindspore import context, Tensor - - -context.set_context(mode=context.GRAPH_MODE, device_target="CPU") - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_rand_like(): - """ - Feature: rand_like - Description: test the function of rand_like. - Expectation: success - """ - input_x = Tensor(np.array([[1, 2, 3, 9], [1, 2, 3, 9]]), mstype.int32) - output = input_x.rand_like(seed=0) - expect_res = np.array([[5.48813504e-01, 7.15189366e-01, 6.02763376e-01, 5.44883183e-01], - [4.23654799e-01, 6.45894113e-01, 4.37587211e-01, 8.91773001e-01]]).astype(np.float64) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_randint_like(): - """ - Feature: randint_like - Description: test the function of randint_like. - Expectation: success - """ - input_x = Tensor(np.array([1., 2., 3., 4., 5.]), mstype.float32) - output = input_x.randint_like(20, 100, seed=0) - expect_res = np.array([64, 67, 84, 87, 87]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_randn_like(): - """ - Feature: randn_like - Description: test the function of randn_like. - Expectation: success - """ - input_p = Tensor(np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]), mstype.int32) - output = input_p.randn_like(seed=0) - expect_res = np.array([[1.76405239e+00, 4.00157213e-01, 9.78738010e-01, 2.24089313e+00, 1.86755800e+00], - [-9.77277875e-01, 9.50088441e-01, -1.51357204e-01, -1.03218853e-01, 4.10598516e-01]]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_as_tensor(): - """ - Feature: as_tensor - Description: test the function of as_tensor. - Expectation: success - """ - input_data = np.array([1, 2, 3]) - ms_tensor = Tensor.as_tensor(input_data) - expect_res = np.array([1, 2, 3]) - assert np.allclose(ms_tensor.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_as_strided(): - """ - Feature: rand_like - Description: test the function of as_stride. - Expectation: success - """ - input_array = np.arange(9, dtype=np.int32).reshape(3, 3) - output = Tensor(input_array).as_strided((2, 2), (1, 1)) - expect_res = np.array([[0, 1], [1, 2]]).astype(np.int32) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_frombuffer(): - """ - Feature: rand_like - Description: test the function of frombuffer. - Expectation: success - """ - input_array = array("d", [1, 2, 3, 4]) - output = Tensor.frombuffer(input_array, mstype.int32) - expect_res = np.array([1, 2, 3, 4]).astype(np.int32) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_cpu -@pytest.mark.platform_arm_cpu -@pytest.mark.env_onecard -def test_empty_strided(): - """ - Feature: rand_like - Description: test the function of empty_strided. - Expectation: success - """ - size = (3, 3) - stride = (1, 3) - output = Tensor.empty_strided(size, stride, seed=0) - expect_res = np.array([[0.00000000e+00, 7.15189366e+10, 0.00000000e+00], - [0.00000000e+00, 0.00000000e+00, 6.45894113e+10], - [0.00000000e+00, 8.91773001e+10, 9.63662761e+10]]).astype(np.float64) - assert np.allclose(output.asnumpy(), expect_res) diff --git a/tests/st/tensor_func/gpu/test_tensor_func_gpu.py b/tests/st/tensor_func/gpu/test_tensor_func_gpu.py deleted file mode 100644 index fe36f76eb88..00000000000 --- a/tests/st/tensor_func/gpu/test_tensor_func_gpu.py +++ /dev/null @@ -1,149 +0,0 @@ -from array import array -import numpy as np -import pytest -import mindspore.common.dtype as mstype -from mindspore import context, Tensor - - -context.set_context(mode=context.GRAPH_MODE, device_target="GPU") - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_multinomial(): - """ - Feature: multinomial - Description: test the function of multinomial. - Expectation: success - """ - input_tensor = Tensor([0., 9., 4., 0.], mstype.float32) - output = input_tensor.multinomial(num_samples=2, seed=10) - expect_res = np.array([2, 1]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_rand_like(): - """ - Feature: rand_like - Description: test the function of rand_like. - Expectation: success - """ - input_x = Tensor(np.array([[1, 2, 3, 9], [1, 2, 3, 9]]), mstype.int8) - output = input_x.rand_like(seed=0) - expect_res = np.array([[5.48813504e-01, 7.15189366e-01, 6.02763376e-01, 5.44883183e-01], - [4.23654799e-01, 6.45894113e-01, 4.37587211e-01, 8.91773001e-01]]).astype(np.float64) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_randint_like(): - """ - Feature: randint_like - Description: test the function of randint_like. - Expectation: success - """ - input_x = Tensor(np.array([1., 2., 3., 4., 5.]), mstype.float32) - output = input_x.randint_like(20, 100, seed=0) - expect_res = np.array([64, 67, 84, 87, 87]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_randn_like(): - """ - Feature: randn_like - Description: test the function of randn_like. - Expectation: success - """ - input_p = Tensor(np.array([[1, 2, 3, 4, 5], [6, 7, 8, 9, 10]]), mstype.int32) - output = input_p.randn_like(seed=0) - expect_res = np.array([[1.76405239e+00, 4.00157213e-01, 9.78738010e-01, 2.24089313e+00, 1.86755800e+00], - [-9.77277875e-01, 9.50088441e-01, -1.51357204e-01, -1.03218853e-01, 4.10598516e-01]]) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_as_tensor(): - """ - Feature: as_tensor - Description: test the function of as_tensor. - Expectation: success - """ - input_data = np.array([1, 2, 3]) - ms_tensor = Tensor.as_tensor(input_data) - expect_res = np.array([1, 2, 3]) - assert np.allclose(ms_tensor.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_as_strided(): - """ - Feature: rand_like - Description: test the function of as_stride. - Expectation: success - """ - input_array = np.arange(9, dtype=np.int32).reshape(3, 3) - output = Tensor(input_array).as_strided((2, 2), (1, 1)) - expect_res = np.array([[0, 1], [1, 2]]).astype(np.int32) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_frombuffer(): - """ - Feature: rand_like - Description: test the function of frombuffer. - Expectation: success - """ - input_array = array("d", [1, 2, 3, 4]) - output = Tensor.frombuffer(input_array, mstype.int32) - expect_res = np.array([1, 2, 3, 4]).astype(np.int32) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_empty_strided(): - """ - Feature: rand_like - Description: test the function of empty_strided. - Expectation: success - """ - size = (3, 3) - stride = (1, 3) - output = Tensor.empty_strided(size, stride, seed=0) - expect_res = np.array([[0.00000000e+00, 7.15189366e+10, 0.00000000e+00], - [0.00000000e+00, 0.00000000e+00, 6.45894113e+10], - [0.00000000e+00, 8.91773001e+10, 9.63662761e+10]]).astype(np.float64) - assert np.allclose(output.asnumpy(), expect_res) - - -@pytest.mark.level0 -@pytest.mark.platform_x86_gpu_training -@pytest.mark.env_onecard -def test_randpermn(): - """ - Feature: rand_like - Description: test the function of randpermn. - Expectation: success - """ - n = Tensor([20], dtype=mstype.int32) - output = n.randperm(max_length=30, pad=-1) - set_a = set(np.linspace(0, 19, 20).astype(int)) - set_b = set(output.asnumpy()) - assert set_a.issubset(set_b)