diff --git a/.jenkins/check/config/filter_pylint.txt b/.jenkins/check/config/filter_pylint.txt index c4df350cb2f..1376e41fd7d 100644 --- a/.jenkins/check/config/filter_pylint.txt +++ b/.jenkins/check/config/filter_pylint.txt @@ -76,6 +76,7 @@ "mindspore/mindspore/python/mindspore/ops/operations/_inner_ops.py" "not-callable" "mindspore/mindspore/python/mindspore/hypercomplex" "useless-return" "mindspore/mindspore/python" "redefined-builtin" +"mindspore/mindspore/python/mindspore/ops/function/array_func.py" "unidiomatic-typecheck" # MindData "mindspore/mindspore/python/mindspore/dataset/__init__.py" "redefined-builtin" diff --git a/docs/api/api_python/ops/mindspore.ops.func_max_unpool1d.rst b/docs/api/api_python/ops/mindspore.ops.func_max_unpool1d.rst index a706a1f5614..b0928111da0 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_max_unpool1d.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_max_unpool1d.rst @@ -18,7 +18,7 @@ mindspore.ops.max_unpool1d - **indices** (Tensor) - 最大值的索引。shape必须与输入 `x` 相同。取值范围需满足 :math:`[0, H_{in} - 1]` 。 数据类型必须是int32或int64。 - **kernel_size** (Union[int, tuple[int]]) - 池化核尺寸大小。 - - **stride** (Union[int, tuple[int]]) - 池化操作的移动步长，若取值为 'None'， `stride` 值与 `kernel_size` + - **stride** (Union[int, tuple[int]]) - 池化操作的移动步长，若取值为 '0'， '(0)' 或 'None'， `stride` 值与 `kernel_size` 相同。默认值：None。 - **padding** (Union[int, tuple[int]]) - 填充值。默认值：0。 - **output_size** (tuple[int], 可选) - 输出shape。默认值：None。 diff --git a/docs/api/api_python/ops/mindspore.ops.func_tensor_split.rst b/docs/api/api_python/ops/mindspore.ops.func_tensor_split.rst index 6c5d831796b..e5119570c8a 100644 --- a/docs/api/api_python/ops/mindspore.ops.func_tensor_split.rst +++ b/docs/api/api_python/ops/mindspore.ops.func_tensor_split.rst @@ -7,7 +7,7 @@ mindspore.ops.tensor_split 参数： - **x** (Tensor) - 待分割的Tensor。 - - **indices_or_sections** (Union[int, tuple(int), list(int)]) - 如果 `indices_or_sections` 是整数类型n，输入将沿 `axis` 轴分割成n份。如果输入沿着 `axis` 轴能被n整除，那么每个切片的大小相同为 :math:`input.size(axis) / n` 。如果不能被n整除，那么前 :math:`input.size(axis) % n` 个切片的大小为 :math:`input.size(axis) // n + 1` ，其余切片的大小为 :math:`input.size(axis) // n` 。 + - **indices_or_sections** (Union[int, tuple(int), list(int)]) - 如果 `indices_or_sections` 是整数类型n，输入将沿 `axis` 轴分割成n份。如果输入沿着 `axis` 轴能被n整除，那么每个切片的大小相同为 :math:`x.size(axis) / n` 。如果不能被n整除，那么前 :math:`x.size(axis) % n` 个切片的大小为 :math:`x.size(axis) // n + 1` ，其余切片的大小为 :math:`x.size(axis) // n` 。 如果 `indices_or_sections` 是由int组成list或者tuple，那么输入将沿着 `axis` 轴在tuple或list中的索引处切分。例如：:math:`indices\_or\_sections=[2, 3]` 和 :math:`axis=0` 将得到切片 :math:`x[:2]` ， :math:`x[2:3]` ，和 :math:`x[3:]` . - **axis** (int) - 指定分割轴。默认值：0。 diff --git a/mindspore/python/mindspore/ops/function/array_func.py b/mindspore/python/mindspore/ops/function/array_func.py index 63702a41838..652d9b80a3c 100644 --- a/mindspore/python/mindspore/ops/function/array_func.py +++ b/mindspore/python/mindspore/ops/function/array_func.py @@ -4960,7 +4960,7 @@ def _split_int(x, split_size_or_sections, axis): arr_shape = x.shape length_along_dim = arr_shape[axis] if split_size_or_sections > length_along_dim: - res = P.Split(axis, length_along_dim)(x) + res = P.Split(axis, 1)(x) elif length_along_dim % split_size_or_sections == 0: sections = length_along_dim // split_size_or_sections res = P.Split(axis, sections)(x) @@ -5036,12 +5036,16 @@ def split(x, split_size_or_sections, axis=0): """ if not isinstance(x, Tensor): raise TypeError(f'expect `x` is a Tensor, but got {type(x)}') - if not isinstance(axis, int): + if type(axis) is not int: raise TypeError(f"Type of Argument `axis` should be integer but got {type(axis)}") axis = _canonicalize_axis(axis, x.ndim) - if isinstance(split_size_or_sections, int): - res = _split_int(x, split_size_or_sections, axis) + if type(split_size_or_sections) is int: + if split_size_or_sections > 0: + res = _split_int(x, split_size_or_sections, axis) + else: + raise ValueError(f"For split, the value of 'split_size_or_sections' must be more than zero, " + f"but got {split_size_or_sections}.") elif isinstance(split_size_or_sections, (list, tuple)): for item in split_size_or_sections: if not isinstance(item, int): @@ -5053,7 +5057,7 @@ def split(x, split_size_or_sections, axis=0): else: raise TypeError(f"Type of Argument `split_size_or_sections` should be integer, tuple(int) or list(int), " \ f"but got {type(split_size_or_sections)}") - return res + return tuple(res) def tril(input_x, diagonal=0): # pylint: disable=redefined-outer-name @@ -5212,9 +5216,10 @@ def _tensor_split_sub_int(x, indices_or_sections, axis): length_along_dim = arr_shape[axis] if indices_or_sections > length_along_dim: res = P.Split(axis, length_along_dim)(x) - indices_or_sections_n = [i for i in np.arange(length_along_dim, indices_or_sections)] + indices_or_sections_n = [length_along_dim, length_along_dim + 1] res2 = _tensor_split_sub_tensors(x, indices_or_sections_n, axis) - res += tuple(res2)[1:] + for _ in np.arange(length_along_dim, indices_or_sections): + res += tuple(res2)[1:] elif length_along_dim % indices_or_sections == 0: res = P.Split(axis, indices_or_sections)(x) else: @@ -5240,9 +5245,9 @@ def tensor_split(x, indices_or_sections, axis=0): indices_or_sections (Union[int, tuple(int), list(int)]): If `indices_or_sections` is an integer n, input is split into n sections along dimension `axis`. If input is divisible by n along dimension `axis`, each section will be - of equal size, :math:`input.size(axis) / n` . If input is not divisible by n, the sizes of the first - :math:`input.size(axis) % n` sections will have size :math:`input.size(axis) // n + 1` , and the rest will - have size :math:`input.size(axis) // n` . + of equal size, :math:`x.size(axis) / n` . If input is not divisible by n, the sizes of the first + :math:`x.size(axis) % n` sections will have size :math:`x.size(axis) // n + 1` , and the rest will + have size :math:`x.size(axis) // n` . If `indices_or_sections` is a list or tuple of ints, then input is split along dimension `axis` at each of the indices in the list, tuple. For instance, :math:`indices\_or\_sections=[2, 3]` and :math:`axis=0` would result in the tensors :math:`x[:2]` , @@ -5273,12 +5278,15 @@ def tensor_split(x, indices_or_sections, axis=0): if not isinstance(x, Tensor): raise TypeError(f'expect `x` is a Tensor, but got {type(x)}') - if not isinstance(axis, int): + if type(axis) is not int: raise TypeError(f"Type of Argument `axis` should be integer but got {type(axis)}") axis = _canonicalize_axis(axis, x.ndim) - if isinstance(indices_or_sections, int): - res = _tensor_split_sub_int(x, indices_or_sections, axis) - + if type(indices_or_sections) is int: + if indices_or_sections > 0: + res = _tensor_split_sub_int(x, indices_or_sections, axis) + else: + raise ValueError(f"For tensor_split, the value of 'indices_or_sections' must be more than zero " + f"but got {indices_or_sections}") elif isinstance(indices_or_sections, (list, tuple)): for item in indices_or_sections: if not isinstance(item, int): diff --git a/mindspore/python/mindspore/ops/function/nn_func.py b/mindspore/python/mindspore/ops/function/nn_func.py index 0dd652fdfff..7fd02e8687c 100644 --- a/mindspore/python/mindspore/ops/function/nn_func.py +++ b/mindspore/python/mindspore/ops/function/nn_func.py @@ -703,7 +703,7 @@ def max_unpool1d(x, indices, kernel_size, stride=None, padding=0, output_size=No Data type must be in int32 or int64. kernel_size (Union[int, tuple[int]]): The size of kernel used to take the maximum value. stride (Union[int, tuple[int]]): The distance of kernel moving, - If stride is None, then stride equal to kernel_size. Default: None. + If stride is 0, (0) or None, then stride equal to kernel_size. Default: None. padding (Union[int, tuple[int]]): The pad value to be filled. Default: 0. output_size (tuple[int], optional): The output shape. Default: None. If output_size == (), then the shape of output computed by `kernel_size`, `stride` and `padding`.