mindspore.ops.StridedSlice

class mindspore.ops.StridedSlice(begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0)

对输入Tensor根据步长和索引进行切片提取。

更多参考详见 mindspore.ops.strided_slice()。

参数：

• begin_mask (int，可选) - 表示切片的起始索引掩码。默认值：0。

• end_mask (int，可选) - 表示切片的结束索引掩码。默认值：0。

• ellipsis_mask (int，可选) - 维度掩码值为1说明不需要进行切片操作。为int型掩码。默认值：0。

• new_axis_mask (int，可选) - 表示切片的新增维度掩码。默认值：0。

• shrink_axis_mask (int，可选) - 表示切片的收缩维度掩码。为int型掩码。默认值：0。

输入：

• input_x (Tensor) - 需要切片处理的输入Tensor。

• begin (tuple[int]) - 指定开始切片的索引。仅支持大于或等于0的int值。

• end (tuple[int]) - 指定结束切片的索引。仅支持大于0的int值。

• strides (tuple[int]) - 指定各维度切片的步长。输入为一个tuple，仅支持int值。strides 的元素必须非零。可能为负值，这会导致反向切片。

输出：

返回根据起始索引、结束索引和步长进行提取出的切片Tensor。

支持平台：

Ascend GPU CPU

样例：

>>> input_x = Tensor([[[1, 1, 1], [2, 2, 2]], [[3, 3, 3], [4, 4, 4]],
...                   [[5, 5, 5], [6, 6, 6]]], mindspore.float32)
>>> #         [[[1. 1. 1.]
>>> #           [2. 2. 2.]]
>>> #
>>> #          [[3. 3. 3.]
>>> #           [4. 4. 4.]]
>>> #
>>> #          [[5. 5. 5.]
>>> #           [6. 6. 6.]]]
>>> # In order to visually view the multi-dimensional array, write the above as follows：
>>> #         [
>>> #             [
>>> #                 [1,1,1]
>>> #                 [2,2,2]
>>> #             ]
>>> #             [
>>> #                 [3,3,3]
>>> #                 [4,4,4]
>>> #             ]
>>> #             [
>>> #                 [5,5,5]
>>> #                 [6,6,6]
>>> #             ]
>>> #         ]
>>> strided_slice = ops.StridedSlice()
>>> output = strided_slice(input_x, (1, 0, 2), (3, 1, 3), (1, 1, 1))
>>> # Take this " output = strided_slice(input_x, (1, 0, 2), (3, 1, 3), (1, 1, 1)) " as an example,
>>> # start = [1, 0, 2] , end = [3, 1, 3], stride = [1, 1, 1], Find a segment of (start, end),
>>> # note that end is an open interval
>>> # To facilitate understanding, this operator can be divided into three steps:
>>> # Step 1: Calculation of the first dimension:
>>> # start = 1, end = 3, stride = 1, So can take 1st, 2nd rows, and then gets the final output at this time.
>>> # output_1th =
>>> # [
>>> #     [
>>> #         [3,3,3]
>>> #         [4,4,4]
>>> #     ]
>>> #     [
>>> #         [5,5,5]
>>> #         [6,6,6]
>>> #     ]
>>> # ]
>>> # Step 2: Calculation of the second dimension
>>> # 2nd dimension, start = 0, end = 1, stride = 1. So only 0th rows can be taken, and the output at this time.
>>> # output_2nd =
>>> # [
>>> #     [
>>> #         [3,3,3]
>>> #     ]
>>> #     [
>>> #         [5,5,5]
>>> #     ]
>>> # ]
>>> # Step 3: Calculation of the third dimension
>>> # 3nd dimension,start = 2, end = 3, stride = 1, So can take 2th cols,
>>> # and you get the final output at this time.
>>> # output_3ed =
>>> # [
>>> #     [
>>> #         [3]
>>> #     ]
>>> #     [
>>> #         [5]
>>> #     ]
>>> # ]
>>> # The final output after finishing is:
>>> print(output)
[[[3.]]
 [[5.]]]
>>> # another example like :
>>> output = strided_slice(input_x, (1, 0, 0), (2, 1, 3), (1, 1, 1))
>>> print(output)
[[[3. 3. 3.]]]