mindspore.ops.StridedSlice
- class mindspore.ops.StridedSlice(begin_mask=0, end_mask=0, ellipsis_mask=0, new_axis_mask=0, shrink_axis_mask=0)[source]
Extracts a strided slice of a tensor.
Refer to
mindspore.ops.strided_slice()
for more details.- Parameters
begin_mask (int, optional) – Starting index of the slice. Default:
0
.end_mask (int, optional) – Ending index of the slice. Default:
0
.ellipsis_mask (int, optional) – An int mask, ignore slicing operation when set to 1. Default:
0
.new_axis_mask (int, optional) – An int mask for adding new dims. Default:
0
.shrink_axis_mask (int, optional) – An int mask for shrinking dims. Default:
0
.
- Inputs:
input_x (Tensor) - The input Tensor to be extracted from.
begin (tuple[int]) - A tuple which represents the location where to start.
end (tuple[int]) - A tuple or which represents the maximum location where to end.
strides (tuple[int]) - A tuple which represents the strides is continuously added before reaching the maximum location. Only int is allowed, it can be negative which results in reversed slicing.
- Outputs:
Tensor, return the extracts a strided slice of a Tensor based on begin/end index and strides.
- Supported Platforms:
Ascend
GPU
CPU
Examples
>>> import mindspore >>> from mindspore import Tensor, ops >>> 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.]]]