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mindspore.ops.UnsortedSegmentSum

class mindspore.ops.UnsortedSegmentSum(*args, **kwargs)

Computes the sum of a tensor along segments.

Calculates a tensor such that $\text{output}[i]=\sum _{segment\mathrm{\_}ids[j]==i}\text{data}[j,\dots ]$, where $j$ is a tuple describing the index of element in data. segment_ids selects which elements in data to sum up. Segment_ids does not need to be sorted, and it does not need to cover all values in the entire valid value range.

Note

If the segment_id i is absent in the segment_ids, then output[i] will be filled with 0.

If the sum of the given segment_ids $i$ is empty, then $\text{output}[i]=0$. If the given segment_ids is negative, the value will be ignored. ‘num_segments’ must be equal to the number of different segment_ids.

Inputs:

• input_x (Tensor) - The shape is $(x_1,x_2,...,x_R)$.

• segment_ids (Tensor) - Set the shape as $(x_1,x_2,...,x_N)$, where 0 < N <= R.

• num_segments (int) - Set $z$ as num_segments.

Outputs:

Tensor, the shape is $(z,x_{N+1},...,x_R)$.

Raises

• TypeError – If num_segments is not an int.

• ValueError – If length of shape of segment_ids is less than 1.

Supported Platforms:

Ascend GPU CPU

Examples

>>> input_x = Tensor([1, 2, 3, 4], mindspore.float32)
>>> segment_ids = Tensor([0, 0, 1, 2], mindspore.int32)
>>> num_segments = 4
>>> output = ops.UnsortedSegmentSum()(input_x, segment_ids, num_segments)
>>> print(output)
[3. 3. 4. 0.]
>>> input_x = Tensor([1, 2, 3, 4, 2, 5], mindspore.float32)
>>> segment_ids = Tensor([0, 0, 1, 2, 3, 4], mindspore.int32)
>>> num_segments = 6
>>> output = ops.UnsortedSegmentSum()(input_x, segment_ids, num_segments)
>>> print(output)
[3. 3. 4. 2. 5. 0.]