openvino/docs/ops/movement/ScatterUpdate_3.md

ScatterUpdate

Versioned name: ScatterUpdate-3

Category: Data movement operations

Short description: ScatterUpdate creates a copy of the first input tensor with updated elements specified with second and third input tensors.

Detailed description: ScatterUpdate creates a copy of the first input tensor with updated elements in positions specified with indices input and values specified with updates tensor starting from the dimension with index axis. For the data tensor of shape [d_0, d_1, ..., d_n], indices tensor of shape [i_0, i_1, ..., i_k] and updates tensor of shape [d_0, d_1, ... d_(axis - 1), i_0, i_1, ..., i_k, d_(axis + 1), ..., d_n] the operation computes for each m, n, ..., p of the indices tensor indices:

data[..., indices[m, n, ..., p], ...] = updates[..., m, n, ..., p, ...]

where first ... in the data corresponds to first axis dimensions, last ... in the data corresponds to the rank(data) - (axis + 1) dimensions.

Several examples for case when axis = 0:

1. indices is a 0D tensor: data[indices, ...] = updates[...]
2. indices is a 1D tensor (for each i): data[indices[i], ...] = updates[i, ...]
3. indices is a ND tensor (for each i, ..., j): data[indices[i, ..., j], ...] = updates[i, ..., j, ...]

This operation is similar to TensorFlow* operation ScatterUpdate but allows scattering for the arbitrary axis.

Attributes: ScatterUpdate does not have attributes.

Inputs:

• 1: data tensor of arbitrary rank r and of type T. Required.

• 2: indices tensor with indices of type T_IND. All index values are expected to be within bounds [0, s - 1] along axis of size s. If multiple indices point to the same output location then the order of updating the values is undefined. If an index points to non-existing output tensor element or is negative then an exception is raised. Required.

• 3: updates tensor of type T. Required.

• 4: axis tensor with scalar or 1D tensor with one element of type T_AXIS specifying axis for scatter. The value can be in range [-r, r - 1] where r is the rank of data. Required.

Outputs:

• 1: tensor with shape equal to data tensor of the type T.

Types

• T: any numeric type.

• T_IND: any supported integer types.

• T_AXIS: any supported integer types.

Example

<layer ... type="ScatterUpdate">
    <input>
        <port id="0">
            <dim>1000</dim>
            <dim>256</dim>
            <dim>10</dim>
            <dim>15</dim>
        </port>
        <port id="1">
            <dim>125</dim>
            <dim>20</dim>
        </port>
        <port id="2">
            <dim>1000</dim>
            <dim>125</dim>
            <dim>20</dim>
            <dim>10</dim>
            <dim>15</dim>
        </port>
        <port id="3">     <!-- value [1] -->
            <dim>1</dim>
        </port>
    </input>
    <output>
        <port id="4" precision="FP32">
            <dim>1000</dim>
            <dim>256</dim>
            <dim>10</dim>
            <dim>15</dim>
        </port>
    </output>
</layer>