ScatterNDUpdate#

Versioned name: ScatterNDUpdate-3

Category: Data movement

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

Detailed description: The operation produces a copy of data tensor and updates its value to values specified by updates at specific index positions specified by indices. The output shape is the same as the shape of data. indices tensor must not have duplicate entries. In case of duplicate entries in indices the result is undefined.

The last dimension of indices can be at most the rank of data.shape. The last dimension of indices corresponds to indices into elements if indices.shape[-1] = data.shape.rank or slices if indices.shape[-1] < data.shape.rank. updates is a tensor with shape indices.shape[:-1] + data.shape[indices.shape[-1]:]

Example 1 that shows update of four single elements in data:

data    = [1, 2, 3, 4, 5, 6, 7, 8]
indices = [[4], [3], [1], [7]]
updates = [9, 10, 11, 12]
output  = [1, 11, 3, 10, 9, 6, 7, 12]

Example 2 that shows update of two slices of 4x4 shape in data:

data    = [[[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
          [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
          [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]],
          [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]
indices = [[0], [2]]
updates = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
          [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]]]
output  = [[[5, 5, 5, 5], [6, 6, 6, 6], [7, 7, 7, 7], [8, 8, 8, 8]],
          [[1, 2, 3, 4], [5, 6, 7, 8], [8, 7, 6, 5], [4, 3, 2, 1]],
          [[1, 1, 1, 1], [2, 2, 2, 2], [3, 3, 3, 3], [4, 4, 4, 4]],
          [[8, 7, 6, 5], [4, 3, 2, 1], [1, 2, 3, 4], [5, 6, 7, 8]]]

Attributes: ScatterNDUpdate does not have attributes.

Inputs:

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

  • 2: indices tensor with indices of arbitrary rank q >= 1 and of type T_IND. All index values i_j in index entry (i_0, i_1, ...,i_k) (where k = indices.shape[-1]) must be within bounds [0, s_j - 1] where s_j = data.shape[j]. k must be at most r. Required.

  • 3: updates tensor of rank r - indices.shape[-1] + q - 1 of type T. If expected updates rank is 0D it can be a tensor with single element. Required.

Outputs:

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

Types

  • T: any numeric type.

  • T_IND: int32 or int64

Example

 <layer ... type="ScatterNDUpdate">
     <input>
         <port id="0">
             <dim>1000</dim>
             <dim>256</dim>
             <dim>10</dim>
             <dim>15</dim>
         </port>
         <port id="1">
             <dim>25</dim>
             <dim>125</dim>
             <dim>3</dim>
         </port>
         <port id="2">
             <dim>25</dim>
             <dim>125</dim>
             <dim>15</dim>
         </port>
     </input>
     <output>
         <port id="3">
             <dim>1000</dim>
             <dim>256</dim>
             <dim>10</dim>
             <dim>15</dim>
         </port>
     </output>
 </layer>