scatter_nd.ts

/**
 * @license
 * Copyright 2018 Google Inc. All Rights Reserved.
 * Licensed under the Apache License, Version 2.0 (the "License");
 * you may not use this file except in compliance with the License.
 * You may obtain a copy of the License at
 *
 * http://www.apache.org/licenses/LICENSE-2.0
 *
 * Unless required by applicable law or agreed to in writing, software
 * distributed under the License is distributed on an "AS IS" BASIS,
 * WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
 * See the License for the specific language governing permissions and
 * limitations under the License.
 * =============================================================================
 */

import {ENV} from '../environment';
import {Tensor} from '../tensor';
import {convertToTensor} from '../tensor_util_env';
import {Rank, ShapeMap, TensorLike} from '../types';

import {op} from './operation';
import * as scatter_nd_util from './scatter_nd_util';

/**
 * Creates a new tensor by applying sparse updates to individual
 * values or slices within a zero tensor of the given shape tensor according to
 * indices. This operator is the inverse of the `tf.gatherND` operator which
 * extracts values or slices from a given tensor.
 *
 * ```js
 * const indices = tf.tensor2d([[4], [3], [1], [7]]);
 * const updates = tf.tensor2d([9, 10, 11, 12]);
 * const shape = [8];
 * tf.scatterND(indices, updates, shape]).print() //[0, 11, 0, 10, 9, 0, 0, 12]
 * ```
 *
 * @param indices The tensor contains the indices into the output tensor.
 * @param updates The tensor contains the value for the indices.
 * @param shape: The shape of the output tensor.
 */
/** @doc {heading: 'Operations', subheading: 'Slicing and Joining'} */
function scatterND_<R extends Rank>(
    indices: Tensor|TensorLike, updates: Tensor|TensorLike,
    shape: ShapeMap[R]): Tensor<R> {
  const $indices = convertToTensor(indices, 'indices', 'scatterND', 'int32');
  const $updates = convertToTensor(updates, 'updates', 'scatterND');
  scatter_nd_util.validateInput($updates, $indices, shape);

  return ENV.engine.runKernel(
             backend => backend.scatterND($indices, $updates, shape),
             {$indices, $updates}) as Tensor<R>;
}

export const scatterND = op({scatterND_});