@tensorflow/tfjs-core/src/ops/sparse_to_dense_util.ts

Hardware-accelerated JavaScript library for machine intelligence

/**
 * @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 {Tensor} from '../tensor';

/**
 * Validate sparseToDense inputs.
 *
 * @param sparseIndices A 0-D, 1-D, or 2-D Tensor of type int32.
 * sparseIndices[i] contains the complete index where sparseValues[i] will be
 * placed.
 * @param sparseValues A 0-D or 1-D Tensor. Values
 * corresponding to each row of sparseIndices, or a scalar value to be used for
 * all sparse indices.
 * @param outputShape number[]. Shape of the dense output tensor.
 * @param validateIndices boolean. indice validation is not supported, error
 * will be thrown if it is set.
 */
export function validateInput(
    sparseIndices: Tensor, sparseValues: Tensor, outputShape: number[],
    defaultValues: Tensor) {
  if (sparseIndices.dtype !== 'int32') {
    throw new Error(
        'tf.sparseToDense() expects the indices to be int32 type,' +
        ` but the dtype was ${sparseIndices.dtype}.`);
  }
  if (sparseIndices.rank > 2) {
    throw new Error(
        'sparseIndices should be a scalar, vector, or matrix,' +
        ` but got shape ${sparseIndices.shape}.`);
  }

  const numElems = sparseIndices.rank > 0 ? sparseIndices.shape[0] : 1;
  const numDims = sparseIndices.rank > 1 ? sparseIndices.shape[1] : 1;

  if (outputShape.length !== numDims) {
    throw new Error(
        'outputShape has incorrect number of elements:,' +
        ` ${outputShape.length}, should be: ${numDims}.`);
  }

  const numValues = sparseValues.size;
  if (!(sparseValues.rank === 0 ||
        sparseValues.rank === 1 && numValues === numElems)) {
    throw new Error(
        'sparseValues has incorrect shape ' +
        `${sparseValues.shape}, should be [] or [${numElems}]`);
  }

  if (sparseValues.dtype !== defaultValues.dtype) {
    throw new Error('sparseValues.dtype must match defaultValues.dtype');
  }
}