@tensorflow/tfjs-core
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Hardware-accelerated JavaScript library for machine intelligence
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JavaScript
/**
* @license
* Copyright 2018 Google LLC. 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 { convertToTensor } from '../tensor_util_env';
import * as util from '../util';
import { gather } from './gather';
import { reshape } from './reshape';
import { squeeze } from './squeeze';
import { whereAsync } from './where_async';
/**
* Apply boolean mask to tensor.
*
* ```js
* const tensor = tf.tensor2d([1, 2, 3, 4, 5, 6], [3, 2]);
* const mask = tf.tensor1d([1, 0, 1], 'bool');
* const result = await tf.booleanMaskAsync(tensor, mask);
* result.print();
* ```
*
* @param tensor N-D tensor.
* @param mask K-D boolean tensor, K <= N and K must be known statically.
* @param axis A 0-D int Tensor representing the axis in tensor to mask from.
* By default, axis is 0 which will mask from the first dimension.
* Otherwise K + axis <= N.
*
* @doc {heading: 'Tensors', subheading: 'Slicing and Joining'}
*/
async function booleanMaskAsync_(tensor, mask, axis) {
const $tensor = convertToTensor(tensor, 'tensor', 'boolMask');
const $mask = convertToTensor(mask, 'mask', 'boolMask', 'bool');
const axisFrom = axis == null ? 0 : axis;
const maskDim = $mask.rank;
const tensorShape = $tensor.shape;
util.assert(maskDim > 0, () => 'mask cannot be scalar');
util.assertShapesMatch(tensorShape.slice(axisFrom, axisFrom + maskDim), $mask.shape, `mask's shape must match the first K dimensions of tensor's shape,`);
let leadingSize = 1;
for (let i = axisFrom; i < axisFrom + maskDim; i++) {
leadingSize *= tensorShape[i];
}
const targetTensorShape = tensorShape.slice(0, axisFrom)
.concat([leadingSize], tensorShape.slice(axisFrom + maskDim));
const reshapedTensor = reshape($tensor, targetTensorShape);
const reshapedMask = reshape($mask, [-1]);
const positivePositions = await whereAsync(reshapedMask);
const indices = squeeze(positivePositions, [1]);
const res = gather(reshapedTensor, indices, axisFrom);
// Ensure no memory leak.
if (tensor !== $tensor) {
$tensor.dispose();
}
if (mask !== $mask) {
$mask.dispose();
}
indices.dispose();
reshapedTensor.dispose();
reshapedMask.dispose();
positivePositions.dispose();
return res;
}
export const booleanMaskAsync = booleanMaskAsync_;
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