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ComplexAbs.ts
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ComplexAbs.ts
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/**
* @license
* Copyright 2020 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 {ComplexAbs, ComplexAbsInputs, KernelConfig, KernelFunc, util} from '@tensorflow/tfjs-core';
import {MathBackendCPU} from '../backend_cpu';
export const complexAbs =
(args: {inputs: ComplexAbsInputs, backend: MathBackendCPU}) => {
const {x} = args.inputs;
const cpuBackend = args.backend;
const resultValues = new Float32Array(util.sizeFromShape(x.shape));
const complexVals = cpuBackend.data.get(x.dataId);
const real = complexVals.complexTensorInfos.real;
const imag = complexVals.complexTensorInfos.imag;
const realVals = cpuBackend.data.get(real.dataId).values as Float32Array;
const imagVals = cpuBackend.data.get(imag.dataId).values as Float32Array;
for (let i = 0; i < realVals.length; i++) {
const real = realVals[i];
const imag = imagVals[i];
resultValues[i] = Math.hypot(real, imag);
}
return cpuBackend.makeOutput(resultValues, x.shape, 'float32');
};
export const complexAbsConfig: KernelConfig = {
kernelName: ComplexAbs,
backendName: 'cpu',
kernelFunc: complexAbs as unknown as KernelFunc,
};