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@tensorflow/tfjs-core

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Hardware-accelerated JavaScript library for machine intelligence

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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 {TypedArray} from '../../../../src/types'; import {transposeImpl as cpuTranspose} from '../../../backends/cpu/kernels/Transpose_impl'; import {Transpose, TransposeAttrs, TransposeInputs} from '../../../kernel_names'; import {KernelConfig, TensorInfo} from '../../../kernel_registry'; import {MathBackendWebGL} from '../backend_webgl'; import {transposeImpl} from './Transpose_impl'; export const transposeConfig: KernelConfig = { kernelName: Transpose, backendName: 'webgl', kernelFunc: ({inputs, attrs, backend}) => { const {x} = inputs as TransposeInputs; const {perm} = attrs as {} as TransposeAttrs; const webglBackend = backend as MathBackendWebGL; const xRank = x.shape.length; const newShape: number[] = new Array(xRank); for (let i = 0; i < newShape.length; i++) { newShape[i] = x.shape[perm[i]]; } let out: TensorInfo; if (webglBackend.shouldExecuteOnCPU([x])) { const xTexData = webglBackend.texData.get(x.dataId); const values = xTexData.values as TypedArray; const outValues = cpuTranspose(values, x.shape, x.dtype, perm, newShape); out = webglBackend.makeTensorInfo(newShape, x.dtype); const outData = webglBackend.texData.get(out.dataId); outData.values = outValues; } else { out = transposeImpl(x, perm, webglBackend); } return out; } };