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chenyc

2025-02-12 ea42ff3ebee1eeb3fb29423aa848a249441db81c

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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 { backend_util, Mean, util } from '@tensorflow/tfjs-core';
import { meanImpl } from './Mean_impl';
import { transposeImpl, transposeImplCPU } from './Transpose_impl';
export const meanConfig = {
    kernelName: Mean,
    backendName: 'webgl',
    kernelFunc: ({ inputs, attrs, backend }) => {
        const { x } = inputs;
        const { keepDims, axis } = attrs;
        const webglBackend = backend;
        const xRank = x.shape.length;
        const origAxes = util.parseAxisParam(axis, x.shape);
        let axes = origAxes;
        const permutedAxes = backend_util.getAxesPermutation(axes, xRank);
        const meanInputIsTransposed = permutedAxes != null;
        const shouldExecuteOnCPU = webglBackend.shouldExecuteOnCPU([x]);
        const intermediates = [];
        let meanInput = x;
        if (meanInputIsTransposed) {
            if (shouldExecuteOnCPU) {
                const xTexData = webglBackend.texData.get(meanInput.dataId);
                const values = xTexData.values;
                const newShape = new Array(xRank);
                for (let i = 0; i < newShape.length; i++) {
                    newShape[i] = x.shape[permutedAxes[i]];
                }
                const meanInputValues = transposeImplCPU(values, x.shape, x.dtype, permutedAxes, newShape);
                meanInput = webglBackend.makeTensorInfo(newShape, x.dtype);
                const meanInputData = webglBackend.texData.get(meanInput.dataId);
                meanInputData.values = meanInputValues;
            }
            else {
                meanInput = transposeImpl(x, permutedAxes, webglBackend);
            }
            intermediates.push(meanInput);
            axes = backend_util.getInnerMostAxes(axes.length, xRank);
        }
        backend_util.assertAxesAreInnerMostDims('sum', axes, xRank);
        const [meanOutShape, reduceShape] = backend_util.computeOutAndReduceShapes(meanInput.shape, axes);
        let outShape = meanOutShape;
        if (keepDims) {
            // rather than reshape at the end, set the target shape here.
            outShape = backend_util.expandShapeToKeepDim(meanOutShape, origAxes);
        }
        const out = meanImpl(meanInput, reduceShape, outShape, webglBackend);
        for (const i of intermediates) {
            webglBackend.disposeIntermediateTensorInfo(i);
        }
        return out;
    }
};
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