@tensorflow/tfjs-backend-wasm
Version:
This package adds a WebAssembly backend to TensorFlow.js. It currently supports the following models from our [models](https://github.com/tensorflow/tfjs-models) repo: - BlazeFace - BodyPix - CocoSSD - Face landmarks detection - HandPose - KNN classifier
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JavaScript
/**
* @license
* Copyright 2023 Google LLC.
* 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 { ResizeNearestNeighborGrad } from '@tensorflow/tfjs-core';
import { cast } from './Cast';
let wasmResizeNearestNeighborGrad;
function setup(backend) {
wasmResizeNearestNeighborGrad = backend.wasm.cwrap(ResizeNearestNeighborGrad, null /*void*/, [
'number',
'number',
'number',
'array',
'array',
'boolean', // alignCorners
]);
}
function resizeNearestNeighborGrad(args) {
const { inputs, backend, attrs } = args;
const { images, dy } = inputs;
const { alignCorners } = attrs;
const dx = backend.makeOutput(images.shape, 'float32');
let xData = backend.dataIdMap.get(images.dataId);
let castedData;
if (xData.dtype !== 'float32') {
castedData = cast({
backend,
inputs: { x: images },
attrs: { dtype: 'float32' },
});
xData = backend.dataIdMap.get(castedData.dataId);
}
wasmResizeNearestNeighborGrad(backend.dataIdMap.get(images.dataId).id, backend.dataIdMap.get(dy.dataId).id, backend.dataIdMap.get(dx.dataId).id, new Uint8Array(new Int32Array(images.shape).buffer), new Uint8Array(new Int32Array(dy.shape).buffer), alignCorners);
if (castedData != null) {
backend.disposeData(castedData.dataId);
}
return dx;
}
export const resizeNearestNeighborGradConfig = {
kernelName: ResizeNearestNeighborGrad,
backendName: 'wasm',
setupFunc: setup,
kernelFunc: resizeNearestNeighborGrad,
};
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