@tensorflow/tfjs-node
Version:
This repository provides native TensorFlow execution in backend JavaScript applications under the Node.js runtime, accelerated by the TensorFlow C binary under the hood. It provides the same API as [TensorFlow.js](https://js.tensorflow.org/api/latest/).
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JavaScript
;
/**
* @license
* Copyright 2019 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.
* =============================================================================
*/
Object.defineProperty(exports, "__esModule", { value: true });
exports.nonMaxSuppressionV5Config = void 0;
var tfjs_1 = require("@tensorflow/tfjs");
var nodejs_kernel_backend_1 = require("../nodejs_kernel_backend");
// TODO(nsthorat, dsmilkov): Remove dependency on tensors, use dataId.
exports.nonMaxSuppressionV5Config = {
kernelName: 'NonMaxSuppressionV5',
backendName: 'tensorflow',
kernelFunc: function (_a) {
var inputs = _a.inputs, backend = _a.backend, attrs = _a.attrs;
var _b = inputs, boxes = _b.boxes, scores = _b.scores;
var _c = attrs, maxOutputSize = _c.maxOutputSize, iouThreshold = _c.iouThreshold, scoreThreshold = _c.scoreThreshold, softNmsSigma = _c.softNmsSigma;
var maxOutputSizeTensor = (0, tfjs_1.scalar)(maxOutputSize, 'int32');
var iouThresholdTensor = (0, tfjs_1.scalar)(iouThreshold);
var scoreThresholdTensor = (0, tfjs_1.scalar)(scoreThreshold);
var softNmsSigmaTensor = (0, tfjs_1.scalar)(softNmsSigma);
var opAttrs = [(0, nodejs_kernel_backend_1.createTensorsTypeOpAttr)('T', boxes.dtype)];
var nodeBackend = backend;
var _d = nodeBackend.executeMultipleOutputs('NonMaxSuppressionV5', opAttrs, [
boxes, scores, maxOutputSizeTensor,
iouThresholdTensor, scoreThresholdTensor, softNmsSigmaTensor
], 3), selectedIndices = _d[0], selectedScores = _d[1], validOutputs = _d[2];
maxOutputSizeTensor.dispose();
iouThresholdTensor.dispose();
scoreThresholdTensor.dispose();
softNmsSigmaTensor.dispose();
validOutputs.dispose();
return [selectedIndices, selectedScores];
}
};