/**
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* @license
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* Copyright 2019 Google LLC. All Rights Reserved.
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* Licensed under the Apache License, Version 2.0 (the "License");
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* you may not use this file except in compliance with the License.
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* You may obtain a copy of the License at
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*
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* http://www.apache.org/licenses/LICENSE-2.0
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*
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* Unless required by applicable law or agreed to in writing, software
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* distributed under the License is distributed on an "AS IS" BASIS,
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* WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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* See the License for the specific language governing permissions and
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* limitations under the License.
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* =============================================================================
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*/
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import {KernelConfig, NonMaxSuppressionV4, NonMaxSuppressionV4Attrs, NonMaxSuppressionV4Inputs, scalar, Tensor1D, Tensor2D} from '@tensorflow/tfjs';
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import {createTensorsTypeOpAttr, NodeJSKernelBackend} from '../nodejs_kernel_backend';
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// TODO(nsthorat, dsmilkov): Remove dependency on tensors, use dataId.
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export const nonMaxSuppressionV4Config: KernelConfig = {
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kernelName: NonMaxSuppressionV4,
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backendName: 'tensorflow',
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kernelFunc: ({inputs, backend, attrs}) => {
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const {boxes, scores} = inputs as NonMaxSuppressionV4Inputs;
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const {maxOutputSize, iouThreshold, scoreThreshold, padToMaxOutputSize} =
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attrs as unknown as NonMaxSuppressionV4Attrs;
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const maxOutputSizeTensor = scalar(maxOutputSize, 'int32');
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const iouThresholdTensor = scalar(iouThreshold, 'float32');
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const scoreThresholdTensor = scalar(scoreThreshold, 'float32');
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const nodeBackend = backend as NodeJSKernelBackend;
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const opAttrs = [
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createTensorsTypeOpAttr('T', boxes.dtype),
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createTensorsTypeOpAttr('T_threshold', 'float32'), {
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name: 'pad_to_max_output_size',
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type: nodeBackend.binding.TF_ATTR_BOOL,
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value: padToMaxOutputSize
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}
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];
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const [selectedIndices, validOutputs] = nodeBackend.executeMultipleOutputs(
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'NonMaxSuppressionV4', opAttrs,
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[
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boxes as Tensor2D, scores as Tensor1D, maxOutputSizeTensor,
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iouThresholdTensor, scoreThresholdTensor
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],
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2);
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maxOutputSizeTensor.dispose();
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iouThresholdTensor.dispose();
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scoreThresholdTensor.dispose();
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return [selectedIndices, validOutputs];
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}
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};
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