205 lines
9.9 KiB
C
205 lines
9.9 KiB
C
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/*
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* SPDX-FileCopyrightText: Copyright (c) 1993-2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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* SPDX-License-Identifier: Apache-2.0
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*
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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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#ifndef NV_INFER_PLUGIN_UTILS_H
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#define NV_INFER_PLUGIN_UTILS_H
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#include "NvInferRuntimeCommon.h"
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//!
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//! \file NvInferPluginUtils.h
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//!
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//! This is the API for the Nvidia provided TensorRT plugin utilities.
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//! It lists all the parameters utilized by the TensorRT plugins.
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//!
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namespace nvinfer1
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{
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namespace plugin
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{
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//!
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//! \struct PriorBoxParameters
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//!
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//! \brief The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all
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//! dimensions (H x W).
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//!
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//! PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer.
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//!
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struct PriorBoxParameters
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{
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float *minSize; //!< Minimum box size in pixels. Can not be nullptr.
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float *maxSize; //!< Maximum box size in pixels. Can be nullptr.
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float *aspectRatios; //!< Aspect ratios of the boxes. Can be nullptr.
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int32_t numMinSize; //!< Number of elements in minSize. Must be larger than 0.
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int32_t numMaxSize; //!< Number of elements in maxSize. Can be 0 or same as numMinSize.
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int32_t numAspectRatios; //!< Number of elements in aspectRatios. Can be 0.
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bool flip; //!< If true, will flip each aspect ratio. For example,
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//!< if there is an aspect ratio "r", the aspect ratio "1.0/r" will be generated as well.
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bool clip; //!< If true, will clip the prior so that it is within [0,1].
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float variance[4]; //!< Variance for adjusting the prior boxes.
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int32_t imgH; //!< Image height. If 0, then the H dimension of the data tensor will be used.
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int32_t imgW; //!< Image width. If 0, then the W dimension of the data tensor will be used.
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float stepH; //!< Step in H. If 0, then (float)imgH/h will be used where h is the H dimension of the 1st input tensor.
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float stepW; //!< Step in W. If 0, then (float)imgW/w will be used where w is the W dimension of the 1st input tensor.
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float offset; //!< Offset to the top left corner of each cell.
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};
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//!
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//! \struct RPROIParams
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//!
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//! \brief RPROIParams is used to create the RPROIPlugin instance.
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//!
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struct RPROIParams
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{
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int32_t poolingH; //!< Height of the output in pixels after ROI pooling on feature map.
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int32_t poolingW; //!< Width of the output in pixels after ROI pooling on feature map.
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int32_t featureStride; //!< Feature stride; ratio of input image size to feature map size.
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//!< Assuming that max pooling layers in the neural network use square filters.
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int32_t preNmsTop; //!< Number of proposals to keep before applying NMS.
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int32_t nmsMaxOut; //!< Number of remaining proposals after applying NMS.
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int32_t anchorsRatioCount; //!< Number of anchor box ratios.
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int32_t anchorsScaleCount; //!< Number of anchor box scales.
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float iouThreshold; //!< IoU (Intersection over Union) threshold used for the NMS step.
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float minBoxSize; //!< Minimum allowed bounding box size before scaling, used for anchor box calculation.
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float spatialScale; //!< Spatial scale between the input image and the last feature map.
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};
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//!
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//! \struct GridAnchorParameters
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//!
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//! \brief The Anchor Generator plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W).
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//! GridAnchorParameters defines a set of parameters for creating the plugin layer for all feature maps.
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//!
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struct GridAnchorParameters
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{
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float minSize; //!< Scale of anchors corresponding to finest resolution.
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float maxSize; //!< Scale of anchors corresponding to coarsest resolution.
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float* aspectRatios; //!< List of aspect ratios to place on each grid point.
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int32_t numAspectRatios; //!< Number of elements in aspectRatios.
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int32_t H; //!< Height of feature map to generate anchors for.
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int32_t W; //!< Width of feature map to generate anchors for.
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float variance[4]; //!< Variance for adjusting the prior boxes.
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};
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//!
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//! \enum CodeTypeSSD
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//!
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//! \brief The type of encoding used for decoding the bounding boxes and loc_data.
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//!
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//! \deprecated Deprecated in TensorRT 10.0. DetectionOutput plugin is deprecated.
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//!
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enum class CodeTypeSSD : int32_t
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{
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CORNER TRT_DEPRECATED_ENUM = 0, //!< Use box corners.
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CENTER_SIZE TRT_DEPRECATED_ENUM = 1, //!< Use box centers and size.
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CORNER_SIZE TRT_DEPRECATED_ENUM = 2, //!< Use box centers and size.
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TF_CENTER TRT_DEPRECATED_ENUM = 3 //!< Use box centers and size but flip x and y coordinates.
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};
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//!
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//! \struct DetectionOutputParameters
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//!
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//! \brief The DetectionOutput plugin layer generates the detection output
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//! based on location and confidence predictions by doing non maximum suppression.
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//!
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//! This plugin first decodes the bounding boxes based on the anchors generated.
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//! It then performs non_max_suppression on the decoded bounding boxes.
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//! DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer.
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//!
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//! \deprecated Deprecated in TensorRT 10.0. DetectionOutput plugin is deprecated.
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//!
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struct TRT_DEPRECATED DetectionOutputParameters
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{
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bool shareLocation; //!< If true, bounding box are shared among different classes.
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bool varianceEncodedInTarget; //!< If true, variance is encoded in target.
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//!< Otherwise we need to adjust the predicted offset accordingly.
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int32_t backgroundLabelId; //!< Background label ID. If there is no background class, set it as -1.
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int32_t numClasses; //!< Number of classes to be predicted.
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int32_t topK; //!< Number of boxes per image with top confidence scores that are fed
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//!< into the NMS algorithm.
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int32_t keepTopK; //!< Number of total bounding boxes to be kept per image after NMS step.
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float confidenceThreshold; //!< Only consider detections whose confidences are larger than a threshold.
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float nmsThreshold; //!< Threshold to be used in NMS.
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CodeTypeSSD codeType; //!< Type of coding method for bbox.
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int32_t inputOrder[3]; //!< Specifies the order of inputs {loc_data, conf_data, priorbox_data}.
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bool confSigmoid; //!< Set to true to calculate sigmoid of confidence scores.
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bool isNormalized; //!< Set to true if bounding box data is normalized by the network.
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bool isBatchAgnostic{true}; //!< Defaults to true. Set to false if prior boxes are unique per batch.
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};
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//!
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//! \brief When performing yolo9000, softmaxTree is helping to do softmax on confidence scores,
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//! for element to get the precise classification through word-tree structured classification definition.
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//!
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struct softmaxTree
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{
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int32_t* leaf;
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int32_t n;
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int32_t* parent;
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int32_t* child;
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int32_t* group;
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char** name;
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int32_t groups;
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int32_t* groupSize;
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int32_t* groupOffset;
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};
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//!
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//! \brief The Region plugin layer performs region proposal calculation.
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//!
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//! Generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell).
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//! For each box, calculating its probabilities of objects detections from 80 pre-defined classifications
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//! (yolo9000 has 9418 pre-defined classifications, and these 9418 items are organized as work-tree structure).
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//! RegionParameters defines a set of parameters for creating the Region plugin layer.
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//!
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struct RegionParameters
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{
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int32_t num; //!< Number of predicted bounding box for each grid cell.
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int32_t coords; //!< Number of coordinates for a bounding box.
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int32_t classes; //!< Number of classifications to be predicted.
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softmaxTree* smTree; //!< Helping structure to do softmax on confidence scores.
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};
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//!
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//! \brief The NMSParameters are used by the BatchedNMSPlugin for performing
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//! the non_max_suppression operation over boxes for object detection networks.
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//!
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//! \deprecated Deprecated in TensorRT 10.0. BatchedNMSPlugin plugin is deprecated.
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//!
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struct TRT_DEPRECATED NMSParameters
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{
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bool shareLocation; //!< If set to true, the boxes inputs are shared across all classes.
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//!< If set to false, the boxes input should account for per class box data.
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int32_t backgroundLabelId; //!< Label ID for the background class.
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//!< If there is no background class, set it as -1
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int32_t numClasses; //!< Number of classes in the network.
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int32_t topK; //!< Number of bounding boxes to be fed into the NMS step.
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int32_t keepTopK; //!< Number of total bounding boxes to be kept per image after NMS step.
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//!< Should be less than or equal to the topK value.
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float scoreThreshold; //!< Scalar threshold for score (low scoring boxes are removed).
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float iouThreshold; //!< A scalar threshold for IOU (new boxes that have high IOU overlap
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//!< with previously selected boxes are removed).
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bool isNormalized; //!< Set to false, if the box coordinates are not normalized,
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//!< i.e. not in the range [0,1]. Defaults to false.
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};
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} // namespace plugin
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} // namespace nvinfer1
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#endif // NV_INFER_PLUGIN_UTILS_H
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