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batchedNMSLandmarkConfPlugin

README.md

BatchedNMSLandmarkConfPlugin

Table Of Contents

Description

The BatchedNMSLandmarkConfPlugin implements a non-maximum suppression (NMS) step over boxes for object detection networks.

Non-maximum suppression is typically the universal step in object detection inference. This plugin is used after you’ve processed the bounding box prediction and object classification to get the final bounding boxes for objects.

With this plugin, you can incorporate the non-maximum suppression step during TensorRT inference. During inference, the neural network generates a fixed number of bounding boxes with box coordinates, identified class and confidence levels. Not all bounding boxes, but the most representative ones, have to be drawn on the original image.

Non-maximum suppression is the way to eliminate the boxes which have low confidence or do not have object in and keep the most representative ones. For example, the objects within an image might be covered by many boxes with different levels of confidence. The goal of the non-maximum suppression step is to find the most confident box for the object and remove all the less confident ones.

This plugin accelerates this non maximum suppression step during TensorRT inference on GPU.

Structure

The BatchedNMSLandmarkConfPlugin takes two inputs, boxes input and scores input.

Boxes input The boxes input are of shape [batch_size, number_boxes, number_classes, number_box_parameters]. The box location usually consists of four parameters such as [x1, y1, x2, y2] where (x1, y1) and (x2, y2) are the coordinates of any diagonal pair of box corners. For example, if your model outputs 8732 bounding boxes given one image, there are 100 candidate classes, the shape of boxes input will be [8732, 100, 4].

Scores input The scores input are of shape [batch_size, number_boxes, number_classes]. Each box has an array of probability for each candidate class.

The boxes input and scores input generates the following four outputs:

  • num_detections The num_detections output is of shape [batch_size]. It is an int32 tensor indicating the number of valid detections per batch item. It can be less than keepTopK. Only the top num_detections[i] entries in nmsed_boxes[i], nmsed_scores[i] and nmsed_classes[i] are valid.

  • nmsed_boxes A [batch_size, keepTopK, 4] float32 tensor containing the coordinates of non-max suppressed boxes.

  • nmsed_scores A [batch_size, keepTopK] float32 tensor containing the scores for the boxes.

  • nmsed_classes A [batch_size, keepTopK] float32 tensor containing the classes for the boxes.

  • nmsed_landmarks A [batch_size, keepTopK, 11] float32 tensor containing the 5 landmarks for the boxes.

Parameters

The BatchedNMSLandmarkConfPlugin has plugin creator class BatchedNMSLandmarkConfPluginCreator and plugin class BatchedNMSLandmarkConfPlugin.

The BatchedNMSLandmarkConfPlugin is created using BatchedNMSLandmarkConfPluginCreator with NMSParameters typed parameters. The NMSParameters data structure is listed as follows and is defined in the NvInferPlugin.h header file.

Type Parameter Description
bool shareLocation If set to true, the boxes input are shared across all classes. If set to false, the boxes input should account for per-class box data.
int backgroundLabelId The label ID for the background class. If there is no background class, set it to -1.
int numClasses The number of classes in the network.
int topK The number of bounding boxes to be fed into the NMS step.
int keepTopK The number of total bounding boxes to be kept per-image after the NMS step. Should be less than or equal to the topK value.
float scoreThreshold The scalar threshold for score (low scoring boxes are removed).
float iouThreshold The scalar threshold for IOU (new boxes that have high IOU overlap with previously selected boxes are removed).
bool isNormalized Set to false if the box coordinates are not normalized, meaning they are not in the range [0,1]. Defaults to true.
bool clipBoxes Forcibly restrict bounding boxes to the normalized range [0,1]. Only applicable if isNormalized is also true. Defaults to true.
int scoreBits The number of bits to represent the score values during radix sort. The number of bits to represent score values(confidences) during radix sort. This valid range is 0 < scoreBits <= 10. The default value is 16(which means to use full bits in radix sort). Setting this parameter to any invalid value will result in the same effect as setting it to 16. This parameter can be tuned to strike for a best trade-off between performance and accuracy. Lowering scoreBits will improve performance but with some minor degradation to the accuracy. This parameter is only valid for FP16 data type for now.
bool caffeSemantics Set to false to disable Caffe semantics for IOU calculation. In Caffe, width and height are incremented by '1' if bbox coordinates are not normalized. Defaults to true.

Algorithms

The NMS algorithm used in this particular plugin first sorts the bounding boxes indices by the score for each class, then sorts the bounding boxes by the updated scores, and finally collects the desired number of bounding boxes with the highest scores.

It is mainly accelerated using the nmsInference kernel defined in the BatchedNMSLandmarkConfInference.cu file.

Specifically, the NMS algorithm:

  • Sorts the bounding box indices by the score for each class. Before sorting, the bounding boxes with a score less than scoreThreshold are discarded by setting their indices to -1 and their scores to 0. This is using the sortScoresPerClass kernel defined in the sortScoresPerClass.cu file.

  • Finds the most confident box for the object and removes all the less confident ones using the iterative non-maximum suppression step step for each class. Starting from the bounding box with the highest score in each class, the bounding boxes that has overlap higher than iouThreshold is suppressed by setting their indices to -1 and their scores to 0. Then all the less confident bounding boxes were suppressed for each class. This is using the allClassNMS kernel defined in the allClassNMS.cu file.

  • Sorts the bounding boxes per image using the updated scores. At this time, all the classes were mixed before sort. Discarded and suppressed bounding boxes will go to the end of the sorted array since their score is 0. This is using the sortScoresPerImage kernel defined in the sortScoresPerImage.cu file.

  • Collects the desired number, keepTopK, of bounding box indices with the highest scores from the top of the sorted array, their bounding box coordinates, and their object classification information. This is using the gatherNMSLandmarkConfOutputs kernel defined in the gatherNMSLandmarkConfOutputs.cu file.

Additional resources

The following resources provide a deeper understanding of the BatchedNMSLandmarkConfPlugin plugin:

Networks

Documentation

License

For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.

Changelog

January 2022 BatchedNMSLandmarkConf plugin now supports IOU calculation that matches Torch/Tensorflow and defaults to the Caffe semantics. This is selected using an optional caffeSemantics plugin attribute.

May 2019 This is the first release of this README.md file.

Known issues

  • When running cub::DeviceSegmentedRadixSort::SortPairsDescending with cuda-memcheck --tool racecheck, it will not work correctly.
  • BatchedNMSLandmarkConf plugin cannot handle greater than 4096 rectangles in the input.