|
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createRPNROIPlugin (int32_t featureStride, int32_t preNmsTop, int32_t nmsMaxOut, float iouThreshold, float minBoxSize, float spatialScale, nvinfer1::DimsHW pooling, nvinfer1::Weights anchorRatios, nvinfer1::Weights anchorScales) |
| | Create a plugin layer that fuses the RPN and ROI pooling using user-defined parameters. Registered plugin type "RPROI_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createNormalizePlugin (nvinfer1::Weights const *scales, bool acrossSpatial, bool channelShared, float eps) |
| | The Normalize plugin layer normalizes the input to have L2 norm of 1 with scale learnable. Registered plugin type "Normalize_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createPriorBoxPlugin (nvinfer1::plugin::PriorBoxParameters param) |
| | The PriorBox plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W). PriorBoxParameters defines a set of parameters for creating the PriorBox plugin layer. Registered plugin type "PriorBox_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createAnchorGeneratorPlugin (nvinfer1::plugin::GridAnchorParameters *param, int32_t numLayers) |
| | The Grid Anchor Generator plugin layer generates the prior boxes of designated sizes and aspect ratios across all dimensions (H x W) for all feature maps. GridAnchorParameters defines a set of parameters for creating the GridAnchorGenerator plugin layer. Registered plugin type "GridAnchor_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createNMSPlugin (nvinfer1::plugin::DetectionOutputParameters param) |
| | The DetectionOutput plugin layer generates the detection output based on location and confidence predictions by doing non maximum suppression. DetectionOutputParameters defines a set of parameters for creating the DetectionOutput plugin layer. Registered plugin type "NMS_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createReorgPlugin (int32_t stride) |
| | The Reorg plugin reshapes input of shape CxHxW into a (C*stride*stride)x(H/stride)x(W/stride) shape, used in YOLOv2. It does that by taking 1 x stride x stride slices from tensor and flattening them into (stride x stride) x 1 x 1 shape. Registered plugin type "Reorg_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createRegionPlugin (nvinfer1::plugin::RegionParameters params) |
| | The Region plugin layer performs region proposal calculation: generate 5 bounding boxes per cell (for yolo9000, generate 3 bounding boxes per cell). For each box, calculating its probablities of objects detections from 80 pre-defined classifications (yolo9000 has 9416 pre-defined classifications, and these 9416 items are organized as work-tree structure). RegionParameters defines a set of parameters for creating the Region plugin layer. Registered plugin type "Region_TRT". Registered plugin version "1". More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createBatchedNMSPlugin (nvinfer1::plugin::NMSParameters param) |
| | The BatchedNMS Plugin performs non_max_suppression on the input boxes, per batch, across all classes. It greedily selects a subset of bounding boxes in descending order of score. Prunes away boxes that have a high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. The plugin expects two inputs. Input0 is expected to be 4-D float boxes tensor of shape [batch_size, num_boxes, q, 4], where q can be either 1 (if shareLocation is true) or num_classes. Input1 is expected to be a 3-D float scores tensor of shape [batch_size, num_boxes, num_classes] representing a single score corresponding to each box. The plugin returns four outputs. num_detections : A [batch_size] int32 tensor indicating the number of valid detections per batch item. 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, max_detections, 4] float32 tensor containing the co-ordinates of non-max suppressed boxes. nmsed_scores : A [batch_size, max_detections] float32 tensor containing the scores for the boxes. nmsed_classes : A [batch_size, max_detections] float32 tensor containing the classes for the boxes. More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createSplitPlugin (int32_t axis, int32_t *output_lengths, int32_t noutput) |
| | The Split Plugin performs a split operation on the input tensor. It splits the input tensor into several output tensors, each of a length corresponding to output_lengths. The split occurs along the axis specified by axis. More...
|
| |
| TRT_DEPRECATED_API nvinfer1::IPluginV2 * | createInstanceNormalizationPlugin (float epsilon, nvinfer1::Weights scale_weights, nvinfer1::Weights bias_weights) |
| | The Instance Normalization Plugin computes the instance normalization of an input tensor. The instance normalization is calculated as found in the paper https://arxiv.org/abs/1607.08022. The calculation is y = scale * (x - mean) / sqrt(variance + epsilon) + bias where mean and variance are computed per instance per channel. More...
|
| |
| bool | initLibNvInferPlugins (void *logger, char const *libNamespace) |
| | Initialize and register all the existing TensorRT plugins to the Plugin Registry with an optional namespace. The plugin library author should ensure that this function name is unique to the library. This function should be called once before accessing the Plugin Registry. More...
|
| |
This is the API for the Nvidia provided TensorRT plugins.
The BatchedNMS Plugin performs non_max_suppression on the input boxes, per batch, across all classes. It greedily selects a subset of bounding boxes in descending order of score. Prunes away boxes that have a high intersection-over-union (IOU) overlap with previously selected boxes. Bounding boxes are supplied as [y1, x1, y2, x2], where (y1, x1) and (y2, x2) are the coordinates of any diagonal pair of box corners and the coordinates can be provided as normalized (i.e., lying in the interval [0, 1]) or absolute. The plugin expects two inputs. Input0 is expected to be 4-D float boxes tensor of shape [batch_size, num_boxes, q, 4], where q can be either 1 (if shareLocation is true) or num_classes. Input1 is expected to be a 3-D float scores tensor of shape [batch_size, num_boxes, num_classes] representing a single score corresponding to each box. The plugin returns four outputs. num_detections : A [batch_size] int32 tensor indicating the number of valid detections per batch item. 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, max_detections, 4] float32 tensor containing the co-ordinates of non-max suppressed boxes. nmsed_scores : A [batch_size, max_detections] float32 tensor containing the scores for the boxes. nmsed_classes : A [batch_size, max_detections] float32 tensor containing the classes for the boxes.
Registered plugin type "BatchedNMS_TRT". Registered plugin version "1".
The batched NMS plugin can require a lot of workspace due to intermediate buffer usage. To get the estimated workspace size for the plugin for a batch size, use the API plugin->getWorkspaceSize(batchSize).
- Deprecated:
- Deprecated in TensorRT 8.5. Use BatchedNMSPluginCreator::createPlugin() to create an instance of "BatchedNMS_TRT" version 1 plugin.
1.9.2