Add switch for batch agnostic mode in NMS plugin

Signed-off-by: Rajeev Rao <[email protected]>

CHANGELOG.md

@@ -13,6 +13,7 @@
   - Algorithms optimization for NMS kernels and ROIAlign kernel
   - Fix invalid cuda config issue when bs is larger than 32
   - Fix issues found  on Jetson NANO
+- Add switch for batch-agnostic mode in NMS plugin
 
 ### Removed
 - Removed fcplugin from demoBERT to improve latency

include/NvInferPluginUtils.h

@@ -177,6 +177,7 @@ enum class CodeTypeSSD : int32_t
 //! \param inputOrder Specifies the order of inputs {loc_data, conf_data, priorbox_data}.
 //! \param confSigmoid Set to true to calculate sigmoid of confidence scores.
 //! \param isNormalized Set to true if bounding box data is normalized by the network.
+//! \param isBatchAgnostic Defaults to true. Set to false if prior boxes are unique per batch
 //!
 struct DetectionOutputParameters
 {
@@ -187,6 +188,7 @@ struct DetectionOutputParameters
     int32_t inputOrder[3];
     bool confSigmoid;
     bool isNormalized;
+    bool isBatchAgnostic{true};
 };
 
 //!

plugin/common/kernels/decodeBBoxes.cu

@@ -87,7 +87,8 @@ __launch_bounds__(nthds_per_cta)
         const bool clip_bbox,
         const T_BBOX* loc_data,
         const T_BBOX* prior_data,
-        T_BBOX* bbox_data)
+        T_BBOX* bbox_data,
+        const bool batch_agnostic)
 {
     for (int index = blockIdx.x * nthds_per_cta + threadIdx.x;
          index < nthreads;
@@ -113,7 +114,7 @@ __launch_bounds__(nthds_per_cta)
         // do not assume each images' anchor boxes are identical
         // e.g., in FasterRCNN, priors are ROIs from proposal layer and are different
         // for each image.
-        const int pi = (batch * 2 * num_priors + d) * 4;
+        const int pi = batch_agnostic ? d * 4 : (batch * 2 * num_priors + d) * 4;
         // Index to the right variances corresponding to the current bounding box
         const int vi = pi + num_priors * 4;
         // Encoding method: CodeTypeSSD::CORNER
@@ -296,15 +297,16 @@ pluginStatus_t decodeBBoxes_gpu(
     const bool clip_bbox,
     const void* loc_data,
     const void* prior_data,
-    void* bbox_data)
+    void* bbox_data,
+    const bool batch_agnostic)
 {
     const int BS = 512;
     const int GS = (nthreads + BS - 1) / BS;
     decodeBBoxes_kernel<T_BBOX, BS><<<GS, BS, 0, stream>>>(nthreads, code_type, variance_encoded_in_target,
                                                            num_priors, share_location, num_loc_classes,
                                                            background_label_id, clip_bbox,
                                                            (const T_BBOX*) loc_data, (const T_BBOX*) prior_data,
-                                                           (T_BBOX*) bbox_data);
+                                                           (T_BBOX*) bbox_data, batch_agnostic);
     CSC(cudaGetLastError(), STATUS_FAILURE);
     return STATUS_SUCCESS;
 }
@@ -321,7 +323,8 @@ typedef pluginStatus_t (*dbbFunc)(cudaStream_t,
                                const bool,
                                const void*,
                                const void*,
-                               void*);
+                               void*,
+                               const bool);
 
 struct dbbLaunchConfig
 {
@@ -361,7 +364,8 @@ pluginStatus_t decodeBBoxes(
     const DataType DT_BBOX,
     const void* loc_data,
     const void* prior_data,
-    void* bbox_data)
+    void* bbox_data,
+    const bool batch_agnostic)
 {
     dbbLaunchConfig lc = dbbLaunchConfig(DT_BBOX);
     for (unsigned i = 0; i < dbbLCOptions.size(); ++i)
@@ -380,7 +384,8 @@ pluginStatus_t decodeBBoxes(
                                           clip_bbox,
                                           loc_data,
                                           prior_data,
-                                          bbox_data);
+                                          bbox_data,
+                                          batch_agnostic);
         }
     }
     return STATUS_BAD_PARAM;

plugin/common/kernels/detectionForward.cu

@@ -41,7 +41,8 @@ pluginStatus_t detectionInference(
     void* workspace,
     bool isNormalized,
     bool confSigmoid,
-    int scoreBits)
+    int scoreBits,
+    const bool isBatchAgnostic)
 {
     // Batch size * number bbox per sample * 4 = total number of bounding boxes * 4
     const int locCount = N * C1;
@@ -70,7 +71,8 @@ pluginStatus_t detectionInference(
                                       DT_BBOX,
                                       locData,
                                       priorData,
-                                      bboxDataRaw);
+                                      bboxDataRaw,
+                                      isBatchAgnostic);
 
     ASSERT_FAILURE(status == STATUS_SUCCESS);
 
@@ -246,7 +248,8 @@ namespace plugin
     void* workspace,
     bool isNormalized,
     bool confSigmoid,
-    int scoreBits)
+    int scoreBits,
+    const bool isBatchAgnostic)
 {
     // Batch size * number bbox per sample * 4 = total number of bounding boxes * 4
     const int locCount = N * C1;
@@ -275,7 +278,8 @@ namespace plugin
                                       DT_BBOX,
                                       locData,
                                       priorData,
-                                      bboxDataRaw);
+                                      bboxDataRaw,
+                                      isBatchAgnostic);
 
     ASSERT_FAILURE(status == STATUS_SUCCESS);
 

plugin/common/kernels/kernel.h

@@ -43,7 +43,7 @@ pluginStatus_t detectionInference(cudaStream_t stream, int N, int C1, int C2, bo
     bool varianceEncodedInTarget, int backgroundLabelId, int numPredsPerClass, int numClasses, int topK, int keepTopK,
     float confidenceThreshold, float nmsThreshold, CodeTypeSSD codeType, DataType DT_BBOX, const void* locData,
     const void* priorData, DataType DT_SCORE, const void* confData, void* keepCount, void* topDetections,
-    void* workspace, bool isNormalized = true, bool confSigmoid = false, int scoreBits = 16);
+    void* workspace, bool isNormalized = true, bool confSigmoid = false, int scoreBits = 16, const bool isBatchAgnostic = true);
 
 pluginStatus_t nmsInference(cudaStream_t stream, int N, int boxesSize, int scoresSize, bool shareLocation,
     int backgroundLabelId, int numPredsPerClass, int numClasses, int topK, int keepTopK, float scoreThreshold,
@@ -84,7 +84,7 @@ size_t detectionForwardPostNMSSize(int N, int numClasses, int topK);
 
 pluginStatus_t decodeBBoxes(cudaStream_t stream, int nthreads, CodeTypeSSD code_type, bool variance_encoded_in_target,
     int num_priors, bool share_location, int num_loc_classes, int background_label_id, bool clip_bbox, DataType DT_BBOX,
-    const void* loc_data, const void* prior_data, void* bbox_data);
+    const void* loc_data, const void* prior_data, void* bbox_data, const bool batch_agnostic);
 
 size_t normalizePluginWorkspaceSize(bool acrossSpatial, int C, int H, int W);
 

plugin/nmsPlugin/nmsPlugin.cpp

@@ -567,6 +567,7 @@ NMSBasePluginCreator::NMSBasePluginCreator() noexcept
     mPluginAttributes.emplace_back(PluginField("isNormalized", nullptr, PluginFieldType::kINT32, 1));
     mPluginAttributes.emplace_back(PluginField("codeType", nullptr, PluginFieldType::kINT32, 1));
     mPluginAttributes.emplace_back(PluginField("scoreBits", nullptr, PluginFieldType::kINT32, 1));
+    mPluginAttributes.emplace_back(PluginField("isBatchAgnostic", nullptr, PluginFieldType::kINT32, 1));
     mFC.nbFields = mPluginAttributes.size();
     mFC.fields = mPluginAttributes.data();
 }
@@ -684,6 +685,11 @@ IPluginV2Ext* NMSPluginCreator::createPlugin(const char* name, const PluginField
             ASSERT(fields[i].type == PluginFieldType::kINT32);
             mScoreBits = *(static_cast<const int32_t*>(fields[i].data));
         }
+        else if (!strcmp(attrName, "isBatchAgnostic"))
+        {
+            ASSERT(fields[i].type == PluginFieldType::kINT32);
+            params.isBatchAgnostic = static_cast<int>(*(static_cast<const int*>(fields[i].data)));
+        }
     }
 
     DetectionOutput* obj = new DetectionOutput(params);