plugins are skipped

AI & Data Science Deep Learning (Training & Inference) TensorRT
1

Hi,

I define three plugin type layers.

one of the plugin is like

layer {
  name: "p5/upsample"
  type: "IPlugin"
  bottom: "p5/reduce"
  top: "p5/upsample"
}

and one of the plugin is like

layer {
  name: "roi_pooling"
  type: "IPlugin"
  bottom: "p2"
  bottom: "p3"
  bottom: "p4"
  bottom: "p5"
  bottom: "all_rois"
  bottom: "all_scores"
  top: "all_roi_pooling"
}

but when creating the plugin, the roi_pooling plugin layer is omitted by pluginFactory.

class PluginFactory : public nvinfer1::IPluginFactory, public nvcaffeparser1::IPluginFactory
{
public:
	// deserialization plugin implementation
	virtual nvinfer1::IPlugin* createPlugin(const char* layerName, const nvinfer1::Weights* weights, int nbWeights) override
	{
          LOG(INFO) << "createPlugin " << layerName; // this prints both upsample and roi_pooling plugin
          assert(isPlugin(layerName));
          // create the instance here
         }

	IPlugin* createPlugin(const char* layerName, const void* serialData, size_t serialLength) override
	{
          LOG(INFO) << "createPlugin " << layerName; // this prints only upsample plugin
          assert(isPlugin(layerName));
          // create the instance here
        }

}

Moreover, when calling serialize by gie engine.

like

gieModelStream = engine->serialize();

the roi pooling plugin does not execute plugin API configure, getWorkSpace and initialize.

for example

template<typename Dtype>
class ROIPool: public IPlugin {
public:
    ROIPool(int proposal_limit, vector<float> scales, int base_roi_scale, int pool_h, int pool_w, float rpn_nms_thresh)
    {
      int getNbOutputs() const override
      {
        LOG(INFO) << "ROIPool getNbOutputs"; // got executed
        return 1;
      }
      Dims getOutputDimensions(int index, const Dims* inputs, int nbInputDims) override
      {
        LOG(INFO) << "roi pool getOutputDimensions"; // got executed
        CHECK(nbInputDims == 6);
        CHECK(inputs[0].nbDims == 3);
        CHECK(inputs[1].nbDims == 3);
        CHECK(inputs[2].nbDims == 3);
        CHECK(inputs[3].nbDims == 3);
        // rois
        CHECK(inputs[4].nbDims == 2);
        // scores
        CHECK(inputs[5].nbDims == 2);
        CHECK(inputs[4].d[0] == inputs[5].d[0]);
        CHECK(index == 0);
        LOG(INFO) << "proposal_limit " << proposal_limit_;
        return Dims4(proposal_limit_, inputs[0].d[0], pooled_height_, pooled_width_);
      }
      void configure(const Dims*inputs, int nbInputs, const Dims* outputs, int nbOutputs, int)	override
      {
        LOG(INFO) << "roi pool configure"; // not executed
        for (int i = 0; i < nbInputs - 2; i++) {
          height_.push_back(inputs[i].d[1]);
          width_.push_back(inputs[i].d[2]);
        }
        num_rois_ = inputs[4].d[0];
        channels_ = inputs[0].d[0];
        output_count_ = channels_ * pooled_height_ * pooled_width_;
      }

      virtual size_t getWorkspaceSize(int maxBatchSize) const override
      {
         LOG(INFO) << "roi pool getWorkspaceSize"; // not executed
         return 0;
      }

      int initialize() override
      {
        LOG(INFO) << "ROIPool initialize"; // not executed
      
        return 0;
      }

    }

Any idea?