Maskrcnn (segmentation) using a converted model in c++ without DeepStream

Hi,

Using maskrcnn from tlt v2, on jetson, following the notebook training in the user guide.
— NOT USING DEEPSTREAM —

  1. Say I managed to convert (using tlt-converter) the model . How do I parse the results in my c/c++ code ? That is, I enqueue the input to the model, and copy the results (say using cudaMemcpyAsync) to host buffer. What format is the data? (in classification its easy - prob/class, in detection also ok - class,prob and bb) Is there any documentation on this ? Just to make clear - I am not using DeepStream.

  2. Another quick q - can I change the input size of the model in the maskrcnn_train_resnet50.txt ? I mean, is it size insensitive like YOLO (up to the x32 multiplication ) ?

Thanks for the help !

  1. See https://docs.nvidia.com/metropolis/TLT/tlt-getting-started-guide/text/deploying_to_deepstream.html#generating-an-engine-using-tlt-converter

For classification use: predictions/Softmax.

  • For DetectNet_v2: output_bbox/BiasAdd,output_cov/Sigmoid
  • For FasterRCNN: dense_class_td/Softmax,dense_regress_td/BiasAdd, proposal
  • For SSD, DSSD, RetinaNet: NMS
  • For YOLOv3: BatchedNMS
  • For MaskRCNN: generate_detections, mask_head/mask_fcn_logits/BiasAdd
  1. Yes, you can change the input size, but currently, the width or height should be multiples of 64.
    Refer to Error running MaskRCNN inference after custom training

Great, thanks.