Custom segmentation model BiSeNet gives empty ouput

Accelerated Computing Intelligent Video Analytics DeepStream SDK
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Please provide complete information as applicable to your setup.

• Hardware Platform (Jetson / GPU) jetson xavier
• DeepStream Version 6.0
• JetPack Version (valid for Jetson only) 4.6.1
• TensorRT Version 8.2.1
• NVIDIA GPU Driver Version (valid for GPU only)
• Issue Type( questions, new requirements, bugs) bug

• How to reproduce the issue ? (This is for bugs. Including which sample app is using, the configuration files content, the command line used and other details for reproducing)

Tried to use a custom segmentation model BiSeNet in deepstream. getting an empty screen as an output in nvsegvisual.

[property]
gpu-id=0
network-input-order=0
offsets=0.485;0.456;0.406
net-scale-factor=0.00392156863
infer-dims=3;320;410
model-color-format=1
onnx-file=model_semifinal_220k.onnx
#model-engine-file=saved_model.trt

batch-size=1
## 0=FP32, 1=INT8, 2=FP16 mode
network-mode=2
interval=0
network-type=2
gie-unique-id=1

num-detected-classes=19
output-blob-names=preds
segmentation-threshold=0.2
segmentation-output-order=1

model_semifinal_220k.onnx (12.8 MB)
saved_model.trt (9.9 MB)

• Requirement details( This is for new requirement. Including the module name-for which plugin or for which sample application, the function description)

change config file in segmentation test.

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  1. is your model bisenetv1 or bisenetv2? please make sure the preprocess parameters are right. please verify your trt can work.
  2. please refer to deepstream sample deepstream-segmentation-test and deepstream_tao_apps/apps/tao_segmentation at master · NVIDIA-AI-IOT/deepstream_tao_apps · GitHub
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hi @fanzh ,
i am currently using bisenetv2, trt is working. Tried changing preprocess parameters as to match BiSeNet/segment.cpp at master · CoinCheung/BiSeNet · GitHub params. cannot find any parameter for variance in nvinfer so changed net-scale-factor and offsets accordingly but still getting same issue.

what i did to input-

  • took value of variance as 0.225
  • divided all offset values by scale
  • multiplied scale by variance

why-

  • deepstream uses preprocessing as y = net scale factor*(x-mean)
  • in bisenet prepprocessing is given as -
    array<float, 3> mean{0.485f, 0.456f, 0.406f};
    array<float, 3> variance{0.229f, 0.224f, 0.225f};
    float scale = 1.f / 255.f;
    for (int i{0}; i < 3; ++ i) {
        variance[i] = 1.f / variance[i];
    }
    vector<float> data(iH * iW * 3);
    for (int h{0}; h < iH; ++h) {
        cv::Vec3b *p = im.ptr<cv::Vec3b>(h);
        for (int w{0}; w < iW; ++w) {
            for (int c{0}; c < 3; ++c) {
                int idx = (2 - c) * iH * iW + h * iW + w; // to rgb order
                data[idx] = (p[w][c] * scale - mean[c]) * variance[c];
            }
        }
    }

looked at sample apps for segmentation they work in same way as my code only diffrence is in config file and model as i am using BiSeNet and they use Unet.

thanks for help

5

  1. please set segmentation-output-order=0, pleae find it in Gst-nvinfer — DeepStream 6.1.1 Release documentation.
    nvinfer is opensource, you can print the classes number in SegmentPostprocessor::fillSegmentationOutput() of nvdsinfer_context_impl_output_parsing.cpp, this model 's classes should be 19.

  2. and deepstream only supports this kind of pre-processing y = net scale factor*(x-mean), please refer to Gst-nvinfer — DeepStream 6.1.1 Release documentation

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i tried printing data from SegmentPostprocessor::fillSegmentationOutput() ,it output width and height as 0. can you help how to solve this

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can’t reproduce this issue after testing bisenetv2 cityscapes, here are cfg and output.
dstest_segmentation_config_semantic.txt (3.1 KB)
w 2048, 1024,19

9

how did you generate your onnx file.

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after using your configuration too i am getting :
out height 0 output width0
out classes 1635021889

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download model_final_v2_city.pth, then generate onnx according to this BiSeNet/tensorrt at master · CoinCheung/BiSeNet · GitHub

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There is no update from you for a period, assuming this is not an issue any more.
Hence we are closing this topic. If need further support, please open a new one.
Thanks

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