TLT Converter UffParser: Unsupported number of graph 0

Accelerated Computing Intelligent Video Analytics TAO Toolkit
1

Hi, there!

Currently, I am trying to convert my .etlt model to .trt inside a Jetson TX2.

In my Jupyter notebook, I performed Int8 optimization by executing this code:

!tlt-int8-tensorfile detectnet_v2 -e $SPECS_DIR/detectnet_v2_train_resnet18_kitti_OCR_third_trial_2.txt \
                                  -m 10 \
                                  -o $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/calibration.tensor \

Then, we exported the model into .etlt by executing this code:

!tlt-export $USER_EXPERIMENT_DIR/experiment_third_trial_OCR/weights/resnet18_detector_OCR_third_trial.tlt \
            -o $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/resnet18_detector_jeff.etlt \
            --outputs output_cov/Sigmoid,output_bbox/BiasAdd \
            --enc_key $KEY \
            --input_dims 3,320,832 \
            --max_workspace_size 1100000 \
            --export_module detectnet_v2 \
            --cal_data_file $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/calibration.tensor \
            --data_type int8 \
            --batches 10 \
            --cal_cache_file $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/calibration.bin \
            --cal_batch_size 4 \
            --verbose

After that, we tried to generate TensorRT engine by executed this code inside the Jupyter:

!tlt-converter $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/resnet18_detector_jeff.etlt \
               -k $KEY \
               -c $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/calibration.bin \
               -o output_cov/Sigmoid,output_bbox/BiasAdd \
               -d 3,320,832 \
               -i nchw \
               -m 64 \
               -t int8 \
               -e $USER_EXPERIMENT_DIR/experiment_dir_final_OCR/resnet18_detector.trt \
               -b 4

All seems to work inside Jupyter x86. Then, we tried to generate TensorRT engine from Jetson TX2, so we tried to execute this code:

./tlt-converter ./resnet18_detector_jeff.etlt \
               -k XXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXXX \
               -c ./calibration.bin \
               -o output_cov/Sigmoid,output_bbox/BiasAdd \
               -d 3,320,832 \
               -i nchw \
               -m 64 \
               -t int8 \
               -e ./resnet18_detector_jetson.trt \
               -b 4

Unfortunately, we met this problem:

[ERROR] UffParser: Unsupported number of graph 0
[ERROR] Failed to parse uff model
[ERROR] Network must have at least one output
[ERROR] Unable to create engine

Do you have any insights about this problem? I would appreciate any ideas.
(Note: by they way, the Readme.md inside https://developer.nvidia.com/tlt-converter is broken.)

Best regards,
Jeff

2

Hi jefflgaol,
What do you mean by “the Readme.md inside https://developer.nvidia.com/tlt-converter is broken”?

Please note that, we must use Jetson platform version of tlt-converter when you want to generate trt engine in Jetson platform.

3

When I accessed the Readme.md, it looked like this:

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That’s why this How to export model using tlt-converter for Jetson Nano - TAO Toolkit - NVIDIA Developer Forums said :
“Hi Guys,
I am training a custom object detection model (resnet10 , detectnet_v2) on my x86 machine. I wish to use the trained models on Jetson Nano. In the sample code, there is an instruction to download tlt-converter for Jetson platform. I downloaded it but the readme is not readbale.
Kindly let me know the instructions to use ‘tlt-converter’ for Jetson Nano.
Thanks.”

Also, I already ran the trt-converter inside Jetson TX2, but got this result:

[ERROR] UffParser: Unsupported number of graph 0
[ERROR] Failed to parse uff model
[ERROR] Network must have at least one output
[ERROR] Unable to create engine

Do you have any insights?

Best regards,
Jeff

4

I will check the Readme.md later.
Anyway, if you can download the tlt-converter successfully, you can check its “help” along with tlt user guide for its usage.

For the error, please check

  1. The $KEY is really set when you train the etlt model. Also make sure it is correct.
  2. The key is correct when you run tlt-converter. The key should be exactly the same as used in the TLT training phase
  3. The etlt model is available

Reference topic:
https://devtalk.nvidia.com/default/topic/1067539/transfer-learning-toolkit/tlt-converter-on-jetson-nano-error-/
https://devtalk.nvidia.com/default/topic/1065680/transfer-learning-toolkit/tlt-converter-uff-parser-error/?offset=11#5397152