CenterPose with TAO Deploy

To generate an optimized TensorRT engine:

1. Generate a CenterPose .onnx file using tao model centerpose export.

2. Specify the .onnx file as the input to tao deploy centerpose gen_trt_engine.

For more information about training a CenterPose model, refer to the CenterPose training documentation.

To convert the .onnx file, you can reuse the spec file from the tao model centerpose export command.

gen_trt_engine

The gen_trt_engine parameter defines TensorRT engine generation.

            

            
gen_trt_engine:
 onnx_file: /path/to/onnx_file
 trt_engine: /path/to/trt_engine
 input_channel: 3
 input_width: 512
 input_height: 512
 tensorrt:
   data_type: fp32
   workspace_size: 1024
   min_batch_size: 1
   opt_batch_size: 2
   max_batch_size: 4
   calibration:
     cal_image_dir: /path/to/cal/images
     cal_cache_file: /path/to/cal.bin
     cal_batch_size: 10
     cal_batches: 1000
        

tensorrt

The tensorrt parameter defines TensorRT engine generation.

calibration

The calibration parameter defines TensorRT engine generation with PTQ INT8 calibration.

Use the following command to run CenterPose engine generation:

            

            
tao deploy centerpose gen_trt_engine -e /path/to/spec.yaml \
           -r /path/to/results \
           gen_trt_engine.onnx_file=/path/to/onnx/file \
           gen_trt_engine.trt_engine=/path/to/engine/file \
           gen_trt_engine.tensorrt.data_type=<data_type>
        

Required Arguments

• -e, --experiment_spec: The experiment spec file to set up TensorRT engine generation.

Optional Arguments

• -r, --results_dir: The directory where the JSON status-log file is saved.

• gen_trt_engine.onnx_file: The .onnx model to be converted.

• gen_trt_engine.trt_engine: The path where the generated engine is stored.

• gen_trt_engine.tensorrt.data_type: The precision to be exported.

Sample Usage

The following is an example of using the gen_trt_engine command to generate an FP16 TensorRT engine:

            

            
tao deploy centerpose gen_trt_engine -e $DEFAULT_SPEC
           gen_trt_engine.onnx_file=$ONNX_FILE \
           gen_trt_engine.trt_engine=$ENGINE_FILE \
           gen_trt_engine.tensorrt.data_type=FP16
        

You can reuse the TAO evaluation spec file for evaluation through a TensorRT engine. The following is a sample spec file:

            

            
evaluate:
  trt_engine: /path/to/engine/file
  opencv: False
  eval_num_symmetry: 1
  results_dir: /path/to/save/results
dataset:
  test_data: /path/to/testing/images/and/json/files
  batch_size: 2
  workers: 4
        

Use the following command to run CenterPose engine evaluation:

            

            
tao deploy centerpose evaluate -e /path/to/spec.yaml \
           -r /path/to/results \
           evaluate.trt_engine=/path/to/engine/file
        

Required Arguments

• -e, --experiment_spec: The experiment spec file for evaluation. This must be the same as the tao evaluate spec file.

Optional Arguments

• -r, --results_dir: The directory where the JSON status-log file and evaluation results are saved.

• evaluate.trt_engine: The engine file for evaluation.

Sample Usage

The following is an example of using the evaluate command to run evaluation with a TensorRT engine:

            

            
tao deploy centerpose evaluate -e $DEFAULT_SPEC
           -r $RESULTS_DIR \
           evaluate.trt_engine=$ENGINE_FILE
        

You can reuse the TAO inference spec file for inference through a TensorRT engine. The following is a sample spec file:

            

            
inference:
  trt_engine: /path/to/engine/file
  visualization_threshold: 0.3
  principle_point_x: 298.3
  principle_point_y: 392.1
  focal_length_x: 651.2
  focal_length_y: 651.2
  skew: 0.0
  axis_size: 0.5
  use_pnp: True
  save_json: True
  save_visualization: True
  opencv: True
dataset:
  inference_data: /path/to/inference/files
  batch_size: 1
  workers: 4
        

Use the following command to run CenterPose engine inference:

            

            
tao deploy centerpose inference -e /path/to/spec.yaml \
           -r /path/to/results \
           inference.trt_engine=/path/to/engine/file
        

Required Arguments

• -e, --experiment_spec: The experiment spec file for inference. This should be the same as the tao inference spec file.

Optional Arguments

• -r, --results_dir: The directory where the JSON status-log file and inference results are saved.

• inference.trt_engine: The engine file for inference.

Sample Usage

The following is an example of using the inference command to run inference with a TensorRT engine:

            

            
tao deploy centerpose inference -e $DEFAULT_SPEC
           -r $RESULTS_DIR \
           evaluate.trt_engine=$ENGINE_FILE
        

The visualization results are stored in $RESULTS_DIR.