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.

Converting an ONNX File into TensorRT Engine

FTMS Client

SPECS=$(tao-client centerpose get-spec --action gen_trt_engine --job_type experiment --id $EXPERIMENT_ID)

See also

For information on how to create an experiment using the remote client, refer to the Creating an experiment section in the Remote Client documentation.

TAO Launcher

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

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

Parameter	Datatype	Default	Description	Supported Values
onnx_file	string		The precision to be used for the TensorRT engine
trt_engine	string		The maximum workspace size for the TensorRT engine
input_channel	unsigned int	3	The input channel size. Only the value 3 is supported.	3
input_width	unsigned int	512	The input width	>0
input_height	unsigned int	512	The input height	>0
batch_size	unsigned int	-1	The batch size of the ONNX model	>=-1

tensorrt

The tensorrt parameter defines TensorRT engine generation.

Parameter	Datatype	Default	Description	Supported Values
data_type	string	fp32	The precision to be used for the TensorRT engine	fp32/fp16/int8
workspace_size	unsigned int	1024	The maximum workspace size for the TensorRT engine	>1024
min_batch_size	unsigned int	1	The minimum batch size used for the optimization profile shape	>0
opt_batch_size	unsigned int	1	The optimal batch size used for the optimization profile shape	>0
max_batch_size	unsigned int	1	The maximum batch size used for the optimization profile shape	>0

calibration

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

Parameter	Datatype	Default	Description	Supported Values
cal_image_dir	string		The list of paths that contain images used for calibration
cal_cache_file	string		The path to the calibration cache file to be dumped
cal_batch_size	unsigned int	1	The batch size per batch during calibration	>0
cal_batches	unsigned int	1	The number of batches to calibrate	>0

Use the following command to run CenterPose engine generation:

FTMS Client

GEN_TRT_ENGINE_JOB_ID=$(tao-client centerpose experiment-run-action --action gen_trt_engine --id $EXPERIMENT_ID --specs "$SPECS")

See also

For information on how to create an experiment using the remote client, refer to the Creating an experiment section in the Remote Client documentation.

TAO Launcher

tao deploy centerpose gen_trt_engine -e /path/to/spec.yaml \
         results_dir=/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

• 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

Running Evaluation Through a TensorRT Engine

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:

FTMS Client

EVAL_JOB_ID=$(tao-client centerpose experiment-run-action --action evaluate --id $EXPERIMENT_ID --parent_job_id $GEN_TRT_ENGINE_JOB_ID --specs "$SPECS")

See also

For information on how to create an experiment using the remote client, refer to the Creating an experiment section in the Remote Client documentation.

TAO Launcher

tao deploy centerpose evaluate -e /path/to/spec.yaml \
         results_dir=/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

• 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 $DEFAULT_SPEC
            results_dir=$RESULTS_DIR \
            evaluate.trt_engine=$ENGINE_FILE

Running Inference Through a TensorRT Engine

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:

FTMS Client

INFERENCE_JOB_ID=$(tao-client centerpose experiment-run-action --action inference --id $EXPERIMENT_ID --parent_job_id $GEN_TRT_ENGINE_JOB_ID --specs "$SPECS")

See also

For information on how to create an experiment using the remote client, refer to the Creating an experiment section in the Remote Client documentation.

TAO Launcher

tao deploy centerpose inference -e /path/to/spec.yaml \
         results_dir=/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

• 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 $DEFAULT_SPEC
            results_dir=$RESULTS_DIR \
            inference.trt_engine=$ENGINE_FILE

The visualization results are stored in $RESULTS_DIR.