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 ClientTAO Launcher

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.

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 ClientTAO Launcher

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.

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 ClientTAO Launcher

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.

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 ClientTAO Launcher

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.