Deformable DETR with TAO Deploy

To generate an optimized TensorRT engine, a Deformable DETR ONNX file, which is first generated using tao model deformable_detr export, is taken as an input to tao deploy dino gen_trt_engine. For more information about training a Deformable DETR model, refer to the Deformable DETR training documentation.

Converting .onnx File into TensorRT Engine

FTMS ClientTAO Launcher

SPECS=$(tao-client deformable_detr 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 a value of 3 is supported.	3
input_width	unsigned int	960	The input width	>0
input_height	unsigned int	544	The input height	>0
batch_size	unsigned int	-1	The batch size of the ONNX model	>=-1

tensorrt

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

Parameter	Datatype	Default	Description	Supported Values
cal_image_dir	string list		The list of paths that contain images used for calibration
cal_cache_file	string		The path to 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 Deformable DETR engine generation:

FTMS ClientTAO Launcher

GEN_TRT_ENGINE_JOB_ID=$(tao-client deformable_detr 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 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
  conf_threshold: 0.0
  input_width: 960
  input_height: 544
dataset:
  test_data_sources:
    image_dir: /data/raw-data/val2017/
    json_file: /data/raw-data/annotations/instances_val2017.json
  num_classes: 91
  batch_size: 8

Use the following command to run Deformable DETR engine evaluation:

FTMS ClientTAO Launcher

EVAL_JOB_ID=$(tao-client deformable_detr 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 TensorRT Engine

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

inference:
  conf_threshold: 0.5
  input_width: 960
  input_height: 544
  trt_engine: /path/to/engine/file
  color_map:
    person: green
    car: red
    cat: blue
dataset:
  infer_data_sources:
    image_dir: /data/raw-data/val2017/
    classmap: /path/to/coco/annotations/coco_classmap.txt
  num_classes: 91
  batch_size: 8

Use the following command to run Deformable DETR engine inference:

FTMS ClientTAO Launcher

INFERENCE_JOB_ID=$(tao-client deformable_detr 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.