DINO with TAO Deploy

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

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

gen_trt_engine

The gen_trt_engine parameter defines TensorRT engine generation.

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gen_trt_engine: onnx_file: /path/to/onnx_file trt_engine: /path/to/trt_engine input_channel: 3 input_width: 960 input_height: 544 tensorrt: data_type: int8 workspace_size: 1024 min_batch_size: 1 opt_batch_size: 10 max_batch_size: 10 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

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 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 list

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 DINO engine generation:

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tao deploy dino 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 will be dumped

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

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

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

Sample Usage

Here’s an example of using the gen_trt_engine command to generate an FP16 TensorRT engine:

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tao deploy dino 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:

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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 DINO engine evaluation:

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tao deploy dino 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 should 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 will be dumped

  • evaluate.trt_engine: The engine file for evaluation

Sample Usage

Here’s an example of using the evaluate command to run evaluation with a TensorRT engine:

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tao deploy dino 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:

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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 DINO engine inference:

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tao deploy dino 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 JSON status-log file and inference results will be dumped

  • inference.trt_engine: The engine file for inference

Sample Usage

Here’s an example of using the inference command to run inference with a TensorRT engine:

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

The visualization will be stored in $RESULTS_DIR/images_annotated, and the KITTI format predictions will be stored under $RESULTS_DIR/labels.

© Copyright 2023, NVIDIA.. Last updated on Jul 27, 2023.