VisualChangeNet-Classification with TAO Deploy#

To generate an optimized TensorRT engine:

  1. Generate a VisualChangeNet .onnx file using tao model visual_changenet export.

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

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

Converting an ONNX File into TensorRT Engine#

gen_trt_engine#

The following is an example configuration file for generating the TensorRT Engine:

task: classify
gen_trt_engine:
  results_dir: "${results_dir}/gen_trt_engine"
  onnx_file: "${results_dir}/export/changenet_model.onnx"
  trt_engine: "${results_dir}/gen_trt_engine/changenet.trt"
  input_channel: 3
  input_width: 128
  input_height: 512
  tensorrt:
    data_type: fp32
    workspace_size: int = 1024
    min_batch_size: int = 1
    opt_batch_size: int = 1
    max_batch_size: int = 1

The task section defines the change detection task for which the .onnx model was generated.

Parameter

Data Type

Default

Description

task

str

classify

A flag to indicate the change detection task. Supports two tasks: ‘segment’ and ‘classify’ for segmentation and classification.

The gen_trt_engine section in the experiment specification file provides options for generating a TensorRT engine from an .onnx file.

Parameter

Datatype

Default

Description

Supported Values

results_dir

string

–

The path to the results directory

–

onnx_file

string

–

The path to the exported ETLT or ONNX model

–

trt_engine

string

–

The absolute path to the generated TensorRT engine

–

input_channel

unsigned int

3

The input channel size. Only a value of 3 is supported.

3

input_width

unsigned int

128

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

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

Use the following command to run VisualChangeNet engine generation:

tao deploy visual_changenet gen_trt_engine -e /path/to/spec.yaml \
           results_dir=/path/to/result_dir \
           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_file: The path to the experiment spec file.

  • results_dir: The global results directory. The engine generation log will be saved in the results_dir.

  • 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 fp32 TensorRT engine:

tao deploy visual_changenet gen_trt_engine -e $DEFAULT_SPEC
           results_dir=$RESULTS_DIR
           gen_trt_engine.onnx_file=$ONNX_FILE \
           gen_trt_engine.trt_engine=$ENGINE_FILE \
           gen_trt_engine.tensorrt.data_type=fp32

Running Inference through TensorRT Engine#

You can reuse the spec file that was specified for TAO inference. The following is an example inference spec:

task: classify
model:
  classify:
    eval_margin: 0.5
dataset:
  classify:
    infer_dataset:
      csv_path: /path/to/infer.csv
      images_dir: /path/to/img_dir
    image_ext: .jpg
    batch_size: 16
    workers: 2
    num_input: 4
    input_map:
      LowAngleLight: 0
      SolderLight: 1
      UniformLight: 2
      WhiteLight: 3
    concat_type: linear
    grid_map:
      x: 2
      y: 2
    output_shape:
      - 128
      - 128
    augmentation_config:
      rgb_input_mean: [0.485, 0.456, 0.406]
      rgb_input_std: [0.229, 0.224, 0.225]
    num_classes: 2
inference:
  gpu_id: 0
  trt_engine: /path/to/engine/file
  results_dir: "${results_dir}/inference"

Use the following command to run VisualChangeNet-Classification engine inference:

tao deploy visual_changenet inference -e /path/to/spec.yaml \
           results_dir=$RESULTS_DIR \
           inference.trt_engine=/path/to/engine/file \
           model.classify.eval_margin=0.5

Required Arguments#

  • -e, --experiment_spec_file: The path to the experiment spec file. This should be the same as the tao inference spec file.

Optional Arguments#

  • results_dir: The directory where JSON status-log file and inference results will be dumped.

  • inference.trt_engine: The engine file for inference.

  • model.classify.eval_margin: The evaluation threshold for VisualChangeNet-Classification.

Sample Usage#

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

tao deploy visual_changenet inference -e $DEFAULT_SPEC
           results_dir=$RESULTS_DIR
           inference.trt_engine=$ENGINE_FILE
           model.classify.eval_margin=$EVAL_MARGIN

Running Evaluation through a TensorRT Engine#

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

task: classify
model:
  classify:
    eval_margin: 0.5
dataset:
  classify:
    infer_dataset:
      csv_path: /path/to/infer.csv
      images_dir: /path/to/img_dir
    image_ext: .jpg
    batch_size: 16
    workers: 2
    num_input: 4
    input_map:
      LowAngleLight: 0
      SolderLight: 1
      UniformLight: 2
      WhiteLight: 3
    concat_type: linear
    grid_map:
      x: 2
      y: 2
    output_shape:
      - 128
      - 128
    augmentation_config:
      rgb_input_mean: [0.485, 0.456, 0.406]
      rgb_input_std: [0.229, 0.224, 0.225]
    num_classes: 2
evaluate:
  gpu_id: 0
  trt_engine: /path/to/engine/file
  results_dir: "${results_dir}/inference"

Use the following command to run VisualChangeNet-Classification engine evaluation:

tao deploy visual_changenet evaluate -e /path/to/spec.yaml \
           results_dir=$RESULTS_DIR \
           evaluate.trt_engine=/path/to/engine/file \
           model.classify.eval_margin=0.5

Required Arguments#

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

Optional Arguments#

  • results_dir: The directory where the JSON status-log file and evaluation results will be dumped.

  • evaluate.trt_engine: The engine file for evaluation.

  • model.classify.eval_margin: The evaluation threshold for VisualChangeNet-Classification.

Sample Usage#

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

tao deploy visual_changenet evaluate -e $DEFAULT_SPEC
           results_dir=$RESULTS_DIR \
           evaluate.trt_engine=$ENGINE_FILE
           model.classify.eval_margin=0=$EVAL_MARGIN
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