VisualChangeNet-Classification with TAO Deploy

To generate an optimized TensorRT engine:

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

Copy
Copied!

            
            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:

Copy
Copied!

            
            tao deploy visual_changenet gen_trt_engine -e /path/to/spec.yaml \
           -r /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:

Copy
Copied!

            
            tao deploy visual_changenet gen_trt_engine -e $DEFAULT_SPEC
           -r $RESULTS_DIR
           gen_trt_engine.onnx_file=$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:

Copy
Copied!

            
            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:

Copy
Copied!

            
            tao deploy visual_changenet inference -e /path/to/spec.yaml \
           -r $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

-r, --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:

Copy
Copied!

            
            tao deploy visual_changenet inference -e $DEFAULT_SPEC
           -r $RESULTS_DIR
           inference.trt_engine=$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:

Copy
Copied!

            
            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:

Copy
Copied!

            
            tao deploy visual_changenet evaluate -e /path/to/spec.yaml \
           -r $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

-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.
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:

Copy
Copied!

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