VisualChangeNet-Segmentation 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:

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            task: segment
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. Currently 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	256	The input width	>0
`input_height`	unsigned int	256	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:

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            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 is 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:

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            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:

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            task: segment
dataset:
  segment:
    dataset: "CNDataset"
    root_dir: /path/to/root/dataset/dir/
    data_name: "LEVIR-CD"
    label_transform: "norm"
    batch_size: 16
    workers: 2
    num_classes: 2
    img_size: 256
    image_folder_name: "A"
    change_image_folder_name: "B"
    list_folder_name: 'list'
    annotation_folder_name: "label"
    test_split: "test"
    predict_split: 'predict'
    label_suffix: .png
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:

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            tao deploy visual_changenet inference -e /path/to/spec.yaml \
           -r $RESULTS_DIR \
           inference.trt_engine=/path/to/engine/file

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.

Sample Usage

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

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

The visualization will be stored in $RESULTS_DIR/trt_inference.

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:

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            task: segment
dataset:
  segment:
    dataset: "CNDataset"
    root_dir: /path/to/root/dataset/dir/
    data_name: "LEVIR-CD"
    label_transform: "norm"
    batch_size: 16
    workers: 2
    num_classes: 2
    img_size: 256
    image_folder_name: "A"
    change_image_folder_name: "B"
    list_folder_name: 'list'
    annotation_folder_name: "label"
    test_split: "test"
    predict_split: 'predict'
    label_suffix: .png
evaluate:
  gpu_id: 0
  trt_engine: /path/to/engine/file
  results_dir: "${results_dir}/inference"

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

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            tao deploy visual_changenet evaluate -e /path/to/spec.yaml \
           -r $RESULTS_DIR \
           evaluate.trt_engine=/path/to/engine/file \

Required Arguments

-e, --experiment_spec: The experiment spec file for evaluation. This must 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 visual_changenet evaluate -e $DEFAULT_SPEC
           -r $RESULTS_DIR \
           evaluate.trt_engine=$ENGINE_FILE

The visualization will be stored in $RESULTS_DIR/trt_evaluate.