VisualChangeNet-Segmentation 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: 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:

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 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:

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: 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:

tao deploy visual_changenet inference -e /path/to/spec.yaml \
           results_dir=$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#

  • 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:

tao deploy visual_changenet inference -e $DEFAULT_SPEC
           results_dir=$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:

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:

tao deploy visual_changenet evaluate -e /path/to/spec.yaml \
           results_dir=$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#

  • 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:

tao deploy visual_changenet evaluate -e $DEFAULT_SPEC
           results_dir=$RESULTS_DIR \
           evaluate.trt_engine=$ENGINE_FILE

The visualization will be stored in $RESULTS_DIR/trt_evaluate.

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