Segformer with TAO Deploy

Segformer ONNX file generated from tao export is taken as an input to tao-deploy to generate optimized TensorRT engine. We do not support Int8 precision for Segformer.

Same spec file can be used as the tao model segformer export command.

trt_config

The gen_trt_engine parameter defines TensorRT engine generation.

            

            
gen_trt_engine:
  onnx_file: /path/to/onnx_file
  trt_engine: /path/to/trt_engine
  input_width: 512
  input_height: 512
  tensorrt:
    data_type: FP32
    workspace_size: 1024
    min_batch_size: 1
    opt_batch_size: 1
    max_batch_size: 1
        

tensorrt

The tensorrt parameter defines TensorRT engine generation.

Use the following command to run Segformer engine generation:

            

            
tao deploy segformer gen_trt_engine -e /path/to/spec.yaml \
           results_dir=/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

• 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:

            

            
tao deploy segformer 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
        

Same spec file as TAO evaluation/ inference spec file. Sample spec file:

            

            
model:
  input_height: 512
  input_width: 512
  backbone:
    type: "mit_b1"
dataset:
  img_norm_cfg:
    mean:
      - 127.5
      - 127.5
      - 127.5
    std:
      - 127.5
      - 127.5
      - 127.5
    test_dataset:
      img_dir: /data/images/val
      ann_dir: /data/masks/val
  input_type: "grayscale"
  data_root: /tlt-pytorch
  palette:
    - seg_class: foreground
      rgb:
        - 0
        - 0
        - 0
      label_id: 0
      mapping_class: foreground
    - seg_class: background
      rgb:
        - 255
        - 255
        - 255
      label_id: 1
      mapping_class: background
  batch_size: 1
  workers_per_gpu: 1
        

Use the following command to run Segformer engine evaluation:

            

            
tao deploy segformer evaluate -e /path/to/spec.yaml \
           results_dir=/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

• results_dir: The directory where the evaluat 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 segformer evaluate -e $DEFAULT_SPEC
           results_dir=$RESULTS_DIR \
           evaluate.trt_engine=$ENGINE_FILE
        

Use the following command to run SegFormer engine inference:

            

            
tao deploy segformer inference -e /path/to/spec.yaml \
           results_dir=/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

• 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

For inference, you can re-use the spec config mentioned under running_evaluation_through_tensorrt_engine. Here’s an example of using the inference command to run inference with a TensorRT engine:

            

            
tao deploy segformer inference -e $DEFAULT_SPEC
           results_dir=$RESULTS_DIR \
           inference.trt_engine=$ENGINE_FILE
        

The mask overlaid visualization will be stored under $RESULTS_DIR/vis_overlay and raw predictions in mask format will be stored under $RESULTS_DIR/mask_labels.