Preprocessing Using Python Backend Example

Preprocessing Using Python Backend Example#

This example shows how to preprocess your inputs using Python backend before it is passed to the TensorRT model for inference. This ensemble model includes an image preprocessing model (preprocess) and a TensorRT model (resnet50_trt) to do inference.

1. Converting PyTorch Model to ONNX format:

Run to convert ResNet50 PyTorch model to ONNX format. Width and height dims are fixed at 224 but dynamic axes arguments for dynamic batching are used. Commands from the 2. and 3. subsections shall be executed within this Docker container.

docker run -it --gpus=all -v $(pwd):/workspace bash
pip install numpy pillow torchvision
python --save model.onnx

2. Create the model repository:

mkdir -p model_repository/ensemble_python_resnet50/1
mkdir -p model_repository/preprocess/1
mkdir -p model_repository/resnet50_trt/1

# Copy the Python model
cp model_repository/preprocess/1

3. Build a TensorRT engine for the ONNX model

Set the arguments for enabling fp16 precision –fp16. To enable dynamic shapes use –minShapes, –optShapes, and maxShapes with –explicitBatch:

trtexec --onnx=model.onnx --saveEngine=./model_repository/resnet50_trt/1/model.plan --explicitBatch --minShapes=input:1x3x224x224 --optShapes=input:1x3x224x224 --maxShapes=input:256x3x224x224 --fp16

4. Run the command below to start the server container:

Under python_backend/examples/preprocessing, run this command to start the server docker container:

docker run --gpus=all -it --rm -p8000:8000 -p8001:8001 -p8002:8002 -v$(pwd):/workspace/ -v/$(pwd)/model_repository:/models bash
pip install numpy pillow torchvision
tritonserver --model-repository=/models

5. Start the client to test:

Under python_backend/examples/preprocessing, run the commands below to start the client Docker container:

wget -O "mug.jpg"
docker run --rm --net=host -v $(pwd):/workspace/ python --image mug.jpg
The result of classification is:COFFEE MUG

Here, since we input an image of “mug” and the inference result is “COFFEE MUG” which is correct.