About This Sample

This sample demonstrates how to use TensorRT and its including suite of parsers to perform inference with ResNet50 models trained with various different frameworks.

Parser Overview

TensorRT uses a suite of parsers to generate TensorRT networks from models trained in different frameworks.

UFF Parser

The UFF parser is used for TensorFlow models. After freezing a TensorFlow graph and writing it to a protobuf file, you can convert it to UFF with the convert-to-uff utility included with TensorRT. This sample ships with a pre-generated UFF file.

Caffe Parser

The Caffe parser is used for Caffe2 models. After training, you can invoke the caffe parser directly on the model file (usually .caffemodel) and deploy file (usually .prototxt).

ONNX Parser

The ONNX parser can be used with any framework that supports the ONNX format. It can be used with .onnx files.

Installing Prerequisites

1. Make sure you have the python dependencies installed.
   • For python2, run python2 -m pip install -r requirements.txt from the top-level of this sample.
   • For python3, run python3 -m pip install -r requirements.txt from the top-level of this sample.

Running the Samples

1. Create a TensorRT inference engine and run inference:

   python uff_resnet50.py [-d DATA_DIR]
   python caffe_resnet50.py [-d DATA_DIR]
   python onnx_resnet50.py [-d DATA_DIR]
   

   The data directory needs to be specified only if TensorRT is not installed in the default location.