jalywang123 / yolov3_onnx_tensorrt

Modifications

1. fix onnx version to work with Upsample layer
2. change rb to r to avoid file parsing problems
3. remove checksum

original code link

Object Detection With The ONNX TensorRT Backend In Python

Table Of Contents

• Description
• How does this sample work?
• Prerequisites
• Running the sample
• Additional resources
• License
• Changelog
• Known issues

Description

This sample, yolov3_onnx, implements a full ONNX-based pipeline for performing inference with the YOLOv3 network, with an input size of 608 x 608 pixels, including pre and post-processing. This sample is based on the YOLOv3-608 paper.

How does this sample work?

First, the original YOLOv3 specification from the paper is converted to the Open Neural Network Exchange (ONNX) format in yolov3_to_onnx.py (only has to be done once).

Second, this ONNX representation of YOLOv3 is used to build a TensorRT engine, followed by inference on a sample image in onnx_to_tensorrt.py. The predicted bounding boxes are finally drawn to the original input image and saved to disk.

After inference, post-processing including bounding-box clustering is applied. The resulting bounding boxes are eventually drawn to a new image file and stored on disk for inspection.

Note: This sample is not supported on Ubuntu 14.04 and older. Additionally, the yolov3_to_onnx.py script does not support Python 3.

Prerequisites

For specific software versions, see the TensorRT Installation Guide.

1. Install ONNX-TensorRT: TensorRT backend for ONNX. ONNX-TensorRT includes layer implementations for the required ONNX operators Upsample and LeakyReLU.

2. Install the dependencies for Python.

   • For Python 2 users, from the root directory, run: python2 -m pip install -r requirements.txt

   • For Python 3 users, from the root directory, run: python3 -m pip install -r requirements.txt

Running the sample

1. Create an ONNX version of YOLOv3 with the following command. The Python script will also download all necessary files from the official mirrors (only once). python yolov3_to_onnx.py

   When running the above command for the first time, the output should look similar to the following:

   Downloading from https://raw.githubusercontent.com/pjreddie/darknet/f86901f6177dfc6116360a13cc06ab680e0c86b0/cfg/yolov3.cfg, this may take a while...
   100% [................................................................................] 8342 / 8342
   Downloading from https://pjreddie.com/media/files/yolov3.weights, this may take a while...
   100% [................................................................................] 248007048 / 248007048
   [...]
   %106_convolutional = Conv[auto_pad = u'SAME_LOWER', dilations = [1, 1], kernel_shape = [1, 1], strides = [1, 1]]
   (%105_convolutional_lrelu, %106_convolutional_conv_weights, %106_convolutional_conv_bias)
   return %082_convolutional, %094_convolutional,%106_convolutional
   }
   

2. Build a TensorRT engine from the generated ONNX file and run inference on a sample image, which will also be downloaded during the first run. python onnx_to_tensorrt.py

   When running the above command for the first time, the output should look similar to the following:

   Downloading from https://github.com/pjreddie/darknet/raw/f86901f6177dfc6116360a13cc06ab680e0c86b0/data/dog.jpg, this may take a while...
   100% [................................................................................] 163759 / 163759
   Building an engine from file yolov3.onnx, this may take a while...
   Running inference on image dog.jpg...
   Saved image with bounding boxes of detected objects to dog_bboxes.jpg.
   

3. Verify that the sample ran successfully. If the sample runs successfully you should see output similar to the following:

   Downloading from https://github.com/pjreddie/darknet/raw/f86901f6177dfc6116360a13cc06ab680e0c86b0/data/dog.jpg, this may take a while…
   100% [......................................................................] 163759 / 163759
   Loading ONNX file from path yolov3.onnx...
   Beginning ONNX file parsing
   Completed parsing of ONNX file
   Building an engine from file yolov3.onnx; this may take a while...
   Completed creating Engine
   Running inference on image dog.jpg...
   [[135.14841333 219.59879284 184.30209195 324.0265199 ]
     [ 98.30805074 135.72613533 499.71263299 299.25579652]
     [478.00605802 81.25702449 210.57787895 86.91502688]] [0.99854713 0.99880403 0.93829258] [16 1 7]
   Saved image with bounding boxes of detected objects to dog_bboxes.png.
   

   You should be able to visually confirm whether the detection was correct.

Additional resources

The following resources provide a deeper understanding about the model used in this sample, as well as the dataset it was trained on:

Model

• YOLOv3: An Incremental Improvement

Dataset

• COCO dataset

Documentation

• YOLOv3-608 paper
• Introduction To NVIDIA’s TensorRT Samples
• Working With TensorRT Using The Python API
• NVIDIA’s TensorRT Documentation Library

License

For terms and conditions for use, reproduction, and distribution, see the TensorRT Software License Agreement documentation.

Changelog

March 2019 This README.md file was recreated, updated and reviewed.

Known issues

There are no known issues in this sample.