AlexNetRT is a simple deployment of AlexNet using TensorRT.

Alexnet's authors are Alex Krizhevsky, Ilya Sutskever and Geoffrey Hinton.

The purpose of this code is to deploy an already trained (using Caffe with Nvidia Digits) network using TensorRT.

The architecture of Alexnet can be seen in the deploy.prototxt in this file, it's also available on Netscope here but you can also get a glimpse in this image:

In order to build this project make sure you have the following requirements:

• CUDA > 8 (I used 9.0)
• CMake > 3.8 (I used 3.12) - This make it easier to find the Cuda dependencies.
• TensorRT >3 (I used 4.0)
• The original .caffemodel that you can get from here or just run:

wget -P data/alexnet/ http://dl.caffe.berkeleyvision.org/bvlc_alexnet.caffemodel

from the root of this project.

• Imagemagick to convert input images to the required format .ppm

To build the code do the following:

git clone https://github.com/bpinaya/AlexNetRT.git alexnetrt && cd alexnetrt
mkdir build && cd build
cmake ..
make

The code itself is documented so you can navigate it and see what each part does, I tried to be as explanatory as possible but also avoided unnecessary details. You can run it by just calling:

./alexnetrt

from the build directory but that will launch with the default image (a dog) and run the inference as you can see in this gif:

You can pass it with any image you want as long as it complies with the input format (a 227x227 .ppm image), if you want to prepare you image use the prepare_image.sh script from the data/alexnet/ folder as:

bash prepare_image.sh image_to_prepare.format

Check the cpp file or run ./alexnetrt --help to check the options, to use a different image as input just run:

./alexnetrt --input ../data/alexnet/cat.ppm

and you'll see something like this:

Finally, making reference to Silicon Valley's TV show you can run it with hotdog mode like this:

./alexnetrt --input ../data/alexnet/hotdog.ppm --hotdog

• Check on FP16 and FP8 precision.
• Run clang-tidy and check for errors.

If you have any question or suggestion for improvement let me know creating an issue, PRs are also welcomed.