by Ciro Santilli (@cirosantilli, 34)
The most important thing this project provides appears to be the .onnx file format, which represents ANN models, pre-trained or not.
Deep learning frameworks can then output such .onnx files for interchangeability and serialization.
Some examples:
ONNX visualizer.
Figure 1.
Netron visualization of the activatedgeek/LeNet-5 ONNX output
.
NumPy does not automatically use the GPU for it: stackoverflow.com/questions/49605231/does-numpy-automatically-detect-and-use-gpu, and PyTorch is one of the most notable compatible implementations, as it uses the same memory structure as NumPy arrays.
Sample runs on P51 to observe the GPU speedup:
$ time ./matmul.py g 10000 1000 10000 100
real    0m22.980s
user    0m22.679s
sys     0m1.129s
$ time ./matmul.py c 10000 1000 10000 100
real    1m9.924s
user    4m16.213s
sys     0m17.293s
Contains several computer vision models, e.g. ResNet, all of them including pre-trained versions on some dataset, which is quite sweet.
pytorch.org/vision/0.13/models.html has a minimal runnable example adapted to python/pytorch/resnet_demo.py.
That example uses a ResNet pre-trained on the COCO dataset to do some inference, tested on Ubuntu 22.10:
cd python/pytorch
wget -O resnet_demo_in.jpg https://upload.wikimedia.org/wikipedia/commons/thumb/6/60/Rooster_portrait2.jpg/400px-Rooster_portrait2.jpg
./resnet_demo.py resnet_demo_in.jpg resnet_demo_out.jpg
This first downloads the model, which is currently 167 MB.
We know it is COCO because of the docs: pytorch.org/vision/0.13/models/generated/torchvision.models.detection.fasterrcnn_resnet50_fpn_v2.html which explains that 
FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULT
is an alias for:
FasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1
The runtime is relatively slow on P51, about 4.7s.
After it finishes, the program prints the recognized classes:
['bird', 'banana']
so we get the expected bird, but also the more intriguing banana.
By looking at the output image with bounding boxes, we understand where the banana came from!
Figure 1.
python/pytorch/resnet_demo_in.jpg
. Source.
Figure 2.
python/pytorch/resnet_demo_out.jpg
. The beak was of course a banana, not a beak!
Version of TensorFlow with a Cirq backend that can run in either quantum computers or classical computer simulations, with the goal of potentially speeding up deep learning applications on a quantum computer some day.

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