Ciro Santilli Matrix multiplication example.
Fundamental since deep learning is mostly matrix multiplication.
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 0m17.293sContains several computer vision models, e.g. ResNet, all of them including pre-trained versions on some dataset, which is quite sweet.
Documentation: pytorch.org/vision/stable/index.html
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:
This first downloads the model, which is currently 167 MB.
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.jpgWe 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
is an alias for:
FasterRCNN_ResNet50_FPN_V2_Weights.DEFAULTFasterRCNN_ResNet50_FPN_V2_Weights.COCO_V1The runtime is relatively slow on P51, about 4.7s.
After it finishes, the program prints the recognized classes:
so we get the expected
['bird', 'banana']bird, but also the more intriguing banana.By looking at the output image with bounding boxes, we understand where the banana came from!
