Well, let me tell ya, friendo, sometimes it’s just easier to have a little bit of extra help from the big dogs.
First what exactly is Jetson Inference and Utils? Essentially, these libraries provide a set of tools for optimizing and deploying deep learning models on Nvidia’s Jetson platform (which is basically a tiny computer designed specifically for AI applications). They offer features like model quantization, pruning, and optimization, as well as support for popular frameworks like TensorFlow and PyTorch.
Now, you might be wondering why would we need to use these tools when there are already so many open-source options out there? Well, let me tell ya, friendo, sometimes it’s just easier to have a little bit of extra help from the big dogs. For example, if you’re working on an image recognition project and want to optimize your model for deployment on a Jetson device, using these libraries can save you hours (if not days) of tinkering with various optimization techniques.
But enough about why we should use Jetson Inference and Utils how! First, make sure you have the necessary dependencies installed:
# Install Nvidia CUDA toolkit (required for running inference on a GPU)
# This command installs the Nvidia CUDA toolkit, which is necessary for running inference on a GPU.
sudo apt-get install nvidia-cuda-toolkit
# Install Jetson Inference and Utils libraries
# These commands install the Jetson Inference and Utils libraries, which are used for optimizing models for deployment on a Jetson device.
pip3 install jetson-inference
pip3 install jetson-utils
Once you have those installed, it’s time to load your model into memory:
# Import necessary libraries
import numpy as np # Import numpy library for array manipulation
from PIL import Image # Import PIL library for image processing
import cv2 # Import OpenCV library for computer vision tasks
import os.path # Import os.path library for file path manipulation
import sys # Import sys library for system-specific parameters and functions
# Add the path to your project directory (assuming it's in a subdirectory)
sys.path.append('../')
# Import custom library for Jetson Inference functions
from jetson_utils import load_model, preprocess_image, postprocess_detection
# Load your model using Jetson Inference library
model = load_model("path/to/your/model.prototxt") # Load model from specified file path
Now that we have our model loaded into memory, let’s test it out on an image:
# Read in the input image and preprocess it for inference (using Jetson Utils library)
# Define the path to the input image
image_path = "path/to/your/input.jpg"
# Open the image using the Image module from the PIL library and convert it to RGB format
img = Image.open(image_path).convert("RGB")
# Get the width and height of the image
width, height = img.size
# Convert the image to a numpy array and preprocess it for inference (using Jetson Utils library)
# Import the preprocess_image function from the Jetson Utils library
from jetson.utils import preprocess_image
# Convert the image to a numpy array and preprocess it using the preprocess_image function
inp = np.array(preprocess_image(img))
Finally, let’s run our model on this input:
# Run model using Jetson Inference library
outputs = model.forward(inp) # Run forward pass of model and get output (using Jetson Inference library)
detections = postprocess_detection(outputs, width, height) # Postprocess output to get list of detected objects (using Jetson Utils library)
And that’s it! You now have a working image recognition system using Jetson Inference and Utils libraries. Of course, there are many more features and options available in these tools I encourage you to check out the documentation for more information. But for now, let’s just enjoy the fact that we can use fancy Nvidia tools without having to write a single line of CUDA code!