Face recognition example

In this example, you learn how to implement inference code with a pytorch model to extract and compare face features.

Extract face feature: The source code can be found at FeatureExtraction.java.
The model github can be found at facenet-pytorch.

Compare face features: The source code can be found at FeatureComparison.java.

Setup guide

To configure your development environment, follow setup.

Run face recognition example

Input image file

You can find the image used in this example in the project test resource folder:
src/test/resources/kana1.jpg

src/test/resources/kana2.jpg

Build the project and run

Use the following command to run the project:

cd examples
./gradlew run -Dmain=ai.djl.examples.inference.face.FeatureExtraction

Your output should look like the following:

[INFO ] - [-0.04026184, -0.019486362, -0.09802659, 0.01700999, 0.037829027, ...]

cd examples
./gradlew run -Dmain=ai.djl.examples.inference.face.FeatureComparison

Your output should look like the following:

[INFO ] - 0.9022607

Reference - how to import pytorch model:

1. Install:

# With pip:
pip install facenet-pytorch

# or clone this repo, removing the '-' to allow python imports:
git clone https://github.com/timesler/facenet-pytorch.git facenet_pytorch

1. In python, import facenet-pytorch and instantiate model, then use torch.jit.trace to generate a torch.jit.ScriptModule via tracing:

from facenet_pytorch import InceptionResnetV1
import torch
from PIL import Image
import ssl

try:
    _create_unverified_https_context = ssl._create_unverified_context
except AttributeError:
    # Legacy Python that doesn't verify HTTPS certificates by default
    pass
else:
    # Handle target environment that doesn't support HTTPS verification
    ssl._create_default_https_context = _create_unverified_https_context

# Create an inception resnet (in eval mode):
resnet = InceptionResnetV1(pretrained='vggface2').eval()

img = Image.open('/path/to/any/face/image.jpg')

# An example input you would normally provide to your model's forward() method.
example = torch.rand(1, 3, 320, 320)

# Use torch.jit.trace to generate a torch.jit.ScriptModule via tracing.
traced_script_module = torch.jit.trace(resnet, example)

# For control flow, use script
#script_module = torch.jit.script(model) 

# Save the TorchScript model
traced_script_module.save("face_feature.pt")

output = traced_script_module(torch.rand(1,3,320, 320))
#print(traced_script_module.code)
print(output)