Upload inference.py

inference.py

@@ -0,0 +1,54 @@
+"""
+PriviGaze Inference Script - Run the student model on-device
+
+Usage:
+    python inference.py --model ./checkpoints/student_best.pt --image face.jpg
+"""
+import argparse
+import torch
+import numpy as np
+from PIL import Image, ImageOps
+from models.student import PriviGazeStudent
+
+
+def load_model(checkpoint_path, device='cpu'):
+    model = PriviGazeStudent()
+    ckpt = torch.load(checkpoint_path, map_location=device)
+    model.load_state_dict(ckpt.get('student_state_dict', ckpt))
+    model.eval().to(device)
+    return model
+
+
+def preprocess(image_path, size=224):
+    img = Image.open(image_path).convert('L')
+    img = img.resize((size, size))
+    arr = np.array(img).astype(np.float32) / 255.0
+    tensor = torch.from_numpy(arr).unsqueeze(0).unsqueeze(0)
+    return tensor * 2 - 1
+
+
+def predict(model, tensor, device='cpu'):
+    tensor = tensor.to(device)
+    with torch.no_grad():
+        pitch, yaw, _ = model(tensor)
+    return pitch.item(), yaw.item()
+
+
+def main():
+    p = argparse.ArgumentParser()
+    p.add_argument('--model', type=str, required=True, help='Path to student checkpoint')
+    p.add_argument('--image', type=str, required=True, help='Path to grayscale face image')
+    p.add_argument('--device', type=str, default='cpu')
+    args = p.parse_args()
+
+    device = torch.device(args.device)
+    model = load_model(args.model, device)
+    tensor = preprocess(args.image)
+    pitch, yaw = predict(model, tensor, device)
+    print(f"Gaze: pitch={pitch:+.2f}deg, yaw={yaw:+.2f}deg")
+    print(f"Direction: {'up' if pitch>0 else 'down'} {abs(pitch):.1f}deg, "
+          f"{'right' if yaw>0 else 'left'} {abs(yaw):.1f}deg")
+
+
+if __name__ == "__main__":
+    main()