example.py

"""Minimal inference example for DeiT-Tiny INT8 using ExecuTorch.

Loads a quantized .pte model and runs inference on a single image,
printing the top-5 ImageNet class predictions with probabilities.
"""

import json
from pathlib import Path

import torch
from executorch.runtime import Runtime
from PIL import Image
from torchvision import transforms
from torchvision.models import GoogLeNet_Weights


# ── Configuration ──────────────────────────────────────────────────────────────
MODEL_PATH = "deit_raspberry_executorch_optimized.pte"
IMAGE_PATH = "sample_input.jpg"
INPUT_SIZE = (224, 224)
RESIZE_SIZE = 256  # resize shorter edge before center crop
MEAN = [0.485, 0.456, 0.406]
STD = [0.229, 0.224, 0.225]
TOP_K = 5


# ── Model Loading ──────────────────────────────────────────────────────────────
def load_model(pte_path: str):
    """Load ExecuTorch .pte model and return the forward method."""
    runtime = Runtime.get()
    program = runtime.load_program(pte_path)
    return program.load_method("forward")


# ── Preprocessing ──────────────────────────────────────────────────────────────
def preprocess(image_path: str) -> torch.Tensor:
    """Load and preprocess image for model input.

    Pipeline:
      1. Resize shortest edge to 256 px (bicubic)
      2. Center-crop to 224x224
      3. Convert to float32 tensor in [0, 1]
      4. Normalize with ImageNet mean/std
    """
    transform = transforms.Compose(
        [
            transforms.Resize(
                RESIZE_SIZE, interpolation=transforms.InterpolationMode.BICUBIC
            ),
            transforms.CenterCrop(INPUT_SIZE),
            transforms.ToTensor(),
            transforms.Normalize(mean=MEAN, std=STD),
        ]
    )
    image = Image.open(image_path).convert("RGB")
    tensor = transform(image)
    return tensor.unsqueeze(0)  # [1, 3, 224, 224]


# ── Inference ──────────────────────────────────────────────────────────────────
def run_inference(method, input_tensor: torch.Tensor) -> torch.Tensor:
    """Run forward pass and return the raw logits tensor."""
    outputs = method.execute([input_tensor])
    return outputs[0]


# ── Postprocessing ─────────────────────────────────────────────────────────────
def postprocess(raw_output: torch.Tensor, labels: list) -> list[dict]:
    """Convert raw logits to top-k class predictions.

    Applies softmax over the 1000-class logit vector, then returns the
    top-k (class name, probability) pairs sorted by descending probability.
    """
    logits = raw_output.squeeze(0)  # [1000]
    probabilities = torch.softmax(logits, dim=-1)
    top_probs, top_indices = torch.topk(probabilities, TOP_K)
    return [
        {"class": labels[idx.item()], "probability": round(prob.item(), 6)}
        for prob, idx in zip(top_probs, top_indices, strict=False)
    ]


# ── Save Results ───────────────────────────────────────────────────────────────
def save_results(results: list[dict], script_dir: Path) -> None:
    """Save top-k predictions to predictions.json in the script's directory."""
    output_path = script_dir / "predictions.json"
    with open(output_path, "w") as f:
        json.dump(results, f, indent=2)
    print(f"Saved predictions to {output_path}")


# ── Main ───────────────────────────────────────────────────────────────────────
def main() -> None:
    script_dir = Path(__file__).parent

    # ImageNet class labels (1000 classes)
    WEIGHTS = GoogLeNet_Weights.IMAGENET1K_V1
    IMAGENET_CLASSES = WEIGHTS.meta["categories"]

    # Load model, run inference, decode results
    method = load_model(str(script_dir / MODEL_PATH))
    input_tensor = preprocess(str(script_dir / IMAGE_PATH))
    raw_output = run_inference(method, input_tensor)
    results = postprocess(raw_output, IMAGENET_CLASSES)

    # Print top-k predictions
    print(f"Top-{TOP_K} predictions:")
    for i, r in enumerate(results, 1):
        print(f"  {i}. {r['class']} ({r['probability'] * 100:.2f}%)")

    save_results(results, script_dir)


if __name__ == "__main__":
    main()