include steps for ai-hub

examples/meter-detection/README.md

@@ -118,7 +118,7 @@ sagemaker:
 Confirm the CLI can see the configured SageMaker role and S3 bucket:
 
 ```bash
-qc-cli infra status --config config.yaml
+qc-cli infra status
 ```
 
 ## 4. Upload The Dataset
@@ -153,7 +153,7 @@ Or pass the job name explicitly:
 qc-cli train status qc-cli-YYYYMMDD-HHMMSS
 ```
 
-## Outputs
+## SageMaker Outputs
 
 When the job completes, SageMaker packages the files written under `/opt/ml/model` into `model.tar.gz`.
 
@@ -167,6 +167,91 @@ metrics.json
 
 The archive is stored under the configured `s3.model_prefix`.
 
+## 6. Configure Qualcomm AI Hub
+
+Authenticate with Qualcomm AI Hub:
+
+```bash
+qai-hub configure --api_token
+```
+
+Add AI Hub settings to `config.yaml`. The input name and image size must match the ONNX model exported by this example:
+
+```yaml
+aihub:
+  device:
+    name: Dragonwing IQ-9075 EVK
+  target_runtime: tflite
+  input_specs:
+    images: [[1, 3, 640, 640], float32]
+  job_name: meter-detection
+  model_name: meter-detection
+  output_dir: build/qai-hub/meter-detection
+```
+
+Use the same image size configured in `sagemaker.training.hyperparameters.imgsz`. For example, a smoke-test model
+trained with `imgsz: 320` requires `images: [[1, 3, 320, 320], float32]`.
+
+## 7. Prepare AI Hub Inputs
+
+Generate calibration samples and a validation input from the downloaded dataset:
+
+```bash
+uv run python examples/meter-detection/prepare_aihub_inputs.py --image-size 640
+```
+
+This writes:
+
+```text
+examples/meter-detection/data/aihub_calibration/*.npy
+examples/meter-detection/data/inputs.npz
+```
+
+The script applies the preprocessing expected by the exported YOLO model: aspect-ratio-preserving letterboxing,
+RGB channel order, channel-first layout, and pixel values normalized to `[0, 1]`.
+
+Set `--image-size` to the training `imgsz` value when it is not `640`.
+
+## 8. Upload To Qualcomm AI Hub
+
+Use the SageMaker job name printed by `qc-cli train start`:
+
+```bash
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --from-job qc-cli-YYYYMMDD-HHMMSS
+```
+
+The command downloads the job's `model.tar.gz`, finds `model.onnx`, uploads it to AI Hub, and runs quantization,
+compilation, validation, and profiling. The uploaded source model uses the configured
+`aihub.model_name`.
+
+If the meter-detection job is still the last training job in `.qc-cli.json`, `--from-job` can be omitted. Keeping it
+explicit prevents accidentally uploading an artifact from a different training run.
+
+To resume after a completed step, use one of:
+
+```bash
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --from-step compile
+```
+
+```bash
+qc-cli ai-hub upload \
+  examples/meter-detection/data/aihub_calibration \
+  examples/meter-detection/data/inputs.npz \
+  --from-step validate
+```
+
+Download the compiled artifact after the workflow completes:
+
+```bash
+qc-cli ai-hub download --output build/qai-hub/meter-detection/model.tflite
+```
+
 ## Training Hyperparameters
 
 Values under `sagemaker.training.hyperparameters` are passed to `source/train.py` as command-line arguments.

examples/meter-detection/prepare_aihub_inputs.py

@@ -0,0 +1,92 @@
+#!/usr/bin/env python3
+"""Prepare Qualcomm AI Hub calibration and validation inputs for the meter detector."""
+
+from __future__ import annotations
+
+import argparse
+from pathlib import Path
+
+import numpy as np
+from PIL import Image
+
+IMAGE_EXTENSIONS = {".jpg", ".jpeg", ".png"}
+
+
+def parse_args() -> argparse.Namespace:
+    parser = argparse.ArgumentParser(description=__doc__)
+    parser.add_argument(
+        "--dataset-dir",
+        type=Path,
+        default=Path("examples/meter-detection/data/electric-meter-detection"),
+        help="Root of the extracted Roboflow dataset.",
+    )
+    parser.add_argument(
+        "--calibration-dir",
+        type=Path,
+        default=Path("examples/meter-detection/data/aihub_calibration"),
+        help="Directory where .npy calibration samples will be written.",
+    )
+    parser.add_argument(
+        "--input-file",
+        type=Path,
+        default=Path("examples/meter-detection/data/inputs.npz"),
+        help="Validation .npz input file for qc-cli ai-hub validate.",
+    )
+    parser.add_argument("--input-name", default="images", help="ONNX input name.")
+    parser.add_argument("--image-size", type=int, default=640, help="Square image size used for ONNX export.")
+    parser.add_argument("--samples", type=int, default=16, help="Number of calibration samples to write.")
+    return parser.parse_args()
+
+
+def preprocess_image(path: Path, image_size: int) -> np.ndarray:
+    """Apply Ultralytics-style letterboxing and produce an NCHW float32 tensor."""
+    with Image.open(path) as source:
+        image = source.convert("RGB")
+
+    scale = min(image_size / image.width, image_size / image.height)
+    resized_width = round(image.width * scale)
+    resized_height = round(image.height * scale)
+    image = image.resize((resized_width, resized_height), Image.Resampling.BILINEAR)
+
+    canvas = Image.new("RGB", (image_size, image_size), (114, 114, 114))
+    left = round((image_size - resized_width) / 2 - 0.1)
+    top = round((image_size - resized_height) / 2 - 0.1)
+    canvas.paste(image, (left, top))
+
+    array = np.asarray(canvas, dtype=np.float32) / 255.0
+    return np.transpose(array, (2, 0, 1))[None, ...].astype(np.float32)
+
+
+def main() -> None:
+    args = parse_args()
+    if args.image_size < 1:
+        raise SystemExit("--image-size must be at least 1")
+    if args.samples < 1:
+        raise SystemExit("--samples must be at least 1")
+
+    images = sorted(
+        path
+        for path in args.dataset_dir.rglob("*")
+        if path.is_file() and path.suffix.lower() in IMAGE_EXTENSIONS and path.parent.name == "images"
+    )
+    if not images:
+        raise SystemExit(f"No images found under {args.dataset_dir}")
+
+    args.calibration_dir.mkdir(parents=True, exist_ok=True)
+    args.input_file.parent.mkdir(parents=True, exist_ok=True)
+    for stale_sample in args.calibration_dir.glob("sample_*.npy"):
+        stale_sample.unlink()
+
+    prepared: list[np.ndarray] = []
+    for index, image_path in enumerate(images[: args.samples]):
+        sample = preprocess_image(image_path, args.image_size)
+        np.save(args.calibration_dir / f"sample_{index:03d}.npy", sample)
+        prepared.append(sample)
+
+    np.savez(args.input_file, **{args.input_name: prepared[0]})  # pyright: ignore[reportArgumentType]
+    print(f"Wrote {len(prepared)} calibration samples to {args.calibration_dir}")
+    print(f"Wrote validation input to {args.input_file}")
+
+
+if __name__ == "__main__":
+    main()