# Qualcomm AI Hub Example

This example takes the ONNX model produced by the SageMaker training example and runs the Qualcomm AI Hub upload workflow:

1. Quantize
2. Compile
3. Validate
4. Profile
5. Download the compiled artifact

## Prerequisites

Run the training example first and wait for it to complete:

```bash
examples/training/run_training.sh --wait
```

The `config.yaml` file must include AI Hub settings:

```yaml
aihub:
  device:
    name: Samsung Galaxy S25 (Family)
  target_runtime: tflite
  input_specs:
    input: [[1, 3, 160, 160], float32]
  output_dir: build/qai-hub
```

Finally, the user needs to authenticate with Qualcomm AI Hub using:

```bash
qai-hub configure --api_token
```

## Prepare Inputs

AI Hub does not consume the raw JPG training images directly. It needs NumPy tensors that match the ONNX model input shape and preprocessing.

To generate calibration and validation inputs:

```bash
python examples/ai-hub/prepare_inputs.py
```

This writes:

```text
examples/training/data/aihub_calibration/*.npy
examples/training/data/inputs.npz
```

The script applies the same image preprocessing used by the training example:

- resize to `160x160`
- convert to channel-first `1x3x160x160`
- normalize with ImageNet mean and standard deviation

## Upload Model to Qualcomm Workbench

The model can be uploaded to Qualcomm Workbench using:

```bash
qc-cli ai-hub upload examples/training/data/aihub_calibration examples/training/data/inputs.npz
```

The first argument is the calibration path for the model and the second argument is the input file, both of which were created by the `prepare_inputs.py` script. For more details, add `--help` after the `upload` command.

The `upload` command runs the following commands in order:
1. `qc-cli ai-hub quantize`
2. `qc-cli ai-hub compile`
3. `qc-cli ai-hub validate`
4. `qc-cli ai-hub profile`

Finally the user can download the model from AI Workbench using the command
```bash
qc-cli ai-hub download
```