# inferer

Python API: `modelconverter.platforms.rvc4.inferer`

Inference with an RVC4 model through SNPE.

Holds the
[Inferer](https://docs.luxonis.com/software-v3/ai-inference/conversion/rvc-conversion/offline/modelconverter/modelconverter-api-reference/platforms/base_inferer.md)
implementation the `infer` command uses for the RVC4 platform: the inputs are written out as raw files and pushed through the
converted DLC model with `snpe-net-run`. It only works inside the RVC4 Docker image, where the SNPE SDK is installed.

## Classes

### RVC4Inferer

Inferer for RVC4 DLC models based on `snpe-net-run`.

#### Methods

##### infer

```python
def infer(inputs: dict[str, Path]) -> dict[str, np.ndarray]:
```

Run the model on a single set of input images.

Every image is read as `float32` and dumped to a raw file referenced from the SNPE input list, `snpe-net-run` is then invoked on
the DLC model, and the raw files it produces are read back and reshaped to the configured output shapes. Four-dimensional outputs
are assumed to be channels-last and are transposed to `NCHW`.

Parameters

 * `inputs` (`dict[str, Path]`): Path to the image for every model input, keyed by input name.

Returns

 * `dict[str, np.ndarray]`: The model outputs, keyed by output name.

##### setup

```python
def setup(self):
```

Set the raw image directory and the input list header.

The header names the outputs SNPE is asked to write out.