##
RyzenAIModel

####
optimum.amd.ryzenai.pipeline

< source >

( task model: typing.Optional[typing.Any] = None vaip_config: typing.Optional[str] = None model_type: typing.Optional[str] = None feature_extractor: typing.Union[str, ForwardRef('PreTrainedFeatureExtractor'), NoneType] = None image_processor: typing.Union[str, transformers.image_processing_utils.BaseImageProcessor, NoneType] = None use_fast: bool = True token: typing.Union[bool, str, NoneType] = None revision: typing.Optional[str] = None **kwargs ) →
Pipeline

Parameters

- task (`str`) — The task defining which pipeline will be returned. Available tasks include:
  - “image-classification”
  - “object-detection”
- model (`Optional[Any]`, defaults to `None`) — The model that will be used by the pipeline to make predictions. This can be a model identifier or an actual instance of a pretrained model. If not provided, the default model for the specified task will be loaded.
- vaip_config (`Optional[str]`, defaults to `None`) — Runtime configuration file for inference with Ryzen IPU. A default config file can be found in the Ryzen AI VOE package, extracted during installation under the name `vaip_config.json`.
- model_type (`Optional[str]`, defaults to `None`) — Model type for the model
- feature_extractor (`Union[str, "PreTrainedFeatureExtractor"]`, defaults to `None`) — The feature extractor that will be used by the pipeline to encode data for the model. This can be a model identifier or an actual pretrained feature extractor.
- image_processor (`Union[str, BaseImageProcessor]`, defaults to `None`) — The image processor that will be used by the pipeline for image-related tasks.
- use_fast (`bool`, defaults to `True`) — Whether or not to use a Fast tokenizer if possible.
- token (`Union[str, bool`], defaults to `None`) — The token to use as HTTP bearer authorization for remote files. If True, will use the token generated when running `huggingface-cli login` (stored in `~/.huggingface`).
- revision (`str`, defaults to `None`) — The specific model version to use, specified as a branch name, tag name, or commit id.
- **kwargs — Additional keyword arguments passed to the underlying pipeline class.

Returns

Pipeline

An instance of the specified pipeline for the given task and model.

Utility method to build a pipeline for various RyzenAI tasks.

This function creates a pipeline for a specified task, utilizing a given model or loading the default model for the task. The pipeline includes components such as a image processor and model.

###
Computer vision

###
class optimum.amd.ryzenai.pipelines.TimmImageClassificationPipeline

< source >

( model: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] tokenizer: typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None feature_extractor: typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None image_processor: typing.Optional[transformers.image_processing_utils.BaseImageProcessor] = None processor: typing.Optional[transformers.processing_utils.ProcessorMixin] = None modelcard: typing.Optional[transformers.modelcard.ModelCard] = None framework: typing.Optional[str] = None task: str = '' device: typing.Union[int, ForwardRef('torch.device'), NoneType] = None binary_output: bool = False **kwargs )

Example usage:

    import requests
    from PIL import Image

    from optimum.amd.ryzenai import pipeline

    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)

    model_id = "mohitsha/timm-resnet18-onnx-quantized-ryzen"

    pipe = pipeline("image-classification", model=model_id, vaip_config="vaip_config.json")
    print(pipe(image))

###
class optimum.amd.ryzenai.pipelines.YoloObjectDetectionPipeline

< source >

( model: typing.Union[ForwardRef('PreTrainedModel'), ForwardRef('TFPreTrainedModel')] tokenizer: typing.Optional[transformers.tokenization_utils.PreTrainedTokenizer] = None feature_extractor: typing.Optional[ForwardRef('SequenceFeatureExtractor')] = None image_processor: typing.Optional[transformers.image_processing_utils.BaseImageProcessor] = None processor: typing.Optional[transformers.processing_utils.ProcessorMixin] = None modelcard: typing.Optional[transformers.modelcard.ModelCard] = None framework: typing.Optional[str] = None task: str = '' device: typing.Union[int, ForwardRef('torch.device'), NoneType] = None binary_output: bool = False **kwargs )

Supported model types

- yolox
- yolov3
- yolov5
- yolov8

Example usage:

    import requests
    from PIL import Image

    from optimum.amd.ryzenai import pipeline

    img = ".\\image.jpg"
    url = "http://images.cocodataset.org/val2017/000000039769.jpg"
    image = Image.open(requests.get(url, stream=True).raw)
    img = ".\\image2.jpg"

    image = Image.open(img)

    model_id = "amd/yolox-s"

    detector = pipeline("object-detection", model=model_id, vaip_config="vaip_config.json", model_type="yolox")
    detector = pipe(image)

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