model.py

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import numpy as np
import triton_python_backend_utils as pb_utils
from transformers import ViTImageProcessor, ViTModel


class TritonPythonModel:
    def initialize(self, args):
        self.feature_extractor = ViTImageProcessor.from_pretrained(
            "google/vit-base-patch16-224-in21k"
        )
        self.model = ViTModel.from_pretrained("google/vit-base-patch16-224-in21k")

    def execute(self, requests):
        responses = []
        for request in requests:
            inp = pb_utils.get_input_tensor_by_name(request, "image")
            input_image = np.squeeze(inp.as_numpy()).transpose((2, 0, 1))
            inputs = self.feature_extractor(images=input_image, return_tensors="pt")

            outputs = self.model(**inputs)

            inference_response = pb_utils.InferenceResponse(
                output_tensors=[
                    pb_utils.Tensor(
                        "last_hidden_state", outputs.last_hidden_state.detach().numpy()
                    )
                ]
            )
            responses.append(inference_response)
        return responses