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zxsanny
47 lines
1.5 KiB
Python
47 lines
1.5 KiB
Python
import abc
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from typing import List, Tuple
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import numpy as np
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import onnxruntime as onnx
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class InferenceEngine(abc.ABC):
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@abc.abstractmethod
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def __init__(self, model_path: str, batch_size: int = 1, **kwargs):
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pass
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@abc.abstractmethod
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def get_input_shape(self) -> Tuple[int, int]:
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pass
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@abc.abstractmethod
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def get_batch_size(self) -> int:
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pass
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@abc.abstractmethod
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def run(self, input_data: np.ndarray) -> List[np.ndarray]:
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pass
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class OnnxEngine(InferenceEngine):
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def __init__(self, model_bytes, batch_size: int = 1, **kwargs):
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self.batch_size = batch_size
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self.session = onnx.InferenceSession(model_bytes, providers=["CUDAExecutionProvider", "CPUExecutionProvider"])
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self.model_inputs = self.session.get_inputs()
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self.input_name = self.model_inputs[0].name
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self.input_shape = self.model_inputs[0].shape
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if self.input_shape[0] != -1:
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self.batch_size = self.input_shape[0]
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model_meta = self.session.get_modelmeta()
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print("Metadata:", model_meta.custom_metadata_map)
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self.class_names = eval(model_meta.custom_metadata_map["names"])
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pass
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def get_input_shape(self) -> Tuple[int, int]:
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shape = self.input_shape
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return shape[2], shape[3]
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def get_batch_size(self) -> int:
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return self.batch_size
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def run(self, input_data: np.ndarray) -> List[np.ndarray]:
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return self.session.run(None, {self.input_name: input_data}) |