2023-01-26 03:04:00 +00:00
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from onnxruntime import InferenceSession
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from os import path
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from typing import Any
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import numpy as np
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import torch
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from ..utils import (
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ServerContext,
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)
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2023-01-28 05:28:14 +00:00
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class OnnxImage():
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2023-01-26 03:04:00 +00:00
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def __init__(self, source) -> None:
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self.source = source
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self.data = self
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def __getitem__(self, *args):
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return torch.from_numpy(self.source.__getitem__(*args)).to(torch.float32)
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def squeeze(self):
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self.source = np.squeeze(self.source, (0))
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return self
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def float(self):
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return self
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def cpu(self):
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return self
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def clamp_(self, min, max):
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self.source = np.clip(self.source, min, max)
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return self
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def numpy(self):
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return self.source
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def size(self):
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return np.shape(self.source)
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2023-01-28 05:28:14 +00:00
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class OnnxNet():
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2023-01-26 03:04:00 +00:00
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'''
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Provides the RRDBNet interface using an ONNX session for DirectML acceleration.
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'''
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def __init__(self, ctx: ServerContext, model: str, provider='DmlExecutionProvider') -> None:
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'''
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TODO: get platform provider from request params
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'''
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model_path = path.join(ctx.model_path, model)
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self.session = InferenceSession(
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model_path, providers=[provider])
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def __call__(self, image: Any) -> Any:
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input_name = self.session.get_inputs()[0].name
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output_name = self.session.get_outputs()[0].name
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output = self.session.run([output_name], {
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input_name: image.cpu().numpy()
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})[0]
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2023-01-28 05:28:14 +00:00
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return OnnxImage(output)
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2023-01-26 03:04:00 +00:00
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def eval(self) -> None:
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pass
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def half(self):
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return self
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def load_state_dict(self, net, strict=True) -> None:
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pass
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def to(self, device):
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return self
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