74 lines
1.7 KiB
Python
74 lines
1.7 KiB
Python
from os import path
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from typing import Any, Optional
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import numpy as np
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import torch
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from ..server import ServerContext
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from ..torch_before_ort import InferenceSession, SessionOptions
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class OnnxTensor:
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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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class OnnxRRDBNet:
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"""
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Provides the RRDBNet interface using an ONNX session.
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"""
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def __init__(
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self,
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server: ServerContext,
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model: str,
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provider: str = "DmlExecutionProvider",
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sess_options: Optional[SessionOptions] = None,
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) -> None:
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model_path = path.join(server.model_path, model)
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self.session = InferenceSession(
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model_path, providers=[provider], provider_options=sess_options
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)
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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], {input_name: image.cpu().numpy()})[0]
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return OnnxTensor(output)
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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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