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You can embed preprocessing function into your model and then convert to ONNX class InferenceModel(torch.nn.Module):
def __init__(self, my_model, mean, std):
super().__init__()
self.my_model = my_model
self.register_buffer("mean", mean)
self.register_buffer("std", std)
def forward(self, x):
x = x - self.mean
x = x / self.std
x = self.my_model(x)
return x |
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Hi,
I found the implementation very efficient and strainght forward. However I would like to know if you can provide me hint for doing inference with OPENCV.
So I can convert the model to ONNX, however how should I handle the preprocessing function?
Any small code will suffice.
Thanks
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