Make wrapper classes for making ML models compatible with tool

Not every ML model / onnx file is compatible with the mlfmu tool and i can be a bit hard/confusing to make an incompatible model compatible.

To make this process easier for our users we can make example classes in tensorflow and pytorch that they can use and extend to make a compatibility wrapper for their trained model.

The class could have implemented some functions and give the user an idea of what they should do to make the wrapper.

class TensorflowWrapper(tensorflow.keras.Model):
   def __init__(self, trained_model):
        self.trained_model = trained_model
    def format_inputs(self, inputs, states, time):
        return 
    def format_outputs(self, inputs, states, time, model_outputs):
         return
    def call(self, x):
           inputs, states, time = x
           formatted_inputs = self.format_inputs(inputs, state, time)
           model_outputs = self.trained_model(formatted_inputs)
           return self.format_outputs(inputs, states, time, model_outputs)

class PytorchWrapper(torch.nn.Modulel):
   def __init__(self, trained_model):
        self.trained_model = trained_model
    def format_inputs(self, inputs, states, time):
        return 
    def format_outputs(self, inputs, states, time, model_outputs):
         return
    def forward(self, x):
           inputs, states, time = x
           formatted_inputs = self.format_inputs(inputs, state, time)
           model_outputs = self.trained_model(formatted_inputs)
           return self.format_outputs(inputs, states, time, model_outputs)

In easy cases just implementing the format_inputs and format_outputs classes could be sufficient to make the compatibility wrapper for their model. For more complex cases one would maybe also need to modify more, but this could be a good starting point for our users.

If we include this we should also include tools for loading onnx files into pytorch/tensorflow and converting pytorch/tensorflow to onnx files. That way a user could go the entire way from an incompatible onnx file to an compatible onnx to a compiled fmu. All within the this tool.

[like] Rostock, Claas reacted to your message:

From: KristofferSkare @.> Sent: Friday, March 15, 2024 2:45:49 PM To: dnv-innersource/mlfmu @.> Cc: Subscribed @.***> Subject: [dnv-innersource/mlfmu] Make wrapper classes for making ML models compatible with tool (Issue #30)

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Not every ML model / onnx file is compatible with the mlfmu tool and i can be a bit hard/confusing to make an incompatible model compatible.

To make this process easier for our users we can make example classes in tensorflow and pytorch that they can use and extend to make a compatibility wrapper for their trained model.

The class could have implemented some functions and give the user an idea of what they should do to make the wrapper.

class TensorflowWrapper(tensorflow.keras.Model): def init(self, trained_model): self.trained_model = trained_model def format_inputs(self, inputs, states, time): return def format_outputs(self, inputs, states, time, model_outputs): return def call(self, x): inputs, states, time = x formatted_inputs = self.format_inputs(inputs, state, time) model_outputs = self.trained_model(formatted_inputs) return self.format_outputs(inputs, states, time, model_outputs)

class PytorchWrapper(torch.nn.Modulel): def init(self, trained_model): self.trained_model = trained_model def format_inputs(self, inputs, states, time): return def format_outputs(self, inputs, states, time, model_outputs): return def forward(self, x): inputs, states, time = x formatted_inputs = self.format_inputs(inputs, state, time) model_outputs = self.trained_model(formatted_inputs) return self.format_outputs(inputs, states, time, model_outputs)

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dnv-opensource / mlfmu

Make wrapper classes for making ML models compatible with tool #30