I’ve created a model with a forward function that takes “x” as input (image of size (3,416,416)). I create a trace of the model using: module = torch.jit.trace(model, example_forward_input), then save that model using module.save("model.pt"). Then I load this model trace into an Android application. When I send an input to the model (from the phone) that is identical to the input used as “example_forward_input”, I get the correct result. However, when I use any other input tensor (same shape), I get poor results. Is this supposed to be the behaviour of the trace function? Is there a function that traces a model that can generalize to any inputs? Any guidance would be much appreciated.
For some more detail: This is a YOLOv3 based model that involves detection and classification. The classification with different inputs into the traced model gives similar results to the same inputs in the model. However, the detection locations differ (in w/h especially) when running an input that was not used as an example through the traced model.
EDIT: I’m guessing this is due to the fact that my forward module uses control-flow that is dependent on the input, as outlined here 12. However, when I try to convert the model to a script module, as outlined on that same page. I get the following error: raise NotSupportedError(ctx_range, _vararg_kwarg_err) torch.jit.frontend.NotSupportedError: Compiled functions can't take variable number of arguments or use keyword-only arguments with defaults:   File "C:\Users\isaac\Anaconda3\envs\SNProject\lib\site-packages\torch\nn\modules\module.py", line 85     def forward(self, *input):                       ~~~~~~ <--- HERE         print(torch.__version__)         r"""Defines the computation performed at every call. As you can see this is coming from the torch library itself. Any suggestions on how to proceed?