Model is not giving correct prediction scores after conversion

[nahla-naz]

Hi,

I created a Pytorch model which is giving >80% accuracy on windows. The prediction scores are within the range of 0 and 1. However, after I convert it to NCNN, it is giving 0 and 1 as scores. I managed to solve this problem by adding a softmax layer:
out = F.softmax(out, dim=1)
Now, the prediction scores are between 0 and 1. But the accuracy is very low.

How can I make the accuracy of NCNN model same as that of Pytorch model?

I converted the model from .pth to .onnx using below method and converted .onnx to .param and .bin using convertmodel.com(without simplifying or optimising).

model=torch.load('model.pth',map_location=torch.device('cpu'))
model.eval()
input_tensor = torch.randn(1, 3, 80, 80 )
exportModel = model
torch.onnx.export(exportModel, input_tensor, 'path' +
('modelNEW.onnx'), export_params=True, input_names=["data"], output_names=["softmax"])

Kindly help. Thanks.