I get different inferences to result from my Image classifier. When I do inference on Pytorch as compared to fastai

Part 1 (2019) Advanced (Part 1 v3)
1

When I use the pickle file of fastai and do prediction using learn.predict() method. And I use the same pickle file and convert that pickle file using this method to PyTorch. Below is the code snippet.

learn = load_learner(src_path,model_name)
var=learn.model
import pickle
new_str=f"/pytorch-weight/{model_name}"
file = open(new_str, 'wb')
pickle.dump(var, file)
# close the file
file.close()

And do the prediction using that generated pickle file in PyTorch but I get the different result as compared to fastai. What is the reason behind this I want to know? Am I doing the right way to convert that pickle file for PyTorch inference?

2

Hi, have you been able to figure this out?
I am also looking into training in fastai but then running inference in plain pytorch.

1 Like
3

Fastai automatically applies the validation transforms. make sure you are using exactly the same transforms in your plain pytorch implementation. (I guess at least resize and normalization)

3 Likes
4

Basically what @florianl said. Remember that fastai runs center crop on the validation set as well

3 Likes
5

Thanks both! Makes sense.