What is the recommended way to generate predictions on new test datasets? This may especially be useful when we want to generate predictions after we load a saved model or when we want to test on different datasets.
Here are some options I am currently considering:
- Run learn.predict on each individual item in the test dataset
- export the model (instead of using save) and reload it using load_learn with test argument
- extract model from learner and predict using standard PyTorch
- create a new learn object with some dummy train/val data and the required test data. Then load the saved weights to this object. The test data may require dummy y-variable too since it is probably required while invoking get_preds.
Some of the suggestions in the older posts like this one don’t seem to be applicable for latest version.
