fastaiv2predicts_full

fastaiv2predictsfull.py

# Assuming you have set up your Dataloader and learner as dls, learn

learn.fine_tune(1, wd=0.5)
learn.export("model.pkl") # Save the model

predictions_path = "../input/fruits/fruits-360_dataset/fruits-360/Test"

def predict_batch(self, item, rm_type_tfms=None, with_input=False): # this bit is slightly complicated. ignore it for now
    dl = self.dls.test_dl(item, rm_type_tfms=rm_type_tfms, num_workers=15)
    ret = self.get_preds(dl=dl, with_input=False, with_decoded=True)
    return ret
import random

predictions_path = Path(predictions_path)
Learner.predict_batch = predict_batch

# Load the model
learn = load_learner("model.pkl")

tst_files = get_image_files(predictions_path) #same as before
tst_files = tst_files.shuffle() 

# Run the predictions
preds = learn.predict_batch(tst_files)
classes = learn.dls.vocab # the original categories
# The following classes[int(x)], preds[2]) will need to be changed to preds[3] if you enable with_input on line 9
preds_mapped = list(map(lambda x: classes[int(x)], preds[2])) #just saving them out