Issue with learn.predict()

Hey guy, I was trying to implement image classification on a custom dataset.

I used the below function for getting the data loaders:

def get_dls(bs, size):
dblock = DataBlock(blocks=(ImageBlock, CategoryBlock),
               get_items=get_image_files,
               get_y=parent_label,
               splitter = TrainTestSplitter(test_size=0.2, random_state=0),
               item_tfms=Resize(224),
               batch_tfms=[*aug_transforms(mult=1.0, size=size,min_scale=1.0, max_warp=0, max_rotate=0),
                           Normalize.from_stats(*imagenet_stats)])
return dblock.dataloaders(path_images, bs=bs)

This has worked for me before, but for some reason, when I try to predict something with this,
the output is like:

I understand that if I take the argmax of the second and third tensors, I’ll get the index of my predicted class, but this does not normally happen, right?

Thanks!

@mayuresh
Hi, would you please share with us the code for creating your learner object?

cutmix = CutMix(1.)
learn = cnn_learner(dls, resnet50, loss_func=LabelSmoothingCrossEntropy(), 
                metrics=[accuracy,top_k_accuracy],
                cbs=cutmix)    

Although I figured out that when I remoe Cutmix/mixup, it works perfectly.