Unable to load saved model

I’m trying to load a saved model using learner_load() but get errors about missing dls.
It works just fine if I do learn.load(filepath) but I need to load the model from only the filepath.

This is the full code to reproduce the error.

how do I load the model correctly?

from fastai.vision.all import *

path = untar_data(URLs.PETS)
files = get_image_files(path/“images”)

def label_func(f): return f[0].isupper()
dls = ImageDataLoaders.from_name_func(path, files, label_func, item_tfms=Resize(224))

learn = vision_learner(dls, resnet34, metrics=error_rate)
p = learn.save(‘pets’)

learn_inf = load_learner(p)

But this throws the error:

AttributeError Traceback (most recent call last)
Cell In[9], line 1
->—> 1 learn_inf = load_learner(p)

File ~/anaconda3/envs/modelling2/lib/python3.10/site-packages/fastai/learner.py:451, in l>oad_learner(fname, cpu, pickle_module)
449 raise
450 if cpu:
→ 451 res.dls.cpu()
452 if hasattr(res, ‘channels_last’): res = res.to_contiguous(to_fp32=True)
453 elif hasattr(res, ‘mixed_precision’): res = res.to_fp32()

AttributeError: ‘dict’ object has no attribute ‘dls’

fastai.version == ‘2.7.11’

learn.save is paired with learn.load and saves the model, and potentially optimizer. learn.export is paired with load_learner for deployment


Thanks, it works now. But I have a bonus question:
learn.export works just fine if I’m using the code from the tutorial. But if I change the loss function to labelsmoothning:

learn = vision_learner(dls, resnet34, metrics=error_rate, loss_func=LabelSmoothingCrossEntropyFlat())

I get the error "

TypeError: cannot pickle ‘code’ object"

should I just set the loss_func to None before saving? Or shoud the LabelSmoothing function be re-written somehow? It might be good to know if I write more custom codes to my models.

Best regards/