No I’m still getting this error when I try to apply it. I’m going to submit a bug report. @sgugger any thoughts? My workflow I’m trying to use is (in Google colab)
learn = load_learner('learnfile.pkl')
test_imgs = get_image_files(img_dir)
tst_dl = learn.dls.test_dl(test_imgs)
targs, preds = learn.tta(dl=tst_dl)
This gives the following error
epoch train_loss valid_loss accuracy time
---------------------------------------------------------------------------
AttributeError Traceback (most recent call last)
<ipython-input-23-ece6d3f653d6> in <module>()
----> 1 learn.tta(dl=tst_dl)
13 frames
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in tta(self, ds_idx, dl, n, item_tfms, batch_tfms, beta, use_max)
562 if item_tfms is not None or batch_tfms is not None: dl = dl.new(after_item=item_tfms, after_batch=batch_tfms)
563 try:
--> 564 self(_before_epoch)
565 with dl.dataset.set_split_idx(0), self.no_mbar():
566 if hasattr(self,'progress'): self.progress.mbar = master_bar(list(range(n)))
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in __call__(self, event_name)
131 def ordered_cbs(self, event): return [cb for cb in sort_by_run(self.cbs) if hasattr(cb, event)]
132
--> 133 def __call__(self, event_name): L(event_name).map(self._call_one)
134
135 def _call_one(self, event_name):
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in map(self, f, *args, **kwargs)
381 else f.format if isinstance(f,str)
382 else f.__getitem__)
--> 383 return self._new(map(g, self))
384
385 def filter(self, f, negate=False, **kwargs):
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in _new(self, items, *args, **kwargs)
331 @property
332 def _xtra(self): return None
--> 333 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
334 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
335 def copy(self): return self._new(self.items.copy())
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(cls, x, *args, **kwargs)
45 return x
46
---> 47 res = super().__call__(*((x,) + args), **kwargs)
48 res._newchk = 0
49 return res
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __init__(self, items, use_list, match, *rest)
322 if items is None: items = []
323 if (use_list is not None) or not _is_array(items):
--> 324 items = list(items) if use_list else _listify(items)
325 if match is not None:
326 if is_coll(match): match = len(match)
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in _listify(o)
235 if isinstance(o, list): return o
236 if isinstance(o, str) or _is_array(o): return [o]
--> 237 if is_iter(o): return list(o)
238 return [o]
239
/usr/local/lib/python3.6/dist-packages/fastcore/foundation.py in __call__(self, *args, **kwargs)
298 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
299 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 300 return self.fn(*fargs, **kwargs)
301
302 # Cell
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _call_one(self, event_name)
135 def _call_one(self, event_name):
136 assert hasattr(event, event_name), event_name
--> 137 [cb(event_name) for cb in sort_by_run(self.cbs)]
138
139 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
/usr/local/lib/python3.6/dist-packages/fastai/learner.py in <listcomp>(.0)
135 def _call_one(self, event_name):
136 assert hasattr(event, event_name), event_name
--> 137 [cb(event_name) for cb in sort_by_run(self.cbs)]
138
139 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
/usr/local/lib/python3.6/dist-packages/fastai/callback/core.py in __call__(self, event_name)
42 (self.run_valid and not getattr(self, 'training', False)))
43 res = None
---> 44 if self.run and _run: res = getattr(self, event_name, noop)()
45 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
46 return res
/usr/local/lib/python3.6/dist-packages/fastai/callback/progress.py in before_epoch(self)
21
22 def before_epoch(self):
---> 23 if getattr(self, 'mbar', False): self.mbar.update(self.epoch)
24
25 def before_train(self): self._launch_pbar()
/usr/local/lib/python3.6/dist-packages/fastprogress/fastprogress.py in update(self, val)
92 yield o
93
---> 94 def update(self, val): self.main_bar.update(val)
95
96 # Cell
/usr/local/lib/python3.6/dist-packages/fastprogress/fastprogress.py in update(self, val)
57 elif val <= self.first_its or val >= self.last_v + self.wait_for or val >= self.total:
58 cur_t = time.time()
---> 59 avg_t = (cur_t - self.start_t) / val
60 self.wait_for = max(int(self.update_every / (avg_t+1e-8)),1)
61 self.pred_t = avg_t * self.total
AttributeError: 'NBProgressBar' object has no attribute 'start_t'