I’m trying to fine-tune a resnet50 model for a classification problem. The code I use is pretty simple (copy-pasted from tutorials), the only addition is that I start from greyscale images (see code below). Anyway, everything seems to work as expected (I can dls.show_batch(), learn.fit_one_cycle(), learn.predict(), … ), but if I try to run learn.summary() I get IndexError: list index out of range.
I guessed it was for the greyscale transformation, however it persists even if I discard the greyscale conversion or if I specify cnn_learner(...., n_in=1, ...).
Do you have any advice?
Thanks in advance 
code:
import fastai
from fastai.vision.all import *
from albumentations.augmentations.transforms import ToGray
# Using versions:
# torch: 1.8.0
# fastai: 2.4
# hyperparams
# augmentation
resize1 = 512
resize2 = 224
max_lighting, p_lighting = 0.1, 0.5
min_zoom, max_zoom = 0.9, 1.1
max_warp, max_rotate = 0, 15.0
# dataloader
bs = 32
class AlbumentationsToGray(Transform):
def __init__(self, aug): self.aug = aug
def encodes(self, img: PILImage):
aug_img = self.aug(image=np.array(img))['image']
# ToGray returns 3 channels with same grey image repeated,
# this causes conflicts with model training:
# [b x (3 chan x image size) VS (b x 3 classes) x 1]
aug_img = PILImage.create(aug_img[:, :, 0], mode='L')
return aug_img
# setup augmentation pipeline
RGB2Grey = AlbumentationsToGray(ToGray(p=1))
tfms = [
# IntToFloatTensor(div_mask=255), # need masks in [0, 1] format
*aug_transforms(
size=resize2,
max_lighting=max_lighting, p_lighting=p_lighting,
min_zoom=min_zoom, max_zoom=max_zoom,
max_warp=max_warp, max_rotate=max_rotate)
]
# initialize dataloader
dls = ImageDataLoaders.from_df(selfsuper_df, DATA_PATH, valid_col='is_valid', label_col='label',
item_tfms=[Resize(resize1)], batch_tfms=tfms,
bs=bs)
# hyperparams
freezed_epochs = 6
loss = CrossEntropyLossFlat()
metrics = [error_rate, accuracy]
early_stopping_patience = 10
learn = cnn_learner(dls, resnet50,
loss_func=loss,
metrics=metrics,
cbs=EarlyStoppingCallback(
monitor='accuracy', patience=early_stopping_patience),
# n_in=1,
)
learn.summary()
where selfsuper_df is a dataframe like this:
stack-trace:
---------------------------------------------------------------------------
IndexError Traceback (most recent call last)
<ipython-input-34-bc39e9e85f86> in <module>
----> 1 learn.summary()
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/callback/hook.py in summary(self)
203 "Print a summary of the model, optimizer and loss function."
204 xb = self.dls.train.one_batch()[:self.dls.train.n_inp]
--> 205 res = module_summary(self, *xb)
206 res += f"Optimizer used: {self.opt_func}\nLoss function: {self.loss_func}\n\n"
207 if self.opt is not None:
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/callback/hook.py in module_summary(learn, *xb)
171 # thus are not counted inside the summary
172 #TODO: find a way to have them counted in param number somehow
--> 173 infos = layer_info(learn, *xb)
174 n,bs = 76,find_bs(xb)
175 inp_sz = _print_shapes(apply(lambda x:x.shape, xb), bs)
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/callback/hook.py in layer_info(learn, *xb)
157 train_only_cbs = [cb for cb in learn.cbs if hasattr(cb, '_only_train_loop')]
158 with learn.removed_cbs(train_only_cbs), learn.no_logging(), learn as l:
--> 159 r = l.get_preds(dl=[batch], inner=True, reorder=False)
160 return h.stored
161
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/learner.py in __exit__(self, exc_type, exc_value, tb)
223 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
224 def __enter__(self): self(_before_epoch); return self
--> 225 def __exit__(self, exc_type, exc_value, tb): self(_after_epoch)
226
227 def validation_context(self, cbs=None, inner=False):
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/learner.py in __call__(self, event_name)
139
140 def ordered_cbs(self, event): return [cb for cb in self.cbs.sorted('order') if hasattr(cb, event)]
--> 141 def __call__(self, event_name): L(event_name).map(self._call_one)
142
143 def _call_one(self, event_name):
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastcore/foundation.py in map(self, f, gen, *args, **kwargs)
152 def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
153
--> 154 def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
155 def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
156 def filter(self, f=noop, negate=False, gen=False, **kwargs):
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastcore/basics.py in map_ex(iterable, f, gen, *args, **kwargs)
664 res = map(g, iterable)
665 if gen: return res
--> 666 return list(res)
667
668 # Cell
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastcore/basics.py in __call__(self, *args, **kwargs)
649 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
650 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 651 return self.func(*fargs, **kwargs)
652
653 # Cell
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/learner.py in _call_one(self, event_name)
143 def _call_one(self, event_name):
144 if not hasattr(event, event_name): raise Exception(f'missing {event_name}')
--> 145 for cb in self.cbs.sorted('order'): cb(event_name)
146
147 def _bn_bias_state(self, with_bias): return norm_bias_params(self.model, with_bias).map(self.opt.state)
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/callback/core.py in __call__(self, event_name)
43 (self.run_valid and not getattr(self, 'training', False)))
44 res = None
---> 45 if self.run and _run: res = getattr(self, event_name, noop)()
46 if event_name=='after_fit': self.run=True #Reset self.run to True at each end of fit
47 return res
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/callback/tracker.py in after_epoch(self)
52 def after_epoch(self):
53 "Compare the value monitored to its best score and maybe stop training."
---> 54 super().after_epoch()
55 if self.new_best: self.wait = 0
56 else:
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastai/callback/tracker.py in after_epoch(self)
35 def after_epoch(self):
36 "Compare the last value to the best up to now"
---> 37 val = self.recorder.values[-1][self.idx]
38 if self.comp(val - self.min_delta, self.best): self.best,self.new_best = val,True
39 else: self.new_best = False
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastcore/foundation.py in __getitem__(self, idx)
109 def _xtra(self): return None
110 def _new(self, items, *args, **kwargs): return type(self)(items, *args, use_list=None, **kwargs)
--> 111 def __getitem__(self, idx): return self._get(idx) if is_indexer(idx) else L(self._get(idx), use_list=None)
112 def copy(self): return self._new(self.items.copy())
113
~/anaconda3/envs/fastai/lib/python3.9/site-packages/fastcore/foundation.py in _get(self, i)
113
114 def _get(self, i):
--> 115 if is_indexer(i) or isinstance(i,slice): return getattr(self.items,'iloc',self.items)[i]
116 i = mask2idxs(i)
117 return (self.items.iloc[list(i)] if hasattr(self.items,'iloc')
IndexError: list index out of range