I must be getting something terribly wrong with the fast-ai library, since I seem to be the only one having this problem. Everytime I try the learning rate finder or training the network, it gives me an error. It took me a week to produce this specific error message, which made me check the mask values. It turns out they are either 0 for background pixels and 255 for foreground ones. This is a problem as I only have two classes. How can I change the 255 values to 1 within my Databunch Object? Is there a way to divide each mask value by 255 or do I need to do it beforehand somehow? I am kinda lost within the process.
Here's the error message i'm getting:
LR Finder is complete, type {learner_name}.recorder.plot() to see the graph.
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
<ipython-input-20-c7a9c29f9dd1> in <module>()
----> 1 learn.lr_find()
2 learn.recorder.plot()
8 frames
/usr/local/lib/python3.6/dist-packages/fastai/train.py in lr_find(learn, start_lr, end_lr, num_it, stop_div, wd)
39 cb = LRFinder(learn, start_lr, end_lr, num_it, stop_div)
40 epochs = int(np.ceil(num_it/len(learn.data.train_dl)))
---> 41 learn.fit(epochs, start_lr, callbacks=[cb], wd=wd)
42
43 def to_fp16(learn:Learner, loss_scale:float=None, max_noskip:int=1000, dynamic:bool=True, clip:float=None,
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(self, epochs, lr, wd, callbacks)
198 else: self.opt.lr,self.opt.wd = lr,wd
199 callbacks = [cb(self) for cb in self.callback_fns + listify(defaults.extra_callback_fns)] + listify(callbacks)
--> 200 fit(epochs, self, metrics=self.metrics, callbacks=self.callbacks+callbacks)
201
202 def create_opt(self, lr:Floats, wd:Floats=0.)->None:
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in fit(epochs, learn, callbacks, metrics)
99 for xb,yb in progress_bar(learn.data.train_dl, parent=pbar):
100 xb, yb = cb_handler.on_batch_begin(xb, yb)
--> 101 loss = loss_batch(learn.model, xb, yb, learn.loss_func, learn.opt, cb_handler)
102 if cb_handler.on_batch_end(loss): break
103
/usr/local/lib/python3.6/dist-packages/fastai/basic_train.py in loss_batch(model, xb, yb, loss_func, opt, cb_handler)
28
29 if not loss_func: return to_detach(out), to_detach(yb[0])
---> 30 loss = loss_func(out, *yb)
31
32 if opt is not None:
/usr/local/lib/python3.6/dist-packages/fastai/layers.py in __call__(self, input, target, **kwargs)
241 if self.floatify: target = target.float()
242 input = input.view(-1,input.shape[-1]) if self.is_2d else input.view(-1)
--> 243 return self.func.__call__(input, target.view(-1), **kwargs)
244
245 def CrossEntropyFlat(*args, axis:int=-1, **kwargs):
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in __call__(self, *input, **kwargs)
539 result = self._slow_forward(*input, **kwargs)
540 else:
--> 541 result = self.forward(*input, **kwargs)
542 for hook in self._forward_hooks.values():
543 hook_result = hook(self, input, result)
/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
914 def forward(self, input, target):
915 return F.cross_entropy(input, target, weight=self.weight,
--> 916 ignore_index=self.ignore_index, reduction=self.reduction)
917
918
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in cross_entropy(input, target, weight, size_average, ignore_index, reduce, reduction)
2007 if size_average is not None or reduce is not None:
2008 reduction = _Reduction.legacy_get_string(size_average, reduce)
-> 2009 return nll_loss(log_softmax(input, 1), target, weight, None, ignore_index, None, reduction)
2010
2011
/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in nll_loss(input, target, weight, size_average, ignore_index, reduce, reduction)
1836 .format(input.size(0), target.size(0)))
1837 if dim == 2:
-> 1838 ret = torch._C._nn.nll_loss(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
1839 elif dim == 4:
1840 ret = torch._C._nn.nll_loss2d(input, target, weight, _Reduction.get_enum(reduction), ignore_index)
RuntimeError: Assertion `cur_target >= 0 && cur_target < n_classes' failed. at /pytorch/aten/src/THNN/generic/ClassNLLCriterion.c:97
Also this is how I set up my data:
data = (SegmentationItemList.from_df(img_df,IMAGE_PATH)
# import from df in greyscale ('L')
.split_by_rand_pct(valid_pct=0.15)
# 1/15 train/validation split
.label_from_func(get_mask, classes = array(['background','cell']))
# segmentation mask and classes
.transform(tfms, tfm_y=True, size=TILE_SHAPE)
# apply data augmentation
.databunch(bs=BATCH_SIZE)
# set batchsize
.normalize()
)
Please tell me if you need any more information. I already tried adding an 'after_open' function, which should have divided all by 255 to the 'label_from_func' part. I also know there is a div attribute within the 'open_image' function of fast-ai, which is supposed to normalize RGB values between 0 and 1, but I couldn't find one for 'label_from_func'.
Edit:
I found this post in the fastai community. However even with these answers i was not able to solve my problem. I tried adding this snippet to pass div=True into the open_mask function, but it did not work:
src.train.y.create_func = partial(open_mask, div=True)
src.valid.y.create_func = partial(open_mask, div=True)
I also tried .set_attr(mask_opener=partial(open_mask, div=True)) after .label_from_func(), but then it throws this attribute error: AttributeError: setattr
Still need help