Hi all,
I would like to know why when I run:
learn = unet_learner(dls, resnet34, loss_func=DiceLoss(),metrics=[FocalTverskyLoss],
self_attention=False, act_cls=Mish, opt_func=ranger)
learn.fit_flat_cos(10)
I get this error:
RuntimeError: The size of tensor a (98304) must match the size of tensor b (32768) at non-singleton dimension 0
I have also tried to use learn.lr_find(), and it surprisingly works.
This is the entire error’s log:
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_36/3795572986.py in <module>
----> 1 learn.fit_one_cycle(10)
/opt/conda/lib/python3.7/site-packages/fastai/callback/schedule.py in fit_one_cycle(self, n_epoch, lr_max, div, div_final, pct_start, wd, moms, cbs, reset_opt)
114 scheds = {'lr': combined_cos(pct_start, lr_max/div, lr_max, lr_max/div_final),
115 'mom': combined_cos(pct_start, *(self.moms if moms is None else moms))}
--> 116 self.fit(n_epoch, cbs=ParamScheduler(scheds)+L(cbs), reset_opt=reset_opt, wd=wd)
117
118 # Cell
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
219 self.opt.set_hypers(lr=self.lr if lr is None else lr)
220 self.n_epoch = n_epoch
--> 221 self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
222
223 def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
161
162 def _with_events(self, f, event_type, ex, final=noop):
--> 163 try: self(f'before_{event_type}'); f()
164 except ex: self(f'after_cancel_{event_type}')
165 self(f'after_{event_type}'); final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_fit(self)
210 for epoch in range(self.n_epoch):
211 self.epoch=epoch
--> 212 self._with_events(self._do_epoch, 'epoch', CancelEpochException)
213
214 def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
161
162 def _with_events(self, f, event_type, ex, final=noop):
--> 163 try: self(f'before_{event_type}'); f()
164 except ex: self(f'after_cancel_{event_type}')
165 self(f'after_{event_type}'); final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_epoch(self)
205 def _do_epoch(self):
206 self._do_epoch_train()
--> 207 self._do_epoch_validate()
208
209 def _do_fit(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _do_epoch_validate(self, ds_idx, dl)
201 if dl is None: dl = self.dls[ds_idx]
202 self.dl = dl
--> 203 with torch.no_grad(): self._with_events(self.all_batches, 'validate', CancelValidException)
204
205 def _do_epoch(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
161
162 def _with_events(self, f, event_type, ex, final=noop):
--> 163 try: self(f'before_{event_type}'); f()
164 except ex: self(f'after_cancel_{event_type}')
165 self(f'after_{event_type}'); final()
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in all_batches(self)
167 def all_batches(self):
168 self.n_iter = len(self.dl)
--> 169 for o in enumerate(self.dl): self.one_batch(*o)
170
171 def _do_one_batch(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in one_batch(self, i, b)
192 b = self._set_device(b)
193 self._split(b)
--> 194 self._with_events(self._do_one_batch, 'batch', CancelBatchException)
195
196 def _do_epoch_train(self):
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
163 try: self(f'before_{event_type}'); f()
164 except ex: self(f'after_cancel_{event_type}')
--> 165 self(f'after_{event_type}'); final()
166
167 def all_batches(self):
/opt/conda/lib/python3.7/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):
/opt/conda/lib/python3.7/site-packages/fastcore/foundation.py in map(self, f, gen, *args, **kwargs)
153 def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
154
--> 155 def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
156 def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
157 def argfirst(self, f, negate=False): return first(i for i,o in self.enumerate() if f(o))
/opt/conda/lib/python3.7/site-packages/fastcore/basics.py in map_ex(iterable, f, gen, *args, **kwargs)
696 res = map(g, iterable)
697 if gen: return res
--> 698 return list(res)
699
700 # Cell
/opt/conda/lib/python3.7/site-packages/fastcore/basics.py in __call__(self, *args, **kwargs)
681 if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
682 fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 683 return self.func(*fargs, **kwargs)
684
685 # Cell
/opt/conda/lib/python3.7/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)
/opt/conda/lib/python3.7/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
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in after_batch(self)
502 if len(self.yb) == 0: return
503 mets = self._train_mets if self.training else self._valid_mets
--> 504 for met in mets: met.accumulate(self.learn)
505 if not self.training: return
506 self.lrs.append(self.opt.hypers[-1]['lr'])
/opt/conda/lib/python3.7/site-packages/fastai/learner.py in accumulate(self, learn)
424 def accumulate(self, learn):
425 bs = find_bs(learn.yb)
--> 426 self.total += learn.to_detach(self.func(learn.pred, *learn.yb))*bs
427 self.count += bs
428 @property
/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1049 if not (self._backward_hooks or self._forward_hooks or self._forward_pre_hooks or _global_backward_hooks
1050 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1051 return forward_call(*input, **kwargs)
1052 # Do not call functions when jit is used
1053 full_backward_hooks, non_full_backward_hooks = [], []
/tmp/ipykernel_36/3481683221.py in forward(self, inputs, targets, smooth, alpha, beta, gamma)
18
19 #True Positives, False Positives & False Negatives
---> 20 TP = (inputs * targets).sum()
21 FP = ((1-targets) * inputs).sum()
22 FN = (targets * (1-inputs)).sum()
/opt/conda/lib/python3.7/site-packages/fastai/torch_core.py in __torch_function__(self, func, types, args, kwargs)
338 convert=False
339 if _torch_handled(args, self._opt, func): convert,types = type(self),(torch.Tensor,)
--> 340 res = super().__torch_function__(func, types, args=args, kwargs=kwargs)
341 if convert: res = convert(res)
342 if isinstance(res, TensorBase): res.set_meta(self, as_copy=True)
/opt/conda/lib/python3.7/site-packages/torch/_tensor.py in __torch_function__(cls, func, types, args, kwargs)
1021
1022 with _C.DisableTorchFunction():
-> 1023 ret = func(*args, **kwargs)
1024 return _convert(ret, cls)
1025
RuntimeError: The size of tensor a (98304) must match the size of tensor b (32768) at non-singleton dimension 0