The deep-tumour-spheroid from waterknight1998

'U2NET' object has no attribute 'loss_grad'

Hi, I am trying to train a u2net model for semantic segmentation which will be used for background remover. I am following your deep tumor spheroid notebook. when I try to find lr using lr_find or fit_one_cycle, I got this error. Any idea what i might be doing wrong?

AttributeError Traceback (most recent call last)
in
----> 1 learn.lr_find() # find learning rate
2 learn.recorder(suggestions=True)

14 frames
/usr/local/lib/python3.7/dist-packages/torch/nn/modules/module.py in getattr(self, name)
1206 return modules[name]
1207 raise AttributeError("'{}' object has no attribute '{}'".format(
-> 1208 type(self).name, name))
1209
1210 def setattr(self, name: str, value: Union[Tensor, 'Module']) -> None:

AttributeError: 'U2NET' object has no attribute 'loss_grad'

PS. I am running it on google colab.

U2Net "no implementation found for 'torch.nn.functional.binary_cross_entropy_with_logits' on types that implement __torch_function__: [<class 'fastai.torch_core.TensorImage'>, <class 'fastai.torch_core.TensorMask'>]"

U2Net

Hello !
Thank you for this awesome implementation of U2Net

I am encountering the following error when runing the learner :

First Error

TypeError                                 Traceback (most recent call last)
<ipython-input-29-0a497bfa9bea> in <module>()
----> 1 learn.lr_find() # find learning rate
      2 learn.recorder # plot learning rate graph

16 frames
/usr/local/lib/python3.6/dist-packages/fastai/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
    220     n_epoch = num_it//len(self.dls.train) + 1
    221     cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
--> 222     with self.no_logging(): self.fit(n_epoch, cbs=cb)
    223     if show_plot: self.recorder.plot_lr_find()
    224     if suggestions:

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
    209             self.opt.set_hypers(lr=self.lr if lr is None else lr)
    210             self.n_epoch = n_epoch
--> 211             self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
    212 
    213     def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    158 
    159     def _with_events(self, f, event_type, ex, final=noop):
--> 160         try: self(f'before_{event_type}');  f()
    161         except ex: self(f'after_cancel_{event_type}')
    162         self(f'after_{event_type}');  final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_fit(self)
    200         for epoch in range(self.n_epoch):
    201             self.epoch=epoch
--> 202             self._with_events(self._do_epoch, 'epoch', CancelEpochException)
    203 
    204     def fit(self, n_epoch, lr=None, wd=None, cbs=None, reset_opt=False):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    158 
    159     def _with_events(self, f, event_type, ex, final=noop):
--> 160         try: self(f'before_{event_type}');  f()
    161         except ex: self(f'after_cancel_{event_type}')
    162         self(f'after_{event_type}');  final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch(self)
    194 
    195     def _do_epoch(self):
--> 196         self._do_epoch_train()
    197         self._do_epoch_validate()
    198 

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch_train(self)
    186     def _do_epoch_train(self):
    187         self.dl = self.dls.train
--> 188         self._with_events(self.all_batches, 'train', CancelTrainException)
    189 
    190     def _do_epoch_validate(self, ds_idx=1, dl=None):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    158 
    159     def _with_events(self, f, event_type, ex, final=noop):
--> 160         try: self(f'before_{event_type}');  f()
    161         except ex: self(f'after_cancel_{event_type}')
    162         self(f'after_{event_type}');  final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in all_batches(self)
    164     def all_batches(self):
    165         self.n_iter = len(self.dl)
--> 166         for o in enumerate(self.dl): self.one_batch(*o)
    167 
    168     def _do_one_batch(self):

/usr/local/lib/python3.6/dist-packages/semtorch/models/archs/u2net.py in one_batch(self, i, b)
    616         self.iter = i
    617         self._split(b)
--> 618         self._with_events(self._do_one_batch, 'batch', CancelBatchException)
    619 
    620 

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    158 
    159     def _with_events(self, f, event_type, ex, final=noop):
--> 160         try: self(f'before_{event_type}');  f()
    161         except ex: self(f'after_cancel_{event_type}')
    162         self(f'after_{event_type}');  final()

/usr/local/lib/python3.6/dist-packages/semtorch/models/archs/u2net.py in _do_one_batch(self)
    602         if len(self.yb) == 0:
    603             return
--> 604         _, self.loss = self.loss_func(d0, d1, d2, d3, d4, d5, d6, *self.yb)
    605         self('after_loss')
    606         if not self.training: return

/usr/local/lib/python3.6/dist-packages/semtorch/models/archs/u2net.py in muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v)
    553 def muti_bce_loss_fusion(d0, d1, d2, d3, d4, d5, d6, labels_v):
    554     aux=labels_v.unsqueeze(1)
--> 555     loss0 = bce_loss(d0, aux)
    556     loss1 = bce_loss(d1, aux)
    557     loss2 = bce_loss(d2, aux)

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
    725             result = self._slow_forward(*input, **kwargs)
    726         else:
--> 727             result = self.forward(*input, **kwargs)
    728         for hook in itertools.chain(
    729                 _global_forward_hooks.values(),

/usr/local/lib/python3.6/dist-packages/torch/nn/modules/loss.py in forward(self, input, target)
    630                                                   self.weight,
    631                                                   pos_weight=self.pos_weight,
--> 632                                                   reduction=self.reduction)
    633 
    634 

/usr/local/lib/python3.6/dist-packages/torch/nn/functional.py in binary_cross_entropy_with_logits(input, target, weight, size_average, reduce, reduction, pos_weight)
   2571                 binary_cross_entropy_with_logits, tens_ops, input, target, weight=weight,
   2572                 size_average=size_average, reduce=reduce, reduction=reduction,
-> 2573                 pos_weight=pos_weight)
   2574     if size_average is not None or reduce is not None:
   2575         reduction_enum = _Reduction.legacy_get_enum(size_average, reduce)

/usr/local/lib/python3.6/dist-packages/torch/overrides.py in handle_torch_function(public_api, relevant_args, *args, **kwargs)
   1069     raise TypeError("no implementation found for '{}' on types that implement "
   1070                     '__torch_function__: {}'
-> 1071                     .format(func_name, list(map(type, overloaded_args))))
   1072 
   1073 def has_torch_function(relevant_args: Iterable[Any]) -> bool:

**TypeError: no implementation found for 'torch.nn.functional.binary_cross_entropy_with_logits' on types that implement __torch_function__: [<class 'fastai.torch_core.TensorImage'>, <class 'fastai.torch_core.TensorMask'>]**

I searched online and found out that it was possible that it was due to the pytorch version running on Colab and the Dice Metric (but I used the one you defined "DiceUSquaredNet" so I ran the following code on top of my colab :

!pip uninstall torch -y
!pip install torch==1.6.0+cu101 torchvision==0.7.0+cu101 -f https://download.pytorch.org/whl/torch_stable.html
!pip install fastai==2.0.19
!pip install fastcore==1.3.1 
!pip install semtorch

Error 2

But then instead I get this error :

AttributeError                            Traceback (most recent call last)
<ipython-input-26-0a497bfa9bea> in <module>()
----> 1 learn.lr_find() # find learning rate
      2 learn.recorder # plot learning rate graph

20 frames
/usr/local/lib/python3.6/dist-packages/fastai/callback/schedule.py in lr_find(self, start_lr, end_lr, num_it, stop_div, show_plot, suggestions)
    226     n_epoch = num_it//len(self.dls.train) + 1
    227     cb=LRFinder(start_lr=start_lr, end_lr=end_lr, num_it=num_it, stop_div=stop_div)
--> 228     with self.no_logging(): self.fit(n_epoch, cbs=cb)
    229     if show_plot: self.recorder.plot_lr_find()
    230     if suggestions:

/usr/local/lib/python3.6/dist-packages/fastcore/logargs.py in _f(*args, **kwargs)
     54         init_args.update(log)
     55         setattr(inst, 'init_args', init_args)
---> 56         return inst if to_return else f(*args, **kwargs)
     57     return _f

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in fit(self, n_epoch, lr, wd, cbs, reset_opt)
    205             self.opt.set_hypers(lr=self.lr if lr is None else lr)
    206             self.n_epoch = n_epoch
--> 207             self._with_events(self._do_fit, 'fit', CancelFitException, self._end_cleanup)
    208 
    209     def _end_cleanup(self): self.dl,self.xb,self.yb,self.pred,self.loss = None,(None,),(None,),None,None

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_fit(self)
    195         for epoch in range(self.n_epoch):
    196             self.epoch=epoch
--> 197             self._with_events(self._do_epoch, 'epoch', CancelEpochException)
    198 
    199     @log_args(but='cbs')

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch(self)
    189 
    190     def _do_epoch(self):
--> 191         self._do_epoch_train()
    192         self._do_epoch_validate()
    193 

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _do_epoch_train(self)
    181     def _do_epoch_train(self):
    182         self.dl = self.dls.train
--> 183         self._with_events(self.all_batches, 'train', CancelTrainException)
    184 
    185     def _do_epoch_validate(self, ds_idx=1, dl=None):

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in all_batches(self)
    159     def all_batches(self):
    160         self.n_iter = len(self.dl)
--> 161         for o in enumerate(self.dl): self.one_batch(*o)
    162 
    163     def _do_one_batch(self):

/usr/local/lib/python3.6/dist-packages/semtorch/models/archs/u2net.py in one_batch(self, i, b)
    616         self.iter = i
    617         self._split(b)
--> 618         self._with_events(self._do_one_batch, 'batch', CancelBatchException)
    619 
    620 

/usr/local/lib/python3.6/dist-packages/fastai/learner.py in _with_events(self, f, event_type, ex, final)
    153 
    154     def _with_events(self, f, event_type, ex, final=noop):
--> 155         try:       self(f'before_{event_type}')       ;f()
    156         except ex: self(f'after_cancel_{event_type}')
    157         finally:   self(f'after_{event_type}')        ;final()

/usr/local/lib/python3.6/dist-packages/semtorch/models/archs/u2net.py in _do_one_batch(self)
    610         del d0, d1, d2, d3, d4, d5, d6
    611         self._step()
--> 612         self('after_step')
    613         self.opt.zero_grad()
    614 

/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, gen, *args, **kwargs)
    224     def range(cls, a, b=None, step=None): return cls(range_of(a, b=b, step=step))
    225 
--> 226     def map(self, f, *args, gen=False, **kwargs): return self._new(map_ex(self, f, *args, gen=gen, **kwargs))
    227     def argwhere(self, f, negate=False, **kwargs): return self._new(argwhere(self, f, negate, **kwargs))
    228     def filter(self, f=noop, negate=False, gen=False, **kwargs):

/usr/local/lib/python3.6/dist-packages/fastcore/basics.py in map_ex(iterable, f, gen, *args, **kwargs)
    541     res = map(g, iterable)
    542     if gen: return res
--> 543     return list(res)
    544 
    545 # Cell

/usr/local/lib/python3.6/dist-packages/fastcore/basics.py in __call__(self, *args, **kwargs)
    531             if isinstance(v,_Arg): kwargs[k] = args.pop(v.i)
    532         fargs = [args[x.i] if isinstance(x, _Arg) else x for x in self.pargs] + args[self.maxi+1:]
--> 533         return self.fn(*fargs, **kwargs)
    534 
    535 # 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/semtorch/models/archs/u2net.py in after_step(self)
    673         self.learn.scaler = GradScaler()
    674 
--> 675     def after_step(self): self.learn.scaler.update()
    676 
    677     def after_fit(self):

**AttributeError: 'USquaredNetLearner' object has no attribute 'scaler'**

I guess this is linked to the downgrade of torch to get rid of the first issue ?

I saw this thread where you had the same problem xuebinqin/U-2-Net#25 and though that it was solved in your module.

I'm not using the exact same dataset as yours, but mine is almost exactly similar, binary segmentation of nuclei cells with images in a path and masks in another. Modified the "codes.txt" to include NoNuclei/Nuclei. And running everything on a colab.

Thank you for your help :)

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deep-tumour-spheroid's Issues

'U2NET' object has no attribute 'loss_grad'

U2Net "no implementation found for 'torch.nn.functional.binary_cross_entropy_with_logits' on types that implement __torch_function__: [<class 'fastai.torch_core.TensorImage'>, <class 'fastai.torch_core.TensorMask'>]"

U2Net

First Error

Error 2

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