# Contribution from @fredguth, https://github.com/fredguth/fastai_playground. from fastai.torch_core import * from fastai.callback import * from fastai.basic_train import * __all__ = ['TerminateOnNaNCallback', 'EarlyStoppingCallback', 'SaveModelCallback', 'TrackerCallback', 'ReduceLROnPlateauCallback', 'TrackEpochCallback' ] class TerminateOnNaNCallback(Callback): "A `Callback` that terminates training if loss is NaN." def __init__(self): self.stop = False def on_batch_end(self, last_loss, epoch, num_batch, **kwargs:Any)->None: "Test if `last_loss` is NaN and interrupts training." if self.stop: return True #to skip validation after stopping during training if torch.isnan(last_loss): print (f'Epoch/Batch ({epoch}/{num_batch}): Invalid loss, terminating training.') return {'stop_epoch': True, 'stop_training': True, 'skip_validate': True} class TrackerCallback(LearnerCallback): "A `LearnerCallback` that keeps track of the best value in `monitor`." def __init__(self, learn:Learner, monitor:str='valid_loss', mode:str='auto'): super().__init__(learn) self.monitor,self.mode = monitor,mode if self.mode not in ['auto', 'min', 'max']: warn(f'{self.__class__} mode {self.mode} is invalid, falling back to "auto" mode.') self.mode = 'auto' mode_dict = {'min': np.less, 'max':np.greater} mode_dict['auto'] = np.less if 'loss' in self.monitor else np.greater self.operator = mode_dict[self.mode] def on_train_begin(self, **kwargs:Any)->None: "Initializes the best value." self.best = float('inf') if self.operator == np.less else -float('inf') def get_monitor_value(self): "Pick the monitored value." if self.monitor=='trn_loss' and len(self.learn.recorder.losses) == 0: return None elif len(self.learn.recorder.val_losses) == 0: return None values = {'train_loss':self.learn.recorder.losses[-1].cpu().numpy(), 'valid_loss':self.learn.recorder.val_losses[-1]} if values['valid_loss'] is None: return if self.learn.recorder.metrics: for m, n in zip(self.learn.recorder.metrics[-1],self.learn.recorder.names[3:-1]): values[n] = m if values.get(self.monitor) is None: warn(f'{self.__class__} conditioned on metric `{self.monitor}` which is not available. Available metrics are: {", ".join(map(str, self.learn.recorder.names[1:-1]))}') return values.get(self.monitor) class EarlyStoppingCallback(TrackerCallback): "A `TrackerCallback` that terminates training when monitored quantity stops improving." def __init__(self, learn:Learner, monitor:str='valid_loss', mode:str='auto', min_delta:int=0, patience:int=0): super().__init__(learn, monitor=monitor, mode=mode) self.min_delta,self.patience = min_delta,patience if self.operator == np.less: self.min_delta *= -1 def on_train_begin(self, **kwargs:Any)->None: "Initialize inner arguments." self.wait = 0 super().on_train_begin(**kwargs) def on_epoch_end(self, epoch, **kwargs:Any)->None: "Compare the value monitored to its best score and maybe stop training." current = self.get_monitor_value() if current is None: return if self.operator(current - self.min_delta, self.best): self.best,self.wait = current,0 else: self.wait += 1 if self.wait > self.patience: print(f'Epoch {epoch}: early stopping') return {"stop_training":True} class SaveModelCallback(TrackerCallback): "A `TrackerCallback` that saves the model when monitored quantity is best." def __init__(self, learn:Learner, monitor:str='valid_loss', mode:str='auto', every:str='improvement', name:str='bestmodel'): super().__init__(learn, monitor=monitor, mode=mode) self.every,self.name = every,name if self.every not in ['improvement', 'epoch']: warn(f'SaveModel every {self.every} is invalid, falling back to "improvement".') self.every = 'improvement' def jump_to_epoch(self, epoch:int)->None: try: self.learn.load(f'{self.name}_{epoch-1}', purge=False) print(f"Loaded {self.name}_{epoch-1}") except: print(f'Model {self.name}_{epoch-1} not found.') def on_epoch_end(self, epoch:int, **kwargs:Any)->None: "Compare the value monitored to its best score and maybe save the model." if self.every=="epoch": self.learn.save(f'{self.name}_{epoch}') else: #every="improvement" current = self.get_monitor_value() if current is not None and self.operator(current, self.best): print(f'Better model found at epoch {epoch} with {self.monitor} value: {current}.') self.best = current self.learn.save(f'{self.name}') def on_train_end(self, **kwargs): "Load the best model." if self.every=="improvement" and (self.learn.path/f'{self.learn.model_dir}/{self.name}.pth').is_file(): self.learn.load(f'{self.name}', purge=False) class ReduceLROnPlateauCallback(TrackerCallback): "A `TrackerCallback` that reduces learning rate when a metric has stopped improving." def __init__(self, learn:Learner, monitor:str='valid_loss', mode:str='auto', patience:int=0, factor:float=0.2, min_delta:int=0): super().__init__(learn, monitor=monitor, mode=mode) self.patience,self.factor,self.min_delta = patience,factor,min_delta if self.operator == np.less: self.min_delta *= -1 def on_train_begin(self, **kwargs:Any)->None: "Initialize inner arguments." self.wait, self.opt = 0, self.learn.opt super().on_train_begin(**kwargs) def on_epoch_end(self, epoch, **kwargs:Any)->None: "Compare the value monitored to its best and maybe reduce lr." current = self.get_monitor_value() if current is None: return if self.operator(current - self.min_delta, self.best): self.best,self.wait = current,0 else: self.wait += 1 if self.wait > self.patience: self.opt.lr *= self.factor self.wait = 0 print(f'Epoch {epoch}: reducing lr to {self.opt.lr}') class TrackEpochCallback(LearnerCallback): _order = -20 #Need to run before fit_one_cycle def __init__(self, learn:Learner, name:str='epoch', epoch_offset:int=None): "Store completed epoch number in `learn.model_dir/name`." super().__init__(learn) learn._test_writeable_path() self.path = learn.path/learn.model_dir/name if epoch_offset is None: if os.path.isfile(self.path): with self.path.open('r') as f: try: self.start_epoch = int(f.read())+1 except: self.start_epoch = 0 else: self.start_epoch = 0 def on_train_begin(self, **kwargs:Any): return {'epoch': self.start_epoch} def on_epoch_end(self, epoch, **kwargs:Any)->None: with self.path.open('w') as f: f.write(f'{epoch}') def restart(self): os.remove(self.path)