| |
|
|
| 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 |
| 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: |
| 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 |
| 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) |
|
|
