def UpdateIterStats(self): """Update tracked iteration statistics.""" for k in self.losses_and_metrics.keys(): if k in self.model.losses: self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k) else: self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k) for k, v in self.smoothed_losses_and_metrics.items(): v.AddValue(self.losses_and_metrics[k]) self.iter_total_loss = np.sum( np.array([self.losses_and_metrics[k] for k in self.model.losses])) #print(self.model.roi_data_loader._cur_img) for i in range(len(self.model.roi_data_loader._cur_img)): self.model.roi_data_loader._cur_loss[ self.model.roi_data_loader._cur_img[i]] = self.iter_total_loss self.smoothed_total_loss.AddValue(self.iter_total_loss) self.cur_epoch_total_loss.append(self.iter_total_loss) self.smoothed_mb_qsize.AddValue( self.model.roi_data_loader._minibatch_queue.qsize()) if self.model.roi_data_loader.next_epoch: self.cur_epoch += 1 self.model.roi_data_loader.next_epoch = False self.last_epoch_mean_loss = sum(self.cur_epoch_total_loss) / len( self.cur_epoch_total_loss) self.cur_epoch_total_loss = []
def UpdateIterStats(self): """Update tracked iteration statistics.""" for k in self.losses_and_metrics.keys(): if k in self.model.losses: self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k) else: self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k) for k, v in self.smoothed_losses_and_metrics.items(): v.AddValue(self.losses_and_metrics[k]) self.iter_total_loss = np.sum( np.array([self.losses_and_metrics[k] for k in self.model.losses])) self.smoothed_total_loss.AddValue(self.iter_total_loss) self.smoothed_mb_qsize.AddValue( self.model.roi_data_loader._minibatch_queue.qsize())
def UpdateIterStats(self, i_iter=0): """Update tracked iteration statistics.""" for k in self.losses_and_metrics.keys(): if k in self.model.losses: self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k) else: self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k) for k, v in self.smoothed_losses_and_metrics.items(): v.AddValue(self.losses_and_metrics[k]) self.writer.add_scalar(k, self.losses_and_metrics[k], i_iter) self.iter_total_loss = np.sum( np.array([self.losses_and_metrics[k] for k in self.model.losses]) ) self.smoothed_total_loss.AddValue(self.iter_total_loss)
def UpdateIterStats(self): """Update tracked iteration statistics.""" for k in self.losses_and_metrics.keys(): if k in self.model.losses: self.losses_and_metrics[k] = nu.sum_multi_gpu_blob(k) else: self.losses_and_metrics[k] = nu.average_multi_gpu_blob(k) for k, v in self.smoothed_losses_and_metrics.items(): v.AddValue(self.losses_and_metrics[k]) self.iter_total_loss = np.sum( np.array([self.losses_and_metrics[k] for k in self.model.losses]) ) self.smoothed_total_loss.AddValue(self.iter_total_loss) self.smoothed_mb_qsize.AddValue( self.model.roi_data_loader._minibatch_queue.qsize() )