def on_epoch_end(self, last_metrics, **kwargs): # Convert from fastai format my_targets = [] for batch_boxes, batch_labels in self.targets: for boxes, labels in zip(batch_boxes, batch_labels): non_pad_inds = labels != 0 boxes = to_box_pixel(boxes, self.h, self.w) my_targets.append( (boxes[non_pad_inds, :], labels[non_pad_inds])) my_outputs = [(boxlist.boxes, boxlist.get_field('labels'), boxlist.get_field('scores')) for boxlist in self.outputs] metric = compute_coco_eval(my_outputs, my_targets, self.num_labels)[0] return add_metrics(last_metrics, metric)
def on_epoch_end(self, last_metrics, **kwargs): return add_metrics(last_metrics, 100 * self.correct / self.total)
def on_epoch_end(self, last_metrics, **kwargs): self.predictions = np.array(self.predictions) metric = self.evaluator.get_final_metric(self.predictions) self.predictions = [] return add_metrics(last_metrics, metric)
def on_epoch_end(self, last_metrics, **kwargs): self.epsilon = self.learn.model.exploration_strategy.epsilon return add_metrics(last_metrics, [self.epsilon])
def on_epoch_end(self, last_metrics, **kwargs: Any): return add_metrics(last_metrics, [sum(self.train_reward), sum(self.valid_reward)])
def on_epoch_end(self, last_metrics, **kwargs): self.epsilon = self.learn.exploration_method.epsilon if last_metrics and last_metrics[-1] is None: del last_metrics[-1] return add_metrics(last_metrics, [float(self.epsilon)])