fix bug

PanopticQuality.py

@@ -170,28 +170,28 @@ class PQMetric(evaluate.Metric):
 
     def _compute(self, *, predictions, references, **kwargs):
         """Called within the evaluate.Metric.compute() method"""
-        tp = self.pq_metric.metric.true_positives.clone()
-        fp = self.pq_metric.metric.false_positives.clone()
-        fn = self.pq_metric.metric.false_negatives.clone()
-        iou = self.pq_metric.metric.iou_sum.clone()
+        tp = self.pq_metric.metric.true_positives.clone().cpu()
+        fp = self.pq_metric.metric.false_positives.clone().cpu()
+        fn = self.pq_metric.metric.false_negatives.clone().cpu()
+        iou = self.pq_metric.metric.iou_sum.clone().cpu()
 
         # compute scores
-        result = self.pq_metric.compute() # shape : (area_rngs, n_classes (sorted things + sorted stuffs), scores (pq, sq, rq))
+        result = self.pq_metric.compute().cpu() # shape : (area_rngs, n_classes (sorted things + sorted stuffs), scores (pq, sq, rq))
 
         result_dict = dict()
 
         if self.per_class:
             if not self.split_sq_rq:
                 result = result.unsqueeze(0)
-            result_dict["scores"] = {self.id2label[numeric_label]: result[:,:, i].tolist() \
+            result_dict["scores"] = {self.id2label[numeric_label]: result[:,:, i].numpy() \
                                      for i, numeric_label in enumerate(self.things_stuffs)}
-            result_dict["scores"].update({"ALL": result.mean(dim=-1).tolist()})
-            result_dict["numbers"] =  {self.id2label[numeric_label]: [tp[:, i].tolist(), fp[:, i].tolist(), fn[:, i].tolist(), iou[:, i].tolist()] \
+            result_dict["scores"].update({"ALL": result.mean(dim=-1).numpy()})
+            result_dict["numbers"] =  {self.id2label[numeric_label]: np.stack([tp[:, i].numpy(), fp[:, i].numpy(), fn[:, i].numpy(), iou[:, i].numpy()])\
                                        for i, numeric_label in enumerate(self.things_stuffs)}
-            result_dict["numbers"].update({"ALL": [tp.sum(dim=1).tolist(), fp.sum(dim=1).tolist(), fn.sum(dim=1).tolist(), iou.sum(dim=1).tolist()]})
+            result_dict["numbers"].update({"ALL": np.stack([tp.sum(dim=1).numpy(), fp.sum(dim=1).numpy(), fn.sum(dim=1).numpy(), iou.sum(dim=1).numpy()])})
         else:
-            result_dict["scores"] = {"ALL": result.tolist() if self.split_sq_rq else ([result.tolist()] if len(self.pq_metric.get_areas())>1 else [[result.tolist()]])}
-            result_dict["numbers"] = {"ALL": [tp.sum(dim=-1).tolist(), fp.sum(dim=-1).tolist(), fn.sum(dim=-1).tolist(), iou.sum(dim=-1).tolist()]}
+            result_dict["scores"] = {"ALL": result.numpy() if self.split_sq_rq else (result.numpy()[np.newaxis, ...] if len(self.pq_metric.get_areas())>1 else result.numpy()[np.newaxis, np.newaxis, ...])}
+            result_dict["numbers"] = {"ALL": np.stack([tp.sum(dim=-1).numpy(), fp.sum(dim=-1).numpy(), fn.sum(dim=-1).numpy(), iou.sum(dim=-1).numpy()])}
         
         return result_dict