update according to ActionDataSample

mmaction/evaluation/metrics/multimodal_metric.py

@@ -355,7 +355,7 @@ def process(self, data_batch, data_samples):
         """
         for sample in data_samples:
             # gt_labels in datasample is a LabelData
-            label = sample['gt_labels']['item'].item()
+            label = sample['gt_label'].item()
             result = {
                 'pred_label': sample.get('pred_label'),
                 'gt_label': label,
@@ -428,7 +428,7 @@ def process(self, data_batch: Sequence[dict],
             predictions (Sequence[dict]): A batch of outputs from the model.
         """
         for data_sample in data_samples:
-            pred_score = data_sample['pred_scores']['item'].cpu()
+            pred_score = data_sample['pred_score'].cpu()
             gt_label = format_label(data_sample['gt_label'])
 
             if 'gt_score' in data_sample:

tests/evaluation/metrics/test_retrieval_metric.py

@@ -59,17 +59,16 @@ class TestRetrievalRecall(TestCase):
     def test_evaluate(self):
         """Test using the metric in the same way as Evalutor."""
         pred = [
-            ActionDataSample().set_pred_score(i).to_dict() for i in [
+            ActionDataSample().set_pred_score(i).set_gt_label(k).to_dict()
+            for i, k in zip([
                 torch.tensor([0.7, 0.0, 0.3]),
                 torch.tensor([0.5, 0.2, 0.3]),
                 torch.tensor([0.4, 0.5, 0.1]),
                 torch.tensor([0.0, 0.0, 1.0]),
                 torch.tensor([0.0, 0.0, 1.0]),
                 torch.tensor([0.0, 0.0, 1.0]),
-            ]
+            ], [[0], [0], [1], [2], [2], [0]])
         ]
-        for sample, label in zip(pred, [[0], [0], [1], [2], [2], [0]]):
-            sample['gt_label'] = label
 
         # Test with score (use score instead of label if score exists)
         metric = METRICS.build(dict(type='RetrievalRecall', topk=1))