fix(tests): fix the tests

tests/test_evaluation/test_evaluator/test_multi_datasets_evaluator.py

@@ -97,11 +97,11 @@ def test_composed_metrics(self):
                 size, batch_size, pred=1, label=1):
             evaluator.process(predictions, data_samples)
 
-        metrics = evaluator.evaluate(size=size)
+        metrics_results, averaged_results = evaluator.evaluate(size=size)
 
-        self.assertAlmostEqual(metrics['Fake/Toy/accuracy'], 1.0)
-        self.assertAlmostEqual(metrics['Fake/Toy/mAP'], 0.0)
-        self.assertEqual(metrics['Fake/Toy/size'], size)
+        self.assertAlmostEqual(metrics_results['Fake/Toy/accuracy'], 1.0)
+        self.assertAlmostEqual(metrics_results['Fake/Toy/mAP'], 0.0)
+        self.assertEqual(metrics_results['Fake/Toy/size'], size)
         with self.assertWarns(Warning):
             evaluator.evaluate(size=0)
 
@@ -124,9 +124,9 @@ def test_composed_metrics(self):
         for data_samples, predictions in generate_test_results(
                 size, batch_size, pred=1, label=1):
             evaluator.process(predictions, data_samples)
-        metrics = evaluator.evaluate(size=size)
-        self.assertIn('Fake/Toy/accuracy', metrics)
-        self.assertIn('Fake/accuracy', metrics)
+        metrics_results, averaged_results = evaluator.evaluate(size=size)
+        self.assertIn('Fake/Toy/accuracy', metrics_results)
+        self.assertIn('Fake/accuracy', metrics_results)
 
         metrics_results = OrderedDict({
             'dataset1/metric1/accuracy': 0.9,
@@ -135,7 +135,7 @@ def test_composed_metrics(self):
             'dataset2/metric2/f1_score': 0.75
         })
 
-        evaluator = MultiDatasetsEvaluator([], [])
+        evaluator = MultiDatasetsEvaluator(cfg, dataset_prefixes=['Fake'])
         averaged_results = evaluator.average_results(metrics_results)
 
         expected_averaged_results = {