remove name

structural_similarity_index_measure.py

@@ -71,13 +71,32 @@ class StructuralSimilarityIndexMeasure(evaluate.Metric):
             description=_DESCRIPTION,
             citation=_CITATION,
             inputs_description=_KWARGS_DESCRIPTION,
-            features=datasets.Features({
-                "predictions": datasets.Sequence(datasets.Array2D("float32")),
-                "references": datasets.Sequence(datasets.Array2D("float32")),
-            }),
+            features=datasets.Features(self._get_feature_types()),
             reference_urls=["https://scikit-image.org/docs/dev/auto_examples/transform/plot_ssim.html"],
         )
 
+    def _get_feature_types(self):
+        if self.config_name == "multilist":
+            return {
+                # 1st Seq - num_samples, 2nd Seq - Height, 3rd Seq - Width
+                "predictions": datasets.Sequence(
+                    datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                ),
+                "references": datasets.Sequence(
+                    datasets.Sequence(datasets.Sequence(datasets.Value("float32")))
+                ),
+            }
+        else:
+            return {
+                # 1st Seq - Height, 2rd Seq - Width
+                "predictions": datasets.Sequence(
+                    datasets.Sequence(datasets.Value("float32"))
+                ),
+                "references": datasets.Sequence(
+                    datasets.Sequence(datasets.Value("float32"))
+                ),
+            }
+
     def _compute(
         self, 
         predictions, 
@@ -89,19 +108,31 @@ class StructuralSimilarityIndexMeasure(evaluate.Metric):
         sample_weight=None,
         **kwargs
     ) -> Dict[str, float]:
-        def func_ssim(args):
+        if self.config_name == "multilist":
+            def func_ssim(args):
+                pred, target = args
+                pred = np.array(pred)
+                target = np.array(target)
+                return structural_similarity(
+                    pred, 
+                    target, 
+                    win_size=win_size, 
+                    gaussian_weights=gaussian_weights, 
+                    data_range=data_range, 
+                    multichannel=multichannel,
+                    **kwargs
+                )
+            return np.average(
+                list(map(func_ssim, zip(predictions, references))), 
+                weights=sample_weight
+            )
+        else:
             return structural_similarity(
-                *args, 
+                np.array(predictions),
+                np.array(references), 
                 win_size=win_size, 
                 gaussian_weights=gaussian_weights, 
                 data_range=data_range, 
                 multichannel=multichannel,
                 **kwargs
-            )
-            
-        return {
-            "ssim": np.average(
-                list(map(func_ssim, zip(predictions, references))), 
-                weights=sample_weight
-            )
-        }
+            )

tests.py

@@ -2,11 +2,11 @@ test_cases = [
     {
         "predictions": [[0.1, 0.1], [1.1, 0.1]],
         "references": [[0.1, 0.1], [1.1, 0.1]],
-        "result": {"Peak Signal-to-Noise Ratio": 23.010298856486173}
+        "result": 1.0
     },
     {
         "predictions": [[0, 1], [0, 0]],
         "references": [[0, 0], [-1, -1]],
-        "result": {"Peak Signal-to-Noise Ratio": 1.2493873660829993}
+        "result": 0.2
     }
 ]