fix dtype inconsistency in fe fittransform pipe

autogl/module/feature/base.py

@@ -68,7 +68,12 @@ class BaseFeatureAtom:
         elif self._data_t == "nx":
             if not hasattr(data, "G") or data.G is None:
                 data.G = to_networkx(data, to_undirected=True)
-
+    def _adjust_to_tensor(self,data):
+        if self._data_t == "tensor":
+            pass
+        else:
+            data_np2tensor(data)
+        
     def _preprocess(self, data):
         pass
 
@@ -98,22 +103,17 @@ class BaseFeatureAtom:
         if not self._check_dataset(dataset):
             return
         dataset = copy.deepcopy(dataset)
-        for p in self._pipe:
-            _dataset = [x for x in dataset]
-            if p._subgraph:
+        with torch.no_grad():
+            for p in self._pipe:
+                _dataset = [x for x in dataset]
                 for i, datai in enumerate(_dataset):
                     p._adjust_t(datai)
                     p._preprocess(datai)
                     p._fit_transform(datai)
                     p._postprocess(datai)
+                    p._adjust_to_tensor(datai)
                     _dataset[i] = datai
-            else:
-                data = dataset.data
-                p._adjust_t(data)
-                p._preprocess(data)
-                data = p._fit_transform(data)
-                p._postprocess(data)
-            dataset = self._rebuild(dataset, _dataset)
+                dataset = self._rebuild(dataset, _dataset)
                 
 
     def transform(self, dataset, inplace=True):
@@ -122,22 +122,17 @@ class BaseFeatureAtom:
             return dataset
         if not inplace:
             dataset = copy.deepcopy(dataset)
-        for p in self._pipe:
-            self._dataset = _dataset = [x for x in dataset]
-            if p._subgraph:
+        with torch.no_grad():
+            for p in self._pipe:
+                self._dataset = _dataset = [x for x in dataset]
                 for i, datai in enumerate(_dataset):
                     p._adjust_t(datai)
                     p._preprocess(datai)
                     datai = p._transform(datai)
                     p._postprocess(datai)
+                    p._adjust_to_tensor(datai)
                     _dataset[i] = datai    
-            else:
-                data = dataset.data
-                p._adjust_t(data)
-                p._preprocess(data)
-                data = p._transform(data)
-                p._postprocess(data)
-            dataset = self._rebuild(dataset, _dataset)
+                dataset = self._rebuild(dataset, _dataset)
         dataset.data = data_np2tensor(dataset.data)
         return dataset