fix datasets for cases in which there is a target attribute but no ta…

…rget values are present in the hdf5 file/s

deeprank2/dataset.py

@@ -614,22 +614,36 @@ def load_one_grid(self, hdf5_path: str, entry_name: str) -> Data:
         """
 
         feature_data = []
-        target_value = None
 
         with h5py.File(hdf5_path, 'r') as hdf5_file:
-            entry_group = hdf5_file[entry_name]
+            grp = hdf5_file[entry_name]
 
-            mapped_features_group = entry_group[gridstorage.MAPPED_FEATURES]
+            mapped_features_group = grp[gridstorage.MAPPED_FEATURES]
             for feature_name in self.features:
                 if feature_name[0] != '_':  # ignore metafeatures
                     feature_data.append(mapped_features_group[feature_name][:])
+            x=torch.tensor(np.expand_dims(np.array(feature_data), axis=0), dtype=torch.float)
 
-            target_value = entry_group[targets.VALUES][self.target][()]
+            # target
+            if self.target is None:
+                y = None
+            else:
+                if targets.VALUES in grp and self.target in grp[targets.VALUES]:
+                    y = torch.tensor([grp[targets.VALUES][self.target][()]], dtype=torch.float)
 
-        # Wrap up the data in this object, for the collate_fn to handle it properly:
-        data = Data(x=torch.tensor(np.expand_dims(np.array(feature_data), axis=0), dtype=torch.float),
-                    y=torch.tensor([target_value], dtype=torch.float))
+                    if self.task == targets.REGRESS and self.target_transform is True:
+                        y = torch.sigmoid(torch.log(y))
+                    elif self.task is not targets.REGRESS and self.target_transform is True:
+                        raise ValueError(f"Task is set to {self.task}. Please set it to regress to transform the target with a sigmoid.")
+                else:
+                    y = None
+                    possible_targets = grp[targets.VALUES].keys()
+                    if self.train:
+                        raise ValueError(f"Target {self.target} missing in entry {entry_name} in file {hdf5_path}, possible targets are {possible_targets}." +
+                                        "\n Use the query class to add more target values to input data.")
 
+        # Wrap up the data in this object, for the collate_fn to handle it properly:
+        data = Data(x=x, y=y)
         data.entry_names = entry_name
 
         return data
@@ -948,9 +962,11 @@ def load_one_graph(self, fname: str, entry_name: str)  -> Data: # pylint: disabl
                         raise ValueError(f"Task is set to {self.task}. Please set it to regress to transform the target with a sigmoid.")
 
                 else:
+                    y = None
                     possible_targets = grp[targets.VALUES].keys()
-                    raise ValueError(f"Target {self.target} missing in entry {entry_name} in file {fname}, possible targets are {possible_targets}." +
-                                     "\n Use the query class to add more target values to input data.")
+                    if self.train:
+                        raise ValueError(f"Target {self.target} missing in entry {entry_name} in file {fname}, possible targets are {possible_targets}." +
+                                        "\n Use the query class to add more target values to input data.")
 
             # positions
             pos = torch.tensor(grp[f"{Nfeat.NODE}/{Nfeat.POSITION}/"][()], dtype=torch.float).contiguous()