edited utils to take list (#115)

* enhanced difference domain
* refactored utils
* fixed typo
* added tests
---------

Co-authored-by: Dario Coscia <93731561+dario-coscia@users.noreply.github.com>

pina/utils.py

@@ -1,4 +1,5 @@
 """Utils module"""
+from torch.utils.data import Dataset, DataLoader
 from functools import reduce
 import types
 
@@ -10,14 +11,14 @@ from .label_tensor import LabelTensor
 import torch
 
 
-def check_consistency(object, object_instance, object_name, subclass=False):
+def check_consistency(object, object_instance, subclass=False):
     """Helper function to check object inheritance consistency. 
        Given a specific ``'object'`` we check if the object is
        instance of a specific ``'object_instance'``, or in case
        ``'subclass=True'`` we check if the object is subclass
        if the ``'object_instance'``.
 
-    :param Object object: The object to check the inheritance 
+    :param (iterable or class object) object: The object to check the inheritance 
     :param Object object_instance: The parent class from where the object
         is expected to inherit
     :param str object_name: The name of the object
@@ -25,12 +26,17 @@ def check_consistency(object, object_instance, object_name, subclass=False):
     :raises ValueError: If the object does not inherit from the
         specified class
     """
-    if not subclass:
-        if not isinstance(object, object_instance):
-            raise ValueError(f"{object_name} must be {object_instance}")
-    else:
-        if not issubclass(object, object_instance):
-            raise ValueError(f"{object_name} must be {object_instance}")
+    if not isinstance(object, (list, set, tuple)):
+        object = [object]
+
+    for obj in object:
+        try:
+            if not subclass:
+                assert isinstance(obj, object_instance)
+            else:
+                assert issubclass(obj, object_instance)
+        except AssertionError:
+            raise ValueError(f"{type(obj).__name__} must be {object_instance}.")
 
 
 def number_parameters(model, aggregate=True, only_trainable=True):  # TODO: check
@@ -180,13 +186,13 @@ def chebyshev_roots(n):
 #         def __len__(self):
 #             return self._len
 
-from torch.utils.data import Dataset, DataLoader
+
 class LabelTensorDataset(Dataset):
     def __init__(self, d):
         for k, v in d.items():
             setattr(self, k, v)
         self.labels = list(d.keys())
-        
+
     def __getitem__(self, index):
         print(index)
         result = {}
@@ -201,7 +207,7 @@ class LabelTensorDataset(Dataset):
                 result[label] = sample_tensor[index]
             except IndexError:
                 result[label] = torch.tensor([])
-        
+
         print(result)
         return result
 
@@ -229,13 +235,13 @@ class LabelTensorDataLoader(DataLoader):
 #         def __len__(self):
 #             return self._len
 
-from torch.utils.data import Dataset, DataLoader
+
 class LabelTensorDataset(Dataset):
     def __init__(self, d):
         for k, v in d.items():
             setattr(self, k, v)
         self.labels = list(d.keys())
-        
+
     def __getitem__(self, index):
         print(index)
         result = {}
@@ -250,7 +256,7 @@ class LabelTensorDataset(Dataset):
                 result[label] = sample_tensor[index]
             except IndexError:
                 result[label] = torch.tensor([])
-        
+
         print(result)
         return result
 
@@ -261,4 +267,4 @@ class LabelTensorDataset(Dataset):
 class LabelTensorDataLoader(DataLoader):
 
     def collate_fn(self, data):
-        pass
+        pass