added List as an option to the unflattened_size

test/test_nn.py

@@ -9283,18 +9283,19 @@ def test_flatten(self):
     def test_unflatten(self):
         tensor_input = torch.randn(2, 50)
 
-        # Unflatten Tensor
+        # Unflatten Tensor (unflattened_size as a tuple of ints and list of ints)
 
-        unflatten = nn.Unflatten(dim=1, unflattened_size=(2, 5, 5))
-        tensor_output = unflatten(tensor_input)
-        self.assertEqual(tensor_output.size(), torch.Size([2, 2, 5, 5]))
+        for us in ((2, 5, 5), [2, 5, 5]):
+            unflatten = nn.Unflatten(dim=1, unflattened_size=us)
+            tensor_output = unflatten(tensor_input)
+            self.assertEqual(tensor_output.size(), torch.Size([2, 2, 5, 5]))
 
         # Unflatten NamedTensor
 
         unflatten = nn.Unflatten(dim='features', unflattened_size=(('C', 2), ('H', 5), ('W', 5)))
         named_tensor_input = tensor_input.refine_names('N', 'features')
         named_tensor_output = unflatten(named_tensor_input)
-        self.assertEqual(tensor_output.size(), torch.Size([2, 2, 5, 5]))
+        self.assertEqual(named_tensor_output.size(), torch.Size([2, 2, 5, 5]))
 
     def test_unflatten_invalid_arg(self):
         # Wrong type for unflattened_size (tuple of floats)
@@ -9304,26 +9305,26 @@ def test_unflatten_invalid_arg(self):
                 r"unflattened_size must be tuple of ints, but found element of type float at pos 2"):
             nn.Unflatten(dim=1, unflattened_size=(2, 5, 5.0))
 
+        # Wrong type for unflattened_size (list of lists and list of tuples)
+        for us in ([['C', 2], ['W', 5], ['H', 5]], [('C', 2), ('W', 5), ('H', 5)]):
+            with self.assertRaisesRegex(
+                    TypeError,
+                    r"unflattened_size must be a tuple of tuples, but found type list"):
+                nn.Unflatten(dim='features', unflattened_size=us)
+
         # Wrong type for unflattened_size (tuple of lists)
 
         with self.assertRaisesRegex(
                 TypeError,
                 r"unflattened_size must be tuple of tuples, but found element of type list at pos 0"):
             nn.Unflatten(dim='features', unflattened_size=(['C', 2], ['W', 5], ['H', 5]))
 
-        # Wrong type for unflattened_size (list of ints)
-
-        with self.assertRaisesRegex(
-                TypeError,
-                r"unflattened_size must be a tuple of ints, but found type list"):
-            nn.Unflatten(dim=1, unflattened_size=[2, 5, 5])
-
-        # Wrong type for unflattened_size (list of lists)
+        # Wrong type for unflattened_size (tuple of dicts)
 
         with self.assertRaisesRegex(
                 TypeError,
-                r"unflattened_size must be a tuple of tuples, but found type list"):
-            nn.Unflatten(dim='features', unflattened_size=[['C', 2], ['W', 5], ['H', 5]])
+                r"unflattened_size must be tuple of tuples, but found element of type dict at pos 0"):
+            nn.Unflatten(dim='features', unflattened_size=({'C': 2}, {'W': 5}, {'H': 5}))
 
     def test_layer_norm_grads_with_create_graph_flag(self):
         atol = 1e-5

torch/nn/modules/flatten.py

@@ -2,7 +2,7 @@
 
 from typing import Tuple, Union
 from torch import Tensor
-from torch import Size
+from torch.types import _size
 
 
 class Flatten(Module):
@@ -53,16 +53,16 @@ class Unflatten(Module):
       be either `int` or `str` when `Tensor` or `NamedTensor` is used, respectively.
 
     * :attr:`unflattened_size` is the new shape of the unflattened dimension of the tensor and it can be
-      a `tuple` of ints or `torch.Size` for `Tensor` input or a `NamedShape` (tuple of `(name, size)` tuples)
-      for `NamedTensor` input.
+      a `tuple` of ints or a `list` of ints or `torch.Size` for `Tensor` input;  a `NamedShape` 
+      (tuple of `(name, size)` tuples) for `NamedTensor` input.
 
     Shape:
         - Input: :math:`(N, *dims)`
         - Output: :math:`(N, C_{\text{out}}, H_{\text{out}}, W_{\text{out}})`
 
     Args:
         dim (Union[int, str]): Dimension to be unflattened
-        unflattened_size (Union[torch.Size, NamedShape]): New shape of the unflattened dimension
+        unflattened_size (Union[torch.Size, Tuple, List, NamedShape]): New shape of the unflattened dimension
 
     Examples:
         >>> input = torch.randn(2, 50)
@@ -71,33 +71,31 @@ class Unflatten(Module):
         >>>     nn.Linear(50, 50),
         >>>     nn.Unflatten(1, (2, 5, 5))
         >>> )
-        >>> output = m(output)
+        >>> output = m(input)
         >>> output.size()
         torch.Size([2, 2, 5, 5])
         >>> # With torch.Size
         >>> m = nn.Sequential(
         >>>     nn.Linear(50, 50),
         >>>     nn.Unflatten(1, torch.Size([2, 5, 5]))
         >>> )
-        >>> output = m(output)
+        >>> output = m(input)
         >>> output.size()
         torch.Size([2, 2, 5, 5])
         >>> # With namedshape (tuple of tuples)
-        >>> m = nn.Sequential(
-        >>>     nn.Linear(50, 50),
-        >>>     nn.Unflatten('features', (('C', 2), ('H', 50), ('W',50)))
-        >>> )
-        >>> output = m(output)
+        >>> input = torch.randn(2, 50, names=('N', 'features'))
+        >>> unflatten = nn.Unflatten('features', (('C', 2), ('H', 5), ('W', 5)))
+        >>> output = unflatten(input)
         >>> output.size()
         torch.Size([2, 2, 5, 5])
     """
     NamedShape = Tuple[Tuple[str, int]]
 
     __constants__ = ['dim', 'unflattened_size']
     dim: Union[int, str]
-    unflattened_size: Union[Size, NamedShape]
+    unflattened_size: Union[_size, NamedShape]
 
-    def __init__(self, dim: Union[int, str], unflattened_size: Union[Size, NamedShape]) -> None:
+    def __init__(self, dim: Union[int, str], unflattened_size: Union[_size, NamedShape]) -> None:
         super(Unflatten, self).__init__()
 
         if isinstance(dim, int):
@@ -121,7 +119,7 @@ def _require_tuple_tuple(self, input):
                         "but found type {}".format(type(input).__name__))
 
     def _require_tuple_int(self, input):
-        if (isinstance(input, tuple)):
+        if (isinstance(input, (tuple, list))):
             for idx, elem in enumerate(input):
                 if not isinstance(elem, int):
                     raise TypeError("unflattened_size must be tuple of ints, " +