Allow map_location in torch.load to be a string

test/test_torch.py

@@ -4400,6 +4400,10 @@ def map_location(storage, loc):
         self.assertEqual(type(tensor), torch.FloatTensor)
         self.assertEqual(tensor, torch.FloatTensor([[1.0, 2.0], [3.0, 4.0]]))
 
+        tensor = torch.load(test_file_path, map_location='cpu')
+        self.assertEqual(type(tensor), torch.FloatTensor)
+        self.assertEqual(tensor, torch.FloatTensor([[1.0, 2.0], [3.0, 4.0]]))
+
     def test_from_buffer(self):
         a = bytearray([1, 2, 3, 4])
         self.assertEqual(torch.ByteStorage.from_buffer(a).tolist(), [1, 2, 3, 4])

torch/serialization.py

@@ -10,6 +10,7 @@
 import warnings
 from contextlib import closing, contextmanager
 from ._utils import _import_dotted_name
+from ._six import string_classes as _string_classes
 if sys.version_info[0] == 2:
     import cPickle as pickle
 else:
@@ -225,7 +226,10 @@ def load(f, map_location=None, pickle_module=pickle):
     the right device. Otherwise, torch.load will fall back to the default behavior,
     as if map_location wasn't specified.
 
-    If map_location is a dict, it will be used to remap location tags
+    If map_location is a string, it should be a device tag, where all tensors
+    should be loaded.
+
+    Otherwise, if map_location is a dict, it will be used to remap location tags
     appearing in the file (keys), to ones that specify where to put the
     storages (values).
 
@@ -236,14 +240,16 @@ def load(f, map_location=None, pickle_module=pickle):
         f: a file-like object (has to implement fileno that returns a file
             descriptor, and must implement seek), or a string containing a file
             name
-        map_location: a function or a dict specifying how to remap storage
+        map_location: a function, string or a dict specifying how to remap storage
             locations
         pickle_module: module used for unpickling metadata and objects (has to
             match the pickle_module used to serialize file)
 
     Example:
         >>> torch.load('tensors.pt')
         # Load all tensors onto the CPU
+        >>> torch.load('tensors.pt', map_location='cpu')
+        # Load all tensors onto the CPU, using a function
         >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage)
         # Load all tensors onto GPU 1
         >>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1))
@@ -273,6 +279,9 @@ def _load(f, map_location, pickle_module):
         def restore_location(storage, location):
             location = map_location.get(location, location)
             return default_restore_location(storage, location)
+    elif isinstance(map_location, _string_classes):
+        def restore_location(storage, location):
+            return default_restore_location(storage, map_location)
     else:
         def restore_location(storage, location):
             result = map_location(storage, location)