Add support for tif

src/lightning/data/streaming/data_processor.py

@@ -496,11 +496,11 @@ def is_path(element: Any) -> bool:
                     return False
 
                 element: str = str(Path(element).resolve())
-                return (
-                    element.startswith(self.input_dir.path)
-                    if self.input_dir.path is not None
-                    else os.path.exists(element)
-                )
+                if _IS_IN_STUDIO and self.input_dir.path is not None:
+                    if self.input_dir.path.startswith("/teamspace/studios/this_studio"):
+                        return os.path.exists(element)
+                    return element.startswith(self.input_dir.path)
+                return os.path.exists(element)
 
             # For speed reasons, we assume starting with `self.input_dir` is enough to be a real file.
             # Other alternative would be too slow.

src/lightning/data/streaming/serializers.py

@@ -40,7 +40,6 @@
     from torchvision.io import decode_jpeg
     from torchvision.transforms.functional import pil_to_tensor
 
-
 class Serializer(ABC):
     """The base interface for any serializers.
 
@@ -288,7 +287,7 @@ def serialize(self, filepath: str) -> Tuple[bytes, Optional[str]]:
             return f.read(), file_extension.replace(".", "").lower()
 
     def deserialize(self, data: bytes) -> Any:
-        pass
+        return data
 
     def can_serialize(self, data: Any) -> bool:
         return isinstance(data, str) and os.path.exists(data)
@@ -326,6 +325,7 @@ def can_serialize(self, data: Any) -> bool:
 _SERIALIZERS = OrderedDict(
     **{
         "video": VideoSerializer(),
+        "tif": FileSerializer(),
         "file": FileSerializer(),
         "pil": PILSerializer(),
         "int": IntSerializer(),

tests/tests_data/streaming/test_serializer.py

@@ -44,6 +44,7 @@
 def test_serializers():
     assert list(_SERIALIZERS.keys()) == [
         "video",
+        "tif",
         "file",
         "pil",
         "int",