diff --git a/torchvision/prototype/datasets/_builtin/celeba.py b/torchvision/prototype/datasets/_builtin/celeba.py
index 829fa2560fd..12771a11efa 100644
--- a/torchvision/prototype/datasets/_builtin/celeba.py
+++ b/torchvision/prototype/datasets/_builtin/celeba.py
@@ -30,25 +30,26 @@ def __init__(
 
     def __iter__(self) -> Iterator[Tuple[str, Dict[str, str]]]:
         for _, file in self.datapipe:
-            lines = (line.decode() for line in file)
-
-            if self.fieldnames:
-                fieldnames = self.fieldnames
-            else:
-                # The first row is skipped, because it only contains the number of samples
-                next(lines)
-
-                # Empty field names are filtered out, because some files have an extra white space after the header
-                # line, which is recognized as extra column
-                fieldnames = [name for name in next(csv.reader([next(lines)], dialect="celeba")) if name]
-                # Some files do not include a label for the image ID column
-                if fieldnames[0] != "image_id":
-                    fieldnames.insert(0, "image_id")
-
-            for line in csv.DictReader(lines, fieldnames=fieldnames, dialect="celeba"):
-                yield line.pop("image_id"), line
-
-            file.close()
+            try:
+                lines = (line.decode() for line in file)
+
+                if self.fieldnames:
+                    fieldnames = self.fieldnames
+                else:
+                    # The first row is skipped, because it only contains the number of samples
+                    next(lines)
+
+                    # Empty field names are filtered out, because some files have an extra white space after the header
+                    # line, which is recognized as extra column
+                    fieldnames = [name for name in next(csv.reader([next(lines)], dialect="celeba")) if name]
+                    # Some files do not include a label for the image ID column
+                    if fieldnames[0] != "image_id":
+                        fieldnames.insert(0, "image_id")
+
+                for line in csv.DictReader(lines, fieldnames=fieldnames, dialect="celeba"):
+                    yield line.pop("image_id"), line
+            finally:
+                file.close()
 
 
 NAME = "celeba"
diff --git a/torchvision/prototype/datasets/_builtin/mnist.py b/torchvision/prototype/datasets/_builtin/mnist.py
index c13836a8c4c..97d729d530b 100644
--- a/torchvision/prototype/datasets/_builtin/mnist.py
+++ b/torchvision/prototype/datasets/_builtin/mnist.py
@@ -37,27 +37,28 @@ def __init__(
 
     def __iter__(self) -> Iterator[torch.Tensor]:
         for _, file in self.datapipe:
-            read = functools.partial(fromfile, file, byte_order="big")
+            try:
+                read = functools.partial(fromfile, file, byte_order="big")
 
-            magic = int(read(dtype=torch.int32, count=1))
-            dtype = self._DTYPE_MAP[magic // 256]
-            ndim = magic % 256 - 1
+                magic = int(read(dtype=torch.int32, count=1))
+                dtype = self._DTYPE_MAP[magic // 256]
+                ndim = magic % 256 - 1
 
-            num_samples = int(read(dtype=torch.int32, count=1))
-            shape = cast(List[int], read(dtype=torch.int32, count=ndim).tolist()) if ndim else []
-            count = prod(shape) if shape else 1
+                num_samples = int(read(dtype=torch.int32, count=1))
+                shape = cast(List[int], read(dtype=torch.int32, count=ndim).tolist()) if ndim else []
+                count = prod(shape) if shape else 1
 
-            start = self.start or 0
-            stop = min(self.stop, num_samples) if self.stop else num_samples
+                start = self.start or 0
+                stop = min(self.stop, num_samples) if self.stop else num_samples
 
-            if start:
-                num_bytes_per_value = (torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits // 8
-                file.seek(num_bytes_per_value * count * start, 1)
+                if start:
+                    num_bytes_per_value = (torch.finfo if dtype.is_floating_point else torch.iinfo)(dtype).bits // 8
+                    file.seek(num_bytes_per_value * count * start, 1)
 
-            for _ in range(stop - start):
-                yield read(dtype=dtype, count=count).reshape(shape)
-
-            file.close()
+                for _ in range(stop - start):
+                    yield read(dtype=dtype, count=count).reshape(shape)
+            finally:
+                file.close()
 
 
 class _MNISTBase(Dataset):
diff --git a/torchvision/prototype/datasets/_builtin/pcam.py b/torchvision/prototype/datasets/_builtin/pcam.py
index 3a9fe6e9031..f533ba18084 100644
--- a/torchvision/prototype/datasets/_builtin/pcam.py
+++ b/torchvision/prototype/datasets/_builtin/pcam.py
@@ -28,12 +28,13 @@ def __iter__(self) -> Iterator[Tuple[str, io.IOBase]]:
         import h5py
 
         for _, handle in self.datapipe:
-            with h5py.File(handle) as data:
-                if self.key is not None:
-                    data = data[self.key]
-                yield from data
-
-            handle.close()
+            try:
+                with h5py.File(handle) as data:
+                    if self.key is not None:
+                        data = data[self.key]
+                    yield from data
+            finally:
+                handle.close()
 
 
 _Resource = namedtuple("_Resource", ("file_name", "gdrive_id", "sha256"))