Migrate mnist dataset from np.frombuffer (#4598)

* Migrate mnist dataset from np.frombuffer

* Add a copy with bytearray for non-writable buffers

* Add byte reversal for mnist
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/datasets/mnist.py

@@ -3,6 +3,7 @@ import os
 import os.path
 import shutil
 import string
+import sys
 import warnings
 from typing import Any, Callable, Dict, List, Optional, Tuple
 from urllib.error import URLError
@@ -489,12 +490,12 @@ def get_int(b: bytes) -> int:
 
 
 SN3_PASCALVINCENT_TYPEMAP = {
-    8: (torch.uint8, np.uint8, np.uint8),
-    9: (torch.int8, np.int8, np.int8),
-    11: (torch.int16, np.dtype(">i2"), "i2"),
-    12: (torch.int32, np.dtype(">i4"), "i4"),
-    13: (torch.float32, np.dtype(">f4"), "f4"),
-    14: (torch.float64, np.dtype(">f8"), "f8"),
+    8: torch.uint8,
+    9: torch.int8,
+    11: torch.int16,
+    12: torch.int32,
+    13: torch.float32,
+    14: torch.float64,
 }
 
 
@@ -511,11 +512,19 @@ def read_sn3_pascalvincent_tensor(path: str, strict: bool = True) -> torch.Tenso
     ty = magic // 256
     assert 1 <= nd <= 3
     assert 8 <= ty <= 14
-    m = SN3_PASCALVINCENT_TYPEMAP[ty]
+    torch_type = SN3_PASCALVINCENT_TYPEMAP[ty]
     s = [get_int(data[4 * (i + 1) : 4 * (i + 2)]) for i in range(nd)]
-    parsed = np.frombuffer(data, dtype=m[1], offset=(4 * (nd + 1)))
+
+    num_bytes_per_value = torch.iinfo(torch_type).bits // 8
+    # The MNIST format uses the big endian byte order. If the system uses little endian byte order by default,
+    # we need to reverse the bytes before we can read them with torch.frombuffer().
+    needs_byte_reversal = sys.byteorder == "little" and num_bytes_per_value > 1
+    parsed = torch.frombuffer(bytearray(data), dtype=torch_type, offset=(4 * (nd + 1)))
+    if needs_byte_reversal:
+        parsed = parsed.flip(0)
+
     assert parsed.shape[0] == np.prod(s) or not strict
-    return torch.from_numpy(parsed.astype(m[2])).view(*s)
+    return parsed.view(*s)
 
 
 def read_label_file(path: str) -> torch.Tensor: