Migrate mnist dataset from np.frombuffer

Author: lc0

torchvision/datasets/mnist.py

@@ -489,12 +489,12 @@ 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 +511,11 @@ 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)))
+    parsed = torch.frombuffer(data, dtype=torch_type, offset=(4 * (nd + 1)))
     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: