replaced deprecated call to ByteTensor with from_numpy (#3813)

replaced byteTensor with from_numpy

fixed lint issues and copy related worning
Co-authored-by: Sofiane Abbar <sofa@fb.com>
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/transforms/functional.py

@@ -124,17 +124,13 @@ def to_tensor(pic):
         return torch.from_numpy(nppic).to(dtype=default_float_dtype)
 
     # handle PIL Image
-    if pic.mode == 'I':
-        img = torch.from_numpy(np.array(pic, np.int32, copy=False))
-    elif pic.mode == 'I;16':
-        img = torch.from_numpy(np.array(pic, np.int16, copy=False))
-    elif pic.mode == 'F':
-        img = torch.from_numpy(np.array(pic, np.float32, copy=False))
-    elif pic.mode == '1':
-        img = 255 * torch.from_numpy(np.array(pic, np.uint8, copy=False))
-    else:
-        img = torch.ByteTensor(torch.ByteStorage.from_buffer(pic.tobytes()))
+    mode_to_nptype = {'I': np.int32, 'I;16': np.int16, 'F': np.float32}
+    img = torch.from_numpy(
+        np.array(pic, mode_to_nptype.get(pic.mode, np.uint8), copy=True)
+    )
 
+    if pic.mode == '1':
+        img = 255 * img
     img = img.view(pic.size[1], pic.size[0], len(pic.getbands()))
     # put it from HWC to CHW format
     img = img.permute((2, 0, 1)).contiguous()