diff --git a/torchvision/prototype/transforms/functional/_color.py b/torchvision/prototype/transforms/functional/_color.py
index 376021f0974..d067559f26a 100644
--- a/torchvision/prototype/transforms/functional/_color.py
+++ b/torchvision/prototype/transforms/functional/_color.py
@@ -388,7 +388,7 @@ def autocontrast_image_tensor(image: torch.Tensor) -> torch.Tensor:
     maximum = float_image.amax(dim=(-2, -1), keepdim=True)
 
     eq_idxs = maximum == minimum
-    inv_scale = maximum.sub_(minimum).div_(bound)
+    inv_scale = maximum.sub_(minimum).mul_(1.0 / bound)
     minimum[eq_idxs] = 0.0
     inv_scale[eq_idxs] = 1.0
 
diff --git a/torchvision/prototype/transforms/functional/_geometry.py b/torchvision/prototype/transforms/functional/_geometry.py
index 909b43739ee..ce97ce0575d 100644
--- a/torchvision/prototype/transforms/functional/_geometry.py
+++ b/torchvision/prototype/transforms/functional/_geometry.py
@@ -390,7 +390,7 @@ def _affine_bounding_box_xyxy(
             device=device,
         )
         new_points = torch.matmul(points, transposed_affine_matrix)
-        tr, _ = torch.min(new_points, dim=0, keepdim=True)
+        tr = torch.amin(new_points, dim=0, keepdim=True)
         # Translate bounding boxes
         out_bboxes.sub_(tr.repeat((1, 2)))
         # Estimate meta-data for image with inverted=True and with center=[0,0]
@@ -701,7 +701,7 @@ def pad_image_tensor(
     # internally.
     torch_padding = _parse_pad_padding(padding)
 
-    if padding_mode not in ["constant", "edge", "reflect", "symmetric"]:
+    if padding_mode not in ("constant", "edge", "reflect", "symmetric"):
         raise ValueError(
             f"`padding_mode` should be either `'constant'`, `'edge'`, `'reflect'` or `'symmetric'`, "
             f"but got `'{padding_mode}'`."
@@ -917,7 +917,7 @@ def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype,
     # x_out = (coeffs[0] * x + coeffs[1] * y + coeffs[2]) / (coeffs[6] * x + coeffs[7] * y + 1)
     # y_out = (coeffs[3] * x + coeffs[4] * y + coeffs[5]) / (coeffs[6] * x + coeffs[7] * y + 1)
     #
-
+    # TODO: should we define them transposed?
     theta1 = torch.tensor(
         [[[coeffs[0], coeffs[1], coeffs[2]], [coeffs[3], coeffs[4], coeffs[5]]]], dtype=dtype, device=device
     )
@@ -925,9 +925,9 @@ def _perspective_grid(coeffs: List[float], ow: int, oh: int, dtype: torch.dtype,
 
     d = 0.5
     base_grid = torch.empty(1, oh, ow, 3, dtype=dtype, device=device)
-    x_grid = torch.linspace(d, ow * 1.0 + d - 1.0, steps=ow, device=device)
+    x_grid = torch.linspace(d, ow + d - 1.0, steps=ow, device=device)
     base_grid[..., 0].copy_(x_grid)
-    y_grid = torch.linspace(d, oh * 1.0 + d - 1.0, steps=oh, device=device).unsqueeze_(-1)
+    y_grid = torch.linspace(d, oh + d - 1.0, steps=oh, device=device).unsqueeze_(-1)
     base_grid[..., 1].copy_(y_grid)
     base_grid[..., 2].fill_(1)
 
@@ -1059,6 +1059,7 @@ def perspective_bounding_box(
         (-perspective_coeffs[0] * perspective_coeffs[7] + perspective_coeffs[1] * perspective_coeffs[6]) / denom,
     ]
 
+    # TODO: should we define them transposed?
     theta1 = torch.tensor(
         [[inv_coeffs[0], inv_coeffs[1], inv_coeffs[2]], [inv_coeffs[3], inv_coeffs[4], inv_coeffs[5]]],
         dtype=dtype,
@@ -1165,6 +1166,7 @@ def elastic_image_tensor(
         return image
 
     shape = image.shape
+    device = image.device
 
     if image.ndim > 4:
         image = image.reshape((-1,) + shape[-3:])
@@ -1172,7 +1174,9 @@ def elastic_image_tensor(
     else:
         needs_unsquash = False
 
-    output = _FT.elastic_transform(image, displacement, interpolation=interpolation.value, fill=fill)
+    image_height, image_width = shape[-2:]
+    grid = _create_identity_grid((image_height, image_width), device=device).add_(displacement.to(device))
+    output = _FT._apply_grid_transform(image, grid, interpolation.value, fill)
 
     if needs_unsquash:
         output = output.reshape(shape)
@@ -1505,8 +1509,7 @@ def five_crop_image_tensor(
     image_height, image_width = image.shape[-2:]
 
     if crop_width > image_width or crop_height > image_height:
-        msg = "Requested crop size {} is bigger than input size {}"
-        raise ValueError(msg.format(size, (image_height, image_width)))
+        raise ValueError(f"Requested crop size {size} is bigger than input size {(image_height, image_width)}")
 
     tl = crop_image_tensor(image, 0, 0, crop_height, crop_width)
     tr = crop_image_tensor(image, 0, image_width - crop_width, crop_height, crop_width)
@@ -1525,8 +1528,7 @@ def five_crop_image_pil(
     image_height, image_width = get_spatial_size_image_pil(image)
 
     if crop_width > image_width or crop_height > image_height:
-        msg = "Requested crop size {} is bigger than input size {}"
-        raise ValueError(msg.format(size, (image_height, image_width)))
+        raise ValueError(f"Requested crop size {size} is bigger than input size {(image_height, image_width)}")
 
     tl = crop_image_pil(image, 0, 0, crop_height, crop_width)
     tr = crop_image_pil(image, 0, image_width - crop_width, crop_height, crop_width)