Merge branch 'main' into fix-docstring-fill-param

Author: abhitorch81

README.md

@@ -21,18 +21,21 @@ versions.
 | `torch`            | `torchvision`      | Python              |
 | ------------------ | ------------------ | ------------------- |
 | `main` / `nightly` | `main` / `nightly` | `>=3.9`, `<=3.12`   |
-| `2.5`              | `0.20`             | `>=3.9`, `<=3.12`   |
-| `2.4`              | `0.19`             | `>=3.8`, `<=3.12`   |
-| `2.3`              | `0.18`             | `>=3.8`, `<=3.12`   |
-| `2.2`              | `0.17`             | `>=3.8`, `<=3.11`   |
-| `2.1`              | `0.16`             | `>=3.8`, `<=3.11`   |
-| `2.0`              | `0.15`             | `>=3.8`, `<=3.11`   |
+| `2.8`              | `0.23`             | `>=3.9`, `<=3.13`   |
+| `2.7`              | `0.22`             | `>=3.9`, `<=3.13`   |
+| `2.6`              | `0.21`             | `>=3.9`, `<=3.12`   |
 
 <details>
     <summary>older versions</summary>
 
 | `torch` | `torchvision`     | Python                    |
 |---------|-------------------|---------------------------|
+| `2.5`              | `0.20`             | `>=3.9`, `<=3.12`   |
+| `2.4`              | `0.19`             | `>=3.8`, `<=3.12`   |
+| `2.3`              | `0.18`             | `>=3.8`, `<=3.12`   |
+| `2.2`              | `0.17`             | `>=3.8`, `<=3.11`   |
+| `2.1`              | `0.16`             | `>=3.8`, `<=3.11`   |
+| `2.0`              | `0.15`             | `>=3.8`, `<=3.11`   |
 | `1.13`  | `0.14`            | `>=3.7.2`, `<=3.10`       |
 | `1.12`  | `0.13`            | `>=3.7`, `<=3.10`         |
 | `1.11`  | `0.12`            | `>=3.7`, `<=3.10`         |

torchvision/transforms/v2/functional/_geometry.py

@@ -451,6 +451,12 @@ def _parallelogram_to_bounding_boxes(parallelogram: torch.Tensor) -> torch.Tenso
         torch.Tensor: Tensor of same shape as input containing the rectangle coordinates.
                      The output maintains the same dtype as the input.
     """
+    dtype = parallelogram.dtype
+    acceptable_dtypes = [torch.float32, torch.float64]
+    need_cast = dtype not in acceptable_dtypes
+    if need_cast:
+        # Up-case to avoid overflow for square operations
+        parallelogram = parallelogram.to(torch.float32)
     out_boxes = parallelogram.clone()
 
     # Calculate parallelogram diagonal vectors
@@ -489,6 +495,10 @@ def _parallelogram_to_bounding_boxes(parallelogram: torch.Tensor) -> torch.Tenso
     out_boxes[..., 1] = torch.where(~mask, parallelogram[..., 3] + delta_y, parallelogram[..., 1])
     out_boxes[..., 4] = torch.where(~mask, parallelogram[..., 6] + delta_x, parallelogram[..., 4])
     out_boxes[..., 5] = torch.where(~mask, parallelogram[..., 7] - delta_y, parallelogram[..., 5])
+
+    if need_cast:
+        out_boxes = out_boxes.to(dtype)
+
     return out_boxes
 
 

torchvision/transforms/v2/functional/_meta.py

@@ -194,11 +194,6 @@ def _cxcywhr_to_xywhr(cxcywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     if not inplace:
         cxcywhr = cxcywhr.clone()
 
-    dtype = cxcywhr.dtype
-    need_cast = not cxcywhr.is_floating_point()
-    if need_cast:
-        cxcywhr = cxcywhr.float()
-
     half_wh = cxcywhr[..., 2:-1].div(-2, rounding_mode=None if cxcywhr.is_floating_point() else "floor").abs_()
     r_rad = cxcywhr[..., 4].mul(torch.pi).div(180.0)
     cos, sin = r_rad.cos(), r_rad.sin()
@@ -207,22 +202,13 @@ def _cxcywhr_to_xywhr(cxcywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     # (cy + width / 2 * sin - height / 2 * cos) = y1
     cxcywhr[..., 1].add_(half_wh[..., 0].mul(sin)).sub_(half_wh[..., 1].mul(cos))
 
-    if need_cast:
-        cxcywhr.round_()
-        cxcywhr = cxcywhr.to(dtype)
-
     return cxcywhr
 
 
 def _xywhr_to_cxcywhr(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     if not inplace:
         xywhr = xywhr.clone()
 
-    dtype = xywhr.dtype
-    need_cast = not xywhr.is_floating_point()
-    if need_cast:
-        xywhr = xywhr.float()
-
     half_wh = xywhr[..., 2:-1].div(-2, rounding_mode=None if xywhr.is_floating_point() else "floor").abs_()
     r_rad = xywhr[..., 4].mul(torch.pi).div(180.0)
     cos, sin = r_rad.cos(), r_rad.sin()
@@ -231,10 +217,6 @@ def _xywhr_to_cxcywhr(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     # (y1 - width / 2 * sin + height / 2 * cos) = cy
     xywhr[..., 1].sub_(half_wh[..., 0].mul(sin)).add_(half_wh[..., 1].mul(cos))
 
-    if need_cast:
-        xywhr.round_()
-        xywhr = xywhr.to(dtype)
-
     return xywhr
 
 
@@ -243,11 +225,6 @@ def _xywhr_to_xyxyxyxy(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     if not inplace:
         xywhr = xywhr.clone()
 
-    dtype = xywhr.dtype
-    need_cast = not xywhr.is_floating_point()
-    if need_cast:
-        xywhr = xywhr.float()
-
     wh = xywhr[..., 2:-1]
     r_rad = xywhr[..., 4].mul(torch.pi).div(180.0)
     cos, sin = r_rad.cos(), r_rad.sin()
@@ -265,10 +242,6 @@ def _xywhr_to_xyxyxyxy(xywhr: torch.Tensor, inplace: bool) -> torch.Tensor:
     # y1 + h * cos = y4
     xywhr[..., 7].add_(wh[..., 1].mul(cos))
 
-    if need_cast:
-        xywhr.round_()
-        xywhr = xywhr.to(dtype)
-
     return xywhr
 
 
@@ -278,9 +251,11 @@ def _xyxyxyxy_to_xywhr(xyxyxyxy: torch.Tensor, inplace: bool) -> torch.Tensor:
         xyxyxyxy = xyxyxyxy.clone()
 
     dtype = xyxyxyxy.dtype
-    need_cast = not xyxyxyxy.is_floating_point()
+    acceptable_dtypes = [torch.float32, torch.float64]  # Ensure consistency between CPU and GPU.
+    need_cast = dtype not in acceptable_dtypes
     if need_cast:
-        xyxyxyxy = xyxyxyxy.float()
+        # Up-case to avoid overflow for square operations
+        xyxyxyxy = xyxyxyxy.to(torch.float32)
 
     r_rad = torch.atan2(xyxyxyxy[..., 1].sub(xyxyxyxy[..., 3]), xyxyxyxy[..., 2].sub(xyxyxyxy[..., 0]))
     # x1, y1, (x2 - x1), (y2 - y1), (x3 - x2), (y3 - y2) x4, y4
@@ -293,7 +268,6 @@ def _xyxyxyxy_to_xywhr(xyxyxyxy: torch.Tensor, inplace: bool) -> torch.Tensor:
     xyxyxyxy[..., 4] = r_rad.div_(torch.pi).mul_(180.0)
 
     if need_cast:
-        xyxyxyxy.round_()
         xyxyxyxy = xyxyxyxy.to(dtype)
 
     return xyxyxyxy[..., :5]