2025-03-19 nightly release (8ea4772)

Author: pytorchbot

torchvision/models/optical_flow/raft.py

@@ -486,7 +486,7 @@ def forward(self, image1, image2, num_flow_updates: int = 12):
         batch_size, _, h, w = image1.shape
         if (h, w) != image2.shape[-2:]:
             raise ValueError(f"input images should have the same shape, instead got ({h}, {w}) != {image2.shape[-2:]}")
-        if not (h % 8 == 0) and (w % 8 == 0):
+        if not ((h % 8 == 0) and (w % 8 == 0)):
             raise ValueError(f"input image H and W should be divisible by 8, instead got {h} (h) and {w} (w)")
 
         fmaps = self.feature_encoder(torch.cat([image1, image2], dim=0))

torchvision/ops/boxes.py

@@ -78,7 +78,8 @@ def batched_nms(
         _log_api_usage_once(batched_nms)
     # Benchmarks that drove the following thresholds are at
     # https://github.com/pytorch/vision/issues/1311#issuecomment-781329339
-    if boxes.numel() > (4000 if boxes.device.type == "cpu" else 20000) and not torchvision._is_tracing():
+    # and https://github.com/pytorch/vision/pull/8925
+    if boxes.numel() > (4000 if boxes.device.type == "cpu" else 100_000) and not torchvision._is_tracing():
         return _batched_nms_vanilla(boxes, scores, idxs, iou_threshold)
     else:
         return _batched_nms_coordinate_trick(boxes, scores, idxs, iou_threshold)

torchvision/transforms/v2/_color.py

@@ -134,7 +134,7 @@ def _check_input(
             raise TypeError(f"{name}={value} should be a single number or a sequence with length 2.")
 
         if not bound[0] <= value[0] <= value[1] <= bound[1]:
-            raise ValueError(f"{name} values should be between {bound}, but got {value}.")
+            raise ValueError(f"{name} values should be between {bound} and increasing, but got {value}.")
 
         return None if value[0] == value[1] == center else (float(value[0]), float(value[1]))