Fix/8877 elastic transform negative sigma

test/test_transforms.py

@@ -2208,6 +2208,14 @@ def test_elastic_transformation():
         transforms.ElasticTransform(alpha=2.0, sigma=[1.0, True])
     with pytest.raises(ValueError, match=r"sigma is a sequence its length should be 2"):
         transforms.ElasticTransform(alpha=2.0, sigma=[1.0, 0.0, 1.0])
+    with pytest.raises(ValueError, match=r"sigma should have non-negative values"):
+        transforms.ElasticTransform(alpha=2.0, sigma=-100.0)
+    with pytest.raises(ValueError, match=r"sigma should have non-negative values"):
+        transforms.ElasticTransform(alpha=2.0, sigma=[-100.0, -100.0])
+    with pytest.raises(ValueError, match=r"sigma should have non-negative values"):
+        transforms.ElasticTransform(alpha=2.0, sigma=[-100.0, 100.0])
+    with pytest.raises(ValueError, match=r"sigma should have non-negative values"):
+        transforms.ElasticTransform(alpha=2.0, sigma=[100.0, -100.0])
 
     t = transforms.transforms.ElasticTransform(alpha=2.0, sigma=2.0, interpolation=Image.BILINEAR)
     assert t.interpolation == transforms.InterpolationMode.BILINEAR

test/test_transforms_v2.py

@@ -3427,6 +3427,11 @@ def test_transform(self, make_input, size, device):
             check_v1_compatibility=check_v1_compatibility,
         )
 
+    @pytest.mark.parametrize("sigma", [-100, -100.0, [-100, -100], [-100.0, 100.0], [100.0, -100.0]])
+    def test_transform_negative_sigma(self, sigma):
+        with pytest.raises(ValueError, match="sigma should have non-negative values"):
+            transforms.ElasticTransform(sigma=sigma)
+
 
 class TestToPureTensor:
     def test_correctness(self):

torchvision/transforms/transforms.py

@@ -2063,6 +2063,7 @@ class ElasticTransform(torch.nn.Module):
     Args:
         alpha (float or sequence of floats): Magnitude of displacements. Default is 50.0.
         sigma (float or sequence of floats): Smoothness of displacements. Default is 5.0.
+            Values must be non-negative.
         interpolation (InterpolationMode): Desired interpolation enum defined by
             :class:`torchvision.transforms.InterpolationMode`. Default is ``InterpolationMode.BILINEAR``.
             If input is Tensor, only ``InterpolationMode.NEAREST``, ``InterpolationMode.BILINEAR`` are supported.
@@ -2104,6 +2105,8 @@ def __init__(self, alpha=50.0, sigma=5.0, interpolation=InterpolationMode.BILINE
             sigma = [float(sigma), float(sigma)]
         if isinstance(sigma, (list, tuple)) and len(sigma) == 1:
             sigma = [sigma[0], sigma[0]]
+        if any(s < 0 for s in sigma):
+            raise ValueError(f"sigma should have non-negative values. Got {list(sigma)}")
 
         self.sigma = sigma
 

torchvision/transforms/v2/_geometry.py

@@ -1053,7 +1053,7 @@ class ElasticTransform(Transform):
         alpha (float or sequence of floats, optional): Magnitude of displacements.
             Default is 50.0. A single value is ``[alpha, alpha]``.
         sigma (float or sequence of floats, optional): Smoothness of displacements.
-            Default is 5.0. A single value is ``[sigma, sigma]``.
+            Default is 5.0. A single value is ``[sigma, sigma]``. Values must be non-negative.
         interpolation (str or InterpolationMode, optional): Desired interpolation enum defined by
             :class:`torchvision.transforms.v2.InterpolationMode`.
             Accepted string values are ``"nearest"``, ``"nearest-exact"``, ``"bilinear"``, ``"bicubic"``,
@@ -1081,6 +1081,8 @@ def __init__(
         super().__init__()
         self.alpha = _setup_number_or_seq(alpha, "alpha")
         self.sigma = _setup_number_or_seq(sigma, "sigma")
+        if any(s < 0 for s in self.sigma):
+            raise ValueError(f"sigma should have non-negative values. Got {list(self.sigma)}")
 
         self.interpolation = interpolation
         self.fill = fill