allow sequence fill for v2 AA scripted (#7919)

Author: pmeier

test/test_transforms_v2_consistency.py

@@ -755,10 +755,11 @@ def test_randaug(self, inpt, interpolation, mocker):
             v2_transforms.InterpolationMode.BILINEAR,
         ],
     )
-    def test_randaug_jit(self, interpolation):
+    @pytest.mark.parametrize("fill", [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1], 1])
+    def test_randaug_jit(self, interpolation, fill):
         inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
-        t_ref = legacy_transforms.RandAugment(interpolation=interpolation, num_ops=1)
-        t = v2_transforms.RandAugment(interpolation=interpolation, num_ops=1)
+        t_ref = legacy_transforms.RandAugment(interpolation=interpolation, num_ops=1, fill=fill)
+        t = v2_transforms.RandAugment(interpolation=interpolation, num_ops=1, fill=fill)
 
         tt_ref = torch.jit.script(t_ref)
         tt = torch.jit.script(t)
@@ -830,10 +831,11 @@ def test_trivial_aug(self, inpt, interpolation, mocker):
             v2_transforms.InterpolationMode.BILINEAR,
         ],
     )
-    def test_trivial_aug_jit(self, interpolation):
+    @pytest.mark.parametrize("fill", [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1], 1])
+    def test_trivial_aug_jit(self, interpolation, fill):
         inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
-        t_ref = legacy_transforms.TrivialAugmentWide(interpolation=interpolation)
-        t = v2_transforms.TrivialAugmentWide(interpolation=interpolation)
+        t_ref = legacy_transforms.TrivialAugmentWide(interpolation=interpolation, fill=fill)
+        t = v2_transforms.TrivialAugmentWide(interpolation=interpolation, fill=fill)
 
         tt_ref = torch.jit.script(t_ref)
         tt = torch.jit.script(t)
@@ -906,11 +908,12 @@ def test_augmix(self, inpt, interpolation, mocker):
             v2_transforms.InterpolationMode.BILINEAR,
         ],
     )
-    def test_augmix_jit(self, interpolation):
+    @pytest.mark.parametrize("fill", [None, 85, (10, -10, 10), 0.7, [0.0, 0.0, 0.0], [1], 1])
+    def test_augmix_jit(self, interpolation, fill):
         inpt = torch.randint(0, 256, size=(1, 3, 256, 256), dtype=torch.uint8)
 
-        t_ref = legacy_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
-        t = v2_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1)
+        t_ref = legacy_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1, fill=fill)
+        t = v2_transforms.AugMix(interpolation=interpolation, mixture_width=1, chain_depth=1, fill=fill)
 
         tt_ref = torch.jit.script(t_ref)
         tt = torch.jit.script(t)

torchvision/transforms/v2/_auto_augment.py

@@ -33,8 +33,8 @@ def __init__(
     def _extract_params_for_v1_transform(self) -> Dict[str, Any]:
         params = super()._extract_params_for_v1_transform()
 
-        if not (params["fill"] is None or isinstance(params["fill"], (int, float))):
-            raise ValueError(f"{type(self).__name__}() can only be scripted for a scalar `fill`, but got {self.fill}.")
+        if isinstance(params["fill"], dict):
+            raise ValueError(f"{type(self).__name__}() can not be scripted for when `fill` is a dictionary.")
 
         return params