Add regnet model from SWAG (#5722)

* Add regnet_y_16gf and regnet_y_32gf from swag

* Format with ufmt

* Add the experiment accuracy
Co-authored-by: Vasilis Vryniotis <datumbox@users.noreply.github.com>

torchvision/models/regnet.py

@@ -412,6 +412,15 @@ _COMMON_META = {
     "interpolation": InterpolationMode.BILINEAR,
 }
 
+_COMMON_SWAG_META = {
+    **_COMMON_META,
+    "publication_year": 2022,
+    "size": (384, 384),
+    "recipe": "https://github.com/facebookresearch/SWAG",
+    "license": "https://github.com/facebookresearch/SWAG/blob/main/LICENSE",
+    "interpolation": InterpolationMode.BICUBIC,
+}
+
 
 class RegNet_Y_400MF_Weights(WeightsEnum):
     IMAGENET1K_V1 = Weights(
@@ -566,6 +575,18 @@ class RegNet_Y_16GF_Weights(WeightsEnum):
             "acc@5": 96.328,
         },
     )
+    IMAGENET1K_SWAG_V1 = Weights(
+        url="https://download.pytorch.org/models/regnet_y_16gf_swag-43afe44d.pth",
+        transforms=partial(
+            ImageClassification, crop_size=384, resize_size=384, interpolation=InterpolationMode.BICUBIC
+        ),
+        meta={
+            **_COMMON_SWAG_META,
+            "num_params": 83590140,
+            "acc@1": 86.012,
+            "acc@5": 98.054,
+        },
+    )
     DEFAULT = IMAGENET1K_V2
 
 
@@ -592,6 +613,18 @@ class RegNet_Y_32GF_Weights(WeightsEnum):
             "acc@5": 96.498,
         },
     )
+    IMAGENET1K_SWAG_V1 = Weights(
+        url="https://download.pytorch.org/models/regnet_y_32gf_swag-04fdfa75.pth",
+        transforms=partial(
+            ImageClassification, crop_size=384, resize_size=384, interpolation=InterpolationMode.BICUBIC
+        ),
+        meta={
+            **_COMMON_SWAG_META,
+            "num_params": 145046770,
+            "acc@1": 86.838,
+            "acc@5": 98.362,
+        },
+    )
     DEFAULT = IMAGENET1K_V2