Fixes 1.0 crop reproducibility issue.

transforms.CenterCrop automatically converts image size into a tuple, but transforms.Resize does not.
When running models on ImageNet at 384x384 with 1.0 crop_pct, this will result in transforms.Resize(384).
Some methods (i.e. ConvNeXt, Swin) implement this with transforms.Resize((384, 384)).
As a result their reported scores on ImageNet at 384 are not reproducible with timm's validation script.

timm/data/transforms_factory.py

@@ -145,6 +145,7 @@ def transforms_imagenet_eval(
             scale_size = tuple([int(x / crop_pct) for x in img_size])
     else:
         scale_size = int(math.floor(img_size / crop_pct))
+        scale_size = (scale_size, scale_size)
 
     tfl = [
         transforms.Resize(scale_size, interpolation=str_to_interp_mode(interpolation)),