remove pil_interp

focalnet.py

@@ -15,7 +15,6 @@ from timm.models.registry import register_model
 from torchvision import transforms
 from timm.data.constants import IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD
 from timm.data import create_transform
-from timm.data.transforms import _pil_interp
 
 class Mlp(nn.Module):
     def __init__(self, in_features, hidden_features=None, out_features=None, act_layer=nn.GELU, drop=0.):
@@ -499,14 +498,14 @@ def build_transforms(img_size, center_crop=False):
     if center_crop:
         size = int((256 / 224) * img_size)
         t.append(
-            transforms.Resize(size, interpolation=_pil_interp('bicubic'))
+            transforms.Resize(size)
         )
         t.append(
             transforms.CenterCrop(img_size)    
         )
     else:
         t.append(
-            transforms.Resize(img_size, interpolation=_pil_interp('bicubic'))
+            transforms.Resize(img_size)
         )        
     t.append(transforms.ToTensor())
     t.append(transforms.Normalize(IMAGENET_DEFAULT_MEAN, IMAGENET_DEFAULT_STD))
@@ -517,14 +516,14 @@ def build_transforms4display(img_size, center_crop=False):
     if center_crop:
         size = int((256 / 224) * img_size)
         t.append(
-            transforms.Resize(size, interpolation=_pil_interp('bicubic'))
+            transforms.Resize(size)
         )
         t.append(
             transforms.CenterCrop(img_size)    
         )
     else:
         t.append(
-            transforms.Resize(img_size, interpolation=_pil_interp('bicubic'))
+            transforms.Resize(img_size)
         )  
     t.append(transforms.ToTensor())
     return transforms.Compose(t)