Created inference file, moved torch no grad to model.py, removed timm user warning and used a different photo from the demo for default inference image.

inference.py

@@ -0,0 +1,17 @@
+import model
+from PIL import Image
+import torch
+
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+file = "./image.png" # input image
+
+model = model.BEN_Base().to(device).eval() #init pipeline
+
+model.loadcheckpoints("./BEN_Base.pth")
+image = Image.open(file)
+mask, foreground = model.inference(image)
+
+mask.save("./mask.png")
+foreground.save("./foreground.png")

model.py

@@ -6,7 +6,7 @@ import torch.nn.functional as F
 import torch.utils.checkpoint as checkpoint
 from einops import rearrange
 from PIL import Image, ImageFilter, ImageOps
-from timm.models.layers import DropPath, to_2tuple, trunc_normal_
+from timm.layers import DropPath, to_2tuple, trunc_normal_
 from torchvision import transforms
 
 class Mlp(nn.Module):
@@ -887,6 +887,7 @@ class BEN_Base(nn.Module):
 
         return final_output.sigmoid()
 
+    @torch.no_grad()
     def inference(self,image):
         image, h, w,original_image =  rgb_loader_refiner(image)