Fix tuple error, mentioned in some of the issues

README.md

@@ -36,7 +36,7 @@ LaMa generalizes surprisingly well to much higher resolutions (~2k❗️) than i
   <img src="https://raw.githubusercontent.com/geekyutao/Inpaint-Anything/main/example/MainFramework.png" />
 </p>
 
-- [Feature Refinement to Improve High Resolution Image Inpainting](https://arxiv.org/abs/2206.13644) / [video](https://www.youtube.com/watch?v=gEukhOheWgE) / code https://github.com/advimman/lama/pull/112 / by Geomagical Labs ([geomagical.com](geomagical.com))
+- [Feature Refinement to Improve High Resolution Image Inpainting](https://arxiv.org/abs/2206.13644) / [video](https://www.youtube.com/watch?v=gEukhOheWgE) / code https://github.com/advimman/lama/pull/112 / by Geomagical Labs ([geomagical.com](https://www.geomagical.com))
 <p align="center">
   <img src="https://raw.githubusercontent.com/senya-ashukha/senya-ashukha.github.io/master/images/FeatureRefinement.png" />
 </p>

saicinpainting/evaluation/refinement.py

@@ -83,6 +83,26 @@ def _l1_loss(
         loss += torch.mean(torch.abs(pred_downscaled[mask_downscaled>=1e-8] - ref[mask_downscaled>=1e-8]))                
     return loss
 
+def feats_type_to_list(feats, feats_type):
+    """unpacks the tuple of features into a list"""
+    if feats_type == tuple:
+        feats = list(feats)
+    elif feats_type == torch.Tensor:
+        feats = [feats]
+    else:
+        raise NotImplementedError("Expected the output of forward_front to be a tuple or a tensor!")
+    return feats
+
+def list_to_feats_type(feats, feats_type):
+    """packs the list of features into the original feature type"""
+    if feats_type == tuple:
+        feats = tuple(feats)
+    elif feats_type == torch.Tensor:
+        feats = feats[0]
+    else:
+        raise NotImplementedError("Expected the output of forward_front to be a tuple or a tensor!")
+    return feats
+
 def _infer(
     image : torch.Tensor, mask : torch.Tensor, 
     forward_front : nn.Module, forward_rears : nn.Module, 
@@ -125,27 +145,30 @@ def _infer(
     if ref_lower_res is not None:
         ref_lower_res = ref_lower_res.detach()
     with torch.no_grad():
-        z1,z2 = forward_front(masked_image)
+        z_feats = forward_front(masked_image)
+        z_feats_type = type(z_feats)
+        z_feats = feats_type_to_list(z_feats, z_feats_type)
     # Inference
     mask = mask.to(devices[-1])
     ekernel = torch.from_numpy(cv2.getStructuringElement(cv2.MORPH_ELLIPSE,(15,15)).astype(bool)).float()
     ekernel = ekernel.to(devices[-1])
     image = image.to(devices[-1])
-    z1, z2 = z1.detach().to(devices[0]), z2.detach().to(devices[0])
-    z1.requires_grad, z2.requires_grad = True, True
+    z_feats = [z_feat.detach().to(devices[0]) for z_feat in z_feats]
+    for z_feat in z_feats:
+        z_feat.requires_grad = True        
 
-    optimizer = Adam([z1,z2], lr=lr)
+    optimizer = Adam(z_feats, lr=lr)
 
     pbar = tqdm(range(n_iters), leave=False)
     for idi in pbar:
         optimizer.zero_grad()
-        input_feat = (z1,z2)
+        input_feat = list_to_feats_type(z_feats, z_feats_type)
         for idd, forward_rear in enumerate(forward_rears):
             output_feat = forward_rear(input_feat)
             if idd < len(devices) - 1:
-                midz1, midz2 = output_feat
-                midz1, midz2 = midz1.to(devices[idd+1]), midz2.to(devices[idd+1])
-                input_feat = (midz1, midz2)
+                mid_z_feats = feats_type_to_list(output_feat, z_feats_type)
+                mid_z_feats = [mid_z_feat.to(devices[idd+1]) for mid_z_feat in mid_z_feats]
+                input_feat = list_to_feats_type(mid_z_feats, z_feats_type)
             else:        
                 pred = output_feat