hotfix tensor dimensionality assertion

.gitignore

@@ -14,6 +14,9 @@ old/*
 ablation.py
 images/
 images/*
+images_comparison
+images_comparison/*
+comp_s2_s2naip.py
 
 # Byte-compiled / optimized / DLL files
 __pycache__/

opensr_model/srmodel.py

@@ -128,7 +128,6 @@ def _tensor_encode(self,X: torch.Tensor):
         if self.encode_conditioning==True and self.sr_type=="SISR":
             # try to upsample->encode conditioning
             X_int = torch.nn.functional.interpolate(X, size=(X.shape[-1]*4,X.shape[-1]*4), mode='bilinear', align_corners=False)
-            print(X_int.shape)
             # encode conditioning
             X_enc = self.model.first_stage_model.encode(X_int).sample()
         # move to same device as the model

opensr_model/utils.py

@@ -120,7 +120,7 @@ def linear_transform_6b(t_input,stage="norm"):
 
     return t_output
 
-def assert_tensor_validity(self,tensor):
+def assert_tensor_validity(tensor):
 
     # ASSERT BATCH DIMENSION
     # if unbatched, add batch dimension
@@ -142,30 +142,29 @@ def assert_tensor_validity(self,tensor):
         # Padding for height and width needs to be added to both sides of the dimension
         # The pad has the format (left, right, top, bottom)
         padding = (pad_width // 2, pad_width - pad_width // 2, pad_height // 2, pad_height - pad_height // 2)
-        self.padding = padding
+        padding = padding
 
         # Apply symmetric padding
         tensor = torch.nn.functional.pad(tensor, padding, mode='reflect')
 
     else: # save padding with 0s
         padding = (0,0,0,0)
-        self.padding = padding
+        padding = padding
 
     return tensor,padding
 
 
 
-def revert_padding(self,tensor,padding):
+def revert_padding(tensor,padding):
     left, right, top, bottom = padding
+    # account for 4x upsampling Factor
+    left, right, top, bottom = left*4, right*4, top*4, bottom*4
     # Calculate the indices to slice from the padded tensor
     start_height = top
     end_height = tensor.size(-2) - bottom
     start_width = left
     end_width = tensor.size(-1) - right
 
-    # account for 4x upsampling Factor
-    start_height,end_height,start_width,end_width = start_height*4,end_height*4,start_width*4,end_width*4
-
     # Slice the tensor to remove padding
     unpadded_tensor = tensor[:,:, start_height:end_height, start_width:end_width]
     return unpadded_tensor

setup.py

@@ -6,7 +6,7 @@
 
 setup(
     name='opensr-model',
-    version='0.2.11',
+    version='0.2.12',
     author = "Simon Donike, Cesar Aybar, Luis Gomez Chova, Freddie Kalaitzis",
     author_email = "accounts@donike.net",
     description = "ESA OpenSR Diffusion model package for Super-Resolution of Senintel-2 Imagery",