doc string

sparsecoding/transforms/images.py

@@ -412,7 +412,7 @@ def quilt(
     patches: torch.Tensor,
     stride: int = None,
 ):
-    """Gather square patches into an image.
+    """Gather square patches into an image. When patches overlap, take the average of overlapping pixels
 
     Inverse of `patchify()`
 
@@ -445,7 +445,6 @@ def quilt(
     if stride is None:
         stride = P
 
-    # Calculate expected number of patches based on stride
     expected_N = (
         int((H - P + 1 + stride) // stride)
         * int((W - P + 1 + stride) // stride)
@@ -471,27 +470,23 @@ def quilt(
             UserWarning,
         )
 
-    # Reshape patches for folding operation
     patches = patches.reshape(-1, N, C*P*P)  # [prod(*), N, C*P*P]
     patches = torch.permute(patches, (0, 2, 1))  # [prod(*), C*P*P, N]
 
-    # Use fold operation with the specified stride
     image = torch.nn.functional.fold(
         input=patches,
         output_size=(H, W),
         kernel_size=P,
         stride=stride,
     )  # [prod(*), C, H, W]
 
-    # Create a ones tensor of the same shape as patches to track overlapping regions
     normalization = torch.nn.functional.fold(
         torch.ones_like(patches),
         output_size=(H, W),
         kernel_size=P,
         stride=stride,
     )
 
-    # Normalize by the count of overlapping patches
-    image = image / (normalization + 1e-6)  # Add small epsilon to avoid division by zero
+    image = image / (normalization + 1e-6) 
 
     return image.reshape(*leading_dims, C, H, W)