linting

sparsecoding/transforms/images.py

@@ -412,9 +412,10 @@ def quilt(
     patches: torch.Tensor,
     stride: int = None,
 ):
-    """Gather square patches into an image, supporting overlapping patches.
-    Works with patches created by `patchify()` with a custom stride.
-    
+    """Gather square patches into an image.
+
+    Inverse of `patchify()`
+
     Parameters
     ----------
     height : int
@@ -430,7 +431,7 @@ def quilt(
     stride : int, optional
         Stride used when creating patches. If None, assumes non-overlapping patches
         (stride = patch_size).
-        
+
     Returns
     -------
     image : Tensor, shape [*, C, height, width]
@@ -440,10 +441,10 @@ def quilt(
     N, C, P = patches.shape[-4:-1]
     H = height
     W = width
-    
+
     if stride is None:
         stride = P
-        
+
     # Calculate expected number of patches based on stride
     expected_N = (
         int((H - P + 1 + stride) // stride)
@@ -454,7 +455,12 @@ def quilt(
             f"Expected {expected_N} patches per image based on stride {stride}, "
             f"got {N} patches."
         )
-
+
+    if stride > P:
+        raise RuntimeError(
+            "Stride cannot be larger than the size of a patch when quilting"
+        )
+
     if (
         H % stride != 0
         or W % stride != 0
@@ -464,28 +470,28 @@ def quilt(
             f"parts on the bottom and/or right may be affected.",
             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
-    
+
     return image.reshape(*leading_dims, C, H, W)