minor formating edits to fix sphinx formatting warnings

rctn · Nov 20, 2024 · c5ed4b1 · c5ed4b1
1 parent afc126e
commit c5ed4b1
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Showing 2 changed files with 24 additions and 20 deletions.
diff --git a/sparsecoding/data/transforms/patch.py b/sparsecoding/data/transforms/patch.py
@@ -16,11 +16,12 @@ def sample_random_patches(
         Patch side length.
     num_patches : int
         Number of patches to sample.
-    image : Tensor, shape [*, C, H, W]
+    image : Tensor, shape [M, C, H, W]
         where:
-            C is the number of channels,
-            H is the image height,
-            W is the image width.
+        M is the number of images,
+        C is the number of channels,
+        H is the image height,
+        W is the image width.
 
     Returns
     -------
@@ -75,23 +76,25 @@ def patchify(
     ----------
     patch_size : int
         Patch side length.
-    image : Tensor, shape [*, C, H, W]
-        where:
-            C is the number of channels,
-            H is the image height,
-            W is the image width.
+    image : Tensor, shape [M, C, H, W]
+        where: 
+        M is the number of images, 
+        C is the number of channels, 
+        H is the image height, 
+        W is the image width.
     stride : int, optional
         Stride between patches in pixel space. If not specified, set to
         `patch_size` (non-overlapping patches).
 
     Returns
     -------
-    patches : Tensor, shape [*, N, C, P, P]
+    patches : Tensor, shape [M, N, C, P, P]
         Non-overlapping patches taken from the input image,
         where:
-            P is the patch size,
-            N is the number of patches, equal to H//P * W//P,
-            C is the number of channels of the input image.
+        M is the number of images,
+        P is the patch size,
+        N is the number of patches, equal to H//P * W//P,
+        C is the number of channels of the input image.
     """
     leading_dims = image.shape[:-3]
     C, H, W = image.shape[-3:]
@@ -141,16 +144,17 @@ def quilt(
         Height for the reconstructed image.
     width : int
         Width for the reconstructed image.
-    patches : Tensor, shape [*, N, C, P, P]
+    patches : Tensor, shape [M, N, C, P, P]
         Non-overlapping patches from an input image,
         where:
-            P is the patch size,
-            N is the number of patches,
-            C is the number of channels in the image.
+        M is the number of images
+        P is the patch size,
+        N is the number of patches,
+        C is the number of channels in the image.
 
     Returns
     -------
-    image : Tensor, shape [*, C, height, width]
+    image : Tensor, shape [M, C, height, width]
         Image reconstructed by stitching together input patches.
     """
     leading_dims = patches.shape[:-4]

diff --git a/sparsecoding/inference.py b/sparsecoding/inference.py
@@ -856,8 +856,8 @@ class OMP(InferenceMethod):
     """
     Infer coefficients for each image in data using elements dictionary.
     Method description can be traced to:
-        "Orthogonal Matching Pursuit: Recursive Function Approximation with Application to Wavelet Decomposition"
-        (Y. Pati & R. Rezaiifar & P. Krishnaprasad, 1993)
+    "Orthogonal Matching Pursuit: Recursive Function Approximation with Application to Wavelet Decomposition"
+    (Y. Pati & R. Rezaiifar & P. Krishnaprasad, 1993)
     """
 
     def __init__(self, sparsity, solver=None, return_all_coefficients=False):