Fix variable naming and padding logic in PatchInferencerEngine for im…

…proved clarity and functionality

minerva/engines/patch_inferencer_engine.py

@@ -213,13 +213,13 @@ def _adjust_patches(
         sl = []
         ref_shape = list(ref_shape)
         arr_shape = list(arrays[0].shape)
-        for idx, lenght, ref in zip([0, *offset], arr_shape, ref_shape):
+        for idx, length, ref in zip([0, *offset], arr_shape, ref_shape):
             if idx > 0:
-                sl.append(slice(0, min(lenght, ref), None))
-                pad_width = [idx, max(ref - lenght - idx, 0)] + pad_width
+                sl.append(slice(0, min(length, ref), None))
+                pad_width = [idx, max(ref - length - idx, 0)] + pad_width
             else:
-                sl.append(slice(np.abs(idx), min(lenght, ref - idx), None))
-                pad_width = [0, max(ref - lenght - idx, 0)] + pad_width
+                sl.append(slice(np.abs(idx), min(length, ref - idx), None))
+                pad_width = [0, max(ref - length - idx, 0)] + pad_width
         adjusted = [
             (
                 torch.nn.functional.pad(
@@ -251,6 +251,7 @@ def _combine_patches(
             )
             reconstructed.append(reconstruct)
             weights.append(weight)
+            print(reconstruct.shape)
         reconstructed = torch.stack(reconstructed, dim=0)
         weights = torch.stack(weights, dim=0)
         return torch.sum(reconstructed * weights, dim=0) / torch.sum(weights, dim=0)
@@ -319,7 +320,7 @@ def __call__(
         slices = [
             tuple(
                 [
-                    slice(i + base, None)  # TODO: if ((i + base >= 0) and (i < in_dim))
+                    slice(i, None)  # TODO: if ((i + base >= 0) and (i < in_dim))
                     for i, base, in_dim in zip([0, *offset], base, x.shape)
                 ]
             )
@@ -328,7 +329,7 @@ def __call__(
 
         torch_pad = []
         for pad_value in reversed(base):
-            torch_pad = torch_pad + [pad_value, pad_value]
+            torch_pad = torch_pad + [0, pad_value]
         x_padded = torch.nn.functional.pad(
             x,
             pad=tuple(torch_pad),
@@ -349,7 +350,7 @@ def __call__(
             else:
                 results.append(inference)
             indexes.append(patch_idx)
-        output_slice = tuple([slice(0, lenght) for lenght in self.ref_shape])
+        output_slice = tuple([slice(0, length) for length in self.ref_shape])
         if self.return_tuple:
             comb_list = []
             for i in range(self.return_tuple):