fixing rebase

Author: azrael417

tests/test_attention.py

@@ -36,7 +36,6 @@
 
 import torch
 from torch.library import opcheck
-import torch.nn as nn
 
 # from torch.autograd import gradcheck
 from torch_harmonics import AttentionS2, NeighborhoodAttentionS2

tests/test_permute.py

@@ -33,7 +33,6 @@
 import unittest
 from parameterized import parameterized, parameterized_class
 
-
 import torch
 from torch.library import opcheck
 from torch_harmonics.utils import permute_to_0231, permute_to_0312

tests/testutils.py

@@ -0,0 +1,62 @@
+# coding=utf-8
+
+# SPDX-FileCopyrightText: Copyright (c) 2025 The torch-harmonics Authors. All rights reserved.
+# SPDX-License-Identifier: BSD-3-Clause
+#
+# Redistribution and use in source and binary forms, with or without
+# modification, are permitted provided that the following conditions are met:
+#
+# 1. Redistributions of source code must retain the above copyright notice, this
+# list of conditions and the following disclaimer.
+#
+# 2. Redistributions in binary form must reproduce the above copyright notice,
+# this list of conditions and the following disclaimer in the documentation
+# and/or other materials provided with the distribution.
+#
+# 3. Neither the name of the copyright holder nor the names of its
+# contributors may be used to endorse or promote products derived from
+# this software without specific prior written permission.
+#
+# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
+# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
+# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
+# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
+# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
+#
+
+import torch
+
+def compare_tensors(msg, tensor1, tensor2, atol=1e-8, rtol=1e-5, verbose=False):
+
+    # some None checks
+    if tensor1 is None and tensor2 is None:
+        allclose = True
+    elif tensor1 is None and tensor2 is not None:
+        allclose = False
+        if verbose:
+            print(f"tensor1 is None and tensor2 is not None")
+    elif tensor1 is not None and tensor2 is None:
+        allclose = False
+        if verbose:
+            print(f"tensor1 is not None and tensor2 is None")
+    else:
+        diff = torch.abs(tensor1 - tensor2)
+        abs_diff = torch.mean(diff, dim=0)
+        rel_diff = torch.mean(diff / torch.clamp(torch.abs(tensor2), min=1e-6), dim=0)
+        allclose = torch.allclose(tensor1, tensor2, atol=atol, rtol=rtol)
+        if not allclose and verbose:
+            print(f"Absolute difference on {msg}: min = {abs_diff.min()}, mean = {abs_diff.mean()}, max = {abs_diff.max()}")
+            print(f"Relative difference on {msg}: min = {rel_diff.min()}, mean = {rel_diff.mean()}, max = {rel_diff.max()}")
+            print(f"Element values with max difference on {msg}: {tensor1.flatten()[diff.argmax()]} and {tensor2.flatten()[diff.argmax()]}")
+            # find violating entry
+            worst_diff = torch.argmax(diff - (atol + rtol * torch.abs(tensor2)))
+            diff_bad = diff.flatten()[worst_diff].item()
+            tensor2_abs_bad = torch.abs(tensor2).flatten()[worst_diff].item()
+            print(f"Worst allclose condition violation: {diff_bad} <= {atol} + {rtol} * {tensor2_abs_bad} = {atol + rtol * tensor2_abs_bad}")
+
+    return allclose