add operator to convert dense to sparse and convert sparse to dense

Author: yifanfeng97

SuperMoon/hyedge/__init__.py

@@ -5,11 +5,13 @@
 from .utils.self_loop import self_loop_add, self_loop_remove
 from .gather_neighbor import neighbor_grid, neighbor_distance, gather_patch_ft
 from .utils.count import count_hyedge, count_node
+from .structure_convert import matrix2index, index2matrix
 
 __all__ = ['pairwise_euclidean_distance',
            'count_hyedge', 'count_node',
            'degree_node', 'degree_hyedge',
            'self_loop_add', 'self_loop_remove',
            'contiguous_hyedge_idx', 'filter_node_index', 'remove_negative_index',
            'neighbor_grid', 'neighbor_distance', 'gather_patch_ft',
+           'matrix2index', 'index2matrix', 
            ]

SuperMoon/hyedge/structure_convert.py

@@ -0,0 +1,17 @@
+import torch
+
+
+def index2matrix(index):
+    assert index.size(0) == 2
+
+    index = index.long()
+    v_len = index.size(1)
+    v = torch.ones(v_len).float()
+    matrix = torch.sparse_coo_tensor(index, v).to_dense()
+    return matrix
+
+
+def matrix2index(matrix: torch.tensor):
+    i_v = matrix.to_sparse()
+    index = i_v.indices()
+    return index;

test/hyedge/test_structure_convert.py

@@ -0,0 +1,21 @@
+import torch
+
+from SuperMoon.hyedge import matrix2index, index2matrix
+
+
+def test_matrix2index():
+    matrix = torch.tensor([[0, 1, 0, 0],
+                           [1, 1, 0, 1],
+                           [0, 0, 1, 0]])
+    index = matrix2index(matrix)
+    assert torch.all(index == torch.tensor([[0, 1, 1, 1, 2],
+                                            [1, 0, 1, 3, 2]]))
+
+
+def test_index2matrix():
+    index = torch.tensor([[0, 1, 1, 1, 2],
+                          [1, 0, 1, 3, 2]])
+    matrix = index2matrix(index)
+    assert torch.all(matrix == torch.tensor([[0, 1, 0, 0],
+                                             [1, 1, 0, 1],
+                                             [0, 0, 1, 0]]).float())