Merge pull request #1225 from Sherry-XLL/master

FIX: fix UserWarning in get_norm_adj_mat and accelerate csr2tensor
RUCAIBox · Apr 4, 2022 · 86b20cd · 86b20cd
2 parents 4f76169 + 390c305
commit 86b20cd
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Showing 4 changed files with 4 additions and 4 deletions.
diff --git a/recbole/model/general_recommender/lightgcn.py b/recbole/model/general_recommender/lightgcn.py
@@ -100,7 +100,7 @@ def get_norm_adj_mat(self):
         L = sp.coo_matrix(L)
         row = L.row
         col = L.col
-        i = torch.LongTensor([row, col])
+        i = torch.LongTensor(np.array([row, col]))
         data = torch.FloatTensor(L.data)
         SparseL = torch.sparse.FloatTensor(i, data, torch.Size(L.shape))
         return SparseL

diff --git a/recbole/model/general_recommender/ncl.py b/recbole/model/general_recommender/ncl.py
@@ -121,7 +121,7 @@ def get_norm_adj_mat(self):
         L = sp.coo_matrix(L)
         row = L.row
         col = L.col
-        i = torch.LongTensor([row, col])
+        i = torch.LongTensor(np.array([row, col]))
         data = torch.FloatTensor(L.data)
         SparseL = torch.sparse.FloatTensor(i, data, torch.Size(L.shape))
         return SparseL

diff --git a/recbole/model/general_recommender/ngcf.py b/recbole/model/general_recommender/ngcf.py
@@ -103,7 +103,7 @@ def get_norm_adj_mat(self):
         L = sp.coo_matrix(L)
         row = L.row
         col = L.col
-        i = torch.LongTensor([row, col])
+        i = torch.LongTensor(np.array([row, col]))
         data = torch.FloatTensor(L.data)
         SparseL = torch.sparse.FloatTensor(i, data, torch.Size(L.shape))
         return SparseL

diff --git a/recbole/model/general_recommender/sgl.py b/recbole/model/general_recommender/sgl.py
@@ -156,7 +156,7 @@ def csr2tensor(self, matrix: sp.csr_matrix):
         """
         matrix = matrix.tocoo()
         x = torch.sparse.FloatTensor(
-            torch.LongTensor([matrix.row.tolist(), matrix.col.tolist()]),
+            torch.LongTensor(np.array([matrix.row, matrix.col])),
             torch.FloatTensor(matrix.data.astype(np.float32)), matrix.shape
         ).to(self.device)
         return x