Merge pull request #61 from mariogeiger/view

view

test/test_storage.py

@@ -122,3 +122,24 @@ def test_coalesce(dtype, device):
     assert storage.row().tolist() == [0, 0, 1, 1]
     assert storage.col().tolist() == [0, 1, 0, 1]
     assert storage.value().tolist() == [1, 2, 3, 4]
+
+
+@pytest.mark.parametrize('dtype,device', product(dtypes, devices))
+def test_sparse_reshape(dtype, device):
+    row, col = tensor([[0, 1, 2, 3], [0, 1, 2, 3]], torch.long, device)
+    storage = SparseStorage(row=row, col=col)
+
+    storage = storage.sparse_reshape(2, 8)
+    assert storage.sparse_sizes() == (2, 8)
+    assert storage.row().tolist() == [0, 0, 1, 1]
+    assert storage.col().tolist() == [0, 5, 2, 7]
+
+    storage = storage.sparse_reshape(-1, 4)
+    assert storage.sparse_sizes() == (4, 4)
+    assert storage.row().tolist() == [0, 1, 2, 3]
+    assert storage.col().tolist() == [0, 1, 2, 3]
+
+    storage = storage.sparse_reshape(2, -1)
+    assert storage.sparse_sizes() == (2, 8)
+    assert storage.row().tolist() == [0, 0, 1, 1]
+    assert storage.col().tolist() == [0, 5, 2, 7]

torch_sparse/storage.py

@@ -260,6 +260,31 @@ class SparseStorage(object):
                              colcount=colcount, csr2csc=self._csr2csc,
                              csc2csr=self._csc2csr, is_sorted=True)
 
+    def sparse_reshape(self, num_rows: int, num_cols: int):
+        assert num_rows > 0 or num_rows == -1
+        assert num_cols > 0 or num_cols == -1
+        assert num_rows > 0 or num_cols > 0
+
+        total = self.sparse_size(0) * self.sparse_size(1)
+
+        if num_rows == -1:
+            num_rows = total // num_cols
+
+        if num_cols == -1:
+            num_cols = total // num_rows
+
+        assert num_rows * num_cols == total
+
+        idx = self.sparse_size(1) * self.row() + self.col()
+
+        row = idx / num_cols
+        col = idx % num_cols
+
+        return SparseStorage(row=row, rowptr=None, col=col, value=self._value,
+                             sparse_sizes=(num_rows, num_cols), rowcount=None,
+                             colptr=None, colcount=None, csr2csc=None,
+                             csc2csr=None, is_sorted=True)
+
     def has_rowcount(self) -> bool:
         return self._rowcount is not None
 

torch_sparse/tensor.py

@@ -171,6 +171,10 @@ class SparseTensor(object):
     def sparse_resize(self, sparse_sizes: Tuple[int, int]):
         return self.from_storage(self.storage.sparse_resize(sparse_sizes))
 
+    def sparse_reshape(self, num_rows: int, num_cols: int):
+        return self.from_storage(
+            self.storage.sparse_reshape(num_rows, num_cols))
+
     def is_coalesced(self) -> bool:
         return self.storage.is_coalesced()