def t(self):
indices = list(self.indices.split(1, dim=0))
indices[-1], indices[-2] = indices[-2], indices[-1]
indices = jt.contrib.concat(indices, dim=0)
shape = list(self.shape)
shape[-1], shape[-2] = shape[-2], shape[-1]
shape = jt.NanoVector(shape)
return SparseVar(indices, self.values, shape)
def test_sparse_var(self):
indices = np.array([[0,1,1],[2,0,2]])
values = np.array([3,4,5]).astype(np.float32)
shape = [2,3]
jt_array = jt.sparse.sparse_array(jt.array(indices),jt.array(values),jt.NanoVector(shape))
torch_tensor = torch.sparse.FloatTensor(torch.from_numpy(indices),torch.from_numpy(values),torch.Size(shape))
jt_numpy = jt_array.to_dense().numpy()
torch_numpy = torch_tensor.to_dense().numpy()
assert np.allclose(jt_numpy,torch_numpy)
def test_slice_bug(self):
a = jt.NanoVector([2, 3, 4, 5])
assert a[:] == [2, 3, 4, 5]
assert a[1:] == [3, 4, 5]