FIX: fix UserWarning in get_norm_adj_mat and accelerate csr2tensor

[Sherry-XLL]

When running LightGCN, NGCF and NCL, there will be a UserWarning as follows:

UserWarning: Creating a tensor from a list of numpy.ndarrays is extremely slow. Please consider converting the list to a single numpy.ndarray with numpy.array() before converting to a tensor.

The reason is that in get_norm_adj_mat, the type of L.row and L.col is numpy.ndarray, so i =torch.LongTensor(np.array([row, col])) creates a tensor from a list of numpy.ndarrays.

RecBole/recbole/model/general_recommender/lightgcn.py

Lines 99 to 103 in 4f76169

	# covert norm_adj matrix to tensor
	L = sp.coo_matrix(L)
	row = L.row
	col = L.col
	i = torch.LongTensor([row, col])

In SGL, numpy.ndarray is converted to list by .tolist() to avoid the warning.

RecBole/recbole/model/general_recommender/sgl.py

Lines 158 to 161 in 4f76169

	x = torch.sparse.FloatTensor(
	torch.LongTensor([matrix.row.tolist(), matrix.col.tolist()]),
	torch.FloatTensor(matrix.data.astype(np.float32)), matrix.shape
	).to(self.device)

I test the running time of three methods on ml-1m, and converting the list to a single numpy.ndarray with numpy.array()before converting to a tensor is the quickest.

• 含 numpy 的 list 直接转 LongTensor:

stime = time.time()
row = L.row
col = L.col
i = torch.LongTensor([row, col])
etime = time.time()
print(f'用时: {etime-stime}s')
0.3401050567626953s

• 先将 list 的 numpy 转为 list，再转 LongTensor:

stime = time.time()
row = L.row.tolist()
col = L.col.tolist()
i = torch.LongTensor([row, col])
etime = time.time()
print(f'用时: {etime-stime}s')
0.14787578582763672s

• 含 numpy 的 list 先转 numpy 再转 LongTensor:

stime = time.time()
row = L.row
col = L.col
i = torch.LongTensor(np.array([row, col]))
etime = time.time()
print(f'用时: {etime-stime}s')
0.0045320987701416016s