[Refactor] Simplify the logic of sparse_conv (#2802)

mmcv/ops/csrc/pytorch/mlu/sparse_conv_mlu.cpp

@@ -147,12 +147,8 @@ torch::Tensor IndiceConvForwardMLUKernelLauncher(
     torch::Tensor indiceNum, int64_t numActOut, int64_t _inverse,
     int64_t _subM) {
   auto indice_num_cpu = indiceNum.to({torch::kCPU});
-  auto indice_num_cpu_64 = indice_num_cpu.data_ptr<int>();
-  int indice_num_len = indiceNum.numel();
-  int64_t indice_num[indice_num_len];
-  for (int i = 0; i < indice_num_len; ++i) {
-    indice_num[i] = (int64_t)(((int *)indice_num_cpu_64)[i]);
-  }
+  auto indice_num_cpu_64 = indice_num_cpu.to(torch::kInt64);
+  auto indice_num = indice_num_cpu_64.data_ptr<int64_t>();
 
   // generate empty output
   int C = filters.dim() == 4 ? filters.size(3) : filters.size(4);
@@ -241,12 +237,8 @@ std::vector<torch::Tensor> IndiceConvBackwardMLUKernelLauncher(
     torch::Tensor indicePairs, torch::Tensor indiceNum, int64_t _inverse,
     int64_t _subM) {
   auto indice_num_cpu = indiceNum.to({torch::kCPU});
-  auto indice_num_cpu_64 = indice_num_cpu.data_ptr<int>();
-  int indice_num_len = indiceNum.numel();
-  int64_t indice_num[indice_num_len];
-  for (int i = 0; i < indice_num_len; ++i) {
-    indice_num[i] = (int64_t)(((int *)(indice_num_cpu_64))[i]);
-  }
+  auto indice_num_cpu_64 = indice_num_cpu.to(torch::kInt64);
+  auto indice_num = indice_num_cpu_64.data_ptr<int64_t>();
 
   // generate empty input_grad
   torch::Tensor input_grad = at::zeros({features.size(0), features.size(1)},