Issue title: multi-GPU training error
 Issue body: trying train CBGS in 8 GPU(2080ti), using the newest repo code, use follow code to start.
```
python3 -m torch.distributed.launch --nproc_per_node=8 ./tools/train.py examples/cbgs/configs/nusc_all_vfev3_spmiddleresnetfhd_rpn2_mghead_syncbn.py --work_dir=/home/ubuntu/Documents/Det3D/trained_model
```
the error looks like happend in syncBN:
```
    return SyncBatchnormFunction.apply(input, z, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.training or not self.track_running_stats, exponential_average_factor, self.process_group, self.channel_last, self.fuse_relu)
  File "/home/ubuntu/.local/lib/python3.6/site-packages/apex/parallel/optimized_sync_batchnorm_kernel.py", line 26, in forward
    mean, var_biased = syncbn.welford_mean_var(input)
RuntimeError: Dimension out of range (expected to be in range of [-2, 1], but got 2) (maybe_wrap_dim at /pytorch/c10/core/WrapDimMinimal.h:20)
frame #0: 38d1:d2b4:3d06:83e8:96a5:8c2:893d:aba5Error38d1:d2b4:3d06:83e8:96a5:8c2:893d:aba5rror(38d1:d2b4:3d06:83e8:96a5:8c2:893d:aba5SourceLocation, st38d1:d2b4:3d06:83e8:96a5:8c2:893d:aba5string const&) + 0x33 (0x7f92265a1813 in /home/ubuntu/.local/lib/python3.6/site-packages/torch/lib/libc10.so)
```

by the way, server environment are using pytorch 1.3.1 + CUDA 10.1 + python3.6
@poodarchu 
 Comments: 
 Comment 0: check your data. Dimension out of range (expected to be in range of [-2, 1], but got 2)
 Comment 1: it locate at backones(SpMiddleResNetModule which contain BN layer) occur this error, it mean I should check the forward pipeline in this part?
```
        # input: # [41, 1600, 1408]
        sparse_shape = np.array(input_shape[38d1:d2b4:3d06:83e8:96a5:8c2:893d:aba5-1]) + [1, 0, 0]
        print("sparse shape:", sparse_shape)

        coors = coors.int()
        ret = spconv.SparseConvTensor(voxel_features, coors, sparse_shape, batch_size)
        ret = self.middle_conv(ret)
        ret = ret.dense()

        N, C, D, H, W = ret.shape
        ret = ret.view(N, C * D, H, W)

        return ret
```

 Comment 2: yes, 1d conv should be used here (sync bn layer)
 Comment 3: the syncbn input tensor shape:
```
syncbn input shape: torch.Size([75100, 16])
syncbn input shape: torch.Size([76985, 16])
syncbn input shape: torch.Size([79617, 16])
syncbn input shape: torch.Size([81551, 16])
syncbn input shape: torch.Size([79113, 16])
syncbn input shape: torch.Size([68469, 16])
syncbn input shape: torch.Size([61096, 16])
syncbn input shape: torch.Size([77184, 16])
```
try to print spconv.SparseConvTensor shape info, but it can't, just use this:
```
print(" ret shape:", ret.dense().shape)
```
here is the output:
```
ret shape: torch.Size([4, 5, 41, 1008, 1008])
 ret shape: torch.Size([4, 5, 41, 1008, 1008])
 ret shape: torch.Size([4, 5, 41, 1008, 1008])
 ret shape: torch.Size([4, 5, 41, 1008, 1008])
 ret shape: torch.Size([4, 5, 41, 1008, 1008])
 ret shape: torch.Size([4, 5, 41, 1008, 1008])
 ret shape: torch.Size([4, 5, 41, 1008, 1008])
```
is there any way to debug the shape info in spconv.SparseTensor?
 Comment 4: I guess you could run it using single gpu; so the problem is relative to BatchNorm Layer; you should use batchnrom 1d .
 Comment 5: it's strange, in the code, the BN layer are set to batchnorm1d
```
## norm_cfg == None
if norm_cfg is None:
            norm_cfg = dict(type="BN1d", eps=1e-3, momentum=0.01)
``` 
but the whole network info output is:
```
(backbone): SpMiddleResNetFHD(
      (middle_conv): SparseSequential(
        (0): SubMConv3d()
        (1): SyncBatchNorm(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
        (2): ReLU()
        (3): SparseBasicBlock(
          (conv1): SubMConv3d()
          (bn1): SyncBatchNorm(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
          (relu): ReLU()
          (conv2): SubMConv3d()
          (bn2): SyncBatchNorm(16, eps=0.001, momentum=0.01, affine=True, track_running_stats=True)
```
 
 Comment 6: reinstall apex fix it
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