Fix bug of multiple pre-processing when segmentation (PyTorch) (#645)

It is very slow in performing segmentation inference.
#531 
#234

And, it is because the dataloader will apply multiple data preprocessing
if self.cache_convert is None.

https://github.com/isl-org/Open3D-ML/blob/fcf97c07bf7a113a47d0fcf63760b245c2a2784e/ml3d/torch/dataloaders/torch_dataloader.py#L77-L83

When running the run_inference method, the cache_convert of dataloader
is None.

https://github.com/isl-org/Open3D-ML/blob/fcf97c07bf7a113a47d0fcf63760b245c2a2784e/ml3d/torch/pipelines/semantic_segmentation.py#L143-L147

This leads to extreme slowness in performing reasoning.

I've added a get_cache method to provide cache to avoid slowdowns caused
by multiple preprocessing during inference.

I tested it on a GV100 GPU with RandLA-Net on the Toronto3D dataset.
Inferencing time for a single scene is only two minutes and 37 seconds.
Reasoning is considerably faster than before

```bash
After: test 0/1: 100%|██████████████████████████████████████████████████████| 4990714/4990714 [02:37<00:00, 31769.86it/s]

Before: test 0/1:   4%|██                                                     | 187127/4990714 [05:12<2:19:39, 573.27it/s]
```

ml3d/torch/dataloaders/torch_dataloader.py

@@ -22,6 +22,7 @@ def __init__(self,
                  sampler=None,
                  use_cache=True,
                  steps_per_epoch=None,
+                 cache_convert=None,
                  **kwargs):
         """Initialize.
 
@@ -38,6 +39,7 @@ def __init__(self,
         self.dataset = dataset
         self.preprocess = preprocess
         self.steps_per_epoch = steps_per_epoch
+        self.cache_convert = cache_convert
 
         if preprocess is not None and use_cache:
             cache_dir = getattr(dataset.cfg, 'cache_dir')
@@ -59,10 +61,7 @@ def __init__(self,
                         continue
                     data = dataset.get_data(idx)
                     # cache the data
-                    self.cache_convert(name, data, attr)
-
-        else:
-            self.cache_convert = None
+                    self.cache_convert(name, data, attr)            
 
         self.transform = transform
 

ml3d/torch/pipelines/semantic_segmentation.py

@@ -136,6 +136,11 @@ def run_inference(self, data):
         model.device = device
         model.eval()
 
+        preprocess_func = model.preprocess
+        processed_data = preprocess_func(data, {'split': 'test'})
+        def get_cache(attr):
+            return processed_data
+
         batcher = self.get_batcher(device)
         infer_dataset = InferenceDummySplit(data)
         self.dataset_split = infer_dataset
@@ -144,7 +149,8 @@ def run_inference(self, data):
                                       preprocess=model.preprocess,
                                       transform=model.transform,
                                       sampler=infer_sampler,
-                                      use_cache=False)
+                                      use_cache=False,                                    
+                                      cache_convert=get_cache)
         infer_loader = DataLoader(infer_split,
                                   batch_size=cfg.batch_size,
                                   sampler=get_sampler(infer_sampler),