Merge branch 'api_test_docstring' into 'master'

api_test_docstring and fix benchmark.md

See merge request open-mmlab/mmdet.3d!146

docs/benchmarks.md

@@ -183,7 +183,7 @@ We compare the training speed (samples/s) with other codebases if they implement
 
   ```bash
   cd tools
-  sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8  --cfg_file ./cfgs/pointpillar.yaml --batch_size 32  --workers 32
+  sh scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8  --cfg_file ./cfgs/kitti_models/pointpillar.yaml --batch_size 32  --workers 32 --epochs 80
   ```
 
 ### SECOND
@@ -198,7 +198,7 @@ We compare the training speed (samples/s) with other codebases if they implement
 
   ```bash
   cd tools
-  ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8  --cfg_file ./cfgs/second.yaml --batch_size 32  --workers 32
+  sh ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8  --cfg_file ./cfgs/kitti_models/second.yaml --batch_size 32  --workers 32 --epochs 80
   ```
 
 ### Part-A2
@@ -213,5 +213,5 @@ We compare the training speed (samples/s) with other codebases if they implement
 
   ```bash
   cd tools
-  ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8  --cfg_file ./cfgs/PartA2.yaml --batch_size 32 --workers 32
+  sh ./scripts/slurm_train.sh ${PARTITION} ${JOB_NAME} 8  --cfg_file ./cfgs/kitti_models/PartA2.yaml --batch_size 32 --workers 32 --epochs 80
   ```

mmdet3d/apis/test.py

@@ -3,6 +3,23 @@ import torch
 
 
 def single_gpu_test(model, data_loader, show=False, out_dir=None):
+    """Test model with single gpu.
+
+    This method tests model with single gpu and gives the 'show' option.
+    By setting ``show=True``, it saves the visualization results under
+    ``out_dir``.
+
+    Args:
+        model (nn.Module): Model to be tested.
+        data_loader (nn.Dataloader): Pytorch data loader.
+        show (bool): Whether to save viualization results.
+            Default: True.
+        out_dir (str): The path to save visualization results.
+            Default: None.
+
+    Returns:
+        list[dict]: The prediction results.
+    """
     model.eval()
     results = []
     dataset = data_loader.dataset