[Feature] Support nuimages visualization (#408)

* support nuimages visualization

* recover config

* support nuimages visualization

* support batchsize > 1

* add comments

mmdet3d/apis/test.py

 import mmcv
+import os
 import torch
+from mmcv.image import tensor2imgs
 
 
-def single_gpu_test(model, data_loader, show=False, out_dir=None):
+def single_gpu_test(model,
+                    data_loader,
+                    show=False,
+                    out_dir=None,
+                    show_score_thr=0.3):
     """Test model with single gpu.
 
     This method tests model with single gpu and gives the 'show' option.
@@ -29,8 +35,42 @@ def single_gpu_test(model, data_loader, show=False, out_dir=None):
             result = model(return_loss=False, rescale=True, **data)
 
         if show:
-            model.module.show_results(data, result, out_dir)
+            # Visualize the results of MMdetection3D model
+            # 'show_results' is MMdetection3D visualization API
+            if hasattr(model.module, 'show_results'):
+                model.module.show_results(data, result, out_dir)
+            # Visualize the results of MMdetection model
+            # 'show_result' is MMdetection visualization API
+            else:
+                batch_size = len(result)
+                if batch_size == 1 and isinstance(data['img'][0],
+                                                  torch.Tensor):
+                    img_tensor = data['img'][0]
+                else:
+                    img_tensor = data['img'][0].data[0]
+                img_metas = data['img_metas'][0].data[0]
+                imgs = tensor2imgs(img_tensor, **img_metas[0]['img_norm_cfg'])
+                assert len(imgs) == len(img_metas)
 
+                for i, (img, img_meta) in enumerate(zip(imgs, img_metas)):
+                    h, w, _ = img_meta['img_shape']
+                    img_show = img[:h, :w, :]
+
+                    ori_h, ori_w = img_meta['ori_shape'][:-1]
+                    img_show = mmcv.imresize(img_show, (ori_w, ori_h))
+
+                    if out_dir:
+                        out_file = os.path.join(out_dir,
+                                                img_meta['ori_filename'])
+                    else:
+                        out_file = None
+
+                    model.module.show_result(
+                        img_show,
+                        result[i],
+                        show=show,
+                        out_file=out_file,
+                        score_thr=show_score_thr)
         results.extend(result)
 
         batch_size = len(result)