abstract load_data_to_gpu function in train/test (#116)

* abstract load_data_to_gpu function in train/test

pcdet/datasets/dataset.py

@@ -144,22 +144,26 @@ class DatasetTemplate(torch_data.Dataset):
         ret = {}
 
         for key, val in data_dict.items():
-            if key in ['voxels', 'voxel_num_points']:
-                ret[key] = np.concatenate(val, axis=0)
-            elif key in ['points', 'voxel_coords']:
-                coors = []
-                for i, coor in enumerate(val):
-                    coor_pad = np.pad(coor, ((0, 0), (1, 0)), mode='constant', constant_values=i)
-                    coors.append(coor_pad)
-                ret[key] = np.concatenate(coors, axis=0)
-            elif key in ['gt_boxes']:
-                max_gt = max([len(x) for x in val])
-                batch_gt_boxes3d = np.zeros((batch_size, max_gt, val[0].shape[-1]), dtype=np.float32)
-                for k in range(batch_size):
-                    batch_gt_boxes3d[k, :val[k].__len__(), :] = val[k]
-                ret[key] = batch_gt_boxes3d
-            else:
-                ret[key] = np.stack(val, axis=0)
+            try:
+                if key in ['voxels', 'voxel_num_points']:
+                    ret[key] = np.concatenate(val, axis=0)
+                elif key in ['points', 'voxel_coords']:
+                    coors = []
+                    for i, coor in enumerate(val):
+                        coor_pad = np.pad(coor, ((0, 0), (1, 0)), mode='constant', constant_values=i)
+                        coors.append(coor_pad)
+                    ret[key] = np.concatenate(coors, axis=0)
+                elif key in ['gt_boxes']:
+                    max_gt = max([len(x) for x in val])
+                    batch_gt_boxes3d = np.zeros((batch_size, max_gt, val[0].shape[-1]), dtype=np.float32)
+                    for k in range(batch_size):
+                        batch_gt_boxes3d[k, :val[k].__len__(), :] = val[k]
+                    ret[key] = batch_gt_boxes3d
+                else:
+                    ret[key] = np.stack(val, axis=0)
+            except:
+                print('Error in collate_batch: key=%s' % key)
+                raise TypeError
 
         ret['batch_size'] = batch_size
         return ret

pcdet/models/__init__.py

@@ -11,16 +11,20 @@ def build_network(model_cfg, num_class, dataset):
     return model
 
 
+def load_data_to_gpu(batch_dict):
+    for key, val in batch_dict.items():
+        if not isinstance(val, np.ndarray):
+            continue
+        if key in ['frame_id', 'metadata', 'calib', 'image_shape']:
+            continue
+        batch_dict[key] = torch.from_numpy(val).float().cuda()
+
+
 def model_fn_decorator():
     ModelReturn = namedtuple('ModelReturn', ['loss', 'tb_dict', 'disp_dict'])
 
     def model_func(model, batch_dict):
-        for key, val in batch_dict.items():
-            if not isinstance(val, np.ndarray):
-                continue
-            if key in ['frame_id']:
-                continue
-            batch_dict[key] = torch.from_numpy(val).float().cuda()
+        load_data_to_gpu(batch_dict)
         ret_dict, tb_dict, disp_dict = model(batch_dict)
 
         loss = ret_dict['loss'].mean()

tools/eval_utils/eval_utils.py

@@ -4,6 +4,7 @@ import pickle
 import numpy as np
 import torch
 from pcdet.utils import common_utils
+from pcdet.models import load_data_to_gpu
 
 
 def statistics_info(cfg, ret_dict, metric, disp_dict):
@@ -51,13 +52,7 @@ def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, sa
         progress_bar = tqdm.tqdm(total=len(dataloader), leave=True, desc='eval', dynamic_ncols=True)
     start_time = time.time()
     for i, batch_dict in enumerate(dataloader):
-        for key, val in batch_dict.items():
-            if not isinstance(val, np.ndarray):
-                continue
-            if key in ['frame_id', 'calib', 'image_shape']:
-                continue
-            batch_dict[key] = torch.from_numpy(val).float().cuda()
-
+        load_data_to_gpu(batch_dict)
         with torch.no_grad():
             pred_dicts, ret_dict = model(batch_dict)
         disp_dict = {}