Merge branch 'master' into dev_v0.6

docs/GETTING_STARTED.md

@@ -94,6 +94,7 @@ and you could refer to `data/waymo/waymo_processed_data_v0_5_0` to see how many
 ```python 
 python -m pcdet.datasets.waymo.waymo_dataset --func create_waymo_infos \
     --cfg_file tools/cfgs/dataset_configs/waymo_dataset.yaml
+# Ignore 'CUDA_ERROR_NO_DEVICE' error as this process does not require GPU.
 ```
 
 Note that you do not need to install `waymo-open-dataset` if you have already processed the data before and do not need to evaluate with official Waymo Metrics. 

tools/eval_utils/eval_utils.py

@@ -19,11 +19,11 @@ def statistics_info(cfg, ret_dict, metric, disp_dict):
         '(%d, %d) / %d' % (metric['recall_roi_%s' % str(min_thresh)], metric['recall_rcnn_%s' % str(min_thresh)], metric['gt_num'])
 
 
-def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, save_to_file=False, result_dir=None):
+def eval_one_epoch(cfg, args, model, dataloader, epoch_id, logger, dist_test=False, result_dir=None):
     result_dir.mkdir(parents=True, exist_ok=True)
 
     final_output_dir = result_dir / 'final_result' / 'data'
-    if save_to_file:
+    if args.save_to_file:
         final_output_dir.mkdir(parents=True, exist_ok=True)
 
     metric = {
@@ -37,6 +37,10 @@ def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, sa
     class_names = dataset.class_names
     det_annos = []
 
+    if getattr(args, 'infer_time', False):
+        start_iter = int(len(dataloader) * 0.1)
+        infer_time_meter = common_utils.AverageMeter()
+
     logger.info('*************** EPOCH %s EVALUATION *****************' % epoch_id)
     if dist_test:
         num_gpus = torch.cuda.device_count()
@@ -53,14 +57,24 @@ def eval_one_epoch(cfg, model, dataloader, epoch_id, logger, dist_test=False, sa
     start_time = time.time()
     for i, batch_dict in enumerate(dataloader):
         load_data_to_gpu(batch_dict)
+
+        if getattr(args, 'infer_time', False):
+            start_time = time.time()
+
         with torch.no_grad():
             pred_dicts, ret_dict = model(batch_dict)
         disp_dict = {}
 
+        if getattr(args, 'infer_time', False):
+            inference_time = time.time() - start_time
+            infer_time_meter.update(inference_time * 1000)
+            # use ms to measure inference time
+            disp_dict['infer_time'] = f'{infer_time_meter.val:.2f}({infer_time_meter.avg:.2f})'
+
         statistics_info(cfg, ret_dict, metric, disp_dict)
         annos = dataset.generate_prediction_dicts(
             batch_dict, pred_dicts, class_names,
-            output_path=final_output_dir if save_to_file else None
+            output_path=final_output_dir if args.save_to_file else None
         )
         det_annos += annos
         if cfg.LOCAL_RANK == 0:

tools/process_tools/create_integrated_databse.py

@@ -0,0 +1,82 @@
+import numpy as np
+import pickle as pkl
+from pathlib import Path
+import tqdm
+import copy
+
+
+def create_integrated_db_with_infos(args, root_path):
+    """
+    Args:
+        args:
+    Returns:
+
+    """
+    # prepare
+    db_infos_path = root_path / args.src_db_info
+    db_info_global_path = str(db_infos_path)[:-4] + '_global' + '.pkl'
+    global_db_path = root_path / (args.new_db_name + '.npy')
+
+    db_infos = pkl.load(open(db_infos_path, 'rb'))
+    db_info_global = copy.deepcopy(db_infos)
+    start_idx = 0
+    global_db_list = []
+
+    for category, class_info in db_infos.items():
+        print('>>> Start processing %s' % category)
+        for idx, info in tqdm.tqdm(enumerate(class_info), total=len(class_info)):
+            obj_path = root_path / info['path']
+            obj_points = np.fromfile(str(obj_path), dtype=np.float32).reshape(
+                [-1, args.num_point_features])
+            num_points = obj_points.shape[0]
+            db_info_global[category][idx]['global_data_offset'] = (start_idx, start_idx + num_points)
+            start_idx += num_points
+            global_db_list.append(obj_points)
+
+    global_db = np.concatenate(global_db_list)
+
+    with open(global_db_path, 'wb') as f:
+        np.save(f, global_db)
+
+    with open(db_info_global_path, 'wb') as f:
+        pkl.dump(db_info_global, f)
+
+    print(f"Successfully create integrated database at {global_db_path}")
+    print(f"Successfully create integrated database info at {db_info_global_path}")
+
+    return db_info_global, global_db
+
+
+def verify(info, whole_db, root_path, num_point_features):
+    obj_path = root_path / info['path']
+    obj_points = np.fromfile(str(obj_path), dtype=np.float32).reshape([-1, num_point_features])
+    mean_origin = obj_points.mean()
+
+    start_idx, end_idx = info['global_data_offset']
+    obj_points_new = whole_db[start_idx:end_idx]
+    mean_new = obj_points_new.mean()
+
+    assert mean_origin == mean_new
+
+    print("Verification pass!")
+
+
+if __name__ == '__main__':
+    import argparse
+
+    parser = argparse.ArgumentParser(description='arg parser')
+    parser.add_argument('--root_path', type=str, default=None, help='specify the root path')
+    parser.add_argument('--src_db_info', type=str, default='waymo_processed_data_v0_5_0_waymo_dbinfos_train_sampled_1.pkl', help='')
+    parser.add_argument('--new_db_name', type=str, default='waymo_processed_data_v0_5_0_gt_database_train_sampled_1_global', help='')
+    parser.add_argument('--num_point_features', type=int, default=5,
+                        help='number of feature channels for points')
+    parser.add_argument('--class_name', type=str, default='Vehicle',
+                        help='category name for verification')
+
+    args = parser.parse_args()
+
+    root_path = Path(args.root_path)
+
+    db_infos_global, whole_db = create_integrated_db_with_infos(args, root_path)
+    # simple verify
+    verify(db_infos_global[args.class_name][0], whole_db, root_path, args.num_point_features)

tools/test.py

@@ -38,6 +38,7 @@ def parse_config():
     parser.add_argument('--eval_all', action='store_true', default=False, help='whether to evaluate all checkpoints')
     parser.add_argument('--ckpt_dir', type=str, default=None, help='specify a ckpt directory to be evaluated if needed')
     parser.add_argument('--save_to_file', action='store_true', default=False, help='')
+    parser.add_argument('--infer_time', action='store_true', default=False, help='calculate inference latency')
 
     args = parser.parse_args()
 
@@ -60,8 +61,8 @@ def eval_single_ckpt(model, test_loader, args, eval_output_dir, logger, epoch_id
 
     # start evaluation
     eval_utils.eval_one_epoch(
-        cfg, model, test_loader, epoch_id, logger, dist_test=dist_test,
-        result_dir=eval_output_dir, save_to_file=args.save_to_file
+        cfg, args, model, test_loader, epoch_id, logger, dist_test=dist_test,
+        result_dir=eval_output_dir
     )
 
 
@@ -118,8 +119,8 @@ def repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir
         # start evaluation
         cur_result_dir = eval_output_dir / ('epoch_%s' % cur_epoch_id) / cfg.DATA_CONFIG.DATA_SPLIT['test']
         tb_dict = eval_utils.eval_one_epoch(
-            cfg, model, test_loader, cur_epoch_id, logger, dist_test=dist_test,
-            result_dir=cur_result_dir, save_to_file=args.save_to_file
+            cfg, model, args, test_loader, cur_epoch_id, logger, dist_test=dist_test,
+            result_dir=cur_result_dir
         )
 
         if cfg.LOCAL_RANK == 0:
@@ -134,6 +135,10 @@ def repeat_eval_ckpt(model, test_loader, args, eval_output_dir, logger, ckpt_dir
 
 def main():
     args, cfg = parse_config()
+
+    if args.infer_time:
+        os.environ['CUDA_LAUNCH_BLOCKING'] = '1'
+
     if args.launcher == 'none':
         dist_test = False
         total_gpus = 1