[Fix]: fix init_model_api (#1739)

demo/mono_det_demo.py

@@ -46,10 +46,7 @@ def main(args):
 
     # init visualizer
     visualizer = VISUALIZERS.build(model.cfg.visualizer)
-    visualizer.dataset_meta = {
-        'CLASSES': model.CLASSES,
-        'PALETTE': model.PALETTE
-    }
+    visualizer.dataset_meta = model.dataset_meta
 
     # test a single image
     result = inference_mono_3d_detector(model, args.img, args.ann,

demo/multi_modality_demo.py

@@ -47,10 +47,7 @@ def main(args):
 
     # init visualizer
     visualizer = VISUALIZERS.build(model.cfg.visualizer)
-    visualizer.dataset_meta = {
-        'CLASSES': model.CLASSES,
-        'PALETTE': model.PALETTE
-    }
+    visualizer.dataset_meta = model.dataset_meta
 
     # test a single image and point cloud sample
     result, data = inference_multi_modality_detector(model, args.pcd, args.img,

demo/pc_seg_demo.py

@@ -36,10 +36,7 @@ def main(args):
 
     # init visualizer
     visualizer = VISUALIZERS.build(model.cfg.visualizer)
-    visualizer.dataset_meta = {
-        'CLASSES': model.CLASSES,
-        'PALETTE': model.PALETTE
-    }
+    visualizer.dataset_meta = model.dataset_meta
 
     # test a single point cloud sample
     result, data = inference_segmentor(model, args.pcd)

demo/pcd_demo.py

@@ -39,9 +39,7 @@ def main(args):
 
     # init visualizer
     visualizer = VISUALIZERS.build(model.cfg.visualizer)
-    visualizer.dataset_meta = {
-        'CLASSES': model.CLASSES,
-    }
+    visualizer.dataset_meta = model.dataset_meta
 
     # test a single point cloud sample
     result, data = inference_detector(model, args.pcd)

mmdet3d/apis/inference.py

@@ -2,7 +2,8 @@
 import warnings
 from copy import deepcopy
 from os import path as osp
-from typing import Sequence, Union
+from pathlib import Path
+from typing import Optional, Sequence, Union
 
 import mmengine
 import numpy as np
@@ -33,36 +34,60 @@ def convert_SyncBN(config):
                 convert_SyncBN(config[item])
 
 
-def init_model(config, checkpoint=None, device='cuda:0'):
+def init_model(config: Union[str, Path, Config],
+               checkpoint: Optional[str] = None,
+               device: str = 'cuda:0',
+               cfg_options: Optional[dict] = None):
     """Initialize a model from config file, which could be a 3D detector or a
     3D segmentor.
 
     Args:
-        config (str or :obj:`mmengine.Config`): Config file path or the config
-            object.
+        config (str, :obj:`Path`, or :obj:`mmengine.Config`): Config file path,
+            :obj:`Path`, or the config object.
         checkpoint (str, optional): Checkpoint path. If left as None, the model
             will not load any weights.
         device (str): Device to use.
+        cfg_options (dict, optional): Options to override some settings in
+            the used config.
 
     Returns:
         nn.Module: The constructed detector.
     """
-    if isinstance(config, str):
+    if isinstance(config, (str, Path)):
         config = Config.fromfile(config)
     elif not isinstance(config, Config):
         raise TypeError('config must be a filename or Config object, '
                         f'but got {type(config)}')
+    if cfg_options is not None:
+        config.merge_from_dict(cfg_options)
+    elif 'init_cfg' in config.model.backbone:
+        config.model.backbone.init_cfg = None
     convert_SyncBN(config.model)
     config.model.train_cfg = None
     model = MODELS.build(config.model)
+
     if checkpoint is not None:
         checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
-        if 'CLASSES' in checkpoint['meta']:
-            model.CLASSES = checkpoint['meta']['CLASSES']
+
+        dataset_meta = checkpoint['meta'].get('dataset_meta', None)
+        # save the dataset_meta in the model for convenience
+        if 'dataset_meta' in checkpoint.get('meta', {}):
+            # mmdet3d 1.x
+            model.dataset_meta = dataset_meta
+        elif 'CLASSES' in checkpoint.get('meta', {}):
+            # < mmdet3d 1.x
+            classes = checkpoint['meta']['CLASSES']
+            model.dataset_meta = {'CLASSES': classes}
+
+            if 'PALETTE' in checkpoint.get('meta', {}):  # 3D Segmentor
+                model.dataset_meta['PALETTE'] = checkpoint['meta']['PALETTE']
         else:
-            model.CLASSES = config.class_names
-        if 'PALETTE' in checkpoint['meta']:  # 3D Segmentor
-            model.PALETTE = checkpoint['meta']['PALETTE']
+            # < mmdet3d 1.x
+            model.dataset_meta = {'CLASSES': config.class_names}
+
+            if 'PALETTE' in checkpoint.get('meta', {}):  # 3D Segmentor
+                model.dataset_meta['PALETTE'] = checkpoint['meta']['PALETTE']
+
     model.cfg = config  # save the config in the model for convenience
     if device != 'cpu':
         torch.cuda.set_device(device)