Merge branch 'master' into v1.0.0.dev0

docs_zh-CN/tutorials/customize_runtime.md

@@ -121,7 +121,7 @@ class MyOptimizerConstructor(object):
         _delete_=True, grad_clip=dict(max_norm=35, norm_type=2))
     ```
 
-    如果您的配置继承了一个已经设置了 `optimizer_config` 的基础配置，那么您可能需要 `_delete_=True` 字段来覆盖基础配置中无用的设置。详见配置文件的[说明文档](https://mmdetection.readthedocs.io/en/latest/tutorials/config.html)。
+    如果您的配置继承了一个已经设置了 `optimizer_config` 的基础配置，那么您可能需要 `_delete_=True` 字段来覆盖基础配置中无用的设置。详见配置文件的[说明文档](https://mmdetection.readthedocs.io/zh_CN/latest/tutorials/config.html)。
 
 - __使用动量规划器 (momentum scheduler) 来加速模型收敛__：
 

docs_zh-CN/useful_tools.md

@@ -266,7 +266,7 @@ python -u tools/data_converter/nuimage_converter.py --data-root ${DATA_ROOT} --v
 -   `--nproc`: 数据准备的进程数，默认为 `4`。由于图片是并行处理的，更大的进程数目能够减少准备时间。
 -   `--extra-tag`: 注释的额外标签，默认为 `nuimages`。这可用于将不同时间处理的不同注释分开以供研究。
 
-更多的数据准备细节参考 [doc](https://mmdetection3d.readthedocs.io/en/latest/data_preparation.html)，nuImages 数据集的细节参考 [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md/)。
+更多的数据准备细节参考 [doc](https://mmdetection3d.readthedocs.io/zh_CN/latest/data_preparation.html)，nuImages 数据集的细节参考 [README](https://github.com/open-mmlab/mmdetection3d/blob/master/configs/nuimages/README.md/)。
 
  
 

mmdet3d/apis/inference.py

@@ -57,7 +57,7 @@ def init_model(config, checkpoint=None, device='cuda:0'):
     config.model.train_cfg = None
     model = build_model(config.model, test_cfg=config.get('test_cfg'))
     if checkpoint is not None:
-        checkpoint = load_checkpoint(model, checkpoint)
+        checkpoint = load_checkpoint(model, checkpoint, map_location='cpu')
         if 'CLASSES' in checkpoint['meta']:
             model.CLASSES = checkpoint['meta']['CLASSES']
         else:

mmdet3d/core/visualizer/show_result.py

@@ -77,7 +77,7 @@ def show_result(points,
                 pred_bboxes,
                 out_dir,
                 filename,
-                show=True,
+                show=False,
                 snapshot=False):
     """Convert results into format that is directly readable for meshlab.
 

mmdet3d/datasets/nuscenes_mono_dataset.py

@@ -781,7 +781,7 @@ def nusc_box_to_cam_box3d(boxes):
     dims = torch.Tensor([b.wlh for b in boxes]).view(-1, 3)
     rots = torch.Tensor([b.orientation.yaw_pitch_roll[0]
                          for b in boxes]).view(-1, 1)
-    velocity = torch.Tensor([b.velocity[:2] for b in boxes]).view(-1, 2)
+    velocity = torch.Tensor([b.velocity[0::2] for b in boxes]).view(-1, 2)
 
     # convert nusbox to cambox convention
     dims[:, [0, 1, 2]] = dims[:, [1, 2, 0]]

mmdet3d/version.py

 # Copyright (c) Open-MMLab. All rights reserved.
 
-__version__ = '0.17.1'
+__version__ = '0.17.2'
 short_version = __version__
 
 

tools/create_data.py

@@ -171,7 +171,7 @@ def waymo_data_prep(root_path,
             save_dir,
             prefix=str(i),
             workers=workers,
-            test_mode=(split == 'test'))
+            test_mode=(split == 'testing'))
         converter.convert()
     # Generate waymo infos
     out_dir = osp.join(out_dir, 'kitti_format')

tools/model_converters/convert_h3dnet_checkpoints.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import tempfile
+import torch
+from mmcv import Config
+from mmcv.runner import load_state_dict
+
+from mmdet3d.models import build_detector
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMDet3D upgrade model version(before v0.6.0) of H3DNet')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--out', help='path of the output checkpoint file')
+    args = parser.parse_args()
+    return args
+
+
+def parse_config(config_strings):
+    """Parse config from strings.
+
+    Args:
+        config_strings (string): strings of model config.
+
+    Returns:
+        Config: model config
+    """
+    temp_file = tempfile.NamedTemporaryFile()
+    config_path = f'{temp_file.name}.py'
+    with open(config_path, 'w') as f:
+        f.write(config_strings)
+
+    config = Config.fromfile(config_path)
+
+    # Update backbone config
+    if 'pool_mod' in config.model.backbone.backbones:
+        config.model.backbone.backbones.pop('pool_mod')
+
+    if 'sa_cfg' not in config.model.backbone:
+        config.model.backbone['sa_cfg'] = dict(
+            type='PointSAModule',
+            pool_mod='max',
+            use_xyz=True,
+            normalize_xyz=True)
+
+    if 'type' not in config.model.rpn_head.vote_aggregation_cfg:
+        config.model.rpn_head.vote_aggregation_cfg['type'] = 'PointSAModule'
+
+    # Update rpn_head config
+    if 'pred_layer_cfg' not in config.model.rpn_head:
+        config.model.rpn_head['pred_layer_cfg'] = dict(
+            in_channels=128, shared_conv_channels=(128, 128), bias=True)
+
+    if 'feat_channels' in config.model.rpn_head:
+        config.model.rpn_head.pop('feat_channels')
+
+    if 'vote_moudule_cfg' in config.model.rpn_head:
+        config.model.rpn_head['vote_module_cfg'] = config.model.rpn_head.pop(
+            'vote_moudule_cfg')
+
+    if config.model.rpn_head.vote_aggregation_cfg.use_xyz:
+        config.model.rpn_head.vote_aggregation_cfg.mlp_channels[0] -= 3
+
+    for cfg in config.model.roi_head.primitive_list:
+        cfg['vote_module_cfg'] = cfg.pop('vote_moudule_cfg')
+        cfg.vote_aggregation_cfg.mlp_channels[0] -= 3
+        if 'type' not in cfg.vote_aggregation_cfg:
+            cfg.vote_aggregation_cfg['type'] = 'PointSAModule'
+
+    if 'type' not in config.model.roi_head.bbox_head.suface_matching_cfg:
+        config.model.roi_head.bbox_head.suface_matching_cfg[
+            'type'] = 'PointSAModule'
+
+    if config.model.roi_head.bbox_head.suface_matching_cfg.use_xyz:
+        config.model.roi_head.bbox_head.suface_matching_cfg.mlp_channels[
+            0] -= 3
+
+    if 'type' not in config.model.roi_head.bbox_head.line_matching_cfg:
+        config.model.roi_head.bbox_head.line_matching_cfg[
+            'type'] = 'PointSAModule'
+
+    if config.model.roi_head.bbox_head.line_matching_cfg.use_xyz:
+        config.model.roi_head.bbox_head.line_matching_cfg.mlp_channels[0] -= 3
+
+    if 'proposal_module_cfg' in config.model.roi_head.bbox_head:
+        config.model.roi_head.bbox_head.pop('proposal_module_cfg')
+
+    temp_file.close()
+
+    return config
+
+
+def main():
+    """Convert keys in checkpoints for VoteNet.
+
+    There can be some breaking changes during the development of mmdetection3d,
+    and this tool is used for upgrading checkpoints trained with old versions
+    (before v0.6.0) to the latest one.
+    """
+    args = parse_args()
+    checkpoint = torch.load(args.checkpoint)
+    cfg = parse_config(checkpoint['meta']['config'])
+    # Build the model and load checkpoint
+    model = build_detector(
+        cfg.model,
+        train_cfg=cfg.get('train_cfg'),
+        test_cfg=cfg.get('test_cfg'))
+    orig_ckpt = checkpoint['state_dict']
+    converted_ckpt = orig_ckpt.copy()
+
+    if cfg['dataset_type'] == 'ScanNetDataset':
+        NUM_CLASSES = 18
+    elif cfg['dataset_type'] == 'SUNRGBDDataset':
+        NUM_CLASSES = 10
+    else:
+        raise NotImplementedError
+
+    RENAME_PREFIX = {
+        'rpn_head.conv_pred.0': 'rpn_head.conv_pred.shared_convs.layer0',
+        'rpn_head.conv_pred.1': 'rpn_head.conv_pred.shared_convs.layer1'
+    }
+
+    DEL_KEYS = [
+        'rpn_head.conv_pred.0.bn.num_batches_tracked',
+        'rpn_head.conv_pred.1.bn.num_batches_tracked'
+    ]
+
+    EXTRACT_KEYS = {
+        'rpn_head.conv_pred.conv_cls.weight':
+        ('rpn_head.conv_pred.conv_out.weight', [(0, 2), (-NUM_CLASSES, -1)]),
+        'rpn_head.conv_pred.conv_cls.bias':
+        ('rpn_head.conv_pred.conv_out.bias', [(0, 2), (-NUM_CLASSES, -1)]),
+        'rpn_head.conv_pred.conv_reg.weight':
+        ('rpn_head.conv_pred.conv_out.weight', [(2, -NUM_CLASSES)]),
+        'rpn_head.conv_pred.conv_reg.bias':
+        ('rpn_head.conv_pred.conv_out.bias', [(2, -NUM_CLASSES)])
+    }
+
+    # Delete some useless keys
+    for key in DEL_KEYS:
+        converted_ckpt.pop(key)
+
+    # Rename keys with specific prefix
+    RENAME_KEYS = dict()
+    for old_key in converted_ckpt.keys():
+        for rename_prefix in RENAME_PREFIX.keys():
+            if rename_prefix in old_key:
+                new_key = old_key.replace(rename_prefix,
+                                          RENAME_PREFIX[rename_prefix])
+                RENAME_KEYS[new_key] = old_key
+    for new_key, old_key in RENAME_KEYS.items():
+        converted_ckpt[new_key] = converted_ckpt.pop(old_key)
+
+    # Extract weights and rename the keys
+    for new_key, (old_key, indices) in EXTRACT_KEYS.items():
+        cur_layers = orig_ckpt[old_key]
+        converted_layers = []
+        for (start, end) in indices:
+            if end != -1:
+                converted_layers.append(cur_layers[start:end])
+            else:
+                converted_layers.append(cur_layers[start:])
+        converted_layers = torch.cat(converted_layers, 0)
+        converted_ckpt[new_key] = converted_layers
+        if old_key in converted_ckpt.keys():
+            converted_ckpt.pop(old_key)
+
+    # Check the converted checkpoint by loading to the model
+    load_state_dict(model, converted_ckpt, strict=True)
+    checkpoint['state_dict'] = converted_ckpt
+    torch.save(checkpoint, args.out)
+
+
+if __name__ == '__main__':
+    main()