[Fix] add H3DNet checkpoint converter

configs/h3dnet/README.md

@@ -22,3 +22,11 @@ We implement H3DNet and provide the result and checkpoints on ScanNet datasets.
 |  Backbone   | Lr schd | Mem (GB) | Inf time (fps) | AP@0.25 |AP@0.5| Download |
 | :---------: | :-----: | :------: | :------------: | :----: |:----: | :------: |
 |    [MultiBackbone](./h3dnet_3x8_scannet-3d-18class.py)     |  3x    |7.9||66.43|48.01|[model](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/h3dnet/h3dnet_scannet-3d-18class/h3dnet_scannet-3d-18class_20200830_000136-02e36246.pth) | [log](https://download.openmmlab.com/mmdetection3d/v0.1.0_models/h3dnet/h3dnet_scannet-3d-18class/h3dnet_scannet-3d-18class_20200830_000136.log.json) |
+
+**Notice**: If your current mmdetection3d version >= 0.6.0, and you are using the checkpoints downloaded from the above links or using checkpoints trained with mmdetection3d version < 0.6.0, the checkpoints have to be first converted via [tools/model_converters/convert_h3dnet_checkpoints.py](../../tools/model_converters/convert_h3dnet_checkpoints.py):
+
+```
+python ./tools/model_converters/convert_h3dnet_checkpoints.py ${ORIGINAL_CHECKPOINT_PATH} --out=${NEW_CHECKPOINT_PATH}
+```
+
+Then you can use the converted checkpoints following [getting_started.md](../../docs/getting_started.md).

docs/compatibility.md

@@ -75,6 +75,6 @@ Please refer to the SUNRGBD [README.md](https://github.com/open-mmlab/mmdetectio
 
 ## 0.6.0
 
-### VoteNet model structure update
+### VoteNet and H3DNet model structure update
 
-In MMDetection 0.6.0, we updated the model structure of VoteNet, therefore model checkpoints generated by MMDetection < 0.6.0 should be first converted to a format compatible with the latest VoteNet structure via this [script](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_votenet_checkpoints.py). For more details, please refer to the VoteNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet/README.md/)
+In MMDetection 0.6.0, we updated the model structures of VoteNet and H3DNet, therefore model checkpoints generated by MMDetection < 0.6.0 should be first converted to a format compatible with the latest structures via [convert_votenet_checkpoints.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_votenet_checkpoints.py) and [convert_h3dnet_checkpoints.py](https://github.com/open-mmlab/mmdetection3d/blob/master/tools/model_converters/convert_h3dnet_checkpoints.py) . For more details, please refer to the VoteNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/votenet/README.md/) and H3DNet [README.md](https://github.com/open-mmlab/mmdetection3d/tree/master/configs/h3dnet/README.md/).

tools/model_converters/convert_h3dnet_checkpoints.py

@@ -0,0 +1,176 @@
+# 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()