Initial snapshot: hand motion pipeline (Dyn-HaMR + dex-retargeting + SPIDER)

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>
2026-09-15 14:13:49 +08:00
commit 7e4ef6f98b
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+# Copyright (c) OpenMMLab. All rights reserved.
+from os.path import dirname, exists, join, relpath
+
+import torch
+from mmcv.runner import build_optimizer
+
+
+def _get_config_directory():
+    """Find the predefined detector config directory."""
+    try:
+        # Assume we are running in the source mmdetection repo
+        repo_dpath = dirname(dirname(__file__))
+    except NameError:
+        # For IPython development when this __file__ is not defined
+        import mmpose
+        repo_dpath = dirname(dirname(mmpose.__file__))
+    config_dpath = join(repo_dpath, 'configs')
+    if not exists(config_dpath):
+        raise Exception('Cannot find config path')
+    return config_dpath
+
+
+def test_config_build_detector():
+    """Test that all detection models defined in the configs can be
+    initialized."""
+    from mmcv import Config
+
+    from mmpose.models import build_posenet
+
+    config_dpath = _get_config_directory()
+    print(f'Found config_dpath = {config_dpath}')
+
+    import glob
+    config_fpaths = list(glob.glob(join(config_dpath, '**', '*.py')))
+    config_fpaths = [p for p in config_fpaths if p.find('_base_') == -1]
+    config_names = [relpath(p, config_dpath) for p in config_fpaths]
+
+    print(f'Using {len(config_names)} config files')
+
+    for config_fname in config_names:
+        config_fpath = join(config_dpath, config_fname)
+        config_mod = Config.fromfile(config_fpath)
+
+        print(f'Building detector, config_fpath = {config_fpath}')
+
+        # Remove pretrained keys to allow for testing in an offline environment
+        if 'pretrained' in config_mod.model:
+            config_mod.model['pretrained'] = None
+
+        detector = build_posenet(config_mod.model)
+        assert detector is not None
+
+        optimizer = build_optimizer(detector, config_mod.optimizer)
+        assert isinstance(optimizer, torch.optim.Optimizer)