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
7002 changed files with 1452249 additions and 0 deletions
@@ -0,0 +1,165 @@
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import warnings
+
+import numpy as np
+import torch
+
+from mmpose.apis import init_pose_model
+
+try:
+    import onnx
+    import onnxruntime as rt
+except ImportError as e:
+    raise ImportError(f'Please install onnx and onnxruntime first. {e}')
+
+try:
+    from mmcv.onnx.symbolic import register_extra_symbolics
+except ModuleNotFoundError:
+    raise NotImplementedError('please update mmcv to version>=1.0.4')
+
+
+def _convert_batchnorm(module):
+    """Convert the syncBNs into normal BN3ds."""
+    module_output = module
+    if isinstance(module, torch.nn.SyncBatchNorm):
+        module_output = torch.nn.BatchNorm3d(module.num_features, module.eps,
+                                             module.momentum, module.affine,
+                                             module.track_running_stats)
+        if module.affine:
+            module_output.weight.data = module.weight.data.clone().detach()
+            module_output.bias.data = module.bias.data.clone().detach()
+            # keep requires_grad unchanged
+            module_output.weight.requires_grad = module.weight.requires_grad
+            module_output.bias.requires_grad = module.bias.requires_grad
+        module_output.running_mean = module.running_mean
+        module_output.running_var = module.running_var
+        module_output.num_batches_tracked = module.num_batches_tracked
+    for name, child in module.named_children():
+        module_output.add_module(name, _convert_batchnorm(child))
+    del module
+    return module_output
+
+
+def pytorch2onnx(model,
+                 input_shape,
+                 opset_version=11,
+                 show=False,
+                 output_file='tmp.onnx',
+                 verify=False):
+    """Convert pytorch model to onnx model.
+
+    Args:
+        model (:obj:`nn.Module`): The pytorch model to be exported.
+        input_shape (tuple[int]): The input tensor shape of the model.
+        opset_version (int): Opset version of onnx used. Default: 11.
+        show (bool): Determines whether to print the onnx model architecture.
+            Default: False.
+        output_file (str): Output onnx model name. Default: 'tmp.onnx'.
+        verify (bool): Determines whether to verify the onnx model.
+            Default: False.
+    """
+    model.cpu().eval()
+
+    one_img = torch.randn(input_shape)
+
+    register_extra_symbolics(opset_version)
+    torch.onnx.export(
+        model,
+        one_img,
+        output_file,
+        export_params=True,
+        keep_initializers_as_inputs=True,
+        verbose=show,
+        opset_version=opset_version)
+
+    print(f'Successfully exported ONNX model: {output_file}')
+    if verify:
+        # check by onnx
+        onnx_model = onnx.load(output_file)
+        onnx.checker.check_model(onnx_model)
+
+        # check the numerical value
+        # get pytorch output
+        pytorch_results = model(one_img)
+        if not isinstance(pytorch_results, (list, tuple)):
+            assert isinstance(pytorch_results, torch.Tensor)
+            pytorch_results = [pytorch_results]
+
+        # get onnx output
+        input_all = [node.name for node in onnx_model.graph.input]
+        input_initializer = [
+            node.name for node in onnx_model.graph.initializer
+        ]
+        net_feed_input = list(set(input_all) - set(input_initializer))
+        assert len(net_feed_input) == 1
+        sess = rt.InferenceSession(output_file)
+        onnx_results = sess.run(None,
+                                {net_feed_input[0]: one_img.detach().numpy()})
+
+        # compare results
+        assert len(pytorch_results) == len(onnx_results)
+        for pt_result, onnx_result in zip(pytorch_results, onnx_results):
+            assert np.allclose(
+                pt_result.detach().cpu(), onnx_result, atol=1.e-5
+            ), 'The outputs are different between Pytorch and ONNX'
+        print('The numerical values are same between Pytorch and ONNX')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='Convert MMPose models to ONNX')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('checkpoint', help='checkpoint file')
+    parser.add_argument('--show', action='store_true', help='show onnx graph')
+    parser.add_argument('--output-file', type=str, default='tmp.onnx')
+    parser.add_argument('--opset-version', type=int, default=11)
+    parser.add_argument(
+        '--verify',
+        action='store_true',
+        help='verify the onnx model output against pytorch output')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[1, 3, 256, 192],
+        help='input size')
+    args = parser.parse_args()
+    return args
+
+
+if __name__ == '__main__':
+    args = parse_args()
+
+    assert args.opset_version == 11, 'MMPose only supports opset 11 now'
+
+    # Following strings of text style are from colorama package
+    bright_style, reset_style = '\x1b[1m', '\x1b[0m'
+    red_text, blue_text = '\x1b[31m', '\x1b[34m'
+    white_background = '\x1b[107m'
+
+    msg = white_background + bright_style + red_text
+    msg += 'DeprecationWarning: This tool will be deprecated in future. '
+    msg += blue_text + 'Welcome to use the unified model deployment toolbox '
+    msg += 'MMDeploy: https://github.com/open-mmlab/mmdeploy'
+    msg += reset_style
+    warnings.warn(msg)
+
+    model = init_pose_model(args.config, args.checkpoint, device='cpu')
+    model = _convert_batchnorm(model)
+
+    # onnx.export does not support kwargs
+    if hasattr(model, 'forward_dummy'):
+        model.forward = model.forward_dummy
+    else:
+        raise NotImplementedError(
+            'Please implement the forward method for exporting.')
+
+    # convert model to onnx file
+    pytorch2onnx(
+        model,
+        args.shape,
+        opset_version=args.opset_version,
+        show=args.show,
+        output_file=args.output_file,
+        verify=args.verify)