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.
+import argparse
+from functools import partial
+
+import torch
+
+from mmpose.apis.inference import init_pose_model
+
+try:
+    from mmcv.cnn import get_model_complexity_info
+except ImportError:
+    raise ImportError('Please upgrade mmcv to >0.6.2')
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description='Train a recognizer')
+    parser.add_argument('config', help='train config file path')
+    parser.add_argument(
+        '--shape',
+        type=int,
+        nargs='+',
+        default=[256, 192],
+        help='input image size')
+    parser.add_argument(
+        '--input-constructor',
+        '-c',
+        type=str,
+        choices=['none', 'batch'],
+        default='none',
+        help='If specified, it takes a callable method that generates '
+        'input. Otherwise, it will generate a random tensor with '
+        'input shape to calculate FLOPs.')
+    parser.add_argument(
+        '--batch-size', '-b', type=int, default=1, help='input batch size')
+    parser.add_argument(
+        '--not-print-per-layer-stat',
+        '-n',
+        action='store_true',
+        help='Whether to print complexity information'
+        'for each layer in a model')
+    args = parser.parse_args()
+    return args
+
+
+def batch_constructor(flops_model, batch_size, input_shape):
+    """Generate a batch of tensors to the model."""
+    batch = {}
+
+    img = torch.ones(()).new_empty(
+        (batch_size, *input_shape),
+        dtype=next(flops_model.parameters()).dtype,
+        device=next(flops_model.parameters()).device)
+
+    batch['img'] = img
+    return batch
+
+
+def main():
+
+    args = parse_args()
+
+    if len(args.shape) == 1:
+        input_shape = (3, args.shape[0], args.shape[0])
+    elif len(args.shape) == 2:
+        input_shape = (3, ) + tuple(args.shape)
+    else:
+        raise ValueError('invalid input shape')
+
+    model = init_pose_model(args.config)
+
+    if args.input_constructor == 'batch':
+        input_constructor = partial(batch_constructor, model, args.batch_size)
+    else:
+        input_constructor = None
+
+    if args.input_constructor == 'batch':
+        input_constructor = partial(batch_constructor, model, args.batch_size)
+    else:
+        input_constructor = None
+
+    if hasattr(model, 'forward_dummy'):
+        model.forward = model.forward_dummy
+    else:
+        raise NotImplementedError(
+            'FLOPs counter is currently not currently supported with {}'.
+            format(model.__class__.__name__))
+
+    flops, params = get_model_complexity_info(
+        model,
+        input_shape,
+        input_constructor=input_constructor,
+        print_per_layer_stat=(not args.not_print_per_layer_stat))
+    split_line = '=' * 30
+    input_shape = (args.batch_size, ) + input_shape
+    print(f'{split_line}\nInput shape: {input_shape}\n'
+          f'Flops: {flops}\nParams: {params}\n{split_line}')
+    print('!!!Please be cautious if you use the results in papers. '
+          'You may need to check if all ops are supported and verify that the '
+          'flops computation is correct.')
+
+
+if __name__ == '__main__':
+    main()