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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+#!/usr/bin/env bash
+# Copyright (c) OpenMMLab. All rights reserved.
+import argparse
+import time
+
+import torch
+from mmcv import Config
+from mmcv.cnn import fuse_conv_bn
+from mmcv.parallel import MMDataParallel
+from mmcv.runner.fp16_utils import wrap_fp16_model
+
+from mmpose.datasets import build_dataloader, build_dataset
+from mmpose.models import build_posenet
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description='MMPose benchmark a recognizer')
+    parser.add_argument('config', help='test config file path')
+    parser.add_argument('--bz', default=32, type=int, help='test config file path')
+    args = parser.parse_args()
+    return args
+
+
+def main():
+    args = parse_args()
+
+    cfg = Config.fromfile(args.config)
+
+    # Since we only care about the forward speed of the network
+    cfg.model.pretrained=None
+    cfg.model.test_cfg.flip_test=False
+    cfg.model.test_cfg.use_udp=False
+    cfg.model.test_cfg.post_process='none'
+
+    # set cudnn_benchmark
+    if cfg.get('cudnn_benchmark', False):
+        torch.backends.cudnn.benchmark = True
+
+    # build the dataloader
+    dataset = build_dataset(cfg.data.val)
+    data_loader = build_dataloader(
+        dataset,
+        samples_per_gpu=args.bz,
+        workers_per_gpu=cfg.data.workers_per_gpu,
+        dist=False,
+        shuffle=False)
+
+    # build the model and load checkpoint
+    model = build_posenet(cfg.model)
+    model = MMDataParallel(model, device_ids=[0])
+    model.eval()
+    
+    # get the example data
+    for i, data in enumerate(data_loader):
+        break
+
+    # the first several iterations may be very slow so skip them
+    num_warmup = 100
+    inference_times = 100
+
+    with torch.no_grad():
+        start_time = time.perf_counter()
+      
+        for i in range(num_warmup):
+            torch.cuda.synchronize()            
+            model(return_loss=False, **data)
+            torch.cuda.synchronize()
+        
+        elapsed = time.perf_counter() - start_time
+        print(f'warmup cost {elapsed} time')
+
+        start_time = time.perf_counter()
+        
+        for i in range(inference_times):
+            torch.cuda.synchronize()
+            model(return_loss=False, **data)
+            torch.cuda.synchronize()
+
+        elapsed = time.perf_counter() - start_time
+        fps = args.bz * inference_times / elapsed
+        print(f'the fps is {fps}')
+
+
+if __name__ == '__main__':
+    main()