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

Co-Authored-By: Claude Fable 5 <noreply@anthropic.com>

third_party/hamer/third-party/ViTPose/mmpose/datasets/samplers/__init__.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+from .distributed_sampler import DistributedSampler
+
+__all__ = ['DistributedSampler']

third_party/hamer/third-party/ViTPose/mmpose/datasets/samplers/distributed_sampler.py

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+# Copyright (c) OpenMMLab. All rights reserved.
+import torch
+from torch.utils.data import DistributedSampler as _DistributedSampler
+
+
+class DistributedSampler(_DistributedSampler):
+    """DistributedSampler inheriting from
+    `torch.utils.data.DistributedSampler`.
+
+    In pytorch of lower versions, there is no `shuffle` argument. This child
+    class will port one to DistributedSampler.
+    """
+
+    def __init__(self,
+                 dataset,
+                 num_replicas=None,
+                 rank=None,
+                 shuffle=True,
+                 seed=0):
+        super().__init__(
+            dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle)
+        # for the compatibility from PyTorch 1.3+
+        self.seed = seed if seed is not None else 0
+
+    def __iter__(self):
+        """Deterministically shuffle based on epoch."""
+        if self.shuffle:
+            g = torch.Generator()
+            g.manual_seed(self.epoch + self.seed)
+            indices = torch.randperm(len(self.dataset), generator=g).tolist()
+        else:
+            indices = torch.arange(len(self.dataset)).tolist()
+
+        # add extra samples to make it evenly divisible
+        indices += indices[:(self.total_size - len(indices))]
+        assert len(indices) == self.total_size
+
+        # subsample
+        indices = indices[self.rank:self.total_size:self.num_replicas]
+        assert len(indices) == self.num_samples
+        return iter(indices)