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 copy
+import platform
+import random
+from functools import partial
+
+import numpy as np
+from mmcv.parallel import collate
+from mmcv.runner import get_dist_info
+from mmcv.utils import Registry, build_from_cfg, is_seq_of
+from mmcv.utils.parrots_wrapper import _get_dataloader
+from torch.utils.data.dataset import ConcatDataset
+
+from .samplers import DistributedSampler
+
+if platform.system() != 'Windows':
+    # https://github.com/pytorch/pytorch/issues/973
+    import resource
+    rlimit = resource.getrlimit(resource.RLIMIT_NOFILE)
+    base_soft_limit = rlimit[0]
+    hard_limit = rlimit[1]
+    soft_limit = min(max(4096, base_soft_limit), hard_limit)
+    resource.setrlimit(resource.RLIMIT_NOFILE, (soft_limit, hard_limit))
+
+DATASETS = Registry('dataset')
+PIPELINES = Registry('pipeline')
+
+
+def _concat_dataset(cfg, default_args=None):
+    types = cfg['type']
+    ann_files = cfg['ann_file']
+    img_prefixes = cfg.get('img_prefix', None)
+    dataset_infos = cfg.get('dataset_info', None)
+
+    num_joints = cfg['data_cfg'].get('num_joints', None)
+    dataset_channel = cfg['data_cfg'].get('dataset_channel', None)
+
+    datasets = []
+    num_dset = len(ann_files)
+    for i in range(num_dset):
+        cfg_copy = copy.deepcopy(cfg)
+        cfg_copy['ann_file'] = ann_files[i]
+
+        if isinstance(types, (list, tuple)):
+            cfg_copy['type'] = types[i]
+        if isinstance(img_prefixes, (list, tuple)):
+            cfg_copy['img_prefix'] = img_prefixes[i]
+        if isinstance(dataset_infos, (list, tuple)):
+            cfg_copy['dataset_info'] = dataset_infos[i]
+
+        if isinstance(num_joints, (list, tuple)):
+            cfg_copy['data_cfg']['num_joints'] = num_joints[i]
+
+        if is_seq_of(dataset_channel, list):
+            cfg_copy['data_cfg']['dataset_channel'] = dataset_channel[i]
+
+        datasets.append(build_dataset(cfg_copy, default_args))
+
+    return ConcatDataset(datasets)
+
+
+def build_dataset(cfg, default_args=None):
+    """Build a dataset from config dict.
+
+    Args:
+        cfg (dict): Config dict. It should at least contain the key "type".
+        default_args (dict, optional): Default initialization arguments.
+            Default: None.
+
+    Returns:
+        Dataset: The constructed dataset.
+    """
+    from .dataset_wrappers import RepeatDataset
+
+    if isinstance(cfg, (list, tuple)):
+        dataset = ConcatDataset([build_dataset(c, default_args) for c in cfg])
+    elif cfg['type'] == 'ConcatDataset':
+        dataset = ConcatDataset(
+            [build_dataset(c, default_args) for c in cfg['datasets']])
+    elif cfg['type'] == 'RepeatDataset':
+        dataset = RepeatDataset(
+            build_dataset(cfg['dataset'], default_args), cfg['times'])
+    elif isinstance(cfg.get('ann_file'), (list, tuple)):
+        dataset = _concat_dataset(cfg, default_args)
+    else:
+        dataset = build_from_cfg(cfg, DATASETS, default_args)
+    return dataset
+
+
+def build_dataloader(dataset,
+                     samples_per_gpu,
+                     workers_per_gpu,
+                     num_gpus=1,
+                     dist=True,
+                     shuffle=True,
+                     seed=None,
+                     drop_last=True,
+                     pin_memory=True,
+                     **kwargs):
+    """Build PyTorch DataLoader.
+
+    In distributed training, each GPU/process has a dataloader.
+    In non-distributed training, there is only one dataloader for all GPUs.
+
+    Args:
+        dataset (Dataset): A PyTorch dataset.
+        samples_per_gpu (int): Number of training samples on each GPU, i.e.,
+            batch size of each GPU.
+        workers_per_gpu (int): How many subprocesses to use for data loading
+            for each GPU.
+        num_gpus (int): Number of GPUs. Only used in non-distributed training.
+        dist (bool): Distributed training/test or not. Default: True.
+        shuffle (bool): Whether to shuffle the data at every epoch.
+            Default: True.
+        drop_last (bool): Whether to drop the last incomplete batch in epoch.
+            Default: True
+        pin_memory (bool): Whether to use pin_memory in DataLoader.
+            Default: True
+        kwargs: any keyword argument to be used to initialize DataLoader
+
+    Returns:
+        DataLoader: A PyTorch dataloader.
+    """
+    rank, world_size = get_dist_info()
+    if dist:
+        sampler = DistributedSampler(
+            dataset, world_size, rank, shuffle=shuffle, seed=seed)
+        shuffle = False
+        batch_size = samples_per_gpu
+        num_workers = workers_per_gpu
+    else:
+        sampler = None
+        batch_size = num_gpus * samples_per_gpu
+        num_workers = num_gpus * workers_per_gpu
+
+    init_fn = partial(
+        worker_init_fn, num_workers=num_workers, rank=rank,
+        seed=seed) if seed is not None else None
+
+    _, DataLoader = _get_dataloader()
+    data_loader = DataLoader(
+        dataset,
+        batch_size=batch_size,
+        sampler=sampler,
+        num_workers=num_workers,
+        collate_fn=partial(collate, samples_per_gpu=samples_per_gpu),
+        pin_memory=pin_memory,
+        shuffle=shuffle,
+        worker_init_fn=init_fn,
+        drop_last=drop_last,
+        **kwargs)
+
+    return data_loader
+
+
+def worker_init_fn(worker_id, num_workers, rank, seed):
+    """Init the random seed for various workers."""
+    # The seed of each worker equals to
+    # num_worker * rank + worker_id + user_seed
+    worker_seed = num_workers * rank + worker_id + seed
+    np.random.seed(worker_seed)
+    random.seed(worker_seed)