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| import torch | |
| # reference: https://github.com/jaywalnut310/vits/blob/main/data_utils.py | |
| class DistributedBucketSampler(torch.utils.data.distributed.DistributedSampler): | |
| """ | |
| Maintain similar input lengths in a batch. | |
| Length groups are specified by boundaries. | |
| Ex) boundaries = [b1, b2, b3] -> any batch is included either {x | b1 < length(x) <=b2} or {x | b2 < length(x) <= b3}. | |
| It removes samples which are not included in the boundaries. | |
| Ex) boundaries = [b1, b2, b3] -> any x s.t. length(x) <= b1 or length(x) > b3 are discarded. | |
| """ | |
| def __init__( | |
| self, | |
| dataset, | |
| batch_size, | |
| boundaries, | |
| num_replicas=None, | |
| rank=None, | |
| shuffle=True, | |
| ): | |
| super().__init__(dataset, num_replicas=num_replicas, rank=rank, shuffle=shuffle) | |
| self.lengths = dataset.lengths | |
| self.batch_size = batch_size | |
| self.boundaries = boundaries | |
| self.buckets, self.num_samples_per_bucket = self._create_buckets() | |
| self.total_size = sum(self.num_samples_per_bucket) | |
| self.num_samples = self.total_size // self.num_replicas | |
| def _create_buckets(self): | |
| buckets = [[] for _ in range(len(self.boundaries) - 1)] | |
| for i in range(len(self.lengths)): | |
| length = self.lengths[i] | |
| idx_bucket = self._bisect(length) | |
| if idx_bucket != -1: | |
| buckets[idx_bucket].append(i) | |
| for i in range(len(buckets) - 1, 0, -1): | |
| # for i in range(len(buckets) - 1, -1, -1): | |
| if len(buckets[i]) == 0: | |
| buckets.pop(i) | |
| self.boundaries.pop(i + 1) | |
| num_samples_per_bucket = [] | |
| for i in range(len(buckets)): | |
| len_bucket = len(buckets[i]) | |
| total_batch_size = self.num_replicas * self.batch_size | |
| rem = ( | |
| total_batch_size - (len_bucket % total_batch_size) | |
| ) % total_batch_size | |
| num_samples_per_bucket.append(len_bucket + rem) | |
| return buckets, num_samples_per_bucket | |
| def __iter__(self): | |
| # deterministically shuffle based on epoch | |
| g = torch.Generator() | |
| g.manual_seed(self.epoch) | |
| indices = [] | |
| if self.shuffle: | |
| for bucket in self.buckets: | |
| indices.append(torch.randperm(len(bucket), generator=g).tolist()) | |
| else: | |
| for bucket in self.buckets: | |
| indices.append(list(range(len(bucket)))) | |
| batches = [] | |
| for i in range(len(self.buckets)): | |
| bucket = self.buckets[i] | |
| len_bucket = len(bucket) | |
| ids_bucket = indices[i] | |
| num_samples_bucket = self.num_samples_per_bucket[i] | |
| # add extra samples to make it evenly divisible | |
| rem = num_samples_bucket - len_bucket | |
| ids_bucket = ( | |
| ids_bucket | |
| + ids_bucket * (rem // len_bucket) | |
| + ids_bucket[: (rem % len_bucket)] | |
| ) | |
| # subsample | |
| ids_bucket = ids_bucket[self.rank :: self.num_replicas] | |
| # batching | |
| for j in range(len(ids_bucket) // self.batch_size): | |
| batch = [ | |
| bucket[idx] | |
| for idx in ids_bucket[ | |
| j * self.batch_size : (j + 1) * self.batch_size | |
| ] | |
| ] | |
| batches.append(batch) | |
| if self.shuffle: | |
| batch_ids = torch.randperm(len(batches), generator=g).tolist() | |
| batches = [batches[i] for i in batch_ids] | |
| self.batches = batches | |
| assert len(self.batches) * self.batch_size == self.num_samples | |
| return iter(self.batches) | |
| def _bisect(self, x, lo=0, hi=None): | |
| if hi is None: | |
| hi = len(self.boundaries) - 1 | |
| if hi > lo: | |
| mid = (hi + lo) // 2 | |
| if self.boundaries[mid] < x and x <= self.boundaries[mid + 1]: | |
| return mid | |
| elif x <= self.boundaries[mid]: | |
| return self._bisect(x, lo, mid) | |
| else: | |
| return self._bisect(x, mid + 1, hi) | |
| else: | |
| return -1 | |
| def __len__(self): | |
| return self.num_samples // self.batch_size |