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252 lines
10 KiB
252 lines
10 KiB
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import math
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import numpy as np
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from paddle import distributed as dist
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from paddle.io import BatchSampler
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from paddle.io import DistributedBatchSampler
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from deepspeech.utils.log import Log
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__all__ = [
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"SortagradDistributedBatchSampler",
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"SortagradBatchSampler",
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]
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logger = Log(__name__).getlog()
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def _batch_shuffle(indices, batch_size, epoch, clipped=False):
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"""Put similarly-sized instances into minibatches for better efficiency
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and make a batch-wise shuffle.
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1. Sort the audio clips by duration.
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2. Generate a random number `k`, k in [0, batch_size).
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3. Randomly shift `k` instances in order to create different batches
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for different epochs. Create minibatches.
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4. Shuffle the minibatches.
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:param indices: indexes. List of int.
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:type indices: list
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:param batch_size: Batch size. This size is also used for generate
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a random number for batch shuffle.
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:type batch_size: int
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:param clipped: Whether to clip the heading (small shift) and trailing
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(incomplete batch) instances.
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:type clipped: bool
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:return: Batch shuffled mainifest.
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:rtype: list
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"""
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rng = np.random.RandomState(epoch)
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shift_len = rng.randint(0, batch_size - 1)
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batch_indices = list(zip(*[iter(indices[shift_len:])] * batch_size))
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rng.shuffle(batch_indices)
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batch_indices = [item for batch in batch_indices for item in batch]
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assert clipped is False
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if not clipped:
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res_len = len(indices) - shift_len - len(batch_indices)
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# when res_len is 0, will return whole list, len(List[-0:]) = len(List[:])
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if res_len != 0:
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batch_indices.extend(indices[-res_len:])
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batch_indices.extend(indices[0:shift_len])
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assert len(indices) == len(
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batch_indices
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), f"_batch_shuffle: {len(indices)} : {len(batch_indices)} : {res_len} - {shift_len}"
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return batch_indices
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class SortagradDistributedBatchSampler(DistributedBatchSampler):
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def __init__(self,
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dataset,
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batch_size,
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num_replicas=None,
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rank=None,
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shuffle=False,
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drop_last=False,
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sortagrad=False,
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shuffle_method="batch_shuffle"):
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"""Sortagrad Sampler for multi gpus.
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Args:
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dataset (paddle.io.Dataset):
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batch_size (int): batch size for one gpu
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num_replicas (int, optional): world size or numbers of gpus. Defaults to None.
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rank (int, optional): rank id. Defaults to None.
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shuffle (bool, optional): True for do shuffle, or else. Defaults to False.
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drop_last (bool, optional): whether drop last batch which is less than batch size. Defaults to False.
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sortagrad (bool, optional): True, do sortgrad in first epoch, then shuffle as usual; or else. Defaults to False.
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shuffle_method (str, optional): shuffle method, "instance_shuffle" or "batch_shuffle". Defaults to "batch_shuffle".
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"""
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super().__init__(dataset, batch_size, num_replicas, rank, shuffle,
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drop_last)
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self._sortagrad = sortagrad
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self._shuffle_method = shuffle_method
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def __iter__(self):
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num_samples = len(self.dataset)
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indices = np.arange(num_samples).tolist()
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indices += indices[:(self.total_size - len(indices))]
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assert len(indices) == self.total_size
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# sort (by duration) or batch-wise shuffle the manifest
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if self.shuffle:
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if self.epoch == 0 and self._sortagrad:
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logger.info(
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f'rank: {dist.get_rank()} dataset sortagrad! epoch {self.epoch}'
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)
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else:
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logger.info(
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f'rank: {dist.get_rank()} dataset shuffle! epoch {self.epoch}'
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)
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if self._shuffle_method == "batch_shuffle":
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# using `batch_size * nrank`, or will cause instability loss and nan or inf grad,
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# since diff batch examlpe length in batches case instability loss in diff rank,
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# e.g. rank0 maxlength 20, rank3 maxlength 1000
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indices = _batch_shuffle(
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indices,
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self.batch_size * self.nranks,
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self.epoch,
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clipped=False)
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elif self._shuffle_method == "instance_shuffle":
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np.random.RandomState(self.epoch).shuffle(indices)
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else:
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raise ValueError("Unknown shuffle method %s." %
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self._shuffle_method)
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assert len(
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indices
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) == self.total_size, f"batch shuffle examples error: {len(indices)} : {self.total_size}"
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# slice `self.batch_size` examples by rank id
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def _get_indices_by_batch_size(indices):
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subsampled_indices = []
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last_batch_size = self.total_size % (self.batch_size * self.nranks)
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assert last_batch_size % self.nranks == 0
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last_local_batch_size = last_batch_size // self.nranks
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for i in range(self.local_rank * self.batch_size,
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len(indices) - last_batch_size,
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self.batch_size * self.nranks):
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subsampled_indices.extend(indices[i:i + self.batch_size])
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indices = indices[len(indices) - last_batch_size:]
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subsampled_indices.extend(
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indices[self.local_rank * last_local_batch_size:(
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self.local_rank + 1) * last_local_batch_size])
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return subsampled_indices
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if self.nranks > 1:
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indices = _get_indices_by_batch_size(indices)
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assert len(indices) == self.num_samples
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_sample_iter = iter(indices)
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batch_indices = []
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for idx in _sample_iter:
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batch_indices.append(idx)
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if len(batch_indices) == self.batch_size:
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logger.debug(
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f"rank: {dist.get_rank()} batch index: {batch_indices} ")
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yield batch_indices
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batch_indices = []
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if not self.drop_last and len(batch_indices) > 0:
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yield batch_indices
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def __len__(self):
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num_samples = self.num_samples
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num_samples += int(not self.drop_last) * (self.batch_size - 1)
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return num_samples // self.batch_size
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class SortagradBatchSampler(BatchSampler):
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def __init__(self,
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dataset,
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batch_size,
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shuffle=False,
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drop_last=False,
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sortagrad=False,
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shuffle_method="batch_shuffle"):
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"""Sortagrad Sampler for one gpu.
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Args:
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dataset (paddle.io.Dataset):
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batch_size (int): batch size for one gpu
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shuffle (bool, optional): True for do shuffle, or else. Defaults to False.
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drop_last (bool, optional): whether drop last batch which is less than batch size. Defaults to False.
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sortagrad (bool, optional): True, do sortgrad in first epoch, then shuffle as usual; or else. Defaults to False.
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shuffle_method (str, optional): shuffle method, "instance_shuffle" or "batch_shuffle". Defaults to "batch_shuffle".
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"""
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self.dataset = dataset
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assert isinstance(batch_size, int) and batch_size > 0, \
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"batch_size should be a positive integer"
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self.batch_size = batch_size
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assert isinstance(shuffle, bool), \
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"shuffle should be a boolean value"
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self.shuffle = shuffle
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assert isinstance(drop_last, bool), \
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"drop_last should be a boolean number"
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self.drop_last = drop_last
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self.epoch = 0
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self.num_samples = int(math.ceil(len(self.dataset) * 1.0))
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self.total_size = self.num_samples
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self._sortagrad = sortagrad
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self._shuffle_method = shuffle_method
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def __iter__(self):
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num_samples = len(self.dataset)
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indices = np.arange(num_samples).tolist()
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indices += indices[:(self.total_size - len(indices))]
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assert len(indices) == self.total_size
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# sort (by duration) or batch-wise shuffle the manifest
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if self.shuffle:
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if self.epoch == 0 and self._sortagrad:
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logger.info(f'dataset sortagrad! epoch {self.epoch}')
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else:
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logger.info(f'dataset shuffle! epoch {self.epoch}')
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if self._shuffle_method == "batch_shuffle":
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indices = _batch_shuffle(
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indices, self.batch_size, self.epoch, clipped=False)
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elif self._shuffle_method == "instance_shuffle":
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np.random.RandomState(self.epoch).shuffle(indices)
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else:
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raise ValueError("Unknown shuffle method %s." %
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self._shuffle_method)
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assert len(
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indices
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) == self.total_size, f"batch shuffle examples error: {len(indices)} : {self.total_size}"
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assert len(indices) == self.num_samples
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_sample_iter = iter(indices)
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batch_indices = []
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for idx in _sample_iter:
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batch_indices.append(idx)
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if len(batch_indices) == self.batch_size:
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logger.debug(
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f"rank: {dist.get_rank()} batch index: {batch_indices} ")
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yield batch_indices
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batch_indices = []
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if not self.drop_last and len(batch_indices) > 0:
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yield batch_indices
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self.epoch += 1
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def __len__(self):
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num_samples = self.num_samples
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num_samples += int(not self.drop_last) * (self.batch_size - 1)
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return num_samples // self.batch_size
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