PaddleSpeech/paddlespeech/t2s/datasets/sampler.py

import math

import numpy as np
from paddle.io import BatchSampler


class ErnieSATSampler(BatchSampler):
    """Sampler that restricts data loading to a subset of the dataset.
    In such case, each process can pass a DistributedBatchSampler instance 
    as a DataLoader sampler, and load a subset of the original dataset that 
    is exclusive to it.
    .. note::
        Dataset is assumed to be of constant size.
        
    Args:
        dataset(paddle.io.Dataset): this could be a `paddle.io.Dataset` implement
                     or other python object which implemented
                     `__len__` for BatchSampler to get sample
                     number of data source.
        batch_size(int): sample indice number in a mini-batch indices.
        num_replicas(int, optional): porcess number in distributed training.
            If :attr:`num_replicas` is None, :attr:`num_replicas` will be
            retrieved from :code:`paddle.distributed.ParallenEnv`.
            Default None.
        rank(int, optional): the rank of the current process among :attr:`num_replicas`
            processes. If :attr:`rank` is None, :attr:`rank` is retrieved from
            :code:`paddle.distributed.ParallenEnv`. Default None.
        shuffle(bool): whther to shuffle indices order before genrating
            batch indices. Default False.
        drop_last(bool): whether drop the last incomplete batch dataset size
            is not divisible by the batch size. Default False
    Examples:
        .. code-block:: python
            import numpy as np
            from paddle.io import Dataset, DistributedBatchSampler
            # init with dataset
            class RandomDataset(Dataset):
                def __init__(self, num_samples):
                    self.num_samples = num_samples
            
                def __getitem__(self, idx):
                    image = np.random.random([784]).astype('float32')
                    label = np.random.randint(0, 9, (1, )).astype('int64')
                    return image, label
                
                def __len__(self):
                    return self.num_samples
  
            dataset = RandomDataset(100)
            sampler = DistributedBatchSampler(dataset, batch_size=64)
            for data in sampler:
                # do something
                break
    """

    def __init__(self,
                 dataset,
                 batch_size,
                 num_replicas=None,
                 rank=None,
                 shuffle=False,
                 drop_last=False):
        self.dataset = dataset

        assert isinstance(batch_size, int) and batch_size > 0, \
                "batch_size should be a positive integer"
        self.batch_size = batch_size
        assert isinstance(shuffle, bool), \
                "shuffle should be a boolean value"
        self.shuffle = shuffle
        assert isinstance(drop_last, bool), \
                "drop_last should be a boolean number"

        from paddle.distributed import ParallelEnv

        if num_replicas is not None:
            assert isinstance(num_replicas, int) and num_replicas > 0, \
                    "num_replicas should be a positive integer"
            self.nranks = num_replicas
        else:
            self.nranks = ParallelEnv().nranks

        if rank is not None:
            assert isinstance(rank, int) and rank >= 0, \
                    "rank should be a non-negative integer"
            self.local_rank = rank
        else:
            self.local_rank = ParallelEnv().local_rank

        self.drop_last = drop_last
        self.epoch = 0
        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.nranks))
        self.total_size = self.num_samples * self.nranks

    def __iter__(self):
        num_samples = len(self.dataset)
        indices = np.arange(num_samples).tolist()
        indices += indices[:(self.total_size - len(indices))]
        assert len(indices) == self.total_size

        # subsample
        def _get_indices_by_batch_size(indices):
            subsampled_indices = []
            last_batch_size = self.total_size % (self.batch_size * self.nranks)
            assert last_batch_size % self.nranks == 0
            last_local_batch_size = last_batch_size // self.nranks

            for i in range(self.local_rank * self.batch_size,
                           len(indices) - last_batch_size,
                           self.batch_size * self.nranks):
                subsampled_indices.extend(indices[i:i + self.batch_size])

            indices = indices[len(indices) - last_batch_size:]
            subsampled_indices.extend(
                indices[self.local_rank * last_local_batch_size:(
                    self.local_rank + 1) * last_local_batch_size])
            return subsampled_indices

        if self.nranks > 1:
            indices = _get_indices_by_batch_size(indices)

        assert len(indices) == self.num_samples
        _sample_iter = iter(indices)

        batch_indices_list = []
        batch_indices = []
        for idx in _sample_iter:
            batch_indices.append(idx)
            if len(batch_indices) == self.batch_size:
                batch_indices_list.append(batch_indices)
                batch_indices = []
        if not self.drop_last and len(batch_indices) > 0:
            batch_indices_list.append(batch_indices)

        if self.shuffle:
            np.random.RandomState(self.epoch).shuffle(batch_indices_list)
            self.epoch += 1

        for batch_indices in batch_indices_list:
            yield batch_indices

    def __len__(self):
        num_samples = self.num_samples
        num_samples += int(not self.drop_last) * (self.batch_size - 1)
        return num_samples // self.batch_size

    def set_epoch(self, epoch):
        """
        Sets the epoch number. When :attr:`shuffle=True`, this number is used
        as seeds of random numbers. By default, users may not set this, all
        replicas (workers) use a different random ordering for each epoch.
        If set same number at each epoch, this sampler will yield the same
        ordering at all epoches.
        Arguments:
            epoch (int): Epoch number.
        Examples:
            .. code-block:: python
    
                import numpy as np
    
                from paddle.io import Dataset, DistributedBatchSampler
    
                # init with dataset
                class RandomDataset(Dataset):
                    def __init__(self, num_samples):
                        self.num_samples = num_samples
                
                    def __getitem__(self, idx):
                        image = np.random.random([784]).astype('float32')
                        label = np.random.randint(0, 9, (1, )).astype('int64')
                        return image, label
                    
                    def __len__(self):
                        return self.num_samples
      
                dataset = RandomDataset(100)
                sampler = DistributedBatchSampler(dataset, batch_size=64)
    
                for epoch in range(10):
                    sampler.set_epoch(epoch)
        """
        self.epoch = epoch