<filename>data_loader_util.py
import torch
from torch.utils.data.sampler import Sampler
import torch.distributed as dist


def synchronize():
    """
    Helper function to synchronize (barrier) among all processes when
    using distributed training
    """
    if not dist.is_available():
        return
    if not dist.is_initialized():
        return
    world_size = dist.get_world_size()
    if world_size == 1:
        return
    dist.barrier()

def get_world_size():
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size()

def get_rank():
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    return dist.get_rank()


class InfSampler(Sampler):
    """Samples elements randomly, without replacement.

      Arguments:
          data_source (Dataset): dataset to sample from
      """

    def __init__(self, data_source, shuffle=False):
        self.data_source = data_source
        self.shuffle = shuffle
        self.reset_permutation()

    def reset_permutation(self):
        perm = len(self.data_source)
        if self.shuffle:
            perm = torch.randperm(perm)
        self._perm = perm.tolist()

    def __iter__(self):
        return self

    def __next__(self):
        if len(self._perm) == 0:
            self.reset_permutation()
        return self._perm.pop()

    def __len__(self):
        return len(self.data_source)

    next = __next__  # Python 2 compatibility



import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
from torch.utils.data.sampler import BatchSampler

class IterationBasedBatchSampler(BatchSampler):
    """
    Wraps a BatchSampler, resampling from it until
    a specified number of iterations have been sampled
    """

    def __init__(self, batch_sampler, num_iterations, start_iter=0):
        self.batch_sampler = batch_sampler
        self.num_iterations = num_iterations
        self.start_iter = start_iter

    def __iter__(self):
        iteration = self.start_iter
        while iteration <= self.num_iterations:
            # if the underlying sampler has a set_epoch method, like
            # DistributedSampler, used for making each process see
            # a different split of the dataset, then set it
            if hasattr(self.batch_sampler.sampler, "set_epoch"):
                self.batch_sampler.sampler.set_epoch(iteration)
            for batch in self.batch_sampler:
                iteration += 1
                if iteration > self.num_iterations:
                    break
                yield batch

    def __len__(self):
        return self.num_iterations

class DistributedSampler(Sampler):
    """Sampler that restricts data loading to a subset of the dataset.
    It is especially useful in conjunction with
    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
    process can pass a DistributedSampler 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.
    Arguments:
        dataset: Dataset used for sampling.
        num_replicas (optional): Number of processes participating in
            distributed training.
        rank (optional): Rank of the current process within num_replicas.
    """

    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
        if num_replicas is None:
            if not dist.is_available():
                raise RuntimeError("Requires distributed package to be available")
            num_replicas = dist.get_world_size()
        if rank is None:
            if not dist.is_available():
                raise RuntimeError("Requires distributed package to be available")
            rank = dist.get_rank()
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.epoch = 0
        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
        self.total_size = self.num_samples * self.num_replicas
        self.shuffle = shuffle

    def __iter__(self):
        if self.shuffle:
            # deterministically shuffle based on epoch
            g = torch.Generator()
            g.manual_seed(self.epoch)
            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
        offset = self.num_samples * self.rank
        indices = indices[offset : offset + self.num_samples]
        assert len(indices) == self.num_samples

        return iter(indices)

    def __len__(self):
        return self.num_samples

    def set_epoch(self, epoch):
        self.epoch = epoch



def make_batch_data_sampler(
        sampler, images_per_batch, num_iters=None, start_iter=0
):

    batch_sampler = torch.utils.data.sampler.BatchSampler(
        sampler, images_per_batch, drop_last=True
    )
    if num_iters is not None:
        batch_sampler = IterationBasedBatchSampler(
            batch_sampler, num_iters, start_iter
        )
    return batch_sampler






<filename>data_loader_util.py
import torch
from torch.utils.data.sampler import Sampler
import torch.distributed as dist


def synchronize():
    """
    Helper function to synchronize (barrier) among all processes when
    using distributed training
    """
    if not dist.is_available():
        return
    if not dist.is_initialized():
        return
    world_size = dist.get_world_size()
    if world_size == 1:
        return
    dist.barrier()

def get_world_size():
    if not dist.is_available():
        return 1
    if not dist.is_initialized():
        return 1
    return dist.get_world_size()

def get_rank():
    if not dist.is_available():
        return 0
    if not dist.is_initialized():
        return 0
    return dist.get_rank()


class InfSampler(Sampler):
    """Samples elements randomly, without replacement.

      Arguments:
          data_source (Dataset): dataset to sample from
      """

    def __init__(self, data_source, shuffle=False):
        self.data_source = data_source
        self.shuffle = shuffle
        self.reset_permutation()

    def reset_permutation(self):
        perm = len(self.data_source)
        if self.shuffle:
            perm = torch.randperm(perm)
        self._perm = perm.tolist()

    def __iter__(self):
        return self

    def __next__(self):
        if len(self._perm) == 0:
            self.reset_permutation()
        return self._perm.pop()

    def __len__(self):
        return len(self.data_source)

    next = __next__  # Python 2 compatibility



import math
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler
from torch.utils.data.sampler import BatchSampler

class IterationBasedBatchSampler(BatchSampler):
    """
    Wraps a BatchSampler, resampling from it until
    a specified number of iterations have been sampled
    """

    def __init__(self, batch_sampler, num_iterations, start_iter=0):
        self.batch_sampler = batch_sampler
        self.num_iterations = num_iterations
        self.start_iter = start_iter

    def __iter__(self):
        iteration = self.start_iter
        while iteration <= self.num_iterations:
            # if the underlying sampler has a set_epoch method, like
            # DistributedSampler, used for making each process see
            # a different split of the dataset, then set it
            if hasattr(self.batch_sampler.sampler, "set_epoch"):
                self.batch_sampler.sampler.set_epoch(iteration)
            for batch in self.batch_sampler:
                iteration += 1
                if iteration > self.num_iterations:
                    break
                yield batch

    def __len__(self):
        return self.num_iterations

class DistributedSampler(Sampler):
    """Sampler that restricts data loading to a subset of the dataset.
    It is especially useful in conjunction with
    :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each
    process can pass a DistributedSampler 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.
    Arguments:
        dataset: Dataset used for sampling.
        num_replicas (optional): Number of processes participating in
            distributed training.
        rank (optional): Rank of the current process within num_replicas.
    """

    def __init__(self, dataset, num_replicas=None, rank=None, shuffle=True):
        if num_replicas is None:
            if not dist.is_available():
                raise RuntimeError("Requires distributed package to be available")
            num_replicas = dist.get_world_size()
        if rank is None:
            if not dist.is_available():
                raise RuntimeError("Requires distributed package to be available")
            rank = dist.get_rank()
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.rank = rank
        self.epoch = 0
        self.num_samples = int(math.ceil(len(self.dataset) * 1.0 / self.num_replicas))
        self.total_size = self.num_samples * self.num_replicas
        self.shuffle = shuffle

    def __iter__(self):
        if self.shuffle:
            # deterministically shuffle based on epoch
            g = torch.Generator()
            g.manual_seed(self.epoch)
            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
        offset = self.num_samples * self.rank
        indices = indices[offset : offset + self.num_samples]
        assert len(indices) == self.num_samples

        return iter(indices)

    def __len__(self):
        return self.num_samples

    def set_epoch(self, epoch):
        self.epoch = epoch



def make_batch_data_sampler(
        sampler, images_per_batch, num_iters=None, start_iter=0
):

    batch_sampler = torch.utils.data.sampler.BatchSampler(
        sampler, images_per_batch, drop_last=True
    )
    if num_iters is not None:
        batch_sampler = IterationBasedBatchSampler(
            batch_sampler, num_iters, start_iter
        )
    return batch_sampler






Helper function to synchronize (barrier) among all processes when     using distributed training Samples elements randomly, without replacement.        Arguments:           data_source (Dataset): dataset to sample from # Python 2 compatibility Wraps a BatchSampler, resampling from it until     a specified number of iterations have been sampled # if the underlying sampler has a set_epoch method, like # DistributedSampler, used for making each process see # a different split of the dataset, then set it Sampler that restricts data loading to a subset of the dataset.     It is especially useful in conjunction with     :class:`torch.nn.parallel.DistributedDataParallel`. In such case, each     process can pass a DistributedSampler 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.     Arguments:         dataset: Dataset used for sampling.         num_replicas (optional): Number of processes participating in             distributed training.         rank (optional): Rank of the current process within num_replicas. # deterministically shuffle based on epoch # add extra samples to make it evenly divisible # subsample