samplers.py

# --------------------------------------------------------
# InternVL
# Copyright (c) 2023 OpenGVLab
# Licensed under The MIT License [see LICENSE for details]
# --------------------------------------------------------

import math
import os

import numpy as np
import torch
import torch.distributed as dist
from torch.utils.data.sampler import Sampler


class SubsetRandomSampler(torch.utils.data.Sampler):
    """Samples elements randomly from a given list of indices, without
    replacement.

    Arguments:
        indices (sequence): a sequence of indices
    """

    def __init__(self, indices):
        self.epoch = 0
        self.indices = indices

    def __iter__(self):
        return (self.indices[i] for i in torch.randperm(len(self.indices)))

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

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


class NodeDistributedSampler(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,
                 local_rank=None,
                 local_size=None):
        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()
        if local_rank is None:
            local_rank = int(os.environ.get('LOCAL_RANK', 0))
        if local_size is None:
            local_size = int(os.environ.get('LOCAL_SIZE', 1))
        self.dataset = dataset
        self.num_replicas = num_replicas
        self.num_parts = local_size
        self.rank = rank
        self.local_rank = local_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.total_size_parts = self.num_samples * self.num_replicas // self.num_parts

    def __iter__(self):
        # deterministically shuffle based on epoch
        g = torch.Generator()
        g.manual_seed(self.epoch)

        t = torch.Generator()
        t.manual_seed(0)

        indices = torch.randperm(len(self.dataset), generator=t).tolist()
        # indices = range(len(self.dataset))
        indices = [i for i in indices if i % self.num_parts == self.local_rank]

        # add extra samples to make it evenly divisible
        indices += indices[:(self.total_size_parts - len(indices))]
        assert len(indices) == self.total_size_parts

        # subsample
        indices = indices[self.rank // self.num_parts:self.
                          total_size_parts:self.num_replicas // self.num_parts]

        index = torch.randperm(len(indices), generator=g).tolist()
        indices = list(np.array(indices)[index])

        assert len(indices) == self.num_samples

        return iter(indices)

    def __len__(self):
        return self.num_samples

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