mae-cnn/pretrain/sampler.py

# Copyright (c) ByteDance, Inc. and its affiliates.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.

import random

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


def worker_init_fn(worker_id):
    # https://pytorch.org/docs/stable/notes/randomness.html#dataloader
    worker_seed = torch.initial_seed() % 2 ** 32
    np.random.seed(worker_seed)
    random.seed(worker_seed)


class DistInfiniteBatchSampler(Sampler):
    def __init__(self, world_size, rank, dataset_len, glb_batch_size, seed=1, filling=False, shuffle=True):
        assert glb_batch_size % world_size == 0
        self.world_size, self.rank = world_size, rank
        self.dataset_len = dataset_len
        self.glb_batch_size = glb_batch_size
        self.batch_size = glb_batch_size // world_size
        
        self.iters_per_ep = (dataset_len + glb_batch_size - 1) // glb_batch_size
        self.filling = filling
        self.shuffle = shuffle
        self.epoch = 0
        self.seed = seed
        self.indices = self.gener_indices()
    
    def gener_indices(self):
        global_max_p = self.iters_per_ep * self.glb_batch_size  # global_max_p % world_size must be 0 cuz glb_batch_size % world_size == 0
        if self.shuffle:
            g = torch.Generator()
            g.manual_seed(self.epoch + self.seed)
            global_indices = torch.randperm(self.dataset_len, generator=g)
        else:
            global_indices = torch.arange(self.dataset_len)
        filling = global_max_p - global_indices.shape[0]
        if filling > 0 and self.filling:
            global_indices = torch.cat((global_indices, global_indices[:filling]))
        global_indices = tuple(global_indices.numpy().tolist())
        
        seps = torch.linspace(0, len(global_indices), self.world_size + 1, dtype=torch.int)
        local_indices = global_indices[seps[self.rank]:seps[self.rank + 1]]
        self.max_p = len(local_indices)
        return local_indices
    
    def __iter__(self):
        self.epoch = 0
        while True:
            self.epoch += 1
            p, q = 0, 0
            while p < self.max_p:
                q = p + self.batch_size
                yield self.indices[p:q]
                p = q
            if self.shuffle:
                self.indices = self.gener_indices()
    
    def __len__(self):
        return self.iters_per_ep


if __name__ == '__main__':
    W = 16
    for rk in range(W):
        ind = DistInfiniteBatchSampler(W, rk, 5024, 5024).gener_indices()
        print(rk, len(ind))