Question about SBI dataloader during training.

[Jiangpeiqi44]

Thanks for your amazing work! I have a question about the SBI dataloader : Are the SBI images kept the same within different epochs? In the source code, the worker_init_fn function is set to ：np.random.seed(np.random.get_state()[1][0] + worker_id) And I found that when num_workers > 0 , the np.random.get_state() of each worker in different epochs is the same. So the random numbers output by dataloader in different epochs is the same, does that means the SBI dataset kept the same in the training phase? Demo code is below:

import torch
from torch.utils.data import Dataset
import numpy as np
import random

class TestDataset(Dataset):
    def __init__(self):
        self.datas = np.arange(16)
        print('init')

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

    def __getitem__(self, index):
        data = self.datas[index]
        random_data = np.random.uniform(0.0, 1.0)
        return  data, random_data

    def worker_init_fn(self, worker_id):
        np.random.seed(np.random.get_state()[1][0] + worker_id)

if __name__ == '__main__':
    simple_dataset = TestDataset()
    dataloader = torch.utils.data.DataLoader(simple_dataset, 
                                             batch_size=2,
                                             shuffle=True,
                                             worker_init_fn=simple_dataset.worker_init_fn,
                                             num_workers=2)
    n_epoch = 3
    seed = 5
    torch.manual_seed(seed)
    np.random.seed(seed)
    random.seed(seed)
    for epoch in range(n_epoch):
        print('epoch_%d'%epoch)
        np.random.seed(seed + epoch)
        for step, data in enumerate(dataloader):
            print(data)

And the output is :

init
epoch_0
[tensor([12,  3], dtype=torch.int32), tensor([0.1520, 0.8205], dtype=torch.float64)]
[tensor([2, 8], dtype=torch.int32), tensor([0.9544, 0.0756], dtype=torch.float64)]
[tensor([ 9, 15], dtype=torch.int32), tensor([0.7755, 0.5411], dtype=torch.float64)]
[tensor([14,  6], dtype=torch.int32), tensor([0.4624, 0.1885], dtype=torch.float64)]
[tensor([ 4, 11], dtype=torch.int32), tensor([0.3422, 0.2918], dtype=torch.float64)]
[tensor([1, 7], dtype=torch.int32), tensor([0.5054, 0.6988], dtype=torch.float64)]
[tensor([10,  0], dtype=torch.int32), tensor([0.4065, 0.4715], dtype=torch.float64)]
[tensor([13,  5], dtype=torch.int32), tensor([0.9122, 0.9069], dtype=torch.float64)]
epoch_1
[tensor([13,  8], dtype=torch.int32), tensor([0.1520, 0.8205], dtype=torch.float64)]
[tensor([15,  7], dtype=torch.int32), tensor([0.9544, 0.0756], dtype=torch.float64)]
[tensor([2, 1], dtype=torch.int32), tensor([0.7755, 0.5411], dtype=torch.float64)]
[tensor([11,  6], dtype=torch.int32), tensor([0.4624, 0.1885], dtype=torch.float64)]
[tensor([14,  4], dtype=torch.int32), tensor([0.3422, 0.2918], dtype=torch.float64)]
[tensor([12,  3], dtype=torch.int32), tensor([0.5054, 0.6988], dtype=torch.float64)]
[tensor([9, 5], dtype=torch.int32), tensor([0.4065, 0.4715], dtype=torch.float64)]
[tensor([10,  0], dtype=torch.int32), tensor([0.9122, 0.9069], dtype=torch.float64)]
epoch_2
[tensor([8, 5], dtype=torch.int32), tensor([0.1520, 0.8205], dtype=torch.float64)]
[tensor([6, 7], dtype=torch.int32), tensor([0.9544, 0.0756], dtype=torch.float64)]
[tensor([ 3, 12], dtype=torch.int32), tensor([0.7755, 0.5411], dtype=torch.float64)]
[tensor([13, 14], dtype=torch.int32), tensor([0.4624, 0.1885], dtype=torch.float64)]
[tensor([9, 1], dtype=torch.int32), tensor([0.3422, 0.2918], dtype=torch.float64)]
[tensor([15, 10], dtype=torch.int32), tensor([0.5054, 0.6988], dtype=torch.float64)]
[tensor([2, 0], dtype=torch.int32), tensor([0.4065, 0.4715], dtype=torch.float64)]
[tensor([ 4, 11], dtype=torch.int32), tensor([0.9122, 0.9069], dtype=torch.float64)]