How can I update the sampler and batch size dynamially

I am using WeightedRandomSampler to generate the weights for different labels.
And I want to change the weights gradually for each epoch.

But when I assign the new sampler back to Dataloader, it is not working.

The same story as for the batch size. Change the batch size after certain epoch.



Just read the class for the dataloader, need to update the batch sampler as well. e.g

batch_sampler =, batch_size, False)
dataloders.sampler = sampler
dataloders.batch_sampler = batch_sampler

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Correct me if I am wrong, but doesn’t this code seem unnecessarily bulky ? Defining 3 classes for sampling seems to be an overkill: sampler, batch_sampler, and dataLoader.

I am not sure if the api allows it but can’t we sample from the loader using something as simple as: loader.get(indices) ? is there a performance gain from creating the above 3 objects ?

Well, update the

dataloders.sampler = sampler

is not necessary. Just to keep the sampler the same as batch_sampler.

I cannot find any elegant way to update the sampler and batch size, but the above way is not too bad ( 3 lines of code)

As the distribution of my training classes is unbalanced. Start sampling equally then gradually converge to final distribution seems working fine.


But how easy it is to sample using something like:
np.random.choice(4, p=(0.1,0.3,0.2,0.4))
with this framework ?

where p defines the sampling probability distribution.