Is WeightedSampler really useful?

Because my dataset is imbalanced, I want to use to sample data. Before do that I did a experiment, and the code is as follow:

import torch
from import Sampler
from import TensorDataset as dset

batch_size = 5
inputs = torch.randn(15,2)
# print(inputs)
target = torch.floor(4*torch.rand(15))
trainData = dset(inputs, target)

count_labels = [sum(target==i) for i in range(4)]

num_sample = len(inputs)
weight = 1.0/torch.Tensor(count_labels).clone().detach()
sampler =, num_sample)
trainLoader =, batch_size , shuffle=False, sampler=sampler)

The output is

tensor([0., 1., 3., 3., 2., 2., 0., 2., 1., 3., 2., 2., 2., 2., 3.])
[tensor(2, dtype=torch.uint8), tensor(2, dtype=torch.uint8), tensor(7, dtype=torch.uint8), tensor(4, dtype=torch.uint8)]
tensor([0.5000, 0.5000, 0.1429, 0.2500])

Then I iterate load data in the following way:

print("load data")
for epoch in range(5):
    for i, (inp, tar) in enumerate(trainLoader):
        print(f"Epoch:{epoch} step:{i} target:{tar}")

The result as follows:

No matter how many times I tried, class 2 is never sampled. I wonder whether my code is wrong or the WeightedSampler is wrong?

The weight should provide a weight for each sample, while in your code it looks like you are trying to pass class weights.
Have a look at this example on how to use the WeightedRandomSampler.


Hi, ptrblck

In the code above, is there anything wrong about datatype conversion?
I just remove .clone().detach() on weight and add .numpy() to weight then pass to sampler, it works fine.
And the Pytorch raise a UserWarning when I run the snippet above:

UserWarning: To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach() or sourceTensor.clone().detach().requires_grad_(True), rather than torch.tensor(sourceTensor).

Does WeightedRandomSampler works only fine with numpy array?

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This warning seems to be related to this issue and should be fixed in the current master (and nightly builds). :wink:

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That’s very kind.

What I am confused about is should we pass a numpy.array to the WeightRandomSampler, since in the snippet above it pass a sequence which wrap tensor to the sampler and it didn’t work.

Thanks in advance.

In the code snipper, weight should be a tensor and should work fine.
You can ignore the warning, since the code still will work.

Could you print the sequence of tensors which is not working?

Oh, thank you very much, it was my problem misunderstanding about the weight you have mentioned in your above post.