Should weighted CrossEntropyLoss and WeightedRandomSampler be used at the same time?

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Is it a good idea to use both weighted CrossEntropyLoss and WeightedRandomSampler at the same time? Or should I only be using one at a time for imbalanced datasets? Should I use weighted CrossEntropyLoss for the training data and WeightedRandomSampler for the validation data?

t_sampler = torch.utils.data.sampler.WeightedRandomSampler(train_weights, len(t_data))
criterion = nn.CrossEntropyLoss(weight=train_weights)

The weights are created with:

from collections import Counter
def count_classes(dataset):
    class_counts = dict(Counter(sample_tup[1] for sample_tup in dataset))
    class_counts = dict(sorted(class_counts.items()))
    return class_counts

train_class_counts = count_classes(t_data)
train_weights = [1 / train_class_counts[class_id] for class_id in range(num_classes)]
train_weights = torch.FloatTensor(train_weights)
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