7/31/2023 0 Comments Weighted random list generatorReturn super(CustomWeightedSampler, self). This generation/randomization is done without bias and uses a pseudo-random generator (PRNG). Torch.multinomial(self.weights, self.num_samples % self.bs, False, generator=self.generator)]) How to make a random sample/selection To randomize a choice or create a sweepstake in this generator, enter elements to pick and the number of items to select, the program will generate the list of winning/lucky items randomly. Torch.multinomial(self.weights, self.bs, False, generator=self.generator)]) Rand_tensor = torch.multinomial(self.weights, self.bs, False, generator=self.generator)įor _ in range((self.num_samples - self.bs) // self.bs): Raise ValueError("bs should be smaller than num_samples " Every time it copies one of these elements over, it adds the weight of the element to a total. random name generator doesnt just pick names at random, it weights the. Then, it copies elements of the list of possibles whos level requirements are met by the passed level. A random name generator is useful for anyone who needs, well, a list of random. If not isinstance(num_samples, int) or isinstance(num_samples, bool) or \ A weighted random generator works by accepting a list of possible results, each with a weight, a minimum level, and a maximum level. Any thoughts on it? from import WeightedRandomSamplerĬlass CustomWeightedSampler(WeightedRandomSampler):ĭef _init_(self, weights: Sequence, num_samples: int, bs: int, I’ve implemented a sampler that seems to work for now but is probably not that optimal. Hence when the same instance is found twice in a mini-batch I have all sorts of errors that arise that I’d rather solve via the sampler rather than modifying my current implementation. ![]() The issue is technical as I am using a stateful dataset where each instance has information stored and processed at each batch.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |