我们从Python开源项目中，提取了以下7个代码示例，用于说明如何使用torch.Generator()。
def __iter__(self): # deterministically shuffle based on epoch g = torch.Generator() g.manual_seed(self.epoch) indices = list(torch.randperm(len(self.dataset), generator=g)) # add extra samples to make it evenly divisible indices += indices[:(self.total_size - len(indices))] assert len(indices) == self.total_size # subsample offset = self.num_samples * self.rank indices = indices[offset:offset + self.num_samples] assert len(indices) == self.num_samples return iter(indices)
def test_RNGStateAliasing(self): # Fork the random number stream at this point gen = torch.Generator() gen.set_state(torch.get_rng_state()) self.assertEqual(gen.get_state(), torch.get_rng_state()) target_value = torch.rand(1000) # Dramatically alter the internal state of the main generator _ = torch.rand(100000) forked_value = torch.rand(gen, 1000) self.assertEqual(target_value, forked_value, 0, "RNG has not forked correctly.")
def test_RNGStateAliasing(self): # Fork the random number stream at this point gen = torch.Generator() gen.set_state(torch.get_rng_state()) self.assertEqual(gen.get_state(), torch.get_rng_state()) target_value = torch.rand(1000) # Dramatically alter the internal state of the main generator _ = torch.rand(100000) forked_value = torch.rand(1000, generator=gen) self.assertEqual(target_value, forked_value, 0, "RNG has not forked correctly.")
