def test_sampling(self):
data = [1, 2, 3, 4, 5]
weights = [1, 2, 3, 4, 5]
sampler = DistributedWeightedRandomSampler(
weights=weights, dataset=data, shuffle=False, generator=torch.Generator().manual_seed(0)
)
samples = np.array([data[i] for i in list(sampler)])
if dist.get_rank() == 0:
np.testing.assert_allclose(samples, np.array([5, 5, 5]))
if dist.get_rank() == 1:
np.testing.assert_allclose(samples, np.array([1, 4, 4]))
def test_num_samples(self):
data = [1, 2, 3, 4, 5]
weights = [1, 2, 3, 4, 5]
sampler = DistributedWeightedRandomSampler(
weights=weights,
num_samples_per_rank=5,
replacement=True,
dataset=data,
shuffle=False,
generator=torch.Generator().manual_seed(123),
)
samples = np.array([data[i] for i in list(sampler)])
if dist.get_rank() == 0:
np.testing.assert_allclose(samples, np.array([3, 1, 5, 1, 5]))
if dist.get_rank() == 1:
np.testing.assert_allclose(samples, np.array([4, 2, 4, 2, 4]))